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+Demosaic/experiment/test/results-Urban100/img094_x1_DM.png filter=lfs diff=lfs merge=lfs -text +Demosaic/experiment/test/results-Urban100/img095_x1_DM.png filter=lfs diff=lfs merge=lfs -text +Demosaic/experiment/test/results-Urban100/img096_x1_DM.png filter=lfs diff=lfs merge=lfs -text +Demosaic/experiment/test/results-Urban100/img097_x1_DM.png filter=lfs diff=lfs merge=lfs -text +Demosaic/experiment/test/results-Urban100/img098_x1_DM.png filter=lfs diff=lfs merge=lfs -text +Demosaic/experiment/test/results-Urban100/img099_x1_DM.png filter=lfs diff=lfs merge=lfs -text +Demosaic/experiment/test/results-Urban100/img100_x1_DM.png filter=lfs diff=lfs merge=lfs -text diff --git a/Demosaic/README.md b/Demosaic/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8353de195f5cc2889fbb14337bb519607661614f --- /dev/null +++ b/Demosaic/README.md @@ -0,0 +1,99 @@ +# Pyramid Attention for Image Restoration +This repository is for PANet and PA-EDSR introduced in the following paper + +[Yiqun Mei](http://yiqunm2.web.illinois.edu/), [Yuchen Fan](https://scholar.google.com/citations?user=BlfdYL0AAAAJ&hl=en), [Yulun Zhang](http://yulunzhang.com/), [Jiahui Yu](https://jiahuiyu.com/), [Yuqian Zhou](https://yzhouas.github.io/), [Ding Liu](https://scholar.google.com/citations?user=PGtHUI0AAAAJ&hl=en), [Yun Fu](http://www1.ece.neu.edu/~yunfu/), [Thomas S. Huang](http://ifp-uiuc.github.io/) and [Honghui Shi](https://www.humphreyshi.com/) "Pyramid Attention for Image Restoration", [[Arxiv]](https://arxiv.org/abs/2004.13824) + +The code is built on [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch) & [RNAN](https://github.com/yulunzhang/RNAN) and tested on Ubuntu 18.04 environment (Python3.6, PyTorch_1.1) with Titan X/1080Ti/V100 GPUs. + +## Contents +1. [Train](#train) +2. [Test](#test) +3. [Results](#results) +4. [Citation](#citation) +5. [Acknowledgements](#acknowledgements) + +## Train +### Prepare training data + +1. Download DIV2K training data (800 training + 100 validtion images) from [DIV2K dataset](https://data.vision.ee.ethz.ch/cvl/DIV2K/) or [SNU_CVLab](https://cv.snu.ac.kr/research/EDSR/DIV2K.tar). + +2. Specify '--dir_data' based on the HR and LR images path. + +3. Organize training data like: +```bash +DIV2K/ +├── DIV2K_train_HR +├── DIV2K_train_LR_bicubic +│ └── X1 +├── DIV2K_valid_HR +└── DIV2K_valid_LR_bicubic + └── X1 +``` +For more informaiton, please refer to [EDSR(PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch). + +### Begin to train + +1. (optional) All the pretrained models and visual results can be downloaded from [Google Drive](https://drive.google.com/open?id=1q9iUzqYX0fVRzDu4J6fvSPRosgOZoJJE). + +2. Cd to 'PANet-PyTorch/[Task]/code', run the following scripts to train models. + + **You can use scripts in file 'demo.sb' to train and test models for our paper.** + + ```bash + # Example Usage: + python main.py --n_GPUs 1 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 80 --save_models --model PANET --scale 1 --patch_size 48 --save PANET_DEMOSAIC --n_feats 64 --data_train DIV2K --chop + + + ``` +## Test +### Quick start + +1. Cd to 'PANet-PyTorch/[Task]/code', run the following scripts. + + **You can use scripts in file 'demo.sb' to produce results for our paper.** + + ```bash + # No self-ensemble, use different testsets to reproduce the results in the paper. + # Example Usage: + python main.py --model PANET --save_results --n_GPUs 1 --chop --n_resblocks 80 --n_feats 64 --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 --pre_train ../model_best.pt --test_only + ``` + +### The whole test pipeline +1. Prepare test data. Organize training data like: +```bash +benchmark/ +├── testset1 +│ └── HR +│ └── LR_bicubic +│ └── X1 +│ └── .. +├── testset2 +``` + + +2. Conduct image CAR. + + See **Quick start** +3. Evaluate the results. + + Run 'Evaluate_PSNR_SSIM.m' to obtain PSNR/SSIM values for paper. + +## Citation +If you find the code helpful in your resarch or work, please cite the following papers. +``` +@article{mei2020pyramid, + title={Pyramid Attention Networks for Image Restoration}, + author={Mei, Yiqun and Fan, Yuchen and Zhang, Yulun and Yu, Jiahui and Zhou, Yuqian and Liu, Ding and Fu, Yun and Huang, Thomas S and Shi, Honghui}, + journal={arXiv preprint arXiv:2004.13824}, + year={2020} +} +@InProceedings{Lim_2017_CVPR_Workshops, + author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu}, + title = {Enhanced Deep Residual Networks for Single Image Super-Resolution}, + booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, + month = {July}, + year = {2017} +} +``` +## Acknowledgements +This code is built on [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch), [RNAN](https://github.com/yulunzhang/RNAN) and [generative-inpainting-pytorch](https://github.com/daa233/generative-inpainting-pytorch). We thank the authors for sharing their codes. diff --git a/Demosaic/code/LICENSE b/Demosaic/code/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..df2326a5dce9106825799c73dfab581cbde1f96c --- /dev/null +++ b/Demosaic/code/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 Sanghyun Son + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Demosaic/code/__init__.py b/Demosaic/code/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Demosaic/code/__pycache__/option.cpython-37.pyc b/Demosaic/code/__pycache__/option.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ac7171c8daefd324d83bd62c5c0dc6ffd08252ac Binary files /dev/null and b/Demosaic/code/__pycache__/option.cpython-37.pyc differ diff --git a/Demosaic/code/__pycache__/template.cpython-37.pyc b/Demosaic/code/__pycache__/template.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d3017feeca04ce7426916ac556ef6401380f4031 Binary files /dev/null and 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MSDataLoader +from torch.utils.data import dataloader +from torch.utils.data import ConcatDataset + +# This is a simple wrapper function for ConcatDataset +class MyConcatDataset(ConcatDataset): + def __init__(self, datasets): + super(MyConcatDataset, self).__init__(datasets) + self.train = datasets[0].train + + def set_scale(self, idx_scale): + for d in self.datasets: + if hasattr(d, 'set_scale'): d.set_scale(idx_scale) + +class Data: + def __init__(self, args): + self.loader_train = None + if not args.test_only: + datasets = [] + for d in args.data_train: + module_name = d if d.find('DIV2K-Q') < 0 else 'DIV2KJPEG' + m = import_module('data.' + module_name.lower()) + datasets.append(getattr(m, module_name)(args, name=d)) + + self.loader_train = dataloader.DataLoader( + MyConcatDataset(datasets), + batch_size=args.batch_size, + shuffle=True, + pin_memory=not args.cpu, + num_workers=args.n_threads, + ) + + self.loader_test = [] + for d in args.data_test: + if d in ['CBSD68','Kodak24','McM','Set5', 'Set14', 'B100', 'Urban100']: + m = import_module('data.benchmark') + testset = getattr(m, 'Benchmark')(args, train=False, name=d) + else: + module_name = d if d.find('DIV2K-Q') < 0 else 'DIV2KJPEG' + m = import_module('data.' + module_name.lower()) + testset = getattr(m, module_name)(args, train=False, name=d) + + self.loader_test.append( + dataloader.DataLoader( + testset, + batch_size=1, + shuffle=False, + pin_memory=not args.cpu, + num_workers=args.n_threads, + ) + ) diff --git a/Demosaic/code/data/__pycache__/__init__.cpython-37.pyc b/Demosaic/code/data/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c5d8cfbfd3b6e26feaff268fe4e1701ab1c11455 Binary files /dev/null and b/Demosaic/code/data/__pycache__/__init__.cpython-37.pyc differ diff --git a/Demosaic/code/data/__pycache__/benchmark.cpython-37.pyc b/Demosaic/code/data/__pycache__/benchmark.cpython-37.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..eece4c005c890483bc2b5f69b50309f9814bf210 Binary files /dev/null and b/Demosaic/code/data/__pycache__/srdata.cpython-37.pyc differ diff --git a/Demosaic/code/data/benchmark.py b/Demosaic/code/data/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..da851038dd982aefc74918b850683878962881ae --- /dev/null +++ b/Demosaic/code/data/benchmark.py @@ -0,0 +1,25 @@ +import os + +from data import common +from data import srdata + +import numpy as np + +import torch +import torch.utils.data as data + +class Benchmark(srdata.SRData): + def __init__(self, args, name='', train=True, benchmark=True): + super(Benchmark, self).__init__( + args, name=name, train=train, benchmark=True + ) + + def _set_filesystem(self, dir_data): + self.apath = os.path.join(dir_data, 'benchmark', self.name) + self.dir_hr = os.path.join(self.apath, 'HR') + if self.input_large: + self.dir_lr = os.path.join(self.apath, 'LR_bicubicL') + else: + self.dir_lr = os.path.join(self.apath, 'LR_bicubic') + self.ext = ('', '.png') + diff --git a/Demosaic/code/data/common.py b/Demosaic/code/data/common.py new file mode 100644 index 0000000000000000000000000000000000000000..0d9abfa15baf5fd2a23a7b305fb83c427ebc4d26 --- /dev/null +++ b/Demosaic/code/data/common.py @@ -0,0 +1,72 @@ +import random + +import numpy as np +import skimage.color as sc + +import torch + +def get_patch(*args, patch_size=96, scale=1, multi=False, input_large=False): + ih, iw = args[0].shape[:2] + + if not input_large: + p = 1 if multi else 1 + tp = p * patch_size + ip = tp // 1 + else: + tp = patch_size + ip = patch_size + + ix = random.randrange(0, iw - ip + 1) + iy = random.randrange(0, ih - ip + 1) + + if not input_large: + tx, ty = 1 * ix, 1 * iy + else: + tx, ty = ix, iy + + ret = [ + args[0][iy:iy + ip, ix:ix + ip, :], + *[a[ty:ty + tp, tx:tx + tp, :] for a in args[1:]] + ] + + return ret + +def set_channel(*args, n_channels=3): + def _set_channel(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + + c = img.shape[2] + if n_channels == 1 and c == 3: + img = np.expand_dims(sc.rgb2ycbcr(img)[:, :, 0], 2) + elif n_channels == 3 and c == 1: + img = np.concatenate([img] * n_channels, 2) + + return img + + return [_set_channel(a) for a in args] + +def np2Tensor(*args, rgb_range=255): + def _np2Tensor(img): + np_transpose = np.ascontiguousarray(img.transpose((2, 0, 1))) + tensor = torch.from_numpy(np_transpose).float() + tensor.mul_(rgb_range / 255) + + return tensor + + return [_np2Tensor(a) for a in args] + +def augment(*args, hflip=True, rot=True): + hflip = hflip and random.random() < 0.5 + vflip = rot and random.random() < 0.5 + rot90 = rot and random.random() < 0.5 + + def _augment(img): + if hflip: img = img[:, ::-1, :] + if vflip: img = img[::-1, :, :] + if rot90: img = img.transpose(1, 0, 2) + + return img + + return [_augment(a) for a in args] + diff --git a/Demosaic/code/data/demo.py b/Demosaic/code/data/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..316b9e0d666f46ec52cfc78f3b9f5c3e26c7e97c --- /dev/null +++ b/Demosaic/code/data/demo.py @@ -0,0 +1,39 @@ +import os + +from data import common + +import numpy as np +import imageio + +import torch +import torch.utils.data as data + +class Demo(data.Dataset): + def __init__(self, args, name='Demo', train=False, benchmark=False): + self.args = args + self.name = name + self.scale = args.scale + self.idx_scale = 0 + self.train = False + self.benchmark = benchmark + + self.filelist = [] + for f in os.listdir(args.dir_demo): + if f.find('.png') >= 0 or f.find('.jp') >= 0: + self.filelist.append(os.path.join(args.dir_demo, f)) + self.filelist.sort() + + def __getitem__(self, idx): + filename = os.path.splitext(os.path.basename(self.filelist[idx]))[0] + lr = imageio.imread(self.filelist[idx]) + lr, = common.set_channel(lr, n_channels=self.args.n_colors) + lr_t, = common.np2Tensor(lr, rgb_range=self.args.rgb_range) + + return lr_t, -1, filename + + def __len__(self): + return len(self.filelist) + + def set_scale(self, idx_scale): + self.idx_scale = idx_scale + diff --git a/Demosaic/code/data/div2k.py b/Demosaic/code/data/div2k.py new file mode 100644 index 0000000000000000000000000000000000000000..f1c9bcd9788731e29a76db864cccd1e42ba9de88 --- /dev/null +++ b/Demosaic/code/data/div2k.py @@ -0,0 +1,32 @@ +import os +from data import srdata + +class DIV2K(srdata.SRData): + def __init__(self, args, name='DIV2K', train=True, benchmark=False): + data_range = [r.split('-') for r in args.data_range.split('/')] + if train: + data_range = data_range[0] + else: + if args.test_only and len(data_range) == 1: + data_range = data_range[0] + else: + data_range = data_range[1] + + self.begin, self.end = list(map(lambda x: int(x), data_range)) + super(DIV2K, self).__init__( + args, name=name, train=train, benchmark=benchmark + ) + + def _scan(self): + names_hr, names_lr = super(DIV2K, self)._scan() + names_hr = names_hr[self.begin - 1:self.end] + names_lr = [n[self.begin - 1:self.end] for n in names_lr] + + return names_hr, names_lr + + def _set_filesystem(self, dir_data): + super(DIV2K, self)._set_filesystem(dir_data) + self.dir_hr = os.path.join(self.apath, 'DIV2K_train_HR') + self.dir_lr = os.path.join(self.apath, 'DIV2K_train_LR_bicubic') + if self.input_large: self.dir_lr += 'L' + diff --git a/Demosaic/code/data/div2kjpeg.py b/Demosaic/code/data/div2kjpeg.py new file mode 100644 index 0000000000000000000000000000000000000000..0bfc6b03b3946ba664861cddf13f95f46c12cee8 --- /dev/null +++ b/Demosaic/code/data/div2kjpeg.py @@ -0,0 +1,20 @@ +import os +from data import srdata +from data import div2k + +class DIV2KJPEG(div2k.DIV2K): + def __init__(self, args, name='', train=True, benchmark=False): + self.q_factor = int(name.replace('DIV2K-Q', '')) + super(DIV2KJPEG, self).__init__( + args, name=name, train=train, benchmark=benchmark + ) + + def _set_filesystem(self, dir_data): + self.apath = os.path.join(dir_data, 'DIV2K') + self.dir_hr = os.path.join(self.apath, 'DIV2K_train_HR') + self.dir_lr = os.path.join( + self.apath, 'DIV2K_Q{}'.format(self.q_factor) + ) + if self.input_large: self.dir_lr += 'L' + self.ext = ('.png', '.jpg') + diff --git a/Demosaic/code/data/sr291.py b/Demosaic/code/data/sr291.py new file mode 100644 index 0000000000000000000000000000000000000000..f60ad951ef7ec61d98456f8a2ccec5296befe4cd --- /dev/null +++ b/Demosaic/code/data/sr291.py @@ -0,0 +1,6 @@ +from data import srdata + +class SR291(srdata.SRData): + def __init__(self, args, name='SR291', train=True, benchmark=False): + super(SR291, self).__init__(args, name=name) + diff --git a/Demosaic/code/data/srdata.py b/Demosaic/code/data/srdata.py new file mode 100644 index 0000000000000000000000000000000000000000..d036b10b7cffe54079ee80bfc171716bd1934d7a --- /dev/null +++ b/Demosaic/code/data/srdata.py @@ -0,0 +1,157 @@ +import os +import glob +import random +import pickle + +from data import common + +import numpy as np +import imageio +import torch +import torch.utils.data as data + +class SRData(data.Dataset): + def __init__(self, args, name='', train=True, benchmark=False): + self.args = args + self.name = name + self.train = train + self.split = 'train' if train else 'test' + self.do_eval = True + self.benchmark = benchmark + self.input_large = (args.model == 'VDSR') + self.scale = args.scale + self.idx_scale = 0 + + self._set_filesystem(args.dir_data) + if args.ext.find('img') < 0: + path_bin = os.path.join(self.apath, 'bin') + os.makedirs(path_bin, exist_ok=True) + + list_hr, list_lr = self._scan() + if args.ext.find('img') >= 0 or benchmark: + self.images_hr, self.images_lr = list_hr, list_lr + elif args.ext.find('sep') >= 0: + os.makedirs( + self.dir_hr.replace(self.apath, path_bin), + exist_ok=True + ) + for s in self.scale: + os.makedirs( + os.path.join( + self.dir_lr.replace(self.apath, path_bin), + 'X{}'.format(s) + ), + exist_ok=True + ) + + self.images_hr, self.images_lr = [], [[] for _ in self.scale] + for h in list_hr: + b = h.replace(self.apath, path_bin) + b = b.replace(self.ext[0], '.pt') + self.images_hr.append(b) + self._check_and_load(args.ext, h, b, verbose=True) + for i, ll in enumerate(list_lr): + for l in ll: + b = l.replace(self.apath, path_bin) + b = b.replace(self.ext[1], '.pt') + self.images_lr[i].append(b) + self._check_and_load(args.ext, l, b, verbose=True) + if train: + n_patches = args.batch_size * args.test_every + n_images = len(args.data_train) * len(self.images_hr) + if n_images == 0: + self.repeat = 0 + else: + self.repeat = max(n_patches // n_images, 1) + + # Below functions as used to prepare images + def _scan(self): + names_hr = sorted( + glob.glob(os.path.join(self.dir_hr, '*' + self.ext[0])) + ) + names_lr = [[] for _ in self.scale] + for f in names_hr: + filename, _ = os.path.splitext(os.path.basename(f)) + for si, s in enumerate(self.scale): + names_lr[si].append(os.path.join( + self.dir_lr, 'X{}/{}x{}{}'.format( + s, filename, s, self.ext[1] + ) + )) + + return names_hr, names_lr + + def _set_filesystem(self, dir_data): + self.apath = os.path.join(dir_data, self.name) + self.dir_hr = os.path.join(self.apath, 'HR') + self.dir_lr = os.path.join(self.apath, 'LR_bicubic') + if self.input_large: self.dir_lr += 'L' + self.ext = ('.png', '.png') + + def _check_and_load(self, ext, img, f, verbose=True): + if not os.path.isfile(f) or ext.find('reset') >= 0: + if verbose: + print('Making a binary: {}'.format(f)) + with open(f, 'wb') as _f: + pickle.dump(imageio.imread(img), _f) + + def __getitem__(self, idx): + lr, hr, filename = self._load_file(idx) + pair = self.get_patch(lr, hr) + pair = common.set_channel(*pair, n_channels=self.args.n_colors) + pair_t = common.np2Tensor(*pair, rgb_range=self.args.rgb_range) + + return pair_t[0], pair_t[1], filename + + def __len__(self): + if self.train: + return len(self.images_hr) * self.repeat + else: + return len(self.images_hr) + + def _get_index(self, idx): + if self.train: + return idx % len(self.images_hr) + else: + return idx + + def _load_file(self, idx): + idx = self._get_index(idx) + f_hr = self.images_hr[idx] + f_lr = self.images_lr[self.idx_scale][idx] + + filename, _ = os.path.splitext(os.path.basename(f_hr)) + if self.args.ext == 'img' or self.benchmark: + hr = imageio.imread(f_hr) + lr = imageio.imread(f_lr) + elif self.args.ext.find('sep') >= 0: + with open(f_hr, 'rb') as _f: + hr = pickle.load(_f) + with open(f_lr, 'rb') as _f: + lr = pickle.load(_f) + + return lr, hr, filename + + def get_patch(self, lr, hr): + scale = self.scale[self.idx_scale] + if self.train: + lr, hr = common.get_patch( + lr, hr, + patch_size=self.args.patch_size, + scale=scale, + multi=(len(self.scale) > 1), + input_large=self.input_large + ) + if not self.args.no_augment: lr, hr = common.augment(lr, hr) + else: + ih, iw = lr.shape[:2] + hr = hr[0:ih * scale, 0:iw * scale] + + return lr, hr + + def set_scale(self, idx_scale): + if not self.input_large: + self.idx_scale = idx_scale + else: + self.idx_scale = random.randint(0, len(self.scale) - 1) + diff --git a/Demosaic/code/data/video.py b/Demosaic/code/data/video.py new file mode 100644 index 0000000000000000000000000000000000000000..76181b6ffa119172df0f46615e6a1e8b867e104c --- /dev/null +++ b/Demosaic/code/data/video.py @@ -0,0 +1,44 @@ +import os + +from data import common + +import cv2 +import numpy as np +import imageio + +import torch +import torch.utils.data as data + +class Video(data.Dataset): + def __init__(self, args, name='Video', train=False, benchmark=False): + self.args = args + self.name = name + self.scale = args.scale + self.idx_scale = 0 + self.train = False + self.do_eval = False + self.benchmark = benchmark + + self.filename, _ = os.path.splitext(os.path.basename(args.dir_demo)) + self.vidcap = cv2.VideoCapture(args.dir_demo) + self.n_frames = 0 + self.total_frames = int(self.vidcap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __getitem__(self, idx): + success, lr = self.vidcap.read() + if success: + self.n_frames += 1 + lr, = common.set_channel(lr, n_channels=self.args.n_colors) + lr_t, = common.np2Tensor(lr, rgb_range=self.args.rgb_range) + + return lr_t, -1, '{}_{:0>5}'.format(self.filename, self.n_frames) + else: + vidcap.release() + return None + + def __len__(self): + return self.total_frames + + def set_scale(self, idx_scale): + self.idx_scale = idx_scale + diff --git a/Demosaic/code/dataloader.py b/Demosaic/code/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..350af9a02281c1300cf9f48ec4f57a7bb2629206 --- /dev/null +++ b/Demosaic/code/dataloader.py @@ -0,0 +1,158 @@ +import threading +import random + +import torch +import torch.multiprocessing as multiprocessing +from torch.utils.data import DataLoader +from torch.utils.data import SequentialSampler +from torch.utils.data import RandomSampler +from torch.utils.data import BatchSampler +from torch.utils.data import _utils +from torch.utils.data.dataloader import _DataLoaderIter + +from torch.utils.data._utils import collate +from torch.utils.data._utils import signal_handling +from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL +from torch.utils.data._utils import ExceptionWrapper +from torch.utils.data._utils import IS_WINDOWS +from torch.utils.data._utils.worker import ManagerWatchdog + +from torch._six import queue + +def _ms_loop(dataset, index_queue, data_queue, done_event, collate_fn, scale, seed, init_fn, worker_id): + try: + collate._use_shared_memory = True + signal_handling._set_worker_signal_handlers() + + torch.set_num_threads(1) + random.seed(seed) + torch.manual_seed(seed) + + data_queue.cancel_join_thread() + + if init_fn is not None: + init_fn(worker_id) + + watchdog = ManagerWatchdog() + + while watchdog.is_alive(): + try: + r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL) + except queue.Empty: + continue + + if r is None: + assert done_event.is_set() + return + elif done_event.is_set(): + continue + + idx, batch_indices = r + try: + idx_scale = 0 + if len(scale) > 1 and dataset.train: + idx_scale = random.randrange(0, len(scale)) + dataset.set_scale(idx_scale) + + samples = collate_fn([dataset[i] for i in batch_indices]) + samples.append(idx_scale) + except Exception: + data_queue.put((idx, ExceptionWrapper(sys.exc_info()))) + else: + data_queue.put((idx, samples)) + del samples + + except KeyboardInterrupt: + pass + +class _MSDataLoaderIter(_DataLoaderIter): + + def __init__(self, loader): + self.dataset = loader.dataset + self.scale = loader.scale + self.collate_fn = loader.collate_fn + self.batch_sampler = loader.batch_sampler + self.num_workers = loader.num_workers + self.pin_memory = loader.pin_memory and torch.cuda.is_available() + self.timeout = loader.timeout + + self.sample_iter = iter(self.batch_sampler) + + base_seed = torch.LongTensor(1).random_().item() + + if self.num_workers > 0: + self.worker_init_fn = loader.worker_init_fn + self.worker_queue_idx = 0 + self.worker_result_queue = multiprocessing.Queue() + self.batches_outstanding = 0 + self.worker_pids_set = False + self.shutdown = False + self.send_idx = 0 + self.rcvd_idx = 0 + self.reorder_dict = {} + self.done_event = multiprocessing.Event() + + base_seed = torch.LongTensor(1).random_()[0] + + self.index_queues = [] + self.workers = [] + for i in range(self.num_workers): + index_queue = multiprocessing.Queue() + index_queue.cancel_join_thread() + w = multiprocessing.Process( + target=_ms_loop, + args=( + self.dataset, + index_queue, + self.worker_result_queue, + self.done_event, + self.collate_fn, + self.scale, + base_seed + i, + self.worker_init_fn, + i + ) + ) + w.daemon = True + w.start() + self.index_queues.append(index_queue) + self.workers.append(w) + + if self.pin_memory: + self.data_queue = queue.Queue() + pin_memory_thread = threading.Thread( + target=_utils.pin_memory._pin_memory_loop, + args=( + self.worker_result_queue, + self.data_queue, + torch.cuda.current_device(), + self.done_event + ) + ) + pin_memory_thread.daemon = True + pin_memory_thread.start() + self.pin_memory_thread = pin_memory_thread + else: + self.data_queue = self.worker_result_queue + + _utils.signal_handling._set_worker_pids( + id(self), tuple(w.pid for w in self.workers) + ) + _utils.signal_handling._set_SIGCHLD_handler() + self.worker_pids_set = True + + for _ in range(2 * self.num_workers): + self._put_indices() + + +class MSDataLoader(DataLoader): + + def __init__(self, cfg, *args, **kwargs): + super(MSDataLoader, self).__init__( + *args, **kwargs, num_workers=cfg.n_threads + ) + self.scale = cfg.scale + + def __iter__(self): + return _MSDataLoaderIter(self) + diff --git a/Demosaic/code/demo.sb b/Demosaic/code/demo.sb new file mode 100644 index 0000000000000000000000000000000000000000..471f3d2db227241b2d475e872e65a249d37d4e10 --- /dev/null +++ b/Demosaic/code/demo.sb @@ -0,0 +1,6 @@ +#!/bin/bash + +# PANET Train +#python main.py --n_GPUs 4 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 80 --save_models --model PANET --scale 1 --patch_size 48 --save PANET_DEMOSAIC --n_feats 64 --data_train DIV2K --chop +# Test +python main.py --model PANET --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 --pre_train ../model_best.pt --test_only \ No newline at end of file diff --git a/Demosaic/code/lambda_networks/__init__.py b/Demosaic/code/lambda_networks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f58c729ec677faaf96122272eb20d982210947fe --- /dev/null +++ b/Demosaic/code/lambda_networks/__init__.py @@ -0,0 +1,4 @@ +from lambda_networks.lambda_networks import LambdaLayer +from lambda_networks.lambda_networks import Recursion +from lambda_networks.rlambda_networks import RLambdaLayer +λLayer = LambdaLayer \ No newline at end of file diff --git a/Demosaic/code/lambda_networks/__pycache__/__init__.cpython-37.pyc b/Demosaic/code/lambda_networks/__pycache__/__init__.cpython-37.pyc new file mode 100644 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a/Demosaic/code/lambda_networks/lambda_networks.py b/Demosaic/code/lambda_networks/lambda_networks.py new file mode 100644 index 0000000000000000000000000000000000000000..b43a3210a202d690615a44fbc63b51e50631bbb2 --- /dev/null +++ b/Demosaic/code/lambda_networks/lambda_networks.py @@ -0,0 +1,140 @@ +import torch +from torch import nn, einsum +import torch.nn.functional as F +from einops import rearrange + +# helpers functions + +def exists(val): + return val is not None + +def default(val, d): + return val if exists(val) else d + +# lambda layer + +class LambdaLayer(nn.Module): + def __init__( + self, + dim, + *, + dim_k, + n = None, + r = None, + heads = 4, + dim_out = None, + dim_u = 1, + norm="batch"): + super().__init__() + dim_out = default(dim_out, dim) + self.u = dim_u # intra-depth dimension + self.heads = heads + + assert (dim_out % heads) == 0, 'values dimension must be divisible by number of heads for multi-head query' + dim_v = dim_out // heads + + self.to_q = nn.Conv2d(dim, dim_k * heads, 1, bias = False) + self.to_k = nn.Conv2d(dim, dim_k * dim_u, 1, bias = False) + self.to_v = nn.Conv2d(dim, dim_v * dim_u, 1, bias = False) + if norm=="instance": + self.norm_q = nn.InstanceNorm2d(dim_k * heads) + self.norm_v = nn.InstanceNorm2d(dim_v * dim_u) + else: + self.norm_q = nn.BatchNorm2d(dim_k * heads) + self.norm_v = nn.BatchNorm2d(dim_v * dim_u) + self.local_contexts = exists(r) + if exists(r): + assert (r % 2) == 1, 'Receptive kernel size should be odd' + self.pos_conv = nn.Conv3d(dim_u, dim_k, (1, r, r), padding = (0, r // 2, r // 2)) + else: + assert exists(n), 'You must specify the total sequence length (h x w)' + self.pos_emb = nn.Parameter(torch.randn(n, n, dim_k, dim_u)) + + + def forward(self, x): + b, c, hh, ww, u, h = *x.shape, self.u, self.heads + + q = self.to_q(x) + k = self.to_k(x) + v = self.to_v(x) + + q = self.norm_q(q) + v = self.norm_v(v) + + q = rearrange(q, 'b (h k) hh ww -> b h k (hh ww)', h = h) + k = rearrange(k, 'b (u k) hh ww -> b u k (hh ww)', u = u) + v = rearrange(v, 'b (u v) hh ww -> b u v (hh ww)', u = u) + + k = k.softmax(dim=-1) + + λc = einsum('b u k m, b u v m -> b k v', k, v) + Yc = einsum('b h k n, b k v -> b h v n', q, λc) + + if self.local_contexts: + v = rearrange(v, 'b u v (hh ww) -> b u v hh ww', hh = hh, ww = ww) + λp = self.pos_conv(v) + Yp = einsum('b h k n, b k v n -> b h v n', q, λp.flatten(3)) + else: + λp = einsum('n m k u, b u v m -> b n k v', self.pos_emb, v) + Yp = einsum('b h k n, b n k v -> b h v n', q, λp) + + Y = Yc + Yp + out = rearrange(Y, 'b h v (hh ww) -> b (h v) hh ww', hh = hh, ww = ww) + return out + + +# i'm not sure whether this will work or not +class Recursion(nn.Module): + def __init__(self, N: int, hidden_dim:int=64): + super(Recursion,self).__init__() + self.N = N + self.lambdaNxN_identity = LambdaLayer(dim=hidden_dim, dim_out=hidden_dim, n=N * N, dim_k=16, heads=2, dim_u=1) + # merge upstream information here + self.lambdaNxN_merge = LambdaLayer(dim=2*hidden_dim, dim_out=hidden_dim, n=N * N, dim_k=16, heads=2, dim_u=1) + self.downscale_conv = nn.Conv2d(hidden_dim, hidden_dim, kernel_size=N, stride=N) + self.upscale_conv = nn.Conv2d(hidden_dim, hidden_dim * N * N, kernel_size=3,padding=1) + self.pixel_shuffle = nn.PixelShuffle(N) + + def forward(self, x: torch.Tensor): + N = self.N + + def to_patch(blocks:torch.Tensor)->torch.Tensor: + shape = blocks.shape + blocks_patch = F.unfold(blocks, kernel_size=N, stride=N) + blocks_patch = blocks_patch.view(shape[0], shape[1], N, N, -1) + num_patch = blocks_patch.shape[-1] + blocks_patch = blocks_patch.permute(0, 4, 1, 2, 3).reshape(-1, shape[1], N, N).contiguous() + return blocks_patch, num_patch + + def combine_patch(processed_patch,shape,num_patch): + processed_patch = processed_patch.reshape(shape[0], num_patch, shape[1], N, N) + processed_patch=processed_patch.permute(0, 2, 3, 4, 1).reshape(shape[0],shape[1] * N * N,num_patch).contiguous() + processed=F.fold(processed_patch,output_size=(shape[-2],shape[-1]),kernel_size=N,stride=N) + return processed + + def process(blocks:torch.Tensor)->torch.Tensor: + shape = blocks.shape + if blocks.shape[-1] == N: + processed = self.lambdaNxN_identity(blocks) + return processed + # to NxN patchs + blocks_patch,num_patch=to_patch(blocks) + # pass through identity + processed_patch = self.lambdaNxN_identity(blocks_patch) + # back to HxW + processed=combine_patch(processed_patch,shape,num_patch) + # get feedback + feedback = process(self.downscale_conv(processed)) + # upscale feedback + upscale_feedback = self.upscale_conv(feedback) + upscale_feedback=self.pixel_shuffle(upscale_feedback) + # combine results + combined = torch.cat([processed, upscale_feedback], dim=1) + combined_shape=combined.shape + combined_patch,num_patch=to_patch(combined) + combined_patch_reduced = self.lambdaNxN_merge(combined_patch) + ret_shape=(combined_shape[0],combined_shape[1]//2,combined_shape[2],combined_shape[3]) + ret=combine_patch(combined_patch_reduced,ret_shape,num_patch) + return ret + + return process(x) \ No newline at end of file diff --git a/Demosaic/code/lambda_networks/rlambda_networks.py b/Demosaic/code/lambda_networks/rlambda_networks.py new file mode 100644 index 0000000000000000000000000000000000000000..49f9a0ce799ac80bae1279a42cd574cddcae205e --- /dev/null +++ b/Demosaic/code/lambda_networks/rlambda_networks.py @@ -0,0 +1,93 @@ +import torch +from torch import nn, einsum +import torch.nn.functional as F +from einops import rearrange + + +# helpers functions + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +# lambda layer + +class RLambdaLayer(nn.Module): + def __init__( + self, + dim, + *, + dim_k, + n=None, + r=None, + heads=4, + dim_out=None, + dim_u=1, + recurrence=None + ): + super().__init__() + dim_out = default(dim_out, dim) + self.u = dim_u # intra-depth dimension + self.heads = heads + + assert (dim_out % heads) == 0, 'values dimension must be divisible by number of heads for multi-head query' + dim_v = dim_out // heads + + self.to_q = nn.Conv2d(dim, dim_k * heads, 1, bias=False) + self.to_k = nn.Conv2d(dim, dim_k * dim_u, 1, bias=False) + self.to_v = nn.Conv2d(dim, dim_v * dim_u, 1, bias=False) + + self.norm_q = nn.BatchNorm2d(dim_k * heads) + self.norm_v = nn.BatchNorm2d(dim_v * dim_u) + + self.local_contexts = exists(r) + self.recurrence = recurrence + if exists(r): + assert (r % 2) == 1, 'Receptive kernel size should be odd' + self.pos_conv = nn.Conv3d(dim_u, dim_k, (1, r, r), padding=(0, r // 2, r // 2)) + else: + assert exists(n), 'You must specify the total sequence length (h x w)' + self.pos_emb = nn.Parameter(torch.randn(n, n, dim_k, dim_u)) + + def apply_lambda(self, lambda_c, lambda_p, x): + b, c, hh, ww, u, h = *x.shape, self.u, self.heads + q = self.to_q(x) + q = self.norm_q(q) + q = rearrange(q, 'b (h k) hh ww -> b h k (hh ww)', h=h) + Yc = einsum('b h k n, b k v -> b h v n', q, lambda_c) + if self.local_contexts: + Yp = einsum('b h k n, b k v n -> b h v n', q, lambda_p.flatten(3)) + else: + Yp = einsum('b h k n, b n k v -> b h v n', q, lambda_p) + Y = Yc + Yp + out = rearrange(Y, 'b h v (hh ww) -> b (h v) hh ww', hh=hh, ww=ww) + return out + + def forward(self, x): + b, c, hh, ww, u, h = *x.shape, self.u, self.heads + + k = self.to_k(x) + v = self.to_v(x) + + v = self.norm_v(v) + + k = rearrange(k, 'b (u k) hh ww -> b u k (hh ww)', u=u) + v = rearrange(v, 'b (u v) hh ww -> b u v (hh ww)', u=u) + + k = k.softmax(dim=-1) + + λc = einsum('b u k m, b u v m -> b k v', k, v) + + if self.local_contexts: + v = rearrange(v, 'b u v (hh ww) -> b u v hh ww', hh=hh, ww=ww) + λp = self.pos_conv(v) + else: + λp = einsum('n m k u, b u v m -> b n k v', self.pos_emb, v) + out = x + for i in range(self.recurrence): + out = self.apply_lambda(λc, λp, out) + return out diff --git a/Demosaic/code/loss/__init__.py b/Demosaic/code/loss/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..76cf0ca9051d0f7fe4c6953846ddc69f403cc701 --- /dev/null +++ b/Demosaic/code/loss/__init__.py @@ -0,0 +1,173 @@ +import os +from importlib import import_module + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F + +def sequence_loss(sr, hr, loss_func, gamma=0.8, max_val=None): + """ Loss function defined over sequence of flow predictions """ + + n_recurrence = len(sr) + total_loss = 0.0 + buffer=[0.0]*n_recurrence + # exlude invalid pixels and extremely large diplacements + for i in range(n_recurrence): + i_weight = gamma**(n_recurrence - i - 1) + i_loss = loss_func(sr[i],hr) + buffer[i]=i_loss.item() + # total_loss += i_weight * (valid[:, None] * i_loss).mean() + total_loss += i_weight * (i_loss) + return total_loss,buffer + +class Loss(nn.modules.loss._Loss): + def __init__(self, args, ckp): + super(Loss, self).__init__() + print('Preparing loss function:') + self.buffer=[0.0]*args.recurrence + self.n_GPUs = args.n_GPUs + self.loss = [] + self.loss_module = nn.ModuleList() + for loss in args.loss.split('+'): + weight, loss_type = loss.split('*') + if loss_type == 'MSE': + loss_function = nn.MSELoss() + elif loss_type == 'L1': + loss_function = nn.L1Loss() + elif loss_type.find('VGG') >= 0: + module = import_module('loss.vgg') + loss_function = getattr(module, 'VGG')( + loss_type[3:], + rgb_range=args.rgb_range + ) + elif loss_type.find('GAN') >= 0: + module = import_module('loss.adversarial') + loss_function = getattr(module, 'Adversarial')( + args, + loss_type + ) + + self.loss.append({ + 'type': loss_type, + 'weight': float(weight), + 'function': loss_function} + ) + if loss_type.find('GAN') >= 0: + self.loss.append({'type': 'DIS', 'weight': 1, 'function': None}) + + if len(self.loss) > 1: + self.loss.append({'type': 'Total', 'weight': 0, 'function': None}) + + for l in self.loss: + if l['function'] is not None: + print('{:.3f} * {}'.format(l['weight'], l['type'])) + # self.loss_module.append(l['function']) + + self.log = torch.Tensor() + + device = torch.device('cpu' if args.cpu else 'cuda') + self.loss_module.to(device) + if args.precision == 'half': self.loss_module.half() + if not args.cpu and args.n_GPUs > 1: + self.loss_module = nn.DataParallel( + self.loss_module, range(args.n_GPUs) + ) + + if args.load != '': self.load(ckp.dir, cpu=args.cpu) + + def forward(self, sr, hr): + losses = [] + for i, l in enumerate(self.loss): + if l['function'] is not None: + if isinstance(sr,list): + # weights=[0.32,0.08,0.02,0.01,0.005] + # weights=weights[::-1] + # weights=[0.01,0.02,0.08,0.32] + # self.buffer=[] + effective_loss,buffer_lst=sequence_loss(sr,hr,l['function']) + # for k in range(len(sr)): + # loss=l['function'](sr[k], hr) + # self.buffer.append(loss.item()) + # effective_loss=loss*weights[k]*l['weight'] + losses.append(effective_loss) + self.buffer=buffer_lst + self.log[-1, i] += effective_loss.item() + else: + loss = l['function'](sr, hr) + effective_loss = l['weight'] * loss + losses.append(effective_loss) + self.buffer[0]=effective_loss.item() + self.log[-1, i] += effective_loss.item() + elif l['type'] == 'DIS': + self.log[-1, i] += self.loss[i - 1]['function'].loss + + loss_sum = sum(losses) + if len(self.loss) > 1: + self.log[-1, -1] += loss_sum.item() + + return loss_sum + + def step(self): + for l in self.get_loss_module(): + if hasattr(l, 'scheduler'): + l.scheduler.step() + + def start_log(self): + self.log = torch.cat((self.log, torch.zeros(1, len(self.loss)))) + + def end_log(self, n_batches): + self.log[-1].div_(n_batches) + + def display_loss(self, batch): + n_samples = batch + 1 + log = [] + for l, c in zip(self.loss, self.log[-1]): + log.append('[{}: {:.4f}]'.format(l['type'], c / n_samples)) + + return ''.join(log) + + def plot_loss(self, apath, epoch): + axis = np.linspace(1, epoch, epoch) + for i, l in enumerate(self.loss): + label = '{} Loss'.format(l['type']) + fig = plt.figure() + plt.title(label) + plt.plot(axis, self.log[:, i].numpy(), label=label) + plt.legend() + plt.xlabel('Epochs') + plt.ylabel('Loss') + plt.grid(True) + plt.savefig(os.path.join(apath, 'loss_{}.pdf'.format(l['type']))) + plt.close(fig) + + def get_loss_module(self): + if self.n_GPUs == 1: + return self.loss_module + else: + return self.loss_module.module + + def save(self, apath): + torch.save(self.state_dict(), os.path.join(apath, 'loss.pt')) + torch.save(self.log, os.path.join(apath, 'loss_log.pt')) + + def load(self, apath, cpu=False): + if cpu: + kwargs = {'map_location': lambda storage, loc: storage} + else: + kwargs = {} + + self.load_state_dict(torch.load( + os.path.join(apath, 'loss.pt'), + **kwargs + )) + self.log = torch.load(os.path.join(apath, 'loss_log.pt')) + for l in self.get_loss_module(): + if hasattr(l, 'scheduler'): + for _ in range(len(self.log)): l.scheduler.step() + diff --git a/Demosaic/code/loss/__pycache__/__init__.cpython-37.pyc b/Demosaic/code/loss/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b5f45cb867546a2cb76624453a99f0ebb2edfead Binary files /dev/null and b/Demosaic/code/loss/__pycache__/__init__.cpython-37.pyc differ diff --git a/Demosaic/code/loss/adversarial.py b/Demosaic/code/loss/adversarial.py new file mode 100644 index 0000000000000000000000000000000000000000..db930a8efbd040b3a2271b20193194a58646086d --- /dev/null +++ b/Demosaic/code/loss/adversarial.py @@ -0,0 +1,112 @@ +import utility +from types import SimpleNamespace + +from model import common +from loss import discriminator + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim + +class Adversarial(nn.Module): + def __init__(self, args, gan_type): + super(Adversarial, self).__init__() + self.gan_type = gan_type + self.gan_k = args.gan_k + self.dis = discriminator.Discriminator(args) + if gan_type == 'WGAN_GP': + # see https://arxiv.org/pdf/1704.00028.pdf pp.4 + optim_dict = { + 'optimizer': 'ADAM', + 'betas': (0, 0.9), + 'epsilon': 1e-8, + 'lr': 1e-5, + 'weight_decay': args.weight_decay, + 'decay': args.decay, + 'gamma': args.gamma + } + optim_args = SimpleNamespace(**optim_dict) + else: + optim_args = args + + self.optimizer = utility.make_optimizer(optim_args, self.dis) + + def forward(self, fake, real): + # updating discriminator... + self.loss = 0 + fake_detach = fake.detach() # do not backpropagate through G + for _ in range(self.gan_k): + self.optimizer.zero_grad() + # d: B x 1 tensor + d_fake = self.dis(fake_detach) + d_real = self.dis(real) + retain_graph = False + if self.gan_type == 'GAN': + loss_d = self.bce(d_real, d_fake) + elif self.gan_type.find('WGAN') >= 0: + loss_d = (d_fake - d_real).mean() + if self.gan_type.find('GP') >= 0: + epsilon = torch.rand_like(fake).view(-1, 1, 1, 1) + hat = fake_detach.mul(1 - epsilon) + real.mul(epsilon) + hat.requires_grad = True + d_hat = self.dis(hat) + gradients = torch.autograd.grad( + outputs=d_hat.sum(), inputs=hat, + retain_graph=True, create_graph=True, only_inputs=True + )[0] + gradients = gradients.view(gradients.size(0), -1) + gradient_norm = gradients.norm(2, dim=1) + gradient_penalty = 10 * gradient_norm.sub(1).pow(2).mean() + loss_d += gradient_penalty + # from ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks + elif self.gan_type == 'RGAN': + better_real = d_real - d_fake.mean(dim=0, keepdim=True) + better_fake = d_fake - d_real.mean(dim=0, keepdim=True) + loss_d = self.bce(better_real, better_fake) + retain_graph = True + + # Discriminator update + self.loss += loss_d.item() + loss_d.backward(retain_graph=retain_graph) + self.optimizer.step() + + if self.gan_type == 'WGAN': + for p in self.dis.parameters(): + p.data.clamp_(-1, 1) + + self.loss /= self.gan_k + + # updating generator... + d_fake_bp = self.dis(fake) # for backpropagation, use fake as it is + if self.gan_type == 'GAN': + label_real = torch.ones_like(d_fake_bp) + loss_g = F.binary_cross_entropy_with_logits(d_fake_bp, label_real) + elif self.gan_type.find('WGAN') >= 0: + loss_g = -d_fake_bp.mean() + elif self.gan_type == 'RGAN': + better_real = d_real - d_fake_bp.mean(dim=0, keepdim=True) + better_fake = d_fake_bp - d_real.mean(dim=0, keepdim=True) + loss_g = self.bce(better_fake, better_real) + + # Generator loss + return loss_g + + def state_dict(self, *args, **kwargs): + state_discriminator = self.dis.state_dict(*args, **kwargs) + state_optimizer = self.optimizer.state_dict() + + return dict(**state_discriminator, **state_optimizer) + + def bce(self, real, fake): + label_real = torch.ones_like(real) + label_fake = torch.zeros_like(fake) + bce_real = F.binary_cross_entropy_with_logits(real, label_real) + bce_fake = F.binary_cross_entropy_with_logits(fake, label_fake) + bce_loss = bce_real + bce_fake + return bce_loss + +# Some references +# https://github.com/kuc2477/pytorch-wgan-gp/blob/master/model.py +# OR +# https://github.com/caogang/wgan-gp/blob/master/gan_cifar10.py diff --git a/Demosaic/code/loss/discriminator.py b/Demosaic/code/loss/discriminator.py new file mode 100644 index 0000000000000000000000000000000000000000..05469d37865fe79642e61fb71d479b96d1688ba4 --- /dev/null +++ b/Demosaic/code/loss/discriminator.py @@ -0,0 +1,55 @@ +from model import common + +import torch.nn as nn + +class Discriminator(nn.Module): + ''' + output is not normalized + ''' + def __init__(self, args): + super(Discriminator, self).__init__() + + in_channels = args.n_colors + out_channels = 64 + depth = 7 + + def _block(_in_channels, _out_channels, stride=1): + return nn.Sequential( + nn.Conv2d( + _in_channels, + _out_channels, + 3, + padding=1, + stride=stride, + bias=False + ), + nn.BatchNorm2d(_out_channels), + nn.LeakyReLU(negative_slope=0.2, inplace=True) + ) + + m_features = [_block(in_channels, out_channels)] + for i in range(depth): + in_channels = out_channels + if i % 2 == 1: + stride = 1 + out_channels *= 2 + else: + stride = 2 + m_features.append(_block(in_channels, out_channels, stride=stride)) + + patch_size = args.patch_size // (2**((depth + 1) // 2)) + m_classifier = [ + nn.Linear(out_channels * patch_size**2, 1024), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Linear(1024, 1) + ] + + self.features = nn.Sequential(*m_features) + self.classifier = nn.Sequential(*m_classifier) + + def forward(self, x): + features = self.features(x) + output = self.classifier(features.view(features.size(0), -1)) + + return output + diff --git a/Demosaic/code/loss/vgg.py b/Demosaic/code/loss/vgg.py new file mode 100644 index 0000000000000000000000000000000000000000..f8391bd6e8aeab1f31fd5d4ca4abbd3da0eaba71 --- /dev/null +++ b/Demosaic/code/loss/vgg.py @@ -0,0 +1,36 @@ +from model import common + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.models as models + +class VGG(nn.Module): + def __init__(self, conv_index, rgb_range=1): + super(VGG, self).__init__() + vgg_features = models.vgg19(pretrained=True).features + modules = [m for m in vgg_features] + if conv_index.find('22') >= 0: + self.vgg = nn.Sequential(*modules[:8]) + elif conv_index.find('54') >= 0: + self.vgg = nn.Sequential(*modules[:35]) + + vgg_mean = (0.485, 0.456, 0.406) + vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range) + self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std) + for p in self.parameters(): + p.requires_grad = False + + def forward(self, sr, hr): + def _forward(x): + x = self.sub_mean(x) + x = self.vgg(x) + return x + + vgg_sr = _forward(sr) + with torch.no_grad(): + vgg_hr = _forward(hr.detach()) + + loss = F.mse_loss(vgg_sr, vgg_hr) + + return loss diff --git a/Demosaic/code/main.py b/Demosaic/code/main.py new file mode 100644 index 0000000000000000000000000000000000000000..9703700d795fc061f82b80603d9ffee9f870dc51 --- /dev/null +++ b/Demosaic/code/main.py @@ -0,0 +1,35 @@ +import torch + +import utility +import data +import model +import loss +from option import args +from trainer import Trainer + +torch.manual_seed(args.seed) +checkpoint = utility.checkpoint(args) + +def main(): + global model + if args.data_test == ['video']: + from videotester import VideoTester + model = model.Model(args,checkpoint) + print('total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0)) + t = VideoTester(args, model, checkpoint) + t.test() + else: + if checkpoint.ok: + loader = data.Data(args) + _model = model.Model(args, checkpoint) + print('total params:%.5fM' % (sum(p.numel() for p in _model.parameters())/1000000.0)) + _loss = loss.Loss(args, checkpoint) if not args.test_only else None + t = Trainer(args, loader, _model, _loss, checkpoint) + while not t.terminate(): + t.train() + t.test() + + checkpoint.done() + +if __name__ == '__main__': + main() diff --git a/Demosaic/code/model/LICENSE b/Demosaic/code/model/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..df2326a5dce9106825799c73dfab581cbde1f96c --- /dev/null +++ b/Demosaic/code/model/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 Sanghyun Son + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Demosaic/code/model/__init__.py b/Demosaic/code/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c78df4fef100d9fcbbaeabe02e958b38a92cd395 --- /dev/null +++ b/Demosaic/code/model/__init__.py @@ -0,0 +1,190 @@ +import os +from importlib import import_module + +import torch +import torch.nn as nn +from torch.autograd import Variable + +class Model(nn.Module): + def __init__(self, args, ckp): + super(Model, self).__init__() + print('Making model...') + + self.scale = args.scale + self.idx_scale = 0 + self.self_ensemble = args.self_ensemble + self.chop = args.chop + self.precision = args.precision + self.cpu = args.cpu + self.device = torch.device('cpu' if args.cpu else 'cuda') + self.n_GPUs = args.n_GPUs + self.save_models = args.save_models + + module = import_module('model.' + args.model.lower()) + self.model = module.make_model(args).to(self.device) + if args.precision == 'half': self.model.half() + + if not args.cpu and args.n_GPUs > 1: + self.model = nn.DataParallel(self.model, range(args.n_GPUs)) + + self.load( + ckp.dir, + pre_train=args.pre_train, + resume=args.resume, + cpu=args.cpu + ) + print(self.model, file=ckp.log_file) + + def forward(self, x, idx_scale): + self.idx_scale = idx_scale + target = self.get_model() + if hasattr(target, 'set_scale'): + target.set_scale(idx_scale) + + if self.self_ensemble and not self.training: + if self.chop: + forward_function = self.forward_chop + else: + forward_function = self.model.forward + + return self.forward_x8(x, forward_function) + elif self.chop and not self.training: + return self.forward_chop(x) + else: + return self.model(x) + + def get_model(self): + if self.n_GPUs == 1: + return self.model + else: + return self.model.module + + def state_dict(self, **kwargs): + target = self.get_model() + return target.state_dict(**kwargs) + + def save(self, apath, epoch, is_best=False): + target = self.get_model() + torch.save( + target.state_dict(), + os.path.join(apath, 'model_latest.pt') + ) + if is_best: + torch.save( + target.state_dict(), + os.path.join(apath, 'model_best.pt') + ) + + if self.save_models: + torch.save( + target.state_dict(), + os.path.join(apath, 'model_{}.pt'.format(epoch)) + ) + + def load(self, apath, pre_train='.', resume=-1, cpu=False): + if cpu: + kwargs = {'map_location': lambda storage, loc: storage} + else: + kwargs = {} + + if resume == -1: + self.get_model().load_state_dict( + torch.load( + os.path.join(apath,'model', 'model_latest.pt'), + **kwargs + ), + strict=False + ) + elif resume == 0: + if pre_train != '.': + print('Loading model from {}'.format(pre_train)) + self.get_model().load_state_dict( + torch.load(pre_train, **kwargs), + strict=False + ) + else: + self.get_model().load_state_dict( + torch.load( + os.path.join(apath, 'model', 'model_{}.pt'.format(resume)), + **kwargs + ), + strict=False + ) + + def forward_chop(self, x, shave=10, min_size=6400): + scale = self.scale[self.idx_scale] + scale = 1 + n_GPUs = min(self.n_GPUs, 4) + b, c, h, w = x.size() + h_half, w_half = h // 2, w // 2 + h_size, w_size = h_half + shave, w_half + shave + lr_list = [ + x[:, :, 0:h_size, 0:w_size], + x[:, :, 0:h_size, (w - w_size):w], + x[:, :, (h - h_size):h, 0:w_size], + x[:, :, (h - h_size):h, (w - w_size):w]] + + if w_size * h_size < min_size: + sr_list = [] + for i in range(0, 4, n_GPUs): + lr_batch = torch.cat(lr_list[i:(i + n_GPUs)], dim=0) + sr_batch = self.model(lr_batch) + sr_list.extend(sr_batch.chunk(n_GPUs, dim=0)) + else: + sr_list = [ + self.forward_chop(patch, shave=shave, min_size=min_size) \ + for patch in lr_list + ] + + h, w = scale * h, scale * w + h_half, w_half = scale * h_half, scale * w_half + h_size, w_size = scale * h_size, scale * w_size + shave *= scale + + output = x.new(b, c, h, w) + output[:, :, 0:h_half, 0:w_half] \ + = sr_list[0][:, :, 0:h_half, 0:w_half] + output[:, :, 0:h_half, w_half:w] \ + = sr_list[1][:, :, 0:h_half, (w_size - w + w_half):w_size] + output[:, :, h_half:h, 0:w_half] \ + = sr_list[2][:, :, (h_size - h + h_half):h_size, 0:w_half] + output[:, :, h_half:h, w_half:w] \ + = sr_list[3][:, :, (h_size - h + h_half):h_size, (w_size - w + w_half):w_size] + + return output + + def forward_x8(self, x, forward_function): + def _transform(v, op): + if self.precision != 'single': v = v.float() + + v2np = v.data.cpu().numpy() + if op == 'v': + tfnp = v2np[:, :, :, ::-1].copy() + elif op == 'h': + tfnp = v2np[:, :, ::-1, :].copy() + elif op == 't': + tfnp = v2np.transpose((0, 1, 3, 2)).copy() + + ret = torch.Tensor(tfnp).to(self.device) + if self.precision == 'half': ret = ret.half() + + return ret + + lr_list = [x] + for tf in 'v', 'h', 't': + lr_list.extend([_transform(t, tf) for t in lr_list]) + + sr_list = [forward_function(aug) for aug in lr_list] + for i in range(len(sr_list)): + if i > 3: + sr_list[i] = _transform(sr_list[i], 't') + if i % 4 > 1: + sr_list[i] = _transform(sr_list[i], 'h') + if (i % 4) % 2 == 1: + sr_list[i] = _transform(sr_list[i], 'v') + + output_cat = torch.cat(sr_list, dim=0) + output = output_cat.mean(dim=0, keepdim=True) + + return output + diff --git a/Demosaic/code/model/__pycache__/__init__.cpython-37.pyc b/Demosaic/code/model/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4bad26772174cee602d57a0d9753f0a1b4152fcd Binary files /dev/null and b/Demosaic/code/model/__pycache__/__init__.cpython-37.pyc differ diff --git a/Demosaic/code/model/__pycache__/attention.cpython-37.pyc b/Demosaic/code/model/__pycache__/attention.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0cbecb314b3bd66d2fc114cbca8cba49e18406e8 Binary files /dev/null and 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/dev/null and b/Demosaic/code/model/__pycache__/raftnetsingle.cpython-37.pyc differ diff --git a/Demosaic/code/model/attention.py b/Demosaic/code/model/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..2f6fd9eed740f04c816ec109f75ee54e5c4f8eeb --- /dev/null +++ b/Demosaic/code/model/attention.py @@ -0,0 +1,94 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import transforms +from torchvision import utils as vutils +from model import common +from utils.tools import extract_image_patches,\ + reduce_mean, reduce_sum, same_padding + +class PyramidAttention(nn.Module): + def __init__(self, level=5, res_scale=1, channel=64, reduction=2, ksize=3, stride=1, softmax_scale=10, average=True, conv=common.default_conv): + super(PyramidAttention, self).__init__() + self.ksize = ksize + self.stride = stride + self.res_scale = res_scale + self.softmax_scale = softmax_scale + self.scale = [1-i/10 for i in range(level)] + self.average = average + escape_NaN = torch.FloatTensor([1e-4]) + self.register_buffer('escape_NaN', escape_NaN) + self.conv_match_L_base = common.BasicBlock(conv,channel,channel//reduction, 1, bn=False, act=nn.PReLU()) + self.conv_match = common.BasicBlock(conv,channel, channel//reduction, 1, bn=False, act=nn.PReLU()) + self.conv_assembly = common.BasicBlock(conv,channel, channel,1,bn=False, act=nn.PReLU()) + + def forward(self, input): + res = input + #theta + match_base = self.conv_match_L_base(input) + shape_base = list(res.size()) + input_groups = torch.split(match_base,1,dim=0) + # patch size for matching + kernel = self.ksize + # raw_w is for reconstruction + raw_w = [] + # w is for matching + w = [] + #build feature pyramid + for i in range(len(self.scale)): + ref = input + if self.scale[i]!=1: + ref = F.interpolate(input, scale_factor=self.scale[i], mode='bicubic') + #feature transformation function f + base = self.conv_assembly(ref) + shape_input = base.shape + #sampling + raw_w_i = extract_image_patches(base, ksizes=[kernel, kernel], + strides=[self.stride,self.stride], + rates=[1, 1], + padding='same') # [N, C*k*k, L] + raw_w_i = raw_w_i.view(shape_input[0], shape_input[1], kernel, kernel, -1) + raw_w_i = raw_w_i.permute(0, 4, 1, 2, 3) # raw_shape: [N, L, C, k, k] + raw_w_i_groups = torch.split(raw_w_i, 1, dim=0) + raw_w.append(raw_w_i_groups) + + #feature transformation function g + ref_i = self.conv_match(ref) + shape_ref = ref_i.shape + #sampling + w_i = extract_image_patches(ref_i, ksizes=[self.ksize, self.ksize], + strides=[self.stride, self.stride], + rates=[1, 1], + padding='same') + w_i = w_i.view(shape_ref[0], shape_ref[1], self.ksize, self.ksize, -1) + w_i = w_i.permute(0, 4, 1, 2, 3) # w shape: [N, L, C, k, k] + w_i_groups = torch.split(w_i, 1, dim=0) + w.append(w_i_groups) + + y = [] + for idx, xi in enumerate(input_groups): + #group in a filter + wi = torch.cat([w[i][idx][0] for i in range(len(self.scale))],dim=0) # [L, C, k, k] + #normalize + max_wi = torch.max(torch.sqrt(reduce_sum(torch.pow(wi, 2), + axis=[1, 2, 3], + keepdim=True)), + self.escape_NaN) + wi_normed = wi/ max_wi + #matching + xi = same_padding(xi, [self.ksize, self.ksize], [1, 1], [1, 1]) # xi: 1*c*H*W + yi = F.conv2d(xi, wi_normed, stride=1) # [1, L, H, W] L = shape_ref[2]*shape_ref[3] + yi = yi.view(1,wi.shape[0], shape_base[2], shape_base[3]) # (B=1, C=32*32, H=32, W=32) + # softmax matching score + yi = F.softmax(yi*self.softmax_scale, dim=1) + + if self.average == False: + yi = (yi == yi.max(dim=1,keepdim=True)[0]).float() + + # deconv for patch pasting + raw_wi = torch.cat([raw_w[i][idx][0] for i in range(len(self.scale))],dim=0) + yi = F.conv_transpose2d(yi, raw_wi, stride=self.stride,padding=1)/4. + y.append(yi) + + y = torch.cat(y, dim=0)+res*self.res_scale # back to the mini-batch + return y \ No newline at end of file diff --git a/Demosaic/code/model/betalambdanet.py b/Demosaic/code/model/betalambdanet.py new file mode 100644 index 0000000000000000000000000000000000000000..bdbb523c8375069e8d047b4337a0bf4ab488fa4e --- /dev/null +++ b/Demosaic/code/model/betalambdanet.py @@ -0,0 +1,97 @@ +from model import common +import torch +import torch.nn as nn +from lambda_networks import LambdaLayer +import torch.cuda.amp as amp + +def make_model(args, parent=False): + return BETALAMBDANET(args) + +class BETALAMBDANET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(BETALAMBDANET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4 + ) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + # m_body.append(msa) + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + #m_tail = [ + # common.Upsampler(conv, scale, n_feats, act=False), + # conv(n_feats, args.n_colors, kernel_size) + #] + m_tail = [ + conv(n_feats, args.n_colors, kernel_size) + ] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + self.beta=nn.Parameter(torch.ones(1)*0.5) + + def forward(self, x): + with amp.autocast(self.amp): + out = self.head(x) + last_output=out + for i in range(self.recurrence): + res = self.body(last_output) + res = self.beta*res + (1-self.beta)*last_output + last_output=res + output = self.tail(last_output) + return [output] + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/common.py b/Demosaic/code/model/common.py new file mode 100644 index 0000000000000000000000000000000000000000..d55d63132c23604c423b9c51e3f8cb3348bb40f6 --- /dev/null +++ b/Demosaic/code/model/common.py @@ -0,0 +1,93 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +def default_conv(in_channels, out_channels, kernel_size,stride=1, bias=True): + return nn.Conv2d( + in_channels, out_channels, kernel_size, + padding=(kernel_size//2),stride=stride, bias=bias) + +def spectral_conv(in_channels, out_channels, kernel_size,stride=1, bias=True): + return nn.utils.spectral_norm(nn.Conv2d( + in_channels, out_channels, kernel_size, + padding=(kernel_size//2),stride=stride, bias=bias)) + +class MeanShift(nn.Conv2d): + def __init__( + self, rgb_range, + rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1): + + super(MeanShift, self).__init__(3, 3, kernel_size=1) + std = torch.Tensor(rgb_std) + self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1) + self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std + for p in self.parameters(): + p.requires_grad = False + +class BasicBlock(nn.Sequential): + def __init__( + self, conv, in_channels, out_channels, kernel_size, stride=1, bias=True, + bn=False, act=nn.PReLU()): + + m = [conv(in_channels, out_channels, kernel_size, bias=bias)] + if bn: + m.append(nn.BatchNorm2d(out_channels)) + if act is not None: + m.append(act) + + super(BasicBlock, self).__init__(*m) + +class ResBlock(nn.Module): + def __init__( + self, conv, n_feats, kernel_size, + bias=True, bn=False, act=nn.PReLU(), res_scale=1): + + super(ResBlock, self).__init__() + m = [] + for i in range(2): + m.append(conv(n_feats, n_feats, kernel_size, bias=bias)) + if bn: + m.append(nn.BatchNorm2d(n_feats)) + if i == 0: + m.append(act) + + self.body = nn.Sequential(*m) + self.res_scale = res_scale + + def forward(self, x): + res = self.body(x).mul(self.res_scale) + res += x + + return res + +class Upsampler(nn.Sequential): + def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True): + + m = [] + if (scale & (scale - 1)) == 0: # Is scale = 2^n? + for _ in range(int(math.log(scale, 2))): + m.append(conv(n_feats, 4 * n_feats, 3, bias)) + m.append(nn.PixelShuffle(2)) + if bn: + m.append(nn.BatchNorm2d(n_feats)) + if act == 'relu': + m.append(nn.ReLU(True)) + elif act == 'prelu': + m.append(nn.PReLU(n_feats)) + + elif scale == 3: + m.append(conv(n_feats, 9 * n_feats, 3, bias)) + m.append(nn.PixelShuffle(3)) + if bn: + m.append(nn.BatchNorm2d(n_feats)) + if act == 'relu': + m.append(nn.ReLU(True)) + elif act == 'prelu': + m.append(nn.PReLU(n_feats)) + else: + raise NotImplementedError + + super(Upsampler, self).__init__(*m) + diff --git a/Demosaic/code/model/ddbpn.py b/Demosaic/code/model/ddbpn.py new file mode 100644 index 0000000000000000000000000000000000000000..f2185fcbde79334e77128463804313e325902536 --- /dev/null +++ b/Demosaic/code/model/ddbpn.py @@ -0,0 +1,131 @@ +# Deep Back-Projection Networks For Super-Resolution +# https://arxiv.org/abs/1803.02735 + +from model import common + +import torch +import torch.nn as nn + + +def make_model(args, parent=False): + return DDBPN(args) + +def projection_conv(in_channels, out_channels, scale, up=True): + kernel_size, stride, padding = { + 2: (6, 2, 2), + 4: (8, 4, 2), + 8: (12, 8, 2) + }[scale] + if up: + conv_f = nn.ConvTranspose2d + else: + conv_f = nn.Conv2d + + return conv_f( + in_channels, out_channels, kernel_size, + stride=stride, padding=padding + ) + +class DenseProjection(nn.Module): + def __init__(self, in_channels, nr, scale, up=True, bottleneck=True): + super(DenseProjection, self).__init__() + if bottleneck: + self.bottleneck = nn.Sequential(*[ + nn.Conv2d(in_channels, nr, 1), + nn.PReLU(nr) + ]) + inter_channels = nr + else: + self.bottleneck = None + inter_channels = in_channels + + self.conv_1 = nn.Sequential(*[ + projection_conv(inter_channels, nr, scale, up), + nn.PReLU(nr) + ]) + self.conv_2 = nn.Sequential(*[ + projection_conv(nr, inter_channels, scale, not up), + nn.PReLU(inter_channels) + ]) + self.conv_3 = nn.Sequential(*[ + projection_conv(inter_channels, nr, scale, up), + nn.PReLU(nr) + ]) + + def forward(self, x): + if self.bottleneck is not None: + x = self.bottleneck(x) + + a_0 = self.conv_1(x) + b_0 = self.conv_2(a_0) + e = b_0.sub(x) + a_1 = self.conv_3(e) + + out = a_0.add(a_1) + + return out + +class DDBPN(nn.Module): + def __init__(self, args): + super(DDBPN, self).__init__() + scale = args.scale[0] + + n0 = 128 + nr = 32 + self.depth = 6 + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + initial = [ + nn.Conv2d(args.n_colors, n0, 3, padding=1), + nn.PReLU(n0), + nn.Conv2d(n0, nr, 1), + nn.PReLU(nr) + ] + self.initial = nn.Sequential(*initial) + + self.upmodules = nn.ModuleList() + self.downmodules = nn.ModuleList() + channels = nr + for i in range(self.depth): + self.upmodules.append( + DenseProjection(channels, nr, scale, True, i > 1) + ) + if i != 0: + channels += nr + + channels = nr + for i in range(self.depth - 1): + self.downmodules.append( + DenseProjection(channels, nr, scale, False, i != 0) + ) + channels += nr + + reconstruction = [ + nn.Conv2d(self.depth * nr, args.n_colors, 3, padding=1) + ] + self.reconstruction = nn.Sequential(*reconstruction) + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + def forward(self, x): + x = self.sub_mean(x) + x = self.initial(x) + + h_list = [] + l_list = [] + for i in range(self.depth - 1): + if i == 0: + l = x + else: + l = torch.cat(l_list, dim=1) + h_list.append(self.upmodules[i](l)) + l_list.append(self.downmodules[i](torch.cat(h_list, dim=1))) + + h_list.append(self.upmodules[-1](torch.cat(l_list, dim=1))) + out = self.reconstruction(torch.cat(h_list, dim=1)) + out = self.add_mean(out) + + return out + diff --git a/Demosaic/code/model/lambdanet.py b/Demosaic/code/model/lambdanet.py new file mode 100644 index 0000000000000000000000000000000000000000..d79493747ab2cacd7e3ad061d579df459aef2f2d --- /dev/null +++ b/Demosaic/code/model/lambdanet.py @@ -0,0 +1,101 @@ +from model import common +import torch.nn as nn +from lambda_networks import LambdaLayer +import torch.cuda.amp as amp + +def make_model(args, parent=False): + return LAMBDANET(args) + +class LAMBDANET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(LAMBDANET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4 + ) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + # m_body.append(msa) + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + #m_tail = [ + # common.Upsampler(conv, scale, n_feats, act=False), + # conv(n_feats, args.n_colors, kernel_size) + #] + m_tail = [ + conv(n_feats, args.n_colors, kernel_size) + ] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + + def forward(self, x): + #x = self.sub_mean(x) + with amp.autocast(self.amp): + last_output=x + output_lst=[None]*self.recurrence + for i in range(self.recurrence): + if self.detach: + last_output=last_output.detach() + out = self.head(last_output) + res = self.body(out) + res += out + output = self.tail(res) + last_output + output_lst[i]=output + last_output=output + #x = self.add_mean(x) + return output_lst + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/lambdanetacta.py b/Demosaic/code/model/lambdanetacta.py new file mode 100644 index 0000000000000000000000000000000000000000..151df05e628d9c5f2c55d128703d018238099705 --- /dev/null +++ b/Demosaic/code/model/lambdanetacta.py @@ -0,0 +1,105 @@ +from model import common +import torch.nn as nn +import torch.nn.functional as F +from lambda_networks import LambdaLayer +import torch.cuda.amp as amp + +def make_model(args, parent=False): + if args.spectral: + return LAMBDANET(args, conv=common.spectral_conv) + else: + return LAMBDANET(args) + +class LAMBDANET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(LAMBDANET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4 + ) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + # m_body.append(msa) + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + #m_tail = [ + # common.Upsampler(conv, scale, n_feats, act=False), + # conv(n_feats, args.n_colors, kernel_size) + #] + m_tail = [ + conv(n_feats, args.n_colors, kernel_size) + ] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + + def forward(self, x): + #x = self.sub_mean(x) + with amp.autocast(self.amp): + last_output=(x-0.5)/0.5 + output_lst=[None]*self.recurrence + for i in range(self.recurrence): + if self.detach: + last_output=last_output.detach().clamp(min=-1,max=1) + out = self.head(last_output) + res = self.body(out) + res += out + output = F.tanh(self.tail(res)) + last_output + output_lst[i]=output*0.5+0.5 + last_output=output + #x = self.add_mean(x) + return output_lst + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/lambdanetactb.py b/Demosaic/code/model/lambdanetactb.py new file mode 100644 index 0000000000000000000000000000000000000000..4ef764b3f14bca5b9f7aec790f17617f02d31365 --- /dev/null +++ b/Demosaic/code/model/lambdanetactb.py @@ -0,0 +1,104 @@ +from model import common +import torch.nn as nn +from lambda_networks import LambdaLayer +import torch.cuda.amp as amp + +def make_model(args, parent=False): + if args.spectral: + return LAMBDANET(args, conv=common.spectral_conv) + else: + return LAMBDANET(args) +class LAMBDANET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(LAMBDANET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4 + ) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + # m_body.append(msa) + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + #m_tail = [ + # common.Upsampler(conv, scale, n_feats, act=False), + # conv(n_feats, args.n_colors, kernel_size) + #] + m_tail = [ + conv(n_feats, args.n_colors, kernel_size) + ] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + + def forward(self, x): + #x = self.sub_mean(x) + with amp.autocast(self.amp): + last_output=(x-0.5)/0.5 + output_lst=[None]*self.recurrence + for i in range(self.recurrence): + if self.detach: + last_output=last_output.detach() + out = self.head(last_output) + res = self.body(out) + res += out + output = self.tail(res) + last_output + output=output.tanh() + output_lst[i]=output*0.5+0.5 + last_output=output + #x = self.add_mean(x) + return output_lst + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/lnet.py b/Demosaic/code/model/lnet.py new file mode 100644 index 0000000000000000000000000000000000000000..8db1cc4637c8c3bd031f989af643f6c015e0fcfd --- /dev/null +++ b/Demosaic/code/model/lnet.py @@ -0,0 +1,82 @@ +from model import common +from model import attention +import torch.nn as nn + +def make_model(args, parent=False): + return PANET(args) + +class PANET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(PANET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + m_body.append(msa) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + #m_tail = [ + # common.Upsampler(conv, scale, n_feats, act=False), + # conv(n_feats, args.n_colors, kernel_size) + #] + m_tail = [ + conv(n_feats, args.n_colors, kernel_size) + ] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + def forward(self, x): + #x = self.sub_mean(x) + x = self.head(x) + + res = self.body(x) + + res += x + + x = self.tail(res) + #x = self.add_mean(x) + + return x + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/mdsr.py b/Demosaic/code/model/mdsr.py new file mode 100644 index 0000000000000000000000000000000000000000..c55931bbd1172be4bf921eefd94cf3d7bea7e41a --- /dev/null +++ b/Demosaic/code/model/mdsr.py @@ -0,0 +1,68 @@ +from model import common + +import torch.nn as nn + +def make_model(args, parent=False): + return MDSR(args) + +class MDSR(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(MDSR, self).__init__() + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + self.scale_idx = 0 + + act = nn.ReLU(True) + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + self.pre_process = nn.ModuleList([ + nn.Sequential( + common.ResBlock(conv, n_feats, 5, act=act), + common.ResBlock(conv, n_feats, 5, act=act) + ) for _ in args.scale + ]) + + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, act=act + ) for _ in range(n_resblocks) + ] + m_body.append(conv(n_feats, n_feats, kernel_size)) + + self.upsample = nn.ModuleList([ + common.Upsampler( + conv, s, n_feats, act=False + ) for s in args.scale + ]) + + m_tail = [conv(n_feats, args.n_colors, kernel_size)] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + def forward(self, x): + x = self.sub_mean(x) + x = self.head(x) + x = self.pre_process[self.scale_idx](x) + + res = self.body(x) + res += x + + x = self.upsample[self.scale_idx](res) + x = self.tail(x) + x = self.add_mean(x) + + return x + + def set_scale(self, scale_idx): + self.scale_idx = scale_idx + diff --git a/Demosaic/code/model/panet.py b/Demosaic/code/model/panet.py new file mode 100644 index 0000000000000000000000000000000000000000..8db1cc4637c8c3bd031f989af643f6c015e0fcfd --- /dev/null +++ b/Demosaic/code/model/panet.py @@ -0,0 +1,82 @@ +from model import common +from model import attention +import torch.nn as nn + +def make_model(args, parent=False): + return PANET(args) + +class PANET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(PANET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + m_body.append(msa) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + #m_tail = [ + # common.Upsampler(conv, scale, n_feats, act=False), + # conv(n_feats, args.n_colors, kernel_size) + #] + m_tail = [ + conv(n_feats, args.n_colors, kernel_size) + ] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + def forward(self, x): + #x = self.sub_mean(x) + x = self.head(x) + + res = self.body(x) + + res += x + + x = self.tail(res) + #x = self.add_mean(x) + + return x + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/raftnet.py b/Demosaic/code/model/raftnet.py new file mode 100644 index 0000000000000000000000000000000000000000..196b760adca47ee167193f0f876e55c9612a45cc --- /dev/null +++ b/Demosaic/code/model/raftnet.py @@ -0,0 +1,146 @@ +from model import common +from model import attention +import torch +from lambda_networks import LambdaLayer +import torch.nn as nn +import torch.cuda.amp as amp + +class ConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(ConvGRU, self).__init__() + self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + + def forward(self, h, x): + hx = torch.cat([h, x], dim=1) + + z = torch.sigmoid(self.convz(hx)) + r = torch.sigmoid(self.convr(hx)) + q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) + + # h = (1-z) * h + z * q + # return h + return (1-z) * h + z * q + +class SepConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(SepConvGRU, self).__init__() + self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + + self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + + + def forward(self, h, x): + # horizontal + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz1(hx)) + r = torch.sigmoid(self.convr1(hx)) + q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + # vertical + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz2(hx)) + r = torch.sigmoid(self.convr2(hx)) + q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + return h + + +def make_model(args, parent=False): + return RAFTNET(args) + +class RAFTNET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(RAFTNET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + # perhaps a shallow network here? + for i in range(5): + m_head.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + # convert feature to image, shared + m_tail=[] + for i in range(5): + m_tail.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + m_tail.append(conv(n_feats, args.n_colors, kernel_size)) + # middle recurrent part + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4 + ) + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + self.gru = ConvGRU(hidden_dim=64,input_dim=64) + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + + def forward(self, x): + with amp.autocast(self.amp): + x=(x-0.5)/0.5 + x = self.head(x) + hidden = x.clone() + output_lst=[None]*self.recurrence + for i in range(self.recurrence): + gru_out=self.gru(hidden,x) + res=self.body(gru_out) + gru_out=res+gru_out + hidden=gru_out + output_lst[i]=self.tail(gru_out)*0.5+0.5 + return output_lst + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/raftnetlayer.py b/Demosaic/code/model/raftnetlayer.py new file mode 100644 index 0000000000000000000000000000000000000000..6cd09de9f525c97fbe3a314f9472226930f7ccc3 --- /dev/null +++ b/Demosaic/code/model/raftnetlayer.py @@ -0,0 +1,153 @@ +from model import common +from model import attention +import torch +from lambda_networks import LambdaLayer +import torch.nn as nn +import torch.cuda.amp as amp + +class ConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(ConvGRU, self).__init__() + self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + + def forward(self, h, x): + hx = torch.cat([h, x], dim=1) + + z = torch.sigmoid(self.convz(hx)) + r = torch.sigmoid(self.convr(hx)) + q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) + + # h = (1-z) * h + z * q + # return h + return (1-z) * h + z * q + +class SepConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(SepConvGRU, self).__init__() + self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + + self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + + + def forward(self, h, x): + # horizontal + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz1(hx)) + r = torch.sigmoid(self.convr1(hx)) + q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + # vertical + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz2(hx)) + r = torch.sigmoid(self.convr2(hx)) + q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + return h + + +def make_model(args, parent=False): + return RAFTNET(args) + +class RAFTNET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(RAFTNET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + # perhaps a shallow network here? + for i in range(2): + m_head.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + # convert feature to image, shared + m_tail=[] + for i in range(2): + m_tail.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + m_tail.append(conv(n_feats, args.n_colors, kernel_size)) + # middle recurrent part + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4, + norm=args.normalization + ) + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + self.hidden_encoder=nn.Sequential( + conv(args.n_colors, n_feats, kernel_size), + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale), + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale), + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale) + ) + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + self.gru = ConvGRU(hidden_dim=64,input_dim=64) + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + + def forward(self, x): + with amp.autocast(self.amp): + x=(x-0.5)/0.5 + hidden = self.hidden_encoder(x) + x = self.head(x) + output_lst=[None]*self.recurrence + for i in range(self.recurrence): + gru_out=self.gru(hidden,x) + res=self.body(gru_out) + gru_out=res+gru_out + hidden=gru_out + output=self.tail(gru_out) + output_lst[i]=output*0.5+0.5 + return output_lst + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/raftnets.py b/Demosaic/code/model/raftnets.py new file mode 100644 index 0000000000000000000000000000000000000000..2b9c583debe3515317df5583d742d26064371139 --- /dev/null +++ b/Demosaic/code/model/raftnets.py @@ -0,0 +1,151 @@ +from model import common +from model import attention +import torch +from lambda_networks import LambdaLayer +import torch.nn as nn +import torch.cuda.amp as amp + +class ConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(ConvGRU, self).__init__() + self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + + def forward(self, h, x): + hx = torch.cat([h, x], dim=1) + + z = torch.sigmoid(self.convz(hx)) + r = torch.sigmoid(self.convr(hx)) + q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) + + # h = (1-z) * h + z * q + # return h + return (1-z) * h + z * q + +class SepConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(SepConvGRU, self).__init__() + self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + + self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + + + def forward(self, h, x): + # horizontal + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz1(hx)) + r = torch.sigmoid(self.convr1(hx)) + q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + # vertical + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz2(hx)) + r = torch.sigmoid(self.convr2(hx)) + q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + return h + + +def make_model(args, parent=False): + return RAFTNET(args) + +class RAFTNET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(RAFTNET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + # perhaps a shallow network here? + for i in range(2): + m_head.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + # convert feature to image, shared + m_tail=[] + for i in range(2): + m_tail.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + m_tail.append(conv(n_feats, args.n_colors, kernel_size)) + # middle recurrent part + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4 + ) + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + self.hidden_encoder=nn.Sequential( + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale), + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale), + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale) + ) + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + self.gru = ConvGRU(hidden_dim=64,input_dim=64) + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + + def forward(self, x): + with amp.autocast(self.amp): + x=(x-0.5)/0.5 + x = self.head(x) + hidden = self.hidden_encoder(x) + output_lst=[None]*self.recurrence + for i in range(self.recurrence): + gru_out=self.gru(hidden,x) + res=self.body(gru_out) + gru_out=res+gru_out + hidden=gru_out + output=self.tail(gru_out) + output_lst[i]=output*0.5+0.5 + return output_lst + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/raftnetsingle.py b/Demosaic/code/model/raftnetsingle.py new file mode 100644 index 0000000000000000000000000000000000000000..74c4b54cdff0c911f9fccd32346016bcc51c566e --- /dev/null +++ b/Demosaic/code/model/raftnetsingle.py @@ -0,0 +1,150 @@ +from model import common +from model import attention +import torch +from lambda_networks import LambdaLayer +import torch.nn as nn +import torch.cuda.amp as amp + +class ConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(ConvGRU, self).__init__() + self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) + + def forward(self, h, x): + hx = torch.cat([h, x], dim=1) + + z = torch.sigmoid(self.convz(hx)) + r = torch.sigmoid(self.convr(hx)) + q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) + + # h = (1-z) * h + z * q + # return h + return (1-z) * h + z * q + +class SepConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(SepConvGRU, self).__init__() + self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + + self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + + + def forward(self, h, x): + # horizontal + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz1(hx)) + r = torch.sigmoid(self.convr1(hx)) + q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + # vertical + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz2(hx)) + r = torch.sigmoid(self.convr2(hx)) + q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + return h + + +def make_model(args, parent=False): + return RAFTNET(args) + +class RAFTNET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(RAFTNET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + # perhaps a shallow network here? + for i in range(2): + m_head.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + # convert feature to image, shared + m_tail=[] + for i in range(2): + m_tail.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + m_tail.append(conv(n_feats, args.n_colors, kernel_size)) + # middle recurrent part + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4, + norm=args.normalization + ) + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + self.hidden_encoder=nn.Sequential( + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale), + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale), + common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale) + ) + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + # self.gru = ConvGRU(hidden_dim=64,input_dim=64) + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + + def forward(self, x): + with amp.autocast(self.amp): + x=(x-0.5)/0.5 + x = self.head(x) + hidden = self.hidden_encoder(x) + output_lst=[None]*self.recurrence + for i in range(self.recurrence): + res=self.body(hidden) + gru_out=res+hidden + output=self.tail(gru_out) + output_lst[i]=output*0.5+0.5 + return output_lst + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/rcan.py b/Demosaic/code/model/rcan.py new file mode 100644 index 0000000000000000000000000000000000000000..4f4e26b923f9b8378766b83f358a5b2c8720f520 --- /dev/null +++ b/Demosaic/code/model/rcan.py @@ -0,0 +1,144 @@ +## ECCV-2018-Image Super-Resolution Using Very Deep Residual Channel Attention Networks +## https://arxiv.org/abs/1807.02758 +from model import common + +import torch.nn as nn + +def make_model(args, parent=False): + return RCAN(args) + +## Channel Attention (CA) Layer +class CALayer(nn.Module): + def __init__(self, channel, reduction=16): + super(CALayer, self).__init__() + # global average pooling: feature --> point + self.avg_pool = nn.AdaptiveAvgPool2d(1) + # feature channel downscale and upscale --> channel weight + self.conv_du = nn.Sequential( + nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True), + nn.ReLU(inplace=True), + nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True), + nn.Sigmoid() + ) + + def forward(self, x): + y = self.avg_pool(x) + y = self.conv_du(y) + return x * y + +## Residual Channel Attention Block (RCAB) +class RCAB(nn.Module): + def __init__( + self, conv, n_feat, kernel_size, reduction, + bias=True, bn=False, act=nn.ReLU(True), res_scale=1): + + super(RCAB, self).__init__() + modules_body = [] + for i in range(2): + modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias)) + if bn: modules_body.append(nn.BatchNorm2d(n_feat)) + if i == 0: modules_body.append(act) + modules_body.append(CALayer(n_feat, reduction)) + self.body = nn.Sequential(*modules_body) + self.res_scale = res_scale + + def forward(self, x): + res = self.body(x) + #res = self.body(x).mul(self.res_scale) + res += x + return res + +## Residual Group (RG) +class ResidualGroup(nn.Module): + def __init__(self, conv, n_feat, kernel_size, reduction, act, res_scale, n_resblocks): + super(ResidualGroup, self).__init__() + modules_body = [] + modules_body = [ + RCAB( + conv, n_feat, kernel_size, reduction, bias=True, bn=False, act=nn.ReLU(True), res_scale=1) \ + for _ in range(n_resblocks)] + modules_body.append(conv(n_feat, n_feat, kernel_size)) + self.body = nn.Sequential(*modules_body) + + def forward(self, x): + res = self.body(x) + res += x + return res + +## Residual Channel Attention Network (RCAN) +class RCAN(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(RCAN, self).__init__() + + n_resgroups = args.n_resgroups + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + reduction = args.reduction + scale = args.scale[0] + act = nn.ReLU(True) + + # RGB mean for DIV2K + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + + # define head module + modules_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + modules_body = [ + ResidualGroup( + conv, n_feats, kernel_size, reduction, act=act, res_scale=args.res_scale, n_resblocks=n_resblocks) \ + for _ in range(n_resgroups)] + + modules_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + modules_tail = [ + common.Upsampler(conv, scale, n_feats, act=False), + conv(n_feats, args.n_colors, kernel_size)] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*modules_head) + self.body = nn.Sequential(*modules_body) + self.tail = nn.Sequential(*modules_tail) + + def forward(self, x): + x = self.sub_mean(x) + x = self.head(x) + + res = self.body(x) + res += x + + x = self.tail(res) + x = self.add_mean(x) + + return x + + def load_state_dict(self, state_dict, strict=False): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') >= 0: + print('Replace pre-trained upsampler to new one...') + else: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + + if strict: + missing = set(own_state.keys()) - set(state_dict.keys()) + if len(missing) > 0: + raise KeyError('missing keys in state_dict: "{}"'.format(missing)) diff --git a/Demosaic/code/model/rdn.py b/Demosaic/code/model/rdn.py new file mode 100644 index 0000000000000000000000000000000000000000..0b7001d85ad760e32fc735267d913a4c5f62cb15 --- /dev/null +++ b/Demosaic/code/model/rdn.py @@ -0,0 +1,105 @@ +# Residual Dense Network for Image Super-Resolution +# https://arxiv.org/abs/1802.08797 + +from model import common + +import torch +import torch.nn as nn + + +def make_model(args, parent=False): + return RDN(args) + +class RDB_Conv(nn.Module): + def __init__(self, inChannels, growRate, kSize=3): + super(RDB_Conv, self).__init__() + Cin = inChannels + G = growRate + self.conv = nn.Sequential(*[ + nn.Conv2d(Cin, G, kSize, padding=(kSize-1)//2, stride=1), + nn.ReLU() + ]) + + def forward(self, x): + out = self.conv(x) + return torch.cat((x, out), 1) + +class RDB(nn.Module): + def __init__(self, growRate0, growRate, nConvLayers, kSize=3): + super(RDB, self).__init__() + G0 = growRate0 + G = growRate + C = nConvLayers + + convs = [] + for c in range(C): + convs.append(RDB_Conv(G0 + c*G, G)) + self.convs = nn.Sequential(*convs) + + # Local Feature Fusion + self.LFF = nn.Conv2d(G0 + C*G, G0, 1, padding=0, stride=1) + + def forward(self, x): + return self.LFF(self.convs(x)) + x + +class RDN(nn.Module): + def __init__(self, args): + super(RDN, self).__init__() + r = args.scale[0] + G0 = args.G0 + kSize = args.RDNkSize + + # number of RDB blocks, conv layers, out channels + self.D, C, G = { + 'A': (20, 6, 32), + 'B': (16, 8, 64), + }[args.RDNconfig] + + # Shallow feature extraction net + self.SFENet1 = nn.Conv2d(args.n_colors, G0, kSize, padding=(kSize-1)//2, stride=1) + self.SFENet2 = nn.Conv2d(G0, G0, kSize, padding=(kSize-1)//2, stride=1) + + # Redidual dense blocks and dense feature fusion + self.RDBs = nn.ModuleList() + for i in range(self.D): + self.RDBs.append( + RDB(growRate0 = G0, growRate = G, nConvLayers = C) + ) + + # Global Feature Fusion + self.GFF = nn.Sequential(*[ + nn.Conv2d(self.D * G0, G0, 1, padding=0, stride=1), + nn.Conv2d(G0, G0, kSize, padding=(kSize-1)//2, stride=1) + ]) + + # Up-sampling net + if r == 2 or r == 3: + self.UPNet = nn.Sequential(*[ + nn.Conv2d(G0, G * r * r, kSize, padding=(kSize-1)//2, stride=1), + nn.PixelShuffle(r), + nn.Conv2d(G, args.n_colors, kSize, padding=(kSize-1)//2, stride=1) + ]) + elif r == 4: + self.UPNet = nn.Sequential(*[ + nn.Conv2d(G0, G * 4, kSize, padding=(kSize-1)//2, stride=1), + nn.PixelShuffle(2), + nn.Conv2d(G, G * 4, kSize, padding=(kSize-1)//2, stride=1), + nn.PixelShuffle(2), + nn.Conv2d(G, args.n_colors, kSize, padding=(kSize-1)//2, stride=1) + ]) + else: + raise ValueError("scale must be 2 or 3 or 4.") + + def forward(self, x): + f__1 = self.SFENet1(x) + x = self.SFENet2(f__1) + + RDBs_out = [] + for i in range(self.D): + x = self.RDBs[i](x) + RDBs_out.append(x) + + x = self.GFF(torch.cat(RDBs_out,1)) + x += f__1 + + return self.UPNet(x) diff --git a/Demosaic/code/model/rlambdanet.py b/Demosaic/code/model/rlambdanet.py new file mode 100644 index 0000000000000000000000000000000000000000..cb01a1b3faf0d9760959c242637dff5438aad8cf --- /dev/null +++ b/Demosaic/code/model/rlambdanet.py @@ -0,0 +1,97 @@ +from model import common +import torch +import torch.nn as nn +from lambda_networks import LambdaLayer +import torch.cuda.amp as amp + +def make_model(args, parent=False): + return RLAMBDANET(args) + +class RLAMBDANET(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(RLAMBDANET, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + scale = args.scale[0] + + rgb_mean = (0.4488, 0.4371, 0.4040) + rgb_std = (1.0, 1.0, 1.0) + self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std) + layer = LambdaLayer( + dim = n_feats, + dim_out = n_feats, + r = 23, # the receptive field for relative positional encoding (23 x 23) + dim_k = 16, + heads = 4, + dim_u = 4 + ) + # msa = attention.PyramidAttention() + # define head module + m_head = [conv(args.n_colors, n_feats, kernel_size)] + + # define body module + m_body = [ + common.ResBlock( + conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale + ) for _ in range(n_resblocks//2) + ] + # m_body.append(msa) + m_body.append(layer) + for i in range(n_resblocks//2): + m_body.append(common.ResBlock(conv,n_feats,kernel_size,nn.PReLU(),res_scale=args.res_scale)) + + m_body.append(conv(n_feats, n_feats, kernel_size)) + + # define tail module + #m_tail = [ + # common.Upsampler(conv, scale, n_feats, act=False), + # conv(n_feats, args.n_colors, kernel_size) + #] + m_tail = [ + conv(n_feats, args.n_colors, kernel_size) + ] + + self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1) + + self.head = nn.Sequential(*m_head) + self.body = nn.Sequential(*m_body) + self.tail = nn.Sequential(*m_tail) + + self.recurrence = args.recurrence + self.detach = args.detach + # self.step_detach = args.step_detach + self.amp = args.amp + # self.beta=nn.Parameter(torch.ones(1)*0.5) + + def forward(self, x): + with amp.autocast(self.amp): + out = self.head(x) + last_output=out + for i in range(self.recurrence): + res = self.body(last_output) + res = res + last_output + last_output=res + output = self.tail(last_output) + return [output] + + def load_state_dict(self, state_dict, strict=True): + own_state = self.state_dict() + for name, param in state_dict.items(): + if name in own_state: + if isinstance(param, nn.Parameter): + param = param.data + try: + own_state[name].copy_(param) + except Exception: + if name.find('tail') == -1: + raise RuntimeError('While copying the parameter named {}, ' + 'whose dimensions in the model are {} and ' + 'whose dimensions in the checkpoint are {}.' + .format(name, own_state[name].size(), param.size())) + elif strict: + if name.find('tail') == -1: + raise KeyError('unexpected key "{}" in state_dict' + .format(name)) + diff --git a/Demosaic/code/model/utils/__init__.py b/Demosaic/code/model/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Demosaic/code/model/utils/__pycache__/__init__.cpython-35.pyc b/Demosaic/code/model/utils/__pycache__/__init__.cpython-35.pyc new file mode 100644 index 0000000000000000000000000000000000000000..40f387d415e1f4bb9bf4ceac0a889a9aebc334be Binary files /dev/null and b/Demosaic/code/model/utils/__pycache__/__init__.cpython-35.pyc differ diff --git a/Demosaic/code/model/utils/__pycache__/tools.cpython-35.pyc b/Demosaic/code/model/utils/__pycache__/tools.cpython-35.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a91ca012e6881f1c14b197e62e83607d0e56de35 Binary files /dev/null and b/Demosaic/code/model/utils/__pycache__/tools.cpython-35.pyc differ diff --git a/Demosaic/code/model/utils/tools.py b/Demosaic/code/model/utils/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..c40cd8dc5e95d353254b950c9ec373126c5fe63e --- /dev/null +++ b/Demosaic/code/model/utils/tools.py @@ -0,0 +1,81 @@ +import os +import torch +import numpy as np +from PIL import Image + +import torch.nn.functional as F + +def normalize(x): + return x.mul_(2).add_(-1) + +def same_padding(images, ksizes, strides, rates): + assert len(images.size()) == 4 + batch_size, channel, rows, cols = images.size() + out_rows = (rows + strides[0] - 1) // strides[0] + out_cols = (cols + strides[1] - 1) // strides[1] + effective_k_row = (ksizes[0] - 1) * rates[0] + 1 + effective_k_col = (ksizes[1] - 1) * rates[1] + 1 + padding_rows = max(0, (out_rows-1)*strides[0]+effective_k_row-rows) + padding_cols = max(0, (out_cols-1)*strides[1]+effective_k_col-cols) + # Pad the input + padding_top = int(padding_rows / 2.) + padding_left = int(padding_cols / 2.) + padding_bottom = padding_rows - padding_top + padding_right = padding_cols - padding_left + paddings = (padding_left, padding_right, padding_top, padding_bottom) + images = torch.nn.ZeroPad2d(paddings)(images) + return images + + +def extract_image_patches(images, ksizes, strides, rates, padding='same'): + """ + Extract patches from images and put them in the C output dimension. + :param padding: + :param images: [batch, channels, in_rows, in_cols]. A 4-D Tensor with shape + :param ksizes: [ksize_rows, ksize_cols]. The size of the sliding window for + each dimension of images + :param strides: [stride_rows, stride_cols] + :param rates: [dilation_rows, dilation_cols] + :return: A Tensor + """ + assert len(images.size()) == 4 + assert padding in ['same', 'valid'] + batch_size, channel, height, width = images.size() + + if padding == 'same': + images = same_padding(images, ksizes, strides, rates) + elif padding == 'valid': + pass + else: + raise NotImplementedError('Unsupported padding type: {}.\ + Only "same" or "valid" are supported.'.format(padding)) + + unfold = torch.nn.Unfold(kernel_size=ksizes, + dilation=rates, + padding=0, + stride=strides) + patches = unfold(images) + return patches # [N, C*k*k, L], L is the total number of such blocks +def reduce_mean(x, axis=None, keepdim=False): + if not axis: + axis = range(len(x.shape)) + for i in sorted(axis, reverse=True): + x = torch.mean(x, dim=i, keepdim=keepdim) + return x + + +def reduce_std(x, axis=None, keepdim=False): + if not axis: + axis = range(len(x.shape)) + for i in sorted(axis, reverse=True): + x = torch.std(x, dim=i, keepdim=keepdim) + return x + + +def reduce_sum(x, axis=None, keepdim=False): + if not axis: + axis = range(len(x.shape)) + for i in sorted(axis, reverse=True): + x = torch.sum(x, dim=i, keepdim=keepdim) + return x + diff --git a/Demosaic/code/model/vdsr.py b/Demosaic/code/model/vdsr.py new file mode 100644 index 0000000000000000000000000000000000000000..492442a212aaceee369fc842abcab114d93f2729 --- /dev/null +++ b/Demosaic/code/model/vdsr.py @@ -0,0 +1,46 @@ +from model import common + +import torch.nn as nn +import torch.nn.init as init + +url = { + 'r20f64': '' +} + +def make_model(args, parent=False): + return VDSR(args) + +class VDSR(nn.Module): + def __init__(self, args, conv=common.default_conv): + super(VDSR, self).__init__() + + n_resblocks = args.n_resblocks + n_feats = args.n_feats + kernel_size = 3 + self.url = url['r{}f{}'.format(n_resblocks, n_feats)] + self.sub_mean = common.MeanShift(args.rgb_range) + self.add_mean = common.MeanShift(args.rgb_range, sign=1) + + def basic_block(in_channels, out_channels, act): + return common.BasicBlock( + conv, in_channels, out_channels, kernel_size, + bias=True, bn=False, act=act + ) + + # define body module + m_body = [] + m_body.append(basic_block(args.n_colors, n_feats, nn.ReLU(True))) + for _ in range(n_resblocks - 2): + m_body.append(basic_block(n_feats, n_feats, nn.ReLU(True))) + m_body.append(basic_block(n_feats, args.n_colors, None)) + + self.body = nn.Sequential(*m_body) + + def forward(self, x): + x = self.sub_mean(x) + res = self.body(x) + res += x + x = self.add_mean(res) + + return x + diff --git a/Demosaic/code/option.py b/Demosaic/code/option.py new file mode 100644 index 0000000000000000000000000000000000000000..eccf3bee8c3b11f93dd0ecbc9035f5b746621706 --- /dev/null +++ b/Demosaic/code/option.py @@ -0,0 +1,171 @@ +import argparse +import template + +parser = argparse.ArgumentParser(description='EDSR and MDSR') + +parser.add_argument('--debug', action='store_true', + help='Enables debug mode') +parser.add_argument('--template', default='.', + help='You can set various templates in option.py') + +# Hardware specifications +parser.add_argument('--n_threads', type=int, default=18, + help='number of threads for data loading') +parser.add_argument('--cpu', action='store_true', + help='use cpu only') +parser.add_argument('--n_GPUs', type=int, default=1, + help='number of GPUs') +parser.add_argument('--seed', type=int, default=1, + help='random seed') + +# Data specifications +parser.add_argument('--dir_data', type=str, default='/data/ssd/public/liuhy/DNDM/dataset',help='dataset directory') +parser.add_argument('--data_train', type=str, default='DIV2K', + help='train dataset name') +parser.add_argument('--data_test', type=str, default='DIV2K', + help='test dataset name') +parser.add_argument('--data_range', type=str, default='1-800/801-805', + help='train/test data range') +parser.add_argument('--ext', type=str, default='sep', + help='dataset file extension') +parser.add_argument('--scale', type=str, default='4', + help='super resolution scale') +parser.add_argument('--patch_size', type=int, default=192, + help='output patch size') +parser.add_argument('--rgb_range', type=int, default=1, + help='maximum value of RGB') +parser.add_argument('--n_colors', type=int, default=3, + help='number of color channels to use') +parser.add_argument('--chop', action='store_true', + help='enable memory-efficient forward') +parser.add_argument('--no_augment', action='store_true', + help='do not use data augmentation') + +# Model specifications +parser.add_argument('--model', default='LAMBDANET', + help='model name') + +parser.add_argument('--act', type=str, default='relu', + help='activation function') +parser.add_argument('--pre_train', type=str, default='.', + help='pre-trained model directory') +parser.add_argument('--extend', type=str, default='.', + help='pre-trained model directory') +parser.add_argument('--n_resblocks', type=int, default=16, + help='number of residual blocks') +parser.add_argument('--recurrence', type=int, default=5, + help='number of recurrence') +parser.add_argument('--n_feats', type=int, default=64, + help='number of feature maps') +parser.add_argument('--res_scale', type=float, default=1, + help='residual scaling') +parser.add_argument('--shift_mean', default=True, + help='subtract pixel mean from the input') +parser.add_argument('--amp', action='store_true', + help='subtract pixel mean from the input') +parser.add_argument('--detach', action='store_true', + help='subtract pixel mean from the input') +parser.add_argument('--spectral', action='store_true', + help='subtract pixel mean from the input') +parser.add_argument('--dilation', action='store_true', + help='use dilated convolution') +parser.add_argument('--precision', type=str, default='single', + choices=('single', 'half'), + help='FP precision for test (single | half)') +parser.add_argument('--normalization', type=str, default='layer',help='normalization type') + +# Option for Residual dense network (RDN) +parser.add_argument('--G0', type=int, default=64, + help='default number of filters. (Use in RDN)') +parser.add_argument('--RDNkSize', type=int, default=3, + help='default kernel size. (Use in RDN)') +parser.add_argument('--RDNconfig', type=str, default='B', + help='parameters config of RDN. (Use in RDN)') + +parser.add_argument('--depth', type=int, default=12, + help='number of residual groups') +# Option for Residual channel attention network (RCAN) +parser.add_argument('--n_resgroups', type=int, default=10, + help='number of residual groups') +parser.add_argument('--reduction', type=int, default=16, + help='number of feature maps reduction') + +# Training specifications +parser.add_argument('--reset', action='store_true', + help='reset the training') +parser.add_argument('--test_every', type=int, default=1000, + help='do test per every N batches') +parser.add_argument('--epochs', type=int, default=1000, + help='number of epochs to train') +parser.add_argument('--batch_size', type=int, default=16, + help='input batch size for training') +parser.add_argument('--split_batch', type=int, default=1, + help='split the batch into smaller chunks') +parser.add_argument('--self_ensemble', action='store_true', + help='use self-ensemble method for test') +parser.add_argument('--test_only', action='store_true', + help='set this option to test the model') +parser.add_argument('--gan_k', type=int, default=1, + help='k value for adversarial loss') + +# Optimization specifications +parser.add_argument('--lr', type=float, default=1e-4, + help='learning rate') +parser.add_argument('--decay', type=str, default='200-400-600-800', + help='learning rate decay type') +parser.add_argument('--gamma', type=float, default=0.5, + help='learning rate decay factor for step decay') +parser.add_argument('--optimizer', default='ADAM', + choices=('SGD', 'ADAM', 'RMSprop', "ADAMW"), + help='optimizer to use (SGD | ADAM | RMSprop)') +parser.add_argument('--momentum', type=float, default=0.9, + help='SGD momentum') +parser.add_argument('--betas', type=tuple, default=(0.9, 0.999), + help='ADAM beta') +parser.add_argument('--epsilon', type=float, default=1e-8, + help='ADAM epsilon for numerical stability') +parser.add_argument('--weight_decay', type=float, default=0, + help='weight decay') +parser.add_argument('--gclip', type=float, default=0, + help='gradient clipping threshold (0 = no clipping)') +parser.add_argument('--decay_gamma', type=float, default=0.8, + help='learning rate decay factor for step decay') + +# Loss specifications +parser.add_argument('--loss', type=str, default='1*L1', + help='loss function configuration') +parser.add_argument('--skip_threshold', type=float, default='1e8', + help='skipping batch that has large error') + +# Log specifications +parser.add_argument('--save', type=str, default='test', + help='file name to save') +parser.add_argument('--load', type=str, default='', + help='file name to load') +parser.add_argument('--resume', type=int, default=0, + help='resume from specific checkpoint') +parser.add_argument('--save_models', action='store_true', + help='save all intermediate models') +parser.add_argument('--print_every', type=int, default=100, + help='how many batches to wait before logging training status') +parser.add_argument('--save_results', action='store_true', + help='save output results') +parser.add_argument('--save_gt', action='store_true', + help='save low-resolution and high-resolution images together') + +args = parser.parse_args() +template.set_template(args) + +args.scale = list(map(lambda x: int(x), args.scale.split('+'))) +args.data_train = args.data_train.split('+') +args.data_test = args.data_test.split('+') + +if args.epochs == 0: + args.epochs = 1e8 + +for arg in vars(args): + if vars(args)[arg] == 'True': + vars(args)[arg] = True + elif vars(args)[arg] == 'False': + vars(args)[arg] = False + diff --git a/Demosaic/code/prepare.sh b/Demosaic/code/prepare.sh new file mode 100644 index 0000000000000000000000000000000000000000..adecc455029e50fffb92dcc2ecf059583be2ac92 --- /dev/null +++ b/Demosaic/code/prepare.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +username=`whoami` +task="DNDM" + +if [ ! -d "/data/ssd/public/$username/$task/dataset" ]; then + workplace=`pwd -P` + 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b/Demosaic/code/runs/test/events.out.tfevents.1605067132.gpu52.cse.cuhk.edu.hk.6936.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0233079f08cec6eb8f396e2395ffca91967f6337cbe493573a9c8a3eb20a75dd +size 40 diff --git a/Demosaic/code/runs/test/events.out.tfevents.1605070051.gpu52.cse.cuhk.edu.hk.27235.0 b/Demosaic/code/runs/test/events.out.tfevents.1605070051.gpu52.cse.cuhk.edu.hk.27235.0 new file mode 100644 index 0000000000000000000000000000000000000000..2283d64be4f80078faa8b3b7a4fca2e20074c45d --- /dev/null +++ b/Demosaic/code/runs/test/events.out.tfevents.1605070051.gpu52.cse.cuhk.edu.hk.27235.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d31cadbc2412bdcf631951ee2df61d4d656628d013636258972daaad5920263 +size 40 diff --git a/Demosaic/code/template.py b/Demosaic/code/template.py new file mode 100644 index 0000000000000000000000000000000000000000..c4842bdaa3e44b3ef51381dad2fe527ac3b5881f --- /dev/null +++ b/Demosaic/code/template.py @@ -0,0 +1,53 @@ +def set_template(args): + # Set the templates here + if args.template.find('jpeg') >= 0: + args.data_train = 'DIV2K_jpeg' + args.data_test = 'DIV2K_jpeg' + args.epochs = 200 + args.decay = '100' + + if args.template.find('EDSR_paper') >= 0: + args.model = 'EDSR' + args.n_resblocks = 32 + args.n_feats = 256 + args.res_scale = 0.1 + + if args.template.find('MDSR') >= 0: + args.model = 'MDSR' + args.patch_size = 48 + args.epochs = 650 + + if args.template.find('DDBPN') >= 0: + args.model = 'DDBPN' + args.patch_size = 128 + args.scale = '4' + + args.data_test = 'Set5' + + args.batch_size = 20 + args.epochs = 1000 + args.decay = '500' + args.gamma = 0.1 + args.weight_decay = 1e-4 + + args.loss = '1*MSE' + + if args.template.find('GAN') >= 0: + args.epochs = 200 + args.lr = 5e-5 + args.decay = '150' + + if args.template.find('RCAN') >= 0: + args.model = 'RCAN' + args.n_resgroups = 10 + args.n_resblocks = 20 + args.n_feats = 64 + args.chop = True + + if args.template.find('VDSR') >= 0: + args.model = 'VDSR' + args.n_resblocks = 20 + args.n_feats = 64 + args.patch_size = 41 + args.lr = 1e-1 + diff --git a/Demosaic/code/train-0-4rec-lambda-a.sh b/Demosaic/code/train-0-4rec-lambda-a.sh new file mode 100644 index 0000000000000000000000000000000000000000..9501a061ca42be5ac71adc351a15c601b6d69e8e --- /dev/null +++ b/Demosaic/code/train-0-4rec-lambda-a.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +bash ./prepare.sh +username=`whoami` + +CUDA_VISIBLE_DEVICES=0 python main.py --n_GPUs 1 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANETACTA --scale 1 --patch_size 48 --save LAMBDANETACTA_DEMOSAIC20_R4_MSE --n_feats 64 --data_train DIV2K --recurrence 4 \ +--dir_data="/data/ssd/public/$username/DNDM/dataset" --data_range "1-800/901-942" \ +--optimizer ADAMW --gclip 1.0 --spectral --loss 1*MSE + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-01-1rec.sh b/Demosaic/code/train-01-1rec.sh new file mode 100644 index 0000000000000000000000000000000000000000..d61d6683c8e76d3572a1d1f67c2256f0a587e24d --- /dev/null +++ b/Demosaic/code/train-01-1rec.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +bash ./prepare.sh + +python main.py --n_GPUs 4 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANET --scale 1 --patch_size 48 --save LAMBDA_DEMOSAIC20_R1 --n_feats 64 --data_train DIV2K --recurrence 1 + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-01-1rec80.sh b/Demosaic/code/train-01-1rec80.sh new file mode 100644 index 0000000000000000000000000000000000000000..6c13627c8ba518bb53d1b1857cef856f15028daa --- /dev/null +++ b/Demosaic/code/train-01-1rec80.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +bash ./prepare.sh + +python main.py --n_GPUs 4 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 80 --save_models --model LAMBDANET --scale 1 --patch_size 48 --save LAMBDA_DEMOSAIC80_R1 --n_feats 64 --data_train DIV2K --recurrence 1 --amp diff --git a/Demosaic/code/train-01-4rec-beta-lambda.sh b/Demosaic/code/train-01-4rec-beta-lambda.sh new file mode 100644 index 0000000000000000000000000000000000000000..2adbae5b97579351ad98538250e149a0f600e0d2 --- /dev/null +++ b/Demosaic/code/train-01-4rec-beta-lambda.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +bash ./prepare.sh +username=`whoami` + +CUDA_VISIBLE_DEVICES=0,1 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model BETALAMBDANET --scale 1 --patch_size 48 --save BETALAMBDA_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 \ +--dir_data="/data/ssd/public/$username/DNDM/dataset" --data_range "1-800/901-942" + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-01-4rec-lambda-a.sh b/Demosaic/code/train-01-4rec-lambda-a.sh new file mode 100644 index 0000000000000000000000000000000000000000..ab9b2c8b58ce8eb9d6eb66304836f6fa29915b92 --- /dev/null +++ b/Demosaic/code/train-01-4rec-lambda-a.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +bash ./prepare.sh +username=`whoami` + +CUDA_VISIBLE_DEVICES=0,1 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANETACTA --scale 1 --patch_size 48 --save LAMBDANETACTA_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 \ +--dir_data="/data/ssd/public/$username/DNDM/dataset" --data_range "1-800/901-942" \ +--optimizer ADAMW --gclip 1.0 --spectral --amp + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-01-4rec-raft.sh b/Demosaic/code/train-01-4rec-raft.sh new file mode 100644 index 0000000000000000000000000000000000000000..c1767d623914fd79317f86ad4e2033b1cabbeef6 --- /dev/null +++ b/Demosaic/code/train-01-4rec-raft.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +bash ./prepare.sh + +CUDA_VISIBLE_DEVICES=0,1 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model RAFTNET --scale 1 --patch_size 48 --save RAFT_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-01-4rec-rweight.sh b/Demosaic/code/train-01-4rec-rweight.sh new file mode 100644 index 0000000000000000000000000000000000000000..968e9a68fb8296b2ecec28921ca0d0573d1d8526 --- /dev/null +++ b/Demosaic/code/train-01-4rec-rweight.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +bash ./prepare.sh + +CUDA_VISIBLE_DEVICES=0,1 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANET --scale 1 --patch_size 48 --save LAMBDA_DEMOSAIC20_R4_RWEIGHT --n_feats 64 --data_train DIV2K --recurrence 4 --amp + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-01-4rec.sh b/Demosaic/code/train-01-4rec.sh new file mode 100644 index 0000000000000000000000000000000000000000..42a30294ddc36b347643b35dfa31d517ea84406c --- /dev/null +++ b/Demosaic/code/train-01-4rec.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +bash ./prepare.sh + +CUDA_VISIBLE_DEVICES=0,1 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANET --scale 1 --patch_size 48 --save LAMBDA_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 --amp + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-1-4rec-lambda-b.sh b/Demosaic/code/train-1-4rec-lambda-b.sh new file mode 100644 index 0000000000000000000000000000000000000000..b4ad42c31f90aab1b5217c1655eeb3836e8a7f6f --- /dev/null +++ b/Demosaic/code/train-1-4rec-lambda-b.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +bash ./prepare.sh +username=`whoami` + +CUDA_VISIBLE_DEVICES=1 python main.py --n_GPUs 1 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANETACTB --scale 1 --patch_size 48 --save LAMBDANETACTB_DEMOSAIC20_R4_MSE --n_feats 64 --data_train DIV2K --recurrence 4 \ +--dir_data "/data/ssd/public/$username/DNDM/dataset" --data_range "1-800/901-942" \ +--optimizer ADAMW --gclip 1.0 --spectral --loss 1*MSE + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-23-4rec-detach-rweight.sh b/Demosaic/code/train-23-4rec-detach-rweight.sh new file mode 100644 index 0000000000000000000000000000000000000000..32d810d79605a41108f137cd9aed5c17329e6c33 --- /dev/null +++ b/Demosaic/code/train-23-4rec-detach-rweight.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +bash ./prepare.sh + +CUDA_VISIBLE_DEVICES=2,3 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANET --scale 1 --patch_size 48 --save LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT --n_feats 64 --data_train DIV2K --recurrence 4 --detach --amp + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-23-4rec-detach.sh b/Demosaic/code/train-23-4rec-detach.sh new file mode 100644 index 0000000000000000000000000000000000000000..f360ccd252b7830d34bd3e26a77a2ba546bad0fb --- /dev/null +++ b/Demosaic/code/train-23-4rec-detach.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +bash ./prepare.sh + +CUDA_VISIBLE_DEVICES=2,3 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANET --scale 1 --patch_size 48 --save LAMBDA_DEMOSAIC20_R4_detach --n_feats 64 --data_train DIV2K --recurrence 4 --detach --amp + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-23-4rec-direct-lambda.sh b/Demosaic/code/train-23-4rec-direct-lambda.sh new file mode 100644 index 0000000000000000000000000000000000000000..dc8fa0afd83155446de9424f5f9c4ba43a2979dd --- /dev/null +++ b/Demosaic/code/train-23-4rec-direct-lambda.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +bash ./prepare.sh +username=`whoami` + +CUDA_VISIBLE_DEVICES=2,3 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model RLAMBDANET --scale 1 --patch_size 48 --save RLAMBDA_DEMOSAIC20_R4_detach --n_feats 64 --data_train DIV2K --recurrence 4 \ +--dir_data "/data/ssd/public/$username/DNDM/dataset" --data_range "1-800/901-942" + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-23-4rec-lambda-b.sh b/Demosaic/code/train-23-4rec-lambda-b.sh new file mode 100644 index 0000000000000000000000000000000000000000..85085de44cc2177631dc708f3833ade14e99dd04 --- /dev/null +++ b/Demosaic/code/train-23-4rec-lambda-b.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +bash ./prepare.sh +username=`whoami` + +CUDA_VISIBLE_DEVICES=2,3 python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANETACTB --scale 1 --patch_size 48 --save LAMBDANETACTB_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 \ +--dir_data "/data/ssd/public/$username/DNDM/dataset" --data_range "1-800/901-942" \ +--optimizer ADAMW --gclip 1.0 --spectral --amp + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-4rec-raft-gpu2 - Copy.sh b/Demosaic/code/train-4rec-raft-gpu2 - Copy.sh new file mode 100644 index 0000000000000000000000000000000000000000..c74c119fc1b06af46c1bbdd4fd3eb8be18ea908a --- /dev/null +++ b/Demosaic/code/train-4rec-raft-gpu2 - Copy.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +bash ./prepare.sh +lst=0,1 +if nvidia-smi -i 0 | grep -q "python"; then + lst=2,3 +fi + +CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model RAFTNET --scale 1 --patch_size 48 --save RAFT_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 --data_range "1-800/901-942" \ +--resume -1 --load RAFT_DEMOSAIC20_R4 + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-4rec-raft-gpu2.sh b/Demosaic/code/train-4rec-raft-gpu2.sh new file mode 100644 index 0000000000000000000000000000000000000000..c74c119fc1b06af46c1bbdd4fd3eb8be18ea908a --- /dev/null +++ b/Demosaic/code/train-4rec-raft-gpu2.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +bash ./prepare.sh +lst=0,1 +if nvidia-smi -i 0 | grep -q "python"; then + lst=2,3 +fi + +CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model RAFTNET --scale 1 --patch_size 48 --save RAFT_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 --data_range "1-800/901-942" \ +--resume -1 --load RAFT_DEMOSAIC20_R4 + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-4rec-raft-s-gpu2.sh b/Demosaic/code/train-4rec-raft-s-gpu2.sh new file mode 100644 index 0000000000000000000000000000000000000000..e8da28651836a2fb07f78c215f15f88ba3431b80 --- /dev/null +++ b/Demosaic/code/train-4rec-raft-s-gpu2.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +bash ./prepare.sh +lst=0,1 +if nvidia-smi -i 0 | grep -q "python"; then + lst=2,3 +fi + +CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 10 --save_models \ +--model RAFTNETS --scale 1 --patch_size 48 --save RAFTS_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 --data_range "1-800/901-942" + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-4rec-rafts-layer-gpu2-bn.sh b/Demosaic/code/train-4rec-rafts-layer-gpu2-bn.sh new file mode 100644 index 0000000000000000000000000000000000000000..920ab17f8b76345aa58ee7d5d68764ffd269ccfa --- /dev/null +++ b/Demosaic/code/train-4rec-rafts-layer-gpu2-bn.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +bash ./prepare.sh +lst=0,1 +if nvidia-smi -i 0 | grep -q "python"; then + lst=2,3 +fi + +CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 10 --save_models \ +--model RAFTNETLAYER --scale 1 --patch_size 48 --save RAFTS_layer_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 5 --data_range "1-800/901-942" \ +--normalization instance --gclip 1.2 + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-4rec-rafts-single-gpu2-bn.sh b/Demosaic/code/train-4rec-rafts-single-gpu2-bn.sh new file mode 100644 index 0000000000000000000000000000000000000000..3189b1dff6b476595192a6cb9755c23d71776f02 --- /dev/null +++ b/Demosaic/code/train-4rec-rafts-single-gpu2-bn.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +bash ./prepare.sh +lst=0,1 +if nvidia-smi -i 0 | grep -q "python"; then + lst=2,3 +fi + +CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 10 --save_models \ +--model RAFTNETSINGLE --scale 1 --patch_size 48 --save RAFTSINGLE_DEMOSAIC20_R1 --n_feats 64 --data_train DIV2K --recurrence 1 --data_range "1-800/901-942" \ +--normalization instance + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-4rec-rafts-single-gpu2.sh b/Demosaic/code/train-4rec-rafts-single-gpu2.sh new file mode 100644 index 0000000000000000000000000000000000000000..3189b1dff6b476595192a6cb9755c23d71776f02 --- /dev/null +++ b/Demosaic/code/train-4rec-rafts-single-gpu2.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +bash ./prepare.sh +lst=0,1 +if nvidia-smi -i 0 | grep -q "python"; then + lst=2,3 +fi + +CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 10 --save_models \ +--model RAFTNETSINGLE --scale 1 --patch_size 48 --save RAFTSINGLE_DEMOSAIC20_R1 --n_feats 64 --data_train DIV2K --recurrence 1 --data_range "1-800/901-942" \ +--normalization instance + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-5rec-rafts-layer-gpu2.sh b/Demosaic/code/train-5rec-rafts-layer-gpu2.sh new file mode 100644 index 0000000000000000000000000000000000000000..920ab17f8b76345aa58ee7d5d68764ffd269ccfa --- /dev/null +++ b/Demosaic/code/train-5rec-rafts-layer-gpu2.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +bash ./prepare.sh +lst=0,1 +if nvidia-smi -i 0 | grep -q "python"; then + lst=2,3 +fi + +CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 2 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 10 --save_models \ +--model RAFTNETLAYER --scale 1 --patch_size 48 --save RAFTS_layer_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 5 --data_range "1-800/901-942" \ +--normalization instance --gclip 1.2 + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/train-lambda-1rec.sh b/Demosaic/code/train-lambda-1rec.sh new file mode 100644 index 0000000000000000000000000000000000000000..f7834e2914a93d7a098612a886fcb2987006dcc0 --- /dev/null +++ b/Demosaic/code/train-lambda-1rec.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +bash ./prepare.sh + +python main.py --n_GPUs 4 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 20 --save_models \ +--model LAMBDANET --scale 1 --patch_size 48 --save LAMBDA_DEMOSAIC20_R1_new --n_feats 64 --data_train DIV2K --recurrence 1 \ + --data_range "1-800/901-942" + +# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \ +# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only diff --git a/Demosaic/code/trainer.py b/Demosaic/code/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6c06e860b59b5b4e52f9600e8d2831cde812ad9b --- /dev/null +++ b/Demosaic/code/trainer.py @@ -0,0 +1,182 @@ +import os +import math +from decimal import Decimal + +import utility + +import torch +import torch.nn.utils as utils +from tqdm import tqdm + +import torch.cuda.amp as amp + +from torch.utils.tensorboard import SummaryWriter +import torchvision + +import numpy as np + +class Trainer(): + def __init__(self, args, loader, my_model, my_loss, ckp): + self.args = args + self.scale = args.scale + + self.ckp = ckp + self.loader_train = loader.loader_train + self.loader_test = loader.loader_test + self.model = my_model + self.loss = my_loss + self.optimizer = utility.make_optimizer(args, self.model) + + if self.args.load != '': + self.optimizer.load(ckp.dir, epoch=len(ckp.log)) + + self.error_last = 1e8 + self.scaler=amp.GradScaler( + enabled=args.amp + ) + self.writter=None + self.recurrence=args.recurrence + if args.recurrence>1: + self.writter=SummaryWriter(f"runs/{args.save}") + + def train(self): + self.loss.step() + epoch = self.optimizer.get_last_epoch() + 1 + lr = self.optimizer.get_lr() + + self.ckp.write_log( + '[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(lr)) + ) + self.loss.start_log() + self.model.train() + + timer_data, timer_model = utility.timer(), utility.timer() + # TEMP + self.loader_train.dataset.set_scale(0) + total=len(self.loader_train) + buffer=[0.0]*self.recurrence + # torch.autograd.set_detect_anomaly(True) + for batch, (lr, hr, _,) in enumerate(self.loader_train): + lr, hr = self.prepare(lr, hr) + # print(lr.min(),lr.max(), hr.min(),hr.max()) + # exit(0) + timer_data.hold() + timer_model.tic() + + self.optimizer.zero_grad() + with amp.autocast(self.args.amp): + sr = self.model(lr, 0) + if len(sr)==1 and isinstance(sr,list): + sr=sr[0] + # loss,buffer_lst=sequence_loss(sr,hr) + loss = self.loss(sr, hr) + self.scaler.scale(loss).backward() + if self.args.gclip > 0: + self.scaler.unscale_(self.optimizer) + utils.clip_grad_value_( + self.model.parameters(), + self.args.gclip + ) + self.scaler.step(self.optimizer) + self.scaler.update() + for i in range(self.recurrence): + buffer[i]+=self.loss.buffer[i] + # self.optimizer.step() + + timer_model.hold() + + if (batch + 1) % self.args.print_every == 0: + self.ckp.write_log('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format( + (batch + 1) * self.args.batch_size, + len(self.loader_train.dataset), + self.loss.display_loss(batch), + timer_model.release(), + timer_data.release())) + + timer_data.tic() + if self.writter: + for i in range(self.recurrence): + grid=torchvision.utils.make_grid(sr[i]) + self.writter.add_image(f"Output{i}",grid,epoch) + self.writter.add_scalar(f"Loss{i}",buffer[i]/total,epoch) + self.writter.add_image("Input",torchvision.utils.make_grid(lr),epoch) + self.writter.add_image("Target",torchvision.utils.make_grid(hr),epoch) + self.loss.end_log(len(self.loader_train)) + self.error_last = self.loss.log[-1, -1] + self.optimizer.schedule() + + def test(self): + torch.set_grad_enabled(False) + + epoch = self.optimizer.get_last_epoch() + self.ckp.write_log('\nEvaluation:') + self.ckp.add_log( + torch.zeros(1, len(self.loader_test), len(self.scale)) + ) + self.model.eval() + + timer_test = utility.timer() + if self.args.save_results: self.ckp.begin_background() + for idx_data, d in enumerate(self.loader_test): + for idx_scale, scale in enumerate(self.scale): + d.dataset.set_scale(idx_scale) + for lr, hr, filename in tqdm(d, ncols=80): + lr, hr = self.prepare(lr, hr) + with amp.autocast(self.args.amp): + sr = self.model(lr, idx_scale) + if isinstance(sr,list): + sr=sr[-1] + sr = utility.quantize(sr, self.args.rgb_range) + + save_list = [sr] + self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr( + sr, hr, scale, self.args.rgb_range, dataset=d + ) + if self.args.save_gt: + save_list.extend([lr, hr]) + + if self.args.save_results: + self.ckp.save_results(d, filename[0], save_list, scale) + + self.ckp.log[-1, idx_data, idx_scale] /= len(d) + best = self.ckp.log.max(0) + self.ckp.write_log( + '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format( + d.dataset.name, + scale, + self.ckp.log[-1, idx_data, idx_scale], + best[0][idx_data, idx_scale], + best[1][idx_data, idx_scale] + 1 + ) + ) + self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc())) + self.ckp.write_log('Saving...') + # torch.cuda.empty_cache() + if self.args.save_results: + self.ckp.end_background() + + if not self.args.test_only: + self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch)) + + self.ckp.write_log( + 'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True + ) + + torch.set_grad_enabled(True) + + def prepare(self, *args): + device = torch.device('cpu' if self.args.cpu else 'cuda') + def _prepare(tensor): + if self.args.precision == 'half': tensor = tensor.half() + return tensor.to(device) + + return [_prepare(a) for a in args] + + def terminate(self): + if self.args.test_only: + self.test() + return True + else: + epoch = self.optimizer.get_last_epoch() + 1 + return epoch >= self.args.epochs + diff --git a/Demosaic/code/utility.py b/Demosaic/code/utility.py new file mode 100644 index 0000000000000000000000000000000000000000..af6fde792f904b6b6a0f73c3566147d7860c5973 --- /dev/null +++ b/Demosaic/code/utility.py @@ -0,0 +1,242 @@ +import os +import math +import time +import datetime +from multiprocessing import Process +from multiprocessing import Queue + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +import numpy as np +import imageio + +import torch +import torch.optim as optim +import torch.optim.lr_scheduler as lrs + +class timer(): + def __init__(self): + self.acc = 0 + self.tic() + + def tic(self): + self.t0 = time.time() + + def toc(self, restart=False): + diff = time.time() - self.t0 + if restart: self.t0 = time.time() + return diff + + def hold(self): + self.acc += self.toc() + + def release(self): + ret = self.acc + self.acc = 0 + + return ret + + def reset(self): + self.acc = 0 + +class checkpoint(): + def __init__(self, args): + self.args = args + self.ok = True + self.log = torch.Tensor() + now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S') + + if not args.load: + if not args.save: + args.save = now + self.dir = os.path.join('..', 'experiment', args.save) + else: + self.dir = os.path.join('..', 'experiment', args.load) + if os.path.exists(self.dir): + self.log = torch.load(self.get_path('psnr_log.pt')) + print('Continue from epoch {}...'.format(len(self.log))) + else: + args.load = '' + + if args.reset: + os.system('rm -rf ' + self.dir) + args.load = '' + + os.makedirs(self.dir, exist_ok=True) + os.makedirs(self.get_path('model'), exist_ok=True) + for d in args.data_test: + os.makedirs(self.get_path('results-{}'.format(d)), exist_ok=True) + + open_type = 'a' if os.path.exists(self.get_path('log.txt'))else 'w' + self.log_file = open(self.get_path('log.txt'), open_type) + with open(self.get_path('config.txt'), open_type) as f: + f.write(now + '\n\n') + for arg in vars(args): + f.write('{}: {}\n'.format(arg, getattr(args, arg))) + f.write('\n') + + self.n_processes = 8 + + def get_path(self, *subdir): + return os.path.join(self.dir, *subdir) + + def save(self, trainer, epoch, is_best=False): + trainer.model.save(self.get_path('model'), epoch, is_best=is_best) + trainer.loss.save(self.dir) + trainer.loss.plot_loss(self.dir, epoch) + + self.plot_psnr(epoch) + trainer.optimizer.save(self.dir) + torch.save(self.log, self.get_path('psnr_log.pt')) + + def add_log(self, log): + self.log = torch.cat([self.log, log]) + + def write_log(self, log, refresh=False): + print(log) + self.log_file.write(log + '\n') + if refresh: + self.log_file.close() + self.log_file = open(self.get_path('log.txt'), 'a') + + def done(self): + self.log_file.close() + + def plot_psnr(self, epoch): + axis = np.linspace(1, epoch, epoch) + for idx_data, d in enumerate(self.args.data_test): + label = 'SR on {}'.format(d) + fig = plt.figure() + plt.title(label) + for idx_scale, scale in enumerate(self.args.scale): + plt.plot( + axis, + self.log[:, idx_data, idx_scale].numpy(), + label='Scale {}'.format(scale) + ) + plt.legend() + plt.xlabel('Epochs') + plt.ylabel('PSNR') + plt.grid(True) + plt.savefig(self.get_path('test_{}.pdf'.format(d))) + plt.close(fig) + + def begin_background(self): + self.queue = Queue() + + def bg_target(queue): + while True: + if not queue.empty(): + filename, tensor = queue.get() + if filename is None: break + imageio.imwrite(filename, tensor.numpy()) + + self.process = [ + Process(target=bg_target, args=(self.queue,)) \ + for _ in range(self.n_processes) + ] + + for p in self.process: p.start() + + def end_background(self): + for _ in range(self.n_processes): self.queue.put((None, None)) + while not self.queue.empty(): time.sleep(1) + for p in self.process: p.join() + + def save_results(self, dataset, filename, save_list, scale): + if self.args.save_results: + filename = self.get_path( + 'results-{}'.format(dataset.dataset.name), + '{}_x{}_'.format(filename, scale) + ) + + postfix = ('DM', 'LQ', 'HQ') + for v, p in zip(save_list, postfix): + normalized = v[0].mul(255 / self.args.rgb_range) + tensor_cpu = normalized.byte().permute(1, 2, 0).cpu() + self.queue.put(('{}{}.png'.format(filename, p), tensor_cpu)) + +def quantize(img, rgb_range): + pixel_range = 255 / rgb_range + return img.mul(pixel_range).clamp(0, 255).round().div(pixel_range) + +def calc_psnr(sr, hr, scale, rgb_range, dataset=None): + if hr.nelement() == 1: return 0 + + diff = (sr - hr) / rgb_range + if dataset and dataset.dataset.benchmark: + shave = scale + if diff.size(1) > 5: + gray_coeffs = [65.738, 129.057, 25.064] + convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256 + diff = diff.mul(convert).sum(dim=1) + else: + shave = scale + 6 + + valid = diff[..., :, :] + mse = valid.pow(2).mean() + + return -10 * math.log10(mse) + +def make_optimizer(args, target): + ''' + make optimizer and scheduler together + ''' + # optimizer + trainable = filter(lambda x: x.requires_grad, target.parameters()) + kwargs_optimizer = {'lr': args.lr, 'weight_decay': args.weight_decay} + + if args.optimizer == 'SGD': + optimizer_class = optim.SGD + kwargs_optimizer['momentum'] = args.momentum + elif args.optimizer == 'ADAM': + optimizer_class = optim.Adam + kwargs_optimizer['betas'] = args.betas + kwargs_optimizer['eps'] = args.epsilon + elif args.optimizer == 'ADAMW': + optimizer_class = optim.AdamW + kwargs_optimizer['betas'] = args.betas + kwargs_optimizer['eps'] = args.epsilon + kwargs_optimizer["weight_decay"]=0.01 + elif args.optimizer == 'RMSprop': + optimizer_class = optim.RMSprop + kwargs_optimizer['eps'] = args.epsilon + + # scheduler + milestones = list(map(lambda x: int(x), args.decay.split('-'))) + kwargs_scheduler = {'milestones': milestones, 'gamma': args.gamma} + scheduler_class = lrs.MultiStepLR + + class CustomOptimizer(optimizer_class): + def __init__(self, *args, **kwargs): + super(CustomOptimizer, self).__init__(*args, **kwargs) + + def _register_scheduler(self, scheduler_class, **kwargs): + self.scheduler = scheduler_class(self, **kwargs) + + def save(self, save_dir): + torch.save(self.state_dict(), self.get_dir(save_dir)) + + def load(self, load_dir, epoch=1): + self.load_state_dict(torch.load(self.get_dir(load_dir))) + if epoch > 1: + for _ in range(epoch): self.scheduler.step() + + def get_dir(self, dir_path): + return os.path.join(dir_path, 'optimizer.pt') + + def schedule(self): + self.scheduler.step() + + def get_lr(self): + return self.scheduler.get_lr()[0] + + def get_last_epoch(self): + return self.scheduler.last_epoch + + optimizer = CustomOptimizer(trainable, **kwargs_optimizer) + optimizer._register_scheduler(scheduler_class, **kwargs_scheduler) + return optimizer + diff --git a/Demosaic/code/utils/__init__.py b/Demosaic/code/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Demosaic/code/utils/__pycache__/__init__.cpython-37.pyc b/Demosaic/code/utils/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..66c823a277ce16399ffa58dcd6b806bd04a54d0e Binary files /dev/null and b/Demosaic/code/utils/__pycache__/__init__.cpython-37.pyc differ diff --git a/Demosaic/code/utils/__pycache__/tools.cpython-37.pyc b/Demosaic/code/utils/__pycache__/tools.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ad4e996bc555ddcb51a4a73e1d2e5f69af6918a4 Binary files /dev/null and b/Demosaic/code/utils/__pycache__/tools.cpython-37.pyc differ diff --git a/Demosaic/code/utils/tools.py b/Demosaic/code/utils/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..c40cd8dc5e95d353254b950c9ec373126c5fe63e --- /dev/null +++ b/Demosaic/code/utils/tools.py @@ -0,0 +1,81 @@ +import os +import torch +import numpy as np +from PIL import Image + +import torch.nn.functional as F + +def normalize(x): + return x.mul_(2).add_(-1) + +def same_padding(images, ksizes, strides, rates): + assert len(images.size()) == 4 + batch_size, channel, rows, cols = images.size() + out_rows = (rows + strides[0] - 1) // strides[0] + out_cols = (cols + strides[1] - 1) // strides[1] + effective_k_row = (ksizes[0] - 1) * rates[0] + 1 + effective_k_col = (ksizes[1] - 1) * rates[1] + 1 + padding_rows = max(0, (out_rows-1)*strides[0]+effective_k_row-rows) + padding_cols = max(0, (out_cols-1)*strides[1]+effective_k_col-cols) + # Pad the input + padding_top = int(padding_rows / 2.) + padding_left = int(padding_cols / 2.) + padding_bottom = padding_rows - padding_top + padding_right = padding_cols - padding_left + paddings = (padding_left, padding_right, padding_top, padding_bottom) + images = torch.nn.ZeroPad2d(paddings)(images) + return images + + +def extract_image_patches(images, ksizes, strides, rates, padding='same'): + """ + Extract patches from images and put them in the C output dimension. + :param padding: + :param images: [batch, channels, in_rows, in_cols]. A 4-D Tensor with shape + :param ksizes: [ksize_rows, ksize_cols]. The size of the sliding window for + each dimension of images + :param strides: [stride_rows, stride_cols] + :param rates: [dilation_rows, dilation_cols] + :return: A Tensor + """ + assert len(images.size()) == 4 + assert padding in ['same', 'valid'] + batch_size, channel, height, width = images.size() + + if padding == 'same': + images = same_padding(images, ksizes, strides, rates) + elif padding == 'valid': + pass + else: + raise NotImplementedError('Unsupported padding type: {}.\ + Only "same" or "valid" are supported.'.format(padding)) + + unfold = torch.nn.Unfold(kernel_size=ksizes, + dilation=rates, + padding=0, + stride=strides) + patches = unfold(images) + return patches # [N, C*k*k, L], L is the total number of such blocks +def reduce_mean(x, axis=None, keepdim=False): + if not axis: + axis = range(len(x.shape)) + for i in sorted(axis, reverse=True): + x = torch.mean(x, dim=i, keepdim=keepdim) + return x + + +def reduce_std(x, axis=None, keepdim=False): + if not axis: + axis = range(len(x.shape)) + for i in sorted(axis, reverse=True): + x = torch.std(x, dim=i, keepdim=keepdim) + return x + + +def reduce_sum(x, axis=None, keepdim=False): + if not axis: + axis = range(len(x.shape)) + for i in sorted(axis, reverse=True): + x = torch.sum(x, dim=i, keepdim=keepdim) + return x + diff --git a/Demosaic/code/videotester.py b/Demosaic/code/videotester.py new file mode 100644 index 0000000000000000000000000000000000000000..4e4f3581a8d7318208a1f7bd3b8eae11bfc13aa4 --- /dev/null +++ b/Demosaic/code/videotester.py @@ -0,0 +1,72 @@ +import os +import math + +import utility +from data import common + +import torch +import cv2 + +from tqdm import tqdm + +class VideoTester(): + def __init__(self, args, my_model, ckp): + self.args = args + self.scale = args.scale + + self.ckp = ckp + self.model = my_model + + self.filename, _ = os.path.splitext(os.path.basename(args.dir_demo)) + + def test(self): + torch.set_grad_enabled(False) + + self.ckp.write_log('\nEvaluation on video:') + self.model.eval() + + timer_test = utility.timer() + for idx_scale, scale in enumerate(self.scale): + vidcap = cv2.VideoCapture(self.args.dir_demo) + total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT)) + vidwri = cv2.VideoWriter( + self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)), + cv2.VideoWriter_fourcc(*'XVID'), + vidcap.get(cv2.CAP_PROP_FPS), + ( + int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)), + int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + ) + ) + + tqdm_test = tqdm(range(total_frames), ncols=80) + for _ in tqdm_test: + success, lr = vidcap.read() + if not success: break + + lr, = common.set_channel(lr, n_channels=self.args.n_colors) + lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range) + lr, = self.prepare(lr.unsqueeze(0)) + sr = self.model(lr, idx_scale) + sr = utility.quantize(sr, self.args.rgb_range).squeeze(0) + + normalized = sr * 255 / self.args.rgb_range + ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy() + vidwri.write(ndarr) + + vidcap.release() + vidwri.release() + + self.ckp.write_log( + 'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True + ) + torch.set_grad_enabled(True) + + def prepare(self, *args): + device = torch.device('cpu' if self.args.cpu else 'cuda') + def _prepare(tensor): + if self.args.precision == 'half': tensor = tensor.half() + return tensor.to(device) + + return [_prepare(a) for a in args] + diff --git a/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/config.txt b/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..d197958f6094ba8b8f4df4476e4388be37a36854 --- /dev/null +++ b/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/config.txt @@ -0,0 +1,520 @@ +2020-11-06-18:54:01 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-18:56:17 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-19:03:06 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-19:04:45 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-19:37:28 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-842 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-19:40:01 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-23:27:46 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-23:29:53 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: BETALAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: BETALAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/log.txt b/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..abffeb95d4eccefbf6a72efd687ac6ab9d600b7f --- /dev/null +++ b/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/log.txt @@ -0,0 +1,1419 @@ +DataParallel( + (module): BETALAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +DataParallel( + (module): BETALAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): BETALAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.8399] 53.9+0.7s +[3200/16000] [L1: 0.5667] 47.8+0.1s +[4800/16000] [L1: 0.4601] 49.9+0.1s +[6400/16000] [L1: 0.3973] 51.9+0.1s +[8000/16000] [L1: 0.3518] 50.7+0.0s +[9600/16000] [L1: 0.3197] 51.6+0.1s +[11200/16000] [L1: 0.2953] 50.5+0.0s +[12800/16000] [L1: 0.2765] 50.2+0.0s +[14400/16000] [L1: 0.2600] 49.8+0.0s +[16000/16000] [L1: 0.2465] 49.4+0.0s + +Evaluation: +DataParallel( + (module): BETALAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.9625] 53.1+0.6s +[3200/16000] [L1: 0.6529] 46.9+0.1s +[4800/16000] [L1: 0.5266] 49.0+0.1s +[6400/16000] [L1: 0.4501] 50.7+0.0s +[8000/16000] [L1: 0.3975] 49.9+0.0s +[9600/16000] [L1: 0.3587] 52.0+0.1s +[11200/16000] [L1: 0.3298] 51.7+0.1s +[12800/16000] [L1: 0.3071] 51.3+0.1s +[14400/16000] [L1: 0.2886] 51.1+0.1s +[16000/16000] [L1: 0.2732] 51.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 9.242 (Best: 9.242 @epoch 1) +Forward: 37.15s + +Saving... +Total: 37.97s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1219] 50.4+0.8s +[3200/16000] [L1: 0.1163] 51.1+0.1s +[4800/16000] [L1: 0.1140] 51.5+0.1s +[6400/16000] [L1: 0.1121] 51.5+0.1s +[8000/16000] [L1: 0.1095] 51.0+0.1s +[9600/16000] [L1: 0.1074] 51.4+0.1s +[11200/16000] [L1: 0.1048] 50.9+0.1s +[12800/16000] [L1: 0.1028] 51.5+0.1s +[14400/16000] [L1: 0.1001] 51.6+0.1s +[16000/16000] [L1: 0.0979] 51.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 8.841 (Best: 9.242 @epoch 1) +Forward: 37.01s + +Saving... +Total: 37.63s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0766] 50.7+0.9s +[3200/16000] [L1: 0.0752] 50.9+0.1s +[4800/16000] [L1: 0.0738] 51.1+0.1s +[6400/16000] [L1: 0.0725] 51.6+0.1s +[8000/16000] [L1: 0.0709] 51.4+0.1s +[9600/16000] [L1: 0.0693] 51.6+0.1s +[11200/16000] [L1: 0.0680] 52.2+0.1s +[12800/16000] [L1: 0.0666] 51.2+0.0s +[14400/16000] [L1: 0.0652] 51.2+0.1s +[16000/16000] [L1: 0.0642] 51.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 8.655 (Best: 9.242 @epoch 1) +Forward: 36.69s + +Saving... +Total: 37.20s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0514] 51.0+0.7s +[3200/16000] [L1: 0.0499] 49.7+0.0s +[4800/16000] [L1: 0.0500] 49.4+0.0s +[6400/16000] [L1: 0.0490] 50.8+0.0s +[8000/16000] [L1: 0.0494] 51.4+0.0s +[9600/16000] [L1: 0.0481] 49.9+0.0s +[11200/16000] [L1: 0.0473] 49.5+0.0s +[12800/16000] [L1: 0.0467] 51.4+0.1s +[14400/16000] [L1: 0.0464] 50.3+0.0s +[16000/16000] [L1: 0.0461] 49.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.560 (Best: 9.242 @epoch 1) +Forward: 36.81s + +Saving... +Total: 37.34s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0393] 51.0+0.8s +[3200/16000] [L1: 0.0423] 52.6+0.1s +[4800/16000] [L1: 0.0403] 51.3+0.1s +[6400/16000] [L1: 0.0392] 51.0+0.0s +[8000/16000] [L1: 0.0391] 51.4+0.1s +[9600/16000] [L1: 0.0385] 51.3+0.1s +[11200/16000] [L1: 0.0383] 51.8+0.0s +[12800/16000] [L1: 0.0383] 51.2+0.0s +[14400/16000] [L1: 0.0382] 51.1+0.0s +[16000/16000] [L1: 0.0377] 50.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.452 (Best: 9.242 @epoch 1) +Forward: 36.58s + +Saving... +Total: 37.01s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0340] 50.9+0.8s +[3200/16000] [L1: 0.0341] 51.7+0.1s +[4800/16000] [L1: 0.0325] 49.8+0.0s +[6400/16000] [L1: 0.0325] 51.7+0.1s +[8000/16000] [L1: 0.0324] 51.4+0.1s +[9600/16000] [L1: 0.0328] 51.0+0.1s +[11200/16000] [L1: 0.0326] 50.9+0.0s +[12800/16000] [L1: 0.0326] 50.8+0.0s +[14400/16000] [L1: 0.0325] 50.2+0.0s +[16000/16000] [L1: 0.0328] 50.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.209 (Best: 9.242 @epoch 1) +Forward: 36.75s + +Saving... +Total: 37.15s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0289] 51.0+0.7s +[3200/16000] [L1: 0.0301] 50.4+0.1s +[4800/16000] [L1: 0.0297] 50.2+0.0s +[6400/16000] [L1: 0.0297] 49.9+0.0s +[8000/16000] [L1: 0.0302] 49.7+0.0s +[9600/16000] [L1: 0.0304] 50.6+0.0s +[11200/16000] [L1: 0.0299] 50.1+0.0s +[12800/16000] [L1: 0.0301] 50.2+0.0s +[14400/16000] [L1: 0.0301] 50.0+0.0s +[16000/16000] [L1: 0.0298] 49.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.177 (Best: 9.242 @epoch 1) +Forward: 36.82s + +Saving... +Total: 37.28s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0295] 50.5+0.8s +[3200/16000] [L1: 0.0302] 51.2+0.1s +[4800/16000] [L1: 0.0296] 51.6+0.1s +[6400/16000] [L1: 0.0289] 51.5+0.1s +[8000/16000] [L1: 0.0288] 49.8+0.0s +[9600/16000] [L1: 0.0289] 51.5+0.1s +[11200/16000] [L1: 0.0285] 51.4+0.0s +[12800/16000] [L1: 0.0282] 52.1+0.0s +[14400/16000] [L1: 0.0281] 51.7+0.0s +[16000/16000] [L1: 0.0286] 51.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.225 (Best: 9.242 @epoch 1) +Forward: 36.72s + +Saving... +Total: 37.26s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0261] 50.5+0.8s +[3200/16000] [L1: 0.0260] 49.5+0.0s +[4800/16000] [L1: 0.0267] 49.1+0.0s +[6400/16000] [L1: 0.0262] 49.9+0.0s +[8000/16000] [L1: 0.0264] 50.6+0.1s +[9600/16000] [L1: 0.0260] 51.2+0.1s +[11200/16000] [L1: 0.0259] 49.7+0.1s +[12800/16000] [L1: 0.0257] 49.9+0.1s +[14400/16000] [L1: 0.0257] 48.9+0.0s +[16000/16000] [L1: 0.0255] 50.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.989 (Best: 9.242 @epoch 1) +Forward: 36.74s + +Saving... +Total: 37.15s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0241] 51.1+0.7s +[3200/16000] [L1: 0.0231] 51.0+0.0s +[4800/16000] [L1: 0.0222] 51.5+0.1s +[6400/16000] [L1: 0.0223] 51.8+0.0s +[8000/16000] [L1: 0.0227] 51.3+0.0s +[9600/16000] [L1: 0.0228] 50.8+0.0s +[11200/16000] [L1: 0.0224] 49.5+0.0s +[12800/16000] [L1: 0.0228] 49.5+0.0s +[14400/16000] [L1: 0.0225] 51.3+0.1s +[16000/16000] [L1: 0.0226] 51.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.853 (Best: 9.242 @epoch 1) +Forward: 36.66s + +Saving... +Total: 37.12s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0253] 50.1+0.8s +[3200/16000] [L1: 0.0232] 51.8+0.1s +[4800/16000] [L1: 0.0225] 52.0+0.0s +[6400/16000] [L1: 0.0221] 51.9+0.1s +[8000/16000] [L1: 0.0218] 49.9+0.0s +[9600/16000] [L1: 0.0215] 48.5+0.0s +[11200/16000] [L1: 0.0223] 51.0+0.0s +[12800/16000] [L1: 0.0219] 51.3+0.0s +[14400/16000] [L1: 0.0221] 51.6+0.0s +[16000/16000] [L1: 0.0219] 49.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.427 (Best: 9.242 @epoch 1) +Forward: 36.72s + +Saving... +Total: 37.18s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0190] 50.7+0.8s +[3200/16000] [L1: 0.0192] 50.9+0.1s +[4800/16000] [L1: 0.0196] 52.3+0.1s +[6400/16000] [L1: 0.0202] 51.8+0.1s +[8000/16000] [L1: 0.0202] 51.4+0.0s +[9600/16000] [L1: 0.0200] 51.5+0.0s +[11200/16000] [L1: 0.0199] 51.1+0.0s +[12800/16000] [L1: 0.0200] 50.8+0.1s +[14400/16000] [L1: 0.0200] 49.7+0.0s +[16000/16000] [L1: 0.0199] 49.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.278 (Best: 9.242 @epoch 1) +Forward: 36.90s + +Saving... +Total: 37.43s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0186] 50.5+0.9s +[3200/16000] [L1: 0.0189] 50.0+0.0s +[4800/16000] [L1: 0.0184] 49.8+0.1s +[6400/16000] [L1: 0.0182] 50.3+0.1s +[8000/16000] [L1: 0.0183] 51.8+0.1s +[9600/16000] [L1: 0.0182] 51.8+0.1s +[11200/16000] [L1: 0.0185] 51.6+0.1s +[12800/16000] [L1: 0.0183] 51.4+0.0s +[14400/16000] [L1: 0.0182] 49.9+0.0s +[16000/16000] [L1: 0.0184] 50.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.229 (Best: 9.242 @epoch 1) +Forward: 36.88s + +Saving... +Total: 37.41s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0209] 50.7+0.8s +[3200/16000] [L1: 0.0195] 51.5+0.0s +[4800/16000] [L1: 0.0197] 50.0+0.0s +[6400/16000] [L1: 0.0187] 49.7+0.0s +[8000/16000] [L1: 0.0185] 51.0+0.1s +[9600/16000] [L1: 0.0191] 51.2+0.0s +[11200/16000] [L1: 0.0188] 51.4+0.1s +[12800/16000] [L1: 0.0184] 51.8+0.0s +[14400/16000] [L1: 0.0181] 51.8+0.0s +[16000/16000] [L1: 0.0180] 51.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.075 (Best: 9.242 @epoch 1) +Forward: 36.78s + +Saving... +Total: 37.16s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0169] 50.9+0.7s +[3200/16000] [L1: 0.0166] 52.0+0.1s +[4800/16000] [L1: 0.0172] 51.8+0.1s +[6400/16000] [L1: 0.0180] 51.1+0.1s +[8000/16000] [L1: 0.0181] 50.3+0.1s +[9600/16000] [L1: 0.0178] 50.9+0.0s +[11200/16000] [L1: 0.0178] 50.4+0.0s +[12800/16000] [L1: 0.0176] 50.7+0.0s +[14400/16000] [L1: 0.0175] 50.3+0.0s +[16000/16000] [L1: 0.0171] 51.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.071 (Best: 9.242 @epoch 1) +Forward: 36.69s + +Saving... +Total: 37.09s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0174] 50.5+0.7s +[3200/16000] [L1: 0.0173] 51.3+0.1s +[4800/16000] [L1: 0.0165] 50.7+0.1s +[6400/16000] [L1: 0.0162] 49.6+0.1s +[8000/16000] [L1: 0.0168] 50.9+0.1s +[9600/16000] [L1: 0.0164] 48.9+0.0s +[11200/16000] [L1: 0.0161] 50.5+0.1s +[12800/16000] [L1: 0.0164] 50.1+0.0s +[14400/16000] [L1: 0.0164] 49.2+0.0s +[16000/16000] [L1: 0.0165] 50.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.554 (Best: 9.242 @epoch 1) +Forward: 36.81s + +Saving... +Total: 37.32s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0169] 51.4+0.8s +[3200/16000] [L1: 0.0164] 49.7+0.0s +[4800/16000] [L1: 0.0162] 48.5+0.0s +[6400/16000] [L1: 0.0159] 48.6+0.0s +[8000/16000] [L1: 0.0161] 48.6+0.0s +[9600/16000] [L1: 0.0160] 48.2+0.0s +[11200/16000] [L1: 0.0158] 48.2+0.0s +[12800/16000] [L1: 0.0158] 48.1+0.0s +[14400/16000] [L1: 0.0157] 49.0+0.0s +[16000/16000] [L1: 0.0155] 50.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.106 (Best: 9.242 @epoch 1) +Forward: 36.74s + +Saving... +Total: 37.13s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0136] 50.9+0.8s +[3200/16000] [L1: 0.0143] 51.0+0.0s +[4800/16000] [L1: 0.0147] 50.3+0.0s +[6400/16000] [L1: 0.0147] 49.0+0.0s +[8000/16000] [L1: 0.0143] 48.9+0.0s +[9600/16000] [L1: 0.0144] 48.9+0.0s +[11200/16000] [L1: 0.0144] 48.9+0.0s +[12800/16000] [L1: 0.0144] 49.7+0.0s +[14400/16000] [L1: 0.0142] 50.7+0.1s +[16000/16000] [L1: 0.0142] 50.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.309 (Best: 9.242 @epoch 1) +Forward: 36.61s + +Saving... +Total: 37.18s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 50.7+0.8s +[3200/16000] [L1: 0.0142] 52.2+0.1s +[4800/16000] [L1: 0.0140] 51.6+0.1s +[6400/16000] [L1: 0.0138] 51.4+0.1s +[8000/16000] [L1: 0.0138] 51.5+0.1s +[9600/16000] [L1: 0.0137] 51.3+0.1s +[11200/16000] [L1: 0.0138] 51.6+0.0s +[12800/16000] [L1: 0.0139] 50.7+0.1s +[14400/16000] [L1: 0.0138] 49.9+0.1s +[16000/16000] [L1: 0.0140] 51.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.066 (Best: 9.242 @epoch 1) +Forward: 36.78s + +Saving... +Total: 37.20s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0151] 50.7+0.7s +[3200/16000] [L1: 0.0145] 50.4+0.1s +[4800/16000] [L1: 0.0142] 49.9+0.0s +[6400/16000] [L1: 0.0140] 51.4+0.1s +[8000/16000] [L1: 0.0139] 52.0+0.1s +[9600/16000] [L1: 0.0145] 51.9+0.1s +[11200/16000] [L1: 0.0144] 52.0+0.1s +[12800/16000] [L1: 0.0143] 51.4+0.1s +[14400/16000] [L1: 0.0149] 49.4+0.0s +[16000/16000] [L1: 0.0148] 49.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.223 (Best: 9.242 @epoch 1) +Forward: 36.78s + +Saving... +Total: 37.28s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0122] 50.3+0.7s +[3200/16000] [L1: 0.0126] 51.4+0.1s +[4800/16000] [L1: 0.0133] 51.8+0.1s +[6400/16000] [L1: 0.0134] 51.8+0.0s +[8000/16000] [L1: 0.0136] 50.1+0.1s +[9600/16000] [L1: 0.0134] 49.9+0.0s +[11200/16000] [L1: 0.0135] 50.3+0.0s +[12800/16000] [L1: 0.0134] 52.0+0.1s +[14400/16000] [L1: 0.0136] 51.3+0.1s +[16000/16000] [L1: 0.0138] 49.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.074 (Best: 9.242 @epoch 1) +Forward: 36.79s + +Saving... +Total: 37.26s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 50.9+1.0s +[3200/16000] [L1: 0.0135] 51.2+0.0s +[4800/16000] [L1: 0.0132] 51.5+0.0s +[6400/16000] [L1: 0.0133] 51.8+0.0s +[8000/16000] [L1: 0.0130] 51.0+0.0s +[9600/16000] [L1: 0.0132] 50.9+0.0s +[11200/16000] [L1: 0.0134] 50.4+0.1s +[12800/16000] [L1: 0.0133] 48.7+0.0s +[14400/16000] [L1: 0.0134] 49.9+0.0s +[16000/16000] [L1: 0.0133] 51.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.211 (Best: 9.242 @epoch 1) +Forward: 36.74s + +Saving... +Total: 37.12s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0121] 50.9+0.8s +[3200/16000] [L1: 0.0122] 51.7+0.1s +[4800/16000] [L1: 0.0127] 51.3+0.1s +[6400/16000] [L1: 0.0125] 50.8+0.1s +[8000/16000] [L1: 0.0128] 51.6+0.1s +DataParallel( + (module): BETALAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.8249] 53.4+0.7s +DataParallel( + (module): BETALAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 diff --git a/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/loss.pt b/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..2cb8aeac7a3784a1b17d156d419747cef2ed44bf --- /dev/null +++ b/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48da99bdddb436cdb4a093ba3a3efbe02ec42ba0bd17f6415b4f6645eb17b79f +size 559 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0000000000000000000000000000000000000000..46b37b7abf8d6bbc00a055c9d7cd2211994bf5e7 Binary files /dev/null and b/Demosaic/experiment/BETALAMBDA_DEMOSAIC20_R4/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4/config.txt b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..3d6894f8f4e0058f4578e027d5b0cf9375e4923a --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4/config.txt @@ -0,0 +1,330 @@ +2020-11-06-23:48:10 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTA +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDANETACTA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-23:53:49 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTA +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 32 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDANETACTA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-23:54:55 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTA +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 128 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDANETACTA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-23:57:48 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTB +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 128 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDANETACTA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-07-00:07:52 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTA +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDANETACTA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4/log.txt b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..29a234f1fd6de14c2b204fc37101d78321eaa9ab --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4/log.txt @@ -0,0 +1,1716 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7904] 82.4+0.7s +[3200/16000] [L1: 1.6993] 74.0+0.1s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[12800/128000] [L1: 1.5900] 343.1+2.0s +[25600/128000] [L1: 1.6570] 341.3+0.1s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7904] 75.5+0.7s +[3200/16000] [L1: 1.6993] 70.2+0.0s +[4800/16000] [L1: 1.6920] 70.5+0.0s +[6400/16000] [L1: 1.6977] 70.5+0.1s +[8000/16000] [L1: 1.7032] 70.0+0.1s +[9600/16000] [L1: 1.7071] 69.4+0.1s +[11200/16000] [L1: 1.7094] 69.1+0.0s +[12800/16000] [L1: 1.7120] 69.1+0.1s +[14400/16000] [L1: 1.7150] 69.5+0.1s +[16000/16000] [L1: 1.7150] 68.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.165 (Best: 8.165 @epoch 1) +Forward: 34.67s + +Saving... +Total: 35.61s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7355] 69.7+0.9s +[3200/16000] [L1: 1.7380] 68.1+0.1s +[4800/16000] [L1: 1.7339] 68.4+0.0s +[6400/16000] [L1: 1.7332] 68.1+0.0s +[8000/16000] [L1: 1.7292] 69.2+0.1s +[9600/16000] [L1: 1.7285] 68.1+0.1s +[11200/16000] [L1: 1.7290] 68.9+0.0s +[12800/16000] [L1: 1.7290] 69.2+0.0s +[14400/16000] [L1: 1.7288] 68.8+0.0s +[16000/16000] [L1: 1.7278] 68.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.177 (Best: 8.177 @epoch 2) +Forward: 34.33s + +Saving... +Total: 34.94s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7239] 69.2+0.9s +[3200/16000] [L1: 1.7194] 69.6+0.1s +[4800/16000] [L1: 1.7211] 68.7+0.1s +[6400/16000] [L1: 1.7224] 68.9+0.0s +[8000/16000] [L1: 1.7244] 69.5+0.1s +[9600/16000] [L1: 1.7256] 69.7+0.1s +[11200/16000] [L1: 1.7250] 70.0+0.0s +[12800/16000] [L1: 1.7250] 68.9+0.0s +[14400/16000] [L1: 1.7258] 68.6+0.1s +[16000/16000] [L1: 1.7265] 67.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.165 (Best: 8.177 @epoch 2) +Forward: 34.60s + +Saving... +Total: 35.19s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7141] 69.3+0.9s +[3200/16000] [L1: 1.7201] 69.7+0.1s +[4800/16000] [L1: 1.7263] 70.1+0.1s +[6400/16000] [L1: 1.7284] 69.6+0.1s +[8000/16000] [L1: 1.7294] 68.3+0.1s +[9600/16000] [L1: 1.7281] 69.5+0.1s +[11200/16000] [L1: 1.7236] 69.5+0.1s +[12800/16000] [L1: 1.7228] 69.6+0.1s +[14400/16000] [L1: 1.7243] 69.6+0.0s +[16000/16000] [L1: 1.7235] 69.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.147 (Best: 8.177 @epoch 2) +Forward: 34.49s + +Saving... +Total: 34.89s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7153] 69.5+0.8s +[3200/16000] [L1: 1.7273] 68.8+0.1s +[4800/16000] [L1: 1.7253] 68.8+0.1s +[6400/16000] [L1: 1.7284] 68.7+0.1s +[8000/16000] [L1: 1.7234] 70.1+0.1s +[9600/16000] [L1: 1.7242] 68.4+0.0s +[11200/16000] [L1: 1.7246] 69.6+0.1s +[12800/16000] [L1: 1.7266] 70.0+0.1s +[14400/16000] [L1: 1.7256] 69.7+0.1s +[16000/16000] [L1: 1.7252] 69.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 8.170 (Best: 8.177 @epoch 2) +Forward: 34.28s + +Saving... +Total: 34.76s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7266] 70.6+1.0s +[3200/16000] [L1: 1.7270] 68.5+0.0s +[4800/16000] [L1: 1.7310] 69.8+0.1s +[6400/16000] [L1: nan] 69.0+0.1s +[8000/16000] [L1: nan] 69.6+0.1s +[9600/16000] [L1: nan] 68.9+0.0s +[11200/16000] [L1: nan] 67.5+0.0s +[12800/16000] [L1: nan] 68.2+0.0s +[14400/16000] [L1: nan] 70.3+0.1s +[16000/16000] [L1: nan] 68.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.45s + +Saving... +Total: 34.96s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.3+0.9s +[3200/16000] [L1: nan] 68.8+0.1s +[4800/16000] [L1: nan] 69.2+0.1s +[6400/16000] [L1: nan] 68.4+0.0s +[8000/16000] [L1: nan] 69.7+0.1s +[9600/16000] [L1: nan] 70.6+0.1s +[11200/16000] [L1: nan] 69.8+0.1s +[12800/16000] [L1: nan] 69.3+0.1s +[14400/16000] [L1: nan] 68.8+0.1s +[16000/16000] [L1: nan] 70.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.50s + +Saving... +Total: 35.03s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 67.9+0.9s +[3200/16000] [L1: nan] 69.5+0.1s +[4800/16000] [L1: nan] 68.7+0.0s +[6400/16000] [L1: nan] 69.5+0.0s +[8000/16000] [L1: nan] 69.7+0.1s +[9600/16000] [L1: nan] 70.7+0.1s +[11200/16000] [L1: nan] 70.2+0.1s +[12800/16000] [L1: nan] 68.8+0.0s +[14400/16000] [L1: nan] 69.6+0.0s +[16000/16000] [L1: nan] 69.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.44s + +Saving... +Total: 34.89s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.8+1.0s +[3200/16000] [L1: nan] 69.4+0.1s +[4800/16000] [L1: nan] 70.4+0.1s +[6400/16000] [L1: nan] 70.6+0.1s +[8000/16000] [L1: nan] 69.3+0.1s +[9600/16000] [L1: nan] 70.4+0.1s +[11200/16000] [L1: nan] 70.2+0.1s +[12800/16000] [L1: nan] 69.0+0.0s +[14400/16000] [L1: nan] 69.2+0.0s +[16000/16000] [L1: nan] 68.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.73s + +Saving... +Total: 35.26s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.0+0.9s +[3200/16000] [L1: nan] 69.1+0.1s +[4800/16000] [L1: nan] 69.8+0.1s +[6400/16000] [L1: nan] 69.6+0.1s +[8000/16000] [L1: nan] 69.5+0.1s +[9600/16000] [L1: nan] 69.5+0.1s +[11200/16000] [L1: nan] 70.4+0.1s +[12800/16000] [L1: nan] 69.4+0.1s +[14400/16000] [L1: nan] 70.0+0.1s +[16000/16000] [L1: nan] 70.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.74s + +Saving... +Total: 35.27s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.8+0.8s +[3200/16000] [L1: nan] 68.7+0.1s +[4800/16000] [L1: nan] 70.5+0.1s +[6400/16000] [L1: nan] 69.4+0.1s +[8000/16000] [L1: nan] 69.0+0.1s +[9600/16000] [L1: nan] 70.6+0.1s +[11200/16000] [L1: nan] 70.6+0.1s +[12800/16000] [L1: nan] 71.2+0.1s +[14400/16000] [L1: nan] 70.9+0.1s +[16000/16000] [L1: nan] 67.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.78s + +Saving... +Total: 35.32s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.6+1.0s +[3200/16000] [L1: nan] 69.5+0.1s +[4800/16000] [L1: nan] 68.7+0.1s +[6400/16000] [L1: nan] 70.0+0.1s +[8000/16000] [L1: nan] 67.7+0.0s +[9600/16000] [L1: nan] 66.6+0.0s +[11200/16000] [L1: nan] 68.7+0.1s +[12800/16000] [L1: nan] 70.2+0.1s +[14400/16000] [L1: nan] 69.9+0.1s +[16000/16000] [L1: nan] 67.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.76s + +Saving... +Total: 35.21s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.8+0.9s +[3200/16000] [L1: nan] 69.6+0.1s +[4800/16000] [L1: nan] 70.0+0.1s +[6400/16000] [L1: nan] 69.5+0.1s +[8000/16000] [L1: nan] 70.2+0.1s +[9600/16000] [L1: nan] 71.0+0.1s +[11200/16000] [L1: nan] 69.8+0.1s +[12800/16000] [L1: nan] 68.0+0.1s +[14400/16000] [L1: nan] 71.2+0.1s +[16000/16000] [L1: nan] 70.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.75s + +Saving... +Total: 35.43s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.0+0.9s +[3200/16000] [L1: nan] 68.9+0.1s +[4800/16000] [L1: nan] 70.0+0.1s +[6400/16000] [L1: nan] 70.0+0.1s +[8000/16000] [L1: nan] 70.7+0.1s +[9600/16000] [L1: nan] 70.0+0.1s +[11200/16000] [L1: nan] 68.5+0.1s +[12800/16000] [L1: nan] 67.8+0.0s +[14400/16000] [L1: nan] 68.3+0.0s +[16000/16000] [L1: nan] 68.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.81s + +Saving... +Total: 35.53s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.8+1.0s +[3200/16000] [L1: nan] 69.9+0.1s +[4800/16000] [L1: nan] 70.1+0.1s +[6400/16000] [L1: nan] 70.0+0.1s +[8000/16000] [L1: nan] 70.5+0.1s +[9600/16000] [L1: nan] 70.0+0.1s +[11200/16000] [L1: nan] 71.3+0.1s +[12800/16000] [L1: nan] 70.8+0.1s +[14400/16000] [L1: nan] 69.4+0.1s +[16000/16000] [L1: nan] 70.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.69s + +Saving... +Total: 35.23s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.0+0.9s +[3200/16000] [L1: nan] 69.0+0.1s +[4800/16000] [L1: nan] 70.5+0.1s +[6400/16000] [L1: nan] 70.2+0.1s +[8000/16000] [L1: nan] 70.5+0.1s +[9600/16000] [L1: nan] 70.0+0.1s +[11200/16000] [L1: nan] 70.1+0.1s +[12800/16000] [L1: nan] 69.4+0.1s +[14400/16000] [L1: nan] 69.0+0.0s +[16000/16000] [L1: nan] 67.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.98s + +Saving... +Total: 35.43s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 71.0+0.9s +[3200/16000] [L1: nan] 71.1+0.1s +[4800/16000] [L1: nan] 70.2+0.1s +[6400/16000] [L1: nan] 70.2+0.1s +[8000/16000] [L1: nan] 68.3+0.0s +[9600/16000] [L1: nan] 69.1+0.0s +[11200/16000] [L1: nan] 69.7+0.0s +[12800/16000] [L1: nan] 69.4+0.1s +[14400/16000] [L1: nan] 68.8+0.0s +[16000/16000] [L1: nan] 69.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.82s + +Saving... +Total: 35.34s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.8+0.8s +[3200/16000] [L1: nan] 67.6+0.1s +[4800/16000] [L1: nan] 71.4+0.1s +[6400/16000] [L1: nan] 70.3+0.1s +[8000/16000] [L1: nan] 71.1+0.1s +[9600/16000] [L1: nan] 71.4+0.1s +[11200/16000] [L1: nan] 70.3+0.1s +[12800/16000] [L1: nan] 70.3+0.0s +[14400/16000] [L1: nan] 69.6+0.1s +[16000/16000] [L1: nan] 68.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.78s + +Saving... +Total: 35.23s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.8+0.9s +[3200/16000] [L1: nan] 69.5+0.1s +[4800/16000] [L1: nan] 69.7+0.1s +[6400/16000] [L1: nan] 70.9+0.1s +[8000/16000] [L1: nan] 70.4+0.1s +[9600/16000] [L1: nan] 70.4+0.1s +[11200/16000] [L1: nan] 70.1+0.1s +[12800/16000] [L1: nan] 69.8+0.1s +[14400/16000] [L1: nan] 70.4+0.1s +[16000/16000] [L1: nan] 69.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.61s + +Saving... +Total: 35.23s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.5+0.9s +[3200/16000] [L1: nan] 69.1+0.1s +[4800/16000] [L1: nan] 70.2+0.1s +[6400/16000] [L1: nan] 69.0+0.1s +[8000/16000] [L1: nan] 68.7+0.1s +[9600/16000] [L1: nan] 69.0+0.1s +[11200/16000] [L1: nan] 69.3+0.0s +[12800/16000] [L1: nan] 69.9+0.0s +[14400/16000] [L1: nan] 68.2+0.0s +[16000/16000] [L1: nan] 69.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.65s + +Saving... +Total: 35.18s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.1+0.9s +[3200/16000] [L1: nan] 70.3+0.1s +[4800/16000] [L1: nan] 71.0+0.1s +[6400/16000] [L1: nan] 68.4+0.0s +[8000/16000] [L1: nan] 67.9+0.0s +[9600/16000] [L1: nan] 69.7+0.0s +[11200/16000] [L1: nan] 70.3+0.1s +[12800/16000] [L1: nan] 70.0+0.1s +[14400/16000] [L1: nan] 70.6+0.1s +[16000/16000] [L1: nan] 67.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.75s + +Saving... +Total: 35.26s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.1+1.0s +[3200/16000] [L1: nan] 69.5+0.1s +[4800/16000] [L1: nan] 69.0+0.1s +[6400/16000] [L1: nan] 69.9+0.0s +[8000/16000] [L1: nan] 70.6+0.1s +[9600/16000] [L1: nan] 69.4+0.1s +[11200/16000] [L1: nan] 70.4+0.1s +[12800/16000] [L1: nan] 70.3+0.1s +[14400/16000] [L1: nan] 70.1+0.1s +[16000/16000] [L1: nan] 68.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.60s + +Saving... +Total: 35.16s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.9+0.9s +[3200/16000] [L1: nan] 69.1+0.1s +[4800/16000] [L1: nan] 70.3+0.1s +[6400/16000] [L1: nan] 69.3+0.1s +[8000/16000] [L1: nan] 69.4+0.1s +[9600/16000] [L1: nan] 69.8+0.1s +[11200/16000] [L1: nan] 71.0+0.1s +[12800/16000] [L1: nan] 70.7+0.1s +[14400/16000] [L1: nan] 70.6+0.1s +[16000/16000] [L1: nan] 69.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.59s + +Saving... +Total: 35.13s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.3+0.9s +[3200/16000] [L1: nan] 69.7+0.1s +[4800/16000] [L1: nan] 70.6+0.1s +[6400/16000] [L1: nan] 69.5+0.0s +[8000/16000] [L1: nan] 70.2+0.1s +[9600/16000] [L1: nan] 70.3+0.1s +[11200/16000] [L1: nan] 69.9+0.1s +[12800/16000] [L1: nan] 70.1+0.1s +[14400/16000] [L1: nan] 70.1+0.1s +[16000/16000] [L1: nan] 70.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.53s + +Saving... +Total: 34.98s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.4+0.9s +[3200/16000] [L1: nan] 69.4+0.1s +[4800/16000] [L1: nan] 70.4+0.1s +[6400/16000] [L1: nan] 70.2+0.1s +[8000/16000] [L1: nan] 69.3+0.1s +[9600/16000] [L1: nan] 68.9+0.1s +[11200/16000] [L1: nan] 69.1+0.0s +[12800/16000] [L1: nan] 70.4+0.1s +[14400/16000] [L1: nan] 69.7+0.1s +[16000/16000] [L1: nan] 69.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.52s + +Saving... +Total: 35.18s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.7+0.9s +[3200/16000] [L1: nan] 69.4+0.1s +[4800/16000] [L1: nan] 69.9+0.1s +[6400/16000] [L1: nan] 69.8+0.1s +[8000/16000] [L1: nan] 68.3+0.0s +[9600/16000] [L1: nan] 67.9+0.0s +[11200/16000] [L1: nan] 70.0+0.1s +[12800/16000] [L1: nan] 70.5+0.1s +[14400/16000] [L1: nan] 70.3+0.1s +[16000/16000] [L1: nan] 69.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.40s + +Saving... +Total: 34.97s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.0+0.9s +[3200/16000] [L1: nan] 68.7+0.1s +[4800/16000] [L1: nan] 69.4+0.1s +[6400/16000] [L1: nan] 69.1+0.1s +[8000/16000] [L1: nan] 70.1+0.1s +[9600/16000] [L1: nan] 69.2+0.1s +[11200/16000] [L1: nan] 69.8+0.1s +[12800/16000] [L1: nan] 68.1+0.1s +[14400/16000] [L1: nan] 70.5+0.1s +[16000/16000] [L1: nan] 69.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.36s + +Saving... +Total: 34.87s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.3+0.8s +[3200/16000] [L1: nan] 69.8+0.1s +[4800/16000] [L1: nan] 70.3+0.1s +[6400/16000] [L1: nan] 70.6+0.1s +[8000/16000] [L1: nan] 71.0+0.1s +[9600/16000] [L1: nan] 68.8+0.1s +[11200/16000] [L1: nan] 68.5+0.1s +[12800/16000] [L1: nan] 69.4+0.1s +[14400/16000] [L1: nan] 69.4+0.0s +[16000/16000] [L1: nan] 69.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.38s + +Saving... +Total: 34.89s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.6+0.9s +[3200/16000] [L1: nan] 68.6+0.0s +[4800/16000] [L1: nan] 67.4+0.0s +[6400/16000] [L1: nan] 71.1+0.1s +[8000/16000] [L1: nan] 70.0+0.1s +[9600/16000] [L1: nan] 70.8+0.1s +[11200/16000] [L1: nan] 68.6+0.1s +[12800/16000] [L1: nan] 70.3+0.1s +[14400/16000] [L1: nan] 69.4+0.0s +[16000/16000] [L1: nan] 70.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.51s + +Saving... +Total: 35.04s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.1+0.9s +[3200/16000] [L1: nan] 68.9+0.1s +[4800/16000] [L1: nan] 69.6+0.1s +[6400/16000] [L1: nan] 69.6+0.1s +[8000/16000] [L1: nan] 70.4+0.1s +[9600/16000] [L1: nan] 69.1+0.1s +[11200/16000] [L1: nan] 70.3+0.1s +[12800/16000] [L1: nan] 71.1+0.1s +[14400/16000] [L1: nan] 67.4+0.0s +[16000/16000] [L1: nan] 68.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.39s + +Saving... +Total: 34.88s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.4+0.9s +[3200/16000] [L1: nan] 70.0+0.1s +[4800/16000] [L1: nan] 69.8+0.1s +[6400/16000] [L1: nan] 71.0+0.1s +[8000/16000] [L1: nan] 70.1+0.1s +[9600/16000] [L1: nan] 70.3+0.1s +[11200/16000] [L1: nan] 69.6+0.0s +[12800/16000] [L1: nan] 70.2+0.1s +[14400/16000] [L1: nan] 69.3+0.1s +[16000/16000] [L1: nan] 68.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.47s + +Saving... +Total: 35.01s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.2+1.0s +[3200/16000] [L1: nan] 69.2+0.1s +[4800/16000] [L1: nan] 69.8+0.1s +[6400/16000] [L1: nan] 69.6+0.1s +[8000/16000] [L1: nan] 69.3+0.1s +[9600/16000] [L1: nan] 69.7+0.1s +[11200/16000] [L1: nan] 69.2+0.1s +[12800/16000] [L1: nan] 68.9+0.0s +[14400/16000] [L1: nan] 68.1+0.0s +[16000/16000] [L1: nan] 68.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.32s + +Saving... +Total: 34.88s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.1+1.0s +[3200/16000] [L1: nan] 69.3+0.1s +[4800/16000] [L1: nan] 69.1+0.1s +[6400/16000] [L1: nan] 69.5+0.1s +[8000/16000] [L1: nan] 68.7+0.1s +[9600/16000] [L1: nan] 68.2+0.0s +[11200/16000] [L1: nan] 68.9+0.0s +[12800/16000] [L1: nan] 70.1+0.1s +[14400/16000] [L1: nan] 70.9+0.1s +[16000/16000] [L1: nan] 69.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.33s + +Saving... +Total: 34.91s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.1+1.0s +[3200/16000] [L1: nan] 69.5+0.1s +[4800/16000] [L1: nan] 67.9+0.1s +[6400/16000] [L1: nan] 69.8+0.1s +[8000/16000] [L1: nan] 68.8+0.1s +[9600/16000] [L1: nan] 69.3+0.1s +[11200/16000] [L1: nan] 69.1+0.0s +[12800/16000] [L1: nan] 70.6+0.1s +[14400/16000] [L1: nan] 70.3+0.1s +[16000/16000] [L1: nan] 68.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.30s + +Saving... +Total: 34.74s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.5+0.9s +[3200/16000] [L1: nan] 69.7+0.1s +[4800/16000] [L1: nan] 69.4+0.1s +[6400/16000] [L1: nan] 70.3+0.1s +[8000/16000] [L1: nan] 70.8+0.1s +[9600/16000] [L1: nan] 69.8+0.1s +[11200/16000] [L1: nan] 69.4+0.1s +[12800/16000] [L1: nan] 69.5+0.1s +[14400/16000] [L1: nan] 70.1+0.1s +[16000/16000] [L1: nan] 69.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.21s + +Saving... +Total: 34.73s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.2+0.8s +[3200/16000] [L1: nan] 69.7+0.1s +[4800/16000] [L1: nan] 68.9+0.1s +[6400/16000] [L1: nan] 68.8+0.1s +[8000/16000] [L1: nan] 70.0+0.1s +[9600/16000] [L1: nan] 69.5+0.1s +[11200/16000] [L1: nan] 70.5+0.1s +[12800/16000] [L1: nan] 69.5+0.1s +[14400/16000] [L1: nan] 70.4+0.1s +[16000/16000] [L1: nan] 67.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.48s + +Saving... +Total: 35.04s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.0+1.0s +[3200/16000] [L1: nan] 69.2+0.1s +[4800/16000] [L1: nan] 70.5+0.1s +[6400/16000] [L1: nan] 70.5+0.1s +[8000/16000] [L1: nan] 70.0+0.1s +[9600/16000] [L1: nan] 68.7+0.0s +[11200/16000] [L1: nan] 69.6+0.1s +[12800/16000] [L1: nan] 69.4+0.1s +[14400/16000] [L1: nan] 70.0+0.1s +[16000/16000] [L1: nan] 66.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.49s + +Saving... +Total: 34.94s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.4+1.1s +[3200/16000] [L1: nan] 70.4+0.1s +[4800/16000] [L1: nan] 69.6+0.1s +[6400/16000] [L1: nan] 70.0+0.1s +[8000/16000] [L1: nan] 70.1+0.1s +[9600/16000] [L1: nan] 70.7+0.1s +[11200/16000] [L1: nan] 70.1+0.1s +[12800/16000] [L1: nan] 70.7+0.1s +[14400/16000] [L1: nan] 70.9+0.1s +[16000/16000] [L1: nan] 69.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.39s + +Saving... +Total: 34.92s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.6+0.9s +[3200/16000] [L1: nan] 68.0+0.1s +[4800/16000] [L1: nan] 70.1+0.1s +[6400/16000] [L1: nan] 70.3+0.1s +[8000/16000] [L1: nan] 69.4+0.1s +[9600/16000] [L1: nan] 69.9+0.1s +[11200/16000] [L1: nan] 69.8+0.1s +[12800/16000] [L1: nan] 69.6+0.1s +[14400/16000] [L1: nan] 69.1+0.0s +[16000/16000] [L1: nan] 68.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.47s + +Saving... +Total: 35.03s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.9+1.0s +[3200/16000] [L1: nan] 69.7+0.1s +[4800/16000] [L1: nan] 67.7+0.0s +[6400/16000] [L1: nan] 69.8+0.1s +[8000/16000] [L1: nan] 69.0+0.1s +[9600/16000] [L1: nan] 68.8+0.1s +[11200/16000] [L1: nan] 69.1+0.1s +[12800/16000] [L1: nan] 68.7+0.0s +[14400/16000] [L1: nan] 69.1+0.1s +[16000/16000] [L1: nan] 65.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.62s + +Saving... +Total: 35.18s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.8+0.9s +[3200/16000] [L1: nan] 70.4+0.1s +[4800/16000] [L1: nan] 70.3+0.1s +[6400/16000] [L1: nan] 70.8+0.1s +[8000/16000] [L1: nan] 69.6+0.1s +[9600/16000] [L1: nan] 68.2+0.1s +[11200/16000] [L1: nan] 69.4+0.1s +[12800/16000] [L1: nan] 69.9+0.1s +[14400/16000] [L1: nan] 69.0+0.0s +[16000/16000] [L1: nan] 69.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.41s + +Saving... +Total: 34.98s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.7+0.8s +[3200/16000] [L1: nan] 68.8+0.1s +[4800/16000] [L1: nan] 68.8+0.1s +[6400/16000] [L1: nan] 68.0+0.0s +[8000/16000] [L1: nan] 69.5+0.0s +[9600/16000] [L1: nan] 70.2+0.1s +[11200/16000] [L1: nan] 69.7+0.1s +[12800/16000] [L1: nan] 70.3+0.1s +[14400/16000] [L1: nan] 69.4+0.1s +[16000/16000] [L1: nan] 70.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.45s + +Saving... +Total: 34.97s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 69.4+1.0s +[3200/16000] [L1: nan] 70.4+0.1s +[4800/16000] [L1: nan] 70.3+0.1s +[6400/16000] [L1: nan] 69.9+0.1s +[8000/16000] [L1: nan] 69.7+0.0s +[9600/16000] [L1: nan] 70.8+0.1s +[11200/16000] [L1: nan] 69.4+0.1s +[12800/16000] [L1: nan] 69.1+0.1s +[14400/16000] [L1: nan] 70.0+0.1s +[16000/16000] [L1: nan] 68.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.48s + +Saving... +Total: 34.96s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.2+1.0s +[3200/16000] [L1: nan] 69.5+0.1s +[4800/16000] [L1: nan] 70.3+0.1s +[6400/16000] [L1: nan] 69.7+0.1s +[8000/16000] [L1: nan] 69.2+0.1s +[9600/16000] [L1: nan] 70.0+0.1s +[11200/16000] [L1: nan] 70.3+0.1s +[12800/16000] [L1: nan] 70.4+0.1s +[14400/16000] [L1: nan] 69.4+0.1s +[16000/16000] [L1: nan] 69.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.27s + +Saving... +Total: 34.70s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.1+0.9s +[3200/16000] [L1: nan] 69.1+0.1s +[4800/16000] [L1: nan] 69.2+0.1s +[6400/16000] [L1: nan] 70.0+0.1s +[8000/16000] [L1: nan] 70.6+0.1s +[9600/16000] [L1: nan] 70.0+0.1s +[11200/16000] [L1: nan] 70.0+0.1s +[12800/16000] [L1: nan] 69.1+0.1s +[14400/16000] [L1: nan] 70.7+0.1s +[16000/16000] [L1: nan] 69.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.27s + +Saving... +Total: 34.84s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.9+0.9s +[3200/16000] [L1: nan] 70.0+0.1s +[4800/16000] [L1: nan] 68.5+0.1s +[6400/16000] [L1: nan] 70.4+0.1s +[8000/16000] [L1: nan] 70.0+0.1s +[9600/16000] [L1: nan] 69.9+0.1s +[11200/16000] [L1: nan] 69.8+0.1s +[12800/16000] [L1: nan] 70.7+0.1s +[14400/16000] [L1: nan] 70.0+0.1s +[16000/16000] [L1: nan] 68.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.34s + +Saving... +Total: 34.90s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: nan] 70.3+0.8s +[3200/16000] [L1: nan] 68.2+0.0s +[4800/16000] [L1: nan] 70.7+0.1s +[6400/16000] [L1: nan] 69.0+0.1s +[8000/16000] [L1: nan] 69.7+0.1s +[9600/16000] [L1: nan] 71.0+0.1s +[11200/16000] [L1: nan] 69.7+0.1s +[12800/16000] [L1: nan] 69.5+0.1s +[14400/16000] [L1: nan] 66.8+0.0s +[16000/16000] [L1: nan] 67.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 6) +Forward: 34.39s + +Saving... +Total: 34.82s + diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4/loss.pt 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+seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTA +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*MSE +skip_threshold: 100000000.0 +save: LAMBDANETACTA_DEMOSAIC20_R4_MSE +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/log.txt b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..331ff47f4e69dc4656403a28a8b14f99bf08ba24 --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/log.txt @@ -0,0 +1,6336 @@ +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0382] 47.0+0.8s +[3200/16000] [MSE: 0.9355] 47.4+0.1s +[4800/16000] [MSE: 0.9247] 47.1+0.0s +[6400/16000] [MSE: 0.9218] 46.9+0.0s +[8000/16000] [MSE: 0.9369] 46.8+0.0s +[9600/16000] [MSE: 0.9371] 46.8+0.0s +[11200/16000] [MSE: 0.9370] 47.0+0.0s +[12800/16000] [MSE: 0.9394] 47.2+0.0s +[14400/16000] [MSE: 0.9432] 47.4+0.0s +[16000/16000] [MSE: 0.9438] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 1) +Forward: 38.77s + +Saving... +Total: 39.62s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9829] 48.3+0.7s +[3200/16000] [MSE: 0.9713] 47.7+0.0s +[4800/16000] [MSE: 0.9626] 47.5+0.0s +[6400/16000] [MSE: 0.9548] 47.4+0.0s +[8000/16000] [MSE: 0.9468] 47.2+0.0s +[9600/16000] [MSE: 0.9432] 47.3+0.0s +[11200/16000] [MSE: 0.9410] 47.3+0.0s +[12800/16000] [MSE: 0.9385] 47.1+0.0s +[14400/16000] [MSE: 0.9366] 47.3+0.0s +[16000/16000] [MSE: 0.9340] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.726 (Best: 8.820 @epoch 1) +Forward: 38.67s + +Saving... +Total: 39.26s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9234] 48.3+0.8s +[3200/16000] [MSE: 0.9180] 48.2+0.1s +[4800/16000] [MSE: 0.9189] 47.8+0.1s +[6400/16000] [MSE: 0.9194] 47.5+0.1s +[8000/16000] [MSE: 0.9204] 47.7+0.0s +[9600/16000] [MSE: 0.9225] 47.7+0.0s +[11200/16000] [MSE: 0.9217] 47.7+0.0s +[12800/16000] [MSE: 0.9217] 47.7+0.0s +[14400/16000] [MSE: 0.9224] 47.5+0.0s +[16000/16000] [MSE: 0.9226] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.964 (Best: 8.820 @epoch 1) +Forward: 38.74s + +Saving... +Total: 39.29s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9092] 48.3+0.7s +[3200/16000] [MSE: 0.9198] 47.8+0.1s +[4800/16000] [MSE: 0.9257] 48.0+0.1s +[6400/16000] [MSE: 0.9277] 47.7+0.0s +[8000/16000] [MSE: 0.9276] 47.3+0.0s +[9600/16000] [MSE: 0.9252] 47.3+0.0s +[11200/16000] [MSE: 0.9200] 46.9+0.0s +[12800/16000] [MSE: 0.9194] 46.5+0.0s +[14400/16000] [MSE: 0.9209] 46.7+0.0s +[16000/16000] [MSE: 0.9198] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 1) +Forward: 38.69s + +Saving... +Total: 39.17s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9108] 48.4+0.7s +[3200/16000] [MSE: 0.9226] 48.2+0.1s +[4800/16000] [MSE: 0.9212] 48.1+0.1s +[6400/16000] [MSE: 0.9221] 47.9+0.1s +[8000/16000] [MSE: 0.9159] 47.7+0.0s +[9600/16000] [MSE: 0.9159] 47.4+0.0s +[11200/16000] [MSE: 0.9162] 47.5+0.0s +[12800/16000] [MSE: 0.9185] 47.3+0.0s +[14400/16000] [MSE: 0.9175] 47.6+0.0s +[16000/16000] [MSE: 0.9165] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 1) +Forward: 38.65s + +Saving... +Total: 39.15s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9156] 48.1+0.8s +[3200/16000] [MSE: 0.9180] 47.8+0.0s +[4800/16000] [MSE: 0.9228] 47.2+0.0s +[6400/16000] [MSE: 0.9190] 47.5+0.0s +[8000/16000] [MSE: 0.9167] 47.2+0.0s +[9600/16000] [MSE: 0.9205] 47.1+0.0s +[11200/16000] [MSE: 0.9198] 46.6+0.0s +[12800/16000] [MSE: 0.9180] 46.9+0.0s +[14400/16000] [MSE: 0.9165] 47.0+0.0s +[16000/16000] [MSE: 0.9162] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 1) +Forward: 38.64s + +Saving... +Total: 39.14s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9196] 48.1+0.8s +[3200/16000] [MSE: 0.9293] 47.8+0.0s +[4800/16000] [MSE: 0.9255] 47.8+0.0s +[6400/16000] [MSE: 0.9242] 47.7+0.0s +[8000/16000] [MSE: 0.9205] 47.7+0.0s +[9600/16000] [MSE: 0.9167] 47.5+0.0s +[11200/16000] [MSE: 0.9152] 47.5+0.0s +[12800/16000] [MSE: 0.9171] 47.5+0.0s +[14400/16000] [MSE: 0.9174] 47.5+0.0s +[16000/16000] [MSE: 0.9189] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 1) +Forward: 38.76s + +Saving... +Total: 39.97s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9163] 48.0+0.8s +[3200/16000] [MSE: 0.9121] 47.4+0.0s +[4800/16000] [MSE: 0.9096] 47.8+0.0s +[6400/16000] [MSE: 0.9141] 47.3+0.0s +[8000/16000] [MSE: 0.9202] 47.2+0.0s +[9600/16000] [MSE: 0.9183] 46.8+0.0s +[11200/16000] [MSE: 0.9188] 47.0+0.0s +[12800/16000] [MSE: 0.9190] 46.7+0.0s +[14400/16000] [MSE: 0.9181] 46.4+0.0s +[16000/16000] [MSE: 0.9184] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 1) +Forward: 38.78s + +Saving... +Total: 39.27s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9159] 48.3+0.7s +[3200/16000] [MSE: 0.9277] 47.7+0.1s +[4800/16000] [MSE: 0.9232] 47.9+0.0s +[6400/16000] [MSE: 0.9227] 47.9+0.0s +[8000/16000] [MSE: 0.9232] 47.5+0.0s +[9600/16000] [MSE: 0.9220] 47.4+0.0s +[11200/16000] [MSE: 0.9229] 47.2+0.0s +[12800/16000] [MSE: 0.9219] 47.4+0.0s +[14400/16000] [MSE: 0.9219] 47.2+0.0s +[16000/16000] [MSE: 0.9208] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 1) +Forward: 38.89s + +Saving... +Total: 39.41s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9189] 48.7+0.7s +[3200/16000] [MSE: 0.9143] 47.4+0.0s +[4800/16000] [MSE: 0.9186] 47.7+0.0s +[6400/16000] [MSE: 2.5758] 47.5+0.0s +[8000/16000] [MSE: 3.5673] 47.3+0.0s +[9600/16000] [MSE: 4.1977] 47.5+0.0s +[11200/16000] [MSE: 4.7014] 47.2+0.0s +[12800/16000] [MSE: 4.5775] 47.4+0.0s +[14400/16000] [MSE: 4.2704] 47.1+0.0s +[16000/16000] [MSE: 3.9783] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.659 (Best: 8.820 @epoch 1) +Forward: 38.71s + +Saving... +Total: 39.14s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.3550] 48.1+0.7s +[3200/16000] [MSE: 1.3412] 47.8+0.1s +[4800/16000] [MSE: 1.3406] 47.9+0.1s +[6400/16000] [MSE: 1.3349] 47.7+0.0s +[8000/16000] [MSE: 1.3569] 47.6+0.0s +[9600/16000] [MSE: 1.3626] 47.5+0.0s +[11200/16000] [MSE: 1.3616] 47.6+0.0s +[12800/16000] [MSE: 1.3604] 47.6+0.0s +[14400/16000] [MSE: 1.3689] 47.0+0.0s +[16000/16000] [MSE: 1.4285] 47.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.276 (Best: 8.820 @epoch 1) +Forward: 38.74s + +Saving... +Total: 39.22s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.3830] 48.5+0.7s +[3200/16000] [MSE: 1.3978] 48.0+0.1s +[4800/16000] [MSE: 1.3484] 47.7+0.0s +[6400/16000] [MSE: 1.3193] 47.6+0.0s +[8000/16000] [MSE: 1.3004] 47.8+0.0s +[9600/16000] [MSE: 1.2878] 47.8+0.0s +[11200/16000] [MSE: 1.2789] 47.4+0.0s +[12800/16000] [MSE: 1.2879] 47.2+0.0s +[14400/16000] [MSE: 1.3326] 47.2+0.0s +[16000/16000] [MSE: 1.3593] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.591 (Best: 8.820 @epoch 1) +Forward: 38.70s + +Saving... +Total: 39.16s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.4620] 48.3+0.8s +[3200/16000] [MSE: 1.3388] 47.6+0.0s +[4800/16000] [MSE: 1.2917] 47.8+0.0s +[6400/16000] [MSE: 1.2848] 47.4+0.0s +[8000/16000] [MSE: 1.5881] 47.2+0.0s +[9600/16000] [MSE: 1.7700] 47.2+0.0s +[11200/16000] [MSE: 1.8090] 47.1+0.0s +[12800/16000] [MSE: 1.7972] 46.8+0.0s +[14400/16000] [MSE: 2.1037] 46.8+0.0s +[16000/16000] [MSE: 2.2225] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.552 (Best: 8.820 @epoch 1) +Forward: 38.57s + +Saving... +Total: 39.13s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.2565] 48.3+0.8s +[3200/16000] [MSE: 2.3912] 47.9+0.0s +[4800/16000] [MSE: 2.1136] 47.8+0.0s +[6400/16000] [MSE: 1.9286] 47.6+0.0s +[8000/16000] [MSE: 1.8194] 47.5+0.0s +[9600/16000] [MSE: 1.7361] 47.0+0.0s +[11200/16000] [MSE: 1.6660] 47.1+0.0s +[12800/16000] [MSE: 1.6111] 47.4+0.0s +[14400/16000] [MSE: 1.5692] 47.7+0.0s +[16000/16000] [MSE: 1.5360] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.118 (Best: 8.820 @epoch 1) +Forward: 38.69s + +Saving... +Total: 39.18s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.2313] 48.1+0.7s +[3200/16000] [MSE: 1.2315] 48.0+0.1s +[4800/16000] [MSE: 1.4392] 47.6+0.0s +[6400/16000] [MSE: 1.4897] 47.7+0.0s +[8000/16000] [MSE: 1.5157] 47.8+0.0s +[9600/16000] [MSE: 1.5274] 47.8+0.0s +[11200/16000] [MSE: 1.5466] 47.9+0.0s +[12800/16000] [MSE: 1.5677] 47.8+0.0s +[14400/16000] [MSE: 1.5775] 47.6+0.0s +[16000/16000] [MSE: 1.5911] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.497 (Best: 8.820 @epoch 1) +Forward: 38.71s + +Saving... +Total: 39.19s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.7090] 48.1+0.9s +[3200/16000] [MSE: 3.0873] 48.0+0.0s +[4800/16000] [MSE: 2.8350] 47.6+0.0s +[6400/16000] [MSE: 2.9137] 48.0+0.0s +[8000/16000] [MSE: 3.3273] 47.6+0.0s +[9600/16000] [MSE: 3.3524] 47.4+0.0s +[11200/16000] [MSE: 3.2391] 47.1+0.0s +[12800/16000] [MSE: 3.1510] 46.9+0.0s +[14400/16000] [MSE: 3.0758] 47.6+0.0s +[16000/16000] [MSE: 3.0329] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.637 (Best: 8.820 @epoch 1) +Forward: 38.75s + +Saving... +Total: 39.26s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.0585] 48.4+0.8s +[3200/16000] [MSE: 1.9638] 48.0+0.1s +[4800/16000] [MSE: 1.9579] 48.0+0.1s +[6400/16000] [MSE: 2.8788] 48.0+0.1s +[8000/16000] [MSE: 2.9730] 47.6+0.0s +[9600/16000] [MSE: 3.0111] 47.3+0.0s +[11200/16000] [MSE: 2.9116] 47.4+0.0s +[12800/16000] [MSE: 2.8364] 46.9+0.0s +[14400/16000] [MSE: 2.8345] 46.8+0.0s +[16000/16000] [MSE: 2.8140] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.591 (Best: 8.820 @epoch 1) +Forward: 38.75s + +Saving... +Total: 39.24s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5682] 48.3+0.7s +[3200/16000] [MSE: 2.4654] 48.1+0.1s +[4800/16000] [MSE: 2.2948] 47.9+0.0s +[6400/16000] [MSE: 2.2065] 47.9+0.0s +[8000/16000] [MSE: 2.1594] 47.5+0.0s +[9600/16000] [MSE: 2.1394] 47.4+0.0s +[11200/16000] [MSE: 2.1656] 47.4+0.0s +[12800/16000] [MSE: 2.2455] 47.2+0.0s +[14400/16000] [MSE: 2.3141] 47.4+0.0s +[16000/16000] [MSE: 2.3623] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.716 (Best: 8.820 @epoch 1) +Forward: 38.66s + +Saving... +Total: 39.16s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.9764] 48.6+0.9s +[3200/16000] [MSE: 1.8636] 48.2+0.1s +[4800/16000] [MSE: 1.8359] 48.0+0.1s +[6400/16000] [MSE: 1.8397] 48.0+0.0s +[8000/16000] [MSE: 1.8437] 47.6+0.0s +[9600/16000] [MSE: 1.8461] 47.6+0.0s +[11200/16000] [MSE: 1.8442] 47.4+0.0s +[12800/16000] [MSE: 1.8482] 47.5+0.0s +[14400/16000] [MSE: 1.8472] 47.0+0.0s +[16000/16000] [MSE: 1.8461] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.099 (Best: 8.820 @epoch 1) +Forward: 38.82s + +Saving... +Total: 39.27s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.8581] 48.6+0.7s +[3200/16000] [MSE: 1.8628] 48.0+0.1s +[4800/16000] [MSE: 2.3159] 48.4+0.1s +[6400/16000] [MSE: 2.2875] 47.7+0.0s +[8000/16000] [MSE: 2.2611] 47.4+0.0s +[9600/16000] [MSE: 2.2603] 47.1+0.0s +[11200/16000] [MSE: 2.2546] 46.8+0.0s +[12800/16000] [MSE: 2.2501] 47.3+0.0s +[14400/16000] [MSE: 2.2473] 46.8+0.0s +[16000/16000] [MSE: 2.2452] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.820 (Best: 8.820 @epoch 1) +Forward: 38.76s + +Saving... +Total: 39.26s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.2085] 48.5+0.7s +[3200/16000] [MSE: 2.2055] 48.2+0.1s +[4800/16000] [MSE: 2.2139] 47.6+0.0s +[6400/16000] [MSE: 2.2177] 47.3+0.0s +[8000/16000] [MSE: 2.3315] 47.9+0.0s +[9600/16000] [MSE: 2.3814] 47.9+0.0s +[11200/16000] [MSE: 2.4659] 48.0+0.0s +[12800/16000] [MSE: 2.4034] 47.8+0.0s +[14400/16000] [MSE: 2.3353] 47.5+0.0s +[16000/16000] [MSE: 2.2757] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.988 (Best: 8.820 @epoch 1) +Forward: 38.66s + +Saving... +Total: 39.14s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.7680] 48.4+0.7s +[3200/16000] [MSE: 1.7406] 47.9+0.0s +[4800/16000] [MSE: 1.7249] 47.8+0.1s +[6400/16000] [MSE: 1.7094] 47.7+0.0s +[8000/16000] [MSE: 1.7100] 47.5+0.0s +[9600/16000] [MSE: 1.6987] 47.0+0.0s +[11200/16000] [MSE: 1.6883] 47.0+0.0s +[12800/16000] [MSE: 1.6870] 47.1+0.0s +[14400/16000] [MSE: 1.6794] 47.1+0.0s +[16000/16000] [MSE: 1.6737] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.820 (Best: 8.820 @epoch 22) +Forward: 38.69s + +Saving... +Total: 39.17s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.6141] 48.0+0.6s +[3200/16000] [MSE: 1.6161] 48.2+0.1s +[4800/16000] [MSE: 1.6306] 47.9+0.1s +[6400/16000] [MSE: 1.6546] 48.1+0.1s +[8000/16000] [MSE: 1.6752] 47.8+0.0s +[9600/16000] [MSE: 1.8507] 47.4+0.0s +[11200/16000] [MSE: 2.0335] 47.5+0.0s +[12800/16000] [MSE: 2.2062] 47.5+0.0s +[14400/16000] [MSE: 2.3589] 47.4+0.0s +[16000/16000] [MSE: 2.5173] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.107 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.09s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.0172] 48.2+0.7s +[3200/16000] [MSE: 3.9766] 47.7+0.0s +[4800/16000] [MSE: 3.9693] 47.2+0.0s +[6400/16000] [MSE: 3.9196] 47.0+0.0s +[8000/16000] [MSE: 3.8845] 47.1+0.0s +[9600/16000] [MSE: 3.8632] 46.5+0.0s +[11200/16000] [MSE: 3.9037] 46.7+0.0s +[12800/16000] [MSE: 3.9388] 46.5+0.0s +[14400/16000] [MSE: 3.8965] 47.0+0.0s +[16000/16000] [MSE: 3.8207] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.251 (Best: 8.820 @epoch 22) +Forward: 38.72s + +Saving... +Total: 39.27s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.9663] 47.7+0.8s +[3200/16000] [MSE: 4.1411] 47.8+0.0s +[4800/16000] [MSE: 4.2142] 47.6+0.0s +[6400/16000] [MSE: 4.2537] 47.9+0.0s +[8000/16000] [MSE: 4.5666] 47.8+0.0s +[9600/16000] [MSE: 4.8807] 47.6+0.0s +[11200/16000] [MSE: 4.9601] 47.4+0.0s +[12800/16000] [MSE: 4.8897] 47.4+0.0s +[14400/16000] [MSE: 4.9703] 47.0+0.0s +[16000/16000] [MSE: 4.9630] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.114 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.16s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.0407] 48.2+0.7s +[3200/16000] [MSE: 4.0250] 47.9+0.1s +[4800/16000] [MSE: 4.0517] 47.4+0.0s +[6400/16000] [MSE: 4.2253] 47.8+0.0s +[8000/16000] [MSE: 4.2606] 47.8+0.0s +[9600/16000] [MSE: 4.3543] 47.7+0.0s +[11200/16000] [MSE: 4.3711] 47.6+0.0s +[12800/16000] [MSE: 4.3733] 47.5+0.0s +[14400/16000] [MSE: 4.4499] 47.2+0.0s +[16000/16000] [MSE: 4.5336] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.017 (Best: 8.820 @epoch 22) +Forward: 38.71s + +Saving... +Total: 39.21s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.9764] 48.2+0.8s +[3200/16000] [MSE: 5.8987] 48.0+0.0s +[4800/16000] [MSE: 5.7416] 47.4+0.0s +[6400/16000] [MSE: 5.8262] 47.7+0.0s +[8000/16000] [MSE: 5.9526] 47.4+0.0s +[9600/16000] [MSE: 6.0537] 47.1+0.0s +[11200/16000] [MSE: 5.9864] 46.9+0.0s +[12800/16000] [MSE: 5.8442] 46.5+0.0s +[14400/16000] [MSE: 5.6792] 46.6+0.0s +[16000/16000] [MSE: 5.5410] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.486 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.03s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.2242] 47.9+0.8s +[3200/16000] [MSE: 4.2712] 47.5+0.0s +[4800/16000] [MSE: 4.4481] 47.8+0.0s +[6400/16000] [MSE: 4.6414] 47.3+0.0s +[8000/16000] [MSE: 4.6729] 47.3+0.0s +[9600/16000] [MSE: 4.5291] 47.0+0.0s +[11200/16000] [MSE: 4.3979] 46.9+0.0s +[12800/16000] [MSE: 4.3194] 47.0+0.0s +[14400/16000] [MSE: 4.2548] 46.7+0.0s +[16000/16000] [MSE: 4.2022] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.864 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.05s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.7034] 48.5+0.8s +[3200/16000] [MSE: 3.7352] 48.2+0.1s +[4800/16000] [MSE: 3.7290] 47.7+0.0s +[6400/16000] [MSE: 3.8365] 47.7+0.0s +[8000/16000] [MSE: 4.5370] 47.7+0.0s +[9600/16000] [MSE: 4.5625] 47.4+0.0s +[11200/16000] [MSE: 4.5451] 47.6+0.0s +[12800/16000] [MSE: 4.5121] 47.4+0.0s +[14400/16000] [MSE: 4.5089] 47.3+0.0s +[16000/16000] [MSE: 4.4745] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.117 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.11s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.1465] 48.1+0.8s +[3200/16000] [MSE: 3.9047] 47.8+0.0s +[4800/16000] [MSE: 3.7944] 47.6+0.0s +[6400/16000] [MSE: 3.7437] 47.2+0.0s +[8000/16000] [MSE: 3.6564] 47.3+0.0s +[9600/16000] [MSE: 3.5913] 47.4+0.0s +[11200/16000] [MSE: 3.4879] 47.1+0.0s +[12800/16000] [MSE: 3.4397] 47.1+0.0s +[14400/16000] [MSE: 3.5556] 47.3+0.0s +[16000/16000] [MSE: 3.6070] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.150 (Best: 8.820 @epoch 22) +Forward: 38.83s + +Saving... +Total: 39.31s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.7666] 48.3+0.7s +[3200/16000] [MSE: 3.6740] 47.8+0.0s +[4800/16000] [MSE: 3.6856] 47.4+0.0s +[6400/16000] [MSE: 3.8088] 47.5+0.0s +[8000/16000] [MSE: 3.8431] 47.3+0.0s +[9600/16000] [MSE: 3.8818] 47.5+0.0s +[11200/16000] [MSE: 3.9037] 47.2+0.0s +[12800/16000] [MSE: 3.9615] 47.2+0.0s +[14400/16000] [MSE: 4.0105] 47.2+0.0s +[16000/16000] [MSE: 4.0289] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.273 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.14s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.9664] 48.3+0.7s +[3200/16000] [MSE: 3.7255] 48.2+0.1s +[4800/16000] [MSE: 3.5429] 47.5+0.0s +[6400/16000] [MSE: 3.4650] 47.6+0.0s +[8000/16000] [MSE: 3.4910] 47.1+0.0s +[9600/16000] [MSE: 3.5285] 47.1+0.0s +[11200/16000] [MSE: 3.5411] 47.3+0.0s +[12800/16000] [MSE: 3.5320] 46.5+0.0s +[14400/16000] [MSE: 3.5379] 46.4+0.0s +[16000/16000] [MSE: 3.5404] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.832 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.10s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4538] 48.1+0.6s +[3200/16000] [MSE: 3.3981] 47.2+0.0s +[4800/16000] [MSE: 3.3816] 47.4+0.0s +[6400/16000] [MSE: 3.3377] 47.6+0.0s +[8000/16000] [MSE: 3.4108] 47.4+0.0s +[9600/16000] [MSE: 3.3499] 47.3+0.0s +[11200/16000] [MSE: 3.3243] 47.3+0.0s +[12800/16000] [MSE: 3.3202] 47.5+0.0s +[14400/16000] [MSE: 3.2979] 47.4+0.0s +[16000/16000] [MSE: 3.2367] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.103 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.08s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.4934] 48.3+0.8s +[3200/16000] [MSE: 2.6758] 48.1+0.1s +[4800/16000] [MSE: 2.7992] 47.8+0.0s +[6400/16000] [MSE: 2.9163] 47.7+0.0s +[8000/16000] [MSE: 2.9723] 47.8+0.1s +[9600/16000] [MSE: 3.2697] 47.6+0.0s +[11200/16000] [MSE: 3.4594] 47.2+0.0s +[12800/16000] [MSE: 3.5771] 47.4+0.0s +[14400/16000] [MSE: 3.7608] 47.3+0.0s +[16000/16000] [MSE: 3.7986] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.087 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.89s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2315] 48.3+0.7s +[3200/16000] [MSE: 2.9680] 47.9+0.1s +[4800/16000] [MSE: 2.8662] 47.8+0.0s +[6400/16000] [MSE: 3.4086] 47.5+0.0s +[8000/16000] [MSE: 3.8058] 47.5+0.0s +[9600/16000] [MSE: 4.0259] 47.4+0.0s +[11200/16000] [MSE: 4.1686] 46.9+0.0s +[12800/16000] [MSE: 4.2495] 46.6+0.0s +[14400/16000] [MSE: 4.2726] 46.4+0.0s +[16000/16000] [MSE: 4.3003] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.039 (Best: 8.820 @epoch 22) +Forward: 38.69s + +Saving... +Total: 39.25s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.6381] 48.3+0.8s +[3200/16000] [MSE: 5.6967] 47.4+0.0s +[4800/16000] [MSE: 5.5199] 47.7+0.0s +[6400/16000] [MSE: 5.4709] 47.5+0.0s +[8000/16000] [MSE: 5.4609] 47.4+0.0s +[9600/16000] [MSE: 5.6150] 47.5+0.0s +[11200/16000] [MSE: 5.6487] 47.4+0.0s +[12800/16000] [MSE: 5.6710] 46.8+0.0s +[14400/16000] [MSE: 5.7805] 46.6+0.0s +[16000/16000] [MSE: 5.6741] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.113 (Best: 8.820 @epoch 22) +Forward: 38.78s + +Saving... +Total: 39.28s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5456] 47.7+0.8s +[3200/16000] [MSE: 3.9651] 47.4+0.0s +[4800/16000] [MSE: 4.9137] 47.6+0.0s +[6400/16000] [MSE: 5.0810] 47.2+0.0s +[8000/16000] [MSE: 5.0733] 47.3+0.0s +[9600/16000] [MSE: 4.6001] 47.4+0.0s +[11200/16000] [MSE: 4.2924] 46.8+0.0s +[12800/16000] [MSE: 4.1770] 46.6+0.0s +[14400/16000] [MSE: 4.1736] 47.0+0.0s +[16000/16000] [MSE: 4.1386] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.869 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.11s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.7929] 48.3+0.7s +[3200/16000] [MSE: 3.9621] 47.8+0.0s +[4800/16000] [MSE: 3.8276] 47.5+0.0s +[6400/16000] [MSE: 3.7551] 47.4+0.0s +[8000/16000] [MSE: 3.7164] 47.2+0.0s +[9600/16000] [MSE: 3.6877] 47.0+0.0s +[11200/16000] [MSE: 3.7243] 46.9+0.0s +[12800/16000] [MSE: 3.7885] 46.4+0.0s +[14400/16000] [MSE: 3.8249] 46.6+0.0s +[16000/16000] [MSE: 3.8364] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.893 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.07s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.9687] 48.2+0.8s +[3200/16000] [MSE: 3.9545] 47.9+0.1s +[4800/16000] [MSE: 3.9475] 47.9+0.0s +[6400/16000] [MSE: 3.6592] 47.3+0.0s +[8000/16000] [MSE: 3.4873] 47.6+0.0s +[9600/16000] [MSE: 3.3735] 47.2+0.0s +[11200/16000] [MSE: 3.2945] 47.2+0.0s +[12800/16000] [MSE: 3.2413] 47.3+0.0s +[14400/16000] [MSE: 3.1852] 47.2+0.0s +[16000/16000] [MSE: 3.1269] 47.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.861 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.06s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.6037] 48.5+0.7s +[3200/16000] [MSE: 2.5427] 48.0+0.1s +[4800/16000] [MSE: 2.6616] 47.5+0.0s +[6400/16000] [MSE: 2.8044] 47.2+0.0s +[8000/16000] [MSE: 2.8469] 47.3+0.0s +[9600/16000] [MSE: 2.8420] 47.6+0.0s +[11200/16000] [MSE: 2.8395] 47.3+0.0s +[12800/16000] [MSE: 2.8741] 47.4+0.0s +[14400/16000] [MSE: 2.9442] 47.3+0.0s +[16000/16000] [MSE: 2.9678] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.594 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.16s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0412] 47.7+1.2s +[3200/16000] [MSE: 2.9422] 47.7+0.0s +[4800/16000] [MSE: 3.2067] 47.5+0.0s +[6400/16000] [MSE: 3.2622] 47.5+0.0s +[8000/16000] [MSE: 3.3486] 47.4+0.0s +[9600/16000] [MSE: 3.3241] 47.1+0.0s +[11200/16000] [MSE: 3.3080] 47.0+0.0s +[12800/16000] [MSE: 3.2943] 46.8+0.0s +[14400/16000] [MSE: 3.2882] 46.5+0.0s +[16000/16000] [MSE: 3.2825] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.391 (Best: 8.820 @epoch 22) +Forward: 38.73s + +Saving... +Total: 39.31s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.3829] 48.4+0.9s +[3200/16000] [MSE: 3.4550] 47.9+0.1s +[4800/16000] [MSE: 3.6099] 47.7+0.0s +[6400/16000] [MSE: 3.6965] 47.7+0.0s +[8000/16000] [MSE: 3.9274] 47.4+0.0s +[9600/16000] [MSE: 4.0473] 47.6+0.0s +[11200/16000] [MSE: 4.1420] 47.3+0.0s +[12800/16000] [MSE: 4.2293] 46.8+0.0s +[14400/16000] [MSE: 4.1980] 47.0+0.0s +[16000/16000] [MSE: 4.1086] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.118 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.06s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1810] 48.2+0.7s +[3200/16000] [MSE: 3.1007] 47.9+0.0s +[4800/16000] [MSE: 2.9865] 47.6+0.0s +[6400/16000] [MSE: 2.8531] 47.6+0.0s +[8000/16000] [MSE: 2.7907] 47.3+0.0s +[9600/16000] [MSE: 2.7032] 47.1+0.0s +[11200/16000] [MSE: 2.6603] 46.7+0.0s +[12800/16000] [MSE: 2.6315] 46.4+0.0s +[14400/16000] [MSE: 2.5626] 46.7+0.0s +[16000/16000] [MSE: 2.5050] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.980 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.15s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.6840] 48.4+0.7s +[3200/16000] [MSE: 1.9907] 47.8+0.0s +[4800/16000] [MSE: 2.1251] 47.4+0.0s +[6400/16000] [MSE: 2.2218] 47.3+0.0s +[8000/16000] [MSE: 2.2825] 47.0+0.0s +[9600/16000] [MSE: 2.3207] 47.0+0.0s +[11200/16000] [MSE: 2.3228] 47.2+0.0s +[12800/16000] [MSE: 2.3257] 47.1+0.0s +[14400/16000] [MSE: 2.3282] 47.2+0.0s +[16000/16000] [MSE: 2.3770] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.704 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.08s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.9347] 48.2+0.7s +[3200/16000] [MSE: 3.8689] 47.9+0.0s +[4800/16000] [MSE: 3.6798] 47.5+0.0s +[6400/16000] [MSE: 3.6047] 47.7+0.0s +[8000/16000] [MSE: 3.6690] 47.3+0.0s +[9600/16000] [MSE: 3.9564] 46.7+0.0s +[11200/16000] [MSE: 4.1275] 47.4+0.0s +[12800/16000] [MSE: 4.1262] 47.2+0.0s +[14400/16000] [MSE: 3.9140] 46.9+0.0s +[16000/16000] [MSE: 3.7039] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.762 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.13s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.8841] 48.4+0.7s +[3200/16000] [MSE: 1.8078] 48.0+0.1s +[4800/16000] [MSE: 1.8515] 47.4+0.0s +[6400/16000] [MSE: 1.8762] 47.4+0.0s +[8000/16000] [MSE: 1.9736] 47.3+0.0s +[9600/16000] [MSE: 2.0336] 47.3+0.0s +[11200/16000] [MSE: 2.0852] 46.9+0.0s +[12800/16000] [MSE: 2.1102] 46.9+0.0s +[14400/16000] [MSE: 2.1153] 46.9+0.0s +[16000/16000] [MSE: 2.1378] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.489 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.19s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.4242] 48.1+0.8s +[3200/16000] [MSE: 2.4263] 47.7+0.0s +[4800/16000] [MSE: 2.7953] 47.6+0.0s +[6400/16000] [MSE: 3.3451] 47.6+0.0s +[8000/16000] [MSE: 3.8591] 47.5+0.0s +[9600/16000] [MSE: 4.2000] 47.3+0.0s +[11200/16000] [MSE: 4.2767] 47.4+0.0s +[12800/16000] [MSE: 4.3250] 47.4+0.0s +[14400/16000] [MSE: 4.3350] 47.0+0.0s +[16000/16000] [MSE: 4.3080] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.027 (Best: 8.820 @epoch 22) +Forward: 38.70s + +Saving... +Total: 39.17s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.9743] 48.4+0.7s +[3200/16000] [MSE: 4.0217] 47.6+0.0s +[4800/16000] [MSE: 4.3317] 47.4+0.0s +[6400/16000] [MSE: 4.6276] 47.3+0.0s +[8000/16000] [MSE: 4.6571] 47.5+0.0s +[9600/16000] [MSE: 4.6368] 47.4+0.0s +[11200/16000] [MSE: 4.5731] 47.4+0.0s +[12800/16000] [MSE: 4.4605] 47.2+0.0s +[14400/16000] [MSE: 4.4183] 47.3+0.0s +[16000/16000] [MSE: 4.3861] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.532 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.14s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.1221] 48.2+0.7s +[3200/16000] [MSE: 4.0926] 47.6+0.0s +[4800/16000] [MSE: 4.3985] 47.3+0.0s +[6400/16000] [MSE: 4.7437] 47.5+0.0s +[8000/16000] [MSE: 4.8681] 47.4+0.0s +[9600/16000] [MSE: 4.8318] 46.9+0.0s +[11200/16000] [MSE: 4.7853] 47.0+0.0s +[12800/16000] [MSE: 4.6898] 47.0+0.0s +[14400/16000] [MSE: 4.5574] 46.6+0.0s +[16000/16000] [MSE: 4.4564] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.091 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.13s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.0577] 48.2+0.7s +[3200/16000] [MSE: 4.1242] 47.9+0.1s +[4800/16000] [MSE: 4.0527] 47.7+0.0s +[6400/16000] [MSE: 3.9412] 47.2+0.0s +[8000/16000] [MSE: 3.8052] 47.2+0.0s +[9600/16000] [MSE: 3.7289] 47.3+0.0s +[11200/16000] [MSE: 3.8401] 47.1+0.0s +[12800/16000] [MSE: 3.8968] 47.1+0.0s +[14400/16000] [MSE: 3.9235] 46.8+0.0s +[16000/16000] [MSE: 3.8465] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.967 (Best: 8.820 @epoch 22) +Forward: 38.72s + +Saving... +Total: 39.23s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4549] 48.1+0.8s +[3200/16000] [MSE: 3.5859] 47.4+0.0s +[4800/16000] [MSE: 3.4592] 47.4+0.0s +[6400/16000] [MSE: 3.3846] 47.2+0.0s +[8000/16000] [MSE: 3.3109] 47.2+0.0s +[9600/16000] [MSE: 3.2457] 47.0+0.0s +[11200/16000] [MSE: 3.2273] 46.7+0.0s +[12800/16000] [MSE: 3.2468] 46.4+0.0s +[14400/16000] [MSE: 3.2700] 46.6+0.0s +[16000/16000] [MSE: 3.3102] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.241 (Best: 8.820 @epoch 22) +Forward: 38.72s + +Saving... +Total: 39.16s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.3254] 48.3+0.8s +[3200/16000] [MSE: 5.5210] 47.8+0.0s +[4800/16000] [MSE: 5.5656] 47.2+0.0s +[6400/16000] [MSE: 5.5629] 47.5+0.0s +[8000/16000] [MSE: 5.4446] 47.4+0.0s +[9600/16000] [MSE: 5.3404] 47.3+0.0s +[11200/16000] [MSE: 5.2464] 47.1+0.0s +[12800/16000] [MSE: 5.3345] 47.2+0.0s +[14400/16000] [MSE: 5.4973] 47.2+0.0s +[16000/16000] [MSE: 5.5987] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.678 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.12s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.0067] 48.2+0.8s +[3200/16000] [MSE: 4.9879] 47.7+0.1s +[4800/16000] [MSE: 4.5620] 47.5+0.0s +[6400/16000] [MSE: 4.3400] 47.2+0.0s +[8000/16000] [MSE: 4.2294] 47.4+0.0s +[9600/16000] [MSE: 4.1429] 47.2+0.0s +[11200/16000] [MSE: 4.0613] 47.2+0.0s +[12800/16000] [MSE: 3.8694] 47.0+0.0s +[14400/16000] [MSE: 3.7511] 47.1+0.0s +[16000/16000] [MSE: 3.6226] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.847 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.15s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.9153] 48.0+0.8s +[3200/16000] [MSE: 1.8490] 47.4+0.0s +[4800/16000] [MSE: 1.7530] 46.8+0.0s +[6400/16000] [MSE: 1.7127] 47.4+0.0s +[8000/16000] [MSE: 1.8393] 47.3+0.0s +[9600/16000] [MSE: 2.1394] 47.4+0.0s +[11200/16000] [MSE: 2.3345] 47.2+0.0s +[12800/16000] [MSE: 2.4596] 46.9+0.0s +[14400/16000] [MSE: 2.5491] 47.3+0.0s +[16000/16000] [MSE: 2.5835] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.527 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.16s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6911] 48.1+0.7s +[3200/16000] [MSE: 3.6991] 47.7+0.0s +[4800/16000] [MSE: 3.7114] 47.4+0.0s +[6400/16000] [MSE: 3.4350] 47.6+0.0s +[8000/16000] [MSE: 3.1716] 47.5+0.0s +[9600/16000] [MSE: 2.9969] 47.6+0.0s +[11200/16000] [MSE: 2.9521] 47.1+0.0s +[12800/16000] [MSE: 2.9328] 46.5+0.0s +[14400/16000] [MSE: 2.9221] 46.2+0.0s +[16000/16000] [MSE: 2.9106] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.782 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.94s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1199] 47.8+0.7s +[3200/16000] [MSE: 3.0546] 47.0+0.0s +[4800/16000] [MSE: 2.9733] 47.0+0.0s +[6400/16000] [MSE: 2.9507] 46.7+0.0s +[8000/16000] [MSE: 2.9536] 46.6+0.0s +[9600/16000] [MSE: 2.9992] 46.2+0.0s +[11200/16000] [MSE: 3.0534] 46.2+0.0s +[12800/16000] [MSE: 3.1280] 46.1+0.0s +[14400/16000] [MSE: 3.2491] 46.3+0.0s +[16000/16000] [MSE: 3.3675] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.009 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.13s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4780] 47.8+0.7s +[3200/16000] [MSE: 3.3224] 47.2+0.0s +[4800/16000] [MSE: 3.2536] 47.4+0.0s +[6400/16000] [MSE: 3.2321] 47.3+0.0s +[8000/16000] [MSE: 3.1779] 47.5+0.0s +[9600/16000] [MSE: 3.2052] 47.3+0.0s +[11200/16000] [MSE: 3.2430] 47.0+0.0s +[12800/16000] [MSE: 3.2394] 46.6+0.0s +[14400/16000] [MSE: 3.2401] 47.5+0.0s +[16000/16000] [MSE: 3.2483] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 8.820 @epoch 22) +Forward: 38.46s + +Saving... +Total: 39.03s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6178] 47.7+1.0s +[3200/16000] [MSE: 3.6090] 47.3+0.0s +[4800/16000] [MSE: 3.6146] 47.2+0.0s +[6400/16000] [MSE: 3.5944] 46.8+0.0s +[8000/16000] [MSE: 3.6200] 46.8+0.0s +[9600/16000] [MSE: 3.6915] 46.8+0.0s +[11200/16000] [MSE: 3.7099] 46.5+0.0s +[12800/16000] [MSE: 3.7108] 45.9+0.0s +[14400/16000] [MSE: 3.7012] 46.4+0.0s +[16000/16000] [MSE: 3.6672] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.232 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.05s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2962] 47.6+0.7s +[3200/16000] [MSE: 3.2683] 47.3+0.0s +[4800/16000] [MSE: 3.2735] 47.3+0.0s +[6400/16000] [MSE: 3.2618] 47.1+0.0s +[8000/16000] [MSE: 3.2478] 47.0+0.0s +[9600/16000] [MSE: 3.2231] 47.4+0.0s +[11200/16000] [MSE: 3.2351] 47.2+0.0s +[12800/16000] [MSE: 3.2746] 47.3+0.0s +[14400/16000] [MSE: 3.3315] 46.8+0.0s +[16000/16000] [MSE: 3.3779] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.190 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.09s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6584] 47.8+0.7s +[3200/16000] [MSE: 3.7489] 47.1+0.0s +[4800/16000] [MSE: 3.7602] 46.9+0.0s +[6400/16000] [MSE: 3.7005] 46.9+0.0s +[8000/16000] [MSE: 3.7366] 47.0+0.0s +[9600/16000] [MSE: 3.6938] 46.5+0.0s +[11200/16000] [MSE: 3.5805] 47.2+0.0s +[12800/16000] [MSE: 3.4798] 46.8+0.0s +[14400/16000] [MSE: 3.4561] 46.8+0.0s +[16000/16000] [MSE: 3.4373] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.166 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.08s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2607] 47.9+0.7s +[3200/16000] [MSE: 3.2610] 47.6+0.1s +[4800/16000] [MSE: 3.1363] 47.3+0.0s +[6400/16000] [MSE: 3.0128] 46.9+0.0s +[8000/16000] [MSE: 3.0426] 47.0+0.0s +[9600/16000] [MSE: 3.0163] 47.1+0.0s +[11200/16000] [MSE: 3.0086] 47.2+0.0s +[12800/16000] [MSE: 3.1030] 47.1+0.0s +[14400/16000] [MSE: 3.2516] 47.2+0.0s +[16000/16000] [MSE: 3.3870] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.235 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.12s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.3568] 47.9+0.7s +[3200/16000] [MSE: 4.4571] 47.3+0.0s +[4800/16000] [MSE: 4.4498] 47.2+0.0s +[6400/16000] [MSE: 4.4102] 47.3+0.0s +[8000/16000] [MSE: 4.3672] 47.0+0.0s +[9600/16000] [MSE: 4.3350] 47.2+0.0s +[11200/16000] [MSE: 4.1778] 47.0+0.0s +[12800/16000] [MSE: 4.0622] 46.8+0.0s +[14400/16000] [MSE: 3.9912] 46.7+0.0s +[16000/16000] [MSE: 3.9324] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.808 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 38.97s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4883] 48.1+0.9s +[3200/16000] [MSE: 3.4968] 47.8+0.1s +[4800/16000] [MSE: 3.4348] 47.4+0.0s +[6400/16000] [MSE: 3.3883] 47.3+0.0s +[8000/16000] [MSE: 3.3639] 47.1+0.0s +[9600/16000] [MSE: 3.3415] 47.2+0.0s +[11200/16000] [MSE: 3.3231] 47.1+0.0s +[12800/16000] [MSE: 3.3019] 46.7+0.0s +[14400/16000] [MSE: 3.2766] 47.3+0.0s +[16000/16000] [MSE: 3.2543] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.557 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.08s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9415] 47.9+0.7s +[3200/16000] [MSE: 2.9321] 47.6+0.1s +[4800/16000] [MSE: 2.9852] 46.8+0.0s +[6400/16000] [MSE: 3.0239] 47.3+0.0s +[8000/16000] [MSE: 3.0100] 47.2+0.0s +[9600/16000] [MSE: 2.9953] 47.5+0.0s +[11200/16000] [MSE: 3.0565] 47.4+0.0s +[12800/16000] [MSE: 3.1172] 46.8+0.0s +[14400/16000] [MSE: 3.1529] 47.0+0.0s +[16000/16000] [MSE: 3.1821] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.115 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.11s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1893] 48.1+0.7s +[3200/16000] [MSE: 3.0237] 47.4+0.0s +[4800/16000] [MSE: 3.0390] 47.3+0.0s +[6400/16000] [MSE: 3.0499] 47.2+0.0s +[8000/16000] [MSE: 3.0568] 46.7+0.0s +[9600/16000] [MSE: 3.0616] 46.2+0.0s +[11200/16000] [MSE: 3.0661] 46.0+0.0s +[12800/16000] [MSE: 3.0681] 46.2+0.0s +[14400/16000] [MSE: 3.0749] 46.7+0.0s +[16000/16000] [MSE: 3.0920] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.406 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.98s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1604] 47.5+0.8s +[3200/16000] [MSE: 3.1617] 47.7+0.1s +[4800/16000] [MSE: 3.1448] 47.3+0.0s +[6400/16000] [MSE: 3.1372] 47.1+0.0s +[8000/16000] [MSE: 3.1313] 47.2+0.0s +[9600/16000] [MSE: 3.1202] 47.1+0.0s +[11200/16000] [MSE: 3.1024] 47.0+0.0s +[12800/16000] [MSE: 3.0908] 47.1+0.0s +[14400/16000] [MSE: 3.0831] 47.1+0.0s +[16000/16000] [MSE: 3.0730] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.436 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.02s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0053] 48.1+0.7s +[3200/16000] [MSE: 2.9769] 47.5+0.0s +[4800/16000] [MSE: 2.9826] 47.2+0.0s +[6400/16000] [MSE: 2.9743] 47.3+0.0s +[8000/16000] [MSE: 2.9518] 47.2+0.0s +[9600/16000] [MSE: 2.8847] 47.2+0.0s +[11200/16000] [MSE: 2.8338] 46.9+0.0s +[12800/16000] [MSE: 2.7828] 47.1+0.0s +[14400/16000] [MSE: 2.7473] 47.1+0.0s +[16000/16000] [MSE: 2.7111] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.686 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 39.02s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.1013] 47.4+0.7s +[3200/16000] [MSE: 2.0603] 47.6+0.1s +[4800/16000] [MSE: 2.0623] 46.9+0.0s +[6400/16000] [MSE: 2.0623] 47.1+0.0s +[8000/16000] [MSE: 2.0964] 47.0+0.0s +[9600/16000] [MSE: 2.1077] 46.6+0.0s +[11200/16000] [MSE: 2.1401] 46.0+0.0s +[12800/16000] [MSE: 2.1795] 46.2+0.0s +[14400/16000] [MSE: 2.2026] 46.3+0.0s +[16000/16000] [MSE: 2.2234] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.820 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.23s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.4343] 48.0+0.8s +[3200/16000] [MSE: 2.4352] 47.2+0.0s +[4800/16000] [MSE: 2.3893] 46.5+0.0s +[6400/16000] [MSE: 2.2576] 46.7+0.0s +[8000/16000] [MSE: 2.1465] 46.5+0.0s +[9600/16000] [MSE: 2.0755] 46.8+0.0s +[11200/16000] [MSE: 2.0239] 46.5+0.0s +[12800/16000] [MSE: 2.0080] 46.1+0.0s +[14400/16000] [MSE: 2.0099] 46.1+0.0s +[16000/16000] [MSE: 2.0054] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.746 (Best: 8.820 @epoch 22) +Forward: 38.43s + +Saving... +Total: 38.93s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.0141] 47.9+0.7s +[3200/16000] [MSE: 2.0125] 47.5+0.0s +[4800/16000] [MSE: 1.9430] 47.4+0.0s +[6400/16000] [MSE: 1.8945] 47.1+0.0s +[8000/16000] [MSE: 1.8546] 47.3+0.0s +[9600/16000] [MSE: 1.8219] 47.4+0.0s +[11200/16000] [MSE: 1.8001] 47.1+0.0s +[12800/16000] [MSE: 1.7817] 46.9+0.0s +[14400/16000] [MSE: 1.7631] 47.0+0.0s +[16000/16000] [MSE: 1.8249] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.197 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.13s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.3667] 47.9+0.7s +[3200/16000] [MSE: 2.4836] 47.4+0.0s +[4800/16000] [MSE: 2.5763] 47.1+0.0s +[6400/16000] [MSE: 2.7436] 46.8+0.0s +[8000/16000] [MSE: 2.8901] 46.9+0.0s +[9600/16000] [MSE: 2.9338] 46.9+0.0s +[11200/16000] [MSE: 2.9332] 46.6+0.0s +[12800/16000] [MSE: 2.9058] 46.4+0.0s +[14400/16000] [MSE: 2.8184] 46.2+0.0s +[16000/16000] [MSE: 2.7433] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.633 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.96s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.0424] 47.7+0.7s +[3200/16000] [MSE: 2.1947] 47.2+0.0s +[4800/16000] [MSE: 2.4449] 47.0+0.0s +[6400/16000] [MSE: 2.5926] 47.1+0.0s +[8000/16000] [MSE: 2.6856] 47.1+0.0s +[9600/16000] [MSE: 2.7199] 46.7+0.0s +[11200/16000] [MSE: 2.7208] 46.8+0.0s +[12800/16000] [MSE: 2.7274] 46.7+0.0s +[14400/16000] [MSE: 2.7235] 46.8+0.0s +[16000/16000] [MSE: 2.7238] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.684 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 38.99s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.7939] 48.0+0.7s +[3200/16000] [MSE: 2.7768] 47.7+0.1s +[4800/16000] [MSE: 2.7655] 47.1+0.0s +[6400/16000] [MSE: 2.7630] 46.9+0.0s +[8000/16000] [MSE: 2.7045] 46.5+0.0s +[9600/16000] [MSE: 2.6554] 46.7+0.0s +[11200/16000] [MSE: 2.6212] 46.5+0.0s +[12800/16000] [MSE: 2.5936] 46.6+0.0s +[14400/16000] [MSE: 2.5643] 46.2+0.0s +[16000/16000] [MSE: 2.5397] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.862 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.96s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.3089] 47.7+1.0s +[3200/16000] [MSE: 2.2897] 47.4+0.0s +[4800/16000] [MSE: 2.2016] 46.9+0.0s +[6400/16000] [MSE: 2.1616] 46.9+0.0s +[8000/16000] [MSE: 2.1321] 46.7+0.0s +[9600/16000] [MSE: 2.1072] 46.1+0.0s +[11200/16000] [MSE: 2.1001] 46.2+0.0s +[12800/16000] [MSE: 2.1529] 46.9+0.0s +[14400/16000] [MSE: 2.2599] 47.0+0.0s +[16000/16000] [MSE: 2.3477] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.903 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.01s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.7833] 47.9+0.7s +[3200/16000] [MSE: 2.7659] 47.4+0.0s +[4800/16000] [MSE: 2.7248] 47.0+0.0s +[6400/16000] [MSE: 2.7323] 47.3+0.0s +[8000/16000] [MSE: 2.7460] 47.1+0.0s +[9600/16000] [MSE: 2.7517] 47.0+0.0s +[11200/16000] [MSE: 2.7354] 47.0+0.0s +[12800/16000] [MSE: 2.7339] 47.2+0.0s +[14400/16000] [MSE: 2.7357] 47.1+0.0s +[16000/16000] [MSE: 2.7312] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.928 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.10s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5370] 48.0+0.7s +[3200/16000] [MSE: 2.5862] 47.1+0.0s +[4800/16000] [MSE: 2.6210] 47.1+0.0s +[6400/16000] [MSE: 2.6353] 46.8+0.0s +[8000/16000] [MSE: 2.6473] 47.0+0.0s +[9600/16000] [MSE: 2.6847] 47.3+0.0s +[11200/16000] [MSE: 2.7379] 47.1+0.0s +[12800/16000] [MSE: 2.7875] 46.8+0.0s +[14400/16000] [MSE: 2.8862] 47.1+0.0s +[16000/16000] [MSE: 2.9816] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.837 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 39.01s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0438] 47.8+0.8s +[3200/16000] [MSE: 2.8496] 47.5+0.1s +[4800/16000] [MSE: 2.6797] 47.3+0.0s +[6400/16000] [MSE: 2.6842] 47.0+0.0s +[8000/16000] [MSE: 2.5665] 46.8+0.0s +[9600/16000] [MSE: 2.4837] 46.9+0.0s +[11200/16000] [MSE: 2.4588] 46.9+0.0s +[12800/16000] [MSE: 2.5546] 46.7+0.0s +[14400/16000] [MSE: 2.7845] 47.2+0.0s +[16000/16000] [MSE: 3.0444] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.397 (Best: 8.820 @epoch 22) +Forward: 38.46s + +Saving... +Total: 38.96s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5729] 47.9+0.7s +[3200/16000] [MSE: 2.4940] 47.4+0.1s +[4800/16000] [MSE: 2.6747] 47.1+0.0s +[6400/16000] [MSE: 3.0197] 46.8+0.0s +[8000/16000] [MSE: 3.0245] 46.5+0.0s +[9600/16000] [MSE: 2.8294] 46.2+0.0s +[11200/16000] [MSE: 2.6856] 46.0+0.0s +[12800/16000] [MSE: 2.5671] 46.0+0.0s +[14400/16000] [MSE: 2.4705] 46.5+0.0s +[16000/16000] [MSE: 2.3952] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.352 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.11s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.7659] 47.8+0.8s +[3200/16000] [MSE: 3.0586] 47.5+0.0s +[4800/16000] [MSE: 4.0601] 47.1+0.0s +[6400/16000] [MSE: 4.7070] 47.0+0.0s +[8000/16000] [MSE: 5.1046] 46.9+0.0s +[9600/16000] [MSE: 5.3470] 46.8+0.0s +[11200/16000] [MSE: 5.5183] 46.8+0.0s +[12800/16000] [MSE: 5.6592] 46.5+0.0s +[14400/16000] [MSE: 5.7422] 47.3+0.0s +[16000/16000] [MSE: 5.7574] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.427 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.23s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.9604] 47.7+0.7s +[3200/16000] [MSE: 5.6559] 47.7+0.1s +[4800/16000] [MSE: 5.0770] 47.3+0.0s +[6400/16000] [MSE: 4.6092] 47.3+0.0s +[8000/16000] [MSE: 4.7771] 46.9+0.0s +[9600/16000] [MSE: 5.2692] 46.8+0.0s +[11200/16000] [MSE: 5.5993] 46.8+0.0s +[12800/16000] [MSE: 5.8062] 46.9+0.0s +[14400/16000] [MSE: 6.0329] 47.2+0.0s +[16000/16000] [MSE: 6.2237] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.928 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.08s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [MSE: 7.8020] 48.1+0.7s +[3200/16000] [MSE: 7.5348] 47.5+0.0s +[4800/16000] [MSE: 7.2333] 46.9+0.0s +[6400/16000] [MSE: 7.0545] 47.1+0.0s +[8000/16000] [MSE: 6.8681] 47.0+0.0s +[9600/16000] [MSE: 6.7611] 46.9+0.0s +[11200/16000] [MSE: 6.6944] 46.8+0.0s +[12800/16000] [MSE: 6.6497] 46.8+0.0s +[14400/16000] [MSE: 6.4895] 46.1+0.0s +[16000/16000] [MSE: 6.2909] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.066 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.16s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.4273] 47.8+0.7s +[3200/16000] [MSE: 4.4793] 47.3+0.1s +[4800/16000] [MSE: 4.5342] 47.0+0.0s +[6400/16000] [MSE: 4.6972] 46.9+0.0s +[8000/16000] [MSE: 4.8165] 46.7+0.0s +[9600/16000] [MSE: 4.8560] 46.4+0.0s +[11200/16000] [MSE: 4.8696] 46.0+0.0s +[12800/16000] [MSE: 4.9042] 46.2+0.0s +[14400/16000] [MSE: 4.9379] 46.1+0.0s +[16000/16000] [MSE: 4.9607] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.000 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 39.00s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.1875] 47.5+0.7s +[3200/16000] [MSE: 5.1678] 47.7+0.1s +[4800/16000] [MSE: 5.2292] 47.4+0.1s +[6400/16000] [MSE: 5.2937] 47.2+0.0s +[8000/16000] [MSE: 5.3239] 46.9+0.0s +[9600/16000] [MSE: 5.3301] 46.8+0.0s +[11200/16000] [MSE: 5.2954] 47.0+0.0s +[12800/16000] [MSE: 5.2056] 47.0+0.0s +[14400/16000] [MSE: 5.0685] 47.0+0.0s +[16000/16000] [MSE: 4.9607] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.872 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.06s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.9005] 47.8+0.7s +[3200/16000] [MSE: 3.6555] 47.0+0.1s +[4800/16000] [MSE: 3.1070] 47.0+0.0s +[6400/16000] [MSE: 2.8185] 47.1+0.0s +[8000/16000] [MSE: 2.7905] 47.1+0.0s +[9600/16000] [MSE: 3.0490] 47.0+0.0s +[11200/16000] [MSE: 3.1572] 47.1+0.0s +[12800/16000] [MSE: 3.1805] 47.0+0.0s +[14400/16000] [MSE: 3.1939] 46.9+0.0s +[16000/16000] [MSE: 3.2518] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.343 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.00s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.8595] 48.0+0.8s +[3200/16000] [MSE: 4.9660] 47.7+0.1s +[4800/16000] [MSE: 5.0568] 47.4+0.0s +[6400/16000] [MSE: 5.1178] 47.7+0.0s +[8000/16000] [MSE: 5.1283] 47.5+0.0s +[9600/16000] [MSE: 5.0035] 47.4+0.0s +[11200/16000] [MSE: 4.8629] 47.5+0.0s +[12800/16000] [MSE: 4.7749] 47.0+0.0s +[14400/16000] [MSE: 4.7219] 47.2+0.0s +[16000/16000] [MSE: 4.6811] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.428 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.00s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.3627] 47.7+0.7s +[3200/16000] [MSE: 4.5867] 47.3+0.1s +[4800/16000] [MSE: 4.5360] 47.2+0.0s +[6400/16000] [MSE: 4.3375] 47.0+0.0s +[8000/16000] [MSE: 4.1306] 46.6+0.0s +[9600/16000] [MSE: 4.0337] 46.7+0.0s +[11200/16000] [MSE: 4.0040] 46.1+0.0s +[12800/16000] [MSE: 4.0922] 46.3+0.0s +[14400/16000] [MSE: 4.2204] 46.5+0.0s +[16000/16000] [MSE: 4.2707] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.095 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.01s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.2040] 47.7+0.7s +[3200/16000] [MSE: 4.2029] 47.7+0.1s +[4800/16000] [MSE: 3.9958] 47.6+0.0s +[6400/16000] [MSE: 3.6165] 47.3+0.0s +[8000/16000] [MSE: 3.3510] 47.3+0.0s +[9600/16000] [MSE: 3.1693] 47.2+0.0s +[11200/16000] [MSE: 3.0434] 46.8+0.0s +[12800/16000] [MSE: 2.9851] 47.0+0.0s +[14400/16000] [MSE: 2.9420] 47.0+0.0s +[16000/16000] [MSE: 2.9003] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.507 (Best: 8.820 @epoch 22) +Forward: 38.75s + +Saving... +Total: 39.24s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5147] 48.0+0.8s +[3200/16000] [MSE: 2.5400] 47.4+0.1s +[4800/16000] [MSE: 3.0339] 47.1+0.0s +[6400/16000] [MSE: 3.4251] 47.2+0.0s +[8000/16000] [MSE: 3.5039] 46.9+0.0s +[9600/16000] [MSE: 3.5050] 46.9+0.0s +[11200/16000] [MSE: 3.5309] 47.0+0.0s +[12800/16000] [MSE: 3.7229] 46.9+0.0s +[14400/16000] [MSE: 3.9237] 46.6+0.0s +[16000/16000] [MSE: 3.9671] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.746 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.04s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.7442] 47.9+0.7s +[3200/16000] [MSE: 3.7114] 47.8+0.1s +[4800/16000] [MSE: 4.3772] 47.2+0.0s +[6400/16000] [MSE: 5.0770] 47.0+0.0s +[8000/16000] [MSE: 5.7656] 46.8+0.0s +[9600/16000] [MSE: 6.1766] 46.8+0.0s +[11200/16000] [MSE: 6.4839] 46.9+0.0s +[12800/16000] [MSE: 6.4900] 46.6+0.0s +[14400/16000] [MSE: 6.4229] 46.7+0.0s +[16000/16000] [MSE: 6.3844] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.825 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.96s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [MSE: 7.0130] 47.7+0.8s +[3200/16000] [MSE: 7.0603] 47.6+0.1s +[4800/16000] [MSE: 7.1216] 47.2+0.0s +[6400/16000] [MSE: 7.1053] 47.1+0.0s +[8000/16000] [MSE: 6.9906] 47.1+0.0s +[9600/16000] [MSE: 6.5100] 46.6+0.0s +[11200/16000] [MSE: 6.1492] 46.6+0.0s +[12800/16000] [MSE: 5.8619] 46.2+0.0s +[14400/16000] [MSE: 5.5761] 46.2+0.0s +[16000/16000] [MSE: 5.3588] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.008 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.23s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.3188] 47.6+0.6s +[3200/16000] [MSE: 2.7489] 47.0+0.0s +[4800/16000] [MSE: 2.8422] 46.7+0.0s +[6400/16000] [MSE: 2.7657] 46.7+0.0s +[8000/16000] [MSE: 2.7029] 46.4+0.0s +[9600/16000] [MSE: 2.6500] 46.4+0.0s +[11200/16000] [MSE: 2.6220] 46.3+0.0s +[12800/16000] [MSE: 2.6017] 45.9+0.0s +[14400/16000] [MSE: 2.5876] 46.8+0.0s +[16000/16000] [MSE: 2.6915] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.524 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.03s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.7839] 48.0+0.7s +[3200/16000] [MSE: 4.1533] 47.6+0.0s +[4800/16000] [MSE: 3.7958] 47.1+0.0s +[6400/16000] [MSE: 3.5230] 46.6+0.0s +[8000/16000] [MSE: 3.4688] 47.0+0.0s +[9600/16000] [MSE: 3.4571] 47.0+0.0s +[11200/16000] [MSE: 3.3639] 47.0+0.0s +[12800/16000] [MSE: 3.3010] 47.0+0.0s +[14400/16000] [MSE: 3.2207] 47.6+0.0s +[16000/16000] [MSE: 3.1491] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.805 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.07s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.8950] 48.2+0.7s +[3200/16000] [MSE: 3.7948] 47.6+0.0s +[4800/16000] [MSE: 4.0711] 47.3+0.0s +[6400/16000] [MSE: 4.1852] 46.9+0.0s +[8000/16000] [MSE: 4.2882] 46.5+0.0s +[9600/16000] [MSE: 4.4143] 46.7+0.0s +[11200/16000] [MSE: 4.5685] 46.4+0.0s +[12800/16000] [MSE: 4.4740] 46.2+0.0s +[14400/16000] [MSE: 4.3247] 46.6+0.0s +[16000/16000] [MSE: 4.2680] 45.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.499 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 39.02s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.1445] 48.1+0.7s +[3200/16000] [MSE: 5.0733] 47.7+0.1s +[4800/16000] [MSE: 4.9061] 46.9+0.0s +[6400/16000] [MSE: 4.7854] 47.1+0.0s +[8000/16000] [MSE: 4.7260] 46.8+0.0s +[9600/16000] [MSE: 4.6373] 46.7+0.0s +[11200/16000] [MSE: 4.5438] 46.2+0.0s +[12800/16000] [MSE: 4.4555] 46.4+0.0s +[14400/16000] [MSE: 4.3985] 46.1+0.0s +[16000/16000] [MSE: 4.3917] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.526 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.95s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.3584] 47.1+0.7s +[3200/16000] [MSE: 3.9333] 47.1+0.0s +[4800/16000] [MSE: 3.7039] 46.8+0.0s +[6400/16000] [MSE: 3.7664] 47.0+0.0s +[8000/16000] [MSE: 3.8954] 47.0+0.0s +[9600/16000] [MSE: 4.1974] 46.8+0.0s +[11200/16000] [MSE: 4.3946] 46.6+0.0s +[12800/16000] [MSE: 4.4244] 46.5+0.0s +[14400/16000] [MSE: 4.3445] 46.6+0.0s +[16000/16000] [MSE: 4.4357] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.279 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.01s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [MSE: 7.5174] 47.9+1.1s +[3200/16000] [MSE: 7.5600] 47.2+0.0s +[4800/16000] [MSE: 7.6011] 47.2+0.0s +[6400/16000] [MSE: 7.2580] 47.2+0.0s +[8000/16000] [MSE: 6.8229] 47.1+0.0s +[9600/16000] [MSE: 6.5300] 47.0+0.0s +[11200/16000] [MSE: 6.3219] 46.5+0.0s +[12800/16000] [MSE: 6.1639] 46.7+0.0s +[14400/16000] [MSE: 6.0382] 47.2+0.0s +[16000/16000] [MSE: 6.0191] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.931 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.10s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [MSE: 6.6975] 48.2+0.8s +[3200/16000] [MSE: 5.6766] 47.4+0.1s +[4800/16000] [MSE: 5.4950] 47.2+0.0s +[6400/16000] [MSE: 5.5236] 47.3+0.0s +[8000/16000] [MSE: 5.5310] 47.3+0.0s +[9600/16000] [MSE: 5.5692] 47.1+0.0s +[11200/16000] [MSE: 5.6028] 46.9+0.0s +[12800/16000] [MSE: 5.6388] 47.1+0.0s +[14400/16000] [MSE: 5.6693] 47.4+0.0s +[16000/16000] [MSE: 5.6529] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.412 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.13s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.7213] 47.9+0.8s +[3200/16000] [MSE: 2.7088] 47.4+0.0s +[4800/16000] [MSE: 2.6719] 47.5+0.0s +[6400/16000] [MSE: 2.6160] 47.0+0.0s +[8000/16000] [MSE: 2.5920] 47.2+0.0s +[9600/16000] [MSE: 2.6684] 46.8+0.0s +[11200/16000] [MSE: 2.7224] 46.4+0.0s +[12800/16000] [MSE: 2.7234] 46.3+0.0s +[14400/16000] [MSE: 2.7129] 46.1+0.0s +[16000/16000] [MSE: 2.7067] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.574 (Best: 8.820 @epoch 22) +Forward: 38.44s + +Saving... +Total: 38.93s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0467] 48.3+0.7s +[3200/16000] [MSE: 3.1981] 47.3+0.0s +[4800/16000] [MSE: 3.3014] 47.3+0.0s +[6400/16000] [MSE: 3.4980] 47.0+0.0s +[8000/16000] [MSE: 3.5667] 46.8+0.0s +[9600/16000] [MSE: 3.6243] 46.7+0.0s +[11200/16000] [MSE: 3.6707] 46.6+0.0s +[12800/16000] [MSE: 3.6866] 47.0+0.0s +[14400/16000] [MSE: 3.6507] 46.7+0.0s +[16000/16000] [MSE: 3.6146] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.550 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 39.01s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0533] 48.0+0.7s +[3200/16000] [MSE: 2.8234] 47.2+0.0s +[4800/16000] [MSE: 2.7421] 47.4+0.0s +[6400/16000] [MSE: 2.7170] 47.1+0.0s +[8000/16000] [MSE: 2.6523] 47.3+0.0s +[9600/16000] [MSE: 2.9502] 47.3+0.0s +[11200/16000] [MSE: 3.3506] 47.2+0.0s +[12800/16000] [MSE: 3.6188] 47.1+0.0s +[14400/16000] [MSE: 3.8028] 47.1+0.0s +[16000/16000] [MSE: 3.9474] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.547 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 38.98s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.4560] 47.8+0.7s +[3200/16000] [MSE: 3.9888] 46.8+0.0s +[4800/16000] [MSE: 3.5675] 46.2+0.0s +[6400/16000] [MSE: 3.3844] 46.1+0.0s +[8000/16000] [MSE: 3.4215] 46.4+0.0s +[9600/16000] [MSE: 3.3436] 46.6+0.0s +[11200/16000] [MSE: 3.2895] 46.2+0.0s +[12800/16000] [MSE: 3.2481] 46.0+0.0s +[14400/16000] [MSE: 3.2273] 46.9+0.0s +[16000/16000] [MSE: 3.2262] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.531 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 39.09s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.8672] 47.8+0.8s +[3200/16000] [MSE: 2.9584] 47.3+0.0s +[4800/16000] [MSE: 2.8204] 47.3+0.0s +[6400/16000] [MSE: 2.7342] 46.9+0.0s +[8000/16000] [MSE: 2.6836] 46.5+0.0s +[9600/16000] [MSE: 2.8578] 47.0+0.0s +[11200/16000] [MSE: 3.0650] 46.4+0.0s +[12800/16000] [MSE: 3.2145] 46.5+0.0s +[14400/16000] [MSE: 3.3355] 46.5+0.0s +[16000/16000] [MSE: 3.5471] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.596 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.16s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [MSE: 6.0340] 47.7+0.8s +[3200/16000] [MSE: 5.8923] 47.4+0.0s +[4800/16000] [MSE: 5.7076] 47.1+0.0s +[6400/16000] [MSE: 5.6709] 47.0+0.0s +[8000/16000] [MSE: 5.6605] 47.0+0.0s +[9600/16000] [MSE: 5.5303] 46.9+0.0s +[11200/16000] [MSE: 5.3040] 46.7+0.0s +[12800/16000] [MSE: 5.1842] 46.8+0.0s +[14400/16000] [MSE: 5.1038] 46.4+0.0s +[16000/16000] [MSE: 4.9554] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.463 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.00s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.8524] 47.9+0.7s +[3200/16000] [MSE: 3.2826] 47.5+0.1s +[4800/16000] [MSE: 3.0774] 47.3+0.0s +[6400/16000] [MSE: 3.0912] 47.2+0.0s +[8000/16000] [MSE: 3.0882] 47.4+0.0s +[9600/16000] [MSE: 3.0815] 47.2+0.0s +[11200/16000] [MSE: 3.0948] 46.8+0.0s +[12800/16000] [MSE: 3.1010] 46.8+0.0s +[14400/16000] [MSE: 3.0918] 46.8+0.0s +[16000/16000] [MSE: 3.0836] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.535 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 38.97s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0111] 47.8+0.8s +[3200/16000] [MSE: 2.9232] 47.1+0.0s +[4800/16000] [MSE: 2.8489] 47.2+0.0s +[6400/16000] [MSE: 2.7559] 47.3+0.0s +[8000/16000] [MSE: 2.8050] 47.1+0.0s +[9600/16000] [MSE: 2.9155] 47.2+0.0s +[11200/16000] [MSE: 3.0078] 47.1+0.0s +[12800/16000] [MSE: 3.0883] 46.9+0.0s +[14400/16000] [MSE: 3.1490] 47.2+0.0s +[16000/16000] [MSE: 3.2998] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.954 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.08s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.4851] 48.0+0.7s +[3200/16000] [MSE: 4.2872] 47.5+0.1s +[4800/16000] [MSE: 4.1425] 47.7+0.0s +[6400/16000] [MSE: 4.0841] 47.3+0.0s +[8000/16000] [MSE: 3.9544] 47.4+0.0s +[9600/16000] [MSE: 3.8488] 47.3+0.0s +[11200/16000] [MSE: 3.7393] 46.6+0.0s +[12800/16000] [MSE: 3.6303] 46.6+0.0s +[14400/16000] [MSE: 3.5367] 46.8+0.0s +[16000/16000] [MSE: 3.4652] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.713 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.11s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1213] 47.8+0.8s +[3200/16000] [MSE: 3.1977] 47.6+0.0s +[4800/16000] [MSE: 3.2056] 47.2+0.0s +[6400/16000] [MSE: 3.1889] 46.7+0.0s +[8000/16000] [MSE: 3.2285] 47.0+0.0s +[9600/16000] [MSE: 3.3989] 46.7+0.0s +[11200/16000] [MSE: 3.4302] 46.5+0.0s +[12800/16000] [MSE: 3.3928] 46.7+0.0s +[14400/16000] [MSE: 3.3633] 46.7+0.0s +[16000/16000] [MSE: 3.4212] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.317 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.05s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1678] 47.3+0.7s +[3200/16000] [MSE: 3.0774] 47.0+0.0s +[4800/16000] [MSE: 3.0240] 47.1+0.0s +[6400/16000] [MSE: 3.1776] 46.9+0.0s +[8000/16000] [MSE: 3.1867] 47.2+0.0s +[9600/16000] [MSE: 3.1210] 46.7+0.0s +[11200/16000] [MSE: 3.0450] 46.5+0.0s +[12800/16000] [MSE: 2.9961] 46.6+0.0s +[14400/16000] [MSE: 2.9983] 46.8+0.0s +[16000/16000] [MSE: 3.0036] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.786 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.18s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6969] 48.1+0.8s +[3200/16000] [MSE: 3.8086] 47.3+0.0s +[4800/16000] [MSE: 3.8739] 47.0+0.0s +[6400/16000] [MSE: 3.7934] 47.3+0.0s +[8000/16000] [MSE: 3.7066] 47.2+0.0s +[9600/16000] [MSE: 3.6482] 46.9+0.0s +[11200/16000] [MSE: 3.5279] 47.1+0.0s +[12800/16000] [MSE: 3.4127] 46.9+0.0s +[14400/16000] [MSE: 3.3478] 47.1+0.0s +[16000/16000] [MSE: 3.2931] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.647 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.05s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5407] 47.8+0.7s +[3200/16000] [MSE: 3.0684] 47.4+0.0s +[4800/16000] [MSE: 3.5148] 47.5+0.0s +[6400/16000] [MSE: 3.5544] 47.4+0.0s +[8000/16000] [MSE: 3.5554] 47.2+0.0s +[9600/16000] [MSE: 3.5740] 46.8+0.0s +[11200/16000] [MSE: 3.5601] 46.8+0.0s +[12800/16000] [MSE: 3.4956] 46.7+0.0s +[14400/16000] [MSE: 3.4559] 46.2+0.0s +[16000/16000] [MSE: 3.4350] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.893 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.07s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1414] 47.7+0.8s +[3200/16000] [MSE: 3.1112] 47.7+0.1s +[4800/16000] [MSE: 3.0831] 47.4+0.1s +[6400/16000] [MSE: 3.0601] 47.1+0.0s +[8000/16000] [MSE: 3.0509] 47.4+0.0s +[9600/16000] [MSE: 3.0483] 47.1+0.0s +[11200/16000] [MSE: 3.0341] 46.9+0.0s +[12800/16000] [MSE: 3.0265] 46.7+0.0s +[14400/16000] [MSE: 3.0246] 46.9+0.0s +[16000/16000] [MSE: 3.0195] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.348 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.07s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9282] 47.8+0.7s +[3200/16000] [MSE: 2.9127] 47.6+0.1s +[4800/16000] [MSE: 2.9121] 47.4+0.0s +[6400/16000] [MSE: 3.0974] 47.1+0.0s +[8000/16000] [MSE: 3.2317] 47.1+0.0s +[9600/16000] [MSE: 3.2954] 47.0+0.0s +[11200/16000] [MSE: 3.3098] 46.9+0.0s +[12800/16000] [MSE: 3.3268] 46.5+0.0s +[14400/16000] [MSE: 3.3104] 46.8+0.0s +[16000/16000] [MSE: 3.2827] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.155 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.18s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9628] 48.1+0.8s +[3200/16000] [MSE: 2.9617] 47.5+0.0s +[4800/16000] [MSE: 2.9477] 46.8+0.0s +[6400/16000] [MSE: 2.8989] 47.0+0.0s +[8000/16000] [MSE: 2.8815] 46.9+0.0s +[9600/16000] [MSE: 2.9632] 46.8+0.0s +[11200/16000] [MSE: 3.2176] 46.9+0.0s +[12800/16000] [MSE: 3.3936] 46.5+0.0s +[14400/16000] [MSE: 3.4177] 46.2+0.0s +[16000/16000] [MSE: 3.3554] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.170 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.96s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.6932] 47.6+0.7s +[3200/16000] [MSE: 2.6845] 47.5+0.0s +[4800/16000] [MSE: 2.6440] 47.2+0.0s +[6400/16000] [MSE: 2.5824] 47.3+0.0s +[8000/16000] [MSE: 2.5999] 47.1+0.0s +[9600/16000] [MSE: 2.7172] 47.0+0.0s +[11200/16000] [MSE: 2.8245] 46.9+0.0s +[12800/16000] [MSE: 2.9127] 46.9+0.0s +[14400/16000] [MSE: 2.9627] 46.6+0.0s +[16000/16000] [MSE: 3.0053] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.171 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.96s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4840] 48.1+0.7s +[3200/16000] [MSE: 3.4312] 47.3+0.0s +[4800/16000] [MSE: 3.4160] 47.2+0.0s +[6400/16000] [MSE: 3.4784] 47.3+0.0s +[8000/16000] [MSE: 3.4659] 47.2+0.0s +[9600/16000] [MSE: 3.4288] 46.5+0.0s +[11200/16000] [MSE: 3.3506] 46.4+0.0s +[12800/16000] [MSE: 3.3047] 46.5+0.0s +[14400/16000] [MSE: 3.2285] 46.8+0.0s +[16000/16000] [MSE: 3.1686] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.900 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.14s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.4402] 47.9+0.7s +[3200/16000] [MSE: 2.4093] 47.3+0.0s +[4800/16000] [MSE: 2.3799] 47.3+0.0s +[6400/16000] [MSE: 2.3208] 47.2+0.0s +[8000/16000] [MSE: 2.2710] 46.6+0.0s +[9600/16000] [MSE: 2.2319] 46.9+0.0s +[11200/16000] [MSE: 2.2348] 46.7+0.0s +[12800/16000] [MSE: 2.2506] 46.2+0.0s +[14400/16000] [MSE: 2.2588] 46.2+0.0s +[16000/16000] [MSE: 2.2819] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.026 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 38.98s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9429] 47.8+0.7s +[3200/16000] [MSE: 2.7622] 47.3+0.1s +[4800/16000] [MSE: 2.8490] 47.4+0.1s +[6400/16000] [MSE: 2.8557] 47.6+0.1s +[8000/16000] [MSE: 2.7228] 47.4+0.0s +[9600/16000] [MSE: 2.7012] 46.8+0.0s +[11200/16000] [MSE: 2.7001] 47.1+0.0s +[12800/16000] [MSE: 2.7039] 47.0+0.0s +[14400/16000] [MSE: 2.6952] 46.8+0.0s +[16000/16000] [MSE: 2.6280] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.705 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.57s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.9734] 47.8+1.0s +[3200/16000] [MSE: 1.9869] 47.5+0.0s +[4800/16000] [MSE: 2.0426] 47.1+0.0s +[6400/16000] [MSE: 2.2711] 46.9+0.0s +[8000/16000] [MSE: 2.3934] 47.2+0.0s +[9600/16000] [MSE: 2.5194] 47.0+0.0s +[11200/16000] [MSE: 2.5948] 46.7+0.0s +[12800/16000] [MSE: 2.6706] 46.6+0.0s +[14400/16000] [MSE: 2.7513] 46.6+0.0s +[16000/16000] [MSE: 2.8101] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.061 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.12s + +[Epoch 119] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.1875] 48.0+0.7s +[3200/16000] [MSE: 5.0676] 47.6+0.0s +[4800/16000] [MSE: 4.9657] 47.0+0.0s +[6400/16000] [MSE: 4.9836] 47.1+0.0s +[8000/16000] [MSE: 4.7871] 46.9+0.0s +[9600/16000] [MSE: 4.6188] 47.2+0.0s +[11200/16000] [MSE: 4.6416] 47.0+0.0s +[12800/16000] [MSE: 4.5894] 47.1+0.0s +[14400/16000] [MSE: 4.6672] 47.2+0.0s +[16000/16000] [MSE: 4.6813] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.055 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.16s + +[Epoch 120] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.5953] 47.9+0.9s +[3200/16000] [MSE: 5.4219] 47.5+0.0s +[4800/16000] [MSE: 4.9937] 47.5+0.1s +[6400/16000] [MSE: 4.4837] 47.3+0.1s +[8000/16000] [MSE: 4.1313] 47.4+0.0s +[9600/16000] [MSE: 3.8963] 47.2+0.0s +[11200/16000] [MSE: 3.7489] 47.2+0.0s +[12800/16000] [MSE: 3.6730] 46.9+0.0s +[14400/16000] [MSE: 3.6738] 47.1+0.0s +[16000/16000] [MSE: 3.6658] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 3.714 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.10s + +[Epoch 121] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5729] 48.0+0.8s +[3200/16000] [MSE: 3.5908] 47.5+0.0s +[4800/16000] [MSE: 3.5927] 47.2+0.0s +[6400/16000] [MSE: 3.5770] 47.2+0.0s +[8000/16000] [MSE: 3.5472] 47.2+0.0s +[9600/16000] [MSE: 4.1049] 47.0+0.0s +[11200/16000] [MSE: 4.4709] 47.0+0.0s +[12800/16000] [MSE: 4.7351] 47.1+0.0s +[14400/16000] [MSE: 4.9364] 46.7+0.0s +[16000/16000] [MSE: 5.0979] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.051 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.99s + +[Epoch 122] Learning rate: 1.00e-4 +[1600/16000] [MSE: 6.2472] 48.0+0.7s +[3200/16000] [MSE: 4.9003] 47.5+0.0s +[4800/16000] [MSE: 4.4017] 47.1+0.0s +[6400/16000] [MSE: 4.3215] 47.3+0.0s +[8000/16000] [MSE: 4.0561] 47.1+0.0s +[9600/16000] [MSE: 3.8059] 46.6+0.0s +[11200/16000] [MSE: 3.5775] 46.6+0.0s +[12800/16000] [MSE: 3.5309] 47.0+0.0s +[14400/16000] [MSE: 3.5007] 47.1+0.0s +[16000/16000] [MSE: 3.4777] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.946 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.05s + +[Epoch 123] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2097] 47.9+0.8s +[3200/16000] [MSE: 3.0754] 47.5+0.0s +[4800/16000] [MSE: 2.7332] 47.5+0.0s +[6400/16000] [MSE: 2.5518] 47.5+0.0s +[8000/16000] [MSE: 2.5385] 47.1+0.0s +[9600/16000] [MSE: 2.6269] 47.3+0.0s +[11200/16000] [MSE: 2.6668] 46.9+0.0s +[12800/16000] [MSE: 2.6820] 47.1+0.0s +[14400/16000] [MSE: 2.6745] 47.0+0.0s +[16000/16000] [MSE: 2.7149] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.051 (Best: 8.820 @epoch 22) +Forward: 38.47s + +Saving... +Total: 39.06s + +[Epoch 124] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.6265] 48.0+0.7s +[3200/16000] [MSE: 2.5724] 47.7+0.1s +[4800/16000] [MSE: 2.5373] 47.6+0.0s +[6400/16000] [MSE: 2.4688] 47.3+0.0s +[8000/16000] [MSE: 2.4133] 47.2+0.0s +[9600/16000] [MSE: 2.3851] 47.2+0.0s +[11200/16000] [MSE: 2.3699] 47.0+0.0s +[12800/16000] [MSE: 2.3612] 47.0+0.0s +[14400/16000] [MSE: 2.3520] 47.0+0.0s +[16000/16000] [MSE: 2.3434] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.051 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 38.99s + +[Epoch 125] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.0478] 48.0+0.7s +[3200/16000] [MSE: 1.9788] 47.5+0.0s +[4800/16000] [MSE: 2.1312] 47.4+0.0s +[6400/16000] [MSE: 2.2585] 47.3+0.0s +[8000/16000] [MSE: 2.2866] 47.0+0.0s +[9600/16000] [MSE: 2.3029] 47.1+0.0s +[11200/16000] [MSE: 2.3091] 46.9+0.0s +[12800/16000] [MSE: 2.3107] 47.1+0.0s +[14400/16000] [MSE: 2.3117] 47.0+0.0s +[16000/16000] [MSE: 2.3230] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.043 (Best: 8.820 @epoch 22) +Forward: 38.47s + +Saving... +Total: 38.98s + +[Epoch 126] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5322] 47.9+0.6s +[3200/16000] [MSE: 2.5405] 47.5+0.1s +[4800/16000] [MSE: 2.4805] 47.6+0.0s +[6400/16000] [MSE: 2.4409] 47.2+0.0s +[8000/16000] [MSE: 2.4149] 46.9+0.0s +[9600/16000] [MSE: 2.3864] 46.8+0.0s +[11200/16000] [MSE: 2.3585] 46.4+0.0s +[12800/16000] [MSE: 2.3358] 46.4+0.0s +[14400/16000] [MSE: 2.3387] 46.2+0.0s +[16000/16000] [MSE: 2.3407] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.995 (Best: 8.820 @epoch 22) +Forward: 38.47s + +Saving... +Total: 38.97s + +[Epoch 127] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.3650] 48.0+0.7s +[3200/16000] [MSE: 2.4797] 47.4+0.1s +[4800/16000] [MSE: 2.5020] 47.3+0.0s +[6400/16000] [MSE: 2.4950] 47.2+0.0s +[8000/16000] [MSE: 2.4940] 47.2+0.0s +[9600/16000] [MSE: 2.4952] 47.2+0.0s +[11200/16000] [MSE: 2.5031] 47.2+0.0s +[12800/16000] [MSE: 2.5161] 46.8+0.0s +[14400/16000] [MSE: 2.5264] 47.1+0.0s +[16000/16000] [MSE: 2.5363] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.514 (Best: 8.820 @epoch 22) +Forward: 38.42s + +Saving... +Total: 38.92s + +[Epoch 128] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5908] 47.9+0.7s +[3200/16000] [MSE: 2.6042] 47.7+0.1s +[4800/16000] [MSE: 2.6825] 47.4+0.0s +[6400/16000] [MSE: 2.6708] 47.1+0.0s +[8000/16000] [MSE: 2.6537] 46.6+0.0s +[9600/16000] [MSE: 2.6515] 46.5+0.0s +[11200/16000] [MSE: 2.7377] 46.6+0.0s +[12800/16000] [MSE: 2.8826] 46.3+0.0s +[14400/16000] [MSE: 2.9641] 46.3+0.0s +[16000/16000] [MSE: 2.9436] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.518 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 39.01s + +[Epoch 129] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.7761] 47.8+1.0s +[3200/16000] [MSE: 2.6841] 47.5+0.0s +[4800/16000] [MSE: 2.6357] 47.4+0.0s +[6400/16000] [MSE: 2.6043] 47.3+0.0s +[8000/16000] [MSE: 2.5791] 47.1+0.0s +[9600/16000] [MSE: 2.6141] 47.0+0.0s +[11200/16000] [MSE: 2.6630] 46.9+0.0s +[12800/16000] [MSE: 2.6601] 47.3+0.0s +[14400/16000] [MSE: 2.6759] 46.9+0.0s +[16000/16000] [MSE: 2.7056] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.513 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.07s + +[Epoch 130] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.5077] 47.9+0.9s +[3200/16000] [MSE: 2.7921] 47.2+0.0s +[4800/16000] [MSE: 2.7630] 46.8+0.0s +[6400/16000] [MSE: 2.8220] 46.8+0.0s +[8000/16000] [MSE: 2.9094] 46.9+0.0s +[9600/16000] [MSE: 2.9080] 47.0+0.0s +[11200/16000] [MSE: 2.8624] 46.7+0.0s +[12800/16000] [MSE: 2.9098] 46.7+0.0s +[14400/16000] [MSE: 2.9379] 47.0+0.0s +[16000/16000] [MSE: 2.9755] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.436 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.03s + +[Epoch 131] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1099] 47.8+0.9s +[3200/16000] [MSE: 2.9271] 47.4+0.0s +[4800/16000] [MSE: 2.8624] 47.2+0.0s +[6400/16000] [MSE: 2.7826] 46.3+0.0s +[8000/16000] [MSE: 2.6748] 46.1+0.0s +[9600/16000] [MSE: 2.5985] 46.1+0.0s +[11200/16000] [MSE: 2.5264] 46.4+0.0s +[12800/16000] [MSE: 2.4539] 46.4+0.0s +[14400/16000] [MSE: 2.3915] 46.5+0.0s +[16000/16000] [MSE: 2.3380] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.758 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 38.99s + +[Epoch 132] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.8670] 48.0+0.7s +[3200/16000] [MSE: 1.8337] 47.4+0.0s +[4800/16000] [MSE: 1.7966] 47.1+0.0s +[6400/16000] [MSE: 1.7760] 47.3+0.0s +[8000/16000] [MSE: 1.7921] 47.0+0.0s +[9600/16000] [MSE: 1.8035] 46.7+0.0s +[11200/16000] [MSE: 1.8078] 46.6+0.0s +[12800/16000] [MSE: 1.8225] 47.0+0.0s +[14400/16000] [MSE: 1.8277] 46.5+0.0s +[16000/16000] [MSE: 1.8339] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.278 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 38.95s + +[Epoch 133] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.8180] 47.9+0.7s +[3200/16000] [MSE: 1.8209] 47.8+0.1s +[4800/16000] [MSE: 1.8723] 47.2+0.0s +[6400/16000] [MSE: 2.1035] 47.1+0.0s +[8000/16000] [MSE: 2.1422] 47.1+0.0s +[9600/16000] [MSE: 2.1781] 46.8+0.0s +[11200/16000] [MSE: 2.1550] 46.6+0.0s +[12800/16000] [MSE: 2.1069] 46.8+0.0s +[14400/16000] [MSE: 2.1060] 46.4+0.0s +[16000/16000] [MSE: 2.1323] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.729 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.13s + +[Epoch 134] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.4167] 48.1+0.7s +[3200/16000] [MSE: 2.4960] 47.7+0.0s +[4800/16000] [MSE: 2.5725] 47.4+0.0s +[6400/16000] [MSE: 2.6114] 46.9+0.0s +[8000/16000] [MSE: 2.6541] 46.4+0.0s +[9600/16000] [MSE: 2.6574] 46.2+0.0s +[11200/16000] [MSE: 2.6591] 46.4+0.0s +[12800/16000] [MSE: 2.6608] 46.2+0.0s +[14400/16000] [MSE: 2.6690] 46.6+0.0s +[16000/16000] [MSE: 2.6724] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.325 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.13s + +[Epoch 135] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.8137] 47.8+0.7s +[3200/16000] [MSE: 2.8091] 47.6+0.0s +[4800/16000] [MSE: 2.7755] 47.6+0.0s +[6400/16000] [MSE: 2.6929] 47.4+0.0s +[8000/16000] [MSE: 2.6317] 47.4+0.0s +[9600/16000] [MSE: 2.6287] 47.1+0.0s +[11200/16000] [MSE: 2.6151] 47.2+0.0s +[12800/16000] [MSE: 2.6126] 47.1+0.0s +[14400/16000] [MSE: 2.6358] 47.3+0.0s +[16000/16000] [MSE: 2.6523] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.785 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 38.99s + +[Epoch 136] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.6977] 48.0+0.7s +[3200/16000] [MSE: 2.7445] 47.6+0.0s +[4800/16000] [MSE: 2.5944] 47.5+0.0s +[6400/16000] [MSE: 2.4670] 47.7+0.0s +[8000/16000] [MSE: 2.4204] 47.4+0.0s +[9600/16000] [MSE: 2.4116] 47.3+0.0s +[11200/16000] [MSE: 2.3836] 47.2+0.0s +[12800/16000] [MSE: 2.3713] 47.2+0.0s +[14400/16000] [MSE: 2.3648] 46.9+0.0s +[16000/16000] [MSE: 2.3544] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.776 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.98s + +[Epoch 137] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.2470] 48.0+0.7s +[3200/16000] [MSE: 2.2176] 47.0+0.1s +[4800/16000] [MSE: 2.2207] 46.6+0.0s +[6400/16000] [MSE: 2.2256] 46.5+0.0s +[8000/16000] [MSE: 2.2349] 47.1+0.0s +[9600/16000] [MSE: 2.2248] 47.2+0.0s +[11200/16000] [MSE: 2.2064] 47.0+0.0s +[12800/16000] [MSE: 2.1931] 47.0+0.0s +[14400/16000] [MSE: 2.1990] 46.9+0.0s +[16000/16000] [MSE: 2.2360] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.460 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.03s + +[Epoch 138] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.4978] 48.1+0.7s +[3200/16000] [MSE: 2.4004] 47.5+0.0s +[4800/16000] [MSE: 2.3020] 47.4+0.0s +[6400/16000] [MSE: 2.3713] 47.4+0.0s +[8000/16000] [MSE: 2.8192] 46.6+0.0s +[9600/16000] [MSE: 3.1604] 46.9+0.0s +[11200/16000] [MSE: 3.3185] 46.8+0.0s +[12800/16000] [MSE: 3.4450] 46.9+0.0s +[14400/16000] [MSE: 3.6514] 47.0+0.0s +[16000/16000] [MSE: 3.8261] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.739 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.99s + +[Epoch 139] Learning rate: 1.00e-4 +[1600/16000] [MSE: 5.2940] 47.9+0.7s +[3200/16000] [MSE: 5.4026] 46.8+0.0s +[4800/16000] [MSE: 5.3922] 46.7+0.0s +[6400/16000] [MSE: 5.3508] 47.2+0.0s +[8000/16000] [MSE: 5.2759] 46.8+0.0s +[9600/16000] [MSE: 5.2262] 46.8+0.0s +[11200/16000] [MSE: 5.1927] 46.3+0.0s +[12800/16000] [MSE: 5.0280] 46.3+0.0s +[14400/16000] [MSE: 4.8633] 46.3+0.0s +[16000/16000] [MSE: 4.7326] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.675 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.07s + +[Epoch 140] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5457] 48.1+0.9s +[3200/16000] [MSE: 3.3147] 47.5+0.0s +[4800/16000] [MSE: 3.2189] 47.1+0.0s +[6400/16000] [MSE: 3.1950] 46.6+0.0s +[8000/16000] [MSE: 3.2374] 46.6+0.0s +[9600/16000] [MSE: 3.2596] 46.7+0.0s +[11200/16000] [MSE: 3.2937] 46.7+0.0s +[12800/16000] [MSE: 3.3406] 46.9+0.0s +[14400/16000] [MSE: 3.4094] 46.8+0.0s +[16000/16000] [MSE: 3.4897] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.743 (Best: 8.820 @epoch 22) +Forward: 38.66s + +Saving... +Total: 39.24s + +[Epoch 141] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.8881] 48.1+0.7s +[3200/16000] [MSE: 4.6544] 47.7+0.1s +[4800/16000] [MSE: 4.4729] 47.3+0.0s +[6400/16000] [MSE: 4.3179] 47.4+0.0s +[8000/16000] [MSE: 4.2354] 47.3+0.0s +[9600/16000] [MSE: 4.1834] 47.0+0.0s +[11200/16000] [MSE: 4.1342] 46.9+0.0s +[12800/16000] [MSE: 4.0178] 46.7+0.0s +[14400/16000] [MSE: 4.0054] 47.0+0.0s +[16000/16000] [MSE: 4.0422] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.504 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.07s + +[Epoch 142] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.7225] 47.9+0.7s +[3200/16000] [MSE: 4.6768] 47.5+0.1s +[4800/16000] [MSE: 4.6196] 47.0+0.0s +[6400/16000] [MSE: 4.5445] 47.1+0.0s +[8000/16000] [MSE: 4.4869] 47.0+0.0s +[9600/16000] [MSE: 4.4465] 47.0+0.0s +[11200/16000] [MSE: 4.3743] 46.6+0.0s +[12800/16000] [MSE: 4.2948] 47.0+0.0s +[14400/16000] [MSE: 4.2242] 46.6+0.0s +[16000/16000] [MSE: 4.1642] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.470 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.10s + +[Epoch 143] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5252] 48.0+0.7s +[3200/16000] [MSE: 3.3613] 47.3+0.0s +[4800/16000] [MSE: 3.3361] 47.2+0.0s +[6400/16000] [MSE: 3.3069] 47.3+0.0s +[8000/16000] [MSE: 3.2705] 46.9+0.0s +[9600/16000] [MSE: 3.2281] 47.1+0.0s +[11200/16000] [MSE: 3.2888] 47.0+0.0s +[12800/16000] [MSE: 3.3407] 46.5+0.0s +[14400/16000] [MSE: 3.3869] 46.6+0.0s +[16000/16000] [MSE: 3.4164] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.930 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.00s + +[Epoch 144] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6234] 47.9+0.8s +[3200/16000] [MSE: 3.5678] 46.7+0.0s +[4800/16000] [MSE: 3.6022] 47.3+0.0s +[6400/16000] [MSE: 3.5575] 47.2+0.0s +[8000/16000] [MSE: 3.5103] 47.2+0.0s +[9600/16000] [MSE: 3.4973] 47.1+0.0s +[11200/16000] [MSE: 3.4898] 46.7+0.0s +[12800/16000] [MSE: 3.4650] 46.8+0.0s +[14400/16000] [MSE: 3.4328] 46.9+0.0s +[16000/16000] [MSE: 3.3916] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.811 (Best: 8.820 @epoch 22) +Forward: 38.45s + +Saving... +Total: 38.94s + +[Epoch 145] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4123] 48.2+0.7s +[3200/16000] [MSE: 3.4399] 47.7+0.1s +[4800/16000] [MSE: 3.4277] 47.3+0.0s +[6400/16000] [MSE: 3.3788] 47.1+0.0s +[8000/16000] [MSE: 3.3019] 47.1+0.0s +[9600/16000] [MSE: 3.2433] 47.0+0.0s +[11200/16000] [MSE: 3.2049] 46.9+0.0s +[12800/16000] [MSE: 3.1909] 47.2+0.0s +[14400/16000] [MSE: 3.1872] 47.0+0.0s +[16000/16000] [MSE: 3.2013] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.855 (Best: 8.820 @epoch 22) +Forward: 38.46s + +Saving... +Total: 39.06s + +[Epoch 146] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2392] 48.3+0.7s +[3200/16000] [MSE: 3.3032] 48.0+0.1s +[4800/16000] [MSE: 3.2938] 47.6+0.1s +[6400/16000] [MSE: 3.3791] 47.6+0.0s +[8000/16000] [MSE: 3.4538] 47.2+0.0s +[9600/16000] [MSE: 3.5139] 47.3+0.0s +[11200/16000] [MSE: 3.5470] 47.2+0.0s +[12800/16000] [MSE: 3.5540] 47.4+0.0s +[14400/16000] [MSE: 3.5825] 47.1+0.0s +[16000/16000] [MSE: 3.6093] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.204 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.04s + +[Epoch 147] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6353] 47.8+0.8s +[3200/16000] [MSE: 3.6273] 47.6+0.1s +[4800/16000] [MSE: 3.6073] 47.7+0.0s +[6400/16000] [MSE: 3.5647] 47.1+0.0s +[8000/16000] [MSE: 3.4927] 46.7+0.0s +[9600/16000] [MSE: 3.4549] 47.2+0.0s +[11200/16000] [MSE: 3.4375] 47.1+0.0s +[12800/16000] [MSE: 3.4138] 47.1+0.0s +[14400/16000] [MSE: 3.3947] 47.2+0.0s +[16000/16000] [MSE: 3.4012] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.896 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.09s + +[Epoch 148] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5490] 47.8+0.7s +[3200/16000] [MSE: 3.5261] 47.4+0.0s +[4800/16000] [MSE: 3.5204] 47.2+0.0s +[6400/16000] [MSE: 3.4811] 47.1+0.0s +[8000/16000] [MSE: 3.4389] 47.1+0.0s +[9600/16000] [MSE: 3.4250] 47.1+0.0s +[11200/16000] [MSE: 3.3676] 46.9+0.0s +[12800/16000] [MSE: 3.3259] 46.8+0.0s +[14400/16000] [MSE: 3.2943] 47.0+0.0s +[16000/16000] [MSE: 3.2667] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.832 (Best: 8.820 @epoch 22) +Forward: 38.45s + +Saving... +Total: 38.95s + +[Epoch 149] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0382] 47.9+0.8s +[3200/16000] [MSE: 3.0326] 47.2+0.0s +[4800/16000] [MSE: 3.0265] 47.2+0.0s +[6400/16000] [MSE: 3.0261] 47.2+0.0s +[8000/16000] [MSE: 3.0284] 47.3+0.0s +[9600/16000] [MSE: 3.0314] 47.3+0.0s +[11200/16000] [MSE: 3.0338] 46.9+0.0s +[12800/16000] [MSE: 3.0605] 47.1+0.0s +[14400/16000] [MSE: 3.1170] 46.9+0.0s +[16000/16000] [MSE: 3.1542] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.835 (Best: 8.820 @epoch 22) +Forward: 38.46s + +Saving... +Total: 38.95s + +[Epoch 150] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4367] 48.0+0.7s +[3200/16000] [MSE: 3.4400] 47.3+0.0s +[4800/16000] [MSE: 3.3933] 47.2+0.0s +[6400/16000] [MSE: 3.3591] 47.3+0.0s +[8000/16000] [MSE: 3.3383] 47.2+0.0s +[9600/16000] [MSE: 3.3270] 47.1+0.0s +[11200/16000] [MSE: 3.3174] 47.0+0.0s +[12800/16000] [MSE: 3.3013] 46.8+0.0s +[14400/16000] [MSE: 3.2632] 46.9+0.0s +[16000/16000] [MSE: 3.2283] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.832 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.10s + +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.8783] 47.9+0.8s +[3200/16000] [MSE: 2.9008] 47.2+0.0s +[4800/16000] [MSE: 2.9853] 47.1+0.0s +[6400/16000] [MSE: 3.0001] 46.8+0.0s +[8000/16000] [MSE: 2.9760] 47.0+0.0s +[9600/16000] [MSE: 2.9740] 46.9+0.0s +[11200/16000] [MSE: 2.9883] 46.4+0.0s +[12800/16000] [MSE: 2.9891] 46.7+0.0s +[14400/16000] [MSE: 2.9843] 46.8+0.0s +[16000/16000] [MSE: 2.9900] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.860 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.03s + +[Epoch 152] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1886] 47.9+0.7s +[3200/16000] [MSE: 3.2730] 47.6+0.0s +[4800/16000] [MSE: 3.2957] 47.3+0.0s +[6400/16000] [MSE: 3.2906] 47.1+0.0s +[8000/16000] [MSE: 3.3002] 47.0+0.0s +[9600/16000] [MSE: 3.3074] 47.2+0.0s +[11200/16000] [MSE: 3.3082] 46.8+0.0s +[12800/16000] [MSE: 3.3042] 47.4+0.0s +[14400/16000] [MSE: 3.2606] 46.7+0.0s +[16000/16000] [MSE: 3.2394] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.068 (Best: 8.820 @epoch 22) +Forward: 38.47s + +Saving... +Total: 38.94s + +[Epoch 153] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9449] 47.5+0.7s +[3200/16000] [MSE: 2.9641] 47.2+0.0s +[4800/16000] [MSE: 3.0656] 47.2+0.0s +[6400/16000] [MSE: 3.1720] 47.3+0.0s +[8000/16000] [MSE: 3.1138] 47.1+0.0s +[9600/16000] [MSE: 3.0715] 46.9+0.0s +[11200/16000] [MSE: 3.0372] 46.3+0.0s +[12800/16000] [MSE: 3.0117] 46.9+0.0s +[14400/16000] [MSE: 2.9900] 46.9+0.0s +[16000/16000] [MSE: 2.9758] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.925 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.06s + +[Epoch 154] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.8551] 47.4+0.8s +[3200/16000] [MSE: 2.8188] 47.0+0.0s +[4800/16000] [MSE: 2.8544] 47.0+0.0s +[6400/16000] [MSE: 2.8707] 47.2+0.0s +[8000/16000] [MSE: 2.8369] 47.0+0.0s +[9600/16000] [MSE: 2.8285] 47.1+0.0s +[11200/16000] [MSE: 2.8200] 47.2+0.0s +[12800/16000] [MSE: 2.8334] 47.3+0.0s +[14400/16000] [MSE: 2.8458] 47.1+0.0s +[16000/16000] [MSE: 2.8625] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.866 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.11s + +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0443] 48.2+0.7s +[3200/16000] [MSE: 3.0725] 47.7+0.1s +[4800/16000] [MSE: 3.3524] 47.4+0.0s +[6400/16000] [MSE: 3.6160] 47.6+0.0s +[8000/16000] [MSE: 3.7762] 47.6+0.0s +[9600/16000] [MSE: 3.8558] 47.1+0.0s +[11200/16000] [MSE: 3.8353] 47.3+0.0s +[12800/16000] [MSE: 3.8471] 47.6+0.0s +[14400/16000] [MSE: 3.8125] 47.6+0.0s +[16000/16000] [MSE: 3.7391] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.817 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 39.03s + +[Epoch 156] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0280] 47.8+0.8s +[3200/16000] [MSE: 2.9500] 47.3+0.0s +[4800/16000] [MSE: 2.9453] 47.0+0.0s +[6400/16000] [MSE: 2.9382] 47.0+0.0s +[8000/16000] [MSE: 2.9745] 47.0+0.0s +[9600/16000] [MSE: 2.9876] 46.8+0.0s +[11200/16000] [MSE: 2.9983] 46.8+0.0s +[12800/16000] [MSE: 3.0093] 47.1+0.0s +[14400/16000] [MSE: 3.0231] 46.8+0.0s +[16000/16000] [MSE: 3.0309] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.830 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 39.10s + +[Epoch 157] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9391] 48.0+0.8s +[3200/16000] [MSE: 2.9537] 47.5+0.1s +[4800/16000] [MSE: 2.9083] 47.5+0.0s +[6400/16000] [MSE: 2.9096] 47.7+0.0s +[8000/16000] [MSE: 2.8624] 47.3+0.0s +[9600/16000] [MSE: 2.8195] 47.2+0.0s +[11200/16000] [MSE: 2.8057] 46.7+0.0s +[12800/16000] [MSE: 2.7974] 46.6+0.0s +[14400/16000] [MSE: 2.7953] 47.2+0.0s +[16000/16000] [MSE: 2.8144] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.804 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 39.03s + +[Epoch 158] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0092] 48.0+0.7s +[3200/16000] [MSE: 3.0218] 47.8+0.1s +[4800/16000] [MSE: 3.0220] 47.1+0.0s +[6400/16000] [MSE: 2.9904] 47.2+0.0s +[8000/16000] [MSE: 3.0166] 47.0+0.0s +[9600/16000] [MSE: 3.0762] 46.8+0.0s +[11200/16000] [MSE: 3.0893] 47.0+0.0s +[12800/16000] [MSE: 3.1015] 46.9+0.0s +[14400/16000] [MSE: 3.1151] 47.2+0.0s +[16000/16000] [MSE: 3.1175] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.794 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 39.01s + +[Epoch 159] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1809] 47.3+0.8s +[3200/16000] [MSE: 3.2059] 47.2+0.0s +[4800/16000] [MSE: 3.2662] 47.3+0.0s +[6400/16000] [MSE: 3.3305] 47.2+0.0s +[8000/16000] [MSE: 3.3816] 47.2+0.0s +[9600/16000] [MSE: 3.4522] 47.1+0.0s +[11200/16000] [MSE: 3.5172] 46.8+0.0s +[12800/16000] [MSE: 3.5634] 46.8+0.0s +[14400/16000] [MSE: 3.6186] 47.1+0.0s +[16000/16000] [MSE: 3.5875] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.836 (Best: 8.820 @epoch 22) +Forward: 38.66s + +Saving... +Total: 39.15s + +[Epoch 160] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2479] 48.2+0.7s +[3200/16000] [MSE: 3.0949] 47.2+0.0s +[4800/16000] [MSE: 3.0248] 47.3+0.0s +[6400/16000] [MSE: 3.0134] 47.3+0.0s +[8000/16000] [MSE: 2.9428] 47.0+0.0s +[9600/16000] [MSE: 2.9376] 47.4+0.0s +[11200/16000] [MSE: 2.9205] 47.1+0.0s +[12800/16000] [MSE: 2.9145] 47.2+0.0s +[14400/16000] [MSE: 2.9109] 47.0+0.0s +[16000/16000] [MSE: 2.9058] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.825 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 38.99s + +[Epoch 161] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.8410] 47.8+0.7s +[3200/16000] [MSE: 2.8591] 47.4+0.0s +[4800/16000] [MSE: 2.8847] 47.1+0.0s +[6400/16000] [MSE: 2.8676] 47.1+0.0s +[8000/16000] [MSE: 2.8532] 46.8+0.0s +[9600/16000] [MSE: 2.8168] 47.0+0.0s +[11200/16000] [MSE: 2.9413] 47.0+0.0s +[12800/16000] [MSE: 3.1357] 46.4+0.0s +[14400/16000] [MSE: 3.1316] 47.0+0.0s +[16000/16000] [MSE: 3.1268] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.852 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.09s + +[Epoch 162] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.7682] 47.9+0.8s +[3200/16000] [MSE: 3.6091] 47.5+0.0s +[4800/16000] [MSE: 3.4928] 47.5+0.0s +[6400/16000] [MSE: 3.6078] 47.4+0.0s +[8000/16000] [MSE: 3.6770] 47.1+0.0s +[9600/16000] [MSE: 3.7243] 46.7+0.0s +[11200/16000] [MSE: 3.7263] 46.5+0.0s +[12800/16000] [MSE: 3.7786] 46.8+0.0s +[14400/16000] [MSE: 3.7581] 46.6+0.0s +[16000/16000] [MSE: 3.7436] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.813 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.08s + +[Epoch 163] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5669] 48.1+0.7s +[3200/16000] [MSE: 3.7067] 47.7+0.0s +[4800/16000] [MSE: 3.9579] 47.1+0.0s +[6400/16000] [MSE: 4.0372] 47.1+0.0s +[8000/16000] [MSE: 4.1480] 46.6+0.0s +[9600/16000] [MSE: 4.2244] 46.7+0.0s +[11200/16000] [MSE: 4.2656] 47.0+0.0s +[12800/16000] [MSE: 4.2910] 47.0+0.0s +[14400/16000] [MSE: 4.2762] 46.6+0.0s +[16000/16000] [MSE: 4.2428] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.169 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 39.02s + +[Epoch 164] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.7743] 48.0+0.7s +[3200/16000] [MSE: 3.9776] 47.7+0.0s +[4800/16000] [MSE: 3.9710] 47.4+0.0s +[6400/16000] [MSE: 3.9985] 47.2+0.0s +[8000/16000] [MSE: 4.0374] 46.7+0.0s +[9600/16000] [MSE: 3.9392] 47.2+0.0s +[11200/16000] [MSE: 3.8537] 47.1+0.0s +[12800/16000] [MSE: 3.7798] 47.1+0.0s +[14400/16000] [MSE: 3.7170] 46.3+0.0s +[16000/16000] [MSE: 3.6839] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.879 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.07s + +[Epoch 165] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.4926] 47.3+0.7s +[3200/16000] [MSE: 3.4602] 47.1+0.0s +[4800/16000] [MSE: 3.4862] 47.2+0.0s +[6400/16000] [MSE: 3.5222] 46.6+0.0s +[8000/16000] [MSE: 3.5426] 46.2+0.0s +[9600/16000] [MSE: 3.5374] 46.1+0.0s +[11200/16000] [MSE: 3.4933] 46.1+0.0s +[12800/16000] [MSE: 3.4452] 46.6+0.0s +[14400/16000] [MSE: 3.4195] 46.4+0.0s +[16000/16000] [MSE: 3.3896] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.858 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 39.03s + +[Epoch 166] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.3227] 48.0+0.8s +[3200/16000] [MSE: 3.4307] 47.6+0.1s +[4800/16000] [MSE: 3.4189] 47.6+0.1s +[6400/16000] [MSE: 3.3286] 47.4+0.0s +[8000/16000] [MSE: 3.3088] 47.0+0.0s +[9600/16000] [MSE: 3.3253] 46.9+0.0s +[11200/16000] [MSE: 3.3358] 46.8+0.0s +[12800/16000] [MSE: 3.3381] 47.1+0.0s +[14400/16000] [MSE: 3.2920] 46.9+0.0s +[16000/16000] [MSE: 3.2573] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.878 (Best: 8.820 @epoch 22) +Forward: 38.50s + +Saving... +Total: 38.98s + +[Epoch 167] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1563] 48.1+0.6s +[3200/16000] [MSE: 3.2976] 47.4+0.1s +[4800/16000] [MSE: 3.3482] 46.9+0.0s +[6400/16000] [MSE: 3.3539] 47.0+0.0s +[8000/16000] [MSE: 3.3476] 46.9+0.0s +[9600/16000] [MSE: 3.4386] 46.8+0.0s +[11200/16000] [MSE: 3.4741] 46.7+0.0s +[12800/16000] [MSE: 3.4891] 47.0+0.0s +[14400/16000] [MSE: 3.4728] 46.8+0.0s +[16000/16000] [MSE: 3.4312] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.972 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.14s + +[Epoch 168] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.0165] 48.1+0.7s +[3200/16000] [MSE: 2.9830] 47.9+0.0s +[4800/16000] [MSE: 3.1881] 47.2+0.0s +[6400/16000] [MSE: 3.4669] 46.9+0.0s +[8000/16000] [MSE: 3.4603] 46.7+0.0s +[9600/16000] [MSE: 3.4569] 46.8+0.0s +[11200/16000] [MSE: 3.4495] 46.8+0.0s +[12800/16000] [MSE: 3.4565] 46.8+0.0s +[14400/16000] [MSE: 3.5699] 46.1+0.0s +[16000/16000] [MSE: 3.5888] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.164 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.99s + +[Epoch 169] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1961] 47.1+0.8s +[3200/16000] [MSE: 3.1607] 46.8+0.1s +[4800/16000] [MSE: 3.2834] 46.9+0.0s +[6400/16000] [MSE: 3.4895] 47.2+0.0s +[8000/16000] [MSE: 3.5926] 47.3+0.0s +[9600/16000] [MSE: 3.6210] 47.2+0.0s +[11200/16000] [MSE: 3.5991] 47.2+0.0s +[12800/16000] [MSE: 3.6524] 46.9+0.0s +[14400/16000] [MSE: 3.7188] 46.5+0.0s +[16000/16000] [MSE: 3.7121] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.318 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.15s + +[Epoch 170] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5122] 48.0+0.8s +[3200/16000] [MSE: 3.5002] 46.9+0.0s +[4800/16000] [MSE: 3.5442] 46.4+0.0s +[6400/16000] [MSE: 3.4722] 46.8+0.0s +[8000/16000] [MSE: 3.5477] 47.3+0.0s +[9600/16000] [MSE: 3.6832] 46.9+0.0s +[11200/16000] [MSE: 3.7882] 47.1+0.0s +[12800/16000] [MSE: 3.8212] 47.1+0.0s +[14400/16000] [MSE: 3.9036] 46.7+0.0s +[16000/16000] [MSE: 3.9462] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.228 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.15s + +[Epoch 171] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.3914] 47.9+0.7s +[3200/16000] [MSE: 4.2787] 47.4+0.0s +[4800/16000] [MSE: 4.1837] 47.3+0.0s +[6400/16000] [MSE: 4.1455] 47.2+0.0s +[8000/16000] [MSE: 4.1293] 46.9+0.0s +[9600/16000] [MSE: 4.2266] 46.2+0.0s +[11200/16000] [MSE: 4.2756] 46.1+0.0s +[12800/16000] [MSE: 4.2985] 46.8+0.0s +[14400/16000] [MSE: 4.2882] 46.3+0.0s +[16000/16000] [MSE: 4.2399] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.159 (Best: 8.820 @epoch 22) +Forward: 38.66s + +Saving... +Total: 39.17s + +[Epoch 172] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.8355] 48.1+0.8s +[3200/16000] [MSE: 3.6912] 47.5+0.1s +[4800/16000] [MSE: 3.7852] 46.8+0.0s +[6400/16000] [MSE: 3.9567] 46.9+0.0s +[8000/16000] [MSE: 4.1035] 47.3+0.0s +[9600/16000] [MSE: 4.0097] 47.3+0.0s +[11200/16000] [MSE: 3.9549] 47.3+0.0s +[12800/16000] [MSE: 3.9233] 47.4+0.0s +[14400/16000] [MSE: 3.8933] 47.6+0.0s +[16000/16000] [MSE: 3.8664] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.318 (Best: 8.820 @epoch 22) +Forward: 38.75s + +Saving... +Total: 39.20s + +[Epoch 173] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6138] 47.8+0.8s +[3200/16000] [MSE: 3.8557] 47.5+0.0s +[4800/16000] [MSE: 3.9830] 47.0+0.0s +[6400/16000] [MSE: 4.0812] 47.0+0.0s +[8000/16000] [MSE: 4.0665] 47.0+0.0s +[9600/16000] [MSE: 4.0310] 46.9+0.0s +[11200/16000] [MSE: 3.9713] 46.9+0.0s +[12800/16000] [MSE: 3.9067] 46.9+0.0s +[14400/16000] [MSE: 3.8474] 47.2+0.0s +[16000/16000] [MSE: 3.8268] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.391 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.19s + +[Epoch 174] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.8439] 48.0+0.8s +[3200/16000] [MSE: 3.9236] 47.5+0.0s +[4800/16000] [MSE: 3.6155] 46.7+0.0s +[6400/16000] [MSE: 3.6777] 47.2+0.0s +[8000/16000] [MSE: 3.6953] 46.7+0.0s +[9600/16000] [MSE: 3.6992] 46.7+0.0s +[11200/16000] [MSE: 3.6994] 46.7+0.0s +[12800/16000] [MSE: 3.7133] 46.8+0.0s +[14400/16000] [MSE: 3.7620] 46.9+0.0s +[16000/16000] [MSE: 3.7960] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.142 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.99s + +[Epoch 175] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.9075] 47.9+0.7s +[3200/16000] [MSE: 3.8339] 47.5+0.1s +[4800/16000] [MSE: 3.7860] 47.2+0.0s +[6400/16000] [MSE: 3.7180] 47.2+0.0s +[8000/16000] [MSE: 3.7197] 46.3+0.0s +[9600/16000] [MSE: 3.7383] 46.4+0.0s +[11200/16000] [MSE: 3.7421] 46.1+0.0s +[12800/16000] [MSE: 3.7963] 46.2+0.0s +[14400/16000] [MSE: 3.8453] 46.5+0.0s +[16000/16000] [MSE: 3.8380] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.148 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 39.05s + +[Epoch 176] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.8609] 47.9+0.7s +[3200/16000] [MSE: 3.8162] 47.6+0.1s +[4800/16000] [MSE: 3.7647] 47.4+0.0s +[6400/16000] [MSE: 3.7249] 47.2+0.0s +[8000/16000] [MSE: 3.6930] 47.0+0.0s +[9600/16000] [MSE: 3.6550] 47.0+0.0s +[11200/16000] [MSE: 3.6303] 46.9+0.0s +[12800/16000] [MSE: 3.6132] 46.9+0.0s +[14400/16000] [MSE: 3.5981] 46.5+0.0s +[16000/16000] [MSE: 3.5954] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.168 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.09s + +[Epoch 177] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5229] 47.8+0.7s +[3200/16000] [MSE: 3.9122] 47.6+0.1s +[4800/16000] [MSE: 3.9663] 47.2+0.0s +[6400/16000] [MSE: 3.7134] 46.3+0.0s +[8000/16000] [MSE: 3.5791] 46.3+0.0s +[9600/16000] [MSE: 3.4698] 46.3+0.0s +[11200/16000] [MSE: 3.4017] 46.4+0.0s +[12800/16000] [MSE: 3.3600] 46.5+0.0s +[14400/16000] [MSE: 3.3433] 46.7+0.0s +[16000/16000] [MSE: 3.3391] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.143 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.08s + +[Epoch 178] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2405] 47.8+0.7s +[3200/16000] [MSE: 3.2379] 47.6+0.0s +[4800/16000] [MSE: 3.2355] 47.3+0.0s +[6400/16000] [MSE: 3.2192] 47.4+0.0s +[8000/16000] [MSE: 3.2916] 47.3+0.0s +[9600/16000] [MSE: 3.4712] 47.1+0.0s +[11200/16000] [MSE: 3.5939] 47.1+0.0s +[12800/16000] [MSE: 3.6760] 47.1+0.0s +[14400/16000] [MSE: 3.6576] 47.0+0.0s +[16000/16000] [MSE: 3.6743] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.764 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 39.12s + +[Epoch 179] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6232] 48.0+0.7s +[3200/16000] [MSE: 3.5986] 47.5+0.0s +[4800/16000] [MSE: 3.6394] 47.4+0.0s +[6400/16000] [MSE: 3.6768] 47.1+0.0s +[8000/16000] [MSE: 3.6824] 47.0+0.0s +[9600/16000] [MSE: 3.6809] 46.2+0.0s +[11200/16000] [MSE: 3.6435] 46.8+0.0s +[12800/16000] [MSE: 3.6148] 46.4+0.0s +[14400/16000] [MSE: 3.6020] 46.3+0.0s +[16000/16000] [MSE: 3.6208] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.777 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.04s + +[Epoch 180] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.5300] 47.8+0.8s +[3200/16000] [MSE: 3.4981] 47.2+0.0s +[4800/16000] [MSE: 3.2488] 46.8+0.0s +[6400/16000] [MSE: 3.1237] 47.0+0.0s +[8000/16000] [MSE: 3.0614] 47.1+0.0s +[9600/16000] [MSE: 3.0249] 46.7+0.0s +[11200/16000] [MSE: 2.9887] 46.4+0.0s +[12800/16000] [MSE: 2.9531] 46.3+0.0s +[14400/16000] [MSE: 2.9291] 46.0+0.0s +[16000/16000] [MSE: 2.9094] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.375 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.11s + +[Epoch 181] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.7189] 47.8+0.7s +[3200/16000] [MSE: 3.0617] 47.7+0.1s +[4800/16000] [MSE: 3.2938] 47.3+0.0s +[6400/16000] [MSE: 3.3017] 47.5+0.0s +[8000/16000] [MSE: 3.2899] 47.3+0.0s +[9600/16000] [MSE: 3.3164] 47.3+0.0s +[11200/16000] [MSE: 3.3899] 47.1+0.0s +[12800/16000] [MSE: 3.4534] 47.0+0.0s +[14400/16000] [MSE: 3.4443] 46.7+0.0s +[16000/16000] [MSE: 3.4442] 45.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.150 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.18s + +[Epoch 182] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.6009] 48.3+0.8s +[3200/16000] [MSE: 3.5731] 47.5+0.0s +[4800/16000] [MSE: 3.5541] 47.4+0.0s +[6400/16000] [MSE: 3.5560] 47.2+0.0s +[8000/16000] [MSE: 3.5596] 47.1+0.0s +[9600/16000] [MSE: 3.5790] 46.2+0.0s +[11200/16000] [MSE: 3.5748] 46.0+0.0s +[12800/16000] [MSE: 3.5717] 46.3+0.0s +[14400/16000] [MSE: 3.5473] 46.5+0.0s +[16000/16000] [MSE: 3.5145] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.977 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.10s + +[Epoch 183] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.1923] 48.0+0.9s +[3200/16000] [MSE: 3.1750] 47.5+0.1s +[4800/16000] [MSE: 3.1606] 47.1+0.0s +[6400/16000] [MSE: 3.1488] 46.8+0.0s +[8000/16000] [MSE: 3.1378] 47.0+0.0s +[9600/16000] [MSE: 3.1842] 47.0+0.0s +[11200/16000] [MSE: 3.2815] 46.7+0.0s +[12800/16000] [MSE: 3.3628] 46.7+0.0s +[14400/16000] [MSE: 3.4192] 46.4+0.0s +[16000/16000] [MSE: 3.4141] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.110 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.06s + +[Epoch 184] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9355] 47.9+1.0s +[3200/16000] [MSE: 3.1609] 47.5+0.1s +[4800/16000] [MSE: 3.2237] 47.3+0.0s +[6400/16000] [MSE: 3.2554] 47.3+0.0s +[8000/16000] [MSE: 3.2347] 47.4+0.0s +[9600/16000] [MSE: 3.1627] 47.0+0.0s +[11200/16000] [MSE: 3.0936] 46.8+0.0s +[12800/16000] [MSE: 3.0683] 46.8+0.0s +[14400/16000] [MSE: 3.0319] 46.3+0.0s +[16000/16000] [MSE: 3.0201] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.159 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.11s + +[Epoch 185] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.8006] 47.8+0.8s +[3200/16000] [MSE: 3.7422] 47.1+0.0s +[4800/16000] [MSE: 3.6167] 47.5+0.0s +[6400/16000] [MSE: 3.5238] 47.3+0.0s +[8000/16000] [MSE: 3.4613] 47.4+0.0s +[9600/16000] [MSE: 3.4195] 47.1+0.0s +[11200/16000] [MSE: 3.3867] 46.8+0.0s +[12800/16000] [MSE: 3.3551] 47.2+0.0s +[14400/16000] [MSE: 3.3178] 47.2+0.0s +[16000/16000] [MSE: 3.2917] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.119 (Best: 8.820 @epoch 22) +Forward: 38.47s + +Saving... +Total: 38.96s + +[Epoch 186] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.9985] 47.6+0.7s +[3200/16000] [MSE: 3.0334] 47.4+0.0s +[4800/16000] [MSE: 3.1011] 47.2+0.0s +[6400/16000] [MSE: 3.1109] 47.2+0.0s +[8000/16000] [MSE: 3.0945] 47.4+0.0s +[9600/16000] [MSE: 3.0759] 47.2+0.0s +[11200/16000] [MSE: 3.0660] 47.1+0.0s +[12800/16000] [MSE: 3.1026] 47.1+0.0s +[14400/16000] [MSE: 3.1399] 46.6+0.0s +[16000/16000] [MSE: 3.1878] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.183 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.98s + +[Epoch 187] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.7350] 48.0+0.7s +[3200/16000] [MSE: 3.5491] 47.6+0.0s +[4800/16000] [MSE: 3.4749] 47.0+0.0s +[6400/16000] [MSE: 3.4230] 47.1+0.0s +[8000/16000] [MSE: 3.3996] 47.1+0.0s +[9600/16000] [MSE: 3.3530] 47.1+0.0s +[11200/16000] [MSE: 3.2711] 47.1+0.0s +[12800/16000] [MSE: 3.2092] 47.3+0.0s +[14400/16000] [MSE: 3.1487] 47.0+0.0s +[16000/16000] [MSE: 3.0982] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.101 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.98s + +[Epoch 188] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.6522] 48.1+0.7s +[3200/16000] [MSE: 2.8734] 47.7+0.1s +[4800/16000] [MSE: 2.9140] 47.8+0.1s +[6400/16000] [MSE: 2.8146] 47.4+0.0s +[8000/16000] [MSE: 2.9754] 47.1+0.0s +[9600/16000] [MSE: 3.1989] 47.3+0.0s +[11200/16000] [MSE: 3.3471] 47.2+0.0s +[12800/16000] [MSE: 3.4860] 47.2+0.0s +[14400/16000] [MSE: 3.5597] 47.0+0.0s +[16000/16000] [MSE: 3.6079] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.043 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.09s + +[Epoch 189] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.3807] 48.2+0.7s +[3200/16000] [MSE: 3.3595] 47.2+0.0s +[4800/16000] [MSE: 3.2689] 46.1+0.0s +[6400/16000] [MSE: 3.1737] 45.8+0.0s +[8000/16000] [MSE: 3.1982] 46.4+0.0s +[9600/16000] [MSE: 3.2509] 46.2+0.0s +[11200/16000] [MSE: 3.3510] 45.9+0.0s +[12800/16000] [MSE: 3.4529] 46.6+0.0s +[14400/16000] [MSE: 3.5019] 46.2+0.0s +[16000/16000] [MSE: 3.5419] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.537 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 39.08s + +[Epoch 190] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.5394] 47.3+0.8s +[3200/16000] [MSE: 4.0746] 46.6+0.0s +[4800/16000] [MSE: 4.0529] 47.0+0.0s +[6400/16000] [MSE: 4.0571] 47.3+0.0s +[8000/16000] [MSE: 3.9625] 47.3+0.0s +[9600/16000] [MSE: 3.9028] 47.1+0.0s +[11200/16000] [MSE: 3.8573] 46.7+0.0s +[12800/16000] [MSE: 3.8199] 47.1+0.0s +[14400/16000] [MSE: 3.7395] 46.6+0.0s +[16000/16000] [MSE: 3.6755] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.891 (Best: 8.820 @epoch 22) +Forward: 38.70s + +Saving... +Total: 39.19s + +[Epoch 191] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2747] 48.0+0.7s +[3200/16000] [MSE: 3.3116] 47.6+0.1s +[4800/16000] [MSE: 3.3035] 47.2+0.0s +[6400/16000] [MSE: 3.4721] 47.2+0.0s +[8000/16000] [MSE: 3.6147] 46.9+0.0s +[9600/16000] [MSE: 3.7080] 47.1+0.0s +[11200/16000] [MSE: 3.7249] 47.3+0.0s +[12800/16000] [MSE: 3.7615] 47.0+0.0s +[14400/16000] [MSE: 3.8013] 47.0+0.0s +[16000/16000] [MSE: 3.8040] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.660 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.19s + +[Epoch 192] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.7609] 47.9+0.7s +[3200/16000] [MSE: 3.7770] 47.2+0.0s +[4800/16000] [MSE: 4.0311] 47.2+0.0s +[6400/16000] [MSE: 4.1494] 47.4+0.0s +[8000/16000] [MSE: 4.2342] 46.9+0.0s +[9600/16000] [MSE: 4.3023] 47.1+0.0s +[11200/16000] [MSE: 4.3646] 46.5+0.0s +[12800/16000] [MSE: 4.3971] 46.5+0.0s +[14400/16000] [MSE: 4.3776] 46.7+0.0s +[16000/16000] [MSE: 4.3459] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.222 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.11s + +[Epoch 193] Learning rate: 1.00e-4 +[1600/16000] [MSE: 4.5800] 47.9+0.7s +[3200/16000] [MSE: 4.6338] 47.2+0.0s +[4800/16000] [MSE: 4.3540] 47.3+0.0s +[6400/16000] [MSE: 4.1784] 47.0+0.0s +[8000/16000] [MSE: 4.0674] 45.9+0.0s +[9600/16000] [MSE: 3.9344] 46.2+0.0s +[11200/16000] [MSE: 3.8126] 46.4+0.0s +[12800/16000] [MSE: 3.7286] 46.6+0.0s +[14400/16000] [MSE: 3.6703] 46.5+0.0s +[16000/16000] [MSE: 3.6289] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.168 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.09s + +[Epoch 194] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2246] 47.9+0.8s +[3200/16000] [MSE: 3.2997] 47.4+0.0s +[4800/16000] [MSE: 3.3028] 47.4+0.0s +[6400/16000] [MSE: 3.3008] 47.2+0.0s +[8000/16000] [MSE: 3.2994] 47.1+0.0s +[9600/16000] [MSE: 3.3182] 47.2+0.0s +[11200/16000] [MSE: 3.3286] 46.6+0.0s +[12800/16000] [MSE: 3.3354] 47.0+0.0s +[14400/16000] [MSE: 3.3453] 46.8+0.0s +[16000/16000] [MSE: 3.3495] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.701 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 39.01s + +[Epoch 195] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2449] 47.8+0.9s +[3200/16000] [MSE: 3.3389] 47.9+0.1s +[4800/16000] [MSE: 3.4319] 47.1+0.0s +[6400/16000] [MSE: 3.5026] 47.4+0.0s +[8000/16000] [MSE: 3.5907] 47.2+0.0s +[9600/16000] [MSE: 3.4331] 47.0+0.0s +[11200/16000] [MSE: 3.3561] 47.0+0.0s +[12800/16000] [MSE: 3.3270] 47.3+0.0s +[14400/16000] [MSE: 3.3376] 46.8+0.0s +[16000/16000] [MSE: 3.3399] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.389 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.10s + +[Epoch 196] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.3435] 48.0+0.8s +[3200/16000] [MSE: 3.2030] 47.4+0.0s +[4800/16000] [MSE: 3.0717] 47.3+0.0s +[6400/16000] [MSE: 2.9845] 47.3+0.0s +[8000/16000] [MSE: 3.0706] 46.8+0.0s +[9600/16000] [MSE: 3.2624] 47.0+0.0s +[11200/16000] [MSE: 3.4796] 46.6+0.0s +[12800/16000] [MSE: 3.5415] 46.5+0.0s +[14400/16000] [MSE: 3.5714] 46.8+0.0s +[16000/16000] [MSE: 3.6013] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.187 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.08s + +[Epoch 197] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.8155] 48.0+0.8s +[3200/16000] [MSE: 3.7504] 47.4+0.0s +[4800/16000] [MSE: 3.7797] 46.6+0.0s +[6400/16000] [MSE: 3.7244] 46.4+0.0s +[8000/16000] [MSE: 3.6914] 46.3+0.0s +[9600/16000] [MSE: 3.6347] 46.4+0.0s +[11200/16000] [MSE: 3.5438] 46.4+0.0s +[12800/16000] [MSE: 3.5014] 46.6+0.0s +[14400/16000] [MSE: 3.4985] 46.3+0.0s +[16000/16000] [MSE: 3.4913] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.062 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 39.56s + +[Epoch 198] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2958] 47.8+0.7s +[3200/16000] [MSE: 3.3677] 47.3+0.0s +[4800/16000] [MSE: 3.3914] 46.9+0.0s +[6400/16000] [MSE: 3.4622] 47.0+0.0s +[8000/16000] [MSE: 3.4754] 46.9+0.0s +[9600/16000] [MSE: 3.4477] 47.1+0.0s +[11200/16000] [MSE: 3.4038] 46.6+0.0s +[12800/16000] [MSE: 3.3750] 46.6+0.0s +[14400/16000] [MSE: 3.3546] 46.5+0.0s +[16000/16000] [MSE: 3.3416] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.861 (Best: 8.820 @epoch 22) +Forward: 38.45s + +Saving... +Total: 38.98s + +[Epoch 199] Learning rate: 1.00e-4 +[1600/16000] [MSE: 3.2346] 47.6+0.8s +[3200/16000] [MSE: 3.1754] 47.3+0.1s +[4800/16000] [MSE: 3.0464] 47.5+0.0s +[6400/16000] [MSE: 2.9533] 47.5+0.0s +[8000/16000] [MSE: 2.8968] 47.4+0.0s +[9600/16000] [MSE: 2.8446] 47.5+0.0s +[11200/16000] [MSE: 2.8076] 47.2+0.0s +[12800/16000] [MSE: 2.7895] 47.2+0.0s +[14400/16000] [MSE: 2.8168] 46.6+0.0s +[16000/16000] [MSE: 2.8257] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.173 (Best: 8.820 @epoch 22) +Forward: 38.44s + +Saving... +Total: 38.92s + +[Epoch 200] Learning rate: 1.00e-4 +[1600/16000] [MSE: 2.8211] 47.4+0.8s +[3200/16000] [MSE: 2.7825] 47.2+0.0s +[4800/16000] [MSE: 2.7619] 47.1+0.0s +[6400/16000] [MSE: 2.7492] 47.0+0.0s +[8000/16000] [MSE: 2.8051] 46.8+0.0s +[9600/16000] [MSE: 2.8276] 46.8+0.0s +[11200/16000] [MSE: 2.8541] 46.5+0.0s +[12800/16000] [MSE: 2.8869] 46.4+0.0s +[14400/16000] [MSE: 2.9128] 46.4+0.0s +[16000/16000] [MSE: 2.9321] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.264 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.16s + +[Epoch 201] Learning rate: 2.50e-5 +[1600/16000] [MSE: 3.0091] 47.6+0.8s +[3200/16000] [MSE: 3.0420] 47.2+0.0s +[4800/16000] [MSE: 3.0993] 47.0+0.0s +[6400/16000] [MSE: 3.2019] 46.7+0.0s +[8000/16000] [MSE: 3.2844] 46.6+0.0s +[9600/16000] [MSE: 3.2492] 46.6+0.0s +[11200/16000] [MSE: 3.2024] 46.3+0.0s +[12800/16000] [MSE: 3.1694] 46.4+0.0s +[14400/16000] [MSE: 3.1473] 46.0+0.0s +[16000/16000] [MSE: 3.1322] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.078 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 39.02s + +[Epoch 202] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1024] 47.3+0.8s +[3200/16000] [MSE: 3.1286] 47.4+0.1s +[4800/16000] [MSE: 3.2205] 47.6+0.0s +[6400/16000] [MSE: 3.2292] 47.1+0.0s +[8000/16000] [MSE: 3.2135] 47.0+0.0s +[9600/16000] [MSE: 3.1998] 47.1+0.0s +[11200/16000] [MSE: 3.1852] 47.0+0.0s +[12800/16000] [MSE: 3.1685] 46.8+0.0s +[14400/16000] [MSE: 3.1774] 46.4+0.0s +[16000/16000] [MSE: 3.2312] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.064 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.09s + +[Epoch 203] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.6158] 48.0+0.7s +[3200/16000] [MSE: 3.5589] 47.3+0.0s +[4800/16000] [MSE: 3.6482] 47.2+0.0s +[6400/16000] [MSE: 3.7047] 47.1+0.0s +[8000/16000] [MSE: 3.7169] 47.1+0.0s +[9600/16000] [MSE: 3.7801] 46.9+0.0s +[11200/16000] [MSE: 3.7896] 46.8+0.0s +[12800/16000] [MSE: 3.7412] 46.8+0.0s +[14400/16000] [MSE: 3.7072] 46.6+0.0s +[16000/16000] [MSE: 3.6823] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.703 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.16s + +[Epoch 204] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4954] 48.0+0.7s +[3200/16000] [MSE: 3.4828] 47.8+0.1s +[4800/16000] [MSE: 3.4846] 47.6+0.0s +[6400/16000] [MSE: 3.4920] 47.2+0.0s +[8000/16000] [MSE: 3.4953] 47.2+0.0s +[9600/16000] [MSE: 3.5148] 47.4+0.0s +[11200/16000] [MSE: 3.5693] 46.9+0.0s +[12800/16000] [MSE: 3.5951] 47.2+0.0s +[14400/16000] [MSE: 3.5828] 47.0+0.0s +[16000/16000] [MSE: 3.5596] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.309 (Best: 8.820 @epoch 22) +Forward: 38.75s + +Saving... +Total: 39.27s + +[Epoch 205] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3506] 48.0+0.7s +[3200/16000] [MSE: 3.5548] 47.8+0.1s +[4800/16000] [MSE: 3.7893] 47.6+0.1s +[6400/16000] [MSE: 3.9086] 47.5+0.0s +[8000/16000] [MSE: 3.9152] 47.8+0.1s +[9600/16000] [MSE: 3.7647] 47.0+0.0s +[11200/16000] [MSE: 3.6527] 47.0+0.0s +[12800/16000] [MSE: 3.5645] 47.2+0.0s +[14400/16000] [MSE: 3.4913] 47.0+0.0s +[16000/16000] [MSE: 3.4345] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.139 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.06s + +[Epoch 206] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.9430] 48.0+0.9s +[3200/16000] [MSE: 3.1063] 47.3+0.0s +[4800/16000] [MSE: 3.2969] 46.8+0.0s +[6400/16000] [MSE: 3.4210] 47.1+0.0s +[8000/16000] [MSE: 3.4906] 47.1+0.0s +[9600/16000] [MSE: 3.4470] 47.0+0.0s +[11200/16000] [MSE: 3.3772] 46.8+0.0s +[12800/16000] [MSE: 3.3251] 47.1+0.0s +[14400/16000] [MSE: 3.3390] 46.4+0.0s +[16000/16000] [MSE: 3.3922] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.385 (Best: 8.820 @epoch 22) +Forward: 38.79s + +Saving... +Total: 39.26s + +[Epoch 207] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.8025] 47.7+0.9s +[3200/16000] [MSE: 3.7872] 47.3+0.0s +[4800/16000] [MSE: 3.7413] 47.1+0.0s +[6400/16000] [MSE: 3.6985] 47.1+0.0s +[8000/16000] [MSE: 3.6843] 46.8+0.0s +[9600/16000] [MSE: 3.6695] 46.7+0.0s +[11200/16000] [MSE: 3.6544] 46.7+0.0s +[12800/16000] [MSE: 3.6346] 46.3+0.0s +[14400/16000] [MSE: 3.6276] 46.3+0.0s +[16000/16000] [MSE: 3.6311] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.921 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.14s + +[Epoch 208] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7248] 47.8+0.7s +[3200/16000] [MSE: 3.7204] 47.7+0.1s +[4800/16000] [MSE: 3.6834] 47.3+0.0s +[6400/16000] [MSE: 3.6792] 47.5+0.0s +[8000/16000] [MSE: 3.6673] 47.5+0.0s +[9600/16000] [MSE: 3.6644] 47.2+0.0s +[11200/16000] [MSE: 3.6473] 46.9+0.0s +[12800/16000] [MSE: 3.5882] 47.0+0.0s +[14400/16000] [MSE: 3.5642] 47.0+0.0s +[16000/16000] [MSE: 3.5501] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.864 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.00s + +[Epoch 209] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4141] 48.3+0.7s +[3200/16000] [MSE: 3.4114] 47.6+0.1s +[4800/16000] [MSE: 3.3879] 47.3+0.0s +[6400/16000] [MSE: 3.3401] 47.2+0.0s +[8000/16000] [MSE: 3.2760] 47.2+0.0s +[9600/16000] [MSE: 3.2258] 46.9+0.0s +[11200/16000] [MSE: 3.1849] 46.7+0.0s +[12800/16000] [MSE: 3.1624] 46.7+0.0s +[14400/16000] [MSE: 3.1488] 46.9+0.0s +[16000/16000] [MSE: 3.1368] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.927 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.06s + +[Epoch 210] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0247] 47.6+0.8s +[3200/16000] [MSE: 3.0187] 47.5+0.0s +[4800/16000] [MSE: 3.0097] 47.3+0.0s +[6400/16000] [MSE: 2.9977] 46.2+0.0s +[8000/16000] [MSE: 3.1020] 46.1+0.0s +[9600/16000] [MSE: 3.1988] 46.6+0.0s +[11200/16000] [MSE: 3.2795] 46.6+0.0s +[12800/16000] [MSE: 3.3321] 46.8+0.0s +[14400/16000] [MSE: 3.3801] 46.5+0.0s +[16000/16000] [MSE: 3.4127] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.705 (Best: 8.820 @epoch 22) +Forward: 38.66s + +Saving... +Total: 39.15s + +[Epoch 211] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7075] 47.8+0.8s +[3200/16000] [MSE: 3.7091] 47.7+0.0s +[4800/16000] [MSE: 3.6993] 47.4+0.0s +[6400/16000] [MSE: 3.6755] 47.5+0.0s +[8000/16000] [MSE: 3.6481] 47.1+0.0s +[9600/16000] [MSE: 3.5700] 46.8+0.0s +[11200/16000] [MSE: 3.5088] 46.7+0.0s +[12800/16000] [MSE: 3.4680] 46.4+0.0s +[14400/16000] [MSE: 3.4338] 46.6+0.0s +[16000/16000] [MSE: 3.4337] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.598 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.12s + +[Epoch 212] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3742] 48.0+0.7s +[3200/16000] [MSE: 3.3584] 47.3+0.0s +[4800/16000] [MSE: 3.3859] 47.3+0.0s +[6400/16000] [MSE: 3.4265] 47.2+0.0s +[8000/16000] [MSE: 3.4332] 47.0+0.0s +[9600/16000] [MSE: 3.4086] 46.6+0.0s +[11200/16000] [MSE: 3.3960] 46.6+0.0s +[12800/16000] [MSE: 3.3830] 46.9+0.0s +[14400/16000] [MSE: 3.3734] 47.0+0.0s +[16000/16000] [MSE: 3.3621] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.733 (Best: 8.820 @epoch 22) +Forward: 38.69s + +Saving... +Total: 39.13s + +[Epoch 213] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.2551] 47.8+0.7s +[3200/16000] [MSE: 3.4164] 46.9+0.0s +[4800/16000] [MSE: 3.5447] 47.2+0.0s +[6400/16000] [MSE: 3.6025] 47.0+0.0s +[8000/16000] [MSE: 3.6345] 47.0+0.0s +[9600/16000] [MSE: 3.6517] 46.8+0.0s +[11200/16000] [MSE: 3.6629] 46.2+0.0s +[12800/16000] [MSE: 3.6651] 46.4+0.0s +[14400/16000] [MSE: 3.6657] 46.4+0.0s +[16000/16000] [MSE: 3.6601] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.694 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.07s + +[Epoch 214] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4969] 48.2+0.7s +[3200/16000] [MSE: 3.3791] 47.3+0.1s +[4800/16000] [MSE: 3.4219] 47.3+0.0s +[6400/16000] [MSE: 3.4413] 47.0+0.0s +[8000/16000] [MSE: 3.5080] 46.9+0.0s +[9600/16000] [MSE: 3.5529] 46.8+0.0s +[11200/16000] [MSE: 3.5625] 46.4+0.0s +[12800/16000] [MSE: 3.5672] 46.7+0.0s +[14400/16000] [MSE: 3.5751] 47.0+0.0s +[16000/16000] [MSE: 3.5746] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.515 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 38.99s + +[Epoch 215] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7161] 48.1+0.7s +[3200/16000] [MSE: 3.7267] 47.4+0.0s +[4800/16000] [MSE: 3.7425] 47.1+0.0s +[6400/16000] [MSE: 3.7371] 46.7+0.0s +[8000/16000] [MSE: 3.6896] 46.8+0.0s +[9600/16000] [MSE: 3.6462] 46.5+0.0s +[11200/16000] [MSE: 3.6145] 46.7+0.0s +[12800/16000] [MSE: 3.5743] 46.7+0.0s +[14400/16000] [MSE: 3.5384] 46.8+0.0s +[16000/16000] [MSE: 3.5105] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.825 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.12s + +[Epoch 216] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.2293] 47.8+0.8s +[3200/16000] [MSE: 3.2229] 47.4+0.0s +[4800/16000] [MSE: 3.2217] 47.1+0.0s +[6400/16000] [MSE: 3.2421] 46.9+0.0s +[8000/16000] [MSE: 3.2413] 47.0+0.0s +[9600/16000] [MSE: 3.2406] 47.0+0.0s +[11200/16000] [MSE: 3.2530] 46.8+0.0s +[12800/16000] [MSE: 3.2641] 46.8+0.0s +[14400/16000] [MSE: 3.2751] 46.6+0.0s +[16000/16000] [MSE: 3.2830] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.851 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.11s + +[Epoch 217] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3720] 47.5+1.0s +[3200/16000] [MSE: 3.3124] 47.3+0.0s +[4800/16000] [MSE: 3.2938] 46.9+0.0s +[6400/16000] [MSE: 3.2199] 46.3+0.0s +[8000/16000] [MSE: 3.2068] 46.2+0.0s +[9600/16000] [MSE: 3.2418] 46.5+0.0s +[11200/16000] [MSE: 3.2244] 46.8+0.0s +[12800/16000] [MSE: 3.2057] 46.9+0.0s +[14400/16000] [MSE: 3.1892] 46.9+0.0s +[16000/16000] [MSE: 3.1765] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.758 (Best: 8.820 @epoch 22) +Forward: 38.74s + +Saving... +Total: 39.19s + +[Epoch 218] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0372] 47.8+0.9s +[3200/16000] [MSE: 3.0571] 47.3+0.0s +[4800/16000] [MSE: 3.0511] 47.2+0.0s +[6400/16000] [MSE: 3.0604] 47.1+0.0s +[8000/16000] [MSE: 3.0528] 46.9+0.0s +[9600/16000] [MSE: 3.0384] 47.0+0.0s +[11200/16000] [MSE: 3.0335] 47.1+0.0s +[12800/16000] [MSE: 3.0299] 47.1+0.0s +[14400/16000] [MSE: 3.0309] 46.5+0.0s +[16000/16000] [MSE: 3.0289] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.918 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.03s + +[Epoch 219] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0049] 48.2+0.7s +[3200/16000] [MSE: 3.0092] 47.2+0.0s +[4800/16000] [MSE: 3.0877] 46.7+0.0s +[6400/16000] [MSE: 3.1007] 46.6+0.0s +[8000/16000] [MSE: 3.1243] 46.8+0.0s +[9600/16000] [MSE: 3.1438] 46.8+0.0s +[11200/16000] [MSE: 3.1298] 46.9+0.0s +[12800/16000] [MSE: 3.1182] 47.0+0.0s +[14400/16000] [MSE: 3.1300] 47.3+0.0s +[16000/16000] [MSE: 3.1929] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.835 (Best: 8.820 @epoch 22) +Forward: 38.69s + +Saving... +Total: 39.16s + +[Epoch 220] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7140] 48.0+0.9s +[3200/16000] [MSE: 3.8484] 47.0+0.0s +[4800/16000] [MSE: 3.9037] 47.1+0.0s +[6400/16000] [MSE: 3.8893] 47.0+0.0s +[8000/16000] [MSE: 3.8298] 46.8+0.0s +[9600/16000] [MSE: 3.7266] 47.0+0.0s +[11200/16000] [MSE: 3.6616] 46.9+0.0s +[12800/16000] [MSE: 3.6375] 47.0+0.0s +[14400/16000] [MSE: 3.6033] 47.0+0.0s +[16000/16000] [MSE: 3.5633] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.718 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 38.96s + +[Epoch 221] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1627] 47.6+0.7s +[3200/16000] [MSE: 3.0482] 47.4+0.0s +[4800/16000] [MSE: 3.0288] 47.3+0.0s +[6400/16000] [MSE: 3.0051] 47.3+0.0s +[8000/16000] [MSE: 2.9976] 46.9+0.0s +[9600/16000] [MSE: 2.9903] 46.7+0.0s +[11200/16000] [MSE: 2.9858] 46.6+0.0s +[12800/16000] [MSE: 2.9839] 46.6+0.0s +[14400/16000] [MSE: 2.9813] 46.3+0.0s +[16000/16000] [MSE: 2.9802] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.730 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.13s + +[Epoch 222] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.9194] 47.8+0.7s +[3200/16000] [MSE: 2.9183] 47.5+0.0s +[4800/16000] [MSE: 2.9126] 47.2+0.0s +[6400/16000] [MSE: 2.9055] 46.9+0.0s +[8000/16000] [MSE: 2.9026] 46.7+0.0s +[9600/16000] [MSE: 2.9102] 46.9+0.0s +[11200/16000] [MSE: 2.9158] 46.7+0.0s +[12800/16000] [MSE: 2.9186] 46.7+0.0s +[14400/16000] [MSE: 2.9243] 46.9+0.0s +[16000/16000] [MSE: 2.9451] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.877 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.18s + +[Epoch 223] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1081] 47.8+0.8s +[3200/16000] [MSE: 3.1312] 47.2+0.1s +[4800/16000] [MSE: 3.1718] 47.3+0.0s +[6400/16000] [MSE: 3.1926] 47.2+0.0s +[8000/16000] [MSE: 3.2084] 47.2+0.0s +[9600/16000] [MSE: 3.2248] 47.2+0.0s +[11200/16000] [MSE: 3.2058] 47.2+0.0s +[12800/16000] [MSE: 3.1788] 47.1+0.0s +[14400/16000] [MSE: 3.1525] 46.8+0.0s +[16000/16000] [MSE: 3.1294] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.859 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.12s + +[Epoch 224] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1699] 47.7+0.7s +[3200/16000] [MSE: 3.2156] 47.3+0.0s +[4800/16000] [MSE: 3.1977] 47.0+0.0s +[6400/16000] [MSE: 3.2081] 46.7+0.0s +[8000/16000] [MSE: 3.2154] 46.8+0.0s +[9600/16000] [MSE: 3.2237] 46.9+0.0s +[11200/16000] [MSE: 3.2236] 46.8+0.0s +[12800/16000] [MSE: 3.2372] 46.7+0.0s +[14400/16000] [MSE: 3.2499] 46.5+0.0s +[16000/16000] [MSE: 3.2630] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.961 (Best: 8.820 @epoch 22) +Forward: 38.75s + +Saving... +Total: 39.23s + +[Epoch 225] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3449] 47.5+0.7s +[3200/16000] [MSE: 3.3493] 47.4+0.0s +[4800/16000] [MSE: 3.3463] 47.0+0.0s +[6400/16000] [MSE: 3.3288] 47.1+0.0s +[8000/16000] [MSE: 3.3126] 47.2+0.0s +[9600/16000] [MSE: 3.2995] 47.2+0.0s +[11200/16000] [MSE: 3.2893] 46.7+0.0s +[12800/16000] [MSE: 3.2894] 46.7+0.0s +[14400/16000] [MSE: 3.2834] 46.6+0.0s +[16000/16000] [MSE: 3.2659] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.378 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.10s + +[Epoch 226] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0497] 47.8+0.7s +[3200/16000] [MSE: 3.1685] 47.2+0.1s +[4800/16000] [MSE: 3.2471] 46.8+0.0s +[6400/16000] [MSE: 3.2885] 47.0+0.0s +[8000/16000] [MSE: 3.2984] 47.1+0.0s +[9600/16000] [MSE: 3.2858] 46.5+0.0s +[11200/16000] [MSE: 3.2769] 46.8+0.0s +[12800/16000] [MSE: 3.2632] 46.9+0.0s +[14400/16000] [MSE: 3.2381] 46.6+0.0s +[16000/16000] [MSE: 3.2155] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.679 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.11s + +[Epoch 227] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.9902] 47.9+0.7s +[3200/16000] [MSE: 2.9808] 47.4+0.0s +[4800/16000] [MSE: 2.9717] 47.4+0.0s +[6400/16000] [MSE: 2.9575] 47.2+0.0s +[8000/16000] [MSE: 2.9629] 47.2+0.0s +[9600/16000] [MSE: 2.9695] 46.8+0.0s +[11200/16000] [MSE: 2.9707] 46.8+0.0s +[12800/16000] [MSE: 2.9712] 46.9+0.0s +[14400/16000] [MSE: 2.9708] 46.9+0.0s +[16000/16000] [MSE: 2.9669] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.658 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.07s + +[Epoch 228] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.9373] 47.9+0.8s +[3200/16000] [MSE: 2.9456] 47.3+0.0s +[4800/16000] [MSE: 2.9703] 47.2+0.0s +[6400/16000] [MSE: 2.9801] 47.4+0.0s +[8000/16000] [MSE: 2.9804] 47.2+0.0s +[9600/16000] [MSE: 2.9867] 47.2+0.0s +[11200/16000] [MSE: 2.9933] 46.9+0.0s +[12800/16000] [MSE: 2.9997] 47.1+0.0s +[14400/16000] [MSE: 3.0070] 47.0+0.0s +[16000/16000] [MSE: 3.0084] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.239 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.15s + +[Epoch 229] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0338] 47.9+0.8s +[3200/16000] [MSE: 3.0288] 47.5+0.1s +[4800/16000] [MSE: 3.0418] 47.2+0.0s +[6400/16000] [MSE: 3.0847] 47.1+0.0s +[8000/16000] [MSE: 3.1490] 46.9+0.0s +[9600/16000] [MSE: 3.2580] 47.0+0.0s +[11200/16000] [MSE: 3.3468] 46.9+0.0s +[12800/16000] [MSE: 3.4288] 46.7+0.0s +[14400/16000] [MSE: 3.5100] 47.0+0.0s +[16000/16000] [MSE: 3.5745] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.894 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.04s + +[Epoch 230] Learning rate: 5.00e-5 +[1600/16000] [MSE: 4.0669] 47.6+0.7s +[3200/16000] [MSE: 4.0412] 47.0+0.0s +[4800/16000] [MSE: 4.0418] 46.7+0.0s +[6400/16000] [MSE: 4.0425] 46.7+0.0s +[8000/16000] [MSE: 4.0397] 46.7+0.0s +[9600/16000] [MSE: 4.0415] 46.3+0.0s +[11200/16000] [MSE: 3.9960] 46.2+0.0s +[12800/16000] [MSE: 3.9466] 46.2+0.0s +[14400/16000] [MSE: 3.8870] 46.4+0.0s +[16000/16000] [MSE: 3.8411] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.068 (Best: 8.820 @epoch 22) +Forward: 38.72s + +Saving... +Total: 39.23s + +[Epoch 231] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4425] 47.7+0.7s +[3200/16000] [MSE: 3.4461] 47.3+0.0s +[4800/16000] [MSE: 3.4204] 47.2+0.0s +[6400/16000] [MSE: 3.3915] 47.2+0.0s +[8000/16000] [MSE: 3.3628] 47.0+0.0s +[9600/16000] [MSE: 3.3523] 46.7+0.0s +[11200/16000] [MSE: 3.3477] 46.4+0.0s +[12800/16000] [MSE: 3.3445] 47.0+0.0s +[14400/16000] [MSE: 3.3409] 46.4+0.0s +[16000/16000] [MSE: 3.3396] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.797 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.05s + +[Epoch 232] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3399] 47.9+0.7s +[3200/16000] [MSE: 3.3280] 47.5+0.1s +[4800/16000] [MSE: 3.3195] 47.3+0.0s +[6400/16000] [MSE: 3.3232] 46.8+0.0s +[8000/16000] [MSE: 3.3447] 47.1+0.0s +[9600/16000] [MSE: 3.3609] 47.1+0.0s +[11200/16000] [MSE: 3.3627] 47.1+0.0s +[12800/16000] [MSE: 3.3672] 46.4+0.0s +[14400/16000] [MSE: 3.3708] 46.6+0.0s +[16000/16000] [MSE: 3.3690] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.205 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.15s + +[Epoch 233] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.2820] 48.0+0.7s +[3200/16000] [MSE: 3.2773] 47.4+0.1s +[4800/16000] [MSE: 3.2683] 46.9+0.0s +[6400/16000] [MSE: 3.3095] 46.9+0.0s +[8000/16000] [MSE: 3.3876] 46.7+0.0s +[9600/16000] [MSE: 3.4366] 46.9+0.0s +[11200/16000] [MSE: 3.4629] 46.6+0.0s +[12800/16000] [MSE: 3.4793] 46.9+0.0s +[14400/16000] [MSE: 3.4908] 46.5+0.0s +[16000/16000] [MSE: 3.4979] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.473 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.17s + +[Epoch 234] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.6228] 47.8+0.8s +[3200/16000] [MSE: 3.6596] 47.5+0.0s +[4800/16000] [MSE: 3.6780] 47.0+0.0s +[6400/16000] [MSE: 3.6914] 46.9+0.0s +[8000/16000] [MSE: 3.6716] 46.9+0.0s +[9600/16000] [MSE: 3.6691] 46.9+0.0s +[11200/16000] [MSE: 3.6657] 47.0+0.0s +[12800/16000] [MSE: 3.6579] 46.9+0.0s +[14400/16000] [MSE: 3.6468] 46.8+0.0s +[16000/16000] [MSE: 3.6323] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.558 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.07s + +[Epoch 235] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4978] 47.9+0.8s +[3200/16000] [MSE: 3.4928] 47.3+0.0s +[4800/16000] [MSE: 3.4965] 46.8+0.0s +[6400/16000] [MSE: 3.4933] 47.1+0.0s +[8000/16000] [MSE: 3.4923] 47.3+0.0s +[9600/16000] [MSE: 3.4939] 46.9+0.0s +[11200/16000] [MSE: 3.4940] 47.0+0.0s +[12800/16000] [MSE: 3.4894] 46.5+0.0s +[14400/16000] [MSE: 3.4822] 46.9+0.0s +[16000/16000] [MSE: 3.4771] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.238 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.01s + +[Epoch 236] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4000] 47.7+0.7s +[3200/16000] [MSE: 3.3886] 47.2+0.0s +[4800/16000] [MSE: 3.3872] 47.2+0.0s +[6400/16000] [MSE: 3.3837] 47.1+0.0s +[8000/16000] [MSE: 3.3831] 47.3+0.0s +[9600/16000] [MSE: 3.3744] 46.7+0.0s +[11200/16000] [MSE: 3.3701] 46.0+0.0s +[12800/16000] [MSE: 3.3688] 46.0+0.0s +[14400/16000] [MSE: 3.3630] 46.3+0.0s +[16000/16000] [MSE: 3.3581] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.193 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 39.06s + +[Epoch 237] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3262] 48.0+0.8s +[3200/16000] [MSE: 3.3468] 47.4+0.1s +[4800/16000] [MSE: 3.3462] 47.2+0.0s +[6400/16000] [MSE: 3.3435] 47.1+0.0s +[8000/16000] [MSE: 3.3382] 47.1+0.0s +[9600/16000] [MSE: 3.3292] 47.1+0.0s +[11200/16000] [MSE: 3.3337] 46.8+0.0s +[12800/16000] [MSE: 3.3488] 46.8+0.0s +[14400/16000] [MSE: 3.3581] 46.6+0.0s +[16000/16000] [MSE: 3.3521] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.109 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.15s + +[Epoch 238] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3285] 47.9+0.7s +[3200/16000] [MSE: 3.3353] 47.6+0.0s +[4800/16000] [MSE: 3.3301] 47.1+0.0s +[6400/16000] [MSE: 3.3258] 47.0+0.0s +[8000/16000] [MSE: 3.3215] 46.8+0.0s +[9600/16000] [MSE: 3.3201] 46.4+0.0s +[11200/16000] [MSE: 3.3173] 46.3+0.0s +[12800/16000] [MSE: 3.3080] 45.9+0.0s +[14400/16000] [MSE: 3.2965] 46.1+0.0s +[16000/16000] [MSE: 3.2796] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.968 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.01s + +[Epoch 239] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1062] 47.5+1.1s +[3200/16000] [MSE: 3.1007] 47.4+0.0s +[4800/16000] [MSE: 3.0972] 47.0+0.0s +[6400/16000] [MSE: 3.0994] 47.1+0.0s +[8000/16000] [MSE: 3.0990] 47.0+0.0s +[9600/16000] [MSE: 3.0968] 47.0+0.0s +[11200/16000] [MSE: 3.1008] 46.9+0.0s +[12800/16000] [MSE: 3.1115] 47.0+0.0s +[14400/16000] [MSE: 3.1173] 47.3+0.0s +[16000/16000] [MSE: 3.1225] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.043 (Best: 8.820 @epoch 22) +Forward: 38.70s + +Saving... +Total: 39.21s + +[Epoch 240] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.2095] 47.7+0.7s +[3200/16000] [MSE: 3.2121] 47.4+0.0s +[4800/16000] [MSE: 3.2275] 47.1+0.0s +[6400/16000] [MSE: 3.1909] 46.9+0.0s +[8000/16000] [MSE: 3.1602] 47.1+0.0s +[9600/16000] [MSE: 3.1478] 47.0+0.0s +[11200/16000] [MSE: 3.1345] 47.1+0.0s +[12800/16000] [MSE: 3.1337] 46.7+0.0s +[14400/16000] [MSE: 3.1420] 47.1+0.0s +[16000/16000] [MSE: 3.1687] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.207 (Best: 8.820 @epoch 22) +Forward: 38.69s + +Saving... +Total: 39.17s + +[Epoch 241] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7888] 47.7+0.7s +[3200/16000] [MSE: 3.7549] 47.3+0.0s +[4800/16000] [MSE: 3.7092] 47.4+0.0s +[6400/16000] [MSE: 3.6737] 46.9+0.0s +[8000/16000] [MSE: 3.6503] 46.8+0.0s +[9600/16000] [MSE: 3.6344] 46.7+0.0s +[11200/16000] [MSE: 3.6410] 47.0+0.0s +[12800/16000] [MSE: 3.6620] 46.8+0.0s +[14400/16000] [MSE: 3.6793] 46.8+0.0s +[16000/16000] [MSE: 3.6883] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.746 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.09s + +[Epoch 242] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.5238] 47.7+0.7s +[3200/16000] [MSE: 3.4257] 47.4+0.0s +[4800/16000] [MSE: 3.3995] 47.0+0.0s +[6400/16000] [MSE: 3.3534] 46.9+0.0s +[8000/16000] [MSE: 3.2991] 46.7+0.0s +[9600/16000] [MSE: 3.2649] 46.9+0.0s +[11200/16000] [MSE: 3.2419] 47.0+0.0s +[12800/16000] [MSE: 3.2267] 46.7+0.0s +[14400/16000] [MSE: 3.2118] 46.7+0.0s +[16000/16000] [MSE: 3.2030] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.987 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.08s + +[Epoch 243] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0814] 47.4+0.7s +[3200/16000] [MSE: 3.0898] 47.4+0.1s +[4800/16000] [MSE: 3.0814] 47.1+0.0s +[6400/16000] [MSE: 3.0828] 46.8+0.0s +[8000/16000] [MSE: 3.0830] 47.2+0.0s +[9600/16000] [MSE: 3.0992] 46.9+0.0s +[11200/16000] [MSE: 3.1563] 46.6+0.0s +[12800/16000] [MSE: 3.2000] 46.3+0.0s +[14400/16000] [MSE: 3.2354] 47.0+0.0s +[16000/16000] [MSE: 3.2667] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.008 (Best: 8.820 @epoch 22) +Forward: 38.70s + +Saving... +Total: 39.18s + +[Epoch 244] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3260] 47.7+0.8s +[3200/16000] [MSE: 3.2834] 47.4+0.0s +[4800/16000] [MSE: 3.2639] 47.4+0.1s +[6400/16000] [MSE: 3.1917] 47.3+0.0s +[8000/16000] [MSE: 3.1301] 47.5+0.0s +[9600/16000] [MSE: 3.0876] 47.3+0.0s +[11200/16000] [MSE: 3.0756] 47.1+0.0s +[12800/16000] [MSE: 3.0781] 46.9+0.0s +[14400/16000] [MSE: 3.1034] 47.4+0.0s +[16000/16000] [MSE: 3.1013] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.139 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.10s + +[Epoch 245] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0806] 47.9+0.7s +[3200/16000] [MSE: 3.0334] 47.3+0.0s +[4800/16000] [MSE: 3.0047] 47.3+0.0s +[6400/16000] [MSE: 3.0036] 46.9+0.0s +[8000/16000] [MSE: 3.0036] 47.1+0.0s +[9600/16000] [MSE: 3.0089] 46.9+0.0s +[11200/16000] [MSE: 3.0244] 47.4+0.0s +[12800/16000] [MSE: 3.0274] 47.1+0.0s +[14400/16000] [MSE: 3.0266] 47.0+0.0s +[16000/16000] [MSE: 3.0194] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.232 (Best: 8.820 @epoch 22) +Forward: 38.76s + +Saving... +Total: 39.24s + +[Epoch 246] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.9546] 47.7+0.7s +[3200/16000] [MSE: 3.0102] 47.2+0.0s +[4800/16000] [MSE: 3.0418] 47.2+0.0s +[6400/16000] [MSE: 3.0562] 47.2+0.0s +[8000/16000] [MSE: 3.0576] 47.3+0.0s +[9600/16000] [MSE: 3.0600] 47.3+0.0s +[11200/16000] [MSE: 3.0514] 47.1+0.0s +[12800/16000] [MSE: 3.0254] 46.9+0.0s +[14400/16000] [MSE: 3.0020] 47.1+0.0s +[16000/16000] [MSE: 2.9862] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.951 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.06s + +[Epoch 247] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.8498] 48.1+0.8s +[3200/16000] [MSE: 2.8589] 47.7+0.1s +[4800/16000] [MSE: 2.8686] 47.3+0.0s +[6400/16000] [MSE: 2.8609] 47.4+0.0s +[8000/16000] [MSE: 2.8769] 47.4+0.0s +[9600/16000] [MSE: 2.9164] 47.5+0.0s +[11200/16000] [MSE: 2.9454] 47.2+0.0s +[12800/16000] [MSE: 2.9624] 47.2+0.0s +[14400/16000] [MSE: 2.9659] 47.5+0.0s +[16000/16000] [MSE: 2.9669] 47.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.736 (Best: 8.820 @epoch 22) +Forward: 38.75s + +Saving... +Total: 39.21s + +[Epoch 248] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.9561] 48.0+0.7s +[3200/16000] [MSE: 2.9774] 47.6+0.1s +[4800/16000] [MSE: 3.0457] 47.3+0.0s +[6400/16000] [MSE: 3.0842] 47.3+0.0s +[8000/16000] [MSE: 3.1291] 47.3+0.0s +[9600/16000] [MSE: 3.1085] 46.9+0.0s +[11200/16000] [MSE: 3.0853] 47.1+0.0s +[12800/16000] [MSE: 3.0430] 47.3+0.0s +[14400/16000] [MSE: 3.0090] 47.0+0.0s +[16000/16000] [MSE: 2.9818] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.028 (Best: 8.820 @epoch 22) +Forward: 38.69s + +Saving... +Total: 39.18s + +[Epoch 249] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0366] 48.0+0.7s +[3200/16000] [MSE: 3.0765] 47.4+0.1s +[4800/16000] [MSE: 3.0782] 47.3+0.0s +[6400/16000] [MSE: 3.0917] 47.2+0.0s +[8000/16000] [MSE: 3.1837] 47.0+0.0s +[9600/16000] [MSE: 3.2032] 47.0+0.0s +[11200/16000] [MSE: 3.2149] 46.9+0.0s +[12800/16000] [MSE: 3.2206] 46.4+0.0s +[14400/16000] [MSE: 3.2271] 46.6+0.0s +[16000/16000] [MSE: 3.2359] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.429 (Best: 8.820 @epoch 22) +Forward: 38.75s + +Saving... +Total: 39.20s + +[Epoch 250] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4880] 47.9+0.8s +[3200/16000] [MSE: 3.5091] 47.3+0.0s +[4800/16000] [MSE: 3.4980] 47.1+0.0s +[6400/16000] [MSE: 3.5326] 46.9+0.0s +[8000/16000] [MSE: 3.5818] 47.3+0.0s +[9600/16000] [MSE: 3.6022] 47.2+0.0s +[11200/16000] [MSE: 3.5545] 47.3+0.0s +[12800/16000] [MSE: 3.4983] 46.9+0.0s +[14400/16000] [MSE: 3.4507] 47.1+0.0s +[16000/16000] [MSE: 3.4048] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.120 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.06s + +[Epoch 251] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0441] 48.2+0.7s +[3200/16000] [MSE: 3.0316] 47.4+0.1s +[4800/16000] [MSE: 2.9885] 46.7+0.0s +[6400/16000] [MSE: 2.9679] 46.5+0.0s +[8000/16000] [MSE: 2.9761] 46.4+0.0s +[9600/16000] [MSE: 2.9944] 46.3+0.0s +[11200/16000] [MSE: 3.0379] 46.9+0.0s +[12800/16000] [MSE: 3.0812] 46.9+0.0s +[14400/16000] [MSE: 3.1143] 47.2+0.0s +[16000/16000] [MSE: 3.1200] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.206 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.05s + +[Epoch 252] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.3544] 47.7+0.7s +[3200/16000] [MSE: 3.3153] 47.1+0.0s +[4800/16000] [MSE: 3.3135] 47.3+0.0s +[6400/16000] [MSE: 3.2963] 47.0+0.0s +[8000/16000] [MSE: 3.2737] 47.3+0.0s +[9600/16000] [MSE: 3.2636] 47.1+0.0s +[11200/16000] [MSE: 3.2604] 47.0+0.0s +[12800/16000] [MSE: 3.2558] 46.9+0.0s +[14400/16000] [MSE: 3.2524] 46.9+0.0s +[16000/16000] [MSE: 3.2528] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.035 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.16s + +[Epoch 253] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1945] 47.8+0.9s +[3200/16000] [MSE: 3.2052] 47.7+0.1s +[4800/16000] [MSE: 3.2064] 47.5+0.0s +[6400/16000] [MSE: 3.1954] 47.6+0.0s +[8000/16000] [MSE: 3.1930] 47.5+0.0s +[9600/16000] [MSE: 3.1734] 47.2+0.0s +[11200/16000] [MSE: 3.1552] 47.0+0.0s +[12800/16000] [MSE: 3.1445] 46.7+0.0s +[14400/16000] [MSE: 3.1291] 46.8+0.0s +[16000/16000] [MSE: 3.1157] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.465 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.08s + +[Epoch 254] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.8774] 48.2+0.7s +[3200/16000] [MSE: 2.8617] 47.1+0.0s +[4800/16000] [MSE: 2.8450] 47.0+0.0s +[6400/16000] [MSE: 2.8178] 47.0+0.0s +[8000/16000] [MSE: 2.8078] 46.8+0.0s +[9600/16000] [MSE: 2.8358] 47.2+0.0s +[11200/16000] [MSE: 2.8566] 47.0+0.0s +[12800/16000] [MSE: 2.8752] 46.6+0.0s +[14400/16000] [MSE: 2.8903] 46.6+0.0s +[16000/16000] [MSE: 2.9079] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.377 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.11s + +[Epoch 255] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1261] 48.3+0.7s +[3200/16000] [MSE: 3.1359] 47.4+0.0s +[4800/16000] [MSE: 3.1069] 47.4+0.0s +[6400/16000] [MSE: 3.0785] 47.1+0.0s +[8000/16000] [MSE: 3.0606] 47.2+0.0s +[9600/16000] [MSE: 3.0198] 47.0+0.0s +[11200/16000] [MSE: 3.0026] 46.7+0.0s +[12800/16000] [MSE: 3.0102] 47.0+0.0s +[14400/16000] [MSE: 3.0338] 47.0+0.0s +[16000/16000] [MSE: 3.0542] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.737 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.13s + +[Epoch 256] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4457] 48.0+0.8s +[3200/16000] [MSE: 3.3025] 47.4+0.0s +[4800/16000] [MSE: 3.2183] 47.2+0.0s +[6400/16000] [MSE: 3.1804] 46.6+0.0s +[8000/16000] [MSE: 3.1773] 46.4+0.0s +[9600/16000] [MSE: 3.1835] 46.6+0.0s +[11200/16000] [MSE: 3.1750] 46.4+0.0s +[12800/16000] [MSE: 3.1668] 46.7+0.0s +[14400/16000] [MSE: 3.1614] 46.8+0.0s +[16000/16000] [MSE: 3.1509] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.926 (Best: 8.820 @epoch 22) +Forward: 38.73s + +Saving... +Total: 39.23s + +[Epoch 257] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.0293] 47.7+0.8s +[3200/16000] [MSE: 3.0579] 47.5+0.0s +[4800/16000] [MSE: 3.0518] 47.4+0.0s +[6400/16000] [MSE: 3.0017] 47.3+0.0s +[8000/16000] [MSE: 2.9768] 47.0+0.0s +[9600/16000] [MSE: 2.9680] 47.0+0.0s +[11200/16000] [MSE: 2.9649] 47.1+0.0s +[12800/16000] [MSE: 2.9465] 47.1+0.0s +[14400/16000] [MSE: 2.9366] 47.3+0.0s +[16000/16000] [MSE: 2.9238] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.211 (Best: 8.820 @epoch 22) +Forward: 38.77s + +Saving... +Total: 39.26s + +[Epoch 258] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.2407] 48.0+0.7s +[3200/16000] [MSE: 3.0416] 47.7+0.1s +[4800/16000] [MSE: 2.9821] 47.3+0.0s +[6400/16000] [MSE: 2.9527] 46.9+0.0s +[8000/16000] [MSE: 3.0020] 46.3+0.0s +[9600/16000] [MSE: 3.1148] 46.7+0.0s +[11200/16000] [MSE: 3.2711] 46.4+0.0s +[12800/16000] [MSE: 3.4035] 46.3+0.0s +[14400/16000] [MSE: 3.4972] 46.5+0.0s +[16000/16000] [MSE: 3.5658] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.996 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.05s + +[Epoch 259] Learning rate: 5.00e-5 +[1600/16000] [MSE: 4.1615] 48.3+0.7s +[3200/16000] [MSE: 4.2139] 48.2+0.1s +[4800/16000] [MSE: 4.3953] 47.9+0.1s +[6400/16000] [MSE: 4.5437] 47.8+0.0s +[8000/16000] [MSE: 4.6394] 47.4+0.0s +[9600/16000] [MSE: 4.7047] 47.5+0.0s +[11200/16000] [MSE: 4.7351] 47.3+0.0s +[12800/16000] [MSE: 4.7761] 47.0+0.0s +[14400/16000] [MSE: 4.8060] 47.2+0.0s +[16000/16000] [MSE: 4.8103] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.219 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.14s + +[Epoch 260] Learning rate: 5.00e-5 +[1600/16000] [MSE: 4.6198] 48.2+0.9s +[3200/16000] [MSE: 4.3734] 47.7+0.1s +[4800/16000] [MSE: 4.4488] 47.4+0.0s +[6400/16000] [MSE: 4.3901] 47.2+0.0s +[8000/16000] [MSE: 4.3270] 47.3+0.0s +[9600/16000] [MSE: 4.2522] 47.5+0.0s +[11200/16000] [MSE: 4.2035] 47.2+0.0s +[12800/16000] [MSE: 4.2077] 47.2+0.0s +[14400/16000] [MSE: 4.2003] 46.7+0.0s +[16000/16000] [MSE: 4.2000] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.021 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.02s + +[Epoch 261] Learning rate: 5.00e-5 +[1600/16000] [MSE: 4.0817] 48.0+1.2s +[3200/16000] [MSE: 4.2653] 47.7+0.1s +[4800/16000] [MSE: 4.4834] 47.6+0.1s +[6400/16000] [MSE: 4.6095] 47.6+0.0s +[8000/16000] [MSE: 4.6808] 47.3+0.0s +[9600/16000] [MSE: 4.7729] 47.2+0.0s +[11200/16000] [MSE: 4.8707] 47.1+0.0s +[12800/16000] [MSE: 4.9485] 46.9+0.0s +[14400/16000] [MSE: 5.0236] 47.0+0.0s +[16000/16000] [MSE: 5.0639] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.037 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.09s + +[Epoch 262] Learning rate: 5.00e-5 +[1600/16000] [MSE: 5.3080] 48.1+0.7s +[3200/16000] [MSE: 5.3218] 47.5+0.1s +[4800/16000] [MSE: 5.3030] 47.3+0.0s +[6400/16000] [MSE: 5.2874] 47.4+0.0s +[8000/16000] [MSE: 5.2770] 47.4+0.0s +[9600/16000] [MSE: 5.2721] 47.2+0.0s +[11200/16000] [MSE: 5.2860] 47.3+0.0s +[12800/16000] [MSE: 5.2974] 47.2+0.0s +[14400/16000] [MSE: 5.2777] 47.0+0.0s +[16000/16000] [MSE: 5.2614] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.966 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.08s + +[Epoch 263] Learning rate: 5.00e-5 +[1600/16000] [MSE: 4.7956] 48.1+0.8s +[3200/16000] [MSE: 4.7157] 47.4+0.1s +[4800/16000] [MSE: 4.6800] 47.6+0.0s +[6400/16000] [MSE: 4.6385] 47.4+0.0s +[8000/16000] [MSE: 4.6748] 47.3+0.0s +[9600/16000] [MSE: 4.7139] 47.0+0.0s +[11200/16000] [MSE: 4.7238] 46.7+0.0s +[12800/16000] [MSE: 4.7378] 47.1+0.0s +[14400/16000] [MSE: 4.7621] 46.6+0.0s +[16000/16000] [MSE: 4.7990] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.144 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 38.99s + +[Epoch 264] Learning rate: 5.00e-5 +[1600/16000] [MSE: 5.2108] 48.0+0.7s +[3200/16000] [MSE: 5.2649] 47.3+0.0s +[4800/16000] [MSE: 5.0830] 47.0+0.0s +[6400/16000] [MSE: 4.9656] 46.5+0.0s +[8000/16000] [MSE: 4.8891] 46.5+0.0s +[9600/16000] [MSE: 4.8315] 46.3+0.0s +[11200/16000] [MSE: 4.7745] 46.6+0.0s +[12800/16000] [MSE: 4.7037] 46.8+0.0s +[14400/16000] [MSE: 4.6529] 46.7+0.0s +[16000/16000] [MSE: 4.6131] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.519 (Best: 8.820 @epoch 22) +Forward: 38.53s + +Saving... +Total: 38.97s + +[Epoch 265] Learning rate: 5.00e-5 +[1600/16000] [MSE: 4.4047] 48.2+0.7s +[3200/16000] [MSE: 4.3811] 47.5+0.0s +[4800/16000] [MSE: 4.3663] 47.4+0.0s +[6400/16000] [MSE: 4.3295] 47.5+0.0s +[8000/16000] [MSE: 4.3194] 47.2+0.0s +[9600/16000] [MSE: 4.2785] 47.3+0.0s +[11200/16000] [MSE: 4.2307] 47.2+0.0s +[12800/16000] [MSE: 4.1772] 47.1+0.0s +[14400/16000] [MSE: 4.1316] 47.0+0.0s +[16000/16000] [MSE: 4.1025] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.521 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.04s + +[Epoch 266] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7566] 48.1+0.7s +[3200/16000] [MSE: 3.7547] 47.7+0.1s +[4800/16000] [MSE: 3.7684] 47.5+0.0s +[6400/16000] [MSE: 3.8466] 47.3+0.0s +[8000/16000] [MSE: 3.8948] 47.0+0.0s +[9600/16000] [MSE: 3.8863] 47.0+0.0s +[11200/16000] [MSE: 3.8836] 47.0+0.0s +[12800/16000] [MSE: 3.8672] 47.1+0.0s +[14400/16000] [MSE: 3.8554] 46.4+0.0s +[16000/16000] [MSE: 3.8704] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.494 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.13s + +[Epoch 267] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.9568] 48.4+0.7s +[3200/16000] [MSE: 3.9626] 47.9+0.1s +[4800/16000] [MSE: 3.9595] 47.4+0.0s +[6400/16000] [MSE: 3.9502] 47.6+0.0s +[8000/16000] [MSE: 3.9374] 47.7+0.0s +[9600/16000] [MSE: 3.9243] 47.5+0.0s +[11200/16000] [MSE: 3.9130] 47.5+0.0s +[12800/16000] [MSE: 3.9193] 47.4+0.0s +[14400/16000] [MSE: 3.9289] 47.4+0.0s +[16000/16000] [MSE: 3.9325] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.507 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 38.97s + +[Epoch 268] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.8957] 48.0+0.7s +[3200/16000] [MSE: 3.8138] 47.6+0.1s +[4800/16000] [MSE: 3.7887] 47.5+0.0s +[6400/16000] [MSE: 3.7874] 47.3+0.0s +[8000/16000] [MSE: 3.7925] 47.4+0.0s +[9600/16000] [MSE: 3.7733] 47.0+0.0s +[11200/16000] [MSE: 3.7559] 46.8+0.0s +[12800/16000] [MSE: 3.7446] 46.9+0.0s +[14400/16000] [MSE: 3.7328] 47.0+0.0s +[16000/16000] [MSE: 3.7172] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.549 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.05s + +[Epoch 269] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.6006] 48.1+0.7s +[3200/16000] [MSE: 3.5841] 47.6+0.1s +[4800/16000] [MSE: 3.5857] 47.5+0.0s +[6400/16000] [MSE: 3.5763] 47.6+0.0s +[8000/16000] [MSE: 3.5625] 47.5+0.0s +[9600/16000] [MSE: 3.5473] 47.3+0.0s +[11200/16000] [MSE: 3.5271] 47.0+0.0s +[12800/16000] [MSE: 3.5119] 47.1+0.0s +[14400/16000] [MSE: 3.4967] 47.4+0.0s +[16000/16000] [MSE: 3.4876] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.493 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.09s + +[Epoch 270] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.6281] 48.4+0.7s +[3200/16000] [MSE: 3.6159] 47.7+0.0s +[4800/16000] [MSE: 3.6376] 47.5+0.0s +[6400/16000] [MSE: 3.6391] 47.8+0.0s +[8000/16000] [MSE: 3.6443] 47.3+0.0s +[9600/16000] [MSE: 3.6450] 47.0+0.0s +[11200/16000] [MSE: 3.6489] 47.1+0.0s +[12800/16000] [MSE: 3.6457] 47.1+0.0s +[14400/16000] [MSE: 3.6467] 47.2+0.0s +[16000/16000] [MSE: 3.6519] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.488 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.13s + +[Epoch 271] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7141] 48.2+0.7s +[3200/16000] [MSE: 3.8708] 47.8+0.0s +[4800/16000] [MSE: 3.9299] 47.4+0.0s +[6400/16000] [MSE: 3.9305] 47.4+0.0s +[8000/16000] [MSE: 3.9652] 47.6+0.0s +[9600/16000] [MSE: 3.9986] 46.9+0.0s +[11200/16000] [MSE: 4.0217] 46.9+0.0s +[12800/16000] [MSE: 4.0282] 47.2+0.0s +[14400/16000] [MSE: 4.0111] 47.5+0.0s +[16000/16000] [MSE: 3.9904] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.496 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.07s + +[Epoch 272] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.7975] 48.2+0.9s +[3200/16000] [MSE: 3.7806] 47.8+0.0s +[4800/16000] [MSE: 3.6228] 47.7+0.0s +[6400/16000] [MSE: 3.4907] 47.6+0.0s +[8000/16000] [MSE: 3.4134] 47.7+0.0s +[9600/16000] [MSE: 3.3574] 47.5+0.0s +[11200/16000] [MSE: 3.3112] 47.2+0.0s +[12800/16000] [MSE: 3.3019] 47.6+0.0s +[14400/16000] [MSE: 3.2984] 47.4+0.0s +[16000/16000] [MSE: 3.2964] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.497 (Best: 8.820 @epoch 22) +Forward: 38.80s + +Saving... +Total: 39.25s + +[Epoch 273] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.1878] 48.3+0.7s +[3200/16000] [MSE: 3.1503] 48.1+0.0s +[4800/16000] [MSE: 3.1239] 47.8+0.0s +[6400/16000] [MSE: 3.1146] 47.3+0.0s +[8000/16000] [MSE: 3.1089] 47.4+0.0s +[9600/16000] [MSE: 3.1054] 46.8+0.0s +[11200/16000] [MSE: 3.1034] 46.6+0.0s +[12800/16000] [MSE: 3.1187] 46.8+0.0s +[14400/16000] [MSE: 3.1322] 46.4+0.0s +[16000/16000] [MSE: 3.1452] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.534 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.06s + +[Epoch 274] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.2214] 48.1+0.7s +[3200/16000] [MSE: 3.0838] 47.8+0.0s +[4800/16000] [MSE: 2.9934] 47.5+0.0s +[6400/16000] [MSE: 2.9266] 47.2+0.0s +[8000/16000] [MSE: 2.8999] 47.4+0.0s +[9600/16000] [MSE: 2.8816] 47.6+0.0s +[11200/16000] [MSE: 2.8642] 46.9+0.0s +[12800/16000] [MSE: 2.8523] 47.0+0.0s +[14400/16000] [MSE: 2.8432] 46.7+0.0s +[16000/16000] [MSE: 2.8375] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.542 (Best: 8.820 @epoch 22) +Forward: 38.70s + +Saving... +Total: 39.66s + +[Epoch 275] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.8293] 48.7+0.7s +[3200/16000] [MSE: 2.8831] 47.6+0.0s +[4800/16000] [MSE: 2.8970] 47.6+0.0s +[6400/16000] [MSE: 2.8999] 47.2+0.0s +[8000/16000] [MSE: 2.8768] 47.1+0.0s +[9600/16000] [MSE: 2.8638] 47.1+0.0s +[11200/16000] [MSE: 2.8529] 46.9+0.0s +[12800/16000] [MSE: 2.8382] 47.5+0.0s +[14400/16000] [MSE: 2.8301] 47.5+0.0s +[16000/16000] [MSE: 2.8215] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.551 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.13s + +[Epoch 276] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.7357] 48.4+0.8s +[3200/16000] [MSE: 2.8043] 48.0+0.1s +[4800/16000] [MSE: 2.8086] 47.7+0.0s +[6400/16000] [MSE: 2.7783] 47.5+0.0s +[8000/16000] [MSE: 2.7582] 47.6+0.0s +[9600/16000] [MSE: 2.7573] 47.5+0.0s +[11200/16000] [MSE: 2.7591] 47.2+0.0s +[12800/16000] [MSE: 2.7649] 47.3+0.0s +[14400/16000] [MSE: 2.7585] 47.0+0.0s +[16000/16000] [MSE: 2.7488] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.691 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.06s + +[Epoch 277] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.6118] 48.4+0.8s +[3200/16000] [MSE: 2.5882] 48.0+0.1s +[4800/16000] [MSE: 2.5794] 47.9+0.0s +[6400/16000] [MSE: 2.5794] 47.7+0.0s +[8000/16000] [MSE: 2.5808] 47.4+0.0s +[9600/16000] [MSE: 2.5816] 47.5+0.0s +[11200/16000] [MSE: 2.5955] 47.3+0.0s +[12800/16000] [MSE: 2.6085] 47.4+0.0s +[14400/16000] [MSE: 2.6265] 46.8+0.0s +[16000/16000] [MSE: 2.6541] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.566 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.18s + +[Epoch 278] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.8533] 48.1+0.8s +[3200/16000] [MSE: 2.6811] 47.8+0.0s +[4800/16000] [MSE: 2.6178] 47.0+0.0s +[6400/16000] [MSE: 2.5729] 46.6+0.0s +[8000/16000] [MSE: 2.5485] 46.9+0.0s +[9600/16000] [MSE: 2.5322] 46.5+0.0s +[11200/16000] [MSE: 2.5206] 46.7+0.0s +[12800/16000] [MSE: 2.5115] 46.9+0.0s +[14400/16000] [MSE: 2.5024] 46.6+0.0s +[16000/16000] [MSE: 2.4892] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.610 (Best: 8.820 @epoch 22) +Forward: 38.76s + +Saving... +Total: 39.20s + +[Epoch 279] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.3985] 48.5+0.7s +[3200/16000] [MSE: 2.3938] 48.0+0.1s +[4800/16000] [MSE: 2.3889] 47.3+0.0s +[6400/16000] [MSE: 2.3708] 47.3+0.0s +[8000/16000] [MSE: 2.3541] 47.4+0.0s +[9600/16000] [MSE: 2.3531] 47.2+0.0s +[11200/16000] [MSE: 2.3981] 47.3+0.0s +[12800/16000] [MSE: 2.4231] 47.5+0.0s +[14400/16000] [MSE: 2.4085] 47.3+0.0s +[16000/16000] [MSE: 2.4164] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.686 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.09s + +[Epoch 280] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.4447] 48.1+0.8s +[3200/16000] [MSE: 2.3764] 48.1+0.0s +[4800/16000] [MSE: 2.3498] 47.6+0.0s +[6400/16000] [MSE: 2.4856] 47.6+0.0s +[8000/16000] [MSE: 2.9829] 47.4+0.0s +[9600/16000] [MSE: 3.0760] 47.3+0.0s +[11200/16000] [MSE: 3.0784] 47.3+0.0s +[12800/16000] [MSE: 3.0726] 47.4+0.0s +[14400/16000] [MSE: 3.0128] 47.3+0.0s +[16000/16000] [MSE: 2.9637] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.685 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.13s + +[Epoch 281] Learning rate: 5.00e-5 +[1600/16000] [MSE: 3.4316] 48.2+0.8s +[3200/16000] [MSE: 3.0851] 48.0+0.1s +[4800/16000] [MSE: 2.8277] 47.4+0.0s +[6400/16000] [MSE: 2.6735] 47.3+0.0s +[8000/16000] [MSE: 2.6059] 46.3+0.0s +[9600/16000] [MSE: 2.6214] 46.3+0.0s +[11200/16000] [MSE: 2.5652] 46.2+0.0s +[12800/16000] [MSE: 2.5125] 46.1+0.0s +[14400/16000] [MSE: 2.4698] 47.1+0.0s +[16000/16000] [MSE: 2.4462] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.761 (Best: 8.820 @epoch 22) +Forward: 38.67s + +Saving... +Total: 39.13s + +[Epoch 282] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.2980] 47.6+0.7s +[3200/16000] [MSE: 2.2920] 47.4+0.0s +[4800/16000] [MSE: 2.2578] 47.4+0.0s +[6400/16000] [MSE: 2.2426] 47.4+0.0s +[8000/16000] [MSE: 2.2306] 47.2+0.0s +[9600/16000] [MSE: 2.2215] 47.2+0.0s +[11200/16000] [MSE: 2.2158] 47.1+0.0s +[12800/16000] [MSE: 2.2143] 47.3+0.0s +[14400/16000] [MSE: 2.2133] 46.9+0.0s +[16000/16000] [MSE: 2.2017] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.616 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.10s + +[Epoch 283] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0823] 47.6+0.9s +[3200/16000] [MSE: 2.1475] 47.6+0.0s +[4800/16000] [MSE: 2.1798] 47.3+0.0s +[6400/16000] [MSE: 2.1887] 47.8+0.0s +[8000/16000] [MSE: 2.1980] 47.3+0.0s +[9600/16000] [MSE: 2.2252] 47.6+0.0s +[11200/16000] [MSE: 2.2303] 47.5+0.0s +[12800/16000] [MSE: 2.2199] 47.3+0.0s +[14400/16000] [MSE: 2.2128] 47.3+0.0s +[16000/16000] [MSE: 2.2068] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.601 (Best: 8.820 @epoch 22) +Forward: 38.71s + +Saving... +Total: 39.20s + +[Epoch 284] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.1645] 48.5+0.7s +[3200/16000] [MSE: 2.1672] 47.9+0.0s +[4800/16000] [MSE: 2.1614] 47.3+0.0s +[6400/16000] [MSE: 2.1877] 46.8+0.0s +[8000/16000] [MSE: 2.1915] 47.3+0.0s +[9600/16000] [MSE: 2.1949] 47.0+0.0s +[11200/16000] [MSE: 2.1911] 47.3+0.0s +[12800/16000] [MSE: 2.1829] 47.3+0.0s +[14400/16000] [MSE: 2.1716] 46.7+0.0s +[16000/16000] [MSE: 2.1567] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.597 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.15s + +[Epoch 285] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0917] 48.1+0.8s +[3200/16000] [MSE: 2.1204] 47.6+0.0s +[4800/16000] [MSE: 2.1273] 47.7+0.0s +[6400/16000] [MSE: 2.1346] 47.4+0.0s +[8000/16000] [MSE: 2.1359] 47.0+0.0s +[9600/16000] [MSE: 2.1385] 47.3+0.0s +[11200/16000] [MSE: 2.1404] 47.2+0.0s +[12800/16000] [MSE: 2.1401] 46.9+0.0s +[14400/16000] [MSE: 2.1389] 47.2+0.0s +[16000/16000] [MSE: 2.1363] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.610 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.05s + +[Epoch 286] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.1218] 48.2+0.9s +[3200/16000] [MSE: 2.1049] 47.6+0.0s +[4800/16000] [MSE: 2.1165] 47.7+0.0s +[6400/16000] [MSE: 2.1282] 47.5+0.0s +[8000/16000] [MSE: 2.1810] 47.1+0.0s +[9600/16000] [MSE: 2.2209] 47.3+0.0s +[11200/16000] [MSE: 2.2467] 47.3+0.0s +[12800/16000] [MSE: 2.2664] 47.4+0.0s +[14400/16000] [MSE: 2.2830] 47.1+0.0s +[16000/16000] [MSE: 2.3161] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.558 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.04s + +[Epoch 287] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.6614] 48.2+0.8s +[3200/16000] [MSE: 2.6521] 47.7+0.1s +[4800/16000] [MSE: 2.6900] 47.3+0.0s +[6400/16000] [MSE: 2.8129] 46.9+0.0s +[8000/16000] [MSE: 2.8233] 46.4+0.0s +[9600/16000] [MSE: 2.8128] 46.7+0.0s +[11200/16000] [MSE: 2.8044] 46.5+0.0s +[12800/16000] [MSE: 2.7986] 46.4+0.0s +[14400/16000] [MSE: 2.7930] 46.5+0.0s +[16000/16000] [MSE: 2.7750] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.564 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.15s + +[Epoch 288] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.4552] 48.2+0.8s +[3200/16000] [MSE: 2.5139] 47.9+0.1s +[4800/16000] [MSE: 2.5652] 47.7+0.1s +[6400/16000] [MSE: 2.5808] 47.6+0.0s +[8000/16000] [MSE: 2.5976] 47.9+0.1s +[9600/16000] [MSE: 2.5906] 47.6+0.0s +[11200/16000] [MSE: 2.5824] 47.4+0.0s +[12800/16000] [MSE: 2.5822] 47.5+0.0s +[14400/16000] [MSE: 2.5803] 47.2+0.0s +[16000/16000] [MSE: 2.5746] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.572 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.16s + +[Epoch 289] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.4461] 48.5+0.7s +[3200/16000] [MSE: 2.4637] 47.9+0.1s +[4800/16000] [MSE: 2.4670] 47.7+0.1s +[6400/16000] [MSE: 2.4724] 48.0+0.1s +[8000/16000] [MSE: 2.4755] 47.8+0.1s +[9600/16000] [MSE: 2.4780] 47.4+0.0s +[11200/16000] [MSE: 2.4802] 47.1+0.0s +[12800/16000] [MSE: 2.4762] 47.6+0.0s +[14400/16000] [MSE: 2.4747] 47.5+0.0s +[16000/16000] [MSE: 2.4718] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.571 (Best: 8.820 @epoch 22) +Forward: 38.70s + +Saving... +Total: 39.20s + +[Epoch 290] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.4738] 48.3+0.7s +[3200/16000] [MSE: 2.5074] 47.8+0.1s +[4800/16000] [MSE: 2.4982] 47.6+0.0s +[6400/16000] [MSE: 2.4494] 47.0+0.0s +[8000/16000] [MSE: 2.4148] 47.4+0.0s +[9600/16000] [MSE: 2.3946] 47.2+0.0s +[11200/16000] [MSE: 2.3793] 47.1+0.0s +[12800/16000] [MSE: 2.3693] 47.5+0.0s +[14400/16000] [MSE: 2.3608] 47.6+0.0s +[16000/16000] [MSE: 2.3550] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.568 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.07s + +[Epoch 291] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.2963] 47.4+0.7s +[3200/16000] [MSE: 2.2849] 47.3+0.0s +[4800/16000] [MSE: 2.2946] 47.1+0.0s +[6400/16000] [MSE: 2.3000] 46.5+0.0s +[8000/16000] [MSE: 2.3022] 46.5+0.0s +[9600/16000] [MSE: 2.3669] 46.8+0.0s +[11200/16000] [MSE: 2.3636] 46.6+0.0s +[12800/16000] [MSE: 2.3470] 46.5+0.0s +[14400/16000] [MSE: 2.3284] 46.2+0.0s +[16000/16000] [MSE: 2.3112] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.615 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.12s + +[Epoch 292] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0253] 48.4+0.7s +[3200/16000] [MSE: 2.0221] 47.4+0.0s +[4800/16000] [MSE: 2.0284] 47.4+0.0s +[6400/16000] [MSE: 2.0311] 47.5+0.0s +[8000/16000] [MSE: 2.0399] 47.2+0.0s +[9600/16000] [MSE: 2.0420] 47.2+0.0s +[11200/16000] [MSE: 2.0446] 47.3+0.0s +[12800/16000] [MSE: 2.0476] 47.2+0.0s +[14400/16000] [MSE: 2.0492] 47.2+0.0s +[16000/16000] [MSE: 2.0465] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.616 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.11s + +[Epoch 293] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0455] 48.2+0.7s +[3200/16000] [MSE: 2.0486] 47.7+0.1s +[4800/16000] [MSE: 2.0669] 47.7+0.0s +[6400/16000] [MSE: 2.0767] 47.6+0.0s +[8000/16000] [MSE: 2.0806] 47.3+0.0s +[9600/16000] [MSE: 2.0945] 47.3+0.0s +[11200/16000] [MSE: 2.0941] 47.2+0.0s +[12800/16000] [MSE: 2.0868] 46.5+0.0s +[14400/16000] [MSE: 2.0793] 47.0+0.0s +[16000/16000] [MSE: 2.0755] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.648 (Best: 8.820 @epoch 22) +Forward: 38.61s + +Saving... +Total: 39.08s + +[Epoch 294] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0081] 47.8+0.9s +[3200/16000] [MSE: 2.0239] 47.1+0.0s +[4800/16000] [MSE: 2.0132] 47.5+0.0s +[6400/16000] [MSE: 2.0373] 47.3+0.0s +[8000/16000] [MSE: 2.0291] 47.4+0.0s +[9600/16000] [MSE: 2.0347] 47.2+0.0s +[11200/16000] [MSE: 2.0538] 47.3+0.0s +[12800/16000] [MSE: 2.0460] 47.2+0.0s +[14400/16000] [MSE: 2.0359] 46.6+0.0s +[16000/16000] [MSE: 2.0237] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.706 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.17s + +[Epoch 295] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9434] 47.9+0.8s +[3200/16000] [MSE: 2.2031] 47.6+0.0s +[4800/16000] [MSE: 2.1050] 47.4+0.0s +[6400/16000] [MSE: 2.0313] 47.5+0.0s +[8000/16000] [MSE: 1.9954] 47.5+0.0s +[9600/16000] [MSE: 1.9732] 46.9+0.0s +[11200/16000] [MSE: 1.9603] 47.1+0.0s +[12800/16000] [MSE: 1.9509] 46.7+0.0s +[14400/16000] [MSE: 1.9486] 46.8+0.0s +[16000/16000] [MSE: 1.9443] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.866 (Best: 8.820 @epoch 22) +Forward: 38.72s + +Saving... +Total: 39.18s + +[Epoch 296] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9174] 47.2+0.8s +[3200/16000] [MSE: 1.9119] 46.9+0.0s +[4800/16000] [MSE: 1.9048] 47.2+0.0s +[6400/16000] [MSE: 1.9024] 47.3+0.0s +[8000/16000] [MSE: 1.9155] 47.2+0.0s +[9600/16000] [MSE: 1.9233] 47.1+0.0s +[11200/16000] [MSE: 1.9032] 47.3+0.0s +[12800/16000] [MSE: 1.8898] 47.0+0.0s +[14400/16000] [MSE: 1.8779] 47.5+0.0s +[16000/16000] [MSE: 1.8702] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.677 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.08s + +[Epoch 297] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.7701] 48.1+0.8s +[3200/16000] [MSE: 1.7816] 47.7+0.0s +[4800/16000] [MSE: 1.7746] 47.5+0.0s +[6400/16000] [MSE: 1.7754] 47.2+0.0s +[8000/16000] [MSE: 1.7753] 46.7+0.0s +[9600/16000] [MSE: 1.7775] 46.7+0.0s +[11200/16000] [MSE: 1.8123] 46.5+0.0s +[12800/16000] [MSE: 1.8380] 47.2+0.0s +[14400/16000] [MSE: 1.8595] 46.6+0.0s +[16000/16000] [MSE: 1.8784] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.599 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.02s + +[Epoch 298] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9913] 47.9+0.7s +[3200/16000] [MSE: 2.0112] 47.7+0.1s +[4800/16000] [MSE: 2.0069] 47.4+0.0s +[6400/16000] [MSE: 2.0150] 47.6+0.0s +[8000/16000] [MSE: 2.0130] 47.5+0.0s +[9600/16000] [MSE: 2.0128] 47.3+0.0s +[11200/16000] [MSE: 2.0150] 47.5+0.0s +[12800/16000] [MSE: 2.0180] 47.4+0.0s +[14400/16000] [MSE: 2.0235] 47.3+0.0s +[16000/16000] [MSE: 2.0238] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.615 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.07s + +[Epoch 299] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0092] 48.0+0.7s +[3200/16000] [MSE: 2.0152] 47.4+0.0s +[4800/16000] [MSE: 2.0112] 47.4+0.0s +[6400/16000] [MSE: 2.0158] 47.2+0.0s +[8000/16000] [MSE: 2.0175] 47.2+0.0s +[9600/16000] [MSE: 2.0155] 46.8+0.0s +[11200/16000] [MSE: 2.0034] 46.2+0.0s +[12800/16000] [MSE: 1.9903] 46.3+0.0s +[14400/16000] [MSE: 1.9789] 46.7+0.0s +[16000/16000] [MSE: 1.9686] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.634 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.22s + +[Epoch 300] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.8527] 47.9+0.8s +[3200/16000] [MSE: 1.8587] 47.3+0.0s +[4800/16000] [MSE: 1.8577] 47.0+0.0s +[6400/16000] [MSE: 1.8619] 46.9+0.0s +[8000/16000] [MSE: 1.8476] 47.3+0.0s +[9600/16000] [MSE: 1.8360] 46.9+0.0s +[11200/16000] [MSE: 1.8284] 47.3+0.0s +[12800/16000] [MSE: 1.8232] 47.0+0.0s +[14400/16000] [MSE: 1.8230] 47.2+0.0s +[16000/16000] [MSE: 1.8236] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.639 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.09s + +[Epoch 301] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.8176] 48.2+0.7s +[3200/16000] [MSE: 1.8136] 47.8+0.1s +[4800/16000] [MSE: 1.8154] 47.7+0.1s +[6400/16000] [MSE: 1.8162] 47.7+0.0s +[8000/16000] [MSE: 1.8259] 47.6+0.0s +[9600/16000] [MSE: 1.8424] 47.1+0.0s +[11200/16000] [MSE: 1.8622] 47.5+0.0s +[12800/16000] [MSE: 1.8766] 47.6+0.0s +[14400/16000] [MSE: 1.8917] 47.3+0.0s +[16000/16000] [MSE: 1.9066] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.625 (Best: 8.820 @epoch 22) +Forward: 38.74s + +Saving... +Total: 39.25s + +[Epoch 302] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0299] 48.0+0.7s +[3200/16000] [MSE: 2.0231] 47.7+0.0s +[4800/16000] [MSE: 2.0193] 47.6+0.0s +[6400/16000] [MSE: 2.0256] 47.6+0.0s +[8000/16000] [MSE: 2.0388] 47.3+0.0s +[9600/16000] [MSE: 2.0493] 46.9+0.0s +[11200/16000] [MSE: 2.0634] 47.3+0.0s +[12800/16000] [MSE: 2.0752] 47.2+0.0s +[14400/16000] [MSE: 2.0865] 47.1+0.0s +[16000/16000] [MSE: 2.0944] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.600 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.13s + +[Epoch 303] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.2216] 47.9+0.7s +[3200/16000] [MSE: 2.2399] 47.3+0.0s +[4800/16000] [MSE: 2.2390] 47.4+0.0s +[6400/16000] [MSE: 2.2375] 47.3+0.0s +[8000/16000] [MSE: 2.2369] 47.2+0.0s +[9600/16000] [MSE: 2.2371] 46.6+0.0s +[11200/16000] [MSE: 2.2371] 47.1+0.0s +[12800/16000] [MSE: 2.2354] 47.2+0.0s +[14400/16000] [MSE: 2.2297] 47.0+0.0s +[16000/16000] [MSE: 2.2252] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.594 (Best: 8.820 @epoch 22) +Forward: 38.71s + +Saving... +Total: 39.22s + +[Epoch 304] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.2957] 48.0+0.7s +[3200/16000] [MSE: 2.4759] 47.8+0.1s +[4800/16000] [MSE: 2.5412] 47.3+0.0s +[6400/16000] [MSE: 2.5712] 46.9+0.0s +[8000/16000] [MSE: 2.5931] 46.9+0.0s +[9600/16000] [MSE: 2.5752] 46.7+0.0s +[11200/16000] [MSE: 2.5479] 47.1+0.0s +[12800/16000] [MSE: 2.5257] 46.9+0.0s +[14400/16000] [MSE: 2.5087] 46.8+0.0s +[16000/16000] [MSE: 2.4927] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.570 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.13s + +[Epoch 305] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.3131] 48.0+0.8s +[3200/16000] [MSE: 2.3367] 47.9+0.1s +[4800/16000] [MSE: 2.3396] 47.8+0.0s +[6400/16000] [MSE: 2.3080] 47.5+0.0s +[8000/16000] [MSE: 2.2797] 47.3+0.0s +[9600/16000] [MSE: 2.2553] 47.2+0.0s +[11200/16000] [MSE: 2.2370] 47.6+0.0s +[12800/16000] [MSE: 2.2221] 47.3+0.0s +[14400/16000] [MSE: 2.2120] 47.4+0.0s +[16000/16000] [MSE: 2.2010] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.579 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.04s + +[Epoch 306] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.1547] 48.2+0.8s +[3200/16000] [MSE: 2.1080] 47.7+0.1s +[4800/16000] [MSE: 2.0661] 47.5+0.1s +[6400/16000] [MSE: 2.0477] 47.8+0.1s +[8000/16000] [MSE: 2.0396] 47.3+0.0s +[9600/16000] [MSE: 2.0397] 47.3+0.0s +[11200/16000] [MSE: 2.0350] 47.0+0.0s +[12800/16000] [MSE: 2.0360] 46.5+0.0s +[14400/16000] [MSE: 2.0359] 46.9+0.0s +[16000/16000] [MSE: 2.0361] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.591 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.08s + +[Epoch 307] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0271] 48.1+0.7s +[3200/16000] [MSE: 2.0189] 47.3+0.0s +[4800/16000] [MSE: 2.0212] 46.8+0.0s +[6400/16000] [MSE: 2.0085] 47.1+0.0s +[8000/16000] [MSE: 1.9905] 47.1+0.0s +[9600/16000] [MSE: 1.9792] 46.9+0.0s +[11200/16000] [MSE: 1.9733] 46.6+0.0s +[12800/16000] [MSE: 1.9649] 46.6+0.0s +[14400/16000] [MSE: 1.9567] 46.4+0.0s +[16000/16000] [MSE: 1.9534] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.704 (Best: 8.820 @epoch 22) +Forward: 38.62s + +Saving... +Total: 39.09s + +[Epoch 308] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9662] 47.1+0.7s +[3200/16000] [MSE: 1.9532] 46.7+0.0s +[4800/16000] [MSE: 1.9410] 46.2+0.0s +[6400/16000] [MSE: 1.9643] 46.5+0.0s +[8000/16000] [MSE: 1.9731] 46.2+0.0s +[9600/16000] [MSE: 1.9792] 46.3+0.0s +[11200/16000] [MSE: 1.9782] 46.9+0.0s +[12800/16000] [MSE: 1.9812] 46.7+0.0s +[14400/16000] [MSE: 1.9877] 46.5+0.0s +[16000/16000] [MSE: 1.9874] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.606 (Best: 8.820 @epoch 22) +Forward: 38.52s + +Saving... +Total: 39.02s + +[Epoch 309] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9943] 47.4+0.9s +[3200/16000] [MSE: 2.0029] 47.2+0.0s +[4800/16000] [MSE: 2.0074] 47.1+0.0s +[6400/16000] [MSE: 1.9756] 47.3+0.0s +[8000/16000] [MSE: 1.9567] 47.0+0.0s +[9600/16000] [MSE: 1.9448] 47.1+0.0s +[11200/16000] [MSE: 1.9422] 46.9+0.0s +[12800/16000] [MSE: 1.9362] 46.9+0.0s +[14400/16000] [MSE: 1.9322] 46.3+0.0s +[16000/16000] [MSE: 1.9293] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.725 (Best: 8.820 @epoch 22) +Forward: 38.56s + +Saving... +Total: 39.05s + +[Epoch 310] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.8408] 47.4+0.7s +[3200/16000] [MSE: 1.8388] 47.1+0.0s +[4800/16000] [MSE: 1.8521] 47.4+0.0s +[6400/16000] [MSE: 1.8735] 47.1+0.0s +[8000/16000] [MSE: 1.9003] 47.1+0.0s +[9600/16000] [MSE: 1.9363] 46.9+0.0s +[11200/16000] [MSE: 1.9641] 47.2+0.0s +[12800/16000] [MSE: 1.9888] 47.1+0.0s +[14400/16000] [MSE: 2.0247] 47.1+0.0s +[16000/16000] [MSE: 2.0549] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.585 (Best: 8.820 @epoch 22) +Forward: 38.64s + +Saving... +Total: 39.28s + +[Epoch 311] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.2979] 48.1+0.9s +[3200/16000] [MSE: 2.1600] 47.4+0.0s +[4800/16000] [MSE: 2.0780] 47.4+0.0s +[6400/16000] [MSE: 2.0394] 47.3+0.0s +[8000/16000] [MSE: 2.0243] 47.1+0.0s +[9600/16000] [MSE: 2.0144] 46.7+0.0s +[11200/16000] [MSE: 2.0048] 47.0+0.0s +[12800/16000] [MSE: 1.9970] 46.7+0.0s +[14400/16000] [MSE: 1.9943] 46.9+0.0s +[16000/16000] [MSE: 1.9891] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.606 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.06s + +[Epoch 312] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9387] 47.7+0.7s +[3200/16000] [MSE: 1.9260] 47.1+0.0s +[4800/16000] [MSE: 1.9209] 47.1+0.0s +[6400/16000] [MSE: 1.9289] 47.1+0.0s +[8000/16000] [MSE: 1.9304] 47.0+0.0s +[9600/16000] [MSE: 1.9348] 46.6+0.0s +[11200/16000] [MSE: 1.9416] 46.9+0.0s +[12800/16000] [MSE: 1.9543] 46.9+0.0s +[14400/16000] [MSE: 1.9870] 46.6+0.0s +[16000/16000] [MSE: 2.0055] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.594 (Best: 8.820 @epoch 22) +Forward: 38.49s + +Saving... +Total: 38.94s + +[Epoch 313] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.1624] 47.5+0.7s +[3200/16000] [MSE: 2.0934] 47.0+0.0s +[4800/16000] [MSE: 2.0284] 46.9+0.0s +[6400/16000] [MSE: 1.9770] 46.9+0.0s +[8000/16000] [MSE: 1.9447] 46.8+0.0s +[9600/16000] [MSE: 1.9346] 46.6+0.0s +[11200/16000] [MSE: 1.9290] 47.0+0.0s +[12800/16000] [MSE: 1.9159] 47.1+0.0s +[14400/16000] [MSE: 1.8997] 46.6+0.0s +[16000/16000] [MSE: 1.8884] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.708 (Best: 8.820 @epoch 22) +Forward: 38.51s + +Saving... +Total: 38.99s + +[Epoch 314] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.8263] 47.7+0.7s +[3200/16000] [MSE: 1.8100] 47.6+0.0s +[4800/16000] [MSE: 1.8160] 47.0+0.0s +[6400/16000] [MSE: 1.8246] 47.0+0.0s +[8000/16000] [MSE: 1.8522] 46.9+0.0s +[9600/16000] [MSE: 1.8834] 46.9+0.0s +[11200/16000] [MSE: 1.8978] 46.7+0.0s +[12800/16000] [MSE: 1.9057] 47.2+0.0s +[14400/16000] [MSE: 1.9072] 47.0+0.0s +[16000/16000] [MSE: 1.9120] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.773 (Best: 8.820 @epoch 22) +Forward: 38.60s + +Saving... +Total: 39.08s + +[Epoch 315] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9795] 48.3+0.6s +[3200/16000] [MSE: 1.9449] 47.7+0.0s +[4800/16000] [MSE: 1.9529] 47.6+0.0s +[6400/16000] [MSE: 1.9225] 47.3+0.0s +[8000/16000] [MSE: 1.8941] 47.1+0.0s +[9600/16000] [MSE: 1.8811] 47.0+0.0s +[11200/16000] [MSE: 1.8664] 47.3+0.0s +[12800/16000] [MSE: 1.8566] 47.3+0.0s +[14400/16000] [MSE: 1.8490] 47.3+0.0s +[16000/16000] [MSE: 1.8446] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.719 (Best: 8.820 @epoch 22) +Forward: 38.58s + +Saving... +Total: 39.10s + +[Epoch 316] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.7774] 47.9+1.1s +[3200/16000] [MSE: 1.7730] 47.4+0.0s +[4800/16000] [MSE: 1.7789] 47.2+0.0s +[6400/16000] [MSE: 1.7799] 47.2+0.0s +[8000/16000] [MSE: 1.7820] 46.9+0.0s +[9600/16000] [MSE: 1.7866] 46.5+0.0s +[11200/16000] [MSE: 1.7865] 46.2+0.0s +[12800/16000] [MSE: 1.8092] 46.3+0.0s +[14400/16000] [MSE: 1.8249] 46.6+0.0s +[16000/16000] [MSE: 1.8399] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.633 (Best: 8.820 @epoch 22) +Forward: 38.68s + +Saving... +Total: 39.13s + +[Epoch 317] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.9330] 47.6+0.7s +[3200/16000] [MSE: 1.9497] 47.0+0.0s +[4800/16000] [MSE: 1.9478] 47.1+0.0s +[6400/16000] [MSE: 1.9422] 46.9+0.0s +[8000/16000] [MSE: 1.9421] 47.0+0.0s +[9600/16000] [MSE: 1.9481] 46.8+0.0s +[11200/16000] [MSE: 1.9560] 47.2+0.0s +[12800/16000] [MSE: 1.9602] 47.0+0.0s +[14400/16000] [MSE: 1.9639] 47.1+0.0s +[16000/16000] [MSE: 1.9652] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.634 (Best: 8.820 @epoch 22) +Forward: 38.59s + +Saving... +Total: 39.06s + +[Epoch 318] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0090] 47.9+0.8s +[3200/16000] [MSE: 2.0503] 47.6+0.1s +[4800/16000] [MSE: 2.0689] 47.5+0.0s +[6400/16000] [MSE: 2.0799] 47.0+0.0s +[8000/16000] [MSE: 2.0829] 47.1+0.0s +[9600/16000] [MSE: 2.0834] 46.9+0.0s +[11200/16000] [MSE: 2.0590] 46.9+0.0s +[12800/16000] [MSE: 2.0437] 47.0+0.0s +[14400/16000] [MSE: 2.0222] 47.0+0.0s +[16000/16000] [MSE: 1.9971] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.673 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.04s + +[Epoch 319] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.7962] 48.2+0.7s +[3200/16000] [MSE: 1.7993] 48.0+0.1s +[4800/16000] [MSE: 1.7889] 47.8+0.1s +[6400/16000] [MSE: 1.7809] 47.8+0.1s +[8000/16000] [MSE: 1.7758] 47.3+0.0s +[9600/16000] [MSE: 1.7739] 47.1+0.0s +[11200/16000] [MSE: 1.7720] 47.1+0.0s +[12800/16000] [MSE: 1.7736] 46.9+0.0s +[14400/16000] [MSE: 1.7756] 46.9+0.0s +[16000/16000] [MSE: 1.7786] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.648 (Best: 8.820 @epoch 22) +Forward: 38.54s + +Saving... +Total: 39.03s + +[Epoch 320] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.8385] 47.6+0.7s +[3200/16000] [MSE: 1.8370] 46.6+0.0s +[4800/16000] [MSE: 1.8237] 46.7+0.0s +[6400/16000] [MSE: 1.8309] 46.4+0.0s +[8000/16000] [MSE: 1.8573] 46.8+0.0s +[9600/16000] [MSE: 1.8715] 47.1+0.0s +[11200/16000] [MSE: 1.8859] 47.4+0.0s +[12800/16000] [MSE: 1.8983] 47.2+0.0s +[14400/16000] [MSE: 1.9054] 46.9+0.0s +[16000/16000] [MSE: 1.9110] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.602 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.11s + +[Epoch 321] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0043] 48.1+0.7s +[3200/16000] [MSE: 2.0385] 47.5+0.1s +[4800/16000] [MSE: 2.0932] 47.0+0.0s +[6400/16000] [MSE: 2.1321] 46.6+0.0s +[8000/16000] [MSE: 2.1553] 46.7+0.0s +[9600/16000] [MSE: 2.1446] 46.6+0.0s +[11200/16000] [MSE: 2.1349] 46.2+0.0s +[12800/16000] [MSE: 2.1451] 46.5+0.0s +[14400/16000] [MSE: 2.1712] 46.3+0.0s +[16000/16000] [MSE: 2.2024] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.568 (Best: 8.820 @epoch 22) +Forward: 38.63s + +Saving... +Total: 39.22s + +[Epoch 322] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.4647] 48.2+0.8s +[3200/16000] [MSE: 2.4365] 47.4+0.1s +[4800/16000] [MSE: 2.4041] 47.3+0.0s +[6400/16000] [MSE: 2.3837] 46.9+0.0s +[8000/16000] [MSE: 2.3732] 47.0+0.0s +[9600/16000] [MSE: 2.4822] 46.9+0.0s +[11200/16000] [MSE: 2.6423] 47.0+0.0s +[12800/16000] [MSE: 2.6953] 47.1+0.0s +[14400/16000] [MSE: 2.7453] 47.0+0.0s +[16000/16000] [MSE: 2.7036] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.654 (Best: 8.820 @epoch 22) +Forward: 38.65s + +Saving... +Total: 39.12s + +[Epoch 323] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.2502] 47.0+0.9s +[3200/16000] [MSE: 2.2347] 46.6+0.0s +[4800/16000] [MSE: 2.2766] 47.3+0.0s +[6400/16000] [MSE: 2.3180] 47.1+0.0s +[8000/16000] [MSE: 2.3366] 46.5+0.0s +[9600/16000] [MSE: 2.3493] 46.7+0.0s +[11200/16000] [MSE: 2.3321] 46.3+0.0s +[12800/16000] [MSE: 2.3109] 46.3+0.0s +[14400/16000] [MSE: 2.2918] 46.1+0.0s +[16000/16000] [MSE: 2.2797] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.654 (Best: 8.820 @epoch 22) +Forward: 38.48s + +Saving... +Total: 38.94s + +[Epoch 324] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0841] 47.6+0.7s +[3200/16000] [MSE: 2.0562] 47.0+0.1s +[4800/16000] [MSE: 2.0344] 47.1+0.0s +[6400/16000] [MSE: 2.0221] 47.0+0.0s +[8000/16000] [MSE: 2.0190] 46.9+0.0s +[9600/16000] [MSE: 2.0110] 47.0+0.0s +[11200/16000] [MSE: 2.0039] 47.1+0.0s +[12800/16000] [MSE: 2.0008] 47.0+0.0s +[14400/16000] [MSE: 1.9974] 47.3+0.0s +[16000/16000] [MSE: 1.9945] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.603 (Best: 8.820 @epoch 22) +Forward: 38.55s + +Saving... +Total: 39.03s + +[Epoch 325] Learning rate: 5.00e-5 +[1600/16000] [MSE: 2.0221] 47.9+0.7s +[3200/16000] [MSE: 2.0168] 47.3+0.0s +[4800/16000] [MSE: 2.0139] 47.3+0.0s +[6400/16000] [MSE: 2.0272] 47.3+0.0s +[8000/16000] [MSE: 2.0803] 46.8+0.0s +[9600/16000] [MSE: 2.1288] 47.1+0.0s +[11200/16000] [MSE: 2.1985] 47.4+0.0s +[12800/16000] [MSE: 2.2526] 47.1+0.0s +[14400/16000] [MSE: 2.2937] 47.2+0.0s +[16000/16000] [MSE: 2.3253] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.578 (Best: 8.820 @epoch 22) +Forward: 38.57s + +Saving... +Total: 39.05s + diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/loss.pt b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..d4ecc616060c5f795d9fb2b67408bfc4da6110ed --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:77b671839c0c879519105cc18dc593f6f79aaf916d4044917896e67f135cfeb6 +size 495 diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/loss_MSE.pdf b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/loss_MSE.pdf new file mode 100644 index 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--- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/optimizer.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e3094b652e9493cbfa0c2e7852bd0fed0635e33ba77f655f1822aa32195a53fb +size 12572847 diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/psnr_log.pt b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/psnr_log.pt new file mode 100644 index 0000000000000000000000000000000000000000..a0ad7631386398069c4e78224e0ba10540f43ad8 --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/psnr_log.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8d8223e5a4f3cdcd1af476004dd95afb92f675b50c196df07d3f3a2eb9cf2fc +size 2040 diff --git a/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/test_DIV2K.pdf b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/test_DIV2K.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7e5ecf1978d86ce15d9a60873001179aec0be2ee Binary files /dev/null and b/Demosaic/experiment/LAMBDANETACTA_DEMOSAIC20_R4_MSE/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/config.txt b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..87eced8a6de72008f73af3d2d7c90dc41196d243 --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/config.txt @@ -0,0 +1,132 @@ +2020-11-07-00:00:55 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTB +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 128 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDANETACTB_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-07-00:07:52 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTB +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDANETACTB_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/log.txt b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..87f53d8d248f4e68f33732eaa80500f2399f5697 --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/log.txt @@ -0,0 +1,1183 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[12800/128000] [L1: 1.4947] 346.5+2.6s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.9414] 77.2+1.1s +[3200/16000] [L1: 1.9495] 72.4+0.0s +[4800/16000] [L1: 1.7959] 71.1+0.0s +[6400/16000] [L1: 1.7738] 71.2+0.0s +[8000/16000] [L1: 1.7664] 71.3+0.0s +[9600/16000] [L1: 1.7596] 72.6+0.0s +[11200/16000] [L1: 1.7520] 71.7+0.0s +[12800/16000] [L1: 1.7474] 68.5+0.0s +[14400/16000] [L1: 1.7479] 72.2+0.1s +[16000/16000] [L1: 1.7423] 73.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.58s + +Saving... +Total: 36.34s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7372] 72.1+0.9s +[3200/16000] [L1: 1.7333] 68.7+0.0s +[4800/16000] [L1: 1.7252] 71.6+0.1s +[6400/16000] [L1: 1.7291] 71.8+0.0s +[8000/16000] [L1: 1.7231] 71.7+0.1s +[9600/16000] [L1: 1.7223] 70.1+0.0s +[11200/16000] [L1: 1.7246] 71.7+0.0s +[12800/16000] [L1: 1.7228] 71.3+0.0s +[14400/16000] [L1: 1.7220] 70.9+0.0s +[16000/16000] [L1: 1.7206] 71.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.17s + +Saving... +Total: 35.79s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7376] 70.4+0.9s +[3200/16000] [L1: 1.7155] 70.3+0.0s +[4800/16000] [L1: 1.7151] 70.2+0.0s +[6400/16000] [L1: 1.7125] 72.4+0.1s +[8000/16000] [L1: 1.7172] 71.8+0.1s +[9600/16000] [L1: 1.7216] 70.1+0.0s +[11200/16000] [L1: 1.7207] 72.5+0.0s +[12800/16000] [L1: 1.7212] 74.8+0.1s +[14400/16000] [L1: 1.7231] 72.0+0.1s +[16000/16000] [L1: 1.7242] 71.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.10s + +Saving... +Total: 35.48s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7003] 72.6+0.8s +[3200/16000] [L1: 1.7091] 71.9+0.0s +[4800/16000] [L1: 1.7221] 73.0+0.1s +[6400/16000] [L1: 1.7260] 73.9+0.0s +[8000/16000] [L1: 1.7295] 73.9+0.0s +[9600/16000] [L1: 1.7274] 72.4+0.0s +[11200/16000] [L1: 1.7176] 70.3+0.0s +[12800/16000] [L1: 1.7176] 73.8+0.1s +[14400/16000] [L1: 1.7209] 73.3+0.0s +[16000/16000] [L1: 1.7186] 72.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 34.98s + +Saving... +Total: 35.49s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.6990] 71.6+0.8s +[3200/16000] [L1: 1.7263] 72.9+0.1s +[4800/16000] [L1: 1.7240] 70.5+0.0s +[6400/16000] [L1: 1.7281] 72.5+0.0s +[8000/16000] [L1: 1.7159] 72.3+0.0s +[9600/16000] [L1: 1.7164] 74.1+0.0s +[11200/16000] [L1: 1.7161] 71.2+0.0s +[12800/16000] [L1: 1.7210] 72.3+0.0s +[14400/16000] [L1: 1.7178] 72.9+0.1s +[16000/16000] [L1: 1.7174] 72.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 34.94s + +Saving... +Total: 35.43s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7136] 73.6+0.9s +[3200/16000] [L1: 1.7179] 73.2+0.1s +[4800/16000] [L1: 1.7287] 74.3+0.1s +[6400/16000] [L1: 1.7209] 74.6+0.1s +[8000/16000] [L1: 1.7157] 74.0+0.1s +[9600/16000] [L1: 1.7228] 74.9+0.1s +[11200/16000] [L1: 1.7216] 72.8+0.1s +[12800/16000] [L1: 1.7175] 74.7+0.1s +[14400/16000] [L1: 1.7124] 72.1+0.1s +[16000/16000] [L1: 1.7143] 73.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.24s + +Saving... +Total: 35.73s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7354] 74.1+1.0s +[3200/16000] [L1: 1.7486] 72.0+0.0s +[4800/16000] [L1: 1.7362] 72.0+0.0s +[6400/16000] [L1: 1.7331] 70.7+0.0s +[8000/16000] [L1: 1.7243] 69.9+0.0s +[9600/16000] [L1: 1.7188] 68.1+0.0s +[11200/16000] [L1: 1.7148] 70.9+0.0s +[12800/16000] [L1: 1.7193] 73.4+0.0s +[14400/16000] [L1: 1.7200] 73.8+0.1s +[16000/16000] [L1: 1.7228] 72.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.25s + +Saving... +Total: 35.71s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7152] 73.1+1.0s +[3200/16000] [L1: 1.7042] 74.0+0.1s +[4800/16000] [L1: 1.7009] 74.6+0.1s +[6400/16000] [L1: 1.7078] 73.3+0.0s +[8000/16000] [L1: 1.7189] 73.5+0.0s +[9600/16000] [L1: 1.7161] 71.9+0.0s +[11200/16000] [L1: 1.7177] 71.2+0.0s +[12800/16000] [L1: 1.7197] 72.7+0.0s +[14400/16000] [L1: 1.7193] 72.7+0.1s +[16000/16000] [L1: 1.7203] 70.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.51s + +Saving... +Total: 35.91s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7085] 71.9+0.7s +[3200/16000] [L1: 1.7333] 71.0+0.1s +[4800/16000] [L1: 1.7253] 73.7+0.1s +[6400/16000] [L1: 1.7261] 73.8+0.0s +[8000/16000] [L1: 1.7281] 68.8+0.0s +[9600/16000] [L1: 1.7281] 70.2+0.0s +[11200/16000] [L1: 1.7299] 71.3+0.1s +[12800/16000] [L1: 1.7278] 70.8+0.0s +[14400/16000] [L1: 1.7281] 73.1+0.0s +[16000/16000] [L1: 1.7271] 74.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.50s + +Saving... +Total: 35.93s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7223] 74.9+0.9s +[3200/16000] [L1: 1.7153] 74.1+0.1s +[4800/16000] [L1: 1.7242] 74.4+0.1s +[6400/16000] [L1: 1.7277] 73.0+0.0s +[8000/16000] [L1: 1.7312] 73.6+0.0s +[9600/16000] [L1: 1.7215] 70.7+0.0s +[11200/16000] [L1: 1.7219] 72.8+0.0s +[12800/16000] [L1: 1.7237] 68.7+0.0s +[14400/16000] [L1: 1.7244] 71.3+0.0s +[16000/16000] [L1: 1.7224] 74.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.34s + +Saving... +Total: 35.84s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7110] 71.0+0.9s +[3200/16000] [L1: 1.7175] 69.1+0.1s +[4800/16000] [L1: 1.7285] 72.3+0.1s +[6400/16000] [L1: 1.7208] 71.5+0.0s +[8000/16000] [L1: 1.7202] 69.3+0.0s +[9600/16000] [L1: 1.7146] 74.2+0.0s +[11200/16000] [L1: 1.7135] 69.5+0.1s +[12800/16000] [L1: 1.7158] 72.2+0.1s +[14400/16000] [L1: 1.7129] 70.1+0.0s +[16000/16000] [L1: 1.7134] 70.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.62s + +Saving... +Total: 36.13s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7099] 73.8+0.8s +[3200/16000] [L1: 1.7359] 71.7+0.1s +[4800/16000] [L1: 1.7373] 72.3+0.1s +[6400/16000] [L1: 1.7306] 71.5+0.1s +[8000/16000] [L1: 1.7270] 68.2+0.1s +[9600/16000] [L1: 1.7240] 70.1+0.0s +[11200/16000] [L1: 1.7243] 70.4+0.1s +[12800/16000] [L1: 1.7269] 72.2+0.1s +[14400/16000] [L1: 1.7230] 71.1+0.1s +[16000/16000] [L1: 1.7214] 74.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.52s + +Saving... +Total: 35.97s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7248] 71.5+0.9s +[3200/16000] [L1: 1.7180] 72.4+0.1s +[4800/16000] [L1: 1.7152] 74.3+0.1s +[6400/16000] [L1: 1.7139] 70.7+0.1s +[8000/16000] [L1: 1.7173] 70.7+0.1s +[9600/16000] [L1: 1.7212] 72.2+0.1s +[11200/16000] [L1: 1.7233] 73.8+0.1s +[12800/16000] [L1: 1.7221] 73.2+0.1s +[14400/16000] [L1: 1.7224] 72.6+0.0s +[16000/16000] [L1: 1.7237] 73.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.40s + +Saving... +Total: 36.13s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7082] 72.9+0.8s +[3200/16000] [L1: 1.7321] 74.1+0.1s +[4800/16000] [L1: 1.7293] 75.1+0.1s +[6400/16000] [L1: 1.7175] 71.8+0.1s +[8000/16000] [L1: 1.7215] 72.7+0.1s +[9600/16000] [L1: 1.7191] 74.3+0.1s +[11200/16000] [L1: 1.7192] 73.8+0.1s +[12800/16000] [L1: 1.7162] 73.1+0.1s +[14400/16000] [L1: 1.7158] 72.0+0.1s +[16000/16000] [L1: 1.7166] 70.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.46s + +Saving... +Total: 35.84s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7176] 73.2+0.8s +[3200/16000] [L1: 1.7123] 75.2+0.1s +[4800/16000] [L1: 1.7015] 73.7+0.1s +[6400/16000] [L1: 1.7088] 72.8+0.1s +[8000/16000] [L1: 1.7102] 72.8+0.1s +[9600/16000] [L1: 1.7077] 73.4+0.0s +[11200/16000] [L1: 1.7056] 71.2+0.0s +[12800/16000] [L1: 1.7093] 71.1+0.0s +[14400/16000] [L1: 1.7081] 72.0+0.0s +[16000/16000] [L1: 1.7088] 71.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.82s + +Saving... +Total: 36.33s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7017] 67.7+0.8s +[3200/16000] [L1: 1.7075] 68.3+0.0s +[4800/16000] [L1: 1.7045] 75.2+0.1s +[6400/16000] [L1: 1.7136] 74.4+0.1s +[8000/16000] [L1: 1.7074] 71.3+0.1s +[9600/16000] [L1: 1.7053] 72.5+0.1s +[11200/16000] [L1: 1.7112] 69.9+0.0s +[12800/16000] [L1: 1.7130] 74.5+0.0s +[14400/16000] [L1: 1.7155] 73.5+0.1s +[16000/16000] [L1: 1.7163] 74.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.52s + +Saving... +Total: 35.99s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7593] 73.5+0.8s +[3200/16000] [L1: 1.7449] 72.4+0.1s +[4800/16000] [L1: 1.7420] 72.9+0.1s +[6400/16000] [L1: 1.7284] 72.7+0.1s +[8000/16000] [L1: 1.7251] 73.7+0.1s +[9600/16000] [L1: 1.7213] 73.7+0.1s +[11200/16000] [L1: 1.7178] 73.3+0.1s +[12800/16000] [L1: 1.7219] 74.2+0.1s +[14400/16000] [L1: 1.7188] 74.8+0.1s +[16000/16000] [L1: 1.7179] 73.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.34s + +Saving... +Total: 35.85s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7229] 73.7+1.0s +[3200/16000] [L1: 1.7211] 68.9+0.1s +[4800/16000] [L1: 1.7251] 69.4+0.1s +[6400/16000] [L1: 1.7277] 72.1+0.1s +[8000/16000] [L1: 1.7230] 73.6+0.1s +[9600/16000] [L1: 1.7297] 72.2+0.1s +[11200/16000] [L1: 1.7303] 74.3+0.1s +[12800/16000] [L1: 1.7264] 71.5+0.1s +[14400/16000] [L1: 1.7241] 74.0+0.1s +[16000/16000] [L1: 1.7285] 73.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.47s + +Saving... +Total: 35.95s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7145] 73.7+1.1s +[3200/16000] [L1: 1.7229] 73.6+0.1s +[4800/16000] [L1: 1.7181] 73.3+0.1s +[6400/16000] [L1: 1.7314] 74.1+0.1s +[8000/16000] [L1: 1.7332] 72.8+0.1s +[9600/16000] [L1: 1.7334] 72.4+0.1s +[11200/16000] [L1: 1.7349] 70.4+0.1s +[12800/16000] [L1: 1.7350] 71.6+0.1s +[14400/16000] [L1: 1.7351] 71.6+0.0s +[16000/16000] [L1: 1.7337] 74.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.48s + +Saving... +Total: 35.86s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7134] 74.7+0.8s +[3200/16000] [L1: 1.7389] 72.0+0.1s +[4800/16000] [L1: 1.7322] 72.9+0.1s +[6400/16000] [L1: 1.7217] 73.3+0.1s +[8000/16000] [L1: 1.7197] 71.1+0.0s +[9600/16000] [L1: 1.7156] 71.0+0.0s +[11200/16000] [L1: 1.7116] 70.8+0.0s +[12800/16000] [L1: 1.7148] 73.7+0.1s +[14400/16000] [L1: 1.7179] 74.4+0.1s +[16000/16000] [L1: 1.7193] 72.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.66s + +Saving... +Total: 36.29s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7067] 71.8+0.9s +[3200/16000] [L1: 1.7135] 74.4+0.1s +[4800/16000] [L1: 1.7254] 71.5+0.1s +[6400/16000] [L1: 1.7298] 73.3+0.0s +[8000/16000] [L1: 1.7285] 74.5+0.1s +[9600/16000] [L1: 1.7288] 73.5+0.1s +[11200/16000] [L1: 1.7266] 73.3+0.0s +[12800/16000] [L1: 1.7307] 74.1+0.1s +[14400/16000] [L1: 1.7328] 71.3+0.1s +[16000/16000] [L1: 1.7317] 71.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.35s + +Saving... +Total: 35.83s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7517] 73.3+0.8s +[3200/16000] [L1: 1.7386] 73.1+0.1s +[4800/16000] [L1: 1.7351] 70.2+0.1s +[6400/16000] [L1: 1.7308] 73.4+0.1s +[8000/16000] [L1: 1.7297] 74.2+0.1s +[9600/16000] [L1: 1.7287] 73.7+0.1s +[11200/16000] [L1: 1.7302] 73.4+0.1s +[12800/16000] [L1: 1.7309] 74.9+0.1s +[14400/16000] [L1: 1.7299] 74.0+0.1s +[16000/16000] [L1: 1.7301] 74.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.08s + +Saving... +Total: 36.25s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7275] 71.6+0.9s +[3200/16000] [L1: 1.7277] 72.5+0.1s +[4800/16000] [L1: 1.7334] 73.6+0.1s +[6400/16000] [L1: 1.7342] 72.8+0.0s +[8000/16000] [L1: 1.7335] 71.2+0.1s +[9600/16000] [L1: 1.7388] 74.1+0.0s +[11200/16000] [L1: 1.7350] 71.5+0.0s +[12800/16000] [L1: 1.7308] 68.0+0.0s +[14400/16000] [L1: 1.7290] 68.4+0.0s +[16000/16000] [L1: 1.7294] 72.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.58s + +Saving... +Total: 36.11s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.6842] 73.5+1.0s +[3200/16000] [L1: 1.6890] 73.6+0.1s +[4800/16000] [L1: 1.7121] 71.9+0.1s +[6400/16000] [L1: 1.7102] 71.3+0.0s +[8000/16000] [L1: 1.7042] 70.3+0.1s +[9600/16000] [L1: 1.7136] 70.9+0.1s +[11200/16000] [L1: 1.7153] 69.9+0.0s +[12800/16000] [L1: 1.7165] 74.0+0.1s +[14400/16000] [L1: 1.7217] 72.4+0.1s +[16000/16000] [L1: 1.7226] 69.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.44s + +Saving... +Total: 35.83s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7147] 73.9+0.9s +[3200/16000] [L1: 1.7066] 73.8+0.1s +[4800/16000] [L1: 1.7149] 70.3+0.1s +[6400/16000] [L1: 1.7147] 70.6+0.0s +[8000/16000] [L1: 1.7119] 73.7+0.1s +[9600/16000] [L1: 1.7146] 73.4+0.1s +[11200/16000] [L1: 1.7135] 73.6+0.1s +[12800/16000] [L1: 1.7142] 72.4+0.0s +[14400/16000] [L1: 1.7185] 72.4+0.0s +[16000/16000] [L1: 1.7218] 71.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.19s + +Saving... +Total: 35.67s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7402] 69.8+0.9s +[3200/16000] [L1: 1.7201] 69.8+0.1s +[4800/16000] [L1: 1.7237] 68.2+0.0s +[6400/16000] [L1: 1.7151] 70.5+0.0s +[8000/16000] [L1: 1.7087] 71.6+0.0s +[9600/16000] [L1: 1.7093] 71.9+0.0s +[11200/16000] [L1: 1.7134] 71.4+0.0s +[12800/16000] [L1: 1.7181] 72.9+0.0s +[14400/16000] [L1: 1.7190] 73.4+0.0s +[16000/16000] [L1: 1.7182] 73.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.18s + +Saving... +Total: 35.85s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7471] 73.6+0.9s +[3200/16000] [L1: 1.7320] 74.2+0.1s +[4800/16000] [L1: 1.7095] 75.4+0.1s +[6400/16000] [L1: 1.7199] 72.0+0.1s +[8000/16000] [L1: 1.7155] 71.0+0.1s +[9600/16000] [L1: 1.7172] 72.7+0.1s +[11200/16000] [L1: 1.7247] 69.5+0.1s +[12800/16000] [L1: 1.7249] 71.6+0.0s +[14400/16000] [L1: 1.7242] 71.1+0.0s +[16000/16000] [L1: 1.7271] 72.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.26s + +Saving... +Total: 35.73s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.6754] 73.8+0.8s +[3200/16000] [L1: 1.7225] 72.1+0.1s +[4800/16000] [L1: 1.7324] 73.3+0.1s +[6400/16000] [L1: 1.7352] 74.1+0.1s +[8000/16000] [L1: 1.7276] 73.2+0.1s +[9600/16000] [L1: 1.7206] 70.5+0.0s +[11200/16000] [L1: 1.7248] 73.7+0.1s +[12800/16000] [L1: 1.7206] 69.8+0.1s +[14400/16000] [L1: 1.7210] 74.9+0.0s +[16000/16000] [L1: 1.7224] 72.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.29s + +Saving... +Total: 35.86s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7243] 72.0+0.8s +[3200/16000] [L1: 1.7454] 70.1+0.0s +[4800/16000] [L1: 1.7241] 72.0+0.1s +[6400/16000] [L1: 1.7218] 71.0+0.1s +[8000/16000] [L1: 1.7202] 71.8+0.1s +[9600/16000] [L1: 1.7249] 71.5+0.1s +[11200/16000] [L1: 1.7251] 74.6+0.1s +[12800/16000] [L1: 1.7209] 71.6+0.1s +[14400/16000] [L1: 1.7215] 70.7+0.1s +[16000/16000] [L1: 1.7210] 72.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.17s + +Saving... +Total: 35.66s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7008] 72.3+1.0s +[3200/16000] [L1: 1.6971] 73.6+0.1s +[4800/16000] [L1: 1.7079] 73.2+0.1s +[6400/16000] [L1: 1.7088] 72.9+0.1s +[8000/16000] [L1: 1.7102] 73.2+0.1s +[9600/16000] [L1: 1.7082] 72.8+0.1s +[11200/16000] [L1: 1.7084] 72.8+0.1s +[12800/16000] [L1: 1.7065] 73.7+0.1s +[14400/16000] [L1: 1.7094] 71.2+0.1s +[16000/16000] [L1: 1.7136] 70.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.14s + +Saving... +Total: 35.63s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7246] 71.6+0.9s +[3200/16000] [L1: 1.7151] 72.9+0.1s +[4800/16000] [L1: 1.7316] 74.5+0.1s +[6400/16000] [L1: 1.7353] 74.6+0.1s +[8000/16000] [L1: 1.7363] 73.3+0.1s +[9600/16000] [L1: 1.7344] 70.3+0.0s +[11200/16000] [L1: 1.7290] 71.8+0.0s +[12800/16000] [L1: 1.7217] 73.9+0.1s +[14400/16000] [L1: 1.7232] 74.0+0.1s +[16000/16000] [L1: 1.7213] 71.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.23s + +Saving... +Total: 35.88s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7344] 74.2+0.9s +[3200/16000] [L1: 1.7331] 71.8+0.0s +[4800/16000] [L1: 1.7310] 75.3+0.0s +[6400/16000] [L1: 1.7261] 75.0+0.1s +[8000/16000] [L1: 1.7210] 68.8+0.0s +[9600/16000] [L1: 1.7172] 72.2+0.1s +[11200/16000] [L1: 1.7168] 74.4+0.1s +[12800/16000] [L1: 1.7187] 70.9+0.1s +[14400/16000] [L1: 1.7196] 70.2+0.0s +[16000/16000] [L1: 1.7175] 75.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.05s + +Saving... +Total: 35.42s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7116] 73.2+0.8s +[3200/16000] [L1: 1.7134] 73.8+0.1s +[4800/16000] [L1: 1.7123] 73.8+0.1s +[6400/16000] [L1: 1.7122] 72.2+0.1s +[8000/16000] [L1: 1.7087] 73.5+0.1s +[9600/16000] [L1: 1.7085] 74.9+0.1s +[11200/16000] [L1: 1.7136] 74.1+0.1s +[12800/16000] [L1: 1.7148] 69.2+0.1s +[14400/16000] [L1: 1.7209] 71.8+0.1s +[16000/16000] [L1: 1.7223] 71.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.01s + +Saving... +Total: 35.50s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7165] 75.3+1.0s +[3200/16000] [L1: 1.7125] 73.8+0.1s +[4800/16000] [L1: 1.7207] 73.7+0.1s +[6400/16000] [L1: 1.7377] 71.4+0.1s +[8000/16000] [L1: 1.7321] 73.3+0.1s +[9600/16000] [L1: 1.7329] 72.5+0.1s +[11200/16000] [L1: 1.7303] 70.2+0.1s +[12800/16000] [L1: 1.7319] 73.5+0.1s +[14400/16000] [L1: 1.7262] 74.3+0.1s +[16000/16000] [L1: 1.7265] 69.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.37s + +Saving... +Total: 35.81s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7418] 73.1+0.8s +[3200/16000] [L1: 1.7297] 71.0+0.1s +[4800/16000] [L1: 1.7271] 70.6+0.0s +[6400/16000] [L1: 1.7298] 72.6+0.1s +[8000/16000] [L1: 1.7230] 74.5+0.1s +[9600/16000] [L1: 1.7185] 71.2+0.1s +[11200/16000] [L1: 1.7223] 71.1+0.0s +[12800/16000] [L1: 1.7221] 71.4+0.1s +[14400/16000] [L1: 1.7232] 75.1+0.1s +[16000/16000] [L1: 1.7231] 74.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.04s + +Saving... +Total: 35.56s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7184] 70.8+0.9s +[3200/16000] [L1: 1.7424] 68.8+0.1s +[4800/16000] [L1: 1.7303] 72.8+0.1s +[6400/16000] [L1: 1.7285] 73.7+0.1s +[8000/16000] [L1: 1.7306] 73.1+0.1s +[9600/16000] [L1: 1.7273] 71.7+0.1s +[11200/16000] [L1: 1.7258] 73.1+0.1s +[12800/16000] [L1: 1.7290] 71.7+0.1s +[14400/16000] [L1: 1.7305] 72.9+0.1s +[16000/16000] [L1: 1.7271] 72.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.14s + +Saving... +Total: 35.63s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7235] 70.8+0.9s +[3200/16000] [L1: 1.7309] 69.1+0.1s +[4800/16000] [L1: 1.7225] 69.7+0.1s +[6400/16000] [L1: 1.7217] 68.8+0.1s +[8000/16000] [L1: 1.7219] 73.5+0.1s +[9600/16000] [L1: 1.7187] 72.2+0.1s +[11200/16000] [L1: 1.7247] 69.7+0.0s +[12800/16000] [L1: 1.7240] 69.0+0.0s +[14400/16000] [L1: 1.7211] 73.3+0.1s +[16000/16000] [L1: 1.7235] 69.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.34s + +Saving... +Total: 35.76s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7415] 73.3+0.9s +[3200/16000] [L1: 1.7409] 74.3+0.1s +[4800/16000] [L1: 1.7356] 72.2+0.1s +[6400/16000] [L1: 1.7312] 72.7+0.1s +[8000/16000] [L1: 1.7245] 72.0+0.1s +[9600/16000] [L1: 1.7247] 72.7+0.0s +[11200/16000] [L1: 1.7211] 71.1+0.1s +[12800/16000] [L1: 1.7263] 71.4+0.0s +[14400/16000] [L1: 1.7239] 71.1+0.0s +[16000/16000] [L1: 1.7264] 72.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.38s + +Saving... +Total: 35.95s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7348] 71.7+0.9s +[3200/16000] [L1: 1.7300] 71.8+0.1s +[4800/16000] [L1: 1.7293] 73.1+0.1s +[6400/16000] [L1: 1.7313] 73.3+0.1s +[8000/16000] [L1: 1.7277] 71.8+0.0s +[9600/16000] [L1: 1.7281] 72.5+0.1s +[11200/16000] [L1: 1.7297] 74.3+0.1s +[12800/16000] [L1: 1.7224] 71.8+0.1s +[14400/16000] [L1: 1.7237] 70.8+0.0s +[16000/16000] [L1: 1.7228] 71.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.32s + +Saving... +Total: 35.86s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7548] 68.9+0.9s +[3200/16000] [L1: 1.7426] 72.5+0.1s +[4800/16000] [L1: 1.7496] 71.0+0.1s +[6400/16000] [L1: 1.7456] 69.5+0.0s +[8000/16000] [L1: 1.7477] 69.0+0.0s +[9600/16000] [L1: 1.7421] 72.2+0.0s +[11200/16000] [L1: 1.7400] 73.5+0.1s +[12800/16000] [L1: 1.7336] 71.3+0.0s +[14400/16000] [L1: 1.7337] 71.1+0.0s +[16000/16000] [L1: 1.7302] 71.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.49s + +Saving... +Total: 36.10s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7035] 69.1+0.9s +[3200/16000] [L1: 1.7178] 71.6+0.1s +[4800/16000] [L1: 1.7283] 71.8+0.1s +[6400/16000] [L1: 1.7316] 74.0+0.1s +[8000/16000] [L1: 1.7382] 74.0+0.1s +[9600/16000] [L1: 1.7332] 72.3+0.1s +[11200/16000] [L1: 1.7278] 73.1+0.1s +[12800/16000] [L1: 1.7256] 71.4+0.1s +[14400/16000] [L1: 1.7245] 69.7+0.0s +[16000/16000] [L1: 1.7219] 70.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.47s + +Saving... +Total: 35.96s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7489] 67.9+0.8s +[3200/16000] [L1: 1.7222] 70.8+0.1s +[4800/16000] [L1: 1.7229] 69.9+0.1s +[6400/16000] [L1: 1.7266] 69.4+0.0s +[8000/16000] [L1: 1.7327] 72.0+0.1s +[9600/16000] [L1: 1.7269] 70.2+0.0s +[11200/16000] [L1: 1.7303] 69.5+0.0s +[12800/16000] [L1: 1.7307] 70.4+0.0s +[14400/16000] [L1: 1.7292] 69.0+0.0s +[16000/16000] [L1: 1.7267] 68.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.45s + +Saving... +Total: 35.93s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7218] 75.5+0.9s +[3200/16000] [L1: 1.7019] 75.9+0.1s +[4800/16000] [L1: 1.7000] 74.2+0.1s +[6400/16000] [L1: 1.7086] 71.0+0.1s +[8000/16000] [L1: 1.7114] 71.1+0.1s +[9600/16000] [L1: 1.7085] 72.5+0.1s +[11200/16000] [L1: 1.7124] 73.0+0.1s +[12800/16000] [L1: 1.7152] 73.3+0.1s +[14400/16000] [L1: 1.7110] 70.6+0.0s +[16000/16000] [L1: 1.7119] 71.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.18s + +Saving... +Total: 35.63s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7358] 72.1+0.9s +[3200/16000] [L1: 1.7240] 71.8+0.1s +[4800/16000] [L1: 1.7193] 71.1+0.1s +[6400/16000] [L1: 1.7223] 71.2+0.1s +[8000/16000] [L1: 1.7134] 71.8+0.1s +[9600/16000] [L1: 1.7168] 73.8+0.1s +[11200/16000] [L1: 1.7142] 72.9+0.1s +[12800/16000] [L1: 1.7164] 71.9+0.1s +[14400/16000] [L1: 1.7179] 71.9+0.1s +[16000/16000] [L1: 1.7202] 72.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.36s + +Saving... +Total: 35.89s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.7242] 74.2+1.0s +[3200/16000] [L1: 1.7394] 74.3+0.1s +[4800/16000] [L1: 1.7331] 73.6+0.1s +[6400/16000] [L1: 1.7267] 74.2+0.1s +[8000/16000] [L1: 1.7246] 72.9+0.1s +[9600/16000] [L1: 1.7208] 73.4+0.1s +[11200/16000] [L1: 1.7192] 71.9+0.0s +[12800/16000] [L1: 1.7194] 73.2+0.1s +[14400/16000] [L1: 1.7182] 72.8+0.1s +[16000/16000] [L1: 1.7168] 67.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: nan (Best: nan @epoch 1) +Forward: 35.20s + +Saving... +Total: 35.73s + diff --git a/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/loss.pt b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..2cb8aeac7a3784a1b17d156d419747cef2ed44bf --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48da99bdddb436cdb4a093ba3a3efbe02ec42ba0bd17f6415b4f6645eb17b79f +size 559 diff --git a/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/loss_L1.pdf b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/loss_L1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e940855d95e4debc377793e69e5fa3f4ef1473e8 Binary files /dev/null and b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4/loss_L1.pdf differ diff --git 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0000000000000000000000000000000000000000..70847bd06a6ab6a23d1835eea8add8b534f825e8 --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4_MSE/config.txt @@ -0,0 +1,66 @@ +2020-11-07-00:06:22 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANETACTB +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAMW +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.0 +loss: 1*MSE +skip_threshold: 100000000.0 +save: LAMBDANETACTB_DEMOSAIC20_R4_MSE +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4_MSE/log.txt b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4_MSE/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..85cdd4a43464a30f02764af6d8fa279c5ca9e61d --- /dev/null +++ b/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4_MSE/log.txt @@ -0,0 +1,6355 @@ +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1360] 47.5+0.6s +[3200/16000] [MSE: 1.0874] 47.1+0.1s +[4800/16000] [MSE: 1.0725] 46.4+0.0s +[6400/16000] [MSE: 1.0645] 47.2+0.0s +[8000/16000] [MSE: 1.0655] 46.5+0.0s +[9600/16000] [MSE: 1.0653] 47.0+0.0s +[11200/16000] [MSE: 1.0635] 46.6+0.0s +[12800/16000] [MSE: 1.0634] 46.4+0.0s +[14400/16000] [MSE: 1.0649] 46.5+0.0s +[16000/16000] [MSE: 1.0617] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.08s + +Saving... +Total: 38.78s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0758] 47.7+0.8s +[3200/16000] [MSE: 1.0708] 47.8+0.0s +[4800/16000] [MSE: 1.0650] 47.6+0.0s +[6400/16000] [MSE: 1.0692] 47.5+0.0s +[8000/16000] [MSE: 1.0623] 47.2+0.0s +[9600/16000] [MSE: 1.0613] 47.0+0.0s +[11200/16000] [MSE: 1.0626] 47.0+0.0s +[12800/16000] [MSE: 1.0613] 47.0+0.0s +[14400/16000] [MSE: 1.0608] 47.4+0.0s +[16000/16000] [MSE: 1.0586] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.40s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0669] 48.1+0.7s +[3200/16000] [MSE: 1.0538] 47.8+0.1s +[4800/16000] [MSE: 1.0541] 47.8+0.1s +[6400/16000] [MSE: 1.0541] 47.5+0.0s +[8000/16000] [MSE: 1.0570] 47.4+0.0s +[9600/16000] [MSE: 1.0591] 47.5+0.0s +[11200/16000] [MSE: 1.0580] 47.3+0.0s +[12800/16000] [MSE: 1.0585] 47.1+0.0s +[14400/16000] [MSE: 1.0603] 47.3+0.0s +[16000/16000] [MSE: 1.0611] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.34s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0389] 47.9+0.7s +[3200/16000] [MSE: 1.0480] 47.3+0.0s +[4800/16000] [MSE: 1.0628] 47.2+0.0s +[6400/16000] [MSE: 1.0680] 47.2+0.0s +[8000/16000] [MSE: 1.0700] 47.0+0.0s +[9600/16000] [MSE: 1.0668] 47.1+0.0s +[11200/16000] [MSE: 1.0573] 46.9+0.0s +[12800/16000] [MSE: 1.0569] 46.9+0.0s +[14400/16000] [MSE: 1.0603] 46.8+0.0s +[16000/16000] [MSE: 1.0584] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.30s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0426] 48.0+0.7s +[3200/16000] [MSE: 1.0665] 47.8+0.1s +[4800/16000] [MSE: 1.0636] 47.8+0.1s +[6400/16000] [MSE: 1.0659] 47.7+0.0s +[8000/16000] [MSE: 1.0539] 47.4+0.0s +[9600/16000] [MSE: 1.0542] 47.1+0.0s +[11200/16000] [MSE: 1.0543] 47.2+0.0s +[12800/16000] [MSE: 1.0590] 47.1+0.0s +[14400/16000] [MSE: 1.0567] 47.2+0.0s +[16000/16000] [MSE: 1.0551] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.32s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0533] 47.8+0.7s +[3200/16000] [MSE: 1.0584] 47.3+0.0s +[4800/16000] [MSE: 1.0683] 46.6+0.0s +[6400/16000] [MSE: 1.0606] 47.0+0.0s +[8000/16000] [MSE: 1.0558] 47.1+0.0s +[9600/16000] [MSE: 1.0630] 47.4+0.0s +[11200/16000] [MSE: 1.0614] 46.4+0.0s +[12800/16000] [MSE: 1.0577] 46.3+0.0s +[14400/16000] [MSE: 1.0541] 46.1+0.0s +[16000/16000] [MSE: 1.0542] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.39s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0645] 48.1+0.7s +[3200/16000] [MSE: 1.0820] 47.2+0.0s +[4800/16000] [MSE: 1.0733] 47.2+0.0s +[6400/16000] [MSE: 1.0709] 47.2+0.0s +[8000/16000] [MSE: 1.0631] 46.6+0.0s +[9600/16000] [MSE: 1.0561] 46.9+0.0s +[11200/16000] [MSE: 1.0527] 46.8+0.0s +[12800/16000] [MSE: 1.0566] 46.9+0.0s +[14400/16000] [MSE: 1.0572] 46.3+0.0s +[16000/16000] [MSE: 1.0601] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.91s + +Saving... +Total: 38.44s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0538] 47.7+0.8s +[3200/16000] [MSE: 1.0453] 47.0+0.0s +[4800/16000] [MSE: 1.0411] 46.2+0.0s +[6400/16000] [MSE: 1.0491] 46.4+0.0s +[8000/16000] [MSE: 1.0607] 46.2+0.0s +[9600/16000] [MSE: 1.0572] 45.9+0.0s +[11200/16000] [MSE: 1.0586] 45.9+0.0s +[12800/16000] [MSE: 1.0594] 46.4+0.0s +[14400/16000] [MSE: 1.0580] 46.2+0.0s +[16000/16000] [MSE: 1.0586] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.89s + +Saving... +Total: 38.41s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0511] 47.8+0.7s +[3200/16000] [MSE: 1.0758] 47.5+0.1s +[4800/16000] [MSE: 1.0670] 47.1+0.0s +[6400/16000] [MSE: 1.0667] 46.9+0.0s +[8000/16000] [MSE: 1.0680] 46.4+0.0s +[9600/16000] [MSE: 1.0663] 47.1+0.0s +[11200/16000] [MSE: 1.0682] 46.4+0.0s +[12800/16000] [MSE: 1.0661] 46.6+0.0s +[14400/16000] [MSE: 1.0664] 46.9+0.0s +[16000/16000] [MSE: 1.0645] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.96s + +Saving... +Total: 38.46s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0597] 47.8+0.8s +[3200/16000] [MSE: 1.0513] 47.5+0.1s +[4800/16000] [MSE: 1.0594] 47.5+0.0s +[6400/16000] [MSE: 1.0633] 47.2+0.0s +[8000/16000] [MSE: 1.0667] 47.3+0.0s +[9600/16000] [MSE: 1.0585] 47.4+0.0s +[11200/16000] [MSE: 1.0594] 47.2+0.0s +[12800/16000] [MSE: 1.0611] 47.0+0.0s +[14400/16000] [MSE: 1.0618] 46.9+0.0s +[16000/16000] [MSE: 1.0594] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.01s + +Saving... +Total: 38.49s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0545] 47.9+0.7s +[3200/16000] [MSE: 1.0574] 47.9+0.1s +[4800/16000] [MSE: 1.0678] 47.4+0.0s +[6400/16000] [MSE: 1.0628] 47.3+0.0s +[8000/16000] [MSE: 1.0617] 47.3+0.0s +[9600/16000] [MSE: 1.0562] 47.4+0.0s +[11200/16000] [MSE: 1.0532] 46.7+0.0s +[12800/16000] [MSE: 1.0561] 47.1+0.0s +[14400/16000] [MSE: 1.0532] 46.7+0.0s +[16000/16000] [MSE: 1.0548] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.91s + +Saving... +Total: 38.39s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0498] 47.9+0.6s +[3200/16000] [MSE: 1.0755] 47.2+0.0s +[4800/16000] [MSE: 1.0789] 46.9+0.0s +[6400/16000] [MSE: 1.0727] 47.2+0.0s +[8000/16000] [MSE: 1.0684] 47.0+0.0s +[9600/16000] [MSE: 1.0640] 47.2+0.0s +[11200/16000] [MSE: 1.0662] 47.0+0.0s +[12800/16000] [MSE: 1.0692] 47.1+0.0s +[14400/16000] [MSE: 1.0655] 47.3+0.0s +[16000/16000] [MSE: 1.0632] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.01s + +Saving... +Total: 38.55s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0651] 47.9+0.7s +[3200/16000] [MSE: 1.0598] 47.5+0.1s +[4800/16000] [MSE: 1.0539] 47.4+0.0s +[6400/16000] [MSE: 1.0533] 47.3+0.0s +[8000/16000] [MSE: 1.0577] 47.0+0.0s +[9600/16000] [MSE: 1.0625] 46.7+0.0s +[11200/16000] [MSE: 1.0640] 47.1+0.0s +[12800/16000] [MSE: 1.0627] 47.4+0.0s +[14400/16000] [MSE: 1.0632] 47.1+0.0s +[16000/16000] [MSE: 1.0647] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.48s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0336] 47.6+1.0s +[3200/16000] [MSE: 1.0604] 47.5+0.0s +[4800/16000] [MSE: 1.0665] 47.3+0.0s +[6400/16000] [MSE: 1.0558] 47.4+0.0s +[8000/16000] [MSE: 1.0588] 47.7+0.0s +[9600/16000] [MSE: 1.0567] 47.2+0.0s +[11200/16000] [MSE: 1.0559] 47.0+0.0s +[12800/16000] [MSE: 1.0524] 47.2+0.0s +[14400/16000] [MSE: 1.0520] 47.2+0.0s +[16000/16000] [MSE: 1.0532] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.39s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0586] 48.0+0.7s +[3200/16000] [MSE: 1.0549] 47.5+0.0s +[4800/16000] [MSE: 1.0419] 47.4+0.0s +[6400/16000] [MSE: 1.0493] 47.3+0.0s +[8000/16000] [MSE: 1.0494] 47.2+0.0s +[9600/16000] [MSE: 1.0470] 47.0+0.0s +[11200/16000] [MSE: 1.0446] 47.1+0.0s +[12800/16000] [MSE: 1.0473] 46.8+0.0s +[14400/16000] [MSE: 1.0457] 46.5+0.0s +[16000/16000] [MSE: 1.0474] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.99s + +Saving... +Total: 38.49s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0372] 47.7+0.7s +[3200/16000] [MSE: 1.0431] 47.3+0.0s +[4800/16000] [MSE: 1.0439] 47.6+0.0s +[6400/16000] [MSE: 1.0538] 47.3+0.0s +[8000/16000] [MSE: 1.0467] 47.3+0.0s +[9600/16000] [MSE: 1.0447] 46.9+0.0s +[11200/16000] [MSE: 1.0495] 47.0+0.0s +[12800/16000] [MSE: 1.0510] 46.6+0.0s +[14400/16000] [MSE: 1.0532] 46.2+0.0s +[16000/16000] [MSE: 1.0540] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.92s + +Saving... +Total: 38.41s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0951] 48.0+0.7s +[3200/16000] [MSE: 1.0787] 47.7+0.1s +[4800/16000] [MSE: 1.0757] 47.7+0.1s +[6400/16000] [MSE: 1.0637] 47.2+0.0s +[8000/16000] [MSE: 1.0603] 47.0+0.0s +[9600/16000] [MSE: 1.0561] 47.0+0.0s +[11200/16000] [MSE: 1.0540] 47.3+0.0s +[12800/16000] [MSE: 1.0598] 46.9+0.0s +[14400/16000] [MSE: 1.0574] 46.7+0.0s +[16000/16000] [MSE: 1.0571] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.91s + +Saving... +Total: 38.45s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0661] 47.9+0.8s +[3200/16000] [MSE: 1.0617] 47.4+0.0s +[4800/16000] [MSE: 1.0630] 47.6+0.0s +[6400/16000] [MSE: 1.0648] 47.1+0.0s +[8000/16000] [MSE: 1.0618] 47.1+0.0s +[9600/16000] [MSE: 1.0684] 46.8+0.0s +[11200/16000] [MSE: 1.0686] 47.0+0.0s +[12800/16000] [MSE: 1.0644] 47.3+0.0s +[14400/16000] [MSE: 1.0624] 47.1+0.0s +[16000/16000] [MSE: 1.0662] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.40s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0515] 48.2+0.8s +[3200/16000] [MSE: 1.0644] 47.8+0.1s +[4800/16000] [MSE: 1.0608] 47.8+0.0s +[6400/16000] [MSE: 1.0720] 47.9+0.0s +[8000/16000] [MSE: 1.0735] 47.3+0.0s +[9600/16000] [MSE: 1.0732] 46.9+0.0s +[11200/16000] [MSE: 1.0734] 46.4+0.0s +[12800/16000] [MSE: 1.0732] 46.5+0.0s +[14400/16000] [MSE: 1.0722] 46.1+0.0s +[16000/16000] [MSE: 1.0712] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.04s + +Saving... +Total: 38.52s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0555] 48.1+0.8s +[3200/16000] [MSE: 1.0790] 48.0+0.1s +[4800/16000] [MSE: 1.0711] 47.6+0.0s +[6400/16000] [MSE: 1.0602] 47.7+0.0s +[8000/16000] [MSE: 1.0570] 47.6+0.1s +[9600/16000] [MSE: 1.0536] 47.5+0.0s +[11200/16000] [MSE: 1.0500] 47.4+0.0s +[12800/16000] [MSE: 1.0531] 47.3+0.0s +[14400/16000] [MSE: 1.0556] 47.1+0.0s +[16000/16000] [MSE: 1.0575] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.04s + +Saving... +Total: 38.55s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0425] 47.8+0.7s +[3200/16000] [MSE: 1.0496] 47.4+0.0s +[4800/16000] [MSE: 1.0606] 47.1+0.0s +[6400/16000] [MSE: 1.0663] 47.2+0.0s +[8000/16000] [MSE: 1.0654] 46.8+0.0s +[9600/16000] [MSE: 1.0657] 46.6+0.0s +[11200/16000] [MSE: 1.0635] 46.7+0.0s +[12800/16000] [MSE: 1.0678] 46.2+0.0s +[14400/16000] [MSE: 1.0696] 46.8+0.0s +[16000/16000] [MSE: 1.0682] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.93s + +Saving... +Total: 38.45s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0863] 47.6+0.8s +[3200/16000] [MSE: 1.0778] 47.7+0.0s +[4800/16000] [MSE: 1.0731] 47.3+0.0s +[6400/16000] [MSE: 1.0702] 47.5+0.0s +[8000/16000] [MSE: 1.0700] 46.9+0.0s +[9600/16000] [MSE: 1.0695] 46.9+0.0s +[11200/16000] [MSE: 1.0700] 46.9+0.0s +[12800/16000] [MSE: 1.0700] 46.9+0.0s +[14400/16000] [MSE: 1.0694] 46.6+0.0s +[16000/16000] [MSE: 1.0693] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.23s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0756] 47.9+0.7s +[3200/16000] [MSE: 1.0684] 47.9+0.0s +[4800/16000] [MSE: 1.0722] 47.5+0.0s +[6400/16000] [MSE: 1.0733] 47.7+0.0s +[8000/16000] [MSE: 1.0709] 47.6+0.0s +[9600/16000] [MSE: 1.0763] 47.0+0.0s +[11200/16000] [MSE: 1.0721] 46.3+0.0s +[12800/16000] [MSE: 1.0679] 47.1+0.0s +[14400/16000] [MSE: 1.0661] 46.1+0.0s +[16000/16000] [MSE: 1.0675] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.33s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0222] 47.7+0.8s +[3200/16000] [MSE: 1.0283] 47.6+0.0s +[4800/16000] [MSE: 1.0470] 47.4+0.0s +[6400/16000] [MSE: 1.0443] 47.3+0.0s +[8000/16000] [MSE: 1.0388] 47.3+0.0s +[9600/16000] [MSE: 1.0478] 47.3+0.0s +[11200/16000] [MSE: 1.0504] 47.4+0.0s +[12800/16000] [MSE: 1.0524] 47.2+0.0s +[14400/16000] [MSE: 1.0574] 47.2+0.0s +[16000/16000] [MSE: 1.0582] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.67s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0503] 47.9+0.8s +[3200/16000] [MSE: 1.0434] 47.8+0.1s +[4800/16000] [MSE: 1.0512] 47.8+0.1s +[6400/16000] [MSE: 1.0530] 47.8+0.1s +[8000/16000] [MSE: 1.0510] 47.4+0.0s +[9600/16000] [MSE: 1.0529] 47.4+0.0s +[11200/16000] [MSE: 1.0528] 47.3+0.0s +[12800/16000] [MSE: 1.0532] 47.3+0.0s +[14400/16000] [MSE: 1.0573] 47.2+0.0s +[16000/16000] [MSE: 1.0615] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.26s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0793] 48.3+0.7s +[3200/16000] [MSE: 1.0629] 47.8+0.1s +[4800/16000] [MSE: 1.0668] 47.5+0.0s +[6400/16000] [MSE: 1.0559] 47.7+0.0s +[8000/16000] [MSE: 1.0480] 47.1+0.0s +[9600/16000] [MSE: 1.0480] 47.3+0.0s +[11200/16000] [MSE: 1.0528] 47.2+0.0s +[12800/16000] [MSE: 1.0564] 47.1+0.0s +[14400/16000] [MSE: 1.0579] 46.9+0.0s +[16000/16000] [MSE: 1.0578] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.91s + +Saving... +Total: 38.38s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0871] 47.7+0.6s +[3200/16000] [MSE: 1.0747] 47.7+0.1s +[4800/16000] [MSE: 1.0535] 47.6+0.0s +[6400/16000] [MSE: 1.0610] 47.0+0.0s +[8000/16000] [MSE: 1.0560] 47.2+0.0s +[9600/16000] [MSE: 1.0567] 47.0+0.0s +[11200/16000] [MSE: 1.0629] 46.8+0.0s +[12800/16000] [MSE: 1.0626] 46.8+0.0s +[14400/16000] [MSE: 1.0625] 46.6+0.0s +[16000/16000] [MSE: 1.0655] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.34s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0137] 47.6+0.7s +[3200/16000] [MSE: 1.0651] 47.1+0.0s +[4800/16000] [MSE: 1.0753] 47.3+0.0s +[6400/16000] [MSE: 1.0767] 47.0+0.0s +[8000/16000] [MSE: 1.0666] 47.0+0.0s +[9600/16000] [MSE: 1.0598] 46.8+0.0s +[11200/16000] [MSE: 1.0630] 46.9+0.0s +[12800/16000] [MSE: 1.0592] 46.7+0.0s +[14400/16000] [MSE: 1.0607] 47.1+0.0s +[16000/16000] [MSE: 1.0616] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.31s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0573] 47.7+0.7s +[3200/16000] [MSE: 1.0813] 47.3+0.0s +[4800/16000] [MSE: 1.0616] 47.1+0.1s +[6400/16000] [MSE: 1.0605] 47.1+0.0s +[8000/16000] [MSE: 1.0609] 47.3+0.0s +[9600/16000] [MSE: 1.0640] 47.4+0.0s +[11200/16000] [MSE: 1.0650] 47.2+0.0s +[12800/16000] [MSE: 1.0616] 47.1+0.0s +[14400/16000] [MSE: 1.0623] 46.7+0.0s +[16000/16000] [MSE: 1.0606] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.97s + +Saving... +Total: 38.46s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0375] 47.8+0.8s +[3200/16000] [MSE: 1.0395] 47.6+0.1s +[4800/16000] [MSE: 1.0475] 47.7+0.0s +[6400/16000] [MSE: 1.0474] 47.0+0.0s +[8000/16000] [MSE: 1.0486] 46.8+0.0s +[9600/16000] [MSE: 1.0465] 47.2+0.0s +[11200/16000] [MSE: 1.0478] 47.1+0.0s +[12800/16000] [MSE: 1.0448] 46.1+0.0s +[14400/16000] [MSE: 1.0477] 46.2+0.0s +[16000/16000] [MSE: 1.0513] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.32s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0690] 48.0+0.7s +[3200/16000] [MSE: 1.0524] 48.2+0.1s +[4800/16000] [MSE: 1.0668] 47.7+0.1s +[6400/16000] [MSE: 1.0709] 47.4+0.1s +[8000/16000] [MSE: 1.0718] 47.5+0.1s +[9600/16000] [MSE: 1.0709] 47.0+0.0s +[11200/16000] [MSE: 1.0653] 47.4+0.0s +[12800/16000] [MSE: 1.0591] 47.4+0.0s +[14400/16000] [MSE: 1.0609] 47.3+0.0s +[16000/16000] [MSE: 1.0594] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.89s + +Saving... +Total: 38.39s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0640] 47.3+0.8s +[3200/16000] [MSE: 1.0661] 47.3+0.0s +[4800/16000] [MSE: 1.0648] 46.3+0.0s +[6400/16000] [MSE: 1.0608] 46.2+0.0s +[8000/16000] [MSE: 1.0548] 47.1+0.0s +[9600/16000] [MSE: 1.0510] 46.4+0.0s +[11200/16000] [MSE: 1.0508] 46.6+0.0s +[12800/16000] [MSE: 1.0528] 46.3+0.0s +[14400/16000] [MSE: 1.0548] 46.3+0.0s +[16000/16000] [MSE: 1.0534] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.38s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0465] 48.1+0.7s +[3200/16000] [MSE: 1.0521] 47.9+0.1s +[4800/16000] [MSE: 1.0518] 47.4+0.0s +[6400/16000] [MSE: 1.0527] 46.7+0.0s +[8000/16000] [MSE: 1.0473] 47.1+0.0s +[9600/16000] [MSE: 1.0483] 46.8+0.0s +[11200/16000] [MSE: 1.0523] 46.8+0.0s +[12800/16000] [MSE: 1.0535] 46.2+0.0s +[14400/16000] [MSE: 1.0593] 45.9+0.0s +[16000/16000] [MSE: 1.0604] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.21s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0622] 47.3+0.8s +[3200/16000] [MSE: 1.0582] 47.5+0.1s +[4800/16000] [MSE: 1.0642] 47.5+0.0s +[6400/16000] [MSE: 1.0785] 47.5+0.0s +[8000/16000] [MSE: 1.0710] 47.1+0.0s +[9600/16000] [MSE: 1.0721] 47.2+0.0s +[11200/16000] [MSE: 1.0704] 47.5+0.0s +[12800/16000] [MSE: 1.0710] 47.4+0.0s +[14400/16000] [MSE: 1.0654] 47.3+0.0s +[16000/16000] [MSE: 1.0656] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.93s + +Saving... +Total: 38.41s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0824] 47.5+0.8s +[3200/16000] [MSE: 1.0723] 47.4+0.0s +[4800/16000] [MSE: 1.0688] 47.0+0.0s +[6400/16000] [MSE: 1.0709] 47.0+0.0s +[8000/16000] [MSE: 1.0647] 46.9+0.0s +[9600/16000] [MSE: 1.0606] 47.0+0.0s +[11200/16000] [MSE: 1.0626] 46.8+0.0s +[12800/16000] [MSE: 1.0625] 47.1+0.0s +[14400/16000] [MSE: 1.0632] 47.0+0.0s +[16000/16000] [MSE: 1.0634] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.46s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0610] 47.6+0.7s +[3200/16000] [MSE: 1.0789] 47.3+0.0s +[4800/16000] [MSE: 1.0654] 47.2+0.0s +[6400/16000] [MSE: 1.0652] 47.0+0.0s +[8000/16000] [MSE: 1.0684] 45.9+0.0s +[9600/16000] [MSE: 1.0663] 46.0+0.0s +[11200/16000] [MSE: 1.0639] 46.4+0.0s +[12800/16000] [MSE: 1.0662] 46.3+0.0s +[14400/16000] [MSE: 1.0679] 46.1+0.0s +[16000/16000] [MSE: 1.0649] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.21s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0535] 48.0+0.8s +[3200/16000] [MSE: 1.0640] 47.5+0.0s +[4800/16000] [MSE: 1.0581] 47.6+0.0s +[6400/16000] [MSE: 1.0578] 47.3+0.1s +[8000/16000] [MSE: 1.0571] 47.4+0.0s +[9600/16000] [MSE: 1.0555] 46.8+0.0s +[11200/16000] [MSE: 1.0611] 46.9+0.0s +[12800/16000] [MSE: 1.0607] 47.0+0.0s +[14400/16000] [MSE: 1.0582] 46.8+0.0s +[16000/16000] [MSE: 1.0598] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.37s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0727] 47.8+0.7s +[3200/16000] [MSE: 1.0763] 47.8+0.1s +[4800/16000] [MSE: 1.0719] 47.5+0.0s +[6400/16000] [MSE: 1.0686] 47.2+0.0s +[8000/16000] [MSE: 1.0616] 47.1+0.0s +[9600/16000] [MSE: 1.0611] 47.1+0.0s +[11200/16000] [MSE: 1.0583] 46.7+0.0s +[12800/16000] [MSE: 1.0624] 47.0+0.0s +[14400/16000] [MSE: 1.0607] 46.9+0.0s +[16000/16000] [MSE: 1.0630] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.25s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0687] 48.1+0.7s +[3200/16000] [MSE: 1.0660] 48.0+0.1s +[4800/16000] [MSE: 1.0642] 47.6+0.1s +[6400/16000] [MSE: 1.0642] 47.3+0.0s +[8000/16000] [MSE: 1.0601] 47.3+0.0s +[9600/16000] [MSE: 1.0630] 46.8+0.0s +[11200/16000] [MSE: 1.0640] 46.9+0.0s +[12800/16000] [MSE: 1.0568] 46.8+0.0s +[14400/16000] [MSE: 1.0586] 47.0+0.0s +[16000/16000] [MSE: 1.0572] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.35s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0867] 48.0+0.7s +[3200/16000] [MSE: 1.0776] 47.6+0.1s +[4800/16000] [MSE: 1.0861] 47.4+0.0s +[6400/16000] [MSE: 1.0828] 47.5+0.0s +[8000/16000] [MSE: 1.0840] 47.5+0.0s +[9600/16000] [MSE: 1.0783] 47.2+0.0s +[11200/16000] [MSE: 1.0766] 47.3+0.0s +[12800/16000] [MSE: 1.0715] 47.2+0.0s +[14400/16000] [MSE: 1.0715] 46.9+0.0s +[16000/16000] [MSE: 1.0687] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.35s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0449] 47.9+0.8s +[3200/16000] [MSE: 1.0583] 47.4+0.0s +[4800/16000] [MSE: 1.0675] 47.5+0.0s +[6400/16000] [MSE: 1.0704] 47.4+0.0s +[8000/16000] [MSE: 1.0764] 46.8+0.0s +[9600/16000] [MSE: 1.0713] 46.6+0.0s +[11200/16000] [MSE: 1.0663] 47.2+0.0s +[12800/16000] [MSE: 1.0634] 46.4+0.0s +[14400/16000] [MSE: 1.0621] 46.1+0.0s +[16000/16000] [MSE: 1.0603] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.99s + +Saving... +Total: 38.53s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0789] 47.9+0.7s +[3200/16000] [MSE: 1.0588] 47.5+0.1s +[4800/16000] [MSE: 1.0560] 47.6+0.1s +[6400/16000] [MSE: 1.0594] 47.5+0.0s +[8000/16000] [MSE: 1.0643] 47.6+0.1s +[9600/16000] [MSE: 1.0605] 47.1+0.0s +[11200/16000] [MSE: 1.0643] 47.0+0.0s +[12800/16000] [MSE: 1.0644] 46.9+0.0s +[14400/16000] [MSE: 1.0638] 46.9+0.0s +[16000/16000] [MSE: 1.0612] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.28s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0580] 47.6+0.7s +[3200/16000] [MSE: 1.0414] 47.8+0.1s +[4800/16000] [MSE: 1.0370] 47.7+0.1s +[6400/16000] [MSE: 1.0451] 47.4+0.0s +[8000/16000] [MSE: 1.0482] 47.6+0.0s +[9600/16000] [MSE: 1.0456] 47.4+0.0s +[11200/16000] [MSE: 1.0499] 47.4+0.0s +[12800/16000] [MSE: 1.0523] 47.2+0.0s +[14400/16000] [MSE: 1.0492] 47.5+0.0s +[16000/16000] [MSE: 1.0498] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.31s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0761] 47.7+0.7s +[3200/16000] [MSE: 1.0653] 47.5+0.0s +[4800/16000] [MSE: 1.0591] 47.7+0.1s +[6400/16000] [MSE: 1.0603] 47.3+0.0s +[8000/16000] [MSE: 1.0531] 47.3+0.0s +[9600/16000] [MSE: 1.0575] 47.0+0.0s +[11200/16000] [MSE: 1.0563] 47.6+0.0s +[12800/16000] [MSE: 1.0583] 46.8+0.0s +[14400/16000] [MSE: 1.0592] 46.5+0.0s +[16000/16000] [MSE: 1.0606] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.29s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0620] 47.5+0.7s +[3200/16000] [MSE: 1.0755] 47.3+0.0s +[4800/16000] [MSE: 1.0694] 47.0+0.0s +[6400/16000] [MSE: 1.0642] 47.1+0.0s +[8000/16000] [MSE: 1.0629] 47.1+0.0s +[9600/16000] [MSE: 1.0597] 46.3+0.0s +[11200/16000] [MSE: 1.0578] 46.7+0.0s +[12800/16000] [MSE: 1.0581] 46.3+0.0s +[14400/16000] [MSE: 1.0574] 46.0+0.0s +[16000/16000] [MSE: 1.0564] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.29s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0491] 47.5+0.7s +[3200/16000] [MSE: 1.0661] 47.3+0.0s +[4800/16000] [MSE: 1.0647] 47.5+0.0s +[6400/16000] [MSE: 1.0766] 47.4+0.0s +[8000/16000] [MSE: 1.0712] 47.4+0.0s +[9600/16000] [MSE: 1.0711] 47.2+0.0s +[11200/16000] [MSE: 1.0725] 47.0+0.0s +[12800/16000] [MSE: 1.0675] 46.8+0.0s +[14400/16000] [MSE: 1.0626] 46.9+0.0s +[16000/16000] [MSE: 1.0601] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.29s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0430] 47.7+0.8s +[3200/16000] [MSE: 1.0284] 47.2+0.0s +[4800/16000] [MSE: 1.0364] 47.8+0.0s +[6400/16000] [MSE: 1.0365] 47.2+0.0s +[8000/16000] [MSE: 1.0374] 47.0+0.0s +[9600/16000] [MSE: 1.0395] 46.8+0.0s +[11200/16000] [MSE: 1.0451] 46.8+0.0s +[12800/16000] [MSE: 1.0491] 47.1+0.0s +[14400/16000] [MSE: 1.0511] 47.2+0.0s +[16000/16000] [MSE: 1.0539] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.22s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0695] 47.9+0.7s +[3200/16000] [MSE: 1.0836] 47.2+0.1s +[4800/16000] [MSE: 1.0779] 47.5+0.0s +[6400/16000] [MSE: 1.0675] 46.8+0.0s +[8000/16000] [MSE: 1.0747] 46.9+0.0s +[9600/16000] [MSE: 1.0711] 47.1+0.0s +[11200/16000] [MSE: 1.0699] 46.7+0.0s +[12800/16000] [MSE: 1.0647] 46.2+0.0s +[14400/16000] [MSE: 1.0629] 46.0+0.0s +[16000/16000] [MSE: 1.0608] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.33s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0708] 47.5+0.8s +[3200/16000] [MSE: 1.0588] 47.2+0.0s +[4800/16000] [MSE: 1.0662] 47.2+0.0s +[6400/16000] [MSE: 1.0730] 46.9+0.0s +[8000/16000] [MSE: 1.0636] 47.2+0.0s +[9600/16000] [MSE: 1.0659] 46.8+0.0s +[11200/16000] [MSE: 1.0662] 46.7+0.0s +[12800/16000] [MSE: 1.0660] 46.9+0.0s +[14400/16000] [MSE: 1.0640] 46.5+0.0s +[16000/16000] [MSE: 1.0609] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.01s + +Saving... +Total: 38.48s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1181] 47.7+0.7s +[3200/16000] [MSE: 1.0983] 47.0+0.0s +[4800/16000] [MSE: 1.0880] 47.0+0.0s +[6400/16000] [MSE: 1.0815] 47.2+0.0s +[8000/16000] [MSE: 1.0676] 47.4+0.0s +[9600/16000] [MSE: 1.0693] 47.5+0.0s +[11200/16000] [MSE: 1.0726] 47.4+0.0s +[12800/16000] [MSE: 1.0689] 47.0+0.0s +[14400/16000] [MSE: 1.0676] 46.9+0.0s +[16000/16000] [MSE: 1.0707] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.92s + +Saving... +Total: 38.38s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0595] 47.8+0.6s +[3200/16000] [MSE: 1.0590] 47.3+0.0s +[4800/16000] [MSE: 1.0412] 47.3+0.0s +[6400/16000] [MSE: 1.0433] 47.6+0.0s +[8000/16000] [MSE: 1.0379] 46.8+0.0s +[9600/16000] [MSE: 1.0411] 47.2+0.0s +[11200/16000] [MSE: 1.0375] 46.5+0.0s +[12800/16000] [MSE: 1.0430] 47.0+0.0s +[14400/16000] [MSE: 1.0444] 46.2+0.0s +[16000/16000] [MSE: 1.0486] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.24s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0336] 47.5+0.8s +[3200/16000] [MSE: 1.0661] 47.3+0.0s +[4800/16000] [MSE: 1.0554] 47.4+0.0s +[6400/16000] [MSE: 1.0618] 47.7+0.0s +[8000/16000] [MSE: 1.0642] 47.6+0.0s +[9600/16000] [MSE: 1.0615] 47.3+0.0s +[11200/16000] [MSE: 1.0578] 47.5+0.0s +[12800/16000] [MSE: 1.0577] 47.4+0.0s +[14400/16000] [MSE: 1.0578] 47.4+0.0s +[16000/16000] [MSE: 1.0546] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.20s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0432] 47.7+0.8s +[3200/16000] [MSE: 1.0497] 47.4+0.0s +[4800/16000] [MSE: 1.0529] 47.4+0.0s +[6400/16000] [MSE: 1.0496] 47.2+0.0s +[8000/16000] [MSE: 1.0550] 47.1+0.0s +[9600/16000] [MSE: 1.0553] 46.8+0.0s +[11200/16000] [MSE: 1.0541] 47.0+0.0s +[12800/16000] [MSE: 1.0551] 47.1+0.0s +[14400/16000] [MSE: 1.0550] 47.3+0.0s +[16000/16000] [MSE: 1.0522] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.35s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0916] 47.7+0.8s +[3200/16000] [MSE: 1.0771] 47.6+0.1s +[4800/16000] [MSE: 1.0654] 47.7+0.0s +[6400/16000] [MSE: 1.0759] 47.7+0.0s +[8000/16000] [MSE: 1.0692] 47.6+0.0s +[9600/16000] [MSE: 1.0715] 47.1+0.0s +[11200/16000] [MSE: 1.0715] 46.5+0.0s +[12800/16000] [MSE: 1.0697] 47.0+0.0s +[14400/16000] [MSE: 1.0711] 47.4+0.0s +[16000/16000] [MSE: 1.0704] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.24s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0664] 47.7+0.7s +[3200/16000] [MSE: 1.0617] 47.4+0.1s +[4800/16000] [MSE: 1.0670] 47.7+0.1s +[6400/16000] [MSE: 1.0701] 47.6+0.1s +[8000/16000] [MSE: 1.0568] 47.8+0.1s +[9600/16000] [MSE: 1.0572] 47.8+0.1s +[11200/16000] [MSE: 1.0553] 47.2+0.0s +[12800/16000] [MSE: 1.0562] 47.2+0.0s +[14400/16000] [MSE: 1.0597] 46.8+0.0s +[16000/16000] [MSE: 1.0599] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.32s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1097] 47.6+0.7s +[3200/16000] [MSE: 1.0853] 47.7+0.1s +[4800/16000] [MSE: 1.0888] 47.6+0.0s +[6400/16000] [MSE: 1.0802] 47.5+0.0s +[8000/16000] [MSE: 1.0729] 47.2+0.0s +[9600/16000] [MSE: 1.0720] 46.9+0.0s +[11200/16000] [MSE: 1.0671] 47.5+0.0s +[12800/16000] [MSE: 1.0647] 47.0+0.0s +[14400/16000] [MSE: 1.0666] 47.3+0.0s +[16000/16000] [MSE: 1.0666] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.24s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0526] 47.8+0.7s +[3200/16000] [MSE: 1.0615] 47.3+0.0s +[4800/16000] [MSE: 1.0630] 47.2+0.0s +[6400/16000] [MSE: 1.0769] 47.1+0.0s +[8000/16000] [MSE: 1.0714] 47.1+0.0s +[9600/16000] [MSE: 1.0656] 47.1+0.0s +[11200/16000] [MSE: 1.0622] 46.7+0.0s +[12800/16000] [MSE: 1.0614] 47.0+0.0s +[14400/16000] [MSE: 1.0624] 46.5+0.0s +[16000/16000] [MSE: 1.0642] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.34s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0998] 47.9+0.8s +[3200/16000] [MSE: 1.0699] 47.4+0.0s +[4800/16000] [MSE: 1.0679] 47.3+0.0s +[6400/16000] [MSE: 1.0628] 46.8+0.0s +[8000/16000] [MSE: 1.0629] 47.1+0.0s +[9600/16000] [MSE: 1.0643] 46.0+0.0s +[11200/16000] [MSE: 1.0667] 46.0+0.0s +[12800/16000] [MSE: 1.0666] 46.4+0.0s +[14400/16000] [MSE: 1.0643] 46.2+0.0s +[16000/16000] [MSE: 1.0642] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.16s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0758] 47.3+0.7s +[3200/16000] [MSE: 1.0612] 47.7+0.1s +[4800/16000] [MSE: 1.0693] 47.8+0.1s +[6400/16000] [MSE: 1.0674] 47.3+0.0s +[8000/16000] [MSE: 1.0693] 47.1+0.0s +[9600/16000] [MSE: 1.0632] 47.2+0.0s +[11200/16000] [MSE: 1.0630] 47.1+0.0s +[12800/16000] [MSE: 1.0663] 47.2+0.0s +[14400/16000] [MSE: 1.0672] 47.3+0.0s +[16000/16000] [MSE: 1.0684] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.89s + +Saving... +Total: 38.38s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0659] 47.8+0.7s +[3200/16000] [MSE: 1.0736] 47.2+0.0s +[4800/16000] [MSE: 1.0576] 47.0+0.0s +[6400/16000] [MSE: 1.0647] 47.1+0.0s +[8000/16000] [MSE: 1.0640] 46.9+0.0s +[9600/16000] [MSE: 1.0609] 46.2+0.0s +[11200/16000] [MSE: 1.0667] 46.1+0.0s +[12800/16000] [MSE: 1.0612] 46.4+0.0s +[14400/16000] [MSE: 1.0630] 46.3+0.0s +[16000/16000] [MSE: 1.0597] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.24s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0322] 47.9+0.7s +[3200/16000] [MSE: 1.0505] 47.9+0.1s +[4800/16000] [MSE: 1.0501] 47.8+0.0s +[6400/16000] [MSE: 1.0520] 47.6+0.0s +[8000/16000] [MSE: 1.0541] 47.0+0.0s +[9600/16000] [MSE: 1.0548] 47.0+0.0s +[11200/16000] [MSE: 1.0540] 46.7+0.0s +[12800/16000] [MSE: 1.0513] 47.2+0.0s +[14400/16000] [MSE: 1.0541] 47.1+0.0s +[16000/16000] [MSE: 1.0565] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.95s + +Saving... +Total: 38.43s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0536] 47.5+0.7s +[3200/16000] [MSE: 1.0637] 47.1+0.0s +[4800/16000] [MSE: 1.0596] 47.1+0.0s +[6400/16000] [MSE: 1.0659] 47.4+0.0s +[8000/16000] [MSE: 1.0640] 47.3+0.0s +[9600/16000] [MSE: 1.0568] 47.4+0.0s +[11200/16000] [MSE: 1.0533] 47.1+0.0s +[12800/16000] [MSE: 1.0539] 47.2+0.0s +[14400/16000] [MSE: 1.0550] 47.1+0.0s +[16000/16000] [MSE: 1.0589] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.22s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0524] 48.0+1.3s +[3200/16000] [MSE: 1.0752] 47.9+0.1s +[4800/16000] [MSE: 1.0741] 47.7+0.1s +[6400/16000] [MSE: 1.0673] 47.4+0.1s +[8000/16000] [MSE: 1.0702] 47.5+0.1s +[9600/16000] [MSE: 1.0692] 47.3+0.0s +[11200/16000] [MSE: 1.0639] 47.2+0.0s +[12800/16000] [MSE: 1.0667] 47.2+0.0s +[14400/16000] [MSE: 1.0673] 47.0+0.0s +[16000/16000] [MSE: 1.0672] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.24s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0398] 47.2+0.9s +[3200/16000] [MSE: 1.0454] 46.0+0.0s +[4800/16000] [MSE: 1.0658] 45.9+0.0s +[6400/16000] [MSE: 1.0659] 45.6+0.0s +[8000/16000] [MSE: 1.0593] 45.6+0.0s +[9600/16000] [MSE: 1.0593] 46.0+0.0s +[11200/16000] [MSE: 1.0618] 46.5+0.0s +[12800/16000] [MSE: 1.0660] 46.2+0.0s +[14400/16000] [MSE: 1.0711] 46.3+0.0s +[16000/16000] [MSE: 1.0724] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.38s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0623] 47.7+0.8s +[3200/16000] [MSE: 1.0501] 47.5+0.0s +[4800/16000] [MSE: 1.0393] 47.2+0.0s +[6400/16000] [MSE: 1.0491] 47.2+0.0s +[8000/16000] [MSE: 1.0564] 47.0+0.0s +[9600/16000] [MSE: 1.0583] 47.1+0.0s +[11200/16000] [MSE: 1.0608] 46.7+0.0s +[12800/16000] [MSE: 1.0627] 47.3+0.0s +[14400/16000] [MSE: 1.0630] 47.1+0.0s +[16000/16000] [MSE: 1.0631] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.32s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0140] 48.1+0.7s +[3200/16000] [MSE: 1.0305] 47.7+0.1s +[4800/16000] [MSE: 1.0426] 47.7+0.1s +[6400/16000] [MSE: 1.0470] 47.9+0.1s +[8000/16000] [MSE: 1.0537] 47.2+0.0s +[9600/16000] [MSE: 1.0586] 47.3+0.0s +[11200/16000] [MSE: 1.0563] 46.9+0.0s +[12800/16000] [MSE: 1.0537] 47.3+0.0s +[14400/16000] [MSE: 1.0529] 47.0+0.0s +[16000/16000] [MSE: 1.0521] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.21s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0909] 47.7+0.8s +[3200/16000] [MSE: 1.0688] 47.0+0.0s +[4800/16000] [MSE: 1.0706] 47.0+0.0s +[6400/16000] [MSE: 1.0695] 47.2+0.0s +[8000/16000] [MSE: 1.0691] 47.3+0.0s +[9600/16000] [MSE: 1.0699] 47.0+0.0s +[11200/16000] [MSE: 1.0734] 47.0+0.0s +[12800/16000] [MSE: 1.0637] 47.1+0.0s +[14400/16000] [MSE: 1.0599] 47.3+0.0s +[16000/16000] [MSE: 1.0590] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.21s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0171] 47.8+0.7s +[3200/16000] [MSE: 1.0467] 47.8+0.1s +[4800/16000] [MSE: 1.0517] 47.7+0.1s +[6400/16000] [MSE: 1.0574] 47.3+0.0s +[8000/16000] [MSE: 1.0565] 46.9+0.0s +[9600/16000] [MSE: 1.0625] 47.0+0.0s +[11200/16000] [MSE: 1.0599] 46.6+0.0s +[12800/16000] [MSE: 1.0637] 47.2+0.0s +[14400/16000] [MSE: 1.0622] 46.1+0.0s +[16000/16000] [MSE: 1.0615] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.90s + +Saving... +Total: 38.51s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1047] 47.5+0.9s +[3200/16000] [MSE: 1.1045] 47.5+0.0s +[4800/16000] [MSE: 1.0918] 47.8+0.0s +[6400/16000] [MSE: 1.0929] 47.5+0.0s +[8000/16000] [MSE: 1.0895] 46.5+0.0s +[9600/16000] [MSE: 1.0830] 47.2+0.0s +[11200/16000] [MSE: 1.0774] 47.1+0.0s +[12800/16000] [MSE: 1.0749] 46.9+0.0s +[14400/16000] [MSE: 1.0706] 46.4+0.0s +[16000/16000] [MSE: 1.0658] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.67s + +Saving... +Total: 38.18s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0841] 47.6+0.6s +[3200/16000] [MSE: 1.0821] 46.8+0.0s +[4800/16000] [MSE: 1.0736] 47.1+0.0s +[6400/16000] [MSE: 1.0712] 47.0+0.0s +[8000/16000] [MSE: 1.0706] 46.9+0.0s +[9600/16000] [MSE: 1.0690] 46.9+0.0s +[11200/16000] [MSE: 1.0691] 46.8+0.0s +[12800/16000] [MSE: 1.0700] 46.8+0.0s +[14400/16000] [MSE: 1.0708] 47.1+0.0s +[16000/16000] [MSE: 1.0701] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.32s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0545] 47.6+0.7s +[3200/16000] [MSE: 1.0405] 47.2+0.0s +[4800/16000] [MSE: 1.0474] 47.2+0.0s +[6400/16000] [MSE: 1.0546] 47.0+0.0s +[8000/16000] [MSE: 1.0467] 47.3+0.0s +[9600/16000] [MSE: 1.0504] 47.0+0.0s +[11200/16000] [MSE: 1.0510] 46.6+0.0s +[12800/16000] [MSE: 1.0501] 46.8+0.0s +[14400/16000] [MSE: 1.0494] 46.8+0.0s +[16000/16000] [MSE: 1.0491] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.20s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0495] 46.6+0.7s +[3200/16000] [MSE: 1.0561] 46.6+0.0s +[4800/16000] [MSE: 1.0629] 46.9+0.0s +[6400/16000] [MSE: 1.0596] 47.1+0.0s +[8000/16000] [MSE: 1.0508] 47.3+0.0s +[9600/16000] [MSE: 1.0579] 46.9+0.0s +[11200/16000] [MSE: 1.0585] 46.8+0.0s +[12800/16000] [MSE: 1.0528] 46.8+0.0s +[14400/16000] [MSE: 1.0528] 47.0+0.0s +[16000/16000] [MSE: 1.0558] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.34s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0837] 47.9+0.7s +[3200/16000] [MSE: 1.0727] 47.5+0.0s +[4800/16000] [MSE: 1.0732] 47.6+0.0s +[6400/16000] [MSE: 1.0605] 47.0+0.0s +[8000/16000] [MSE: 1.0594] 47.3+0.0s +[9600/16000] [MSE: 1.0636] 47.2+0.0s +[11200/16000] [MSE: 1.0616] 47.3+0.0s +[12800/16000] [MSE: 1.0604] 46.8+0.0s +[14400/16000] [MSE: 1.0612] 47.0+0.0s +[16000/16000] [MSE: 1.0631] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.33s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9909] 47.7+0.8s +[3200/16000] [MSE: 1.0126] 47.7+0.0s +[4800/16000] [MSE: 1.0312] 47.6+0.0s +[6400/16000] [MSE: 1.0377] 47.6+0.0s +[8000/16000] [MSE: 1.0463] 47.5+0.0s +[9600/16000] [MSE: 1.0502] 47.3+0.0s +[11200/16000] [MSE: 1.0477] 46.7+0.0s +[12800/16000] [MSE: 1.0501] 46.8+0.0s +[14400/16000] [MSE: 1.0498] 46.9+0.0s +[16000/16000] [MSE: 1.0541] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.31s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0282] 48.0+0.7s +[3200/16000] [MSE: 1.0497] 47.6+0.1s +[4800/16000] [MSE: 1.0523] 47.4+0.0s +[6400/16000] [MSE: 1.0634] 47.5+0.0s +[8000/16000] [MSE: 1.0644] 47.3+0.0s +[9600/16000] [MSE: 1.0622] 47.4+0.0s +[11200/16000] [MSE: 1.0619] 47.0+0.0s +[12800/16000] [MSE: 1.0632] 47.1+0.0s +[14400/16000] [MSE: 1.0643] 47.1+0.0s +[16000/16000] [MSE: 1.0643] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.30s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0422] 48.0+0.9s +[3200/16000] [MSE: 1.0540] 47.4+0.1s +[4800/16000] [MSE: 1.0655] 47.5+0.0s +[6400/16000] [MSE: 1.0590] 47.3+0.0s +[8000/16000] [MSE: 1.0591] 47.1+0.0s +[9600/16000] [MSE: 1.0600] 47.3+0.0s +[11200/16000] [MSE: 1.0545] 47.0+0.0s +[12800/16000] [MSE: 1.0566] 47.3+0.0s +[14400/16000] [MSE: 1.0588] 47.2+0.0s +[16000/16000] [MSE: 1.0589] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.36s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0281] 47.4+0.7s +[3200/16000] [MSE: 1.0448] 47.5+0.0s +[4800/16000] [MSE: 1.0490] 47.5+0.0s +[6400/16000] [MSE: 1.0537] 47.7+0.1s +[8000/16000] [MSE: 1.0517] 47.6+0.1s +[9600/16000] [MSE: 1.0546] 47.3+0.0s +[11200/16000] [MSE: 1.0516] 47.2+0.0s +[12800/16000] [MSE: 1.0483] 47.5+0.0s +[14400/16000] [MSE: 1.0511] 47.3+0.0s +[16000/16000] [MSE: 1.0540] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.37s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0905] 47.1+0.6s +[3200/16000] [MSE: 1.0698] 46.8+0.0s +[4800/16000] [MSE: 1.0652] 46.9+0.0s +[6400/16000] [MSE: 1.0678] 47.2+0.0s +[8000/16000] [MSE: 1.0680] 46.2+0.0s +[9600/16000] [MSE: 1.0663] 45.9+0.0s +[11200/16000] [MSE: 1.0653] 46.6+0.0s +[12800/16000] [MSE: 1.0615] 46.2+0.0s +[14400/16000] [MSE: 1.0612] 46.1+0.0s +[16000/16000] [MSE: 1.0577] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.36s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0223] 47.7+0.7s +[3200/16000] [MSE: 1.0352] 47.4+0.0s +[4800/16000] [MSE: 1.0448] 47.3+0.0s +[6400/16000] [MSE: 1.0522] 47.3+0.0s +[8000/16000] [MSE: 1.0576] 47.0+0.0s +[9600/16000] [MSE: 1.0575] 47.0+0.0s +[11200/16000] [MSE: 1.0602] 47.3+0.0s +[12800/16000] [MSE: 1.0640] 46.9+0.0s +[14400/16000] [MSE: 1.0669] 47.1+0.0s +[16000/16000] [MSE: 1.0629] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.38s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0760] 47.7+0.8s +[3200/16000] [MSE: 1.0662] 47.7+0.1s +[4800/16000] [MSE: 1.0551] 47.5+0.0s +[6400/16000] [MSE: 1.0618] 47.4+0.0s +[8000/16000] [MSE: 1.0615] 47.1+0.0s +[9600/16000] [MSE: 1.0667] 47.1+0.0s +[11200/16000] [MSE: 1.0663] 46.7+0.0s +[12800/16000] [MSE: 1.0669] 46.8+0.0s +[14400/16000] [MSE: 1.0690] 47.1+0.0s +[16000/16000] [MSE: 1.0691] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.24s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0548] 47.7+0.6s +[3200/16000] [MSE: 1.0524] 47.4+0.0s +[4800/16000] [MSE: 1.0567] 47.3+0.0s +[6400/16000] [MSE: 1.0545] 46.5+0.0s +[8000/16000] [MSE: 1.0516] 46.5+0.0s +[9600/16000] [MSE: 1.0526] 46.4+0.0s +[11200/16000] [MSE: 1.0578] 46.7+0.0s +[12800/16000] [MSE: 1.0583] 46.3+0.0s +[14400/16000] [MSE: 1.0584] 46.1+0.0s +[16000/16000] [MSE: 1.0551] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.29s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1138] 47.6+0.7s +[3200/16000] [MSE: 1.0858] 47.1+0.0s +[4800/16000] [MSE: 1.0787] 47.3+0.0s +[6400/16000] [MSE: 1.0771] 47.0+0.0s +[8000/16000] [MSE: 1.0711] 46.9+0.0s +[9600/16000] [MSE: 1.0669] 47.1+0.0s +[11200/16000] [MSE: 1.0654] 47.0+0.0s +[12800/16000] [MSE: 1.0606] 46.8+0.0s +[14400/16000] [MSE: 1.0578] 46.8+0.0s +[16000/16000] [MSE: 1.0579] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.68s + +Saving... +Total: 38.18s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0816] 47.9+0.7s +[3200/16000] [MSE: 1.0749] 47.5+0.0s +[4800/16000] [MSE: 1.0653] 47.5+0.0s +[6400/16000] [MSE: 1.0686] 47.1+0.0s +[8000/16000] [MSE: 1.0697] 46.9+0.0s +[9600/16000] [MSE: 1.0694] 46.1+0.0s +[11200/16000] [MSE: 1.0707] 47.1+0.0s +[12800/16000] [MSE: 1.0697] 46.8+0.0s +[14400/16000] [MSE: 1.0658] 46.8+0.0s +[16000/16000] [MSE: 1.0622] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.98s + +Saving... +Total: 38.45s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0871] 48.0+0.8s +[3200/16000] [MSE: 1.0790] 47.5+0.0s +[4800/16000] [MSE: 1.0746] 47.5+0.0s +[6400/16000] [MSE: 1.0709] 47.3+0.0s +[8000/16000] [MSE: 1.0710] 47.0+0.0s +[9600/16000] [MSE: 1.0727] 47.2+0.0s +[11200/16000] [MSE: 1.0695] 47.1+0.0s +[12800/16000] [MSE: 1.0683] 47.4+0.0s +[14400/16000] [MSE: 1.0648] 47.3+0.0s +[16000/16000] [MSE: 1.0604] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.18s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0729] 48.0+0.9s +[3200/16000] [MSE: 1.0677] 47.7+0.1s +[4800/16000] [MSE: 1.0598] 47.4+0.0s +[6400/16000] [MSE: 1.0575] 47.1+0.0s +[8000/16000] [MSE: 1.0604] 47.1+0.0s +[9600/16000] [MSE: 1.0627] 47.0+0.0s +[11200/16000] [MSE: 1.0594] 47.3+0.0s +[12800/16000] [MSE: 1.0623] 47.3+0.0s +[14400/16000] [MSE: 1.0642] 47.4+0.0s +[16000/16000] [MSE: 1.0636] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.98s + +Saving... +Total: 38.44s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0388] 47.5+0.7s +[3200/16000] [MSE: 1.0591] 47.3+0.0s +[4800/16000] [MSE: 1.0533] 47.1+0.0s +[6400/16000] [MSE: 1.0514] 46.6+0.0s +[8000/16000] [MSE: 1.0542] 46.8+0.0s +[9600/16000] [MSE: 1.0502] 47.1+0.0s +[11200/16000] [MSE: 1.0526] 47.2+0.0s +[12800/16000] [MSE: 1.0525] 46.7+0.0s +[14400/16000] [MSE: 1.0565] 46.7+0.0s +[16000/16000] [MSE: 1.0571] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.31s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0427] 48.2+0.7s +[3200/16000] [MSE: 1.0488] 47.5+0.1s +[4800/16000] [MSE: 1.0570] 47.8+0.0s +[6400/16000] [MSE: 1.0556] 47.4+0.0s +[8000/16000] [MSE: 1.0594] 47.2+0.0s +[9600/16000] [MSE: 1.0548] 47.1+0.0s +[11200/16000] [MSE: 1.0585] 46.9+0.0s +[12800/16000] [MSE: 1.0608] 47.0+0.0s +[14400/16000] [MSE: 1.0592] 46.7+0.0s +[16000/16000] [MSE: 1.0589] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.36s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0671] 47.6+0.7s +[3200/16000] [MSE: 1.0589] 47.3+0.0s +[4800/16000] [MSE: 1.0586] 47.3+0.0s +[6400/16000] [MSE: 1.0522] 46.6+0.0s +[8000/16000] [MSE: 1.0506] 46.8+0.0s +[9600/16000] [MSE: 1.0453] 46.4+0.0s +[11200/16000] [MSE: 1.0455] 46.0+0.0s +[12800/16000] [MSE: 1.0482] 45.9+0.0s +[14400/16000] [MSE: 1.0480] 46.2+0.0s +[16000/16000] [MSE: 1.0530] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.25s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0680] 47.7+0.7s +[3200/16000] [MSE: 1.0737] 47.6+0.0s +[4800/16000] [MSE: 1.0710] 47.1+0.0s +[6400/16000] [MSE: 1.0655] 47.5+0.0s +[8000/16000] [MSE: 1.0653] 47.0+0.0s +[9600/16000] [MSE: 1.0628] 47.1+0.0s +[11200/16000] [MSE: 1.0669] 47.0+0.0s +[12800/16000] [MSE: 1.0676] 46.3+0.0s +[14400/16000] [MSE: 1.0638] 46.2+0.0s +[16000/16000] [MSE: 1.0635] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.30s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0923] 47.6+0.7s +[3200/16000] [MSE: 1.0841] 47.6+0.1s +[4800/16000] [MSE: 1.0628] 47.4+0.0s +[6400/16000] [MSE: 1.0633] 47.6+0.0s +[8000/16000] [MSE: 1.0654] 47.4+0.0s +[9600/16000] [MSE: 1.0629] 47.2+0.0s +[11200/16000] [MSE: 1.0599] 47.0+0.0s +[12800/16000] [MSE: 1.0592] 47.4+0.0s +[14400/16000] [MSE: 1.0617] 47.0+0.0s +[16000/16000] [MSE: 1.0636] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.31s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0694] 47.6+0.7s +[3200/16000] [MSE: 1.0492] 47.2+0.0s +[4800/16000] [MSE: 1.0517] 47.0+0.0s +[6400/16000] [MSE: 1.0650] 47.1+0.0s +[8000/16000] [MSE: 1.0648] 47.4+0.0s +[9600/16000] [MSE: 1.0662] 47.0+0.0s +[11200/16000] [MSE: 1.0656] 46.8+0.0s +[12800/16000] [MSE: 1.0627] 46.9+0.0s +[14400/16000] [MSE: 1.0592] 47.1+0.0s +[16000/16000] [MSE: 1.0577] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.20s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0558] 47.9+0.7s +[3200/16000] [MSE: 1.0430] 47.3+0.0s +[4800/16000] [MSE: 1.0471] 47.0+0.0s +[6400/16000] [MSE: 1.0511] 46.7+0.0s +[8000/16000] [MSE: 1.0620] 46.2+0.0s +[9600/16000] [MSE: 1.0594] 46.4+0.0s +[11200/16000] [MSE: 1.0611] 46.5+0.0s +[12800/16000] [MSE: 1.0663] 46.0+0.0s +[14400/16000] [MSE: 1.0660] 46.6+0.0s +[16000/16000] [MSE: 1.0652] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.23s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0602] 47.4+0.7s +[3200/16000] [MSE: 1.0647] 47.0+0.0s +[4800/16000] [MSE: 1.0626] 46.8+0.0s +[6400/16000] [MSE: 1.0593] 46.7+0.0s +[8000/16000] [MSE: 1.0663] 46.5+0.0s +[9600/16000] [MSE: 1.0641] 46.2+0.0s +[11200/16000] [MSE: 1.0646] 46.2+0.0s +[12800/16000] [MSE: 1.0625] 46.3+0.0s +[14400/16000] [MSE: 1.0608] 46.1+0.0s +[16000/16000] [MSE: 1.0625] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.31s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0728] 48.0+0.8s +[3200/16000] [MSE: 1.0716] 47.7+0.1s +[4800/16000] [MSE: 1.0721] 47.3+0.0s +[6400/16000] [MSE: 1.0663] 46.4+0.0s +[8000/16000] [MSE: 1.0724] 46.4+0.0s +[9600/16000] [MSE: 1.0730] 46.1+0.0s +[11200/16000] [MSE: 1.0676] 46.4+0.0s +[12800/16000] [MSE: 1.0642] 46.1+0.0s +[14400/16000] [MSE: 1.0628] 45.9+0.0s +[16000/16000] [MSE: 1.0629] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.25s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0755] 47.7+0.8s +[3200/16000] [MSE: 1.0611] 47.8+0.1s +[4800/16000] [MSE: 1.0522] 47.8+0.1s +[6400/16000] [MSE: 1.0554] 47.9+0.1s +[8000/16000] [MSE: 1.0582] 47.8+0.0s +[9600/16000] [MSE: 1.0580] 47.6+0.0s +[11200/16000] [MSE: 1.0538] 47.4+0.0s +[12800/16000] [MSE: 1.0531] 47.2+0.0s +[14400/16000] [MSE: 1.0543] 47.4+0.1s +[16000/16000] [MSE: 1.0562] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.33s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0517] 47.7+0.8s +[3200/16000] [MSE: 1.0534] 47.5+0.1s +[4800/16000] [MSE: 1.0616] 47.6+0.1s +[6400/16000] [MSE: 1.0625] 47.7+0.1s +[8000/16000] [MSE: 1.0602] 47.2+0.0s +[9600/16000] [MSE: 1.0540] 47.1+0.0s +[11200/16000] [MSE: 1.0519] 47.3+0.0s +[12800/16000] [MSE: 1.0552] 46.8+0.0s +[14400/16000] [MSE: 1.0516] 46.4+0.0s +[16000/16000] [MSE: 1.0572] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.25s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0388] 47.8+0.7s +[3200/16000] [MSE: 1.0421] 47.3+0.1s +[4800/16000] [MSE: 1.0566] 46.9+0.0s +[6400/16000] [MSE: 1.0692] 46.8+0.0s +[8000/16000] [MSE: 1.0665] 47.0+0.0s +[9600/16000] [MSE: 1.0657] 46.7+0.0s +[11200/16000] [MSE: 1.0601] 47.3+0.0s +[12800/16000] [MSE: 1.0606] 46.3+0.0s +[14400/16000] [MSE: 1.0641] 46.9+0.0s +[16000/16000] [MSE: 1.0620] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.32s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0402] 47.4+0.7s +[3200/16000] [MSE: 1.0492] 47.3+0.0s +[4800/16000] [MSE: 1.0558] 47.4+0.0s +[6400/16000] [MSE: 1.0444] 47.4+0.0s +[8000/16000] [MSE: 1.0534] 47.0+0.0s +[9600/16000] [MSE: 1.0577] 47.0+0.0s +[11200/16000] [MSE: 1.0566] 46.8+0.0s +[12800/16000] [MSE: 1.0554] 46.5+0.0s +[14400/16000] [MSE: 1.0564] 47.2+0.0s +[16000/16000] [MSE: 1.0584] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.40s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1005] 47.7+0.7s +[3200/16000] [MSE: 1.0795] 47.1+0.0s +[4800/16000] [MSE: 1.0700] 47.2+0.0s +[6400/16000] [MSE: 1.0594] 47.0+0.0s +[8000/16000] [MSE: 1.0625] 46.8+0.0s +[9600/16000] [MSE: 1.0699] 46.7+0.0s +[11200/16000] [MSE: 1.0675] 46.8+0.0s +[12800/16000] [MSE: 1.0640] 46.8+0.0s +[14400/16000] [MSE: 1.0675] 46.7+0.0s +[16000/16000] [MSE: 1.0679] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.28s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0470] 47.5+0.7s +[3200/16000] [MSE: 1.0668] 47.4+0.0s +[4800/16000] [MSE: 1.0752] 47.2+0.0s +[6400/16000] [MSE: 1.0783] 47.6+0.0s +[8000/16000] [MSE: 1.0717] 47.5+0.0s +[9600/16000] [MSE: 1.0637] 47.3+0.0s +[11200/16000] [MSE: 1.0601] 47.1+0.0s +[12800/16000] [MSE: 1.0596] 47.0+0.0s +[14400/16000] [MSE: 1.0632] 47.4+0.0s +[16000/16000] [MSE: 1.0643] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.92s + +Saving... +Total: 38.41s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0573] 48.0+0.6s +[3200/16000] [MSE: 1.0712] 47.7+0.1s +[4800/16000] [MSE: 1.0674] 47.8+0.1s +[6400/16000] [MSE: 1.0642] 47.8+0.0s +[8000/16000] [MSE: 1.0583] 47.7+0.1s +[9600/16000] [MSE: 1.0588] 47.7+0.1s +[11200/16000] [MSE: 1.0581] 47.4+0.0s +[12800/16000] [MSE: 1.0602] 47.3+0.0s +[14400/16000] [MSE: 1.0609] 47.4+0.0s +[16000/16000] [MSE: 1.0606] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.30s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0777] 47.9+0.8s +[3200/16000] [MSE: 1.0762] 47.4+0.0s +[4800/16000] [MSE: 1.0713] 47.1+0.0s +[6400/16000] [MSE: 1.0607] 47.1+0.0s +[8000/16000] [MSE: 1.0536] 47.0+0.0s +[9600/16000] [MSE: 1.0515] 46.6+0.0s +[11200/16000] [MSE: 1.0511] 46.3+0.0s +[12800/16000] [MSE: 1.0490] 46.6+0.0s +[14400/16000] [MSE: 1.0477] 46.5+0.0s +[16000/16000] [MSE: 1.0452] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.27s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0855] 47.5+0.8s +[3200/16000] [MSE: 1.0784] 47.2+0.1s +[4800/16000] [MSE: 1.0636] 47.5+0.0s +[6400/16000] [MSE: 1.0672] 47.0+0.0s +[8000/16000] [MSE: 1.0684] 47.2+0.0s +[9600/16000] [MSE: 1.0666] 47.4+0.0s +[11200/16000] [MSE: 1.0649] 46.8+0.0s +[12800/16000] [MSE: 1.0615] 46.7+0.0s +[14400/16000] [MSE: 1.0646] 46.4+0.0s +[16000/16000] [MSE: 1.0642] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.28s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0606] 47.8+0.6s +[3200/16000] [MSE: 1.0482] 47.6+0.1s +[4800/16000] [MSE: 1.0585] 47.7+0.1s +[6400/16000] [MSE: 1.0617] 47.4+0.1s +[8000/16000] [MSE: 1.0541] 47.0+0.0s +[9600/16000] [MSE: 1.0563] 46.8+0.0s +[11200/16000] [MSE: 1.0517] 46.5+0.0s +[12800/16000] [MSE: 1.0511] 46.3+0.0s +[14400/16000] [MSE: 1.0570] 46.5+0.0s +[16000/16000] [MSE: 1.0611] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.33s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1014] 47.9+0.8s +[3200/16000] [MSE: 1.0754] 47.5+0.1s +[4800/16000] [MSE: 1.0750] 47.1+0.0s +[6400/16000] [MSE: 1.0746] 47.0+0.0s +[8000/16000] [MSE: 1.0713] 46.8+0.0s +[9600/16000] [MSE: 1.0656] 46.8+0.0s +[11200/16000] [MSE: 1.0652] 47.1+0.0s +[12800/16000] [MSE: 1.0627] 45.8+0.0s +[14400/16000] [MSE: 1.0626] 46.4+0.0s +[16000/16000] [MSE: 1.0636] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.30s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0853] 47.7+0.7s +[3200/16000] [MSE: 1.0813] 47.6+0.0s +[4800/16000] [MSE: 1.0720] 47.6+0.0s +[6400/16000] [MSE: 1.0647] 47.6+0.0s +[8000/16000] [MSE: 1.0605] 47.3+0.0s +[9600/16000] [MSE: 1.0584] 47.5+0.0s +[11200/16000] [MSE: 1.0556] 47.0+0.0s +[12800/16000] [MSE: 1.0576] 46.5+0.0s +[14400/16000] [MSE: 1.0540] 47.3+0.0s +[16000/16000] [MSE: 1.0560] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.27s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0885] 47.9+0.9s +[3200/16000] [MSE: 1.0656] 47.6+0.0s +[4800/16000] [MSE: 1.0671] 47.0+0.0s +[6400/16000] [MSE: 1.0691] 46.5+0.0s +[8000/16000] [MSE: 1.0674] 47.2+0.0s +[9600/16000] [MSE: 1.0728] 47.1+0.0s +[11200/16000] [MSE: 1.0678] 47.0+0.0s +[12800/16000] [MSE: 1.0677] 47.1+0.0s +[14400/16000] [MSE: 1.0719] 46.8+0.0s +[16000/16000] [MSE: 1.0769] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.31s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0874] 47.7+0.7s +[3200/16000] [MSE: 1.0621] 47.3+0.0s +[4800/16000] [MSE: 1.0740] 47.5+0.0s +[6400/16000] [MSE: 1.0697] 47.1+0.0s +[8000/16000] [MSE: 1.0651] 47.1+0.0s +[9600/16000] [MSE: 1.0642] 47.1+0.0s +[11200/16000] [MSE: 1.0704] 46.7+0.0s +[12800/16000] [MSE: 1.0674] 46.7+0.0s +[14400/16000] [MSE: 1.0689] 46.8+0.0s +[16000/16000] [MSE: 1.0677] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.28s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0490] 47.9+0.7s +[3200/16000] [MSE: 1.0654] 47.4+0.0s +[4800/16000] [MSE: 1.0573] 47.4+0.0s +[6400/16000] [MSE: 1.0569] 47.1+0.0s +[8000/16000] [MSE: 1.0594] 47.1+0.0s +[9600/16000] [MSE: 1.0590] 47.2+0.0s +[11200/16000] [MSE: 1.0574] 47.0+0.0s +[12800/16000] [MSE: 1.0570] 47.1+0.0s +[14400/16000] [MSE: 1.0574] 46.8+0.0s +[16000/16000] [MSE: 1.0597] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.24s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0701] 47.9+0.7s +[3200/16000] [MSE: 1.0612] 47.9+0.1s +[4800/16000] [MSE: 1.0604] 47.7+0.0s +[6400/16000] [MSE: 1.0626] 47.4+0.0s +[8000/16000] [MSE: 1.0668] 47.4+0.0s +[9600/16000] [MSE: 1.0641] 47.2+0.0s +[11200/16000] [MSE: 1.0648] 47.1+0.0s +[12800/16000] [MSE: 1.0622] 46.8+0.0s +[14400/16000] [MSE: 1.0631] 46.8+0.0s +[16000/16000] [MSE: 1.0668] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.34s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0811] 47.3+0.7s +[3200/16000] [MSE: 1.0668] 47.3+0.0s +[4800/16000] [MSE: 1.0681] 47.4+0.0s +[6400/16000] [MSE: 1.0672] 47.2+0.0s +[8000/16000] [MSE: 1.0618] 47.0+0.0s +[9600/16000] [MSE: 1.0600] 47.1+0.0s +[11200/16000] [MSE: 1.0562] 47.2+0.0s +[12800/16000] [MSE: 1.0598] 47.2+0.0s +[14400/16000] [MSE: 1.0604] 47.3+0.0s +[16000/16000] [MSE: 1.0594] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.34s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0854] 47.7+0.8s +[3200/16000] [MSE: 1.0902] 47.5+0.0s +[4800/16000] [MSE: 1.0839] 47.2+0.0s +[6400/16000] [MSE: 1.0681] 47.4+0.0s +[8000/16000] [MSE: 1.0669] 46.5+0.0s +[9600/16000] [MSE: 1.0641] 47.0+0.0s +[11200/16000] [MSE: 1.0618] 46.6+0.0s +[12800/16000] [MSE: 1.0633] 47.1+0.0s +[14400/16000] [MSE: 1.0612] 46.9+0.0s +[16000/16000] [MSE: 1.0567] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.38s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0960] 48.1+0.7s +[3200/16000] [MSE: 1.0867] 47.2+0.0s +[4800/16000] [MSE: 1.0846] 47.6+0.0s +[6400/16000] [MSE: 1.0751] 47.3+0.0s +[8000/16000] [MSE: 1.0778] 47.3+0.0s +[9600/16000] [MSE: 1.0764] 47.4+0.0s +[11200/16000] [MSE: 1.0773] 47.3+0.0s +[12800/16000] [MSE: 1.0753] 47.2+0.0s +[14400/16000] [MSE: 1.0752] 47.3+0.0s +[16000/16000] [MSE: 1.0737] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.20s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0640] 47.6+0.7s +[3200/16000] [MSE: 1.0738] 47.5+0.0s +[4800/16000] [MSE: 1.0817] 47.5+0.0s +[6400/16000] [MSE: 1.0694] 46.9+0.0s +[8000/16000] [MSE: 1.0608] 47.1+0.0s +[9600/16000] [MSE: 1.0593] 46.6+0.0s +[11200/16000] [MSE: 1.0583] 46.7+0.0s +[12800/16000] [MSE: 1.0569] 46.7+0.0s +[14400/16000] [MSE: 1.0633] 46.6+0.0s +[16000/16000] [MSE: 1.0650] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.29s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0307] 47.5+0.7s +[3200/16000] [MSE: 1.0567] 46.9+0.0s +[4800/16000] [MSE: 1.0453] 46.5+0.0s +[6400/16000] [MSE: 1.0491] 46.7+0.0s +[8000/16000] [MSE: 1.0464] 46.2+0.0s +[9600/16000] [MSE: 1.0430] 46.3+0.0s +[11200/16000] [MSE: 1.0521] 46.2+0.0s +[12800/16000] [MSE: 1.0507] 46.4+0.0s +[14400/16000] [MSE: 1.0529] 46.3+0.0s +[16000/16000] [MSE: 1.0545] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.32s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0470] 47.5+0.7s +[3200/16000] [MSE: 1.0610] 47.5+0.1s +[4800/16000] [MSE: 1.0560] 47.4+0.0s +[6400/16000] [MSE: 1.0594] 47.6+0.0s +[8000/16000] [MSE: 1.0634] 47.2+0.0s +[9600/16000] [MSE: 1.0643] 47.1+0.0s +[11200/16000] [MSE: 1.0646] 46.4+0.0s +[12800/16000] [MSE: 1.0626] 46.5+0.0s +[14400/16000] [MSE: 1.0635] 46.1+0.0s +[16000/16000] [MSE: 1.0639] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.20s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0650] 47.7+0.7s +[3200/16000] [MSE: 1.0608] 47.5+0.1s +[4800/16000] [MSE: 1.0494] 47.7+0.1s +[6400/16000] [MSE: 1.0562] 47.6+0.1s +[8000/16000] [MSE: 1.0525] 47.6+0.1s +[9600/16000] [MSE: 1.0560] 47.2+0.0s +[11200/16000] [MSE: 1.0606] 47.2+0.0s +[12800/16000] [MSE: 1.0613] 47.1+0.0s +[14400/16000] [MSE: 1.0632] 46.8+0.0s +[16000/16000] [MSE: 1.0603] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.24s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0392] 47.6+0.8s +[3200/16000] [MSE: 1.0578] 47.0+0.0s +[4800/16000] [MSE: 1.0651] 47.4+0.0s +[6400/16000] [MSE: 1.0597] 47.3+0.0s +[8000/16000] [MSE: 1.0611] 47.5+0.0s +[9600/16000] [MSE: 1.0651] 47.1+0.0s +[11200/16000] [MSE: 1.0670] 46.9+0.0s +[12800/16000] [MSE: 1.0721] 46.5+0.0s +[14400/16000] [MSE: 1.0775] 46.4+0.0s +[16000/16000] [MSE: 1.0749] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.34s + +[Epoch 119] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0495] 47.6+0.7s +[3200/16000] [MSE: 1.0449] 47.3+0.0s +[4800/16000] [MSE: 1.0384] 47.0+0.0s +[6400/16000] [MSE: 1.0381] 47.3+0.0s +[8000/16000] [MSE: 1.0446] 46.9+0.0s +[9600/16000] [MSE: 1.0396] 47.0+0.0s +[11200/16000] [MSE: 1.0483] 47.0+0.0s +[12800/16000] [MSE: 1.0514] 47.1+0.0s +[14400/16000] [MSE: 1.0538] 46.7+0.0s +[16000/16000] [MSE: 1.0526] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.29s + +[Epoch 120] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0876] 47.5+0.6s +[3200/16000] [MSE: 1.0814] 47.6+0.1s +[4800/16000] [MSE: 1.0695] 47.7+0.0s +[6400/16000] [MSE: 1.0623] 47.5+0.0s +[8000/16000] [MSE: 1.0552] 46.9+0.0s +[9600/16000] [MSE: 1.0550] 47.5+0.0s +[11200/16000] [MSE: 1.0557] 47.0+0.0s +[12800/16000] [MSE: 1.0548] 47.2+0.0s +[14400/16000] [MSE: 1.0551] 47.2+0.0s +[16000/16000] [MSE: 1.0557] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.33s + +[Epoch 121] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0394] 47.9+0.7s +[3200/16000] [MSE: 1.0565] 47.6+0.0s +[4800/16000] [MSE: 1.0654] 47.5+0.0s +[6400/16000] [MSE: 1.0708] 46.5+0.0s +[8000/16000] [MSE: 1.0718] 46.9+0.0s +[9600/16000] [MSE: 1.0737] 46.1+0.0s +[11200/16000] [MSE: 1.0717] 46.6+0.0s +[12800/16000] [MSE: 1.0671] 46.6+0.0s +[14400/16000] [MSE: 1.0629] 46.9+0.0s +[16000/16000] [MSE: 1.0651] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.22s + +[Epoch 122] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0454] 46.7+0.9s +[3200/16000] [MSE: 1.0491] 47.4+0.0s +[4800/16000] [MSE: 1.0628] 47.2+0.0s +[6400/16000] [MSE: 1.0614] 47.2+0.0s +[8000/16000] [MSE: 1.0571] 47.2+0.0s +[9600/16000] [MSE: 1.0621] 46.8+0.0s +[11200/16000] [MSE: 1.0594] 47.2+0.0s +[12800/16000] [MSE: 1.0600] 47.1+0.0s +[14400/16000] [MSE: 1.0584] 47.0+0.0s +[16000/16000] [MSE: 1.0640] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.68s + +Saving... +Total: 38.34s + +[Epoch 123] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0301] 47.9+0.9s +[3200/16000] [MSE: 1.0541] 47.6+0.1s +[4800/16000] [MSE: 1.0722] 47.3+0.1s +[6400/16000] [MSE: 1.0696] 47.1+0.1s +[8000/16000] [MSE: 1.0707] 47.5+0.0s +[9600/16000] [MSE: 1.0668] 47.5+0.0s +[11200/16000] [MSE: 1.0699] 47.3+0.0s +[12800/16000] [MSE: 1.0695] 47.0+0.0s +[14400/16000] [MSE: 1.0670] 46.9+0.0s +[16000/16000] [MSE: 1.0665] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.35s + +[Epoch 124] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0481] 47.4+0.7s +[3200/16000] [MSE: 1.0635] 47.5+0.1s +[4800/16000] [MSE: 1.0768] 47.5+0.0s +[6400/16000] [MSE: 1.0715] 47.1+0.0s +[8000/16000] [MSE: 1.0683] 46.9+0.0s +[9600/16000] [MSE: 1.0712] 46.8+0.0s +[11200/16000] [MSE: 1.0691] 46.7+0.0s +[12800/16000] [MSE: 1.0648] 47.0+0.0s +[14400/16000] [MSE: 1.0638] 46.9+0.0s +[16000/16000] [MSE: 1.0666] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.92s + +Saving... +Total: 38.48s + +[Epoch 125] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0786] 47.4+0.7s +[3200/16000] [MSE: 1.0724] 47.1+0.0s +[4800/16000] [MSE: 1.0659] 46.9+0.0s +[6400/16000] [MSE: 1.0646] 47.1+0.0s +[8000/16000] [MSE: 1.0619] 47.0+0.0s +[9600/16000] [MSE: 1.0602] 47.2+0.0s +[11200/16000] [MSE: 1.0619] 46.4+0.0s +[12800/16000] [MSE: 1.0625] 46.0+0.0s +[14400/16000] [MSE: 1.0627] 46.3+0.0s +[16000/16000] [MSE: 1.0641] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.89s + +Saving... +Total: 38.39s + +[Epoch 126] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0453] 47.8+0.7s +[3200/16000] [MSE: 1.0446] 47.8+0.1s +[4800/16000] [MSE: 1.0508] 47.4+0.0s +[6400/16000] [MSE: 1.0481] 47.2+0.0s +[8000/16000] [MSE: 1.0465] 47.5+0.0s +[9600/16000] [MSE: 1.0501] 47.0+0.0s +[11200/16000] [MSE: 1.0581] 47.7+0.0s +[12800/16000] [MSE: 1.0574] 47.2+0.0s +[14400/16000] [MSE: 1.0559] 47.0+0.0s +[16000/16000] [MSE: 1.0560] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.94s + +Saving... +Total: 38.64s + +[Epoch 127] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0647] 48.1+0.7s +[3200/16000] [MSE: 1.0819] 47.5+0.1s +[4800/16000] [MSE: 1.0748] 47.5+0.0s +[6400/16000] [MSE: 1.0677] 47.4+0.1s +[8000/16000] [MSE: 1.0630] 47.5+0.0s +[9600/16000] [MSE: 1.0585] 47.5+0.1s +[11200/16000] [MSE: 1.0611] 47.6+0.1s +[12800/16000] [MSE: 1.0597] 47.1+0.0s +[14400/16000] [MSE: 1.0536] 47.0+0.0s +[16000/16000] [MSE: 1.0543] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.99s + +Saving... +Total: 38.51s + +[Epoch 128] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0662] 47.8+0.7s +[3200/16000] [MSE: 1.0892] 47.1+0.0s +[4800/16000] [MSE: 1.0644] 47.5+0.0s +[6400/16000] [MSE: 1.0683] 47.4+0.0s +[8000/16000] [MSE: 1.0642] 47.1+0.0s +[9600/16000] [MSE: 1.0646] 46.8+0.0s +[11200/16000] [MSE: 1.0680] 46.8+0.0s +[12800/16000] [MSE: 1.0648] 47.1+0.0s +[14400/16000] [MSE: 1.0684] 46.8+0.0s +[16000/16000] [MSE: 1.0697] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.37s + +[Epoch 129] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0289] 47.5+0.8s +[3200/16000] [MSE: 1.0478] 47.4+0.0s +[4800/16000] [MSE: 1.0617] 47.5+0.0s +[6400/16000] [MSE: 1.0642] 47.3+0.0s +[8000/16000] [MSE: 1.0696] 46.9+0.0s +[9600/16000] [MSE: 1.0679] 47.0+0.0s +[11200/16000] [MSE: 1.0656] 46.7+0.0s +[12800/16000] [MSE: 1.0668] 47.3+0.0s +[14400/16000] [MSE: 1.0657] 47.0+0.0s +[16000/16000] [MSE: 1.0601] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.93s + +Saving... +Total: 38.46s + +[Epoch 130] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0668] 47.6+0.7s +[3200/16000] [MSE: 1.0763] 47.3+0.0s +[4800/16000] [MSE: 1.0819] 46.8+0.0s +[6400/16000] [MSE: 1.0784] 46.5+0.0s +[8000/16000] [MSE: 1.0698] 46.3+0.0s +[9600/16000] [MSE: 1.0743] 46.2+0.0s +[11200/16000] [MSE: 1.0682] 46.2+0.0s +[12800/16000] [MSE: 1.0660] 46.0+0.0s +[14400/16000] [MSE: 1.0619] 46.0+0.0s +[16000/16000] [MSE: 1.0625] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.41s + +[Epoch 131] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0595] 47.8+0.8s +[3200/16000] [MSE: 1.0580] 47.4+0.0s +[4800/16000] [MSE: 1.0668] 47.1+0.0s +[6400/16000] [MSE: 1.0697] 47.5+0.0s +[8000/16000] [MSE: 1.0709] 47.4+0.0s +[9600/16000] [MSE: 1.0693] 47.4+0.0s +[11200/16000] [MSE: 1.0776] 47.3+0.0s +[12800/16000] [MSE: 1.0734] 46.9+0.0s +[14400/16000] [MSE: 1.0715] 47.1+0.0s +[16000/16000] [MSE: 1.0733] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.36s + +[Epoch 132] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0698] 47.5+0.7s +[3200/16000] [MSE: 1.0559] 47.3+0.0s +[4800/16000] [MSE: 1.0593] 47.3+0.0s +[6400/16000] [MSE: 1.0569] 47.3+0.0s +[8000/16000] [MSE: 1.0582] 47.3+0.0s +[9600/16000] [MSE: 1.0551] 46.3+0.0s +[11200/16000] [MSE: 1.0547] 46.0+0.0s +[12800/16000] [MSE: 1.0608] 46.2+0.0s +[14400/16000] [MSE: 1.0646] 46.9+0.0s +[16000/16000] [MSE: 1.0643] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.31s + +[Epoch 133] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0670] 47.7+0.8s +[3200/16000] [MSE: 1.0562] 47.6+0.1s +[4800/16000] [MSE: 1.0565] 47.2+0.0s +[6400/16000] [MSE: 1.0507] 47.2+0.0s +[8000/16000] [MSE: 1.0543] 47.2+0.0s +[9600/16000] [MSE: 1.0582] 47.0+0.0s +[11200/16000] [MSE: 1.0566] 47.3+0.0s +[12800/16000] [MSE: 1.0543] 46.6+0.0s +[14400/16000] [MSE: 1.0564] 47.0+0.0s +[16000/16000] [MSE: 1.0561] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.23s + +[Epoch 134] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0661] 47.6+0.6s +[3200/16000] [MSE: 1.0574] 47.0+0.0s +[4800/16000] [MSE: 1.0807] 46.8+0.0s +[6400/16000] [MSE: 1.0816] 46.5+0.0s +[8000/16000] [MSE: 1.0838] 46.3+0.0s +[9600/16000] [MSE: 1.0784] 46.3+0.0s +[11200/16000] [MSE: 1.0720] 46.6+0.0s +[12800/16000] [MSE: 1.0691] 46.7+0.0s +[14400/16000] [MSE: 1.0722] 46.1+0.0s +[16000/16000] [MSE: 1.0681] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.46s + +[Epoch 135] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0258] 47.5+0.8s +[3200/16000] [MSE: 1.0305] 47.0+0.0s +[4800/16000] [MSE: 1.0395] 47.2+0.0s +[6400/16000] [MSE: 1.0439] 47.2+0.0s +[8000/16000] [MSE: 1.0447] 47.1+0.0s +[9600/16000] [MSE: 1.0475] 47.0+0.0s +[11200/16000] [MSE: 1.0436] 47.0+0.0s +[12800/16000] [MSE: 1.0459] 46.8+0.0s +[14400/16000] [MSE: 1.0510] 46.9+0.0s +[16000/16000] [MSE: 1.0508] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.23s + +[Epoch 136] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0461] 47.8+0.6s +[3200/16000] [MSE: 1.0426] 47.1+0.0s +[4800/16000] [MSE: 1.0526] 47.2+0.0s +[6400/16000] [MSE: 1.0479] 47.1+0.0s +[8000/16000] [MSE: 1.0517] 46.9+0.0s +[9600/16000] [MSE: 1.0505] 46.5+0.0s +[11200/16000] [MSE: 1.0551] 46.6+0.0s +[12800/16000] [MSE: 1.0584] 46.3+0.0s +[14400/16000] [MSE: 1.0556] 46.5+0.0s +[16000/16000] [MSE: 1.0551] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.36s + +[Epoch 137] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0936] 47.1+0.7s +[3200/16000] [MSE: 1.0852] 47.2+0.0s +[4800/16000] [MSE: 1.0814] 47.2+0.0s +[6400/16000] [MSE: 1.0818] 47.3+0.0s +[8000/16000] [MSE: 1.0709] 47.3+0.0s +[9600/16000] [MSE: 1.0729] 47.3+0.0s +[11200/16000] [MSE: 1.0724] 46.8+0.0s +[12800/16000] [MSE: 1.0696] 46.9+0.0s +[14400/16000] [MSE: 1.0703] 46.9+0.0s +[16000/16000] [MSE: 1.0679] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.90s + +Saving... +Total: 38.43s + +[Epoch 138] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0624] 47.9+0.7s +[3200/16000] [MSE: 1.0576] 47.5+0.0s +[4800/16000] [MSE: 1.0498] 47.4+0.0s +[6400/16000] [MSE: 1.0503] 47.2+0.0s +[8000/16000] [MSE: 1.0546] 47.2+0.0s +[9600/16000] [MSE: 1.0542] 47.3+0.0s +[11200/16000] [MSE: 1.0513] 47.1+0.0s +[12800/16000] [MSE: 1.0551] 47.0+0.0s +[14400/16000] [MSE: 1.0586] 46.6+0.0s +[16000/16000] [MSE: 1.0618] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.40s + +[Epoch 139] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0515] 47.7+0.7s +[3200/16000] [MSE: 1.0485] 47.6+0.0s +[4800/16000] [MSE: 1.0640] 47.4+0.0s +[6400/16000] [MSE: 1.0581] 47.2+0.0s +[8000/16000] [MSE: 1.0540] 47.2+0.0s +[9600/16000] [MSE: 1.0592] 47.2+0.0s +[11200/16000] [MSE: 1.0663] 47.1+0.0s +[12800/16000] [MSE: 1.0614] 46.9+0.0s +[14400/16000] [MSE: 1.0633] 47.0+0.0s +[16000/16000] [MSE: 1.0633] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.23s + +[Epoch 140] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0467] 47.8+0.7s +[3200/16000] [MSE: 1.0502] 47.6+0.0s +[4800/16000] [MSE: 1.0467] 47.5+0.0s +[6400/16000] [MSE: 1.0557] 47.2+0.0s +[8000/16000] [MSE: 1.0634] 46.9+0.0s +[9600/16000] [MSE: 1.0654] 46.9+0.0s +[11200/16000] [MSE: 1.0660] 46.7+0.0s +[12800/16000] [MSE: 1.0687] 45.7+0.0s +[14400/16000] [MSE: 1.0708] 46.2+0.0s +[16000/16000] [MSE: 1.0683] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.36s + +[Epoch 141] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0825] 47.6+0.8s +[3200/16000] [MSE: 1.0657] 47.8+0.1s +[4800/16000] [MSE: 1.0662] 47.2+0.0s +[6400/16000] [MSE: 1.0670] 47.2+0.0s +[8000/16000] [MSE: 1.0682] 47.5+0.0s +[9600/16000] [MSE: 1.0708] 47.1+0.0s +[11200/16000] [MSE: 1.0702] 47.3+0.0s +[12800/16000] [MSE: 1.0690] 47.2+0.0s +[14400/16000] [MSE: 1.0663] 47.1+0.0s +[16000/16000] [MSE: 1.0673] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.37s + +[Epoch 142] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0510] 47.7+0.7s +[3200/16000] [MSE: 1.0620] 47.4+0.0s +[4800/16000] [MSE: 1.0513] 46.9+0.0s +[6400/16000] [MSE: 1.0556] 47.3+0.0s +[8000/16000] [MSE: 1.0555] 46.9+0.0s +[9600/16000] [MSE: 1.0518] 46.6+0.0s +[11200/16000] [MSE: 1.0566] 47.0+0.0s +[12800/16000] [MSE: 1.0562] 47.0+0.0s +[14400/16000] [MSE: 1.0554] 46.5+0.0s +[16000/16000] [MSE: 1.0572] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.27s + +[Epoch 143] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0701] 47.6+0.7s +[3200/16000] [MSE: 1.0830] 47.2+0.0s +[4800/16000] [MSE: 1.0830] 47.0+0.0s +[6400/16000] [MSE: 1.0737] 47.0+0.0s +[8000/16000] [MSE: 1.0707] 47.2+0.0s +[9600/16000] [MSE: 1.0693] 47.2+0.0s +[11200/16000] [MSE: 1.0675] 47.4+0.0s +[12800/16000] [MSE: 1.0630] 47.1+0.0s +[14400/16000] [MSE: 1.0659] 47.0+0.0s +[16000/16000] [MSE: 1.0655] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.36s + +[Epoch 144] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0670] 47.7+0.8s +[3200/16000] [MSE: 1.0611] 47.5+0.0s +[4800/16000] [MSE: 1.0533] 47.0+0.0s +[6400/16000] [MSE: 1.0545] 47.4+0.0s +[8000/16000] [MSE: 1.0552] 46.9+0.0s +[9600/16000] [MSE: 1.0576] 46.9+0.0s +[11200/16000] [MSE: 1.0548] 46.5+0.0s +[12800/16000] [MSE: 1.0601] 46.6+0.0s +[14400/16000] [MSE: 1.0603] 46.7+0.0s +[16000/16000] [MSE: 1.0610] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.94s + +Saving... +Total: 38.43s + +[Epoch 145] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0394] 48.1+0.7s +[3200/16000] [MSE: 1.0583] 47.4+0.1s +[4800/16000] [MSE: 1.0596] 47.0+0.0s +[6400/16000] [MSE: 1.0545] 47.4+0.0s +[8000/16000] [MSE: 1.0493] 46.9+0.0s +[9600/16000] [MSE: 1.0531] 47.1+0.0s +[11200/16000] [MSE: 1.0527] 46.9+0.0s +[12800/16000] [MSE: 1.0563] 46.8+0.0s +[14400/16000] [MSE: 1.0605] 46.9+0.0s +[16000/16000] [MSE: 1.0573] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.41s + +[Epoch 146] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0708] 47.7+0.8s +[3200/16000] [MSE: 1.0623] 47.4+0.0s +[4800/16000] [MSE: 1.0619] 47.3+0.0s +[6400/16000] [MSE: 1.0782] 47.6+0.0s +[8000/16000] [MSE: 1.0718] 47.4+0.0s +[9600/16000] [MSE: 1.0655] 47.2+0.0s +[11200/16000] [MSE: 1.0641] 47.3+0.0s +[12800/16000] [MSE: 1.0626] 47.1+0.0s +[14400/16000] [MSE: 1.0650] 47.4+0.0s +[16000/16000] [MSE: 1.0664] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.33s + +[Epoch 147] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0896] 47.7+0.7s +[3200/16000] [MSE: 1.0965] 47.7+0.1s +[4800/16000] [MSE: 1.0847] 47.4+0.0s +[6400/16000] [MSE: 1.0876] 47.7+0.1s +[8000/16000] [MSE: 1.0812] 47.4+0.0s +[9600/16000] [MSE: 1.0824] 47.4+0.0s +[11200/16000] [MSE: 1.0784] 47.5+0.0s +[12800/16000] [MSE: 1.0750] 47.6+0.0s +[14400/16000] [MSE: 1.0715] 47.6+0.0s +[16000/16000] [MSE: 1.0685] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.35s + +[Epoch 148] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0718] 47.9+0.7s +[3200/16000] [MSE: 1.0782] 47.6+0.0s +[4800/16000] [MSE: 1.0660] 46.9+0.0s +[6400/16000] [MSE: 1.0694] 47.0+0.0s +[8000/16000] [MSE: 1.0698] 47.0+0.0s +[9600/16000] [MSE: 1.0659] 47.1+0.0s +[11200/16000] [MSE: 1.0621] 46.7+0.0s +[12800/16000] [MSE: 1.0642] 47.1+0.0s +[14400/16000] [MSE: 1.0642] 47.1+0.0s +[16000/16000] [MSE: 1.0604] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.32s + +[Epoch 149] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0725] 47.9+0.7s +[3200/16000] [MSE: 1.0554] 47.7+0.1s +[4800/16000] [MSE: 1.0504] 47.6+0.1s +[6400/16000] [MSE: 1.0498] 47.5+0.0s +[8000/16000] [MSE: 1.0503] 47.0+0.0s +[9600/16000] [MSE: 1.0511] 47.0+0.0s +[11200/16000] [MSE: 1.0573] 46.7+0.0s +[12800/16000] [MSE: 1.0597] 46.3+0.0s +[14400/16000] [MSE: 1.0631] 46.5+0.0s +[16000/16000] [MSE: 1.0632] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.41s + +[Epoch 150] Learning rate: 1.00e-4 +[1600/16000] [MSE: 0.9920] 47.8+0.7s +[3200/16000] [MSE: 1.0353] 47.2+0.0s +[4800/16000] [MSE: 1.0293] 47.1+0.0s +[6400/16000] [MSE: 1.0362] 47.2+0.0s +[8000/16000] [MSE: 1.0495] 46.7+0.0s +[9600/16000] [MSE: 1.0550] 47.2+0.0s +[11200/16000] [MSE: 1.0579] 46.7+0.0s +[12800/16000] [MSE: 1.0567] 46.6+0.0s +[14400/16000] [MSE: 1.0594] 46.1+0.0s +[16000/16000] [MSE: 1.0600] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.26s + +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0427] 47.7+0.8s +[3200/16000] [MSE: 1.0531] 47.4+0.0s +[4800/16000] [MSE: 1.0577] 47.2+0.0s +[6400/16000] [MSE: 1.0598] 46.9+0.0s +[8000/16000] [MSE: 1.0601] 46.9+0.0s +[9600/16000] [MSE: 1.0629] 46.8+0.0s +[11200/16000] [MSE: 1.0697] 46.9+0.0s +[12800/16000] [MSE: 1.0668] 47.0+0.0s +[14400/16000] [MSE: 1.0624] 47.0+0.0s +[16000/16000] [MSE: 1.0657] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.27s + +[Epoch 152] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0329] 47.8+0.6s +[3200/16000] [MSE: 1.0556] 47.4+0.0s +[4800/16000] [MSE: 1.0567] 47.5+0.0s +[6400/16000] [MSE: 1.0495] 47.3+0.0s +[8000/16000] [MSE: 1.0498] 47.3+0.0s +[9600/16000] [MSE: 1.0493] 47.0+0.0s +[11200/16000] [MSE: 1.0507] 47.2+0.0s +[12800/16000] [MSE: 1.0536] 47.4+0.0s +[14400/16000] [MSE: 1.0567] 47.0+0.0s +[16000/16000] [MSE: 1.0588] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.37s + +[Epoch 153] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0923] 48.0+0.7s +[3200/16000] [MSE: 1.0924] 47.2+0.0s +[4800/16000] [MSE: 1.0706] 47.5+0.0s +[6400/16000] [MSE: 1.0628] 47.0+0.0s +[8000/16000] [MSE: 1.0589] 47.1+0.0s +[9600/16000] [MSE: 1.0631] 47.3+0.0s +[11200/16000] [MSE: 1.0629] 47.2+0.0s +[12800/16000] [MSE: 1.0604] 46.8+0.0s +[14400/16000] [MSE: 1.0585] 46.9+0.0s +[16000/16000] [MSE: 1.0598] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.98s + +Saving... +Total: 38.50s + +[Epoch 154] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0366] 47.7+0.7s +[3200/16000] [MSE: 1.0455] 47.2+0.0s +[4800/16000] [MSE: 1.0574] 47.1+0.0s +[6400/16000] [MSE: 1.0502] 46.6+0.0s +[8000/16000] [MSE: 1.0448] 46.9+0.0s +[9600/16000] [MSE: 1.0512] 46.4+0.0s +[11200/16000] [MSE: 1.0485] 46.6+0.0s +[12800/16000] [MSE: 1.0470] 47.1+0.0s +[14400/16000] [MSE: 1.0478] 46.7+0.0s +[16000/16000] [MSE: 1.0499] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.46s + +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0295] 47.8+0.7s +[3200/16000] [MSE: 1.0587] 47.4+0.0s +[4800/16000] [MSE: 1.0727] 47.4+0.1s +[6400/16000] [MSE: 1.0737] 47.3+0.0s +[8000/16000] [MSE: 1.0698] 47.7+0.0s +[9600/16000] [MSE: 1.0665] 47.1+0.0s +[11200/16000] [MSE: 1.0630] 47.4+0.0s +[12800/16000] [MSE: 1.0642] 47.4+0.0s +[14400/16000] [MSE: 1.0641] 47.1+0.0s +[16000/16000] [MSE: 1.0667] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.41s + +[Epoch 156] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0615] 47.8+0.8s +[3200/16000] [MSE: 1.0343] 47.4+0.0s +[4800/16000] [MSE: 1.0471] 47.2+0.0s +[6400/16000] [MSE: 1.0503] 47.1+0.0s +[8000/16000] [MSE: 1.0522] 46.8+0.0s +[9600/16000] [MSE: 1.0516] 46.8+0.0s +[11200/16000] [MSE: 1.0582] 47.0+0.0s +[12800/16000] [MSE: 1.0553] 47.0+0.0s +[14400/16000] [MSE: 1.0549] 47.0+0.0s +[16000/16000] [MSE: 1.0574] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.51s + +[Epoch 157] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0452] 47.9+0.8s +[3200/16000] [MSE: 1.0537] 47.6+0.0s +[4800/16000] [MSE: 1.0539] 47.3+0.0s +[6400/16000] [MSE: 1.0706] 47.3+0.0s +[8000/16000] [MSE: 1.0660] 47.3+0.0s +[9600/16000] [MSE: 1.0655] 47.2+0.0s +[11200/16000] [MSE: 1.0597] 47.0+0.0s +[12800/16000] [MSE: 1.0598] 46.9+0.0s +[14400/16000] [MSE: 1.0598] 47.1+0.0s +[16000/16000] [MSE: 1.0581] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.25s + +[Epoch 158] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0500] 47.9+0.8s +[3200/16000] [MSE: 1.0421] 47.2+0.0s +[4800/16000] [MSE: 1.0523] 46.9+0.0s +[6400/16000] [MSE: 1.0527] 47.4+0.0s +[8000/16000] [MSE: 1.0576] 47.5+0.0s +[9600/16000] [MSE: 1.0606] 47.0+0.0s +[11200/16000] [MSE: 1.0556] 47.1+0.0s +[12800/16000] [MSE: 1.0546] 47.2+0.0s +[14400/16000] [MSE: 1.0574] 47.3+0.0s +[16000/16000] [MSE: 1.0583] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.31s + +[Epoch 159] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0513] 47.5+0.7s +[3200/16000] [MSE: 1.0490] 47.4+0.0s +[4800/16000] [MSE: 1.0526] 47.1+0.0s +[6400/16000] [MSE: 1.0560] 47.4+0.0s +[8000/16000] [MSE: 1.0617] 47.5+0.0s +[9600/16000] [MSE: 1.0634] 47.2+0.0s +[11200/16000] [MSE: 1.0659] 47.2+0.0s +[12800/16000] [MSE: 1.0655] 47.0+0.0s +[14400/16000] [MSE: 1.0623] 46.8+0.0s +[16000/16000] [MSE: 1.0613] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.23s + +[Epoch 160] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0835] 48.1+0.7s +[3200/16000] [MSE: 1.0672] 47.3+0.0s +[4800/16000] [MSE: 1.0544] 47.6+0.0s +[6400/16000] [MSE: 1.0625] 47.2+0.0s +[8000/16000] [MSE: 1.0542] 46.8+0.0s +[9600/16000] [MSE: 1.0544] 47.4+0.0s +[11200/16000] [MSE: 1.0542] 46.8+0.0s +[12800/16000] [MSE: 1.0569] 47.1+0.0s +[14400/16000] [MSE: 1.0600] 46.5+0.0s +[16000/16000] [MSE: 1.0612] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.40s + +[Epoch 161] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0282] 47.8+0.7s +[3200/16000] [MSE: 1.0460] 47.3+0.0s +[4800/16000] [MSE: 1.0620] 47.1+0.0s +[6400/16000] [MSE: 1.0552] 47.0+0.0s +[8000/16000] [MSE: 1.0643] 47.1+0.0s +[9600/16000] [MSE: 1.0600] 46.9+0.0s +[11200/16000] [MSE: 1.0639] 47.2+0.0s +[12800/16000] [MSE: 1.0620] 47.0+0.0s +[14400/16000] [MSE: 1.0619] 46.6+0.0s +[16000/16000] [MSE: 1.0617] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.26s + +[Epoch 162] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0487] 47.7+0.8s +[3200/16000] [MSE: 1.0507] 47.3+0.0s +[4800/16000] [MSE: 1.0476] 47.1+0.0s +[6400/16000] [MSE: 1.0549] 47.4+0.0s +[8000/16000] [MSE: 1.0529] 47.3+0.0s +[9600/16000] [MSE: 1.0501] 47.2+0.0s +[11200/16000] [MSE: 1.0504] 47.2+0.0s +[12800/16000] [MSE: 1.0572] 47.4+0.0s +[14400/16000] [MSE: 1.0554] 46.9+0.0s +[16000/16000] [MSE: 1.0591] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.91s + +Saving... +Total: 38.42s + +[Epoch 163] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0406] 47.5+0.8s +[3200/16000] [MSE: 1.0455] 47.1+0.0s +[4800/16000] [MSE: 1.0479] 47.3+0.0s +[6400/16000] [MSE: 1.0461] 47.2+0.0s +[8000/16000] [MSE: 1.0514] 47.0+0.0s +[9600/16000] [MSE: 1.0524] 46.6+0.0s +[11200/16000] [MSE: 1.0559] 46.8+0.0s +[12800/16000] [MSE: 1.0539] 46.9+0.0s +[14400/16000] [MSE: 1.0503] 47.1+0.0s +[16000/16000] [MSE: 1.0524] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.26s + +[Epoch 164] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0943] 47.6+0.7s +[3200/16000] [MSE: 1.0877] 47.3+0.1s +[4800/16000] [MSE: 1.0714] 47.0+0.0s +[6400/16000] [MSE: 1.0604] 47.5+0.0s +[8000/16000] [MSE: 1.0664] 46.7+0.0s +[9600/16000] [MSE: 1.0585] 46.9+0.0s +[11200/16000] [MSE: 1.0617] 46.8+0.0s +[12800/16000] [MSE: 1.0612] 46.9+0.0s +[14400/16000] [MSE: 1.0585] 47.3+0.0s +[16000/16000] [MSE: 1.0561] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.23s + +[Epoch 165] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0457] 47.7+0.7s +[3200/16000] [MSE: 1.0626] 47.6+0.1s +[4800/16000] [MSE: 1.0653] 47.6+0.1s +[6400/16000] [MSE: 1.0699] 47.4+0.0s +[8000/16000] [MSE: 1.0616] 47.4+0.0s +[9600/16000] [MSE: 1.0618] 47.1+0.0s +[11200/16000] [MSE: 1.0584] 47.3+0.0s +[12800/16000] [MSE: 1.0586] 47.0+0.0s +[14400/16000] [MSE: 1.0629] 46.9+0.0s +[16000/16000] [MSE: 1.0599] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.35s + +[Epoch 166] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.1008] 47.8+0.9s +[3200/16000] [MSE: 1.0916] 47.7+0.1s +[4800/16000] [MSE: 1.0836] 47.4+0.1s +[6400/16000] [MSE: 1.0786] 47.4+0.0s +[8000/16000] [MSE: 1.0720] 47.4+0.0s +[9600/16000] [MSE: 1.0693] 47.2+0.0s +[11200/16000] [MSE: 1.0637] 47.3+0.0s +[12800/16000] [MSE: 1.0637] 47.2+0.0s +[14400/16000] [MSE: 1.0678] 47.1+0.0s +[16000/16000] [MSE: 1.0669] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.35s + +[Epoch 167] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0533] 48.0+0.8s +[3200/16000] [MSE: 1.0560] 47.7+0.1s +[4800/16000] [MSE: 1.0481] 47.5+0.0s +[6400/16000] [MSE: 1.0565] 47.5+0.1s +[8000/16000] [MSE: 1.0592] 47.2+0.1s +[9600/16000] [MSE: 1.0600] 47.7+0.0s +[11200/16000] [MSE: 1.0586] 47.2+0.0s +[12800/16000] [MSE: 1.0590] 46.8+0.0s +[14400/16000] [MSE: 1.0547] 46.9+0.0s +[16000/16000] [MSE: 1.0522] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.34s + +[Epoch 168] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0539] 47.9+0.8s +[3200/16000] [MSE: 1.0614] 47.3+0.0s +[4800/16000] [MSE: 1.0514] 47.1+0.0s +[6400/16000] [MSE: 1.0547] 47.2+0.0s +[8000/16000] [MSE: 1.0524] 46.8+0.0s +[9600/16000] [MSE: 1.0494] 47.1+0.0s +[11200/16000] [MSE: 1.0509] 46.8+0.0s +[12800/16000] [MSE: 1.0539] 47.1+0.0s +[14400/16000] [MSE: 1.0536] 46.8+0.0s +[16000/16000] [MSE: 1.0553] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.89s + +Saving... +Total: 38.40s + +[Epoch 169] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0769] 47.4+0.8s +[3200/16000] [MSE: 1.0785] 47.5+0.1s +[4800/16000] [MSE: 1.0725] 47.0+0.0s +[6400/16000] [MSE: 1.0655] 47.2+0.0s +[8000/16000] [MSE: 1.0666] 47.0+0.0s +[9600/16000] [MSE: 1.0664] 47.0+0.0s +[11200/16000] [MSE: 1.0654] 47.3+0.0s +[12800/16000] [MSE: 1.0676] 47.0+0.0s +[14400/16000] [MSE: 1.0664] 47.1+0.0s +[16000/16000] [MSE: 1.0661] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.29s + +[Epoch 170] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0797] 47.8+0.7s +[3200/16000] [MSE: 1.0853] 47.4+0.0s +[4800/16000] [MSE: 1.0766] 47.4+0.0s +[6400/16000] [MSE: 1.0688] 47.1+0.0s +[8000/16000] [MSE: 1.0663] 47.5+0.0s +[9600/16000] [MSE: 1.0669] 47.3+0.0s +[11200/16000] [MSE: 1.0652] 47.3+0.0s +[12800/16000] [MSE: 1.0629] 47.3+0.0s +[14400/16000] [MSE: 1.0642] 46.3+0.0s +[16000/16000] [MSE: 1.0611] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.26s + +[Epoch 171] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0536] 47.8+0.7s +[3200/16000] [MSE: 1.0457] 47.3+0.0s +[4800/16000] [MSE: 1.0485] 47.1+0.0s +[6400/16000] [MSE: 1.0557] 46.8+0.0s +[8000/16000] [MSE: 1.0552] 46.8+0.0s +[9600/16000] [MSE: 1.0555] 46.7+0.0s +[11200/16000] [MSE: 1.0608] 46.9+0.0s +[12800/16000] [MSE: 1.0582] 46.9+0.0s +[14400/16000] [MSE: 1.0558] 46.6+0.0s +[16000/16000] [MSE: 1.0561] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.37s + +[Epoch 172] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0821] 48.0+0.7s +[3200/16000] [MSE: 1.0780] 47.7+0.1s +[4800/16000] [MSE: 1.0730] 47.2+0.0s +[6400/16000] [MSE: 1.0732] 46.9+0.0s +[8000/16000] [MSE: 1.0709] 47.2+0.0s +[9600/16000] [MSE: 1.0682] 47.0+0.0s +[11200/16000] [MSE: 1.0661] 46.9+0.0s +[12800/16000] [MSE: 1.0633] 47.2+0.0s +[14400/16000] [MSE: 1.0625] 46.6+0.0s +[16000/16000] [MSE: 1.0612] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.31s + +[Epoch 173] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0647] 48.0+0.8s +[3200/16000] [MSE: 1.0645] 47.6+0.1s +[4800/16000] [MSE: 1.0633] 47.4+0.1s +[6400/16000] [MSE: 1.0625] 47.3+0.0s +[8000/16000] [MSE: 1.0583] 47.6+0.1s +[9600/16000] [MSE: 1.0585] 47.4+0.0s +[11200/16000] [MSE: 1.0602] 47.1+0.0s +[12800/16000] [MSE: 1.0609] 47.2+0.0s +[14400/16000] [MSE: 1.0606] 47.0+0.0s +[16000/16000] [MSE: 1.0608] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.95s + +Saving... +Total: 38.44s + +[Epoch 174] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0613] 47.8+0.7s +[3200/16000] [MSE: 1.0581] 47.6+0.0s +[4800/16000] [MSE: 1.0710] 47.3+0.0s +[6400/16000] [MSE: 1.0648] 47.4+0.0s +[8000/16000] [MSE: 1.0627] 47.4+0.0s +[9600/16000] [MSE: 1.0631] 46.9+0.0s +[11200/16000] [MSE: 1.0603] 46.8+0.0s +[12800/16000] [MSE: 1.0605] 46.9+0.0s +[14400/16000] [MSE: 1.0577] 46.9+0.0s +[16000/16000] [MSE: 1.0577] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.94s + +Saving... +Total: 38.50s + +[Epoch 175] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0782] 48.0+0.7s +[3200/16000] [MSE: 1.0824] 47.8+0.1s +[4800/16000] [MSE: 1.0694] 47.3+0.0s +[6400/16000] [MSE: 1.0671] 47.5+0.0s +[8000/16000] [MSE: 1.0610] 47.5+0.0s +[9600/16000] [MSE: 1.0655] 47.5+0.0s +[11200/16000] [MSE: 1.0595] 46.4+0.0s +[12800/16000] [MSE: 1.0616] 46.2+0.0s +[14400/16000] [MSE: 1.0639] 46.6+0.0s +[16000/16000] [MSE: 1.0623] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.42s + +[Epoch 176] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0644] 47.6+0.8s +[3200/16000] [MSE: 1.0639] 47.1+0.0s +[4800/16000] [MSE: 1.0704] 47.2+0.0s +[6400/16000] [MSE: 1.0709] 47.5+0.0s +[8000/16000] [MSE: 1.0700] 47.5+0.0s +[9600/16000] [MSE: 1.0665] 46.7+0.0s +[11200/16000] [MSE: 1.0626] 47.0+0.0s +[12800/16000] [MSE: 1.0661] 47.1+0.0s +[14400/16000] [MSE: 1.0641] 46.6+0.0s +[16000/16000] [MSE: 1.0641] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.26s + +[Epoch 177] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0589] 48.0+0.7s +[3200/16000] [MSE: 1.0753] 47.8+0.1s +[4800/16000] [MSE: 1.0642] 47.9+0.1s +[6400/16000] [MSE: 1.0628] 47.5+0.0s +[8000/16000] [MSE: 1.0587] 47.8+0.1s +[9600/16000] [MSE: 1.0559] 47.4+0.0s +[11200/16000] [MSE: 1.0608] 47.8+0.0s +[12800/16000] [MSE: 1.0575] 47.2+0.0s +[14400/16000] [MSE: 1.0558] 47.1+0.0s +[16000/16000] [MSE: 1.0551] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.70s + +Saving... +Total: 38.22s + +[Epoch 178] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0469] 47.8+0.7s +[3200/16000] [MSE: 1.0452] 47.3+0.0s +[4800/16000] [MSE: 1.0482] 47.5+0.0s +[6400/16000] [MSE: 1.0550] 47.4+0.0s +[8000/16000] [MSE: 1.0608] 47.7+0.0s +[9600/16000] [MSE: 1.0606] 47.5+0.0s +[11200/16000] [MSE: 1.0618] 46.6+0.0s +[12800/16000] [MSE: 1.0607] 46.7+0.0s +[14400/16000] [MSE: 1.0641] 46.5+0.0s +[16000/16000] [MSE: 1.0657] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.44s + +[Epoch 179] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0796] 47.4+0.8s +[3200/16000] [MSE: 1.0804] 47.1+0.0s +[4800/16000] [MSE: 1.0704] 47.1+0.0s +[6400/16000] [MSE: 1.0706] 46.7+0.0s +[8000/16000] [MSE: 1.0691] 46.4+0.0s +[9600/16000] [MSE: 1.0687] 46.8+0.0s +[11200/16000] [MSE: 1.0690] 47.1+0.0s +[12800/16000] [MSE: 1.0709] 47.2+0.0s +[14400/16000] [MSE: 1.0671] 47.0+0.0s +[16000/16000] [MSE: 1.0647] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.35s + +[Epoch 180] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0835] 47.6+0.7s +[3200/16000] [MSE: 1.0648] 47.3+0.0s +[4800/16000] [MSE: 1.0729] 47.1+0.0s +[6400/16000] [MSE: 1.0731] 46.7+0.0s +[8000/16000] [MSE: 1.0744] 46.7+0.0s +[9600/16000] [MSE: 1.0755] 46.6+0.0s +[11200/16000] [MSE: 1.0698] 46.6+0.0s +[12800/16000] [MSE: 1.0686] 46.7+0.0s +[14400/16000] [MSE: 1.0706] 46.7+0.0s +[16000/16000] [MSE: 1.0682] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.27s + +[Epoch 181] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0710] 47.8+0.7s +[3200/16000] [MSE: 1.0482] 47.3+0.0s +[4800/16000] [MSE: 1.0636] 47.1+0.0s +[6400/16000] [MSE: 1.0633] 46.7+0.0s +[8000/16000] [MSE: 1.0596] 46.8+0.0s +[9600/16000] [MSE: 1.0545] 46.7+0.0s +[11200/16000] [MSE: 1.0549] 46.8+0.0s +[12800/16000] [MSE: 1.0545] 47.2+0.0s +[14400/16000] [MSE: 1.0539] 46.7+0.0s +[16000/16000] [MSE: 1.0521] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.33s + +[Epoch 182] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0659] 47.8+0.7s +[3200/16000] [MSE: 1.0776] 47.0+0.0s +[4800/16000] [MSE: 1.0755] 47.1+0.0s +[6400/16000] [MSE: 1.0730] 47.3+0.0s +[8000/16000] [MSE: 1.0741] 46.9+0.0s +[9600/16000] [MSE: 1.0662] 47.1+0.0s +[11200/16000] [MSE: 1.0667] 47.1+0.0s +[12800/16000] [MSE: 1.0638] 47.1+0.0s +[14400/16000] [MSE: 1.0636] 46.9+0.0s +[16000/16000] [MSE: 1.0609] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.24s + +[Epoch 183] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0864] 47.8+0.7s +[3200/16000] [MSE: 1.0851] 47.2+0.0s +[4800/16000] [MSE: 1.0761] 47.1+0.0s +[6400/16000] [MSE: 1.0683] 46.6+0.0s +[8000/16000] [MSE: 1.0642] 46.7+0.0s +[9600/16000] [MSE: 1.0654] 46.1+0.0s +[11200/16000] [MSE: 1.0670] 46.4+0.0s +[12800/16000] [MSE: 1.0649] 46.2+0.0s +[14400/16000] [MSE: 1.0625] 46.2+0.0s +[16000/16000] [MSE: 1.0660] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.38s + +[Epoch 184] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0813] 47.6+0.8s +[3200/16000] [MSE: 1.0771] 47.4+0.0s +[4800/16000] [MSE: 1.0638] 47.3+0.0s +[6400/16000] [MSE: 1.0736] 47.5+0.0s +[8000/16000] [MSE: 1.0715] 47.1+0.0s +[9600/16000] [MSE: 1.0664] 47.2+0.0s +[11200/16000] [MSE: 1.0644] 46.5+0.0s +[12800/16000] [MSE: 1.0622] 46.8+0.0s +[14400/16000] [MSE: 1.0607] 46.9+0.0s +[16000/16000] [MSE: 1.0590] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.25s + +[Epoch 185] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0655] 47.5+0.7s +[3200/16000] [MSE: 1.0467] 47.5+0.1s +[4800/16000] [MSE: 1.0499] 47.0+0.0s +[6400/16000] [MSE: 1.0572] 47.2+0.0s +[8000/16000] [MSE: 1.0574] 47.0+0.0s +[9600/16000] [MSE: 1.0564] 47.1+0.0s +[11200/16000] [MSE: 1.0562] 46.5+0.0s +[12800/16000] [MSE: 1.0589] 46.7+0.0s +[14400/16000] [MSE: 1.0598] 46.9+0.0s +[16000/16000] [MSE: 1.0615] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.31s + +[Epoch 186] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0578] 47.7+0.9s +[3200/16000] [MSE: 1.0421] 47.5+0.1s +[4800/16000] [MSE: 1.0583] 47.6+0.1s +[6400/16000] [MSE: 1.0590] 47.1+0.0s +[8000/16000] [MSE: 1.0511] 47.5+0.0s +[9600/16000] [MSE: 1.0479] 47.0+0.0s +[11200/16000] [MSE: 1.0482] 47.0+0.0s +[12800/16000] [MSE: 1.0472] 47.2+0.0s +[14400/16000] [MSE: 1.0477] 46.5+0.0s +[16000/16000] [MSE: 1.0481] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.99s + +Saving... +Total: 38.51s + +[Epoch 187] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0800] 47.7+0.7s +[3200/16000] [MSE: 1.0757] 47.5+0.0s +[4800/16000] [MSE: 1.0632] 47.2+0.0s +[6400/16000] [MSE: 1.0630] 47.2+0.0s +[8000/16000] [MSE: 1.0602] 47.0+0.0s +[9600/16000] [MSE: 1.0584] 47.3+0.0s +[11200/16000] [MSE: 1.0583] 47.1+0.0s +[12800/16000] [MSE: 1.0595] 47.1+0.0s +[14400/16000] [MSE: 1.0615] 47.2+0.0s +[16000/16000] [MSE: 1.0616] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.36s + +[Epoch 188] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0331] 47.6+0.7s +[3200/16000] [MSE: 1.0574] 47.3+0.0s +[4800/16000] [MSE: 1.0508] 47.1+0.0s +[6400/16000] [MSE: 1.0477] 47.1+0.0s +[8000/16000] [MSE: 1.0547] 46.8+0.0s +[9600/16000] [MSE: 1.0541] 46.9+0.0s +[11200/16000] [MSE: 1.0547] 47.1+0.0s +[12800/16000] [MSE: 1.0588] 47.1+0.0s +[14400/16000] [MSE: 1.0584] 47.0+0.0s +[16000/16000] [MSE: 1.0580] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.29s + +[Epoch 189] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0396] 48.0+0.7s +[3200/16000] [MSE: 1.0373] 47.3+0.0s +[4800/16000] [MSE: 1.0511] 47.2+0.0s +[6400/16000] [MSE: 1.0544] 47.2+0.0s +[8000/16000] [MSE: 1.0705] 46.5+0.0s +[9600/16000] [MSE: 1.0627] 47.0+0.0s +[11200/16000] [MSE: 1.0647] 47.0+0.0s +[12800/16000] [MSE: 1.0650] 45.8+0.0s +[14400/16000] [MSE: 1.0601] 46.0+0.0s +[16000/16000] [MSE: 1.0591] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.39s + +[Epoch 190] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0607] 47.7+0.7s +[3200/16000] [MSE: 1.0474] 47.9+0.1s +[4800/16000] [MSE: 1.0531] 47.5+0.0s +[6400/16000] [MSE: 1.0558] 47.5+0.0s +[8000/16000] [MSE: 1.0546] 47.0+0.0s +[9600/16000] [MSE: 1.0606] 47.3+0.0s +[11200/16000] [MSE: 1.0586] 47.4+0.0s +[12800/16000] [MSE: 1.0559] 47.0+0.0s +[14400/16000] [MSE: 1.0562] 47.0+0.0s +[16000/16000] [MSE: 1.0549] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.29s + +[Epoch 191] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0610] 47.6+0.7s +[3200/16000] [MSE: 1.0515] 47.3+0.0s +[4800/16000] [MSE: 1.0467] 47.0+0.0s +[6400/16000] [MSE: 1.0477] 46.9+0.0s +[8000/16000] [MSE: 1.0523] 47.2+0.0s +[9600/16000] [MSE: 1.0551] 47.3+0.0s +[11200/16000] [MSE: 1.0601] 47.4+0.0s +[12800/16000] [MSE: 1.0601] 47.3+0.0s +[14400/16000] [MSE: 1.0635] 47.1+0.0s +[16000/16000] [MSE: 1.0596] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.25s + +[Epoch 192] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0515] 47.9+0.7s +[3200/16000] [MSE: 1.0554] 47.8+0.1s +[4800/16000] [MSE: 1.0518] 47.7+0.1s +[6400/16000] [MSE: 1.0473] 47.9+0.1s +[8000/16000] [MSE: 1.0509] 47.4+0.0s +[9600/16000] [MSE: 1.0564] 47.5+0.0s +[11200/16000] [MSE: 1.0541] 47.2+0.0s +[12800/16000] [MSE: 1.0489] 47.2+0.0s +[14400/16000] [MSE: 1.0521] 47.2+0.0s +[16000/16000] [MSE: 1.0568] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.32s + +[Epoch 193] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0719] 48.0+0.7s +[3200/16000] [MSE: 1.0667] 47.6+0.1s +[4800/16000] [MSE: 1.0688] 47.1+0.0s +[6400/16000] [MSE: 1.0638] 47.3+0.0s +[8000/16000] [MSE: 1.0599] 47.0+0.0s +[9600/16000] [MSE: 1.0543] 47.3+0.0s +[11200/16000] [MSE: 1.0537] 47.1+0.0s +[12800/16000] [MSE: 1.0554] 47.1+0.0s +[14400/16000] [MSE: 1.0548] 46.7+0.0s +[16000/16000] [MSE: 1.0572] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.01s + +Saving... +Total: 38.54s + +[Epoch 194] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0702] 47.7+0.7s +[3200/16000] [MSE: 1.0861] 47.2+0.0s +[4800/16000] [MSE: 1.0786] 46.9+0.0s +[6400/16000] [MSE: 1.0686] 46.7+0.0s +[8000/16000] [MSE: 1.0598] 47.1+0.0s +[9600/16000] [MSE: 1.0625] 46.8+0.0s +[11200/16000] [MSE: 1.0595] 47.2+0.0s +[12800/16000] [MSE: 1.0608] 46.4+0.0s +[14400/16000] [MSE: 1.0610] 47.0+0.0s +[16000/16000] [MSE: 1.0601] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.34s + +[Epoch 195] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0204] 47.3+0.8s +[3200/16000] [MSE: 1.0239] 47.3+0.0s +[4800/16000] [MSE: 1.0412] 47.2+0.0s +[6400/16000] [MSE: 1.0465] 47.2+0.0s +[8000/16000] [MSE: 1.0501] 47.2+0.0s +[9600/16000] [MSE: 1.0464] 47.3+0.0s +[11200/16000] [MSE: 1.0476] 47.3+0.0s +[12800/16000] [MSE: 1.0505] 47.4+0.0s +[14400/16000] [MSE: 1.0508] 47.4+0.0s +[16000/16000] [MSE: 1.0517] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.28s + +[Epoch 196] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0896] 47.6+0.8s +[3200/16000] [MSE: 1.0698] 47.4+0.0s +[4800/16000] [MSE: 1.0679] 46.9+0.0s +[6400/16000] [MSE: 1.0674] 47.3+0.0s +[8000/16000] [MSE: 1.0649] 47.2+0.0s +[9600/16000] [MSE: 1.0616] 46.8+0.0s +[11200/16000] [MSE: 1.0576] 47.3+0.0s +[12800/16000] [MSE: 1.0556] 47.3+0.0s +[14400/16000] [MSE: 1.0579] 46.9+0.0s +[16000/16000] [MSE: 1.0618] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.00s + +Saving... +Total: 38.52s + +[Epoch 197] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0863] 47.6+0.7s +[3200/16000] [MSE: 1.0900] 47.5+0.0s +[4800/16000] [MSE: 1.0772] 47.4+0.0s +[6400/16000] [MSE: 1.0660] 47.4+0.0s +[8000/16000] [MSE: 1.0733] 47.3+0.0s +[9600/16000] [MSE: 1.0736] 47.1+0.0s +[11200/16000] [MSE: 1.0699] 47.0+0.0s +[12800/16000] [MSE: 1.0709] 46.7+0.0s +[14400/16000] [MSE: 1.0685] 46.5+0.0s +[16000/16000] [MSE: 1.0657] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.24s + +[Epoch 198] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0755] 47.9+0.7s +[3200/16000] [MSE: 1.0942] 47.7+0.0s +[4800/16000] [MSE: 1.0715] 47.1+0.0s +[6400/16000] [MSE: 1.0733] 47.2+0.0s +[8000/16000] [MSE: 1.0703] 47.4+0.0s +[9600/16000] [MSE: 1.0704] 47.7+0.0s +[11200/16000] [MSE: 1.0737] 47.2+0.0s +[12800/16000] [MSE: 1.0695] 47.3+0.0s +[14400/16000] [MSE: 1.0688] 46.8+0.0s +[16000/16000] [MSE: 1.0647] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.28s + +[Epoch 199] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0991] 47.5+0.8s +[3200/16000] [MSE: 1.0939] 47.6+0.1s +[4800/16000] [MSE: 1.0722] 47.4+0.0s +[6400/16000] [MSE: 1.0756] 47.6+0.0s +[8000/16000] [MSE: 1.0742] 47.4+0.0s +[9600/16000] [MSE: 1.0696] 47.3+0.0s +[11200/16000] [MSE: 1.0641] 47.4+0.0s +[12800/16000] [MSE: 1.0640] 47.0+0.0s +[14400/16000] [MSE: 1.0641] 47.0+0.0s +[16000/16000] [MSE: 1.0657] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.42s + +[Epoch 200] Learning rate: 1.00e-4 +[1600/16000] [MSE: 1.0725] 47.6+0.9s +[3200/16000] [MSE: 1.0713] 47.1+0.0s +[4800/16000] [MSE: 1.0605] 47.1+0.0s +[6400/16000] [MSE: 1.0573] 47.1+0.0s +[8000/16000] [MSE: 1.0657] 46.8+0.0s +[9600/16000] [MSE: 1.0684] 47.1+0.0s +[11200/16000] [MSE: 1.0641] 46.3+0.0s +[12800/16000] [MSE: 1.0636] 46.4+0.0s +[14400/16000] [MSE: 1.0670] 46.2+0.0s +[16000/16000] [MSE: 1.0658] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.48s + +[Epoch 201] Learning rate: 2.50e-5 +[1600/16000] [MSE: 1.0804] 47.7+0.8s +[3200/16000] [MSE: 1.0690] 47.2+0.0s +[4800/16000] [MSE: 1.0730] 47.1+0.0s +[6400/16000] [MSE: 1.0718] 47.3+0.0s +[8000/16000] [MSE: 1.0648] 46.7+0.0s +[9600/16000] [MSE: 1.0617] 46.7+0.0s +[11200/16000] [MSE: 1.0611] 46.5+0.0s +[12800/16000] [MSE: 1.0597] 46.9+0.0s +[14400/16000] [MSE: 1.0620] 46.3+0.0s +[16000/16000] [MSE: 1.0598] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.37s + +[Epoch 202] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0596] 47.7+0.8s +[3200/16000] [MSE: 1.0566] 47.3+0.0s +[4800/16000] [MSE: 1.0545] 47.1+0.0s +[6400/16000] [MSE: 1.0623] 47.0+0.0s +[8000/16000] [MSE: 1.0575] 46.8+0.0s +[9600/16000] [MSE: 1.0563] 47.0+0.0s +[11200/16000] [MSE: 1.0607] 47.2+0.0s +[12800/16000] [MSE: 1.0558] 46.6+0.0s +[14400/16000] [MSE: 1.0562] 47.4+0.0s +[16000/16000] [MSE: 1.0636] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.99s + +Saving... +Total: 38.50s + +[Epoch 203] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0893] 47.5+0.6s +[3200/16000] [MSE: 1.0654] 47.4+0.1s +[4800/16000] [MSE: 1.0609] 47.5+0.0s +[6400/16000] [MSE: 1.0588] 46.6+0.0s +[8000/16000] [MSE: 1.0590] 46.8+0.0s +[9600/16000] [MSE: 1.0510] 46.9+0.0s +[11200/16000] [MSE: 1.0518] 46.7+0.0s +[12800/16000] [MSE: 1.0484] 47.1+0.0s +[14400/16000] [MSE: 1.0492] 47.2+0.0s +[16000/16000] [MSE: 1.0539] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.24s + +[Epoch 204] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0978] 47.7+0.7s +[3200/16000] [MSE: 1.0819] 47.7+0.1s +[4800/16000] [MSE: 1.0616] 47.1+0.0s +[6400/16000] [MSE: 1.0627] 47.6+0.0s +[8000/16000] [MSE: 1.0637] 46.9+0.0s +[9600/16000] [MSE: 1.0589] 46.9+0.0s +[11200/16000] [MSE: 1.0629] 47.2+0.0s +[12800/16000] [MSE: 1.0678] 47.1+0.0s +[14400/16000] [MSE: 1.0699] 47.1+0.0s +[16000/16000] [MSE: 1.0617] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.31s + +[Epoch 205] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0861] 47.7+0.7s +[3200/16000] [MSE: 1.0816] 47.5+0.0s +[4800/16000] [MSE: 1.0660] 47.2+0.0s +[6400/16000] [MSE: 1.0671] 47.5+0.0s +[8000/16000] [MSE: 1.0712] 47.0+0.0s +[9600/16000] [MSE: 1.0688] 47.3+0.0s +[11200/16000] [MSE: 1.0689] 47.3+0.0s +[12800/16000] [MSE: 1.0630] 47.3+0.0s +[14400/16000] [MSE: 1.0591] 47.4+0.0s +[16000/16000] [MSE: 1.0607] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.38s + +[Epoch 206] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0645] 47.9+0.8s +[3200/16000] [MSE: 1.0668] 47.6+0.0s +[4800/16000] [MSE: 1.0623] 47.4+0.0s +[6400/16000] [MSE: 1.0582] 47.5+0.0s +[8000/16000] [MSE: 1.0659] 47.2+0.0s +[9600/16000] [MSE: 1.0612] 47.7+0.0s +[11200/16000] [MSE: 1.0562] 47.1+0.0s +[12800/16000] [MSE: 1.0559] 46.9+0.0s +[14400/16000] [MSE: 1.0569] 46.8+0.0s +[16000/16000] [MSE: 1.0576] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.29s + +[Epoch 207] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0916] 47.7+0.7s +[3200/16000] [MSE: 1.0689] 47.2+0.0s +[4800/16000] [MSE: 1.0703] 47.6+0.0s +[6400/16000] [MSE: 1.0609] 47.4+0.0s +[8000/16000] [MSE: 1.0609] 47.2+0.0s +[9600/16000] [MSE: 1.0574] 47.3+0.0s +[11200/16000] [MSE: 1.0534] 46.8+0.0s +[12800/16000] [MSE: 1.0501] 47.0+0.0s +[14400/16000] [MSE: 1.0490] 47.3+0.0s +[16000/16000] [MSE: 1.0501] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.17s + +[Epoch 208] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0246] 48.0+0.7s +[3200/16000] [MSE: 1.0412] 47.7+0.1s +[4800/16000] [MSE: 1.0488] 47.2+0.0s +[6400/16000] [MSE: 1.0545] 47.3+0.0s +[8000/16000] [MSE: 1.0577] 47.4+0.0s +[9600/16000] [MSE: 1.0597] 47.4+0.0s +[11200/16000] [MSE: 1.0611] 47.2+0.0s +[12800/16000] [MSE: 1.0629] 47.5+0.0s +[14400/16000] [MSE: 1.0676] 47.2+0.0s +[16000/16000] [MSE: 1.0681] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.36s + +[Epoch 209] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0410] 47.6+0.7s +[3200/16000] [MSE: 1.0438] 47.0+0.0s +[4800/16000] [MSE: 1.0623] 46.9+0.0s +[6400/16000] [MSE: 1.0676] 46.8+0.0s +[8000/16000] [MSE: 1.0639] 46.6+0.0s +[9600/16000] [MSE: 1.0677] 47.0+0.0s +[11200/16000] [MSE: 1.0613] 46.8+0.0s +[12800/16000] [MSE: 1.0629] 46.9+0.0s +[14400/16000] [MSE: 1.0607] 46.9+0.0s +[16000/16000] [MSE: 1.0615] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.66s + +Saving... +Total: 38.14s + +[Epoch 210] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0796] 48.0+0.7s +[3200/16000] [MSE: 1.0761] 47.7+0.1s +[4800/16000] [MSE: 1.0698] 47.3+0.0s +[6400/16000] [MSE: 1.0692] 47.4+0.0s +[8000/16000] [MSE: 1.0643] 47.1+0.0s +[9600/16000] [MSE: 1.0658] 47.1+0.0s +[11200/16000] [MSE: 1.0638] 46.8+0.0s +[12800/16000] [MSE: 1.0636] 46.7+0.0s +[14400/16000] [MSE: 1.0616] 46.6+0.0s +[16000/16000] [MSE: 1.0583] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.97s + +Saving... +Total: 38.45s + +[Epoch 211] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0846] 47.8+0.7s +[3200/16000] [MSE: 1.0446] 47.5+0.1s +[4800/16000] [MSE: 1.0631] 47.5+0.0s +[6400/16000] [MSE: 1.0534] 47.4+0.0s +[8000/16000] [MSE: 1.0478] 47.1+0.0s +[9600/16000] [MSE: 1.0528] 47.1+0.0s +[11200/16000] [MSE: 1.0517] 47.2+0.0s +[12800/16000] [MSE: 1.0569] 47.3+0.0s +[14400/16000] [MSE: 1.0557] 47.3+0.0s +[16000/16000] [MSE: 1.0607] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.38s + +[Epoch 212] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0831] 48.0+0.8s +[3200/16000] [MSE: 1.0833] 47.8+0.1s +[4800/16000] [MSE: 1.0719] 47.5+0.0s +[6400/16000] [MSE: 1.0783] 47.7+0.0s +[8000/16000] [MSE: 1.0777] 47.2+0.0s +[9600/16000] [MSE: 1.0679] 47.0+0.0s +[11200/16000] [MSE: 1.0686] 46.8+0.0s +[12800/16000] [MSE: 1.0669] 46.7+0.0s +[14400/16000] [MSE: 1.0677] 47.2+0.0s +[16000/16000] [MSE: 1.0679] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.97s + +Saving... +Total: 38.45s + +[Epoch 213] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0213] 47.9+0.8s +[3200/16000] [MSE: 1.0324] 47.4+0.0s +[4800/16000] [MSE: 1.0573] 47.3+0.0s +[6400/16000] [MSE: 1.0536] 47.3+0.0s +[8000/16000] [MSE: 1.0550] 46.6+0.0s +[9600/16000] [MSE: 1.0511] 46.8+0.0s +[11200/16000] [MSE: 1.0528] 47.0+0.0s +[12800/16000] [MSE: 1.0512] 46.9+0.0s +[14400/16000] [MSE: 1.0511] 46.3+0.0s +[16000/16000] [MSE: 1.0500] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.32s + +[Epoch 214] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0962] 47.8+0.7s +[3200/16000] [MSE: 1.0776] 47.4+0.0s +[4800/16000] [MSE: 1.0826] 47.5+0.0s +[6400/16000] [MSE: 1.0896] 48.1+0.1s +[8000/16000] [MSE: 1.0783] 47.7+0.1s +[9600/16000] [MSE: 1.0736] 47.7+0.0s +[11200/16000] [MSE: 1.0665] 47.7+0.1s +[12800/16000] [MSE: 1.0677] 47.3+0.0s +[14400/16000] [MSE: 1.0613] 47.6+0.0s +[16000/16000] [MSE: 1.0635] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.95s + +Saving... +Total: 38.46s + +[Epoch 215] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0647] 48.0+0.7s +[3200/16000] [MSE: 1.0696] 47.5+0.0s +[4800/16000] [MSE: 1.0621] 47.3+0.0s +[6400/16000] [MSE: 1.0652] 47.5+0.0s +[8000/16000] [MSE: 1.0645] 47.1+0.0s +[9600/16000] [MSE: 1.0674] 47.3+0.0s +[11200/16000] [MSE: 1.0621] 47.1+0.0s +[12800/16000] [MSE: 1.0633] 46.9+0.0s +[14400/16000] [MSE: 1.0602] 46.5+0.0s +[16000/16000] [MSE: 1.0599] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.23s + +[Epoch 216] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0231] 47.8+0.7s +[3200/16000] [MSE: 1.0326] 47.4+0.0s +[4800/16000] [MSE: 1.0464] 47.3+0.0s +[6400/16000] [MSE: 1.0467] 47.2+0.0s +[8000/16000] [MSE: 1.0474] 47.2+0.0s +[9600/16000] [MSE: 1.0448] 47.4+0.0s +[11200/16000] [MSE: 1.0503] 46.6+0.0s +[12800/16000] [MSE: 1.0536] 46.9+0.0s +[14400/16000] [MSE: 1.0560] 46.4+0.0s +[16000/16000] [MSE: 1.0552] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.31s + +[Epoch 217] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0446] 47.9+0.8s +[3200/16000] [MSE: 1.0319] 47.3+0.0s +[4800/16000] [MSE: 1.0474] 47.0+0.0s +[6400/16000] [MSE: 1.0353] 46.7+0.0s +[8000/16000] [MSE: 1.0404] 47.2+0.0s +[9600/16000] [MSE: 1.0422] 47.1+0.0s +[11200/16000] [MSE: 1.0428] 47.1+0.0s +[12800/16000] [MSE: 1.0487] 46.4+0.0s +[14400/16000] [MSE: 1.0500] 47.2+0.0s +[16000/16000] [MSE: 1.0533] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.17s + +[Epoch 218] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0767] 47.8+0.7s +[3200/16000] [MSE: 1.0895] 47.3+0.0s +[4800/16000] [MSE: 1.0784] 47.3+0.0s +[6400/16000] [MSE: 1.0786] 47.3+0.0s +[8000/16000] [MSE: 1.0744] 47.2+0.0s +[9600/16000] [MSE: 1.0721] 47.2+0.0s +[11200/16000] [MSE: 1.0691] 47.1+0.0s +[12800/16000] [MSE: 1.0697] 47.2+0.0s +[14400/16000] [MSE: 1.0665] 47.2+0.0s +[16000/16000] [MSE: 1.0648] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.89s + +Saving... +Total: 38.35s + +[Epoch 219] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0642] 47.6+0.7s +[3200/16000] [MSE: 1.0622] 47.2+0.0s +[4800/16000] [MSE: 1.0739] 47.3+0.0s +[6400/16000] [MSE: 1.0751] 46.8+0.0s +[8000/16000] [MSE: 1.0692] 47.3+0.0s +[9600/16000] [MSE: 1.0721] 47.2+0.0s +[11200/16000] [MSE: 1.0722] 47.0+0.0s +[12800/16000] [MSE: 1.0711] 46.8+0.0s +[14400/16000] [MSE: 1.0675] 47.0+0.0s +[16000/16000] [MSE: 1.0678] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.19s + +[Epoch 220] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0526] 47.8+0.6s +[3200/16000] [MSE: 1.0303] 47.3+0.0s +[4800/16000] [MSE: 1.0324] 47.2+0.0s +[6400/16000] [MSE: 1.0422] 46.9+0.0s +[8000/16000] [MSE: 1.0364] 46.8+0.0s +[9600/16000] [MSE: 1.0399] 46.7+0.0s +[11200/16000] [MSE: 1.0445] 46.7+0.0s +[12800/16000] [MSE: 1.0469] 46.1+0.0s +[14400/16000] [MSE: 1.0495] 46.4+0.0s +[16000/16000] [MSE: 1.0512] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.68s + +Saving... +Total: 38.19s + +[Epoch 221] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0836] 47.8+0.7s +[3200/16000] [MSE: 1.0756] 47.5+0.0s +[4800/16000] [MSE: 1.0689] 47.6+0.0s +[6400/16000] [MSE: 1.0632] 47.7+0.1s +[8000/16000] [MSE: 1.0644] 47.5+0.0s +[9600/16000] [MSE: 1.0614] 47.6+0.0s +[11200/16000] [MSE: 1.0630] 47.6+0.0s +[12800/16000] [MSE: 1.0643] 47.5+0.0s +[14400/16000] [MSE: 1.0664] 47.3+0.0s +[16000/16000] [MSE: 1.0635] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.32s + +[Epoch 222] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0486] 47.7+0.7s +[3200/16000] [MSE: 1.0490] 47.3+0.0s +[4800/16000] [MSE: 1.0575] 47.0+0.0s +[6400/16000] [MSE: 1.0489] 47.0+0.0s +[8000/16000] [MSE: 1.0458] 46.7+0.0s +[9600/16000] [MSE: 1.0503] 46.9+0.0s +[11200/16000] [MSE: 1.0494] 46.9+0.0s +[12800/16000] [MSE: 1.0523] 46.8+0.0s +[14400/16000] [MSE: 1.0540] 46.7+0.0s +[16000/16000] [MSE: 1.0563] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.34s + +[Epoch 223] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0610] 47.6+0.8s +[3200/16000] [MSE: 1.0709] 47.7+0.1s +[4800/16000] [MSE: 1.0670] 47.5+0.0s +[6400/16000] [MSE: 1.0694] 47.3+0.0s +[8000/16000] [MSE: 1.0745] 47.1+0.0s +[9600/16000] [MSE: 1.0741] 47.4+0.0s +[11200/16000] [MSE: 1.0699] 47.5+0.0s +[12800/16000] [MSE: 1.0724] 47.2+0.0s +[14400/16000] [MSE: 1.0746] 47.3+0.0s +[16000/16000] [MSE: 1.0720] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.88s + +Saving... +Total: 38.53s + +[Epoch 224] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0636] 47.6+0.7s +[3200/16000] [MSE: 1.0671] 47.5+0.1s +[4800/16000] [MSE: 1.0569] 47.1+0.0s +[6400/16000] [MSE: 1.0551] 46.9+0.0s +[8000/16000] [MSE: 1.0589] 47.3+0.0s +[9600/16000] [MSE: 1.0594] 46.5+0.0s +[11200/16000] [MSE: 1.0585] 46.3+0.0s +[12800/16000] [MSE: 1.0576] 46.5+0.0s +[14400/16000] [MSE: 1.0597] 46.2+0.0s +[16000/16000] [MSE: 1.0604] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.27s + +[Epoch 225] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0452] 47.9+0.7s +[3200/16000] [MSE: 1.0477] 47.6+0.1s +[4800/16000] [MSE: 1.0540] 47.6+0.0s +[6400/16000] [MSE: 1.0468] 47.4+0.0s +[8000/16000] [MSE: 1.0487] 47.6+0.0s +[9600/16000] [MSE: 1.0508] 47.4+0.0s +[11200/16000] [MSE: 1.0482] 47.0+0.0s +[12800/16000] [MSE: 1.0525] 47.1+0.0s +[14400/16000] [MSE: 1.0517] 47.0+0.0s +[16000/16000] [MSE: 1.0513] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.22s + +[Epoch 226] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0409] 48.0+0.7s +[3200/16000] [MSE: 1.0458] 47.5+0.1s +[4800/16000] [MSE: 1.0539] 46.8+0.0s +[6400/16000] [MSE: 1.0552] 46.6+0.0s +[8000/16000] [MSE: 1.0591] 46.4+0.0s +[9600/16000] [MSE: 1.0597] 46.6+0.0s +[11200/16000] [MSE: 1.0622] 46.3+0.0s +[12800/16000] [MSE: 1.0601] 46.6+0.0s +[14400/16000] [MSE: 1.0584] 46.7+0.0s +[16000/16000] [MSE: 1.0590] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.69s + +Saving... +Total: 38.21s + +[Epoch 227] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0930] 47.7+0.7s +[3200/16000] [MSE: 1.0669] 47.3+0.0s +[4800/16000] [MSE: 1.0676] 47.1+0.0s +[6400/16000] [MSE: 1.0720] 47.0+0.0s +[8000/16000] [MSE: 1.0711] 47.1+0.0s +[9600/16000] [MSE: 1.0688] 47.1+0.0s +[11200/16000] [MSE: 1.0678] 46.6+0.0s +[12800/16000] [MSE: 1.0673] 47.0+0.0s +[14400/16000] [MSE: 1.0662] 47.0+0.0s +[16000/16000] [MSE: 1.0610] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.23s + +[Epoch 228] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0182] 48.0+0.8s +[3200/16000] [MSE: 1.0337] 47.4+0.1s +[4800/16000] [MSE: 1.0542] 47.3+0.0s +[6400/16000] [MSE: 1.0553] 47.5+0.0s +[8000/16000] [MSE: 1.0474] 47.5+0.0s +[9600/16000] [MSE: 1.0458] 47.4+0.0s +[11200/16000] [MSE: 1.0461] 47.3+0.0s +[12800/16000] [MSE: 1.0455] 47.3+0.0s +[14400/16000] [MSE: 1.0474] 47.1+0.0s +[16000/16000] [MSE: 1.0497] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.30s + +[Epoch 229] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0905] 47.9+0.8s +[3200/16000] [MSE: 1.0802] 47.6+0.1s +[4800/16000] [MSE: 1.0678] 47.5+0.0s +[6400/16000] [MSE: 1.0727] 47.5+0.0s +[8000/16000] [MSE: 1.0708] 47.4+0.0s +[9600/16000] [MSE: 1.0694] 47.4+0.0s +[11200/16000] [MSE: 1.0684] 46.6+0.0s +[12800/16000] [MSE: 1.0640] 46.8+0.0s +[14400/16000] [MSE: 1.0609] 47.1+0.0s +[16000/16000] [MSE: 1.0611] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.27s + +[Epoch 230] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0286] 47.9+0.7s +[3200/16000] [MSE: 1.0314] 47.6+0.1s +[4800/16000] [MSE: 1.0469] 47.5+0.1s +[6400/16000] [MSE: 1.0623] 48.0+0.1s +[8000/16000] [MSE: 1.0554] 47.7+0.0s +[9600/16000] [MSE: 1.0600] 47.5+0.1s +[11200/16000] [MSE: 1.0591] 47.5+0.1s +[12800/16000] [MSE: 1.0627] 47.8+0.1s +[14400/16000] [MSE: 1.0581] 47.1+0.0s +[16000/16000] [MSE: 1.0590] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.34s + +[Epoch 231] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0884] 47.8+0.7s +[3200/16000] [MSE: 1.0606] 47.3+0.0s +[4800/16000] [MSE: 1.0624] 47.1+0.0s +[6400/16000] [MSE: 1.0689] 47.0+0.0s +[8000/16000] [MSE: 1.0637] 46.8+0.0s +[9600/16000] [MSE: 1.0660] 47.0+0.0s +[11200/16000] [MSE: 1.0647] 47.2+0.0s +[12800/16000] [MSE: 1.0652] 47.0+0.0s +[14400/16000] [MSE: 1.0649] 47.3+0.0s +[16000/16000] [MSE: 1.0632] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.22s + +[Epoch 232] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0572] 47.9+0.7s +[3200/16000] [MSE: 1.0590] 47.8+0.1s +[4800/16000] [MSE: 1.0599] 47.9+0.1s +[6400/16000] [MSE: 1.0601] 47.4+0.1s +[8000/16000] [MSE: 1.0607] 47.4+0.0s +[9600/16000] [MSE: 1.0639] 47.5+0.0s +[11200/16000] [MSE: 1.0625] 47.7+0.1s +[12800/16000] [MSE: 1.0620] 47.5+0.0s +[14400/16000] [MSE: 1.0580] 47.5+0.0s +[16000/16000] [MSE: 1.0583] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.27s + +[Epoch 233] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0193] 47.6+0.7s +[3200/16000] [MSE: 1.0387] 47.4+0.0s +[4800/16000] [MSE: 1.0420] 46.4+0.0s +[6400/16000] [MSE: 1.0404] 46.1+0.0s +[8000/16000] [MSE: 1.0436] 46.3+0.0s +[9600/16000] [MSE: 1.0446] 46.4+0.0s +[11200/16000] [MSE: 1.0483] 46.7+0.0s +[12800/16000] [MSE: 1.0498] 46.3+0.0s +[14400/16000] [MSE: 1.0563] 46.0+0.0s +[16000/16000] [MSE: 1.0525] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.37s + +[Epoch 234] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0870] 47.9+0.8s +[3200/16000] [MSE: 1.0727] 47.7+0.1s +[4800/16000] [MSE: 1.0628] 47.4+0.0s +[6400/16000] [MSE: 1.0659] 47.3+0.0s +[8000/16000] [MSE: 1.0623] 46.9+0.0s +[9600/16000] [MSE: 1.0659] 46.8+0.0s +[11200/16000] [MSE: 1.0641] 47.1+0.0s +[12800/16000] [MSE: 1.0636] 47.1+0.0s +[14400/16000] [MSE: 1.0621] 47.0+0.0s +[16000/16000] [MSE: 1.0588] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.27s + +[Epoch 235] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0417] 48.0+0.7s +[3200/16000] [MSE: 1.0532] 47.6+0.1s +[4800/16000] [MSE: 1.0607] 47.6+0.0s +[6400/16000] [MSE: 1.0547] 47.3+0.0s +[8000/16000] [MSE: 1.0577] 47.2+0.0s +[9600/16000] [MSE: 1.0611] 47.0+0.0s +[11200/16000] [MSE: 1.0621] 47.0+0.0s +[12800/16000] [MSE: 1.0637] 46.8+0.0s +[14400/16000] [MSE: 1.0646] 46.5+0.0s +[16000/16000] [MSE: 1.0608] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.22s + +[Epoch 236] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0610] 48.1+0.6s +[3200/16000] [MSE: 1.0440] 47.5+0.1s +[4800/16000] [MSE: 1.0590] 47.3+0.0s +[6400/16000] [MSE: 1.0643] 47.3+0.0s +[8000/16000] [MSE: 1.0654] 47.1+0.0s +[9600/16000] [MSE: 1.0684] 46.7+0.0s +[11200/16000] [MSE: 1.0655] 46.8+0.0s +[12800/16000] [MSE: 1.0650] 47.5+0.0s +[14400/16000] [MSE: 1.0629] 47.1+0.0s +[16000/16000] [MSE: 1.0609] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.18s + +[Epoch 237] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0864] 47.6+0.8s +[3200/16000] [MSE: 1.0906] 47.5+0.0s +[4800/16000] [MSE: 1.0611] 47.3+0.0s +[6400/16000] [MSE: 1.0789] 47.0+0.0s +[8000/16000] [MSE: 1.0737] 47.1+0.0s +[9600/16000] [MSE: 1.0707] 46.9+0.0s +[11200/16000] [MSE: 1.0688] 47.0+0.0s +[12800/16000] [MSE: 1.0643] 46.9+0.0s +[14400/16000] [MSE: 1.0618] 47.2+0.0s +[16000/16000] [MSE: 1.0579] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.24s + +[Epoch 238] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0349] 47.5+0.8s +[3200/16000] [MSE: 1.0633] 47.1+0.0s +[4800/16000] [MSE: 1.0618] 47.0+0.0s +[6400/16000] [MSE: 1.0513] 47.0+0.0s +[8000/16000] [MSE: 1.0484] 46.6+0.0s +[9600/16000] [MSE: 1.0530] 46.9+0.0s +[11200/16000] [MSE: 1.0534] 47.0+0.0s +[12800/16000] [MSE: 1.0527] 46.9+0.0s +[14400/16000] [MSE: 1.0555] 47.1+0.0s +[16000/16000] [MSE: 1.0575] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.66s + +Saving... +Total: 38.14s + +[Epoch 239] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0394] 47.7+0.8s +[3200/16000] [MSE: 1.0304] 47.5+0.0s +[4800/16000] [MSE: 1.0594] 47.5+0.0s +[6400/16000] [MSE: 1.0654] 47.2+0.0s +[8000/16000] [MSE: 1.0619] 47.2+0.0s +[9600/16000] [MSE: 1.0570] 47.0+0.0s +[11200/16000] [MSE: 1.0522] 46.6+0.0s +[12800/16000] [MSE: 1.0540] 47.2+0.0s +[14400/16000] [MSE: 1.0520] 46.7+0.0s +[16000/16000] [MSE: 1.0535] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.28s + +[Epoch 240] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0455] 47.7+0.7s +[3200/16000] [MSE: 1.0397] 47.7+0.1s +[4800/16000] [MSE: 1.0505] 47.6+0.1s +[6400/16000] [MSE: 1.0530] 47.5+0.0s +[8000/16000] [MSE: 1.0569] 47.3+0.1s +[9600/16000] [MSE: 1.0631] 47.4+0.0s +[11200/16000] [MSE: 1.0715] 47.4+0.0s +[12800/16000] [MSE: 1.0667] 46.8+0.0s +[14400/16000] [MSE: 1.0633] 46.7+0.0s +[16000/16000] [MSE: 1.0629] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.35s + +[Epoch 241] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0610] 48.0+0.7s +[3200/16000] [MSE: 1.0606] 47.6+0.0s +[4800/16000] [MSE: 1.0633] 47.4+0.0s +[6400/16000] [MSE: 1.0618] 46.9+0.0s +[8000/16000] [MSE: 1.0659] 46.8+0.0s +[9600/16000] [MSE: 1.0636] 46.5+0.0s +[11200/16000] [MSE: 1.0622] 46.7+0.0s +[12800/16000] [MSE: 1.0603] 46.8+0.0s +[14400/16000] [MSE: 1.0602] 46.6+0.0s +[16000/16000] [MSE: 1.0602] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.23s + +[Epoch 242] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0432] 47.5+0.7s +[3200/16000] [MSE: 1.0452] 47.2+0.0s +[4800/16000] [MSE: 1.0623] 47.3+0.0s +[6400/16000] [MSE: 1.0563] 46.6+0.0s +[8000/16000] [MSE: 1.0575] 47.0+0.0s +[9600/16000] [MSE: 1.0594] 47.0+0.0s +[11200/16000] [MSE: 1.0593] 47.2+0.0s +[12800/16000] [MSE: 1.0577] 46.5+0.0s +[14400/16000] [MSE: 1.0582] 46.7+0.0s +[16000/16000] [MSE: 1.0589] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.94s + +Saving... +Total: 38.45s + +[Epoch 243] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0428] 47.5+0.8s +[3200/16000] [MSE: 1.0666] 47.6+0.0s +[4800/16000] [MSE: 1.0637] 47.5+0.0s +[6400/16000] [MSE: 1.0600] 47.1+0.0s +[8000/16000] [MSE: 1.0475] 47.0+0.0s +[9600/16000] [MSE: 1.0554] 47.4+0.0s +[11200/16000] [MSE: 1.0587] 47.4+0.0s +[12800/16000] [MSE: 1.0575] 47.0+0.0s +[14400/16000] [MSE: 1.0576] 47.0+0.0s +[16000/16000] [MSE: 1.0579] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.35s + +[Epoch 244] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0262] 47.3+0.8s +[3200/16000] [MSE: 1.0105] 47.5+0.0s +[4800/16000] [MSE: 1.0284] 47.7+0.0s +[6400/16000] [MSE: 1.0445] 47.7+0.0s +[8000/16000] [MSE: 1.0502] 47.4+0.0s +[9600/16000] [MSE: 1.0470] 47.5+0.0s +[11200/16000] [MSE: 1.0495] 47.7+0.0s +[12800/16000] [MSE: 1.0563] 47.4+0.0s +[14400/16000] [MSE: 1.0545] 47.2+0.0s +[16000/16000] [MSE: 1.0540] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.35s + +[Epoch 245] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.1081] 47.5+0.8s +[3200/16000] [MSE: 1.0865] 47.4+0.0s +[4800/16000] [MSE: 1.0823] 47.5+0.0s +[6400/16000] [MSE: 1.0780] 47.5+0.0s +[8000/16000] [MSE: 1.0726] 47.2+0.0s +[9600/16000] [MSE: 1.0645] 47.3+0.0s +[11200/16000] [MSE: 1.0625] 46.8+0.0s +[12800/16000] [MSE: 1.0600] 46.4+0.0s +[14400/16000] [MSE: 1.0610] 47.0+0.0s +[16000/16000] [MSE: 1.0623] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.29s + +[Epoch 246] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0731] 47.5+0.8s +[3200/16000] [MSE: 1.0700] 47.4+0.0s +[4800/16000] [MSE: 1.0478] 47.5+0.0s +[6400/16000] [MSE: 1.0563] 47.5+0.0s +[8000/16000] [MSE: 1.0514] 46.9+0.0s +[9600/16000] [MSE: 1.0482] 46.5+0.0s +[11200/16000] [MSE: 1.0441] 46.7+0.0s +[12800/16000] [MSE: 1.0487] 46.0+0.0s +[14400/16000] [MSE: 1.0515] 45.9+0.0s +[16000/16000] [MSE: 1.0526] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.26s + +[Epoch 247] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0680] 48.0+0.7s +[3200/16000] [MSE: 1.0565] 47.7+0.1s +[4800/16000] [MSE: 1.0588] 47.8+0.1s +[6400/16000] [MSE: 1.0571] 47.3+0.0s +[8000/16000] [MSE: 1.0650] 47.5+0.0s +[9600/16000] [MSE: 1.0654] 47.3+0.0s +[11200/16000] [MSE: 1.0620] 47.4+0.0s +[12800/16000] [MSE: 1.0629] 47.5+0.0s +[14400/16000] [MSE: 1.0589] 47.1+0.0s +[16000/16000] [MSE: 1.0597] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.34s + +[Epoch 248] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0795] 47.8+0.8s +[3200/16000] [MSE: 1.0785] 47.3+0.0s +[4800/16000] [MSE: 1.0716] 47.4+0.0s +[6400/16000] [MSE: 1.0687] 47.3+0.0s +[8000/16000] [MSE: 1.0631] 47.4+0.0s +[9600/16000] [MSE: 1.0632] 47.3+0.0s +[11200/16000] [MSE: 1.0617] 46.7+0.0s +[12800/16000] [MSE: 1.0658] 47.3+0.0s +[14400/16000] [MSE: 1.0657] 47.0+0.0s +[16000/16000] [MSE: 1.0646] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.23s + +[Epoch 249] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0688] 47.7+0.9s +[3200/16000] [MSE: 1.0579] 47.5+0.0s +[4800/16000] [MSE: 1.0450] 47.7+0.0s +[6400/16000] [MSE: 1.0476] 47.9+0.0s +[8000/16000] [MSE: 1.0474] 47.6+0.0s +[9600/16000] [MSE: 1.0511] 47.4+0.0s +[11200/16000] [MSE: 1.0569] 47.2+0.0s +[12800/16000] [MSE: 1.0566] 46.5+0.0s +[14400/16000] [MSE: 1.0607] 46.8+0.0s +[16000/16000] [MSE: 1.0646] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.26s + +[Epoch 250] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0592] 47.7+0.8s +[3200/16000] [MSE: 1.0753] 47.8+0.0s +[4800/16000] [MSE: 1.0658] 47.6+0.0s +[6400/16000] [MSE: 1.0628] 47.2+0.0s +[8000/16000] [MSE: 1.0584] 47.1+0.0s +[9600/16000] [MSE: 1.0665] 47.2+0.0s +[11200/16000] [MSE: 1.0625] 47.1+0.0s +[12800/16000] [MSE: 1.0639] 47.4+0.0s +[14400/16000] [MSE: 1.0668] 47.6+0.0s +[16000/16000] [MSE: 1.0655] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.01s + +Saving... +Total: 38.50s + +[Epoch 251] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0568] 47.7+0.7s +[3200/16000] [MSE: 1.0483] 47.5+0.0s +[4800/16000] [MSE: 1.0613] 47.0+0.0s +[6400/16000] [MSE: 1.0583] 46.7+0.0s +[8000/16000] [MSE: 1.0582] 47.3+0.0s +[9600/16000] [MSE: 1.0598] 47.3+0.0s +[11200/16000] [MSE: 1.0622] 46.9+0.0s +[12800/16000] [MSE: 1.0629] 47.2+0.0s +[14400/16000] [MSE: 1.0634] 46.7+0.0s +[16000/16000] [MSE: 1.0610] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.31s + +[Epoch 252] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0640] 47.8+0.7s +[3200/16000] [MSE: 1.0397] 47.5+0.0s +[4800/16000] [MSE: 1.0505] 47.2+0.0s +[6400/16000] [MSE: 1.0464] 47.0+0.0s +[8000/16000] [MSE: 1.0480] 47.1+0.0s +[9600/16000] [MSE: 1.0470] 46.9+0.0s +[11200/16000] [MSE: 1.0505] 46.9+0.0s +[12800/16000] [MSE: 1.0508] 46.7+0.0s +[14400/16000] [MSE: 1.0526] 47.3+0.0s +[16000/16000] [MSE: 1.0553] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.24s + +[Epoch 253] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0713] 47.4+0.7s +[3200/16000] [MSE: 1.0727] 47.3+0.0s +[4800/16000] [MSE: 1.0713] 47.4+0.0s +[6400/16000] [MSE: 1.0674] 47.3+0.0s +[8000/16000] [MSE: 1.0676] 47.1+0.0s +[9600/16000] [MSE: 1.0597] 47.1+0.0s +[11200/16000] [MSE: 1.0579] 47.0+0.0s +[12800/16000] [MSE: 1.0593] 46.6+0.0s +[14400/16000] [MSE: 1.0616] 46.6+0.0s +[16000/16000] [MSE: 1.0613] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.33s + +[Epoch 254] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0757] 47.8+0.7s +[3200/16000] [MSE: 1.0659] 47.3+0.0s +[4800/16000] [MSE: 1.0615] 47.4+0.0s +[6400/16000] [MSE: 1.0559] 47.7+0.0s +[8000/16000] [MSE: 1.0574] 47.6+0.0s +[9600/16000] [MSE: 1.0605] 47.5+0.0s +[11200/16000] [MSE: 1.0619] 47.4+0.0s +[12800/16000] [MSE: 1.0644] 47.3+0.0s +[14400/16000] [MSE: 1.0626] 47.2+0.0s +[16000/16000] [MSE: 1.0632] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.32s + +[Epoch 255] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0714] 47.5+0.8s +[3200/16000] [MSE: 1.0579] 47.6+0.1s +[4800/16000] [MSE: 1.0576] 47.6+0.0s +[6400/16000] [MSE: 1.0675] 47.3+0.0s +[8000/16000] [MSE: 1.0662] 46.8+0.0s +[9600/16000] [MSE: 1.0721] 46.4+0.0s +[11200/16000] [MSE: 1.0738] 47.1+0.0s +[12800/16000] [MSE: 1.0713] 46.1+0.0s +[14400/16000] [MSE: 1.0722] 46.0+0.0s +[16000/16000] [MSE: 1.0722] 45.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.45s + +[Epoch 256] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0428] 47.7+0.9s +[3200/16000] [MSE: 1.0680] 47.3+0.0s +[4800/16000] [MSE: 1.0527] 47.6+0.1s +[6400/16000] [MSE: 1.0551] 47.1+0.0s +[8000/16000] [MSE: 1.0572] 47.2+0.0s +[9600/16000] [MSE: 1.0597] 47.2+0.0s +[11200/16000] [MSE: 1.0603] 47.2+0.0s +[12800/16000] [MSE: 1.0595] 46.9+0.0s +[14400/16000] [MSE: 1.0613] 46.5+0.0s +[16000/16000] [MSE: 1.0603] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.78s + +Saving... +Total: 38.31s + +[Epoch 257] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0445] 47.2+0.7s +[3200/16000] [MSE: 1.0669] 47.4+0.1s +[4800/16000] [MSE: 1.0646] 47.2+0.0s +[6400/16000] [MSE: 1.0598] 47.2+0.0s +[8000/16000] [MSE: 1.0694] 47.2+0.0s +[9600/16000] [MSE: 1.0743] 47.2+0.0s +[11200/16000] [MSE: 1.0725] 47.4+0.0s +[12800/16000] [MSE: 1.0690] 46.7+0.0s +[14400/16000] [MSE: 1.0691] 47.2+0.0s +[16000/16000] [MSE: 1.0714] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.36s + +[Epoch 258] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0872] 47.7+0.6s +[3200/16000] [MSE: 1.0754] 47.5+0.1s +[4800/16000] [MSE: 1.0744] 47.5+0.0s +[6400/16000] [MSE: 1.0730] 47.5+0.0s +[8000/16000] [MSE: 1.0626] 47.4+0.0s +[9600/16000] [MSE: 1.0612] 47.5+0.0s +[11200/16000] [MSE: 1.0620] 47.4+0.0s +[12800/16000] [MSE: 1.0643] 47.1+0.0s +[14400/16000] [MSE: 1.0642] 47.3+0.0s +[16000/16000] [MSE: 1.0623] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.25s + +[Epoch 259] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0545] 47.8+0.8s +[3200/16000] [MSE: 1.0448] 47.6+0.0s +[4800/16000] [MSE: 1.0372] 47.7+0.0s +[6400/16000] [MSE: 1.0470] 47.7+0.0s +[8000/16000] [MSE: 1.0561] 47.4+0.0s +[9600/16000] [MSE: 1.0556] 47.5+0.0s +[11200/16000] [MSE: 1.0593] 47.5+0.0s +[12800/16000] [MSE: 1.0593] 47.2+0.0s +[14400/16000] [MSE: 1.0605] 47.2+0.0s +[16000/16000] [MSE: 1.0584] 47.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.34s + +[Epoch 260] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0284] 47.9+0.7s +[3200/16000] [MSE: 1.0450] 47.7+0.0s +[4800/16000] [MSE: 1.0417] 47.8+0.0s +[6400/16000] [MSE: 1.0415] 47.8+0.0s +[8000/16000] [MSE: 1.0444] 47.5+0.0s +[9600/16000] [MSE: 1.0516] 47.5+0.0s +[11200/16000] [MSE: 1.0536] 47.1+0.0s +[12800/16000] [MSE: 1.0555] 46.8+0.0s +[14400/16000] [MSE: 1.0549] 47.0+0.0s +[16000/16000] [MSE: 1.0533] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.36s + +[Epoch 261] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0796] 47.4+0.7s +[3200/16000] [MSE: 1.0707] 47.2+0.0s +[4800/16000] [MSE: 1.0607] 47.4+0.0s +[6400/16000] [MSE: 1.0575] 47.3+0.0s +[8000/16000] [MSE: 1.0583] 47.0+0.0s +[9600/16000] [MSE: 1.0553] 46.8+0.0s +[11200/16000] [MSE: 1.0625] 46.5+0.0s +[12800/16000] [MSE: 1.0609] 46.3+0.0s +[14400/16000] [MSE: 1.0582] 46.5+0.0s +[16000/16000] [MSE: 1.0585] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.33s + +[Epoch 262] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0721] 48.0+0.8s +[3200/16000] [MSE: 1.0828] 47.8+0.0s +[4800/16000] [MSE: 1.0708] 47.7+0.0s +[6400/16000] [MSE: 1.0644] 47.7+0.1s +[8000/16000] [MSE: 1.0628] 47.8+0.1s +[9600/16000] [MSE: 1.0606] 47.4+0.0s +[11200/16000] [MSE: 1.0620] 47.1+0.0s +[12800/16000] [MSE: 1.0689] 47.0+0.0s +[14400/16000] [MSE: 1.0702] 47.2+0.0s +[16000/16000] [MSE: 1.0695] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.34s + +[Epoch 263] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0678] 47.5+0.7s +[3200/16000] [MSE: 1.0580] 46.8+0.0s +[4800/16000] [MSE: 1.0585] 46.2+0.0s +[6400/16000] [MSE: 1.0583] 46.8+0.0s +[8000/16000] [MSE: 1.0634] 47.1+0.0s +[9600/16000] [MSE: 1.0648] 46.1+0.0s +[11200/16000] [MSE: 1.0594] 46.3+0.0s +[12800/16000] [MSE: 1.0590] 46.2+0.0s +[14400/16000] [MSE: 1.0564] 46.4+0.0s +[16000/16000] [MSE: 1.0585] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.23s + +[Epoch 264] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0023] 47.7+0.7s +[3200/16000] [MSE: 1.0214] 47.5+0.1s +[4800/16000] [MSE: 1.0295] 47.0+0.0s +[6400/16000] [MSE: 1.0443] 46.9+0.0s +[8000/16000] [MSE: 1.0455] 46.9+0.0s +[9600/16000] [MSE: 1.0522] 47.0+0.0s +[11200/16000] [MSE: 1.0520] 46.7+0.0s +[12800/16000] [MSE: 1.0544] 46.9+0.0s +[14400/16000] [MSE: 1.0517] 47.1+0.0s +[16000/16000] [MSE: 1.0523] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.29s + +[Epoch 265] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0924] 47.8+0.8s +[3200/16000] [MSE: 1.0744] 47.3+0.0s +[4800/16000] [MSE: 1.0710] 47.3+0.0s +[6400/16000] [MSE: 1.0646] 47.3+0.0s +[8000/16000] [MSE: 1.0690] 47.4+0.0s +[9600/16000] [MSE: 1.0648] 47.4+0.0s +[11200/16000] [MSE: 1.0639] 47.3+0.0s +[12800/16000] [MSE: 1.0680] 47.1+0.0s +[14400/16000] [MSE: 1.0663] 47.2+0.0s +[16000/16000] [MSE: 1.0618] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.34s + +[Epoch 266] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0303] 47.7+0.8s +[3200/16000] [MSE: 1.0345] 47.5+0.0s +[4800/16000] [MSE: 1.0389] 47.0+0.0s +[6400/16000] [MSE: 1.0450] 47.3+0.0s +[8000/16000] [MSE: 1.0542] 46.9+0.0s +[9600/16000] [MSE: 1.0568] 46.7+0.0s +[11200/16000] [MSE: 1.0566] 46.5+0.0s +[12800/16000] [MSE: 1.0532] 46.2+0.0s +[14400/16000] [MSE: 1.0540] 46.4+0.0s +[16000/16000] [MSE: 1.0566] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.93s + +Saving... +Total: 38.50s + +[Epoch 267] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0721] 47.8+0.8s +[3200/16000] [MSE: 1.0650] 47.3+0.0s +[4800/16000] [MSE: 1.0526] 47.3+0.0s +[6400/16000] [MSE: 1.0526] 47.0+0.0s +[8000/16000] [MSE: 1.0555] 46.5+0.0s +[9600/16000] [MSE: 1.0560] 46.8+0.0s +[11200/16000] [MSE: 1.0553] 46.8+0.0s +[12800/16000] [MSE: 1.0538] 47.0+0.0s +[14400/16000] [MSE: 1.0515] 46.9+0.0s +[16000/16000] [MSE: 1.0548] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.33s + +[Epoch 268] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0837] 47.8+0.7s +[3200/16000] [MSE: 1.0769] 47.5+0.1s +[4800/16000] [MSE: 1.0655] 47.4+0.0s +[6400/16000] [MSE: 1.0720] 47.6+0.0s +[8000/16000] [MSE: 1.0775] 47.6+0.0s +[9600/16000] [MSE: 1.0690] 47.4+0.0s +[11200/16000] [MSE: 1.0695] 46.9+0.0s +[12800/16000] [MSE: 1.0714] 46.3+0.0s +[14400/16000] [MSE: 1.0622] 46.3+0.0s +[16000/16000] [MSE: 1.0590] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.29s + +[Epoch 269] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0889] 48.1+0.7s +[3200/16000] [MSE: 1.0892] 47.5+0.0s +[4800/16000] [MSE: 1.0776] 47.5+0.0s +[6400/16000] [MSE: 1.0701] 47.4+0.0s +[8000/16000] [MSE: 1.0779] 47.7+0.0s +[9600/16000] [MSE: 1.0719] 47.3+0.0s +[11200/16000] [MSE: 1.0723] 47.2+0.0s +[12800/16000] [MSE: 1.0729] 47.6+0.0s +[14400/16000] [MSE: 1.0729] 47.4+0.0s +[16000/16000] [MSE: 1.0717] 47.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.42s + +[Epoch 270] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0884] 47.8+0.6s +[3200/16000] [MSE: 1.0660] 47.4+0.0s +[4800/16000] [MSE: 1.0707] 47.2+0.0s +[6400/16000] [MSE: 1.0642] 47.6+0.0s +[8000/16000] [MSE: 1.0679] 46.7+0.0s +[9600/16000] [MSE: 1.0714] 47.2+0.0s +[11200/16000] [MSE: 1.0727] 46.5+0.0s +[12800/16000] [MSE: 1.0672] 46.8+0.0s +[14400/16000] [MSE: 1.0635] 47.1+0.0s +[16000/16000] [MSE: 1.0647] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.23s + +[Epoch 271] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0821] 48.1+0.7s +[3200/16000] [MSE: 1.0740] 47.2+0.0s +[4800/16000] [MSE: 1.0682] 47.1+0.0s +[6400/16000] [MSE: 1.0661] 46.8+0.0s +[8000/16000] [MSE: 1.0671] 46.9+0.0s +[9600/16000] [MSE: 1.0742] 47.0+0.0s +[11200/16000] [MSE: 1.0726] 46.9+0.0s +[12800/16000] [MSE: 1.0710] 46.8+0.0s +[14400/16000] [MSE: 1.0715] 47.3+0.0s +[16000/16000] [MSE: 1.0739] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.94s + +Saving... +Total: 38.43s + +[Epoch 272] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0627] 47.5+0.8s +[3200/16000] [MSE: 1.0519] 47.4+0.0s +[4800/16000] [MSE: 1.0515] 47.4+0.0s +[6400/16000] [MSE: 1.0582] 47.6+0.0s +[8000/16000] [MSE: 1.0512] 47.4+0.0s +[9600/16000] [MSE: 1.0498] 47.4+0.0s +[11200/16000] [MSE: 1.0519] 47.3+0.0s +[12800/16000] [MSE: 1.0505] 47.3+0.0s +[14400/16000] [MSE: 1.0533] 47.1+0.0s +[16000/16000] [MSE: 1.0546] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.36s + +[Epoch 273] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0521] 47.5+0.8s +[3200/16000] [MSE: 1.0561] 47.3+0.0s +[4800/16000] [MSE: 1.0546] 47.1+0.0s +[6400/16000] [MSE: 1.0560] 47.5+0.0s +[8000/16000] [MSE: 1.0546] 46.8+0.0s +[9600/16000] [MSE: 1.0583] 46.9+0.0s +[11200/16000] [MSE: 1.0584] 46.6+0.0s +[12800/16000] [MSE: 1.0542] 46.4+0.0s +[14400/16000] [MSE: 1.0505] 46.9+0.0s +[16000/16000] [MSE: 1.0542] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.90s + +Saving... +Total: 38.36s + +[Epoch 274] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0567] 47.7+0.8s +[3200/16000] [MSE: 1.0666] 47.7+0.1s +[4800/16000] [MSE: 1.0612] 47.5+0.0s +[6400/16000] [MSE: 1.0561] 47.4+0.0s +[8000/16000] [MSE: 1.0630] 47.2+0.0s +[9600/16000] [MSE: 1.0644] 47.5+0.0s +[11200/16000] [MSE: 1.0633] 47.3+0.0s +[12800/16000] [MSE: 1.0600] 47.0+0.0s +[14400/16000] [MSE: 1.0576] 47.0+0.0s +[16000/16000] [MSE: 1.0553] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.34s + +[Epoch 275] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0569] 47.4+0.7s +[3200/16000] [MSE: 1.0511] 47.2+0.0s +[4800/16000] [MSE: 1.0534] 47.0+0.0s +[6400/16000] [MSE: 1.0692] 47.3+0.0s +[8000/16000] [MSE: 1.0606] 47.1+0.0s +[9600/16000] [MSE: 1.0649] 47.5+0.0s +[11200/16000] [MSE: 1.0662] 47.2+0.0s +[12800/16000] [MSE: 1.0590] 46.9+0.0s +[14400/16000] [MSE: 1.0599] 47.2+0.0s +[16000/16000] [MSE: 1.0583] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.32s + +[Epoch 276] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0457] 47.8+0.7s +[3200/16000] [MSE: 1.0626] 47.6+0.1s +[4800/16000] [MSE: 1.0689] 47.5+0.0s +[6400/16000] [MSE: 1.0655] 47.5+0.0s +[8000/16000] [MSE: 1.0627] 47.1+0.0s +[9600/16000] [MSE: 1.0635] 47.1+0.0s +[11200/16000] [MSE: 1.0623] 47.3+0.0s +[12800/16000] [MSE: 1.0634] 46.8+0.0s +[14400/16000] [MSE: 1.0641] 45.8+0.0s +[16000/16000] [MSE: 1.0606] 45.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.32s + +[Epoch 277] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0441] 47.9+0.8s +[3200/16000] [MSE: 1.0475] 47.8+0.1s +[4800/16000] [MSE: 1.0419] 47.7+0.1s +[6400/16000] [MSE: 1.0482] 48.0+0.1s +[8000/16000] [MSE: 1.0508] 47.9+0.0s +[9600/16000] [MSE: 1.0451] 47.4+0.0s +[11200/16000] [MSE: 1.0491] 47.5+0.0s +[12800/16000] [MSE: 1.0457] 47.6+0.0s +[14400/16000] [MSE: 1.0422] 47.6+0.0s +[16000/16000] [MSE: 1.0456] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.57s + +[Epoch 278] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0686] 48.0+0.8s +[3200/16000] [MSE: 1.0662] 47.6+0.0s +[4800/16000] [MSE: 1.0697] 47.7+0.0s +[6400/16000] [MSE: 1.0522] 47.0+0.0s +[8000/16000] [MSE: 1.0529] 46.6+0.0s +[9600/16000] [MSE: 1.0504] 46.6+0.0s +[11200/16000] [MSE: 1.0494] 46.1+0.0s +[12800/16000] [MSE: 1.0510] 46.3+0.0s +[14400/16000] [MSE: 1.0519] 46.3+0.0s +[16000/16000] [MSE: 1.0549] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.75s + +Saving... +Total: 38.21s + +[Epoch 279] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0460] 47.7+0.7s +[3200/16000] [MSE: 1.0425] 47.7+0.1s +[4800/16000] [MSE: 1.0530] 47.5+0.0s +[6400/16000] [MSE: 1.0617] 47.5+0.0s +[8000/16000] [MSE: 1.0619] 47.8+0.0s +[9600/16000] [MSE: 1.0658] 47.2+0.0s +[11200/16000] [MSE: 1.0656] 47.1+0.0s +[12800/16000] [MSE: 1.0606] 46.6+0.0s +[14400/16000] [MSE: 1.0591] 47.3+0.0s +[16000/16000] [MSE: 1.0617] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.31s + +[Epoch 280] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0752] 47.9+0.8s +[3200/16000] [MSE: 1.0538] 47.4+0.1s +[4800/16000] [MSE: 1.0599] 47.3+0.0s +[6400/16000] [MSE: 1.0646] 47.3+0.0s +[8000/16000] [MSE: 1.0676] 47.2+0.0s +[9600/16000] [MSE: 1.0662] 47.2+0.0s +[11200/16000] [MSE: 1.0616] 47.4+0.0s +[12800/16000] [MSE: 1.0609] 47.4+0.0s +[14400/16000] [MSE: 1.0629] 47.4+0.0s +[16000/16000] [MSE: 1.0633] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.26s + +[Epoch 281] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0792] 48.0+0.7s +[3200/16000] [MSE: 1.0707] 47.1+0.0s +[4800/16000] [MSE: 1.0680] 47.0+0.0s +[6400/16000] [MSE: 1.0588] 46.9+0.0s +[8000/16000] [MSE: 1.0646] 47.3+0.0s +[9600/16000] [MSE: 1.0589] 47.5+0.0s +[11200/16000] [MSE: 1.0625] 47.1+0.0s +[12800/16000] [MSE: 1.0603] 47.0+0.0s +[14400/16000] [MSE: 1.0603] 47.2+0.0s +[16000/16000] [MSE: 1.0599] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.27s + +[Epoch 282] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0904] 47.8+0.7s +[3200/16000] [MSE: 1.0856] 47.6+0.1s +[4800/16000] [MSE: 1.0661] 47.6+0.1s +[6400/16000] [MSE: 1.0657] 47.5+0.0s +[8000/16000] [MSE: 1.0614] 47.7+0.0s +[9600/16000] [MSE: 1.0593] 47.7+0.1s +[11200/16000] [MSE: 1.0576] 47.8+0.1s +[12800/16000] [MSE: 1.0628] 47.2+0.0s +[14400/16000] [MSE: 1.0660] 47.4+0.0s +[16000/16000] [MSE: 1.0658] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.96s + +Saving... +Total: 38.43s + +[Epoch 283] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0321] 47.7+0.8s +[3200/16000] [MSE: 1.0277] 47.5+0.0s +[4800/16000] [MSE: 1.0487] 47.3+0.0s +[6400/16000] [MSE: 1.0571] 47.4+0.0s +[8000/16000] [MSE: 1.0625] 47.4+0.0s +[9600/16000] [MSE: 1.0555] 47.2+0.0s +[11200/16000] [MSE: 1.0553] 47.5+0.0s +[12800/16000] [MSE: 1.0543] 47.2+0.0s +[14400/16000] [MSE: 1.0601] 47.3+0.0s +[16000/16000] [MSE: 1.0585] 47.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.03s + +Saving... +Total: 38.51s + +[Epoch 284] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0750] 48.0+0.8s +[3200/16000] [MSE: 1.0702] 48.0+0.1s +[4800/16000] [MSE: 1.0574] 47.9+0.1s +[6400/16000] [MSE: 1.0577] 47.7+0.0s +[8000/16000] [MSE: 1.0605] 47.2+0.0s +[9600/16000] [MSE: 1.0601] 47.5+0.0s +[11200/16000] [MSE: 1.0549] 47.4+0.0s +[12800/16000] [MSE: 1.0486] 47.2+0.0s +[14400/16000] [MSE: 1.0489] 46.8+0.0s +[16000/16000] [MSE: 1.0474] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.03s + +Saving... +Total: 38.55s + +[Epoch 285] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0648] 47.4+0.7s +[3200/16000] [MSE: 1.0588] 47.2+0.0s +[4800/16000] [MSE: 1.0548] 47.5+0.0s +[6400/16000] [MSE: 1.0595] 47.7+0.0s +[8000/16000] [MSE: 1.0625] 47.2+0.0s +[9600/16000] [MSE: 1.0619] 47.3+0.0s +[11200/16000] [MSE: 1.0639] 47.8+0.0s +[12800/16000] [MSE: 1.0620] 47.6+0.0s +[14400/16000] [MSE: 1.0589] 47.5+0.0s +[16000/16000] [MSE: 1.0588] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.92s + +Saving... +Total: 38.39s + +[Epoch 286] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0566] 47.9+0.6s +[3200/16000] [MSE: 1.0328] 47.7+0.0s +[4800/16000] [MSE: 1.0492] 47.1+0.0s +[6400/16000] [MSE: 1.0501] 47.1+0.0s +[8000/16000] [MSE: 1.0477] 47.1+0.0s +[9600/16000] [MSE: 1.0523] 47.0+0.0s +[11200/16000] [MSE: 1.0545] 46.8+0.0s +[12800/16000] [MSE: 1.0566] 47.3+0.0s +[14400/16000] [MSE: 1.0585] 46.9+0.0s +[16000/16000] [MSE: 1.0587] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.33s + +[Epoch 287] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0636] 47.7+0.7s +[3200/16000] [MSE: 1.0620] 47.4+0.0s +[4800/16000] [MSE: 1.0625] 47.3+0.0s +[6400/16000] [MSE: 1.0607] 47.2+0.0s +[8000/16000] [MSE: 1.0508] 47.3+0.0s +[9600/16000] [MSE: 1.0534] 46.9+0.0s +[11200/16000] [MSE: 1.0541] 46.6+0.0s +[12800/16000] [MSE: 1.0529] 47.1+0.0s +[14400/16000] [MSE: 1.0510] 47.0+0.0s +[16000/16000] [MSE: 1.0547] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.84s + +Saving... +Total: 38.33s + +[Epoch 288] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0739] 47.8+0.6s +[3200/16000] [MSE: 1.0619] 47.5+0.1s +[4800/16000] [MSE: 1.0566] 47.4+0.0s +[6400/16000] [MSE: 1.0599] 47.2+0.0s +[8000/16000] [MSE: 1.0683] 47.3+0.0s +[9600/16000] [MSE: 1.0658] 47.4+0.0s +[11200/16000] [MSE: 1.0639] 46.8+0.0s +[12800/16000] [MSE: 1.0630] 46.8+0.0s +[14400/16000] [MSE: 1.0643] 47.0+0.0s +[16000/16000] [MSE: 1.0640] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.45s + +[Epoch 289] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0362] 47.6+0.8s +[3200/16000] [MSE: 1.0584] 47.3+0.0s +[4800/16000] [MSE: 1.0548] 47.1+0.0s +[6400/16000] [MSE: 1.0595] 47.1+0.0s +[8000/16000] [MSE: 1.0599] 47.0+0.0s +[9600/16000] [MSE: 1.0585] 47.2+0.0s +[11200/16000] [MSE: 1.0582] 47.4+0.0s +[12800/16000] [MSE: 1.0551] 47.0+0.0s +[14400/16000] [MSE: 1.0566] 47.2+0.0s +[16000/16000] [MSE: 1.0547] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.30s + +[Epoch 290] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0250] 47.5+0.7s +[3200/16000] [MSE: 1.0411] 47.6+0.1s +[4800/16000] [MSE: 1.0502] 47.6+0.1s +[6400/16000] [MSE: 1.0574] 47.3+0.0s +[8000/16000] [MSE: 1.0582] 47.1+0.0s +[9600/16000] [MSE: 1.0594] 47.4+0.0s +[11200/16000] [MSE: 1.0601] 47.0+0.0s +[12800/16000] [MSE: 1.0634] 46.9+0.0s +[14400/16000] [MSE: 1.0633] 46.9+0.0s +[16000/16000] [MSE: 1.0643] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.32s + +[Epoch 291] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0597] 47.6+0.6s +[3200/16000] [MSE: 1.0324] 47.7+0.1s +[4800/16000] [MSE: 1.0495] 47.2+0.0s +[6400/16000] [MSE: 1.0596] 47.0+0.0s +[8000/16000] [MSE: 1.0625] 47.4+0.0s +[9600/16000] [MSE: 1.0583] 47.3+0.0s +[11200/16000] [MSE: 1.0542] 47.2+0.0s +[12800/16000] [MSE: 1.0583] 46.7+0.0s +[14400/16000] [MSE: 1.0572] 46.3+0.0s +[16000/16000] [MSE: 1.0602] 45.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.34s + +[Epoch 292] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0623] 47.8+0.7s +[3200/16000] [MSE: 1.0852] 47.8+0.1s +[4800/16000] [MSE: 1.0715] 47.4+0.1s +[6400/16000] [MSE: 1.0676] 47.5+0.0s +[8000/16000] [MSE: 1.0682] 47.3+0.0s +[9600/16000] [MSE: 1.0651] 47.2+0.0s +[11200/16000] [MSE: 1.0666] 46.9+0.0s +[12800/16000] [MSE: 1.0694] 47.2+0.0s +[14400/16000] [MSE: 1.0690] 47.3+0.0s +[16000/16000] [MSE: 1.0652] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 38.03s + +Saving... +Total: 38.52s + +[Epoch 293] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0792] 47.9+0.7s +[3200/16000] [MSE: 1.0681] 47.5+0.1s +[4800/16000] [MSE: 1.0689] 47.7+0.0s +[6400/16000] [MSE: 1.0741] 47.6+0.0s +[8000/16000] [MSE: 1.0712] 47.2+0.0s +[9600/16000] [MSE: 1.0772] 47.5+0.0s +[11200/16000] [MSE: 1.0728] 47.3+0.0s +[12800/16000] [MSE: 1.0658] 47.4+0.0s +[14400/16000] [MSE: 1.0654] 47.0+0.0s +[16000/16000] [MSE: 1.0700] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.42s + +[Epoch 294] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0159] 47.9+1.0s +[3200/16000] [MSE: 1.0413] 47.7+0.0s +[4800/16000] [MSE: 1.0490] 47.0+0.0s +[6400/16000] [MSE: 1.0581] 47.3+0.0s +[8000/16000] [MSE: 1.0608] 46.8+0.0s +[9600/16000] [MSE: 1.0621] 47.0+0.0s +[11200/16000] [MSE: 1.0572] 46.9+0.0s +[12800/16000] [MSE: 1.0573] 47.2+0.0s +[14400/16000] [MSE: 1.0615] 47.0+0.0s +[16000/16000] [MSE: 1.0626] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.29s + +[Epoch 295] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0756] 47.7+0.7s +[3200/16000] [MSE: 1.0812] 47.3+0.0s +[4800/16000] [MSE: 1.0812] 47.5+0.0s +[6400/16000] [MSE: 1.0686] 47.7+0.0s +[8000/16000] [MSE: 1.0730] 47.1+0.0s +[9600/16000] [MSE: 1.0722] 47.1+0.0s +[11200/16000] [MSE: 1.0732] 47.1+0.0s +[12800/16000] [MSE: 1.0675] 47.2+0.0s +[14400/16000] [MSE: 1.0689] 47.3+0.0s +[16000/16000] [MSE: 1.0629] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.27s + +[Epoch 296] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0828] 47.8+0.7s +[3200/16000] [MSE: 1.0771] 47.9+0.1s +[4800/16000] [MSE: 1.0689] 47.4+0.0s +[6400/16000] [MSE: 1.0559] 47.3+0.0s +[8000/16000] [MSE: 1.0549] 47.5+0.0s +[9600/16000] [MSE: 1.0589] 47.0+0.0s +[11200/16000] [MSE: 1.0586] 47.1+0.0s +[12800/16000] [MSE: 1.0577] 46.9+0.0s +[14400/16000] [MSE: 1.0557] 47.2+0.0s +[16000/16000] [MSE: 1.0575] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.96s + +Saving... +Total: 38.47s + +[Epoch 297] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0325] 48.0+0.7s +[3200/16000] [MSE: 1.0494] 47.5+0.1s +[4800/16000] [MSE: 1.0438] 47.4+0.0s +[6400/16000] [MSE: 1.0477] 47.4+0.0s +[8000/16000] [MSE: 1.0509] 47.0+0.0s +[9600/16000] [MSE: 1.0511] 46.9+0.0s +[11200/16000] [MSE: 1.0599] 46.5+0.0s +[12800/16000] [MSE: 1.0630] 46.7+0.0s +[14400/16000] [MSE: 1.0647] 46.6+0.0s +[16000/16000] [MSE: 1.0698] 46.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.36s + +[Epoch 298] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0084] 47.9+0.7s +[3200/16000] [MSE: 1.0413] 47.0+0.0s +[4800/16000] [MSE: 1.0356] 47.4+0.0s +[6400/16000] [MSE: 1.0529] 47.4+0.0s +[8000/16000] [MSE: 1.0491] 47.5+0.0s +[9600/16000] [MSE: 1.0506] 47.5+0.0s +[11200/16000] [MSE: 1.0556] 47.3+0.0s +[12800/16000] [MSE: 1.0578] 47.3+0.0s +[14400/16000] [MSE: 1.0582] 47.5+0.0s +[16000/16000] [MSE: 1.0585] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.25s + +[Epoch 299] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0507] 47.6+0.7s +[3200/16000] [MSE: 1.0602] 47.3+0.0s +[4800/16000] [MSE: 1.0499] 47.4+0.0s +[6400/16000] [MSE: 1.0560] 46.6+0.0s +[8000/16000] [MSE: 1.0610] 46.4+0.0s +[9600/16000] [MSE: 1.0619] 46.4+0.0s +[11200/16000] [MSE: 1.0600] 46.6+0.0s +[12800/16000] [MSE: 1.0623] 46.1+0.0s +[14400/16000] [MSE: 1.0611] 46.6+0.0s +[16000/16000] [MSE: 1.0632] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.38s + +[Epoch 300] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0327] 47.7+1.0s +[3200/16000] [MSE: 1.0596] 47.6+0.1s +[4800/16000] [MSE: 1.0726] 47.3+0.0s +[6400/16000] [MSE: 1.0669] 47.1+0.0s +[8000/16000] [MSE: 1.0623] 47.2+0.0s +[9600/16000] [MSE: 1.0567] 47.1+0.0s +[11200/16000] [MSE: 1.0591] 46.8+0.0s +[12800/16000] [MSE: 1.0589] 47.0+0.0s +[14400/16000] [MSE: 1.0626] 46.9+0.0s +[16000/16000] [MSE: 1.0632] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.31s + +[Epoch 301] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0524] 48.0+0.8s +[3200/16000] [MSE: 1.0506] 47.2+0.0s +[4800/16000] [MSE: 1.0552] 46.7+0.0s +[6400/16000] [MSE: 1.0496] 46.5+0.0s +[8000/16000] [MSE: 1.0492] 46.4+0.0s +[9600/16000] [MSE: 1.0538] 46.5+0.0s +[11200/16000] [MSE: 1.0542] 47.0+0.0s +[12800/16000] [MSE: 1.0562] 46.9+0.0s +[14400/16000] [MSE: 1.0525] 46.8+0.0s +[16000/16000] [MSE: 1.0572] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.25s + +[Epoch 302] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0898] 48.0+0.7s +[3200/16000] [MSE: 1.0672] 47.4+0.1s +[4800/16000] [MSE: 1.0642] 47.4+0.0s +[6400/16000] [MSE: 1.0635] 47.3+0.0s +[8000/16000] [MSE: 1.0569] 47.6+0.0s +[9600/16000] [MSE: 1.0521] 47.0+0.0s +[11200/16000] [MSE: 1.0550] 46.9+0.0s +[12800/16000] [MSE: 1.0542] 47.1+0.0s +[14400/16000] [MSE: 1.0565] 46.5+0.0s +[16000/16000] [MSE: 1.0574] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.71s + +Saving... +Total: 38.20s + +[Epoch 303] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0656] 47.6+0.7s +[3200/16000] [MSE: 1.0637] 47.5+0.0s +[4800/16000] [MSE: 1.0603] 47.5+0.0s +[6400/16000] [MSE: 1.0589] 47.4+0.0s +[8000/16000] [MSE: 1.0631] 47.5+0.0s +[9600/16000] [MSE: 1.0629] 47.3+0.0s +[11200/16000] [MSE: 1.0641] 47.0+0.0s +[12800/16000] [MSE: 1.0677] 47.3+0.0s +[14400/16000] [MSE: 1.0649] 46.9+0.0s +[16000/16000] [MSE: 1.0613] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.86s + +Saving... +Total: 38.36s + +[Epoch 304] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0935] 47.7+0.8s +[3200/16000] [MSE: 1.0603] 47.6+0.1s +[4800/16000] [MSE: 1.0608] 47.3+0.0s +[6400/16000] [MSE: 1.0654] 47.3+0.0s +[8000/16000] [MSE: 1.0674] 47.3+0.0s +[9600/16000] [MSE: 1.0724] 47.0+0.0s +[11200/16000] [MSE: 1.0740] 47.3+0.0s +[12800/16000] [MSE: 1.0721] 46.9+0.0s +[14400/16000] [MSE: 1.0736] 46.4+0.0s +[16000/16000] [MSE: 1.0713] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.90s + +Saving... +Total: 38.37s + +[Epoch 305] Learning rate: 5.00e-5 +[1600/16000] [MSE: 0.9828] 47.9+0.8s +[3200/16000] [MSE: 1.0248] 47.5+0.0s +[4800/16000] [MSE: 1.0286] 47.4+0.0s +[6400/16000] [MSE: 1.0432] 47.4+0.0s +[8000/16000] [MSE: 1.0465] 47.4+0.0s +[9600/16000] [MSE: 1.0533] 46.9+0.0s +[11200/16000] [MSE: 1.0540] 47.3+0.0s +[12800/16000] [MSE: 1.0517] 46.9+0.0s +[14400/16000] [MSE: 1.0521] 46.4+0.0s +[16000/16000] [MSE: 1.0485] 46.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.98s + +Saving... +Total: 38.45s + +[Epoch 306] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0556] 48.2+0.8s +[3200/16000] [MSE: 1.0913] 47.6+0.1s +[4800/16000] [MSE: 1.0822] 47.4+0.1s +[6400/16000] [MSE: 1.0677] 47.4+0.0s +[8000/16000] [MSE: 1.0627] 47.5+0.0s +[9600/16000] [MSE: 1.0652] 47.4+0.0s +[11200/16000] [MSE: 1.0617] 47.5+0.0s +[12800/16000] [MSE: 1.0634] 47.0+0.0s +[14400/16000] [MSE: 1.0682] 46.5+0.0s +[16000/16000] [MSE: 1.0695] 46.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.36s + +[Epoch 307] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0474] 47.6+0.7s +[3200/16000] [MSE: 1.0431] 47.6+0.0s +[4800/16000] [MSE: 1.0494] 47.1+0.0s +[6400/16000] [MSE: 1.0476] 47.1+0.0s +[8000/16000] [MSE: 1.0536] 47.4+0.0s +[9600/16000] [MSE: 1.0558] 47.3+0.0s +[11200/16000] [MSE: 1.0611] 47.0+0.0s +[12800/16000] [MSE: 1.0588] 47.2+0.0s +[14400/16000] [MSE: 1.0558] 47.4+0.0s +[16000/16000] [MSE: 1.0572] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.96s + +Saving... +Total: 38.46s + +[Epoch 308] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.1113] 48.1+0.7s +[3200/16000] [MSE: 1.1020] 48.0+0.1s +[4800/16000] [MSE: 1.0809] 47.6+0.0s +[6400/16000] [MSE: 1.0785] 47.5+0.0s +[8000/16000] [MSE: 1.0727] 47.3+0.0s +[9600/16000] [MSE: 1.0750] 47.1+0.0s +[11200/16000] [MSE: 1.0659] 47.2+0.0s +[12800/16000] [MSE: 1.0655] 46.8+0.0s +[14400/16000] [MSE: 1.0695] 46.9+0.0s +[16000/16000] [MSE: 1.0679] 46.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.92s + +Saving... +Total: 38.42s + +[Epoch 309] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0238] 47.4+0.8s +[3200/16000] [MSE: 1.0412] 47.5+0.0s +[4800/16000] [MSE: 1.0477] 47.4+0.0s +[6400/16000] [MSE: 1.0507] 47.4+0.0s +[8000/16000] [MSE: 1.0555] 47.6+0.0s +[9600/16000] [MSE: 1.0610] 47.1+0.0s +[11200/16000] [MSE: 1.0676] 46.7+0.0s +[12800/16000] [MSE: 1.0647] 46.4+0.0s +[14400/16000] [MSE: 1.0638] 47.0+0.0s +[16000/16000] [MSE: 1.0692] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.79s + +Saving... +Total: 38.29s + +[Epoch 310] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0411] 47.8+0.7s +[3200/16000] [MSE: 1.0489] 47.7+0.1s +[4800/16000] [MSE: 1.0414] 47.1+0.0s +[6400/16000] [MSE: 1.0450] 47.0+0.0s +[8000/16000] [MSE: 1.0482] 46.8+0.0s +[9600/16000] [MSE: 1.0540] 46.2+0.0s +[11200/16000] [MSE: 1.0594] 46.6+0.0s +[12800/16000] [MSE: 1.0641] 46.4+0.0s +[14400/16000] [MSE: 1.0640] 46.1+0.0s +[16000/16000] [MSE: 1.0646] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.47s + +[Epoch 311] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0782] 47.3+0.8s +[3200/16000] [MSE: 1.0694] 47.4+0.0s +[4800/16000] [MSE: 1.0670] 47.1+0.0s +[6400/16000] [MSE: 1.0662] 47.3+0.0s +[8000/16000] [MSE: 1.0721] 47.2+0.0s +[9600/16000] [MSE: 1.0690] 46.9+0.0s +[11200/16000] [MSE: 1.0674] 46.9+0.0s +[12800/16000] [MSE: 1.0636] 47.1+0.0s +[14400/16000] [MSE: 1.0653] 47.0+0.0s +[16000/16000] [MSE: 1.0619] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.80s + +Saving... +Total: 38.29s + +[Epoch 312] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0935] 47.7+0.8s +[3200/16000] [MSE: 1.0703] 47.7+0.1s +[4800/16000] [MSE: 1.0587] 47.5+0.0s +[6400/16000] [MSE: 1.0702] 47.4+0.0s +[8000/16000] [MSE: 1.0682] 47.1+0.0s +[9600/16000] [MSE: 1.0704] 47.0+0.0s +[11200/16000] [MSE: 1.0732] 47.0+0.0s +[12800/16000] [MSE: 1.0673] 46.4+0.0s +[14400/16000] [MSE: 1.0630] 46.4+0.0s +[16000/16000] [MSE: 1.0654] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.73s + +Saving... +Total: 38.22s + +[Epoch 313] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0765] 47.7+0.6s +[3200/16000] [MSE: 1.0871] 47.1+0.0s +[4800/16000] [MSE: 1.0838] 47.0+0.0s +[6400/16000] [MSE: 1.0710] 47.1+0.0s +[8000/16000] [MSE: 1.0651] 47.1+0.0s +[9600/16000] [MSE: 1.0625] 47.2+0.0s +[11200/16000] [MSE: 1.0633] 46.9+0.0s +[12800/16000] [MSE: 1.0651] 47.2+0.0s +[14400/16000] [MSE: 1.0629] 47.0+0.0s +[16000/16000] [MSE: 1.0600] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.25s + +[Epoch 314] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0500] 47.7+0.8s +[3200/16000] [MSE: 1.0455] 47.2+0.0s +[4800/16000] [MSE: 1.0615] 46.4+0.0s +[6400/16000] [MSE: 1.0571] 46.8+0.0s +[8000/16000] [MSE: 1.0586] 46.3+0.0s +[9600/16000] [MSE: 1.0633] 46.5+0.0s +[11200/16000] [MSE: 1.0653] 46.2+0.0s +[12800/16000] [MSE: 1.0677] 46.2+0.0s +[14400/16000] [MSE: 1.0666] 46.3+0.0s +[16000/16000] [MSE: 1.0682] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.72s + +Saving... +Total: 38.20s + +[Epoch 315] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0745] 47.7+0.7s +[3200/16000] [MSE: 1.0844] 47.5+0.1s +[4800/16000] [MSE: 1.0858] 47.7+0.0s +[6400/16000] [MSE: 1.0742] 47.3+0.0s +[8000/16000] [MSE: 1.0671] 47.4+0.0s +[9600/16000] [MSE: 1.0675] 47.6+0.0s +[11200/16000] [MSE: 1.0637] 47.4+0.0s +[12800/16000] [MSE: 1.0630] 47.4+0.0s +[14400/16000] [MSE: 1.0609] 47.1+0.0s +[16000/16000] [MSE: 1.0636] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.28s + +[Epoch 316] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0528] 47.8+0.8s +[3200/16000] [MSE: 1.0391] 47.4+0.0s +[4800/16000] [MSE: 1.0476] 47.1+0.0s +[6400/16000] [MSE: 1.0462] 47.2+0.0s +[8000/16000] [MSE: 1.0476] 47.1+0.0s +[9600/16000] [MSE: 1.0524] 47.1+0.0s +[11200/16000] [MSE: 1.0476] 47.2+0.0s +[12800/16000] [MSE: 1.0466] 47.1+0.0s +[14400/16000] [MSE: 1.0439] 46.9+0.0s +[16000/16000] [MSE: 1.0487] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.77s + +Saving... +Total: 38.25s + +[Epoch 317] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0257] 47.8+0.6s +[3200/16000] [MSE: 1.0550] 47.6+0.0s +[4800/16000] [MSE: 1.0583] 47.3+0.0s +[6400/16000] [MSE: 1.0500] 47.4+0.0s +[8000/16000] [MSE: 1.0483] 47.2+0.0s +[9600/16000] [MSE: 1.0548] 47.3+0.0s +[11200/16000] [MSE: 1.0596] 47.4+0.0s +[12800/16000] [MSE: 1.0614] 47.1+0.0s +[14400/16000] [MSE: 1.0631] 47.1+0.0s +[16000/16000] [MSE: 1.0613] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.35s + +[Epoch 318] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0578] 47.9+0.8s +[3200/16000] [MSE: 1.0493] 47.6+0.1s +[4800/16000] [MSE: 1.0541] 47.3+0.0s +[6400/16000] [MSE: 1.0572] 47.0+0.0s +[8000/16000] [MSE: 1.0588] 47.0+0.0s +[9600/16000] [MSE: 1.0619] 47.2+0.0s +[11200/16000] [MSE: 1.0591] 46.2+0.0s +[12800/16000] [MSE: 1.0620] 46.1+0.0s +[14400/16000] [MSE: 1.0629] 46.6+0.0s +[16000/16000] [MSE: 1.0595] 46.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.87s + +Saving... +Total: 38.34s + +[Epoch 319] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0480] 47.5+0.7s +[3200/16000] [MSE: 1.0501] 47.3+0.0s +[4800/16000] [MSE: 1.0525] 47.3+0.0s +[6400/16000] [MSE: 1.0509] 46.8+0.0s +[8000/16000] [MSE: 1.0508] 46.6+0.0s +[9600/16000] [MSE: 1.0528] 46.2+0.0s +[11200/16000] [MSE: 1.0473] 46.6+0.0s +[12800/16000] [MSE: 1.0476] 46.4+0.0s +[14400/16000] [MSE: 1.0474] 45.9+0.0s +[16000/16000] [MSE: 1.0483] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.85s + +Saving... +Total: 38.30s + +[Epoch 320] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0804] 47.6+0.7s +[3200/16000] [MSE: 1.0965] 46.9+0.0s +[4800/16000] [MSE: 1.0750] 47.0+0.0s +[6400/16000] [MSE: 1.0722] 46.7+0.0s +[8000/16000] [MSE: 1.0672] 46.6+0.0s +[9600/16000] [MSE: 1.0673] 47.1+0.0s +[11200/16000] [MSE: 1.0669] 46.7+0.0s +[12800/16000] [MSE: 1.0682] 46.6+0.0s +[14400/16000] [MSE: 1.0669] 46.4+0.0s +[16000/16000] [MSE: 1.0638] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.74s + +Saving... +Total: 38.23s + +[Epoch 321] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0932] 48.0+0.7s +[3200/16000] [MSE: 1.0744] 47.6+0.0s +[4800/16000] [MSE: 1.0675] 47.0+0.0s +[6400/16000] [MSE: 1.0635] 46.3+0.0s +[8000/16000] [MSE: 1.0714] 47.3+0.0s +[9600/16000] [MSE: 1.0678] 47.0+0.0s +[11200/16000] [MSE: 1.0623] 46.6+0.0s +[12800/16000] [MSE: 1.0683] 46.0+0.0s +[14400/16000] [MSE: 1.0668] 46.2+0.0s +[16000/16000] [MSE: 1.0638] 45.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.81s + +Saving... +Total: 38.41s + +[Epoch 322] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0693] 47.9+0.9s +[3200/16000] [MSE: 1.0813] 47.7+0.1s +[4800/16000] [MSE: 1.0694] 47.2+0.0s +[6400/16000] [MSE: 1.0689] 47.2+0.0s +[8000/16000] [MSE: 1.0715] 47.0+0.0s +[9600/16000] [MSE: 1.0672] 47.3+0.0s +[11200/16000] [MSE: 1.0741] 46.6+0.0s +[12800/16000] [MSE: 1.0750] 46.6+0.0s +[14400/16000] [MSE: 1.0663] 47.2+0.0s +[16000/16000] [MSE: 1.0657] 46.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.89s + +Saving... +Total: 38.38s + +[Epoch 323] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0530] 47.9+0.7s +[3200/16000] [MSE: 1.0295] 47.1+0.0s +[4800/16000] [MSE: 1.0292] 46.8+0.0s +[6400/16000] [MSE: 1.0387] 46.8+0.0s +[8000/16000] [MSE: 1.0424] 46.8+0.0s +[9600/16000] [MSE: 1.0442] 47.0+0.0s +[11200/16000] [MSE: 1.0476] 47.1+0.0s +[12800/16000] [MSE: 1.0504] 47.4+0.0s +[14400/16000] [MSE: 1.0493] 46.8+0.0s +[16000/16000] [MSE: 1.0523] 45.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.32s + +[Epoch 324] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0457] 47.9+0.7s +[3200/16000] [MSE: 1.0618] 47.5+0.0s +[4800/16000] [MSE: 1.0548] 47.7+0.0s +[6400/16000] [MSE: 1.0596] 47.4+0.0s +[8000/16000] [MSE: 1.0654] 47.3+0.0s +[9600/16000] [MSE: 1.0602] 47.3+0.0s +[11200/16000] [MSE: 1.0568] 47.6+0.0s +[12800/16000] [MSE: 1.0567] 47.3+0.0s +[14400/16000] [MSE: 1.0559] 47.1+0.0s +[16000/16000] [MSE: 1.0551] 46.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.76s + +Saving... +Total: 38.22s + +[Epoch 325] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0544] 47.4+0.6s +[3200/16000] [MSE: 1.0584] 47.4+0.0s +[4800/16000] [MSE: 1.0557] 47.1+0.0s +[6400/16000] [MSE: 1.0553] 47.1+0.0s +[8000/16000] [MSE: 1.0536] 47.2+0.0s +[9600/16000] [MSE: 1.0553] 47.1+0.0s +[11200/16000] [MSE: 1.0578] 47.2+0.0s +[12800/16000] [MSE: 1.0625] 47.1+0.0s +[14400/16000] [MSE: 1.0652] 46.9+0.0s +[16000/16000] [MSE: 1.0657] 46.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.82s + +Saving... +Total: 38.34s + +[Epoch 326] Learning rate: 5.00e-5 +[1600/16000] [MSE: 1.0862] 47.6+0.7s +[3200/16000] [MSE: 1.0688] 47.5+0.0s +[4800/16000] [MSE: 1.0754] 47.3+0.0s +[6400/16000] [MSE: 1.0676] 47.2+0.0s +[8000/16000] [MSE: 1.0646] 46.9+0.0s +[9600/16000] [MSE: 1.0729] 46.9+0.0s +[11200/16000] [MSE: 1.0735] 47.0+0.0s +[12800/16000] [MSE: 1.0706] 46.6+0.0s +[14400/16000] [MSE: 1.0761] 47.1+0.0s +[16000/16000] [MSE: 1.0742] 46.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 7.056 @epoch 1) +Forward: 37.83s + +Saving... +Total: 38.32s + diff --git a/Demosaic/experiment/LAMBDANETACTB_DEMOSAIC20_R4_MSE/loss.pt 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['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-00:12:14 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-10:36:22 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-11:55:01 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-12:05:25 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/log.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..ab4946fc1c24de7ce817353d9bcb6c6f50329234 --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/log.txt @@ -0,0 +1,9129 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1961] 23.8+0.8s +[3200/16000] [L1: 0.1351] 13.2+0.1s +[4800/16000] [L1: 0.1085] 13.4+0.1s +[6400/16000] [L1: 0.0937] 14.0+0.1s +[8000/16000] [L1: 0.0833] 13.0+0.1s +[9600/16000] [L1: 0.0753] 14.0+0.1s +[11200/16000] [L1: 0.0692] 13.7+0.1s +[12800/16000] [L1: 0.0645] 12.9+0.0s +[14400/16000] [L1: 0.0604] 13.5+0.1s +[16000/16000] [L1: 0.0570] 13.1+0.0s + +Evaluation: +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1961] 26.8+0.6s +[3200/16000] [L1: 0.1352] 15.9+0.0s +[4800/16000] [L1: 0.1091] 15.1+0.0s +[6400/16000] [L1: 0.0940] 16.5+0.0s +[8000/16000] [L1: 0.0837] 15.2+0.0s +[9600/16000] [L1: 0.0754] 15.1+0.0s +[11200/16000] [L1: 0.0693] 15.1+0.0s +[12800/16000] [L1: 0.0644] 14.7+0.0s +[14400/16000] [L1: 0.0604] 14.6+0.0s +[16000/16000] [L1: 0.0572] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.958 (Best: 30.958 @epoch 1) +Forward: 9.34s + +Saving... +Total: 10.31s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0237] 16.7+0.8s +[3200/16000] [L1: 0.0230] 17.3+0.1s +[4800/16000] [L1: 0.0229] 16.6+0.1s +[6400/16000] [L1: 0.0226] 17.0+0.1s +[8000/16000] [L1: 0.0222] 16.3+0.1s +[9600/16000] [L1: 0.0217] 16.5+0.1s +[11200/16000] [L1: 0.0216] 16.6+0.1s +[12800/16000] [L1: 0.0213] 16.7+0.1s +[14400/16000] [L1: 0.0209] 16.5+0.1s +[16000/16000] [L1: 0.0205] 17.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 32.524 (Best: 32.524 @epoch 2) +Forward: 9.15s + +Saving... +Total: 9.79s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0160] 16.0+0.9s +[3200/16000] [L1: 0.0160] 16.6+0.1s +[4800/16000] [L1: 0.0160] 15.6+0.0s +[6400/16000] [L1: 0.0159] 16.0+0.0s +[8000/16000] [L1: 0.0158] 16.6+0.0s +[9600/16000] [L1: 0.0158] 16.4+0.0s +[11200/16000] [L1: 0.0156] 16.3+0.0s +[12800/16000] [L1: 0.0154] 16.2+0.0s +[14400/16000] [L1: 0.0153] 15.5+0.0s +[16000/16000] [L1: 0.0153] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.005 (Best: 36.005 @epoch 3) +Forward: 9.27s + +Saving... +Total: 9.83s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0132] 16.7+0.9s +[3200/16000] [L1: 0.0135] 15.8+0.0s +[4800/16000] [L1: 0.0134] 16.2+0.0s +[6400/16000] [L1: 0.0132] 17.2+0.0s +[8000/16000] [L1: 0.0130] 15.2+0.0s +[9600/16000] [L1: 0.0131] 15.4+0.0s +[11200/16000] [L1: 0.0130] 15.0+0.0s +[12800/16000] [L1: 0.0129] 16.0+0.0s +[14400/16000] [L1: 0.0129] 15.2+0.0s +[16000/16000] [L1: 0.0127] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.911 (Best: 36.005 @epoch 3) +Forward: 9.14s + +Saving... +Total: 9.68s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0125] 16.1+0.9s +[3200/16000] [L1: 0.0119] 16.5+0.1s +[4800/16000] [L1: 0.0119] 16.8+0.1s +[6400/16000] [L1: 0.0119] 17.0+0.1s +[8000/16000] [L1: 0.0118] 16.7+0.1s +[9600/16000] [L1: 0.0119] 16.2+0.1s +[11200/16000] [L1: 0.0117] 16.2+0.1s +[12800/16000] [L1: 0.0118] 16.6+0.1s +[14400/16000] [L1: 0.0117] 16.8+0.0s +[16000/16000] [L1: 0.0116] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.764 (Best: 36.764 @epoch 5) +Forward: 9.21s + +Saving... +Total: 9.76s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0103] 16.1+0.8s +[3200/16000] [L1: 0.0107] 16.3+0.0s +[4800/16000] [L1: 0.0108] 17.0+0.0s +[6400/16000] [L1: 0.0108] 14.6+0.0s +[8000/16000] [L1: 0.0108] 15.4+0.0s +[9600/16000] [L1: 0.0108] 14.8+0.0s +[11200/16000] [L1: 0.0107] 15.0+0.0s +[12800/16000] [L1: 0.0105] 15.4+0.0s +[14400/16000] [L1: 0.0105] 16.0+0.0s +[16000/16000] [L1: 0.0106] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.964 (Best: 38.964 @epoch 6) +Forward: 9.16s + +Saving... +Total: 9.81s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0098] 16.4+1.0s +[3200/16000] [L1: 0.0100] 16.8+0.1s +[4800/16000] [L1: 0.0100] 16.4+0.1s +[6400/16000] [L1: 0.0099] 16.5+0.1s +[8000/16000] [L1: 0.0099] 16.6+0.1s +[9600/16000] [L1: 0.0099] 17.1+0.1s +[11200/16000] [L1: 0.0098] 16.5+0.1s +[12800/16000] [L1: 0.0098] 16.9+0.1s +[14400/16000] [L1: 0.0099] 16.9+0.1s +[16000/16000] [L1: 0.0099] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 37.923 (Best: 38.964 @epoch 6) +Forward: 9.16s + +Saving... +Total: 9.65s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0096] 16.5+0.9s +[3200/16000] [L1: 0.0096] 16.5+0.1s +[4800/16000] [L1: 0.0097] 16.8+0.1s +[6400/16000] [L1: 0.0096] 16.7+0.1s +[8000/16000] [L1: 0.0095] 17.1+0.1s +[9600/16000] [L1: 0.0094] 16.9+0.1s +[11200/16000] [L1: 0.0094] 16.2+0.1s +[12800/16000] [L1: 0.0094] 16.5+0.1s +[14400/16000] [L1: 0.0093] 16.8+0.1s +[16000/16000] [L1: 0.0093] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.680 (Best: 38.964 @epoch 6) +Forward: 9.16s + +Saving... +Total: 9.70s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0093] 15.7+0.9s +[3200/16000] [L1: 0.0094] 17.0+0.1s +[4800/16000] [L1: 0.0093] 14.3+0.0s +[6400/16000] [L1: 0.0094] 14.3+0.0s +[8000/16000] [L1: 0.0093] 15.6+0.1s +[9600/16000] [L1: 0.0093] 17.0+0.1s +[11200/16000] [L1: 0.0092] 16.9+0.1s +[12800/16000] [L1: 0.0093] 16.7+0.1s +[14400/16000] [L1: 0.0093] 16.8+0.1s +[16000/16000] [L1: 0.0092] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.473 (Best: 39.473 @epoch 9) +Forward: 9.22s + +Saving... +Total: 9.84s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0090] 14.1+0.9s +[3200/16000] [L1: 0.0089] 15.3+0.0s +[4800/16000] [L1: 0.0088] 16.3+0.0s +[6400/16000] [L1: 0.0087] 16.1+0.0s +[8000/16000] [L1: 0.0088] 16.4+0.1s +[9600/16000] [L1: 0.0087] 17.0+0.1s +[11200/16000] [L1: 0.0087] 16.7+0.1s +[12800/16000] [L1: 0.0089] 16.7+0.1s +[14400/16000] [L1: 0.0088] 16.3+0.1s +[16000/16000] [L1: 0.0088] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.358 (Best: 39.473 @epoch 9) +Forward: 9.34s + +Saving... +Total: 9.81s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0082] 16.8+0.9s +[3200/16000] [L1: 0.0082] 17.1+0.1s +[4800/16000] [L1: 0.0084] 16.3+0.1s +[6400/16000] [L1: 0.0085] 16.0+0.1s +[8000/16000] [L1: 0.0086] 16.5+0.1s +[9600/16000] [L1: 0.0085] 17.0+0.1s +[11200/16000] [L1: 0.0085] 16.8+0.1s +[12800/16000] [L1: 0.0085] 15.9+0.1s +[14400/16000] [L1: 0.0084] 16.4+0.1s +[16000/16000] [L1: 0.0085] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.151 (Best: 40.151 @epoch 11) +Forward: 9.19s + +Saving... +Total: 9.64s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0082] 14.8+0.8s +[3200/16000] [L1: 0.0083] 16.1+0.0s +[4800/16000] [L1: 0.0082] 15.0+0.0s +[6400/16000] [L1: 0.0082] 16.5+0.0s +[8000/16000] [L1: 0.0082] 15.7+0.1s +[9600/16000] [L1: 0.0081] 15.8+0.0s +[11200/16000] [L1: 0.0081] 16.1+0.0s +[12800/16000] [L1: 0.0081] 16.0+0.0s +[14400/16000] [L1: 0.0081] 16.9+0.0s +[16000/16000] [L1: 0.0081] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.701 (Best: 40.701 @epoch 12) +Forward: 9.11s + +Saving... +Total: 9.58s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0079] 14.0+0.8s +[3200/16000] [L1: 0.0080] 15.3+0.0s +[4800/16000] [L1: 0.0079] 15.3+0.0s +[6400/16000] [L1: 0.0078] 15.1+0.0s +[8000/16000] [L1: 0.0079] 15.6+0.0s +[9600/16000] [L1: 0.0079] 15.4+0.0s +[11200/16000] [L1: 0.0079] 15.4+0.0s +[12800/16000] [L1: 0.0079] 16.3+0.0s +[14400/16000] [L1: 0.0079] 16.2+0.0s +[16000/16000] [L1: 0.0079] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.624 (Best: 40.701 @epoch 12) +Forward: 9.05s + +Saving... +Total: 9.54s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0078] 15.5+1.1s +[3200/16000] [L1: 0.0077] 14.9+0.0s +[4800/16000] [L1: 0.0077] 14.6+0.0s +[6400/16000] [L1: 0.0077] 15.3+0.0s +[8000/16000] [L1: 0.0077] 14.9+0.0s +[9600/16000] [L1: 0.0077] 17.0+0.0s +[11200/16000] [L1: 0.0077] 16.9+0.0s +[12800/16000] [L1: 0.0077] 16.6+0.0s +[14400/16000] [L1: 0.0077] 15.2+0.0s +[16000/16000] [L1: 0.0077] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.776 (Best: 40.776 @epoch 14) +Forward: 9.23s + +Saving... +Total: 9.78s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0072] 15.8+0.9s +[3200/16000] [L1: 0.0074] 15.7+0.1s +[4800/16000] [L1: 0.0074] 16.0+0.0s +[6400/16000] [L1: 0.0075] 16.0+0.1s +[8000/16000] [L1: 0.0075] 15.8+0.0s +[9600/16000] [L1: 0.0075] 16.8+0.1s +[11200/16000] [L1: 0.0074] 16.3+0.1s +[12800/16000] [L1: 0.0074] 16.7+0.1s +[14400/16000] [L1: 0.0074] 15.9+0.1s +[16000/16000] [L1: 0.0074] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.770 (Best: 40.776 @epoch 14) +Forward: 9.06s + +Saving... +Total: 9.44s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0074] 16.8+0.8s +[3200/16000] [L1: 0.0073] 16.4+0.1s +[4800/16000] [L1: 0.0074] 15.7+0.0s +[6400/16000] [L1: 0.0073] 16.6+0.0s +[8000/16000] [L1: 0.0073] 16.9+0.1s +[9600/16000] [L1: 0.0073] 17.1+0.1s +[11200/16000] [L1: 0.0072] 16.3+0.1s +[12800/16000] [L1: 0.0072] 16.7+0.1s +[14400/16000] [L1: 0.0072] 17.1+0.1s +[16000/16000] [L1: 0.0072] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 41.247 (Best: 41.247 @epoch 16) +Forward: 9.18s + +Saving... +Total: 9.64s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0071] 15.0+0.8s +[3200/16000] [L1: 0.0071] 15.7+0.0s +[4800/16000] [L1: 0.0070] 15.5+0.0s +[6400/16000] [L1: 0.0070] 15.5+0.0s +[8000/16000] [L1: 0.0071] 15.3+0.0s +[9600/16000] [L1: 0.0071] 14.6+0.0s +[11200/16000] [L1: 0.0071] 16.8+0.0s +[12800/16000] [L1: 0.0071] 16.4+0.1s +[14400/16000] [L1: 0.0070] 16.7+0.1s +[16000/16000] [L1: 0.0070] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.473 (Best: 41.473 @epoch 17) +Forward: 9.12s + +Saving... +Total: 9.53s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0068] 16.7+0.8s +[3200/16000] [L1: 0.0068] 16.2+0.1s +[4800/16000] [L1: 0.0068] 16.7+0.1s +[6400/16000] [L1: 0.0069] 15.5+0.1s +[8000/16000] [L1: 0.0068] 16.1+0.1s +[9600/16000] [L1: 0.0069] 16.4+0.1s +[11200/16000] [L1: 0.0068] 15.9+0.1s +[12800/16000] [L1: 0.0069] 16.7+0.0s +[14400/16000] [L1: 0.0068] 17.0+0.1s +[16000/16000] [L1: 0.0069] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.424 (Best: 41.473 @epoch 17) +Forward: 9.08s + +Saving... +Total: 9.62s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0067] 16.4+0.9s +[3200/16000] [L1: 0.0067] 15.1+0.0s +[4800/16000] [L1: 0.0068] 15.7+0.0s +[6400/16000] [L1: 0.0068] 15.8+0.0s +[8000/16000] [L1: 0.0068] 16.6+0.1s +[9600/16000] [L1: 0.0067] 15.4+0.0s +[11200/16000] [L1: 0.0068] 15.6+0.0s +[12800/16000] [L1: 0.0067] 15.7+0.0s +[14400/16000] [L1: 0.0067] 15.3+0.0s +[16000/16000] [L1: 0.0067] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.273 (Best: 41.473 @epoch 17) +Forward: 9.18s + +Saving... +Total: 9.61s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0068] 16.5+0.9s +[3200/16000] [L1: 0.0067] 16.6+0.1s +[4800/16000] [L1: 0.0067] 15.7+0.0s +[6400/16000] [L1: 0.0067] 16.4+0.1s +[8000/16000] [L1: 0.0066] 16.1+0.1s +[9600/16000] [L1: 0.0067] 16.0+0.1s +[11200/16000] [L1: 0.0067] 16.2+0.1s +[12800/16000] [L1: 0.0066] 16.4+0.0s +[14400/16000] [L1: 0.0066] 15.5+0.1s +[16000/16000] [L1: 0.0066] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.401 (Best: 41.473 @epoch 17) +Forward: 9.22s + +Saving... +Total: 9.72s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0064] 16.6+0.9s +[3200/16000] [L1: 0.0064] 16.3+0.1s +[4800/16000] [L1: 0.0064] 15.7+0.0s +[6400/16000] [L1: 0.0064] 16.0+0.0s +[8000/16000] [L1: 0.0064] 16.5+0.0s +[9600/16000] [L1: 0.0064] 16.7+0.1s +[11200/16000] [L1: 0.0064] 16.6+0.1s +[12800/16000] [L1: 0.0065] 15.7+0.0s +[14400/16000] [L1: 0.0064] 17.0+0.1s +[16000/16000] [L1: 0.0064] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.658 (Best: 41.658 @epoch 21) +Forward: 9.27s + +Saving... +Total: 9.89s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0064] 15.5+0.9s +[3200/16000] [L1: 0.0064] 15.4+0.0s +[4800/16000] [L1: 0.0064] 17.0+0.1s +[6400/16000] [L1: 0.0064] 17.1+0.1s +[8000/16000] [L1: 0.0064] 16.0+0.1s +[9600/16000] [L1: 0.0064] 16.0+0.1s +[11200/16000] [L1: 0.0063] 16.4+0.1s +[12800/16000] [L1: 0.0063] 15.1+0.1s +[14400/16000] [L1: 0.0063] 16.0+0.1s +[16000/16000] [L1: 0.0063] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.178 (Best: 42.178 @epoch 22) +Forward: 9.05s + +Saving... +Total: 9.47s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0065] 17.1+0.9s +[3200/16000] [L1: 0.0064] 16.7+0.1s +[4800/16000] [L1: 0.0064] 15.5+0.0s +[6400/16000] [L1: 0.0063] 15.9+0.0s +[8000/16000] [L1: 0.0063] 16.7+0.1s +[9600/16000] [L1: 0.0063] 16.3+0.1s +[11200/16000] [L1: 0.0063] 16.0+0.0s +[12800/16000] [L1: 0.0063] 15.8+0.0s +[14400/16000] [L1: 0.0062] 16.3+0.0s +[16000/16000] [L1: 0.0062] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.272 (Best: 42.272 @epoch 23) +Forward: 9.27s + +Saving... +Total: 9.72s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0061] 15.5+0.9s +[3200/16000] [L1: 0.0061] 14.7+0.0s +[4800/16000] [L1: 0.0061] 15.0+0.0s +[6400/16000] [L1: 0.0061] 16.1+0.1s +[8000/16000] [L1: 0.0061] 15.4+0.1s +[9600/16000] [L1: 0.0061] 16.7+0.1s +[11200/16000] [L1: 0.0061] 15.8+0.1s +[12800/16000] [L1: 0.0061] 16.5+0.1s +[14400/16000] [L1: 0.0061] 15.5+0.0s +[16000/16000] [L1: 0.0061] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.063 (Best: 42.272 @epoch 23) +Forward: 9.11s + +Saving... +Total: 9.66s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0061] 15.3+0.9s +[3200/16000] [L1: 0.0061] 16.1+0.0s +[4800/16000] [L1: 0.0061] 16.4+0.0s +[6400/16000] [L1: 0.0061] 15.8+0.0s +[8000/16000] [L1: 0.0061] 16.1+0.0s +[9600/16000] [L1: 0.0061] 16.3+0.0s +[11200/16000] [L1: 0.0061] 15.6+0.0s +[12800/16000] [L1: 0.0061] 16.5+0.1s +[14400/16000] [L1: 0.0061] 15.7+0.0s +[16000/16000] [L1: 0.0061] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.793 (Best: 42.272 @epoch 23) +Forward: 9.18s + +Saving... +Total: 9.60s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0060] 15.9+0.9s +[3200/16000] [L1: 0.0059] 16.7+0.0s +[4800/16000] [L1: 0.0060] 16.4+0.1s +[6400/16000] [L1: 0.0059] 16.1+0.0s +[8000/16000] [L1: 0.0059] 16.0+0.0s +[9600/16000] [L1: 0.0060] 16.8+0.0s +[11200/16000] [L1: 0.0060] 16.8+0.0s +[12800/16000] [L1: 0.0060] 15.9+0.0s +[14400/16000] [L1: 0.0060] 16.5+0.0s +[16000/16000] [L1: 0.0060] 17.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.214 (Best: 42.272 @epoch 23) +Forward: 9.22s + +Saving... +Total: 9.67s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0058] 16.6+0.9s +[3200/16000] [L1: 0.0058] 17.1+0.1s +[4800/16000] [L1: 0.0058] 16.8+0.1s +[6400/16000] [L1: 0.0059] 17.0+0.1s +[8000/16000] [L1: 0.0059] 17.4+0.1s +[9600/16000] [L1: 0.0059] 16.0+0.0s +[11200/16000] [L1: 0.0059] 16.6+0.1s +[12800/16000] [L1: 0.0059] 16.6+0.1s +[14400/16000] [L1: 0.0059] 15.9+0.1s +[16000/16000] [L1: 0.0059] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.450 (Best: 42.450 @epoch 27) +Forward: 9.21s + +Saving... +Total: 9.65s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0061] 17.2+1.0s +[3200/16000] [L1: 0.0060] 16.5+0.1s +[4800/16000] [L1: 0.0060] 16.6+0.1s +[6400/16000] [L1: 0.0060] 16.8+0.1s +[8000/16000] [L1: 0.0059] 17.2+0.1s +[9600/16000] [L1: 0.0059] 16.2+0.1s +[11200/16000] [L1: 0.0059] 15.7+0.1s +[12800/16000] [L1: 0.0059] 17.2+0.1s +[14400/16000] [L1: 0.0059] 17.0+0.1s +[16000/16000] [L1: 0.0059] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.554 (Best: 42.554 @epoch 28) +Forward: 9.12s + +Saving... +Total: 9.72s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 16.2+1.0s +[3200/16000] [L1: 0.0057] 16.7+0.1s +[4800/16000] [L1: 0.0057] 17.2+0.1s +[6400/16000] [L1: 0.0057] 16.8+0.1s +[8000/16000] [L1: 0.0057] 17.0+0.1s +[9600/16000] [L1: 0.0058] 16.6+0.1s +[11200/16000] [L1: 0.0058] 16.4+0.1s +[12800/16000] [L1: 0.0058] 16.5+0.1s +[14400/16000] [L1: 0.0057] 16.2+0.1s +[16000/16000] [L1: 0.0057] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 42.671 (Best: 42.671 @epoch 29) +Forward: 9.16s + +Saving... +Total: 9.80s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 16.6+1.0s +[3200/16000] [L1: 0.0094] 16.7+0.1s +[4800/16000] [L1: 0.0106] 16.2+0.1s +[6400/16000] [L1: 0.0095] 15.9+0.0s +[8000/16000] [L1: 0.0089] 16.0+0.1s +[9600/16000] [L1: 0.0084] 16.1+0.0s +[11200/16000] [L1: 0.0080] 16.0+0.1s +[12800/16000] [L1: 0.0078] 16.1+0.0s +[14400/16000] [L1: 0.0076] 16.3+0.1s +[16000/16000] [L1: 0.0074] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.601 (Best: 42.671 @epoch 29) +Forward: 9.16s + +Saving... +Total: 9.71s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 16.2+0.9s +[3200/16000] [L1: 0.0058] 15.6+0.1s +[4800/16000] [L1: 0.0058] 17.2+0.1s +[6400/16000] [L1: 0.0058] 17.2+0.1s +[8000/16000] [L1: 0.0057] 16.2+0.1s +[9600/16000] [L1: 0.0058] 16.2+0.1s +[11200/16000] [L1: 0.0058] 16.5+0.1s +[12800/16000] [L1: 0.0058] 16.7+0.1s +[14400/16000] [L1: 0.0058] 16.7+0.1s +[16000/16000] [L1: 0.0058] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.674 (Best: 42.674 @epoch 31) +Forward: 9.12s + +Saving... +Total: 9.66s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.8+0.9s +[3200/16000] [L1: 0.0057] 16.1+0.0s +[4800/16000] [L1: 0.0057] 16.0+0.0s +[6400/16000] [L1: 0.0057] 15.6+0.0s +[8000/16000] [L1: 0.0057] 16.9+0.0s +[9600/16000] [L1: 0.0057] 16.8+0.1s +[11200/16000] [L1: 0.0057] 16.3+0.1s +[12800/16000] [L1: 0.0057] 15.7+0.0s +[14400/16000] [L1: 0.0057] 16.3+0.0s +[16000/16000] [L1: 0.0057] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 42.577 (Best: 42.674 @epoch 31) +Forward: 9.20s + +Saving... +Total: 10.34s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.8+0.9s +[3200/16000] [L1: 0.0057] 16.6+0.1s +[4800/16000] [L1: 0.0057] 16.1+0.1s +[6400/16000] [L1: 0.0056] 17.1+0.1s +[8000/16000] [L1: 0.0056] 16.8+0.1s +[9600/16000] [L1: 0.0056] 16.5+0.1s +[11200/16000] [L1: 0.0056] 17.2+0.1s +[12800/16000] [L1: 0.0056] 16.1+0.0s +[14400/16000] [L1: 0.0056] 16.7+0.1s +[16000/16000] [L1: 0.0056] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.728 (Best: 42.728 @epoch 33) +Forward: 9.22s + +Saving... +Total: 9.76s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.9+0.9s +[3200/16000] [L1: 0.0056] 15.1+0.0s +[4800/16000] [L1: 0.0056] 16.7+0.0s +[6400/16000] [L1: 0.0056] 15.9+0.0s +[8000/16000] [L1: 0.0056] 15.1+0.0s +[9600/16000] [L1: 0.0056] 14.7+0.0s +[11200/16000] [L1: 0.0056] 16.1+0.0s +[12800/16000] [L1: 0.0056] 16.6+0.0s +[14400/16000] [L1: 0.0056] 16.6+0.1s +[16000/16000] [L1: 0.0056] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.677 (Best: 42.728 @epoch 33) +Forward: 9.18s + +Saving... +Total: 9.76s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 17.0+1.0s +[3200/16000] [L1: 0.0055] 16.5+0.1s +[4800/16000] [L1: 0.0055] 16.7+0.1s +[6400/16000] [L1: 0.0055] 16.6+0.1s +[8000/16000] [L1: 0.0055] 17.1+0.1s +[9600/16000] [L1: 0.0055] 16.3+0.0s +[11200/16000] [L1: 0.0055] 16.9+0.1s +[12800/16000] [L1: 0.0055] 16.3+0.0s +[14400/16000] [L1: 0.0055] 16.4+0.1s +[16000/16000] [L1: 0.0055] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.857 (Best: 42.857 @epoch 35) +Forward: 9.09s + +Saving... +Total: 9.77s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.7+0.9s +[3200/16000] [L1: 0.0054] 16.0+0.0s +[4800/16000] [L1: 0.0054] 16.3+0.0s +[6400/16000] [L1: 0.0055] 17.2+0.1s +[8000/16000] [L1: 0.0055] 15.7+0.0s +[9600/16000] [L1: 0.0055] 16.1+0.0s +[11200/16000] [L1: 0.0055] 16.0+0.0s +[12800/16000] [L1: 0.0056] 16.0+0.0s +[14400/16000] [L1: 0.0056] 16.6+0.0s +[16000/16000] [L1: 0.0056] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.902 (Best: 42.902 @epoch 36) +Forward: 9.22s + +Saving... +Total: 9.70s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 16.1+0.8s +[3200/16000] [L1: 0.0055] 16.5+0.0s +[4800/16000] [L1: 0.0055] 17.3+0.1s +[6400/16000] [L1: 0.0055] 15.2+0.0s +[8000/16000] [L1: 0.0055] 16.3+0.0s +[9600/16000] [L1: 0.0055] 16.0+0.0s +[11200/16000] [L1: 0.0055] 15.2+0.0s +[12800/16000] [L1: 0.0055] 17.0+0.1s +[14400/16000] [L1: 0.0055] 17.1+0.0s +[16000/16000] [L1: 0.0055] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.886 (Best: 42.902 @epoch 36) +Forward: 9.29s + +Saving... +Total: 9.78s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 16.5+0.9s +[3200/16000] [L1: 0.0056] 16.2+0.1s +[4800/16000] [L1: 0.0055] 16.2+0.0s +[6400/16000] [L1: 0.0055] 15.9+0.0s +[8000/16000] [L1: 0.0055] 16.8+0.1s +[9600/16000] [L1: 0.0055] 15.9+0.0s +[11200/16000] [L1: 0.0055] 16.5+0.1s +[12800/16000] [L1: 0.0055] 17.6+0.1s +[14400/16000] [L1: 0.0054] 16.3+0.0s +[16000/16000] [L1: 0.0055] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.838 (Best: 42.902 @epoch 36) +Forward: 9.49s + +Saving... +Total: 10.03s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.9+0.9s +[3200/16000] [L1: 0.0056] 15.6+0.0s +[4800/16000] [L1: 0.0055] 16.5+0.0s +[6400/16000] [L1: 0.0055] 13.9+0.0s +[8000/16000] [L1: 0.0055] 14.4+0.0s +[9600/16000] [L1: 0.0055] 13.9+0.0s +[11200/16000] [L1: 0.0054] 16.0+0.0s +[12800/16000] [L1: 0.0054] 14.9+0.0s +[14400/16000] [L1: 0.0054] 15.5+0.0s +[16000/16000] [L1: 0.0054] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.005 (Best: 43.005 @epoch 39) +Forward: 9.07s + +Saving... +Total: 9.67s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 16.4+1.0s +[3200/16000] [L1: 0.0055] 16.0+0.1s +[4800/16000] [L1: 0.0055] 16.0+0.0s +[6400/16000] [L1: 0.0055] 16.2+0.1s +[8000/16000] [L1: 0.0055] 16.1+0.1s +[9600/16000] [L1: 0.0055] 16.4+0.1s +[11200/16000] [L1: 0.0055] 16.9+0.1s +[12800/16000] [L1: 0.0055] 16.1+0.1s +[14400/16000] [L1: 0.0054] 17.2+0.1s +[16000/16000] [L1: 0.0054] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.945 (Best: 43.005 @epoch 39) +Forward: 9.15s + +Saving... +Total: 9.53s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 16.0+0.8s +[3200/16000] [L1: 0.0053] 16.1+0.1s +[4800/16000] [L1: 0.0053] 15.0+0.0s +[6400/16000] [L1: 0.0053] 17.0+0.1s +[8000/16000] [L1: 0.0053] 16.5+0.0s +[9600/16000] [L1: 0.0054] 15.9+0.0s +[11200/16000] [L1: 0.0054] 16.3+0.0s +[12800/16000] [L1: 0.0054] 16.1+0.0s +[14400/16000] [L1: 0.0054] 16.4+0.0s +[16000/16000] [L1: 0.0054] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.981 (Best: 43.005 @epoch 39) +Forward: 9.19s + +Saving... +Total: 9.74s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.6+1.0s +[3200/16000] [L1: 0.0054] 16.0+0.0s +[4800/16000] [L1: 0.0054] 16.7+0.1s +[6400/16000] [L1: 0.0054] 15.3+0.0s +[8000/16000] [L1: 0.0054] 15.2+0.0s +[9600/16000] [L1: 0.0054] 16.5+0.1s +[11200/16000] [L1: 0.0054] 15.3+0.0s +[12800/16000] [L1: 0.0054] 16.6+0.0s +[14400/16000] [L1: 0.0053] 16.1+0.0s +[16000/16000] [L1: 0.0053] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.059 (Best: 43.059 @epoch 42) +Forward: 9.29s + +Saving... +Total: 9.87s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.7+1.0s +[3200/16000] [L1: 0.0054] 16.1+0.0s +[4800/16000] [L1: 0.0054] 15.7+0.0s +[6400/16000] [L1: 0.0053] 14.6+0.0s +[8000/16000] [L1: 0.0053] 15.3+0.0s +[9600/16000] [L1: 0.0053] 16.0+0.0s +[11200/16000] [L1: 0.0053] 16.4+0.0s +[12800/16000] [L1: 0.0053] 16.0+0.0s +[14400/16000] [L1: 0.0054] 16.8+0.0s +[16000/16000] [L1: 0.0054] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.521 (Best: 43.059 @epoch 42) +Forward: 9.12s + +Saving... +Total: 9.52s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.8+1.0s +[3200/16000] [L1: 0.0054] 15.9+0.0s +[4800/16000] [L1: 0.0053] 16.6+0.1s +[6400/16000] [L1: 0.0053] 16.8+0.1s +[8000/16000] [L1: 0.0053] 16.4+0.1s +[9600/16000] [L1: 0.0053] 16.1+0.0s +[11200/16000] [L1: 0.0053] 17.1+0.1s +[12800/16000] [L1: 0.0053] 16.9+0.1s +[14400/16000] [L1: 0.0053] 16.1+0.0s +[16000/16000] [L1: 0.0053] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.086 (Best: 43.086 @epoch 44) +Forward: 9.14s + +Saving... +Total: 9.67s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 16.7+0.9s +[3200/16000] [L1: 0.0053] 16.3+0.1s +[4800/16000] [L1: 0.0053] 16.2+0.1s +[6400/16000] [L1: 0.0053] 16.8+0.1s +[8000/16000] [L1: 0.0053] 17.0+0.1s +[9600/16000] [L1: 0.0053] 16.6+0.1s +[11200/16000] [L1: 0.0053] 17.1+0.1s +[12800/16000] [L1: 0.0053] 16.8+0.1s +[14400/16000] [L1: 0.0053] 16.4+0.1s +[16000/16000] [L1: 0.0053] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.062 (Best: 43.086 @epoch 44) +Forward: 9.27s + +Saving... +Total: 9.79s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 16.7+0.9s +[3200/16000] [L1: 0.0052] 16.2+0.1s +[4800/16000] [L1: 0.0053] 16.1+0.1s +[6400/16000] [L1: 0.0053] 15.4+0.0s +[8000/16000] [L1: 0.0052] 14.8+0.0s +[9600/16000] [L1: 0.0053] 16.1+0.0s +[11200/16000] [L1: 0.0053] 15.0+0.0s +[12800/16000] [L1: 0.0052] 15.9+0.0s +[14400/16000] [L1: 0.0052] 16.1+0.1s +[16000/16000] [L1: 0.0052] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.095 (Best: 43.095 @epoch 46) +Forward: 9.06s + +Saving... +Total: 9.68s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 16.0+1.0s +[3200/16000] [L1: 0.0052] 16.1+0.1s +[4800/16000] [L1: 0.0052] 16.6+0.1s +[6400/16000] [L1: 0.0052] 16.2+0.1s +[8000/16000] [L1: 0.0052] 16.8+0.1s +[9600/16000] [L1: 0.0052] 16.5+0.1s +[11200/16000] [L1: 0.0052] 16.1+0.1s +[12800/16000] [L1: 0.0052] 16.2+0.1s +[14400/16000] [L1: 0.0052] 15.9+0.1s +[16000/16000] [L1: 0.0052] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.179 (Best: 43.179 @epoch 47) +Forward: 9.17s + +Saving... +Total: 9.72s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 17.2+0.9s +[3200/16000] [L1: 0.0052] 15.6+0.0s +[4800/16000] [L1: 0.0052] 14.9+0.0s +[6400/16000] [L1: 0.0051] 15.0+0.0s +[8000/16000] [L1: 0.0052] 14.4+0.0s +[9600/16000] [L1: 0.0051] 16.5+0.0s +[11200/16000] [L1: 0.0052] 17.1+0.1s +[12800/16000] [L1: 0.0051] 15.0+0.0s +[14400/16000] [L1: 0.0052] 14.8+0.0s +[16000/16000] [L1: 0.0052] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.130 (Best: 43.179 @epoch 47) +Forward: 9.16s + +Saving... +Total: 9.61s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 16.4+1.1s +[3200/16000] [L1: 0.0053] 16.3+0.0s +[4800/16000] [L1: 0.0054] 15.7+0.0s +[6400/16000] [L1: 0.0053] 15.9+0.0s +[8000/16000] [L1: 0.0053] 16.2+0.1s +[9600/16000] [L1: 0.0053] 16.5+0.0s +[11200/16000] [L1: 0.0053] 15.6+0.0s +[12800/16000] [L1: 0.0053] 16.4+0.0s +[14400/16000] [L1: 0.0053] 15.7+0.1s +[16000/16000] [L1: 0.0053] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.205 (Best: 43.205 @epoch 49) +Forward: 9.19s + +Saving... +Total: 9.66s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.1+0.8s +[3200/16000] [L1: 0.0050] 16.6+0.1s +[4800/16000] [L1: 0.0051] 16.5+0.0s +[6400/16000] [L1: 0.0051] 16.0+0.1s +[8000/16000] [L1: 0.0052] 16.4+0.1s +[9600/16000] [L1: 0.0052] 15.4+0.0s +[11200/16000] [L1: 0.0052] 15.1+0.0s +[12800/16000] [L1: 0.0052] 16.5+0.0s +[14400/16000] [L1: 0.0052] 16.2+0.0s +[16000/16000] [L1: 0.0052] 14.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.203 (Best: 43.205 @epoch 49) +Forward: 9.22s + +Saving... +Total: 9.68s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.8+0.8s +[3200/16000] [L1: 0.0052] 16.2+0.1s +[4800/16000] [L1: 0.0052] 15.7+0.0s +[6400/16000] [L1: 0.0052] 17.0+0.1s +[8000/16000] [L1: 0.0052] 15.9+0.0s +[9600/16000] [L1: 0.0052] 16.9+0.1s +[11200/16000] [L1: 0.0052] 15.9+0.0s +[12800/16000] [L1: 0.0052] 14.6+0.0s +[14400/16000] [L1: 0.0052] 15.2+0.0s +[16000/16000] [L1: 0.0052] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.181 (Best: 43.205 @epoch 49) +Forward: 9.21s + +Saving... +Total: 9.76s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.2+1.0s +[3200/16000] [L1: 0.0054] 16.0+0.0s +[4800/16000] [L1: 0.0053] 15.9+0.1s +[6400/16000] [L1: 0.0053] 16.0+0.1s +[8000/16000] [L1: 0.0052] 14.4+0.0s +[9600/16000] [L1: 0.0052] 15.7+0.0s +[11200/16000] [L1: 0.0052] 15.4+0.0s +[12800/16000] [L1: 0.0052] 15.8+0.0s +[14400/16000] [L1: 0.0052] 16.5+0.1s +[16000/16000] [L1: 0.0052] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.214 (Best: 43.214 @epoch 52) +Forward: 9.18s + +Saving... +Total: 9.65s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 16.1+0.8s +[3200/16000] [L1: 0.0052] 15.5+0.0s +[4800/16000] [L1: 0.0052] 16.5+0.0s +[6400/16000] [L1: 0.0052] 16.8+0.0s +[8000/16000] [L1: 0.0052] 16.5+0.0s +[9600/16000] [L1: 0.0052] 16.2+0.0s +[11200/16000] [L1: 0.0052] 15.6+0.0s +[12800/16000] [L1: 0.0052] 15.6+0.0s +[14400/16000] [L1: 0.0052] 15.3+0.0s +[16000/16000] [L1: 0.0052] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.361 (Best: 43.361 @epoch 53) +Forward: 9.23s + +Saving... +Total: 9.76s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.1+0.9s +[3200/16000] [L1: 0.0051] 15.2+0.0s +[4800/16000] [L1: 0.0051] 16.1+0.0s +[6400/16000] [L1: 0.0051] 15.2+0.0s +[8000/16000] [L1: 0.0052] 15.5+0.0s +[9600/16000] [L1: 0.0051] 16.6+0.1s +[11200/16000] [L1: 0.0051] 15.1+0.0s +[12800/16000] [L1: 0.0051] 15.8+0.0s +[14400/16000] [L1: 0.0052] 16.7+0.0s +[16000/16000] [L1: 0.0051] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.282 (Best: 43.361 @epoch 53) +Forward: 9.16s + +Saving... +Total: 9.75s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 14.4+0.9s +[3200/16000] [L1: 0.0052] 16.9+0.0s +[4800/16000] [L1: 0.0052] 16.6+0.1s +[6400/16000] [L1: 0.0052] 16.3+0.0s +[8000/16000] [L1: 0.0052] 17.2+0.0s +[9600/16000] [L1: 0.0052] 15.6+0.0s +[11200/16000] [L1: 0.0051] 16.4+0.1s +[12800/16000] [L1: 0.0051] 16.8+0.1s +[14400/16000] [L1: 0.0051] 16.2+0.0s +[16000/16000] [L1: 0.0051] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.160 (Best: 43.361 @epoch 53) +Forward: 9.12s + +Saving... +Total: 9.63s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.1+0.8s +[3200/16000] [L1: 0.0050] 16.8+0.1s +[4800/16000] [L1: 0.0051] 16.0+0.0s +[6400/16000] [L1: 0.0051] 16.5+0.1s +[8000/16000] [L1: 0.0051] 16.5+0.1s +[9600/16000] [L1: 0.0051] 16.1+0.0s +[11200/16000] [L1: 0.0051] 15.8+0.0s +[12800/16000] [L1: 0.0051] 17.2+0.1s +[14400/16000] [L1: 0.0051] 15.6+0.0s +[16000/16000] [L1: 0.0051] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.375 (Best: 43.375 @epoch 56) +Forward: 9.17s + +Saving... +Total: 9.82s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.4+1.0s +[3200/16000] [L1: 0.0050] 15.5+0.0s +[4800/16000] [L1: 0.0050] 16.0+0.1s +[6400/16000] [L1: 0.0051] 16.7+0.1s +[8000/16000] [L1: 0.0050] 16.1+0.1s +[9600/16000] [L1: 0.0051] 17.1+0.1s +[11200/16000] [L1: 0.0051] 16.5+0.1s +[12800/16000] [L1: 0.0051] 17.4+0.1s +[14400/16000] [L1: 0.0051] 16.6+0.1s +[16000/16000] [L1: 0.0051] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.393 (Best: 43.393 @epoch 57) +Forward: 9.14s + +Saving... +Total: 9.60s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 16.0+0.8s +[3200/16000] [L1: 0.0051] 16.1+0.1s +[4800/16000] [L1: 0.0051] 15.6+0.1s +[6400/16000] [L1: 0.0051] 16.0+0.1s +[8000/16000] [L1: 0.0051] 16.1+0.0s +[9600/16000] [L1: 0.0051] 15.1+0.0s +[11200/16000] [L1: 0.0051] 16.3+0.1s +[12800/16000] [L1: 0.0051] 16.0+0.0s +[14400/16000] [L1: 0.0051] 16.3+0.1s +[16000/16000] [L1: 0.0051] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.364 (Best: 43.393 @epoch 57) +Forward: 9.35s + +Saving... +Total: 9.86s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 16.6+0.9s +[3200/16000] [L1: 0.0051] 16.0+0.1s +[4800/16000] [L1: 0.0051] 16.8+0.0s +[6400/16000] [L1: 0.0051] 14.7+0.0s +[8000/16000] [L1: 0.0051] 16.2+0.0s +[9600/16000] [L1: 0.0051] 16.5+0.1s +[11200/16000] [L1: 0.0051] 16.7+0.1s +[12800/16000] [L1: 0.0051] 15.8+0.0s +[14400/16000] [L1: 0.0051] 15.5+0.0s +[16000/16000] [L1: 0.0051] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.342 (Best: 43.393 @epoch 57) +Forward: 9.34s + +Saving... +Total: 10.22s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 16.6+0.8s +[3200/16000] [L1: 0.0051] 16.7+0.1s +[4800/16000] [L1: 0.0051] 16.7+0.1s +[6400/16000] [L1: 0.0051] 17.1+0.1s +[8000/16000] [L1: 0.0051] 16.1+0.1s +[9600/16000] [L1: 0.0052] 17.0+0.1s +[11200/16000] [L1: 0.0051] 17.2+0.1s +[12800/16000] [L1: 0.0051] 16.4+0.0s +[14400/16000] [L1: 0.0051] 16.6+0.1s +[16000/16000] [L1: 0.0051] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.394 (Best: 43.394 @epoch 60) +Forward: 9.08s + +Saving... +Total: 9.52s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 17.1+0.8s +[3200/16000] [L1: 0.0050] 16.8+0.1s +[4800/16000] [L1: 0.0051] 16.9+0.1s +[6400/16000] [L1: 0.0051] 17.0+0.1s +[8000/16000] [L1: 0.0051] 16.7+0.1s +[9600/16000] [L1: 0.0051] 16.0+0.0s +[11200/16000] [L1: 0.0051] 15.7+0.0s +[12800/16000] [L1: 0.0051] 16.8+0.0s +[14400/16000] [L1: 0.0051] 15.9+0.0s +[16000/16000] [L1: 0.0051] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.054 (Best: 43.394 @epoch 60) +Forward: 9.18s + +Saving... +Total: 9.68s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.6+0.9s +[3200/16000] [L1: 0.0050] 16.6+0.1s +[4800/16000] [L1: 0.0050] 16.7+0.1s +[6400/16000] [L1: 0.0050] 16.3+0.1s +[8000/16000] [L1: 0.0050] 15.2+0.0s +[9600/16000] [L1: 0.0050] 15.5+0.0s +[11200/16000] [L1: 0.0050] 16.1+0.0s +[12800/16000] [L1: 0.0050] 15.6+0.0s +[14400/16000] [L1: 0.0051] 15.1+0.0s +[16000/16000] [L1: 0.0051] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.367 (Best: 43.394 @epoch 60) +Forward: 9.06s + +Saving... +Total: 9.65s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.7+1.0s +[3200/16000] [L1: 0.0050] 16.3+0.1s +[4800/16000] [L1: 0.0050] 15.8+0.1s +[6400/16000] [L1: 0.0051] 16.2+0.1s +[8000/16000] [L1: 0.0051] 16.9+0.1s +[9600/16000] [L1: 0.0051] 15.9+0.0s +[11200/16000] [L1: 0.0051] 16.0+0.0s +[12800/16000] [L1: 0.0051] 15.9+0.0s +[14400/16000] [L1: 0.0050] 15.5+0.0s +[16000/16000] [L1: 0.0050] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.470 (Best: 43.470 @epoch 63) +Forward: 9.12s + +Saving... +Total: 9.64s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.0+0.9s +[3200/16000] [L1: 0.0050] 16.5+0.1s +[4800/16000] [L1: 0.0050] 16.8+0.1s +[6400/16000] [L1: 0.0050] 16.4+0.1s +[8000/16000] [L1: 0.0050] 16.7+0.1s +[9600/16000] [L1: 0.0050] 16.2+0.0s +[11200/16000] [L1: 0.0050] 16.0+0.1s +[12800/16000] [L1: 0.0051] 17.5+0.1s +[14400/16000] [L1: 0.0050] 16.4+0.1s +[16000/16000] [L1: 0.0050] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.425 (Best: 43.470 @epoch 63) +Forward: 9.26s + +Saving... +Total: 9.81s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.7+0.9s +[3200/16000] [L1: 0.0049] 16.2+0.1s +[4800/16000] [L1: 0.0050] 16.0+0.1s +[6400/16000] [L1: 0.0050] 16.4+0.0s +[8000/16000] [L1: 0.0050] 16.2+0.1s +[9600/16000] [L1: 0.0050] 16.3+0.1s +[11200/16000] [L1: 0.0050] 17.3+0.1s +[12800/16000] [L1: 0.0050] 15.8+0.1s +[14400/16000] [L1: 0.0050] 16.1+0.1s +[16000/16000] [L1: 0.0050] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.450 (Best: 43.470 @epoch 63) +Forward: 9.14s + +Saving... +Total: 9.67s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.3+1.0s +[3200/16000] [L1: 0.0050] 15.5+0.0s +[4800/16000] [L1: 0.0050] 15.7+0.0s +[6400/16000] [L1: 0.0050] 16.0+0.0s +[8000/16000] [L1: 0.0050] 16.6+0.0s +[9600/16000] [L1: 0.0050] 17.2+0.1s +[11200/16000] [L1: 0.0050] 15.6+0.0s +[12800/16000] [L1: 0.0050] 15.7+0.0s +[14400/16000] [L1: 0.0050] 15.9+0.0s +[16000/16000] [L1: 0.0050] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.439 (Best: 43.470 @epoch 63) +Forward: 9.21s + +Saving... +Total: 9.63s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.6+0.8s +[3200/16000] [L1: 0.0050] 17.2+0.1s +[4800/16000] [L1: 0.0050] 17.0+0.1s +[6400/16000] [L1: 0.0050] 16.3+0.1s +[8000/16000] [L1: 0.0050] 16.6+0.1s +[9600/16000] [L1: 0.0050] 16.2+0.1s +[11200/16000] [L1: 0.0050] 15.7+0.0s +[12800/16000] [L1: 0.0050] 16.3+0.1s +[14400/16000] [L1: 0.0050] 15.8+0.0s +[16000/16000] [L1: 0.0050] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.542 (Best: 43.542 @epoch 67) +Forward: 9.38s + +Saving... +Total: 9.92s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 16.3+0.9s +[3200/16000] [L1: 0.0051] 16.0+0.0s +[4800/16000] [L1: 0.0050] 16.0+0.0s +[6400/16000] [L1: 0.0050] 16.3+0.0s +[8000/16000] [L1: 0.0050] 16.8+0.1s +[9600/16000] [L1: 0.0050] 16.7+0.1s +[11200/16000] [L1: 0.0050] 15.9+0.1s +[12800/16000] [L1: 0.0050] 16.5+0.0s +[14400/16000] [L1: 0.0050] 16.5+0.0s +[16000/16000] [L1: 0.0050] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.325 (Best: 43.542 @epoch 67) +Forward: 9.20s + +Saving... +Total: 9.67s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.8+0.9s +[3200/16000] [L1: 0.0049] 16.0+0.0s +[4800/16000] [L1: 0.0049] 16.5+0.1s +[6400/16000] [L1: 0.0049] 16.7+0.0s +[8000/16000] [L1: 0.0049] 16.2+0.0s +[9600/16000] [L1: 0.0049] 17.1+0.1s +[11200/16000] [L1: 0.0049] 17.2+0.1s +[12800/16000] [L1: 0.0049] 16.8+0.1s +[14400/16000] [L1: 0.0049] 16.6+0.1s +[16000/16000] [L1: 0.0049] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.505 (Best: 43.542 @epoch 67) +Forward: 9.04s + +Saving... +Total: 9.58s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.6+0.9s +[3200/16000] [L1: 0.0050] 16.0+0.1s +[4800/16000] [L1: 0.0050] 16.4+0.1s +[6400/16000] [L1: 0.0049] 16.9+0.1s +[8000/16000] [L1: 0.0050] 15.4+0.0s +[9600/16000] [L1: 0.0050] 16.1+0.1s +[11200/16000] [L1: 0.0050] 16.0+0.0s +[12800/16000] [L1: 0.0050] 16.1+0.1s +[14400/16000] [L1: 0.0050] 17.1+0.1s +[16000/16000] [L1: 0.0050] 17.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.583 (Best: 43.583 @epoch 70) +Forward: 9.06s + +Saving... +Total: 9.59s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.7+0.9s +[3200/16000] [L1: 0.0049] 16.7+0.1s +[4800/16000] [L1: 0.0049] 15.5+0.1s +[6400/16000] [L1: 0.0049] 16.9+0.1s +[8000/16000] [L1: 0.0050] 16.1+0.0s +[9600/16000] [L1: 0.0050] 17.1+0.1s +[11200/16000] [L1: 0.0049] 17.1+0.1s +[12800/16000] [L1: 0.0049] 16.1+0.1s +[14400/16000] [L1: 0.0049] 16.8+0.1s +[16000/16000] [L1: 0.0050] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.499 (Best: 43.583 @epoch 70) +Forward: 9.08s + +Saving... +Total: 9.69s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.9+0.9s +[3200/16000] [L1: 0.0050] 16.4+0.1s +[4800/16000] [L1: 0.0049] 14.7+0.0s +[6400/16000] [L1: 0.0050] 16.0+0.0s +[8000/16000] [L1: 0.0050] 14.9+0.0s +[9600/16000] [L1: 0.0049] 14.9+0.0s +[11200/16000] [L1: 0.0049] 16.5+0.0s +[12800/16000] [L1: 0.0049] 15.3+0.0s +[14400/16000] [L1: 0.0049] 15.3+0.0s +[16000/16000] [L1: 0.0049] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.511 (Best: 43.583 @epoch 70) +Forward: 8.98s + +Saving... +Total: 9.44s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.7+0.9s +[3200/16000] [L1: 0.0050] 16.9+0.1s +[4800/16000] [L1: 0.0050] 16.5+0.1s +[6400/16000] [L1: 0.0050] 16.7+0.1s +[8000/16000] [L1: 0.0050] 15.9+0.1s +[9600/16000] [L1: 0.0049] 16.8+0.1s +[11200/16000] [L1: 0.0049] 15.7+0.0s +[12800/16000] [L1: 0.0049] 16.7+0.1s +[14400/16000] [L1: 0.0049] 17.6+0.1s +[16000/16000] [L1: 0.0050] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.570 (Best: 43.583 @epoch 70) +Forward: 9.00s + +Saving... +Total: 9.42s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 14.9+0.8s +[3200/16000] [L1: 0.0051] 15.8+0.0s +[4800/16000] [L1: 0.0050] 17.0+0.1s +[6400/16000] [L1: 0.0050] 15.6+0.0s +[8000/16000] [L1: 0.0050] 16.8+0.0s +[9600/16000] [L1: 0.0050] 16.0+0.0s +[11200/16000] [L1: 0.0050] 16.6+0.0s +[12800/16000] [L1: 0.0050] 15.5+0.0s +[14400/16000] [L1: 0.0050] 15.8+0.1s +[16000/16000] [L1: 0.0050] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.572 (Best: 43.583 @epoch 70) +Forward: 9.02s + +Saving... +Total: 9.53s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.3+0.9s +[3200/16000] [L1: 0.0050] 15.6+0.0s +[4800/16000] [L1: 0.0049] 16.3+0.0s +[6400/16000] [L1: 0.0049] 15.7+0.0s +[8000/16000] [L1: 0.0049] 14.6+0.0s +[9600/16000] [L1: 0.0050] 15.8+0.0s +[11200/16000] [L1: 0.0049] 17.1+0.0s +[12800/16000] [L1: 0.0050] 15.9+0.0s +[14400/16000] [L1: 0.0049] 17.1+0.0s +[16000/16000] [L1: 0.0049] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.577 (Best: 43.583 @epoch 70) +Forward: 9.22s + +Saving... +Total: 9.66s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.1+0.8s +[3200/16000] [L1: 0.0049] 15.9+0.1s +[4800/16000] [L1: 0.0049] 15.5+0.0s +[6400/16000] [L1: 0.0049] 16.9+0.1s +[8000/16000] [L1: 0.0049] 16.5+0.1s +[9600/16000] [L1: 0.0049] 16.7+0.1s +[11200/16000] [L1: 0.0049] 15.3+0.0s +[12800/16000] [L1: 0.0049] 16.6+0.0s +[14400/16000] [L1: 0.0049] 16.0+0.0s +[16000/16000] [L1: 0.0049] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.477 (Best: 43.583 @epoch 70) +Forward: 9.20s + +Saving... +Total: 9.70s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.8+0.9s +[3200/16000] [L1: 0.0049] 16.5+0.1s +[4800/16000] [L1: 0.0049] 15.3+0.0s +[6400/16000] [L1: 0.0048] 16.7+0.1s +[8000/16000] [L1: 0.0048] 16.1+0.0s +[9600/16000] [L1: 0.0049] 16.6+0.1s +[11200/16000] [L1: 0.0049] 15.2+0.0s +[12800/16000] [L1: 0.0049] 16.2+0.0s +[14400/16000] [L1: 0.0049] 15.3+0.0s +[16000/16000] [L1: 0.0049] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.656 (Best: 43.656 @epoch 77) +Forward: 9.08s + +Saving... +Total: 9.65s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.9+0.8s +[3200/16000] [L1: 0.0048] 16.0+0.1s +[4800/16000] [L1: 0.0048] 17.3+0.1s +[6400/16000] [L1: 0.0049] 16.8+0.1s +[8000/16000] [L1: 0.0049] 16.3+0.0s +[9600/16000] [L1: 0.0048] 16.8+0.1s +[11200/16000] [L1: 0.0049] 16.1+0.0s +[12800/16000] [L1: 0.0049] 16.6+0.1s +[14400/16000] [L1: 0.0049] 16.7+0.1s +[16000/16000] [L1: 0.0049] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.648 (Best: 43.656 @epoch 77) +Forward: 9.08s + +Saving... +Total: 9.51s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.4+1.0s +[3200/16000] [L1: 0.0048] 14.3+0.0s +[4800/16000] [L1: 0.0048] 16.6+0.1s +[6400/16000] [L1: 0.0048] 16.1+0.1s +[8000/16000] [L1: 0.0048] 17.4+0.1s +[9600/16000] [L1: 0.0049] 16.4+0.1s +[11200/16000] [L1: 0.0049] 17.0+0.1s +[12800/16000] [L1: 0.0049] 16.8+0.1s +[14400/16000] [L1: 0.0049] 16.7+0.1s +[16000/16000] [L1: 0.0049] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.434 (Best: 43.656 @epoch 77) +Forward: 8.97s + +Saving... +Total: 9.52s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.8+0.9s +[3200/16000] [L1: 0.0049] 16.4+0.1s +[4800/16000] [L1: 0.0049] 17.2+0.1s +[6400/16000] [L1: 0.0049] 16.9+0.1s +[8000/16000] [L1: 0.0049] 15.8+0.0s +[9600/16000] [L1: 0.0049] 15.2+0.0s +[11200/16000] [L1: 0.0049] 16.9+0.0s +[12800/16000] [L1: 0.0049] 16.5+0.1s +[14400/16000] [L1: 0.0049] 16.8+0.1s +[16000/16000] [L1: 0.0049] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.658 (Best: 43.658 @epoch 80) +Forward: 9.17s + +Saving... +Total: 9.75s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.4+0.9s +[3200/16000] [L1: 0.0050] 15.3+0.0s +[4800/16000] [L1: 0.0049] 15.4+0.0s +[6400/16000] [L1: 0.0049] 16.8+0.0s +[8000/16000] [L1: 0.0049] 16.7+0.1s +[9600/16000] [L1: 0.0049] 16.3+0.0s +[11200/16000] [L1: 0.0049] 16.8+0.0s +[12800/16000] [L1: 0.0049] 16.0+0.0s +[14400/16000] [L1: 0.0049] 16.5+0.0s +[16000/16000] [L1: 0.0049] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.584 (Best: 43.658 @epoch 80) +Forward: 9.29s + +Saving... +Total: 9.90s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.7+0.9s +[3200/16000] [L1: 0.0048] 16.3+0.1s +[4800/16000] [L1: 0.0048] 15.5+0.0s +[6400/16000] [L1: 0.0048] 14.2+0.0s +[8000/16000] [L1: 0.0048] 13.9+0.0s +[9600/16000] [L1: 0.0049] 15.9+0.0s +[11200/16000] [L1: 0.0049] 16.0+0.1s +[12800/16000] [L1: 0.0049] 16.2+0.0s +[14400/16000] [L1: 0.0049] 16.6+0.0s +[16000/16000] [L1: 0.0049] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.561 (Best: 43.658 @epoch 80) +Forward: 9.22s + +Saving... +Total: 9.71s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 17.5+0.9s +[3200/16000] [L1: 0.0047] 16.3+0.1s +[4800/16000] [L1: 0.0048] 16.2+0.0s +[6400/16000] [L1: 0.0048] 15.9+0.0s +[8000/16000] [L1: 0.0048] 16.3+0.0s +[9600/16000] [L1: 0.0048] 15.9+0.0s +[11200/16000] [L1: 0.0048] 15.8+0.0s +[12800/16000] [L1: 0.0048] 15.3+0.0s +[14400/16000] [L1: 0.0048] 15.8+0.0s +[16000/16000] [L1: 0.0048] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.666 (Best: 43.666 @epoch 83) +Forward: 9.26s + +Saving... +Total: 9.78s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 16.2+1.0s +[3200/16000] [L1: 0.0049] 16.6+0.0s +[4800/16000] [L1: 0.0049] 15.7+0.1s +[6400/16000] [L1: 0.0049] 16.3+0.1s +[8000/16000] [L1: 0.0049] 16.2+0.0s +[9600/16000] [L1: 0.0049] 15.1+0.0s +[11200/16000] [L1: 0.0049] 15.1+0.0s +[12800/16000] [L1: 0.0049] 15.9+0.0s +[14400/16000] [L1: 0.0049] 15.4+0.0s +[16000/16000] [L1: 0.0049] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.682 (Best: 43.682 @epoch 84) +Forward: 9.07s + +Saving... +Total: 9.64s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.2+0.9s +[3200/16000] [L1: 0.0049] 16.8+0.0s +[4800/16000] [L1: 0.0048] 16.3+0.0s +[6400/16000] [L1: 0.0048] 15.8+0.0s +[8000/16000] [L1: 0.0048] 15.4+0.0s +[9600/16000] [L1: 0.0048] 16.4+0.0s +[11200/16000] [L1: 0.0048] 16.0+0.0s +[12800/16000] [L1: 0.0048] 15.9+0.0s +[14400/16000] [L1: 0.0048] 16.4+0.0s +[16000/16000] [L1: 0.0049] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.718 (Best: 43.718 @epoch 85) +Forward: 9.35s + +Saving... +Total: 9.77s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.6+0.8s +[3200/16000] [L1: 0.0046] 16.9+0.0s +[4800/16000] [L1: 0.0047] 17.1+0.1s +[6400/16000] [L1: 0.0047] 15.3+0.0s +[8000/16000] [L1: 0.0047] 15.8+0.0s +[9600/16000] [L1: 0.0048] 14.4+0.0s +[11200/16000] [L1: 0.0048] 15.7+0.0s +[12800/16000] [L1: 0.0048] 15.8+0.0s +[14400/16000] [L1: 0.0048] 16.1+0.0s +[16000/16000] [L1: 0.0048] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.659 (Best: 43.718 @epoch 85) +Forward: 9.19s + +Saving... +Total: 9.60s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.8+0.8s +[3200/16000] [L1: 0.0048] 16.3+0.1s +[4800/16000] [L1: 0.0048] 16.8+0.1s +[6400/16000] [L1: 0.0048] 16.8+0.0s +[8000/16000] [L1: 0.0048] 16.2+0.1s +[9600/16000] [L1: 0.0048] 17.2+0.1s +[11200/16000] [L1: 0.0048] 16.9+0.1s +[12800/16000] [L1: 0.0048] 16.4+0.0s +[14400/16000] [L1: 0.0048] 16.9+0.1s +[16000/16000] [L1: 0.0048] 16.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.793 (Best: 43.793 @epoch 87) +Forward: 9.12s + +Saving... +Total: 9.56s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.6+0.8s +[3200/16000] [L1: 0.0047] 16.7+0.1s +[4800/16000] [L1: 0.0048] 15.5+0.1s +[6400/16000] [L1: 0.0048] 16.5+0.1s +[8000/16000] [L1: 0.0049] 16.6+0.1s +[9600/16000] [L1: 0.0048] 16.3+0.0s +[11200/16000] [L1: 0.0049] 16.6+0.0s +[12800/16000] [L1: 0.0049] 15.7+0.0s +[14400/16000] [L1: 0.0049] 15.4+0.0s +[16000/16000] [L1: 0.0049] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.667 (Best: 43.793 @epoch 87) +Forward: 9.17s + +Saving... +Total: 9.59s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 17.1+1.0s +[3200/16000] [L1: 0.0048] 17.1+0.1s +[4800/16000] [L1: 0.0048] 16.4+0.1s +[6400/16000] [L1: 0.0048] 17.1+0.1s +[8000/16000] [L1: 0.0048] 16.3+0.1s +[9600/16000] [L1: 0.0048] 16.4+0.1s +[11200/16000] [L1: 0.0048] 15.8+0.1s +[12800/16000] [L1: 0.0048] 16.3+0.1s +[14400/16000] [L1: 0.0048] 16.7+0.1s +[16000/16000] [L1: 0.0048] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.738 (Best: 43.793 @epoch 87) +Forward: 9.20s + +Saving... +Total: 9.73s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.0+0.9s +[3200/16000] [L1: 0.0049] 16.6+0.0s +[4800/16000] [L1: 0.0049] 15.6+0.0s +[6400/16000] [L1: 0.0049] 15.5+0.0s +[8000/16000] [L1: 0.0049] 17.5+0.1s +[9600/16000] [L1: 0.0049] 16.6+0.1s +[11200/16000] [L1: 0.0049] 16.6+0.1s +[12800/16000] [L1: 0.0049] 16.7+0.1s +[14400/16000] [L1: 0.0049] 16.7+0.0s +[16000/16000] [L1: 0.0049] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.724 (Best: 43.793 @epoch 87) +Forward: 9.33s + +Saving... +Total: 9.83s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.4+1.0s +[3200/16000] [L1: 0.0048] 15.5+0.1s +[4800/16000] [L1: 0.0048] 15.8+0.1s +[6400/16000] [L1: 0.0048] 15.4+0.0s +[8000/16000] [L1: 0.0048] 15.6+0.1s +[9600/16000] [L1: 0.0048] 15.9+0.0s +[11200/16000] [L1: 0.0048] 16.1+0.0s +[12800/16000] [L1: 0.0048] 16.2+0.0s +[14400/16000] [L1: 0.0048] 16.9+0.1s +[16000/16000] [L1: 0.0048] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.777 (Best: 43.793 @epoch 87) +Forward: 9.17s + +Saving... +Total: 9.68s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 17.4+1.0s +[3200/16000] [L1: 0.0049] 16.9+0.1s +[4800/16000] [L1: 0.0049] 16.6+0.1s +[6400/16000] [L1: 0.0049] 17.0+0.1s +[8000/16000] [L1: 0.0048] 15.9+0.1s +[9600/16000] [L1: 0.0048] 17.5+0.1s +[11200/16000] [L1: 0.0048] 16.0+0.1s +[12800/16000] [L1: 0.0048] 16.9+0.1s +[14400/16000] [L1: 0.0048] 16.2+0.1s +[16000/16000] [L1: 0.0048] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.701 (Best: 43.793 @epoch 87) +Forward: 9.23s + +Saving... +Total: 9.69s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.6+1.1s +[3200/16000] [L1: 0.0048] 16.0+0.1s +[4800/16000] [L1: 0.0048] 16.7+0.1s +[6400/16000] [L1: 0.0048] 16.4+0.1s +[8000/16000] [L1: 0.0048] 15.4+0.0s +[9600/16000] [L1: 0.0048] 16.7+0.1s +[11200/16000] [L1: 0.0048] 16.9+0.1s +[12800/16000] [L1: 0.0048] 16.7+0.1s +[14400/16000] [L1: 0.0048] 15.1+0.0s +[16000/16000] [L1: 0.0048] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.696 (Best: 43.793 @epoch 87) +Forward: 9.22s + +Saving... +Total: 9.70s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.0+0.9s +[3200/16000] [L1: 0.0047] 16.4+0.1s +[4800/16000] [L1: 0.0047] 16.0+0.1s +[6400/16000] [L1: 0.0047] 17.0+0.1s +[8000/16000] [L1: 0.0047] 16.4+0.1s +[9600/16000] [L1: 0.0048] 16.2+0.1s +[11200/16000] [L1: 0.0047] 16.2+0.1s +[12800/16000] [L1: 0.0048] 16.6+0.1s +[14400/16000] [L1: 0.0048] 16.1+0.1s +[16000/16000] [L1: 0.0048] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.703 (Best: 43.793 @epoch 87) +Forward: 9.16s + +Saving... +Total: 9.62s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.4+1.0s +[3200/16000] [L1: 0.0049] 15.9+0.0s +[4800/16000] [L1: 0.0048] 16.9+0.0s +[6400/16000] [L1: 0.0048] 16.5+0.0s +[8000/16000] [L1: 0.0048] 15.6+0.0s +[9600/16000] [L1: 0.0048] 14.6+0.0s +[11200/16000] [L1: 0.0048] 16.3+0.0s +[12800/16000] [L1: 0.0048] 16.2+0.0s +[14400/16000] [L1: 0.0048] 14.6+0.0s +[16000/16000] [L1: 0.0048] 14.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.787 (Best: 43.793 @epoch 87) +Forward: 9.27s + +Saving... +Total: 9.79s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.7+0.9s +[3200/16000] [L1: 0.0048] 17.1+0.1s +[4800/16000] [L1: 0.0048] 16.5+0.1s +[6400/16000] [L1: 0.0048] 16.3+0.1s +[8000/16000] [L1: 0.0048] 15.6+0.0s +[9600/16000] [L1: 0.0048] 16.0+0.0s +[11200/16000] [L1: 0.0048] 15.4+0.0s +[12800/16000] [L1: 0.0048] 16.0+0.0s +[14400/16000] [L1: 0.0048] 16.6+0.0s +[16000/16000] [L1: 0.0048] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.723 (Best: 43.793 @epoch 87) +Forward: 9.07s + +Saving... +Total: 9.55s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.6+1.0s +[3200/16000] [L1: 0.0047] 16.8+0.1s +[4800/16000] [L1: 0.0047] 16.3+0.0s +[6400/16000] [L1: 0.0048] 17.2+0.1s +[8000/16000] [L1: 0.0048] 16.6+0.1s +[9600/16000] [L1: 0.0048] 16.8+0.1s +[11200/16000] [L1: 0.0048] 17.4+0.1s +[12800/16000] [L1: 0.0048] 17.0+0.1s +[14400/16000] [L1: 0.0048] 16.5+0.1s +[16000/16000] [L1: 0.0048] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.782 (Best: 43.793 @epoch 87) +Forward: 9.13s + +Saving... +Total: 9.59s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.6+1.0s +[3200/16000] [L1: 0.0047] 15.9+0.0s +[4800/16000] [L1: 0.0048] 16.6+0.0s +[6400/16000] [L1: 0.0047] 16.7+0.0s +[8000/16000] [L1: 0.0047] 15.6+0.0s +[9600/16000] [L1: 0.0047] 15.6+0.0s +[11200/16000] [L1: 0.0048] 17.1+0.1s +[12800/16000] [L1: 0.0047] 16.6+0.0s +[14400/16000] [L1: 0.0048] 16.1+0.0s +[16000/16000] [L1: 0.0047] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.762 (Best: 43.793 @epoch 87) +Forward: 9.12s + +Saving... +Total: 9.63s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.5+0.8s +[3200/16000] [L1: 0.0048] 16.8+0.0s +[4800/16000] [L1: 0.0048] 15.7+0.0s +[6400/16000] [L1: 0.0048] 16.7+0.0s +[8000/16000] [L1: 0.0048] 16.2+0.0s +[9600/16000] [L1: 0.0048] 15.4+0.0s +[11200/16000] [L1: 0.0048] 15.6+0.0s +[12800/16000] [L1: 0.0048] 16.9+0.0s +[14400/16000] [L1: 0.0048] 15.3+0.0s +[16000/16000] [L1: 0.0048] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.781 (Best: 43.793 @epoch 87) +Forward: 9.01s + +Saving... +Total: 9.49s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 17.2+1.0s +[3200/16000] [L1: 0.0048] 16.5+0.1s +[4800/16000] [L1: 0.0048] 17.1+0.1s +[6400/16000] [L1: 0.0048] 15.5+0.0s +[8000/16000] [L1: 0.0049] 16.0+0.0s +[9600/16000] [L1: 0.0048] 16.2+0.0s +[11200/16000] [L1: 0.0048] 16.1+0.0s +[12800/16000] [L1: 0.0048] 16.9+0.0s +[14400/16000] [L1: 0.0048] 17.0+0.1s +[16000/16000] [L1: 0.0048] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.702 (Best: 43.793 @epoch 87) +Forward: 9.00s + +Saving... +Total: 9.55s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.8+1.0s +[3200/16000] [L1: 0.0048] 13.5+0.0s +[4800/16000] [L1: 0.0048] 15.1+0.0s +[6400/16000] [L1: 0.0048] 14.6+0.0s +[8000/16000] [L1: 0.0048] 15.2+0.0s +[9600/16000] [L1: 0.0048] 15.7+0.0s +[11200/16000] [L1: 0.0048] 15.9+0.0s +[12800/16000] [L1: 0.0048] 16.4+0.1s +[14400/16000] [L1: 0.0048] 15.9+0.0s +[16000/16000] [L1: 0.0048] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.693 (Best: 43.793 @epoch 87) +Forward: 9.21s + +Saving... +Total: 9.63s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.8+0.9s +[3200/16000] [L1: 0.0048] 15.0+0.0s +[4800/16000] [L1: 0.0048] 15.0+0.0s +[6400/16000] [L1: 0.0048] 15.7+0.0s +[8000/16000] [L1: 0.0048] 16.3+0.0s +[9600/16000] [L1: 0.0048] 15.9+0.0s +[11200/16000] [L1: 0.0048] 16.1+0.0s +[12800/16000] [L1: 0.0048] 16.3+0.0s +[14400/16000] [L1: 0.0048] 15.9+0.0s +[16000/16000] [L1: 0.0048] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.780 (Best: 43.793 @epoch 87) +Forward: 9.02s + +Saving... +Total: 9.38s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.3+0.8s +[3200/16000] [L1: 0.0049] 16.5+0.1s +[4800/16000] [L1: 0.0048] 16.1+0.1s +[6400/16000] [L1: 0.0048] 15.6+0.0s +[8000/16000] [L1: 0.0048] 16.0+0.1s +[9600/16000] [L1: 0.0048] 16.2+0.1s +[11200/16000] [L1: 0.0048] 15.8+0.0s +[12800/16000] [L1: 0.0048] 16.5+0.1s +[14400/16000] [L1: 0.0048] 15.7+0.0s +[16000/16000] [L1: 0.0048] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.785 (Best: 43.793 @epoch 87) +Forward: 9.21s + +Saving... +Total: 9.76s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.3+0.9s +[3200/16000] [L1: 0.0047] 17.1+0.1s +[4800/16000] [L1: 0.0047] 14.9+0.0s +[6400/16000] [L1: 0.0047] 14.8+0.0s +[8000/16000] [L1: 0.0047] 15.0+0.0s +[9600/16000] [L1: 0.0047] 15.8+0.0s +[11200/16000] [L1: 0.0047] 14.8+0.0s +[12800/16000] [L1: 0.0048] 14.9+0.0s +[14400/16000] [L1: 0.0048] 15.0+0.0s +[16000/16000] [L1: 0.0048] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.760 (Best: 43.793 @epoch 87) +Forward: 9.13s + +Saving... +Total: 9.60s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.8+0.9s +[3200/16000] [L1: 0.0047] 16.6+0.0s +[4800/16000] [L1: 0.0048] 15.9+0.1s +[6400/16000] [L1: 0.0048] 16.2+0.0s +[8000/16000] [L1: 0.0048] 17.0+0.1s +[9600/16000] [L1: 0.0048] 16.7+0.1s +[11200/16000] [L1: 0.0048] 16.1+0.0s +[12800/16000] [L1: 0.0048] 16.6+0.0s +[14400/16000] [L1: 0.0048] 16.4+0.0s +[16000/16000] [L1: 0.0048] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.717 (Best: 43.793 @epoch 87) +Forward: 9.10s + +Saving... +Total: 9.55s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.4+0.8s +[3200/16000] [L1: 0.0048] 15.7+0.1s +[4800/16000] [L1: 0.0048] 15.6+0.1s +[6400/16000] [L1: 0.0047] 16.4+0.0s +[8000/16000] [L1: 0.0048] 16.4+0.1s +[9600/16000] [L1: 0.0048] 16.5+0.1s +[11200/16000] [L1: 0.0048] 15.6+0.0s +[12800/16000] [L1: 0.0048] 16.0+0.1s +[14400/16000] [L1: 0.0048] 16.3+0.0s +[16000/16000] [L1: 0.0048] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.824 (Best: 43.824 @epoch 106) +Forward: 9.08s + +Saving... +Total: 9.72s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.4+1.1s +[3200/16000] [L1: 0.0047] 14.9+0.0s +[4800/16000] [L1: 0.0047] 15.1+0.0s +[6400/16000] [L1: 0.0047] 14.9+0.0s +[8000/16000] [L1: 0.0047] 15.1+0.0s +[9600/16000] [L1: 0.0047] 15.1+0.0s +[11200/16000] [L1: 0.0047] 15.3+0.0s +[12800/16000] [L1: 0.0047] 15.6+0.0s +[14400/16000] [L1: 0.0047] 15.3+0.0s +[16000/16000] [L1: 0.0047] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.871 (Best: 43.871 @epoch 107) +Forward: 9.20s + +Saving... +Total: 9.83s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.1+0.9s +[3200/16000] [L1: 0.0046] 16.0+0.1s +[4800/16000] [L1: 0.0047] 15.9+0.0s +[6400/16000] [L1: 0.0047] 16.6+0.0s +[8000/16000] [L1: 0.0047] 17.2+0.0s +[9600/16000] [L1: 0.0047] 15.3+0.0s +[11200/16000] [L1: 0.0047] 16.6+0.0s +[12800/16000] [L1: 0.0047] 15.0+0.0s +[14400/16000] [L1: 0.0047] 15.6+0.0s +[16000/16000] [L1: 0.0047] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.779 (Best: 43.871 @epoch 107) +Forward: 9.22s + +Saving... +Total: 9.74s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.8+0.9s +[3200/16000] [L1: 0.0047] 16.8+0.1s +[4800/16000] [L1: 0.0047] 16.5+0.1s +[6400/16000] [L1: 0.0048] 17.1+0.1s +[8000/16000] [L1: 0.0047] 16.1+0.1s +[9600/16000] [L1: 0.0048] 16.9+0.1s +[11200/16000] [L1: 0.0048] 15.9+0.1s +[12800/16000] [L1: 0.0048] 17.2+0.1s +[14400/16000] [L1: 0.0048] 17.4+0.1s +[16000/16000] [L1: 0.0048] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.812 (Best: 43.871 @epoch 107) +Forward: 9.07s + +Saving... +Total: 9.60s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.7+1.0s +[3200/16000] [L1: 0.0047] 17.2+0.1s +[4800/16000] [L1: 0.0047] 17.1+0.1s +[6400/16000] [L1: 0.0048] 17.1+0.1s +[8000/16000] [L1: 0.0047] 17.0+0.1s +[9600/16000] [L1: 0.0048] 16.8+0.1s +[11200/16000] [L1: 0.0047] 16.8+0.1s +[12800/16000] [L1: 0.0048] 16.8+0.1s +[14400/16000] [L1: 0.0048] 14.8+0.0s +[16000/16000] [L1: 0.0048] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.836 (Best: 43.871 @epoch 107) +Forward: 9.18s + +Saving... +Total: 9.69s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.1+0.9s +[3200/16000] [L1: 0.0048] 16.7+0.1s +[4800/16000] [L1: 0.0048] 16.3+0.1s +[6400/16000] [L1: 0.0048] 17.1+0.1s +[8000/16000] [L1: 0.0048] 17.1+0.1s +[9600/16000] [L1: 0.0047] 15.6+0.1s +[11200/16000] [L1: 0.0047] 16.9+0.1s +[12800/16000] [L1: 0.0048] 16.6+0.1s +[14400/16000] [L1: 0.0047] 15.9+0.0s +[16000/16000] [L1: 0.0047] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.782 (Best: 43.871 @epoch 107) +Forward: 8.92s + +Saving... +Total: 9.52s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 17.1+1.0s +[3200/16000] [L1: 0.0048] 17.1+0.1s +[4800/16000] [L1: 0.0047] 16.7+0.1s +[6400/16000] [L1: 0.0047] 16.2+0.1s +[8000/16000] [L1: 0.0047] 16.5+0.1s +[9600/16000] [L1: 0.0047] 16.4+0.1s +[11200/16000] [L1: 0.0047] 15.9+0.1s +[12800/16000] [L1: 0.0047] 16.7+0.1s +[14400/16000] [L1: 0.0047] 15.9+0.1s +[16000/16000] [L1: 0.0047] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.745 (Best: 43.871 @epoch 107) +Forward: 8.97s + +Saving... +Total: 9.48s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.5+0.9s +[3200/16000] [L1: 0.0048] 16.6+0.1s +[4800/16000] [L1: 0.0048] 15.9+0.1s +[6400/16000] [L1: 0.0048] 15.0+0.0s +[8000/16000] [L1: 0.0048] 15.0+0.0s +[9600/16000] [L1: 0.0048] 14.8+0.0s +[11200/16000] [L1: 0.0048] 14.8+0.0s +[12800/16000] [L1: 0.0047] 16.4+0.0s +[14400/16000] [L1: 0.0048] 14.3+0.0s +[16000/16000] [L1: 0.0047] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.818 (Best: 43.871 @epoch 107) +Forward: 9.14s + +Saving... +Total: 9.57s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.7+0.9s +[3200/16000] [L1: 0.0047] 15.4+0.0s +[4800/16000] [L1: 0.0047] 14.9+0.1s +[6400/16000] [L1: 0.0048] 14.9+0.0s +[8000/16000] [L1: 0.0048] 14.9+0.0s +[9600/16000] [L1: 0.0047] 15.3+0.1s +[11200/16000] [L1: 0.0047] 15.1+0.0s +[12800/16000] [L1: 0.0047] 14.8+0.0s +[14400/16000] [L1: 0.0047] 16.6+0.0s +[16000/16000] [L1: 0.0047] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.846 (Best: 43.871 @epoch 107) +Forward: 9.07s + +Saving... +Total: 9.59s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.2+0.9s +[3200/16000] [L1: 0.0047] 16.9+0.1s +[4800/16000] [L1: 0.0047] 16.2+0.1s +[6400/16000] [L1: 0.0047] 16.2+0.1s +[8000/16000] [L1: 0.0047] 15.9+0.1s +[9600/16000] [L1: 0.0047] 16.3+0.1s +[11200/16000] [L1: 0.0047] 16.2+0.1s +[12800/16000] [L1: 0.0047] 15.9+0.1s +[14400/16000] [L1: 0.0047] 16.4+0.1s +[16000/16000] [L1: 0.0047] 16.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.792 (Best: 43.871 @epoch 107) +Forward: 9.32s + +Saving... +Total: 9.90s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 15.3+1.0s +[3200/16000] [L1: 0.0047] 15.2+0.0s +[4800/16000] [L1: 0.0047] 15.5+0.0s +[6400/16000] [L1: 0.0047] 16.6+0.0s +[8000/16000] [L1: 0.0047] 14.8+0.0s +[9600/16000] [L1: 0.0047] 15.2+0.0s +[11200/16000] [L1: 0.0047] 15.5+0.0s +[12800/16000] [L1: 0.0047] 16.2+0.0s +[14400/16000] [L1: 0.0047] 15.0+0.0s +[16000/16000] [L1: 0.0047] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.788 (Best: 43.871 @epoch 107) +Forward: 9.19s + +Saving... +Total: 9.60s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 17.0+1.0s +[3200/16000] [L1: 0.0048] 16.2+0.1s +[4800/16000] [L1: 0.0048] 17.3+0.1s +[6400/16000] [L1: 0.0048] 16.9+0.1s +[8000/16000] [L1: 0.0048] 16.9+0.1s +[9600/16000] [L1: 0.0048] 16.7+0.1s +[11200/16000] [L1: 0.0048] 17.1+0.1s +[12800/16000] [L1: 0.0048] 17.2+0.1s +[14400/16000] [L1: 0.0048] 17.2+0.1s +[16000/16000] [L1: 0.0048] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.850 (Best: 43.871 @epoch 107) +Forward: 9.09s + +Saving... +Total: 9.63s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.7+1.1s +[3200/16000] [L1: 0.0047] 16.7+0.1s +[4800/16000] [L1: 0.0047] 15.9+0.1s +[6400/16000] [L1: 0.0047] 15.9+0.0s +[8000/16000] [L1: 0.0047] 16.5+0.0s +[9600/16000] [L1: 0.0047] 15.4+0.0s +[11200/16000] [L1: 0.0047] 15.8+0.0s +[12800/16000] [L1: 0.0047] 15.9+0.0s +[14400/16000] [L1: 0.0047] 16.6+0.0s +[16000/16000] [L1: 0.0047] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.820 (Best: 43.871 @epoch 107) +Forward: 9.06s + +Saving... +Total: 9.66s + +[Epoch 119] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.9+1.0s +[3200/16000] [L1: 0.0048] 16.5+0.1s +[4800/16000] [L1: 0.0048] 17.0+0.1s +[6400/16000] [L1: 0.0048] 16.9+0.1s +[8000/16000] [L1: 0.0048] 16.3+0.1s +[9600/16000] [L1: 0.0048] 16.4+0.1s +[11200/16000] [L1: 0.0048] 16.0+0.1s +[12800/16000] [L1: 0.0048] 16.9+0.1s +[14400/16000] [L1: 0.0048] 16.0+0.1s +[16000/16000] [L1: 0.0048] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.765 (Best: 43.871 @epoch 107) +Forward: 9.14s + +Saving... +Total: 9.65s + +[Epoch 120] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 15.9+0.9s +[3200/16000] [L1: 0.0046] 15.8+0.1s +[4800/16000] [L1: 0.0046] 16.9+0.1s +[6400/16000] [L1: 0.0046] 16.1+0.1s +[8000/16000] [L1: 0.0047] 15.7+0.0s +[9600/16000] [L1: 0.0047] 16.5+0.1s +[11200/16000] [L1: 0.0047] 16.6+0.1s +[12800/16000] [L1: 0.0047] 16.1+0.1s +[14400/16000] [L1: 0.0047] 15.8+0.1s +[16000/16000] [L1: 0.0047] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.759 (Best: 43.871 @epoch 107) +Forward: 9.14s + +Saving... +Total: 9.71s + +[Epoch 121] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.2+1.0s +[3200/16000] [L1: 0.0047] 15.5+0.1s +[4800/16000] [L1: 0.0047] 17.0+0.1s +[6400/16000] [L1: 0.0047] 16.7+0.1s +[8000/16000] [L1: 0.0048] 16.5+0.1s +[9600/16000] [L1: 0.0048] 17.3+0.1s +[11200/16000] [L1: 0.0048] 15.8+0.1s +[12800/16000] [L1: 0.0048] 16.8+0.1s +[14400/16000] [L1: 0.0048] 16.6+0.1s +[16000/16000] [L1: 0.0048] 16.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.786 (Best: 43.871 @epoch 107) +Forward: 9.33s + +Saving... +Total: 9.82s + +[Epoch 122] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 17.2+1.0s +[3200/16000] [L1: 0.0047] 16.0+0.1s +[4800/16000] [L1: 0.0047] 16.2+0.1s +[6400/16000] [L1: 0.0047] 16.6+0.1s +[8000/16000] [L1: 0.0047] 16.9+0.1s +[9600/16000] [L1: 0.0047] 16.4+0.1s +[11200/16000] [L1: 0.0047] 16.6+0.1s +[12800/16000] [L1: 0.0047] 16.6+0.1s +[14400/16000] [L1: 0.0047] 16.2+0.1s +[16000/16000] [L1: 0.0047] 16.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.846 (Best: 43.871 @epoch 107) +Forward: 9.20s + +Saving... +Total: 9.85s + +[Epoch 123] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.2+1.0s +[3200/16000] [L1: 0.0049] 16.5+0.1s +[4800/16000] [L1: 0.0048] 15.8+0.1s +[6400/16000] [L1: 0.0048] 17.1+0.1s +[8000/16000] [L1: 0.0048] 17.0+0.1s +[9600/16000] [L1: 0.0048] 16.9+0.1s +[11200/16000] [L1: 0.0048] 16.9+0.1s +[12800/16000] [L1: 0.0048] 16.5+0.1s +[14400/16000] [L1: 0.0048] 16.9+0.1s +[16000/16000] [L1: 0.0048] 17.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.704 (Best: 43.871 @epoch 107) +Forward: 9.30s + +Saving... +Total: 9.82s + +[Epoch 124] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.3+0.9s +[3200/16000] [L1: 0.0046] 17.4+0.1s +[4800/16000] [L1: 0.0046] 17.6+0.1s +[6400/16000] [L1: 0.0047] 17.2+0.1s +[8000/16000] [L1: 0.0047] 16.5+0.1s +[9600/16000] [L1: 0.0047] 17.0+0.1s +[11200/16000] [L1: 0.0047] 17.4+0.1s +[12800/16000] [L1: 0.0047] 17.5+0.1s +[14400/16000] [L1: 0.0047] 17.4+0.1s +[16000/16000] [L1: 0.0047] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.824 (Best: 43.871 @epoch 107) +Forward: 9.15s + +Saving... +Total: 9.58s + +[Epoch 125] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.3+0.8s +[3200/16000] [L1: 0.0048] 16.7+0.1s +[4800/16000] [L1: 0.0047] 17.6+0.1s +[6400/16000] [L1: 0.0047] 17.2+0.1s +[8000/16000] [L1: 0.0047] 16.6+0.1s +[9600/16000] [L1: 0.0048] 16.3+0.1s +[11200/16000] [L1: 0.0047] 16.9+0.1s +[12800/16000] [L1: 0.0047] 16.6+0.1s +[14400/16000] [L1: 0.0047] 16.5+0.1s +[16000/16000] [L1: 0.0047] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.739 (Best: 43.871 @epoch 107) +Forward: 9.18s + +Saving... +Total: 9.63s + +[Epoch 126] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.7+0.9s +[3200/16000] [L1: 0.0047] 16.2+0.0s +[4800/16000] [L1: 0.0048] 14.7+0.0s +[6400/16000] [L1: 0.0048] 13.6+0.0s +[8000/16000] [L1: 0.0048] 15.2+0.0s +[9600/16000] [L1: 0.0047] 16.5+0.1s +[11200/16000] [L1: 0.0047] 16.3+0.0s +[12800/16000] [L1: 0.0047] 15.4+0.0s +[14400/16000] [L1: 0.0047] 15.6+0.0s +[16000/16000] [L1: 0.0047] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.732 (Best: 43.871 @epoch 107) +Forward: 9.09s + +Saving... +Total: 9.62s + +[Epoch 127] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 17.1+0.8s +[3200/16000] [L1: 0.0049] 16.8+0.1s +[4800/16000] [L1: 0.0048] 15.7+0.1s +[6400/16000] [L1: 0.0048] 16.5+0.1s +[8000/16000] [L1: 0.0048] 16.7+0.1s +[9600/16000] [L1: 0.0047] 17.7+0.1s +[11200/16000] [L1: 0.0047] 17.0+0.1s +[12800/16000] [L1: 0.0047] 16.6+0.1s +[14400/16000] [L1: 0.0047] 17.0+0.1s +[16000/16000] [L1: 0.0047] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.862 (Best: 43.871 @epoch 107) +Forward: 9.24s + +Saving... +Total: 9.68s + +[Epoch 128] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.7+0.9s +[3200/16000] [L1: 0.0047] 15.9+0.1s +[4800/16000] [L1: 0.0047] 16.3+0.1s +[6400/16000] [L1: 0.0046] 16.5+0.1s +[8000/16000] [L1: 0.0047] 16.8+0.1s +[9600/16000] [L1: 0.0047] 17.8+0.1s +[11200/16000] [L1: 0.0047] 16.7+0.1s +[12800/16000] [L1: 0.0047] 16.6+0.1s +[14400/16000] [L1: 0.0047] 17.0+0.1s +[16000/16000] [L1: 0.0047] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.693 (Best: 43.871 @epoch 107) +Forward: 9.09s + +Saving... +Total: 9.64s + +[Epoch 129] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.3+0.9s +[3200/16000] [L1: 0.0047] 16.3+0.1s +[4800/16000] [L1: 0.0047] 16.5+0.1s +[6400/16000] [L1: 0.0047] 16.3+0.0s +[8000/16000] [L1: 0.0047] 17.1+0.1s +[9600/16000] [L1: 0.0047] 16.4+0.1s +[11200/16000] [L1: 0.0046] 16.8+0.1s +[12800/16000] [L1: 0.0046] 15.5+0.1s +[14400/16000] [L1: 0.0047] 16.2+0.0s +[16000/16000] [L1: 0.0047] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.765 (Best: 43.871 @epoch 107) +Forward: 9.30s + +Saving... +Total: 9.86s + +[Epoch 130] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.3+0.9s +[3200/16000] [L1: 0.0047] 16.3+0.1s +[4800/16000] [L1: 0.0047] 16.7+0.1s +[6400/16000] [L1: 0.0047] 15.3+0.0s +[8000/16000] [L1: 0.0047] 17.3+0.1s +[9600/16000] [L1: 0.0046] 16.7+0.1s +[11200/16000] [L1: 0.0047] 16.7+0.1s +[12800/16000] [L1: 0.0047] 16.9+0.1s +[14400/16000] [L1: 0.0047] 15.7+0.1s +[16000/16000] [L1: 0.0047] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.786 (Best: 43.871 @epoch 107) +Forward: 9.16s + +Saving... +Total: 9.59s + +[Epoch 131] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 16.0+0.8s +[3200/16000] [L1: 0.0045] 15.5+0.1s +[4800/16000] [L1: 0.0046] 15.4+0.0s +[6400/16000] [L1: 0.0047] 15.5+0.0s +[8000/16000] [L1: 0.0047] 16.9+0.1s +[9600/16000] [L1: 0.0047] 16.1+0.1s +[11200/16000] [L1: 0.0047] 16.1+0.0s +[12800/16000] [L1: 0.0047] 15.1+0.0s +[14400/16000] [L1: 0.0047] 16.1+0.0s +[16000/16000] [L1: 0.0047] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.710 (Best: 43.871 @epoch 107) +Forward: 8.96s + +Saving... +Total: 9.36s + +[Epoch 132] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.6+0.8s +[3200/16000] [L1: 0.0047] 16.5+0.1s +[4800/16000] [L1: 0.0047] 16.5+0.1s +[6400/16000] [L1: 0.0047] 16.8+0.1s +[8000/16000] [L1: 0.0047] 17.1+0.1s +[9600/16000] [L1: 0.0046] 16.6+0.1s +[11200/16000] [L1: 0.0046] 16.7+0.1s +[12800/16000] [L1: 0.0047] 17.3+0.1s +[14400/16000] [L1: 0.0047] 15.7+0.1s +[16000/16000] [L1: 0.0047] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.769 (Best: 43.871 @epoch 107) +Forward: 9.25s + +Saving... +Total: 9.76s + +[Epoch 133] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.7+0.9s +[3200/16000] [L1: 0.0047] 16.2+0.0s +[4800/16000] [L1: 0.0047] 16.7+0.0s +[6400/16000] [L1: 0.0047] 16.7+0.1s +[8000/16000] [L1: 0.0047] 16.0+0.1s +[9600/16000] [L1: 0.0047] 15.8+0.1s +[11200/16000] [L1: 0.0047] 16.3+0.0s +[12800/16000] [L1: 0.0047] 16.7+0.0s +[14400/16000] [L1: 0.0047] 16.2+0.0s +[16000/16000] [L1: 0.0047] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.721 (Best: 43.871 @epoch 107) +Forward: 9.22s + +Saving... +Total: 9.63s + +[Epoch 134] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.8+0.9s +[3200/16000] [L1: 0.0047] 15.5+0.0s +[4800/16000] [L1: 0.0047] 16.2+0.0s +[6400/16000] [L1: 0.0047] 17.1+0.1s +[8000/16000] [L1: 0.0047] 16.7+0.1s +[9600/16000] [L1: 0.0047] 15.2+0.1s +[11200/16000] [L1: 0.0047] 15.7+0.0s +[12800/16000] [L1: 0.0047] 16.3+0.0s +[14400/16000] [L1: 0.0047] 16.0+0.0s +[16000/16000] [L1: 0.0047] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.873 (Best: 43.873 @epoch 134) +Forward: 9.25s + +Saving... +Total: 9.69s + +[Epoch 135] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 17.1+0.9s +[3200/16000] [L1: 0.0046] 16.6+0.0s +[4800/16000] [L1: 0.0046] 16.9+0.1s +[6400/16000] [L1: 0.0046] 15.8+0.0s +[8000/16000] [L1: 0.0047] 15.5+0.0s +[9600/16000] [L1: 0.0047] 15.7+0.1s +[11200/16000] [L1: 0.0046] 15.4+0.0s +[12800/16000] [L1: 0.0047] 15.8+0.0s +[14400/16000] [L1: 0.0047] 14.8+0.0s +[16000/16000] [L1: 0.0047] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.865 (Best: 43.873 @epoch 134) +Forward: 9.05s + +Saving... +Total: 9.48s + +[Epoch 136] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.0+0.8s +[3200/16000] [L1: 0.0047] 15.7+0.0s +[4800/16000] [L1: 0.0046] 15.7+0.0s +[6400/16000] [L1: 0.0047] 15.7+0.0s +[8000/16000] [L1: 0.0046] 16.0+0.1s +[9600/16000] [L1: 0.0047] 16.6+0.1s +[11200/16000] [L1: 0.0046] 15.6+0.0s +[12800/16000] [L1: 0.0047] 15.7+0.0s +[14400/16000] [L1: 0.0046] 16.5+0.0s +[16000/16000] [L1: 0.0046] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.831 (Best: 43.873 @epoch 134) +Forward: 8.96s + +Saving... +Total: 9.49s + +[Epoch 137] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.5+0.9s +[3200/16000] [L1: 0.0047] 16.0+0.0s +[4800/16000] [L1: 0.0047] 15.4+0.0s +[6400/16000] [L1: 0.0047] 16.3+0.0s +[8000/16000] [L1: 0.0047] 15.5+0.0s +[9600/16000] [L1: 0.0047] 16.3+0.0s +[11200/16000] [L1: 0.0047] 17.3+0.0s +[12800/16000] [L1: 0.0047] 16.7+0.0s +[14400/16000] [L1: 0.0047] 15.7+0.0s +[16000/16000] [L1: 0.0047] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.795 (Best: 43.873 @epoch 134) +Forward: 9.22s + +Saving... +Total: 9.80s + +[Epoch 138] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 15.9+0.9s +[3200/16000] [L1: 0.0047] 15.8+0.0s +[4800/16000] [L1: 0.0046] 16.5+0.0s +[6400/16000] [L1: 0.0046] 16.1+0.0s +[8000/16000] [L1: 0.0046] 16.0+0.0s +[9600/16000] [L1: 0.0046] 16.1+0.0s +[11200/16000] [L1: 0.0046] 16.5+0.0s +[12800/16000] [L1: 0.0046] 16.4+0.1s +[14400/16000] [L1: 0.0046] 15.4+0.0s +[16000/16000] [L1: 0.0046] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.760 (Best: 43.873 @epoch 134) +Forward: 9.13s + +Saving... +Total: 9.60s + +[Epoch 139] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.5+0.8s +[3200/16000] [L1: 0.0203] 16.0+0.1s +[4800/16000] [L1: 0.0185] 15.5+0.0s +[6400/16000] [L1: 0.0156] 15.9+0.0s +[8000/16000] [L1: 0.0138] 16.2+0.0s +[9600/16000] [L1: 0.0125] 15.5+0.1s +[11200/16000] [L1: 0.0116] 16.4+0.1s +[12800/16000] [L1: 0.0108] 16.7+0.1s +[14400/16000] [L1: 0.0102] 17.2+0.1s +[16000/16000] [L1: 0.0097] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.186 (Best: 43.873 @epoch 134) +Forward: 9.21s + +Saving... +Total: 10.39s + +[Epoch 140] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 16.8+1.0s +[3200/16000] [L1: 0.0051] 16.1+0.1s +[4800/16000] [L1: 0.0050] 15.4+0.0s +[6400/16000] [L1: 0.0050] 15.5+0.0s +[8000/16000] [L1: 0.0050] 16.1+0.0s +[9600/16000] [L1: 0.0050] 15.8+0.0s +[11200/16000] [L1: 0.0050] 16.1+0.0s +[12800/16000] [L1: 0.0049] 16.1+0.0s +[14400/16000] [L1: 0.0050] 15.4+0.0s +[16000/16000] [L1: 0.0049] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.544 (Best: 43.873 @epoch 134) +Forward: 9.25s + +Saving... +Total: 9.86s + +[Epoch 141] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.2+0.9s +[3200/16000] [L1: 0.0048] 16.3+0.1s +[4800/16000] [L1: 0.0048] 16.3+0.1s +[6400/16000] [L1: 0.0048] 15.2+0.0s +[8000/16000] [L1: 0.0048] 15.9+0.0s +[9600/16000] [L1: 0.0048] 15.6+0.0s +[11200/16000] [L1: 0.0048] 16.1+0.0s +[12800/16000] [L1: 0.0048] 16.4+0.0s +[14400/16000] [L1: 0.0047] 15.8+0.0s +[16000/16000] [L1: 0.0047] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.785 (Best: 43.873 @epoch 134) +Forward: 9.27s + +Saving... +Total: 9.80s + +[Epoch 142] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 14.4+0.9s +[3200/16000] [L1: 0.0046] 16.0+0.0s +[4800/16000] [L1: 0.0046] 15.8+0.0s +[6400/16000] [L1: 0.0046] 16.4+0.0s +[8000/16000] [L1: 0.0047] 16.0+0.1s +[9600/16000] [L1: 0.0047] 16.7+0.1s +[11200/16000] [L1: 0.0047] 16.4+0.0s +[12800/16000] [L1: 0.0047] 15.8+0.0s +[14400/16000] [L1: 0.0047] 16.6+0.0s +[16000/16000] [L1: 0.0047] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.809 (Best: 43.873 @epoch 134) +Forward: 8.99s + +Saving... +Total: 9.52s + +[Epoch 143] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.3+1.0s +[3200/16000] [L1: 0.0047] 16.1+0.0s +[4800/16000] [L1: 0.0047] 15.5+0.0s +[6400/16000] [L1: 0.0047] 15.5+0.0s +[8000/16000] [L1: 0.0047] 15.7+0.0s +[9600/16000] [L1: 0.0047] 15.9+0.1s +[11200/16000] [L1: 0.0047] 16.7+0.1s +[12800/16000] [L1: 0.0047] 16.8+0.0s +[14400/16000] [L1: 0.0047] 16.9+0.1s +[16000/16000] [L1: 0.0047] 17.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.799 (Best: 43.873 @epoch 134) +Forward: 9.05s + +Saving... +Total: 9.50s + +[Epoch 144] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.1+0.9s +[3200/16000] [L1: 0.0047] 17.2+0.1s +[4800/16000] [L1: 0.0047] 15.9+0.0s +[6400/16000] [L1: 0.0047] 16.6+0.1s +[8000/16000] [L1: 0.0047] 15.1+0.0s +[9600/16000] [L1: 0.0047] 16.1+0.0s +[11200/16000] [L1: 0.0047] 16.7+0.1s +[12800/16000] [L1: 0.0047] 16.4+0.1s +[14400/16000] [L1: 0.0047] 16.3+0.0s +[16000/16000] [L1: 0.0047] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.878 (Best: 43.878 @epoch 144) +Forward: 9.09s + +Saving... +Total: 9.64s + +[Epoch 145] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.9+1.1s +[3200/16000] [L1: 0.0045] 17.2+0.1s +[4800/16000] [L1: 0.0046] 16.7+0.1s +[6400/16000] [L1: 0.0046] 17.0+0.1s +[8000/16000] [L1: 0.0046] 15.9+0.1s +[9600/16000] [L1: 0.0047] 15.5+0.1s +[11200/16000] [L1: 0.0046] 17.5+0.1s +[12800/16000] [L1: 0.0046] 16.6+0.0s +[14400/16000] [L1: 0.0046] 16.3+0.0s +[16000/16000] [L1: 0.0047] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.925 (Best: 43.925 @epoch 145) +Forward: 9.18s + +Saving... +Total: 9.76s + +[Epoch 146] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.3+0.8s +[3200/16000] [L1: 0.0047] 16.8+0.1s +[4800/16000] [L1: 0.0047] 17.4+0.1s +[6400/16000] [L1: 0.0047] 17.2+0.1s +[8000/16000] [L1: 0.0047] 16.5+0.1s +[9600/16000] [L1: 0.0047] 16.0+0.0s +[11200/16000] [L1: 0.0047] 16.3+0.1s +[12800/16000] [L1: 0.0047] 16.1+0.0s +[14400/16000] [L1: 0.0047] 15.8+0.1s +[16000/16000] [L1: 0.0047] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.886 (Best: 43.925 @epoch 145) +Forward: 9.07s + +Saving... +Total: 9.59s + +[Epoch 147] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.5+1.0s +[3200/16000] [L1: 0.0047] 16.2+0.1s +[4800/16000] [L1: 0.0047] 16.8+0.1s +[6400/16000] [L1: 0.0047] 15.4+0.0s +[8000/16000] [L1: 0.0047] 15.1+0.0s +[9600/16000] [L1: 0.0047] 15.4+0.0s +[11200/16000] [L1: 0.0046] 16.6+0.1s +[12800/16000] [L1: 0.0046] 16.5+0.1s +[14400/16000] [L1: 0.0046] 15.9+0.0s +[16000/16000] [L1: 0.0046] 16.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.722 (Best: 43.925 @epoch 145) +Forward: 9.12s + +Saving... +Total: 9.72s + +[Epoch 148] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.4+0.9s +[3200/16000] [L1: 0.0047] 17.1+0.1s +[4800/16000] [L1: 0.0047] 17.0+0.1s +[6400/16000] [L1: 0.0047] 16.8+0.1s +[8000/16000] [L1: 0.0047] 16.4+0.1s +[9600/16000] [L1: 0.0047] 15.6+0.1s +[11200/16000] [L1: 0.0047] 16.5+0.1s +[12800/16000] [L1: 0.0047] 16.1+0.1s +[14400/16000] [L1: 0.0047] 15.5+0.1s +[16000/16000] [L1: 0.0047] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.781 (Best: 43.925 @epoch 145) +Forward: 9.11s + +Saving... +Total: 9.64s + +[Epoch 149] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.7+0.9s +[3200/16000] [L1: 0.0046] 16.7+0.1s +[4800/16000] [L1: 0.0047] 17.0+0.1s +[6400/16000] [L1: 0.0047] 16.4+0.1s +[8000/16000] [L1: 0.0047] 15.8+0.1s +[9600/16000] [L1: 0.0047] 15.6+0.1s +[11200/16000] [L1: 0.0047] 17.3+0.1s +[12800/16000] [L1: 0.0047] 15.6+0.0s +[14400/16000] [L1: 0.0047] 16.0+0.0s +[16000/16000] [L1: 0.0047] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.869 (Best: 43.925 @epoch 145) +Forward: 9.06s + +Saving... +Total: 9.51s + +[Epoch 150] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.7+0.8s +[3200/16000] [L1: 0.0047] 16.4+0.1s +[4800/16000] [L1: 0.0047] 16.5+0.1s +[6400/16000] [L1: 0.0047] 16.6+0.1s +[8000/16000] [L1: 0.0047] 16.2+0.1s +[9600/16000] [L1: 0.0047] 17.1+0.1s +[11200/16000] [L1: 0.0047] 17.0+0.1s +[12800/16000] [L1: 0.0047] 16.8+0.1s +[14400/16000] [L1: 0.0047] 15.9+0.1s +[16000/16000] [L1: 0.0047] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.821 (Best: 43.925 @epoch 145) +Forward: 9.19s + +Saving... +Total: 9.65s + +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.4+0.8s +[3200/16000] [L1: 0.0046] 16.1+0.1s +[4800/16000] [L1: 0.0046] 16.4+0.1s +[6400/16000] [L1: 0.0046] 17.2+0.1s +[8000/16000] [L1: 0.0046] 16.6+0.1s +[9600/16000] [L1: 0.0046] 16.8+0.1s +[11200/16000] [L1: 0.0046] 15.9+0.1s +[12800/16000] [L1: 0.0046] 15.5+0.1s +[14400/16000] [L1: 0.0047] 16.0+0.1s +[16000/16000] [L1: 0.0047] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.774 (Best: 43.925 @epoch 145) +Forward: 9.19s + +Saving... +Total: 9.69s + +[Epoch 152] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.9+1.0s +[3200/16000] [L1: 0.0047] 17.2+0.1s +[4800/16000] [L1: 0.0047] 17.1+0.1s +[6400/16000] [L1: 0.0047] 16.8+0.1s +[8000/16000] [L1: 0.0047] 17.3+0.1s +[9600/16000] [L1: 0.0047] 16.9+0.1s +[11200/16000] [L1: 0.0046] 17.1+0.1s +[12800/16000] [L1: 0.0046] 17.0+0.1s +[14400/16000] [L1: 0.0046] 16.5+0.1s +[16000/16000] [L1: 0.0046] 17.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.769 (Best: 43.925 @epoch 145) +Forward: 9.30s + +Saving... +Total: 9.82s + +[Epoch 153] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.9+0.9s +[3200/16000] [L1: 0.0047] 16.7+0.1s +[4800/16000] [L1: 0.0046] 17.0+0.1s +[6400/16000] [L1: 0.0047] 16.0+0.1s +[8000/16000] [L1: 0.0047] 16.6+0.1s +[9600/16000] [L1: 0.0047] 17.0+0.1s +[11200/16000] [L1: 0.0047] 16.7+0.1s +[12800/16000] [L1: 0.0047] 16.7+0.1s +[14400/16000] [L1: 0.0047] 17.1+0.1s +[16000/16000] [L1: 0.0047] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.840 (Best: 43.925 @epoch 145) +Forward: 9.37s + +Saving... +Total: 9.90s + +[Epoch 154] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.9+0.9s +[3200/16000] [L1: 0.0047] 16.1+0.0s +[4800/16000] [L1: 0.0047] 16.6+0.0s +[6400/16000] [L1: 0.0047] 16.2+0.0s +[8000/16000] [L1: 0.0046] 16.8+0.0s +[9600/16000] [L1: 0.0047] 15.7+0.0s +[11200/16000] [L1: 0.0047] 16.1+0.0s +[12800/16000] [L1: 0.0047] 15.8+0.0s +[14400/16000] [L1: 0.0047] 16.2+0.0s +[16000/16000] [L1: 0.0047] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.888 (Best: 43.925 @epoch 145) +Forward: 9.12s + +Saving... +Total: 9.70s + +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.3+0.9s +[3200/16000] [L1: 0.0047] 15.4+0.1s +[4800/16000] [L1: 0.0047] 17.1+0.1s +[6400/16000] [L1: 0.0047] 16.1+0.1s +[8000/16000] [L1: 0.0047] 16.4+0.0s +[9600/16000] [L1: 0.0047] 16.1+0.0s +[11200/16000] [L1: 0.0047] 14.5+0.0s +[12800/16000] [L1: 0.0047] 15.2+0.0s +[14400/16000] [L1: 0.0047] 16.6+0.1s +[16000/16000] [L1: 0.0047] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.853 (Best: 43.925 @epoch 145) +Forward: 9.21s + +Saving... +Total: 9.65s + +[Epoch 156] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.9+0.9s +[3200/16000] [L1: 0.0047] 16.2+0.1s +[4800/16000] [L1: 0.0047] 16.9+0.0s +[6400/16000] [L1: 0.0047] 16.2+0.0s +[8000/16000] [L1: 0.0047] 16.2+0.0s +[9600/16000] [L1: 0.0046] 17.0+0.1s +[11200/16000] [L1: 0.0047] 15.9+0.0s +[12800/16000] [L1: 0.0046] 15.2+0.0s +[14400/16000] [L1: 0.0046] 16.4+0.0s +[16000/16000] [L1: 0.0046] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.847 (Best: 43.925 @epoch 145) +Forward: 9.14s + +Saving... +Total: 9.68s + +[Epoch 157] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.5+0.9s +[3200/16000] [L1: 0.0046] 17.3+0.1s +[4800/16000] [L1: 0.0046] 15.4+0.0s +[6400/16000] [L1: 0.0046] 16.4+0.1s +[8000/16000] [L1: 0.0047] 16.8+0.1s +[9600/16000] [L1: 0.0047] 17.2+0.1s +[11200/16000] [L1: 0.0047] 16.1+0.1s +[12800/16000] [L1: 0.0047] 16.7+0.1s +[14400/16000] [L1: 0.0047] 15.7+0.0s +[16000/16000] [L1: 0.0047] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.854 (Best: 43.925 @epoch 145) +Forward: 9.20s + +Saving... +Total: 9.66s + +[Epoch 158] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.3+0.9s +[3200/16000] [L1: 0.0046] 15.7+0.0s +[4800/16000] [L1: 0.0046] 15.3+0.0s +[6400/16000] [L1: 0.0046] 15.9+0.0s +[8000/16000] [L1: 0.0046] 14.7+0.0s +[9600/16000] [L1: 0.0046] 15.4+0.0s +[11200/16000] [L1: 0.0046] 15.9+0.0s +[12800/16000] [L1: 0.0046] 15.3+0.0s +[14400/16000] [L1: 0.0046] 15.0+0.0s +[16000/16000] [L1: 0.0046] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.912 (Best: 43.925 @epoch 145) +Forward: 9.07s + +Saving... +Total: 9.47s + +[Epoch 159] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 15.6+0.9s +[3200/16000] [L1: 0.0047] 16.5+0.0s +[4800/16000] [L1: 0.0046] 16.1+0.1s +[6400/16000] [L1: 0.0046] 15.6+0.0s +[8000/16000] [L1: 0.0046] 16.1+0.0s +[9600/16000] [L1: 0.0046] 16.7+0.1s +[11200/16000] [L1: 0.0046] 16.2+0.1s +[12800/16000] [L1: 0.0046] 16.8+0.1s +[14400/16000] [L1: 0.0046] 15.7+0.1s +[16000/16000] [L1: 0.0046] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.876 (Best: 43.925 @epoch 145) +Forward: 9.05s + +Saving... +Total: 9.62s + +[Epoch 160] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 16.8+1.0s +[3200/16000] [L1: 0.0046] 16.1+0.1s +[4800/16000] [L1: 0.0046] 16.9+0.1s +[6400/16000] [L1: 0.0046] 16.1+0.1s +[8000/16000] [L1: 0.0046] 16.2+0.1s +[9600/16000] [L1: 0.0046] 16.8+0.1s +[11200/16000] [L1: 0.0047] 16.6+0.1s +[12800/16000] [L1: 0.0047] 15.8+0.0s +[14400/16000] [L1: 0.0046] 16.8+0.1s +[16000/16000] [L1: 0.0046] 17.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.785 (Best: 43.925 @epoch 145) +Forward: 9.40s + +Saving... +Total: 9.88s + +[Epoch 161] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 15.3+0.9s +[3200/16000] [L1: 0.0046] 16.4+0.1s +[4800/16000] [L1: 0.0046] 16.4+0.1s +[6400/16000] [L1: 0.0046] 16.3+0.1s +[8000/16000] [L1: 0.0046] 15.9+0.1s +[9600/16000] [L1: 0.0046] 16.5+0.1s +[11200/16000] [L1: 0.0046] 15.4+0.0s +[12800/16000] [L1: 0.0046] 16.1+0.0s +[14400/16000] [L1: 0.0046] 15.4+0.0s +[16000/16000] [L1: 0.0046] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.896 (Best: 43.925 @epoch 145) +Forward: 9.13s + +Saving... +Total: 9.66s + +[Epoch 162] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.8+0.9s +[3200/16000] [L1: 0.0047] 16.4+0.0s +[4800/16000] [L1: 0.0047] 16.4+0.1s +[6400/16000] [L1: 0.0046] 15.6+0.0s +[8000/16000] [L1: 0.0046] 16.8+0.1s +[9600/16000] [L1: 0.0047] 16.1+0.0s +[11200/16000] [L1: 0.0047] 16.5+0.1s +[12800/16000] [L1: 0.0047] 16.4+0.1s +[14400/16000] [L1: 0.0047] 16.0+0.0s +[16000/16000] [L1: 0.0046] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.847 (Best: 43.925 @epoch 145) +Forward: 9.28s + +Saving... +Total: 9.82s + +[Epoch 163] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.7+0.9s +[3200/16000] [L1: 0.0046] 15.1+0.0s +[4800/16000] [L1: 0.0046] 14.7+0.0s +[6400/16000] [L1: 0.0046] 15.6+0.0s +[8000/16000] [L1: 0.0047] 16.5+0.0s +[9600/16000] [L1: 0.0046] 15.3+0.0s +[11200/16000] [L1: 0.0046] 15.1+0.0s +[12800/16000] [L1: 0.0046] 14.9+0.0s +[14400/16000] [L1: 0.0046] 16.2+0.0s +[16000/16000] [L1: 0.0046] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.794 (Best: 43.925 @epoch 145) +Forward: 9.24s + +Saving... +Total: 9.76s + +[Epoch 164] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 17.0+0.9s +[3200/16000] [L1: 0.0045] 16.9+0.1s +[4800/16000] [L1: 0.0046] 16.7+0.1s +[6400/16000] [L1: 0.0046] 17.3+0.1s +[8000/16000] [L1: 0.0046] 17.0+0.1s +[9600/16000] [L1: 0.0046] 15.7+0.0s +[11200/16000] [L1: 0.0046] 17.0+0.1s +[12800/16000] [L1: 0.0046] 16.8+0.1s +[14400/16000] [L1: 0.0046] 16.5+0.0s +[16000/16000] [L1: 0.0047] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.772 (Best: 43.925 @epoch 145) +Forward: 9.22s + +Saving... +Total: 9.72s + +[Epoch 165] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 17.1+0.9s +[3200/16000] [L1: 0.0047] 17.0+0.1s +[4800/16000] [L1: 0.0046] 17.1+0.1s +[6400/16000] [L1: 0.0047] 16.4+0.0s +[8000/16000] [L1: 0.0047] 17.1+0.1s +[9600/16000] [L1: 0.0047] 15.1+0.0s +[11200/16000] [L1: 0.0047] 16.7+0.0s +[12800/16000] [L1: 0.0047] 16.4+0.0s +[14400/16000] [L1: 0.0047] 14.9+0.0s +[16000/16000] [L1: 0.0047] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.872 (Best: 43.925 @epoch 145) +Forward: 9.24s + +Saving... +Total: 9.71s + +[Epoch 166] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 16.7+0.9s +[3200/16000] [L1: 0.0046] 16.3+0.1s +[4800/16000] [L1: 0.0046] 15.7+0.1s +[6400/16000] [L1: 0.0046] 16.3+0.1s +[8000/16000] [L1: 0.0046] 16.2+0.1s +[9600/16000] [L1: 0.0046] 16.2+0.1s +[11200/16000] [L1: 0.0046] 15.7+0.0s +[12800/16000] [L1: 0.0046] 15.2+0.0s +[14400/16000] [L1: 0.0046] 16.3+0.0s +[16000/16000] [L1: 0.0046] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.862 (Best: 43.925 @epoch 145) +Forward: 9.17s + +Saving... +Total: 9.65s + +[Epoch 167] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.6+1.1s +[3200/16000] [L1: 0.0047] 16.4+0.1s +[4800/16000] [L1: 0.0047] 16.4+0.0s +[6400/16000] [L1: 0.0047] 16.1+0.0s +[8000/16000] [L1: 0.0047] 16.3+0.1s +[9600/16000] [L1: 0.0047] 15.2+0.0s +[11200/16000] [L1: 0.0047] 15.3+0.0s +[12800/16000] [L1: 0.0046] 14.9+0.0s +[14400/16000] [L1: 0.0046] 16.0+0.0s +[16000/16000] [L1: 0.0047] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.846 (Best: 43.925 @epoch 145) +Forward: 9.14s + +Saving... +Total: 9.72s + +[Epoch 168] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.4+0.9s +[3200/16000] [L1: 0.0046] 16.5+0.1s +[4800/16000] [L1: 0.0046] 15.8+0.1s +[6400/16000] [L1: 0.0046] 16.5+0.1s +[8000/16000] [L1: 0.0046] 16.6+0.1s +[9600/16000] [L1: 0.0046] 16.6+0.1s +[11200/16000] [L1: 0.0046] 16.6+0.1s +[12800/16000] [L1: 0.0046] 16.7+0.1s +[14400/16000] [L1: 0.0046] 15.8+0.1s +[16000/16000] [L1: 0.0046] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.906 (Best: 43.925 @epoch 145) +Forward: 9.22s + +Saving... +Total: 9.73s + +[Epoch 169] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.9+0.9s +[3200/16000] [L1: 0.0047] 16.4+0.1s +[4800/16000] [L1: 0.0047] 16.9+0.1s +[6400/16000] [L1: 0.0046] 16.1+0.1s +[8000/16000] [L1: 0.0047] 16.5+0.1s +[9600/16000] [L1: 0.0047] 16.8+0.1s +[11200/16000] [L1: 0.0047] 15.0+0.1s +[12800/16000] [L1: 0.0047] 15.8+0.0s +[14400/16000] [L1: 0.0047] 16.7+0.1s +[16000/16000] [L1: 0.0047] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.842 (Best: 43.925 @epoch 145) +Forward: 9.33s + +Saving... +Total: 9.87s + +[Epoch 170] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.4+0.9s +[3200/16000] [L1: 0.0046] 17.0+0.1s +[4800/16000] [L1: 0.0045] 16.7+0.1s +[6400/16000] [L1: 0.0046] 16.2+0.1s +[8000/16000] [L1: 0.0046] 16.6+0.0s +[9600/16000] [L1: 0.0046] 16.1+0.1s +[11200/16000] [L1: 0.0046] 16.5+0.1s +[12800/16000] [L1: 0.0046] 16.9+0.1s +[14400/16000] [L1: 0.0046] 16.8+0.1s +[16000/16000] [L1: 0.0046] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.789 (Best: 43.925 @epoch 145) +Forward: 9.13s + +Saving... +Total: 9.70s + +[Epoch 171] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.0+0.9s +[3200/16000] [L1: 0.0047] 16.7+0.0s +[4800/16000] [L1: 0.0047] 16.9+0.1s +[6400/16000] [L1: 0.0047] 16.2+0.0s +[8000/16000] [L1: 0.0047] 17.0+0.0s +[9600/16000] [L1: 0.0047] 17.0+0.1s +[11200/16000] [L1: 0.0047] 14.6+0.0s +[12800/16000] [L1: 0.0047] 14.9+0.0s +[14400/16000] [L1: 0.0047] 15.6+0.0s +[16000/16000] [L1: 0.0047] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.851 (Best: 43.925 @epoch 145) +Forward: 9.21s + +Saving... +Total: 9.60s + +[Epoch 172] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.5+0.8s +[3200/16000] [L1: 0.0046] 16.9+0.1s +[4800/16000] [L1: 0.0046] 16.5+0.1s +[6400/16000] [L1: 0.0046] 16.2+0.1s +[8000/16000] [L1: 0.0046] 16.3+0.1s +[9600/16000] [L1: 0.0046] 16.2+0.1s +[11200/16000] [L1: 0.0046] 15.1+0.0s +[12800/16000] [L1: 0.0046] 15.0+0.0s +[14400/16000] [L1: 0.0046] 14.9+0.0s +[16000/16000] [L1: 0.0046] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.883 (Best: 43.925 @epoch 145) +Forward: 9.15s + +Saving... +Total: 9.71s + +[Epoch 173] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0044] 16.9+1.0s +[3200/16000] [L1: 0.0044] 16.7+0.1s +[4800/16000] [L1: 0.0045] 17.0+0.1s +[6400/16000] [L1: 0.0045] 16.9+0.1s +[8000/16000] [L1: 0.0046] 16.6+0.1s +[9600/16000] [L1: 0.0046] 16.9+0.1s +[11200/16000] [L1: 0.0046] 17.1+0.1s +[12800/16000] [L1: 0.0046] 16.5+0.1s +[14400/16000] [L1: 0.0046] 17.5+0.1s +[16000/16000] [L1: 0.0046] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.735 (Best: 43.925 @epoch 145) +Forward: 9.14s + +Saving... +Total: 9.74s + +[Epoch 174] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.9+1.0s +[3200/16000] [L1: 0.0047] 17.2+0.1s +[4800/16000] [L1: 0.0047] 17.1+0.1s +[6400/16000] [L1: 0.0046] 17.5+0.1s +[8000/16000] [L1: 0.0046] 17.6+0.1s +[9600/16000] [L1: 0.0046] 17.6+0.1s +[11200/16000] [L1: 0.0046] 16.3+0.1s +[12800/16000] [L1: 0.0046] 17.2+0.1s +[14400/16000] [L1: 0.0046] 17.1+0.1s +[16000/16000] [L1: 0.0046] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.840 (Best: 43.925 @epoch 145) +Forward: 9.11s + +Saving... +Total: 9.59s + +[Epoch 175] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 15.6+1.0s +[3200/16000] [L1: 0.0046] 15.8+0.0s +[4800/16000] [L1: 0.0046] 16.3+0.1s +[6400/16000] [L1: 0.0046] 16.2+0.1s +[8000/16000] [L1: 0.0046] 16.7+0.1s +[9600/16000] [L1: 0.0046] 17.4+0.1s +[11200/16000] [L1: 0.0046] 17.3+0.1s +[12800/16000] [L1: 0.0046] 15.4+0.0s +[14400/16000] [L1: 0.0046] 17.0+0.1s +[16000/16000] [L1: 0.0046] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.817 (Best: 43.925 @epoch 145) +Forward: 9.14s + +Saving... +Total: 9.68s + +[Epoch 176] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 17.4+0.9s +[3200/16000] [L1: 0.0046] 16.9+0.1s +[4800/16000] [L1: 0.0046] 17.1+0.1s +[6400/16000] [L1: 0.0045] 17.0+0.1s +[8000/16000] [L1: 0.0046] 17.7+0.1s +[9600/16000] [L1: 0.0046] 15.9+0.1s +[11200/16000] [L1: 0.0046] 16.3+0.1s +[12800/16000] [L1: 0.0046] 16.3+0.1s +[14400/16000] [L1: 0.0046] 16.8+0.1s +[16000/16000] [L1: 0.0046] 16.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.783 (Best: 43.925 @epoch 145) +Forward: 9.14s + +Saving... +Total: 9.67s + +[Epoch 177] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.4+0.9s +[3200/16000] [L1: 0.0047] 16.2+0.0s +[4800/16000] [L1: 0.0047] 16.0+0.1s +[6400/16000] [L1: 0.0046] 15.2+0.0s +[8000/16000] [L1: 0.0047] 16.2+0.0s +[9600/16000] [L1: 0.0047] 16.5+0.0s +[11200/16000] [L1: 0.0047] 15.8+0.0s +[12800/16000] [L1: 0.0047] 15.7+0.0s +[14400/16000] [L1: 0.0047] 15.1+0.0s +[16000/16000] [L1: 0.0046] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.856 (Best: 43.925 @epoch 145) +Forward: 9.01s + +Saving... +Total: 9.57s + +[Epoch 178] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 16.1+0.9s +[3200/16000] [L1: 0.0046] 16.3+0.0s +[4800/16000] [L1: 0.0046] 16.7+0.1s +[6400/16000] [L1: 0.0046] 16.0+0.1s +[8000/16000] [L1: 0.0046] 14.8+0.0s +[9600/16000] [L1: 0.0046] 15.2+0.0s +[11200/16000] [L1: 0.0046] 17.0+0.1s +[12800/16000] [L1: 0.0046] 17.0+0.0s +[14400/16000] [L1: 0.0046] 15.9+0.1s +[16000/16000] [L1: 0.0046] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.877 (Best: 43.925 @epoch 145) +Forward: 9.15s + +Saving... +Total: 9.68s + +[Epoch 179] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 14.5+1.0s +[3200/16000] [L1: 0.0047] 15.1+0.0s +[4800/16000] [L1: 0.0047] 16.5+0.0s +[6400/16000] [L1: 0.0046] 16.2+0.0s +[8000/16000] [L1: 0.0046] 16.0+0.0s +[9600/16000] [L1: 0.0046] 15.1+0.0s +[11200/16000] [L1: 0.0046] 17.0+0.0s +[12800/16000] [L1: 0.0046] 16.3+0.0s +[14400/16000] [L1: 0.0046] 16.4+0.0s +[16000/16000] [L1: 0.0046] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.875 (Best: 43.925 @epoch 145) +Forward: 9.08s + +Saving... +Total: 9.51s + +[Epoch 180] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.1+0.8s +[3200/16000] [L1: 0.0046] 14.6+0.0s +[4800/16000] [L1: 0.0046] 15.9+0.0s +[6400/16000] [L1: 0.0046] 16.7+0.1s +[8000/16000] [L1: 0.0046] 15.9+0.1s +[9600/16000] [L1: 0.0046] 15.8+0.0s +[11200/16000] [L1: 0.0046] 15.8+0.0s +[12800/16000] [L1: 0.0046] 15.6+0.0s +[14400/16000] [L1: 0.0046] 15.5+0.0s +[16000/16000] [L1: 0.0046] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.842 (Best: 43.925 @epoch 145) +Forward: 9.28s + +Saving... +Total: 9.81s + +[Epoch 181] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.5+0.9s +[3200/16000] [L1: 0.0046] 15.6+0.1s +[4800/16000] [L1: 0.0046] 16.6+0.1s +[6400/16000] [L1: 0.0046] 17.1+0.1s +[8000/16000] [L1: 0.0046] 16.6+0.1s +[9600/16000] [L1: 0.0046] 15.6+0.1s +[11200/16000] [L1: 0.0046] 16.4+0.1s +[12800/16000] [L1: 0.0046] 15.6+0.0s +[14400/16000] [L1: 0.0046] 15.7+0.0s +[16000/16000] [L1: 0.0046] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.860 (Best: 43.925 @epoch 145) +Forward: 9.08s + +Saving... +Total: 9.64s + +[Epoch 182] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.0+0.9s +[3200/16000] [L1: 0.0047] 16.7+0.1s +[4800/16000] [L1: 0.0047] 17.2+0.1s +[6400/16000] [L1: 0.0047] 16.5+0.0s +[8000/16000] [L1: 0.0046] 16.2+0.0s +[9600/16000] [L1: 0.0046] 15.8+0.0s +[11200/16000] [L1: 0.0046] 14.2+0.0s +[12800/16000] [L1: 0.0046] 15.5+0.0s +[14400/16000] [L1: 0.0046] 16.4+0.0s +[16000/16000] [L1: 0.0046] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.933 (Best: 43.933 @epoch 182) +Forward: 9.19s + +Saving... +Total: 9.78s + +[Epoch 183] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.7+1.0s +[3200/16000] [L1: 0.0047] 16.9+0.1s +[4800/16000] [L1: 0.0047] 16.1+0.1s +[6400/16000] [L1: 0.0046] 16.5+0.1s +[8000/16000] [L1: 0.0046] 16.5+0.1s +[9600/16000] [L1: 0.0046] 16.8+0.1s +[11200/16000] [L1: 0.0046] 16.6+0.1s +[12800/16000] [L1: 0.0046] 16.7+0.1s +[14400/16000] [L1: 0.0046] 15.7+0.1s +[16000/16000] [L1: 0.0046] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.861 (Best: 43.933 @epoch 182) +Forward: 9.37s + +Saving... +Total: 9.87s + +[Epoch 184] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.0+0.9s +[3200/16000] [L1: 0.0046] 16.0+0.1s +[4800/16000] [L1: 0.0046] 16.1+0.1s +[6400/16000] [L1: 0.0046] 16.6+0.1s +[8000/16000] [L1: 0.0046] 16.5+0.0s +[9600/16000] [L1: 0.0046] 17.0+0.1s +[11200/16000] [L1: 0.0046] 16.2+0.1s +[12800/16000] [L1: 0.0046] 15.6+0.1s +[14400/16000] [L1: 0.0046] 16.3+0.0s +[16000/16000] [L1: 0.0046] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.924 (Best: 43.933 @epoch 182) +Forward: 9.37s + +Saving... +Total: 9.90s + +[Epoch 185] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 14.6+0.9s +[3200/16000] [L1: 0.0046] 15.8+0.1s +[4800/16000] [L1: 0.0046] 16.5+0.1s +[6400/16000] [L1: 0.0046] 17.0+0.1s +[8000/16000] [L1: 0.0046] 16.5+0.1s +[9600/16000] [L1: 0.0046] 16.4+0.1s +[11200/16000] [L1: 0.0046] 16.5+0.1s +[12800/16000] [L1: 0.0046] 16.6+0.1s +[14400/16000] [L1: 0.0046] 16.7+0.1s +[16000/16000] [L1: 0.0046] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.889 (Best: 43.933 @epoch 182) +Forward: 9.24s + +Saving... +Total: 9.80s + +[Epoch 186] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.8+0.8s +[3200/16000] [L1: 0.0046] 17.3+0.1s +[4800/16000] [L1: 0.0046] 16.1+0.0s +[6400/16000] [L1: 0.0046] 17.4+0.1s +[8000/16000] [L1: 0.0046] 16.4+0.1s +[9600/16000] [L1: 0.0046] 16.6+0.1s +[11200/16000] [L1: 0.0046] 17.0+0.1s +[12800/16000] [L1: 0.0046] 17.7+0.1s +[14400/16000] [L1: 0.0046] 17.5+0.1s +[16000/16000] [L1: 0.0046] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.886 (Best: 43.933 @epoch 182) +Forward: 9.12s + +Saving... +Total: 9.69s + +[Epoch 187] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 17.0+0.9s +[3200/16000] [L1: 0.0045] 16.5+0.1s +[4800/16000] [L1: 0.0045] 16.3+0.1s +[6400/16000] [L1: 0.0045] 16.1+0.1s +[8000/16000] [L1: 0.0046] 17.0+0.1s +[9600/16000] [L1: 0.0045] 15.6+0.1s +[11200/16000] [L1: 0.0045] 14.8+0.0s +[12800/16000] [L1: 0.0045] 15.4+0.0s +[14400/16000] [L1: 0.0046] 15.9+0.0s +[16000/16000] [L1: 0.0046] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.878 (Best: 43.933 @epoch 182) +Forward: 9.29s + +Saving... +Total: 9.70s + +[Epoch 188] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 16.2+0.9s +[3200/16000] [L1: 0.0046] 16.5+0.1s +[4800/16000] [L1: 0.0046] 16.4+0.1s +[6400/16000] [L1: 0.0046] 16.4+0.1s +[8000/16000] [L1: 0.0046] 16.5+0.1s +[9600/16000] [L1: 0.0046] 16.0+0.1s +[11200/16000] [L1: 0.0046] 15.8+0.1s +[12800/16000] [L1: 0.0046] 15.0+0.0s +[14400/16000] [L1: 0.0046] 16.6+0.1s +[16000/16000] [L1: 0.0046] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.923 (Best: 43.933 @epoch 182) +Forward: 9.13s + +Saving... +Total: 9.67s + +[Epoch 189] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 14.9+0.9s +[3200/16000] [L1: 0.0046] 15.7+0.0s +[4800/16000] [L1: 0.0046] 16.9+0.0s +[6400/16000] [L1: 0.0046] 15.1+0.0s +[8000/16000] [L1: 0.0046] 16.2+0.0s +[9600/16000] [L1: 0.0046] 16.3+0.1s +[11200/16000] [L1: 0.0046] 16.8+0.0s +[12800/16000] [L1: 0.0046] 16.6+0.1s +[14400/16000] [L1: 0.0046] 16.9+0.1s +[16000/16000] [L1: 0.0046] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.885 (Best: 43.933 @epoch 182) +Forward: 9.21s + +Saving... +Total: 9.67s + +[Epoch 190] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0044] 15.5+1.0s +[3200/16000] [L1: 0.0046] 16.1+0.0s +[4800/16000] [L1: 0.0045] 17.2+0.1s +[6400/16000] [L1: 0.0045] 16.2+0.1s +[8000/16000] [L1: 0.0046] 16.4+0.0s +[9600/16000] [L1: 0.0046] 16.4+0.1s +[11200/16000] [L1: 0.0046] 16.3+0.0s +[12800/16000] [L1: 0.0046] 14.8+0.0s +[14400/16000] [L1: 0.0046] 16.2+0.0s +[16000/16000] [L1: 0.0046] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.937 (Best: 43.937 @epoch 190) +Forward: 9.32s + +Saving... +Total: 9.98s + +[Epoch 191] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.2+1.0s +[3200/16000] [L1: 0.0046] 16.2+0.1s +[4800/16000] [L1: 0.0046] 16.1+0.0s +[6400/16000] [L1: 0.0046] 15.7+0.0s +[8000/16000] [L1: 0.0168] 16.0+0.1s +[9600/16000] [L1: 0.0158] 16.0+0.0s +[11200/16000] [L1: 0.0147] 15.1+0.0s +[12800/16000] [L1: 0.0137] 15.2+0.0s +[14400/16000] [L1: 0.0130] 15.1+0.0s +[16000/16000] [L1: 0.0123] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.327 (Best: 43.937 @epoch 190) +Forward: 9.23s + +Saving... +Total: 9.72s + +[Epoch 192] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0061] 13.9+0.9s +[3200/16000] [L1: 0.0061] 15.5+0.0s +[4800/16000] [L1: 0.0061] 16.7+0.0s +[6400/16000] [L1: 0.0060] 16.1+0.0s +[8000/16000] [L1: 0.0059] 16.6+0.0s +[9600/16000] [L1: 0.0059] 15.6+0.0s +[11200/16000] [L1: 0.0059] 16.4+0.0s +[12800/16000] [L1: 0.0058] 14.6+0.0s +[14400/16000] [L1: 0.0057] 15.0+0.0s +[16000/16000] [L1: 0.0057] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.339 (Best: 43.937 @epoch 190) +Forward: 9.31s + +Saving... +Total: 9.89s + +[Epoch 193] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.9+1.0s +[3200/16000] [L1: 0.0052] 15.7+0.1s +[4800/16000] [L1: 0.0052] 15.0+0.0s +[6400/16000] [L1: 0.0052] 15.7+0.0s +[8000/16000] [L1: 0.0052] 15.4+0.0s +[9600/16000] [L1: 0.0052] 15.4+0.0s +[11200/16000] [L1: 0.0052] 15.4+0.0s +[12800/16000] [L1: 0.0052] 16.5+0.0s +[14400/16000] [L1: 0.0052] 15.1+0.0s +[16000/16000] [L1: 0.0052] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.107 (Best: 43.937 @epoch 190) +Forward: 9.01s + +Saving... +Total: 9.52s + +[Epoch 194] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 16.0+0.9s +[3200/16000] [L1: 0.0050] 15.1+0.0s +[4800/16000] [L1: 0.0050] 16.0+0.1s +[6400/16000] [L1: 0.0049] 16.6+0.1s +[8000/16000] [L1: 0.0049] 16.1+0.1s +[9600/16000] [L1: 0.0049] 16.5+0.1s +[11200/16000] [L1: 0.0049] 15.3+0.0s +[12800/16000] [L1: 0.0049] 16.2+0.1s +[14400/16000] [L1: 0.0049] 16.1+0.0s +[16000/16000] [L1: 0.0049] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.702 (Best: 43.937 @epoch 190) +Forward: 9.20s + +Saving... +Total: 9.75s + +[Epoch 195] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.5+1.0s +[3200/16000] [L1: 0.0049] 15.7+0.0s +[4800/16000] [L1: 0.0049] 16.5+0.0s +[6400/16000] [L1: 0.0048] 14.8+0.0s +[8000/16000] [L1: 0.0048] 15.1+0.0s +[9600/16000] [L1: 0.0048] 15.8+0.0s +[11200/16000] [L1: 0.0048] 15.5+0.0s +[12800/16000] [L1: 0.0048] 16.2+0.0s +[14400/16000] [L1: 0.0048] 16.4+0.0s +[16000/16000] [L1: 0.0047] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.305 (Best: 43.937 @epoch 190) +Forward: 9.39s + +Saving... +Total: 9.86s + +[Epoch 196] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.6+0.8s +[3200/16000] [L1: 0.0048] 15.7+0.0s +[4800/16000] [L1: 0.0047] 15.6+0.0s +[6400/16000] [L1: 0.0046] 16.9+0.0s +[8000/16000] [L1: 0.0047] 16.4+0.0s +[9600/16000] [L1: 0.0046] 15.5+0.0s +[11200/16000] [L1: 0.0046] 16.5+0.0s +[12800/16000] [L1: 0.0046] 15.6+0.0s +[14400/16000] [L1: 0.0046] 16.0+0.0s +[16000/16000] [L1: 0.0046] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.960 (Best: 43.960 @epoch 196) +Forward: 9.23s + +Saving... +Total: 9.69s + +[Epoch 197] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 15.5+0.9s +[3200/16000] [L1: 0.0046] 15.0+0.0s +[4800/16000] [L1: 0.0045] 15.7+0.0s +[6400/16000] [L1: 0.0045] 16.2+0.0s +[8000/16000] [L1: 0.0045] 16.1+0.1s +[9600/16000] [L1: 0.0046] 16.5+0.0s +[11200/16000] [L1: 0.0046] 15.9+0.0s +[12800/16000] [L1: 0.0046] 15.4+0.0s +[14400/16000] [L1: 0.0046] 16.4+0.1s +[16000/16000] [L1: 0.0046] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.862 (Best: 43.960 @epoch 196) +Forward: 9.27s + +Saving... +Total: 9.77s + +[Epoch 198] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.2+0.9s +[3200/16000] [L1: 0.0048] 15.5+0.1s +[4800/16000] [L1: 0.0047] 13.9+0.0s +[6400/16000] [L1: 0.0047] 16.3+0.1s +[8000/16000] [L1: 0.0047] 16.6+0.1s +[9600/16000] [L1: 0.0046] 16.3+0.1s +[11200/16000] [L1: 0.0046] 15.7+0.1s +[12800/16000] [L1: 0.0046] 16.0+0.1s +[14400/16000] [L1: 0.0046] 16.5+0.1s +[16000/16000] [L1: 0.0046] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.986 (Best: 43.986 @epoch 198) +Forward: 9.01s + +Saving... +Total: 9.63s + +[Epoch 199] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0045] 16.5+1.0s +[3200/16000] [L1: 0.0045] 16.9+0.1s +[4800/16000] [L1: 0.0046] 16.4+0.1s +[6400/16000] [L1: 0.0046] 16.2+0.1s +[8000/16000] [L1: 0.0046] 17.0+0.1s +[9600/16000] [L1: 0.0046] 16.8+0.1s +[11200/16000] [L1: 0.0046] 16.3+0.1s +[12800/16000] [L1: 0.0046] 16.7+0.1s +[14400/16000] [L1: 0.0046] 16.9+0.1s +[16000/16000] [L1: 0.0052] 16.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 27.109 (Best: 43.986 @epoch 198) +Forward: 9.29s + +Saving... +Total: 9.84s + +[Epoch 200] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0123] 15.5+0.9s +[3200/16000] [L1: 0.0092] 15.0+0.0s +[4800/16000] [L1: 0.0080] 15.5+0.0s +[6400/16000] [L1: 0.0073] 16.7+0.1s +[8000/16000] [L1: 0.0069] 17.0+0.1s +[9600/16000] [L1: 0.0066] 16.3+0.1s +[11200/16000] [L1: 0.0064] 16.0+0.0s +[12800/16000] [L1: 0.0062] 16.8+0.1s +[14400/16000] [L1: 0.0061] 16.7+0.1s +[16000/16000] [L1: 0.0060] 17.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.235 (Best: 43.986 @epoch 198) +Forward: 9.27s + +Saving... +Total: 9.86s + +[Epoch 201] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0047] 15.7+0.9s +[3200/16000] [L1: 0.0048] 15.4+0.0s +[4800/16000] [L1: 0.0048] 16.3+0.0s +[6400/16000] [L1: 0.0048] 16.3+0.1s +[8000/16000] [L1: 0.0048] 15.2+0.0s +[9600/16000] [L1: 0.0048] 16.5+0.1s +[11200/16000] [L1: 0.0048] 16.4+0.0s +[12800/16000] [L1: 0.0048] 16.4+0.0s +[14400/16000] [L1: 0.0047] 16.6+0.1s +[16000/16000] [L1: 0.0047] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 42.736 (Best: 43.986 @epoch 198) +Forward: 9.26s + +Saving... +Total: 9.89s + +[Epoch 202] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0047] 16.6+0.9s +[3200/16000] [L1: 0.0047] 16.4+0.1s +[4800/16000] [L1: 0.0047] 16.8+0.1s +[6400/16000] [L1: 0.0046] 17.3+0.1s +[8000/16000] [L1: 0.0046] 16.7+0.1s +[9600/16000] [L1: 0.0046] 16.2+0.1s +[11200/16000] [L1: 0.0046] 15.5+0.0s +[12800/16000] [L1: 0.0046] 15.8+0.1s +[14400/16000] [L1: 0.0046] 17.2+0.1s +[16000/16000] [L1: 0.0046] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.696 (Best: 43.986 @epoch 198) +Forward: 9.09s + +Saving... +Total: 9.60s + +[Epoch 203] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.0+1.0s +[3200/16000] [L1: 0.0045] 16.9+0.1s +[4800/16000] [L1: 0.0045] 16.5+0.1s +[6400/16000] [L1: 0.0045] 16.5+0.1s +[8000/16000] [L1: 0.0045] 15.9+0.0s +[9600/16000] [L1: 0.0045] 16.5+0.1s +[11200/16000] [L1: 0.0045] 16.8+0.1s +[12800/16000] [L1: 0.0045] 16.7+0.0s +[14400/16000] [L1: 0.0045] 15.1+0.0s +[16000/16000] [L1: 0.0045] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.909 (Best: 43.986 @epoch 198) +Forward: 9.12s + +Saving... +Total: 9.74s + +[Epoch 204] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.8+1.0s +[3200/16000] [L1: 0.0044] 16.7+0.1s +[4800/16000] [L1: 0.0045] 15.2+0.0s +[6400/16000] [L1: 0.0045] 16.0+0.1s +[8000/16000] [L1: 0.0045] 16.0+0.0s +[9600/16000] [L1: 0.0045] 16.8+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 17.2+0.1s +[14400/16000] [L1: 0.0045] 15.9+0.0s +[16000/16000] [L1: 0.0044] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.965 (Best: 43.986 @epoch 198) +Forward: 9.07s + +Saving... +Total: 9.47s + +[Epoch 205] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.2+0.9s +[3200/16000] [L1: 0.0044] 15.4+0.0s +[4800/16000] [L1: 0.0044] 16.8+0.1s +[6400/16000] [L1: 0.0044] 15.8+0.0s +[8000/16000] [L1: 0.0044] 16.8+0.0s +[9600/16000] [L1: 0.0044] 15.9+0.1s +[11200/16000] [L1: 0.0044] 16.2+0.1s +[12800/16000] [L1: 0.0044] 15.2+0.0s +[14400/16000] [L1: 0.0045] 16.7+0.0s +[16000/16000] [L1: 0.0045] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.988 (Best: 43.988 @epoch 205) +Forward: 9.41s + +Saving... +Total: 9.95s + +[Epoch 206] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.8+0.9s +[3200/16000] [L1: 0.0044] 15.8+0.1s +[4800/16000] [L1: 0.0044] 16.0+0.1s +[6400/16000] [L1: 0.0044] 15.8+0.1s +[8000/16000] [L1: 0.0044] 15.9+0.0s +[9600/16000] [L1: 0.0044] 15.7+0.0s +[11200/16000] [L1: 0.0044] 15.0+0.0s +[12800/16000] [L1: 0.0044] 16.4+0.1s +[14400/16000] [L1: 0.0044] 16.1+0.1s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.009 (Best: 44.009 @epoch 206) +Forward: 9.32s + +Saving... +Total: 9.87s + +[Epoch 207] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.5+1.1s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 15.3+0.0s +[6400/16000] [L1: 0.0044] 14.9+0.0s +[8000/16000] [L1: 0.0044] 16.5+0.1s +[9600/16000] [L1: 0.0044] 17.2+0.1s +[11200/16000] [L1: 0.0044] 15.8+0.0s +[12800/16000] [L1: 0.0044] 16.7+0.0s +[14400/16000] [L1: 0.0045] 15.9+0.0s +[16000/16000] [L1: 0.0044] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.995 (Best: 44.009 @epoch 206) +Forward: 9.22s + +Saving... +Total: 9.73s + +[Epoch 208] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.8+1.0s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0043] 16.6+0.1s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 15.2+0.1s +[9600/16000] [L1: 0.0044] 16.3+0.1s +[11200/16000] [L1: 0.0044] 17.1+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.014 (Best: 44.014 @epoch 208) +Forward: 9.31s + +Saving... +Total: 9.84s + +[Epoch 209] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.7+0.9s +[3200/16000] [L1: 0.0045] 16.4+0.1s +[4800/16000] [L1: 0.0044] 16.6+0.1s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 16.4+0.1s +[14400/16000] [L1: 0.0045] 17.1+0.1s +[16000/16000] [L1: 0.0045] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.027 (Best: 44.027 @epoch 209) +Forward: 9.20s + +Saving... +Total: 9.70s + +[Epoch 210] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.2+1.0s +[3200/16000] [L1: 0.0044] 16.0+0.0s +[4800/16000] [L1: 0.0044] 17.2+0.1s +[6400/16000] [L1: 0.0045] 16.1+0.0s +[8000/16000] [L1: 0.0045] 16.7+0.0s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.0s +[12800/16000] [L1: 0.0044] 15.3+0.0s +[14400/16000] [L1: 0.0044] 16.2+0.0s +[16000/16000] [L1: 0.0044] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.031 (Best: 44.031 @epoch 210) +Forward: 9.20s + +Saving... +Total: 9.82s + +[Epoch 211] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.5+1.0s +[3200/16000] [L1: 0.0045] 15.9+0.0s +[4800/16000] [L1: 0.0045] 15.4+0.0s +[6400/16000] [L1: 0.0044] 16.8+0.0s +[8000/16000] [L1: 0.0044] 16.5+0.0s +[9600/16000] [L1: 0.0044] 16.9+0.0s +[11200/16000] [L1: 0.0044] 16.6+0.0s +[12800/16000] [L1: 0.0044] 15.7+0.0s +[14400/16000] [L1: 0.0044] 15.5+0.0s +[16000/16000] [L1: 0.0044] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.999 (Best: 44.031 @epoch 210) +Forward: 9.34s + +Saving... +Total: 10.16s + +[Epoch 212] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.5+1.0s +[3200/16000] [L1: 0.0045] 16.9+0.1s +[4800/16000] [L1: 0.0045] 17.0+0.1s +[6400/16000] [L1: 0.0045] 16.9+0.1s +[8000/16000] [L1: 0.0045] 16.5+0.1s +[9600/16000] [L1: 0.0045] 16.4+0.1s +[11200/16000] [L1: 0.0045] 16.2+0.1s +[12800/16000] [L1: 0.0045] 16.5+0.0s +[14400/16000] [L1: 0.0044] 17.4+0.1s +[16000/16000] [L1: 0.0044] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.016 (Best: 44.031 @epoch 210) +Forward: 9.29s + +Saving... +Total: 9.76s + +[Epoch 213] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0046] 17.0+1.0s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0044] 15.9+0.0s +[6400/16000] [L1: 0.0044] 17.0+0.1s +[8000/16000] [L1: 0.0045] 16.5+0.1s +[9600/16000] [L1: 0.0045] 15.7+0.0s +[11200/16000] [L1: 0.0045] 15.8+0.0s +[12800/16000] [L1: 0.0045] 15.2+0.0s +[14400/16000] [L1: 0.0045] 17.1+0.1s +[16000/16000] [L1: 0.0045] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.991 (Best: 44.031 @epoch 210) +Forward: 9.41s + +Saving... +Total: 9.90s + +[Epoch 214] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.5+0.9s +[3200/16000] [L1: 0.0045] 15.7+0.1s +[4800/16000] [L1: 0.0045] 15.9+0.0s +[6400/16000] [L1: 0.0044] 16.0+0.0s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 15.9+0.0s +[11200/16000] [L1: 0.0044] 15.8+0.0s +[12800/16000] [L1: 0.0044] 15.2+0.0s +[14400/16000] [L1: 0.0044] 15.6+0.1s +[16000/16000] [L1: 0.0044] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.998 (Best: 44.031 @epoch 210) +Forward: 9.18s + +Saving... +Total: 9.68s + +[Epoch 215] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 17.1+1.0s +[3200/16000] [L1: 0.0045] 16.6+0.0s +[4800/16000] [L1: 0.0045] 17.0+0.0s +[6400/16000] [L1: 0.0045] 17.2+0.1s +[8000/16000] [L1: 0.0045] 16.6+0.1s +[9600/16000] [L1: 0.0045] 16.7+0.1s +[11200/16000] [L1: 0.0044] 15.7+0.0s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0044] 16.1+0.1s +[16000/16000] [L1: 0.0045] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.963 (Best: 44.031 @epoch 210) +Forward: 9.13s + +Saving... +Total: 9.64s + +[Epoch 216] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0046] 15.9+1.0s +[3200/16000] [L1: 0.0045] 16.4+0.1s +[4800/16000] [L1: 0.0044] 14.9+0.0s +[6400/16000] [L1: 0.0044] 17.1+0.0s +[8000/16000] [L1: 0.0044] 16.8+0.0s +[9600/16000] [L1: 0.0044] 16.3+0.0s +[11200/16000] [L1: 0.0044] 16.3+0.0s +[12800/16000] [L1: 0.0044] 16.5+0.0s +[14400/16000] [L1: 0.0044] 16.3+0.0s +[16000/16000] [L1: 0.0044] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.040 (Best: 44.040 @epoch 216) +Forward: 9.37s + +Saving... +Total: 9.84s + +[Epoch 217] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.6+1.0s +[3200/16000] [L1: 0.0044] 16.3+0.0s +[4800/16000] [L1: 0.0045] 16.1+0.0s +[6400/16000] [L1: 0.0044] 15.6+0.1s +[8000/16000] [L1: 0.0045] 16.4+0.1s +[9600/16000] [L1: 0.0045] 15.7+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.013 (Best: 44.040 @epoch 216) +Forward: 9.00s + +Saving... +Total: 9.48s + +[Epoch 218] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.7+1.0s +[3200/16000] [L1: 0.0045] 16.0+0.1s +[4800/16000] [L1: 0.0044] 16.2+0.1s +[6400/16000] [L1: 0.0044] 16.0+0.1s +[8000/16000] [L1: 0.0044] 16.1+0.1s +[9600/16000] [L1: 0.0044] 16.8+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.7+0.1s +[16000/16000] [L1: 0.0044] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.011 (Best: 44.040 @epoch 216) +Forward: 9.10s + +Saving... +Total: 9.46s + +[Epoch 219] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.3+0.9s +[3200/16000] [L1: 0.0045] 16.5+0.1s +[4800/16000] [L1: 0.0045] 17.1+0.1s +[6400/16000] [L1: 0.0045] 15.6+0.0s +[8000/16000] [L1: 0.0045] 15.7+0.0s +[9600/16000] [L1: 0.0044] 15.9+0.0s +[11200/16000] [L1: 0.0044] 15.3+0.0s +[12800/16000] [L1: 0.0044] 15.8+0.0s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.978 (Best: 44.040 @epoch 216) +Forward: 9.34s + +Saving... +Total: 9.82s + +[Epoch 220] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.9+1.0s +[3200/16000] [L1: 0.0043] 16.1+0.0s +[4800/16000] [L1: 0.0043] 16.5+0.0s +[6400/16000] [L1: 0.0043] 16.5+0.0s +[8000/16000] [L1: 0.0044] 14.9+0.0s +[9600/16000] [L1: 0.0044] 14.0+0.0s +[11200/16000] [L1: 0.0044] 15.0+0.0s +[12800/16000] [L1: 0.0044] 14.9+0.0s +[14400/16000] [L1: 0.0044] 15.7+0.0s +[16000/16000] [L1: 0.0044] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.009 (Best: 44.040 @epoch 216) +Forward: 9.25s + +Saving... +Total: 9.75s + +[Epoch 221] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.5+1.0s +[3200/16000] [L1: 0.0046] 16.1+0.1s +[4800/16000] [L1: 0.0045] 16.7+0.1s +[6400/16000] [L1: 0.0045] 17.0+0.1s +[8000/16000] [L1: 0.0044] 16.9+0.0s +[9600/16000] [L1: 0.0045] 16.5+0.0s +[11200/16000] [L1: 0.0045] 15.5+0.0s +[12800/16000] [L1: 0.0044] 15.1+0.0s +[14400/16000] [L1: 0.0045] 16.2+0.0s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.012 (Best: 44.040 @epoch 216) +Forward: 9.07s + +Saving... +Total: 9.60s + +[Epoch 222] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.2+0.9s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 16.6+0.1s +[6400/16000] [L1: 0.0044] 16.9+0.1s +[8000/16000] [L1: 0.0044] 15.4+0.0s +[9600/16000] [L1: 0.0044] 16.1+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 17.1+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.919 (Best: 44.040 @epoch 216) +Forward: 9.24s + +Saving... +Total: 9.67s + +[Epoch 223] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0046] 15.3+0.9s +[3200/16000] [L1: 0.0045] 15.3+0.0s +[4800/16000] [L1: 0.0045] 16.2+0.0s +[6400/16000] [L1: 0.0045] 15.8+0.0s +[8000/16000] [L1: 0.0045] 16.5+0.0s +[9600/16000] [L1: 0.0045] 16.2+0.1s +[11200/16000] [L1: 0.0045] 14.6+0.0s +[12800/16000] [L1: 0.0045] 15.0+0.0s +[14400/16000] [L1: 0.0045] 14.7+0.0s +[16000/16000] [L1: 0.0045] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.001 (Best: 44.040 @epoch 216) +Forward: 9.15s + +Saving... +Total: 9.67s + +[Epoch 224] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.9+1.1s +[3200/16000] [L1: 0.0043] 15.9+0.1s +[4800/16000] [L1: 0.0044] 15.0+0.0s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 16.5+0.1s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 17.0+0.1s +[12800/16000] [L1: 0.0044] 15.7+0.1s +[14400/16000] [L1: 0.0044] 17.3+0.1s +[16000/16000] [L1: 0.0044] 17.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.007 (Best: 44.040 @epoch 216) +Forward: 9.19s + +Saving... +Total: 9.66s + +[Epoch 225] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.5+0.9s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0044] 16.8+0.1s +[6400/16000] [L1: 0.0044] 15.4+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 16.2+0.0s +[11200/16000] [L1: 0.0044] 16.1+0.0s +[12800/16000] [L1: 0.0044] 15.0+0.0s +[14400/16000] [L1: 0.0044] 14.7+0.0s +[16000/16000] [L1: 0.0044] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.963 (Best: 44.040 @epoch 216) +Forward: 9.20s + +Saving... +Total: 9.57s + +[Epoch 226] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.4+1.0s +[3200/16000] [L1: 0.0045] 16.2+0.1s +[4800/16000] [L1: 0.0045] 16.0+0.0s +[6400/16000] [L1: 0.0045] 16.5+0.1s +[8000/16000] [L1: 0.0045] 16.8+0.1s +[9600/16000] [L1: 0.0044] 16.0+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.7+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.002 (Best: 44.040 @epoch 216) +Forward: 9.34s + +Saving... +Total: 9.86s + +[Epoch 227] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.8+1.0s +[3200/16000] [L1: 0.0045] 16.5+0.1s +[4800/16000] [L1: 0.0045] 16.8+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.0s +[8000/16000] [L1: 0.0044] 16.3+0.0s +[9600/16000] [L1: 0.0044] 16.8+0.0s +[11200/16000] [L1: 0.0044] 15.9+0.0s +[12800/16000] [L1: 0.0044] 15.4+0.0s +[14400/16000] [L1: 0.0044] 15.5+0.0s +[16000/16000] [L1: 0.0044] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.002 (Best: 44.040 @epoch 216) +Forward: 9.25s + +Saving... +Total: 9.77s + +[Epoch 228] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.9+1.0s +[3200/16000] [L1: 0.0043] 16.9+0.1s +[4800/16000] [L1: 0.0044] 16.8+0.0s +[6400/16000] [L1: 0.0044] 16.8+0.0s +[8000/16000] [L1: 0.0044] 16.8+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.0s +[12800/16000] [L1: 0.0044] 15.7+0.0s +[14400/16000] [L1: 0.0044] 16.9+0.0s +[16000/16000] [L1: 0.0044] 17.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.933 (Best: 44.040 @epoch 216) +Forward: 9.23s + +Saving... +Total: 9.74s + +[Epoch 229] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.4+1.0s +[3200/16000] [L1: 0.0044] 16.4+0.1s +[4800/16000] [L1: 0.0044] 16.2+0.0s +[6400/16000] [L1: 0.0044] 15.9+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.0s +[9600/16000] [L1: 0.0044] 17.1+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 17.3+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.975 (Best: 44.040 @epoch 216) +Forward: 9.24s + +Saving... +Total: 9.66s + +[Epoch 230] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.5+0.9s +[3200/16000] [L1: 0.0044] 16.1+0.1s +[4800/16000] [L1: 0.0044] 17.0+0.1s +[6400/16000] [L1: 0.0044] 16.6+0.1s +[8000/16000] [L1: 0.0044] 16.2+0.1s +[9600/16000] [L1: 0.0044] 16.9+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.0s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.006 (Best: 44.040 @epoch 216) +Forward: 9.16s + +Saving... +Total: 9.64s + +[Epoch 231] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0046] 15.8+0.9s +[3200/16000] [L1: 0.0045] 15.4+0.0s +[4800/16000] [L1: 0.0045] 16.0+0.0s +[6400/16000] [L1: 0.0045] 14.4+0.0s +[8000/16000] [L1: 0.0045] 15.9+0.0s +[9600/16000] [L1: 0.0044] 14.8+0.0s +[11200/16000] [L1: 0.0044] 14.8+0.0s +[12800/16000] [L1: 0.0044] 15.0+0.0s +[14400/16000] [L1: 0.0044] 15.1+0.0s +[16000/16000] [L1: 0.0044] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.008 (Best: 44.040 @epoch 216) +Forward: 9.31s + +Saving... +Total: 9.81s + +[Epoch 232] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0046] 14.7+1.0s +[3200/16000] [L1: 0.0045] 16.4+0.0s +[4800/16000] [L1: 0.0045] 16.1+0.1s +[6400/16000] [L1: 0.0045] 15.4+0.0s +[8000/16000] [L1: 0.0045] 16.0+0.1s +[9600/16000] [L1: 0.0045] 16.4+0.1s +[11200/16000] [L1: 0.0045] 16.1+0.0s +[12800/16000] [L1: 0.0045] 16.8+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0044] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.985 (Best: 44.040 @epoch 216) +Forward: 9.26s + +Saving... +Total: 9.74s + +[Epoch 233] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.0+1.0s +[3200/16000] [L1: 0.0046] 17.1+0.1s +[4800/16000] [L1: 0.0045] 16.4+0.1s +[6400/16000] [L1: 0.0045] 16.3+0.1s +[8000/16000] [L1: 0.0045] 16.0+0.1s +[9600/16000] [L1: 0.0045] 16.7+0.1s +[11200/16000] [L1: 0.0045] 16.2+0.1s +[12800/16000] [L1: 0.0045] 15.9+0.0s +[14400/16000] [L1: 0.0044] 14.8+0.0s +[16000/16000] [L1: 0.0044] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.032 (Best: 44.040 @epoch 216) +Forward: 9.27s + +Saving... +Total: 9.70s + +[Epoch 234] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 17.0+1.0s +[3200/16000] [L1: 0.0044] 17.2+0.1s +[4800/16000] [L1: 0.0044] 16.4+0.1s +[6400/16000] [L1: 0.0044] 16.6+0.1s +[8000/16000] [L1: 0.0044] 17.1+0.1s +[9600/16000] [L1: 0.0044] 16.9+0.1s +[11200/16000] [L1: 0.0044] 16.9+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 16.2+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.965 (Best: 44.040 @epoch 216) +Forward: 9.06s + +Saving... +Total: 9.47s + +[Epoch 235] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+0.9s +[3200/16000] [L1: 0.0044] 17.1+0.1s +[4800/16000] [L1: 0.0044] 16.5+0.1s +[6400/16000] [L1: 0.0044] 16.8+0.1s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0045] 17.0+0.1s +[11200/16000] [L1: 0.0044] 17.2+0.1s +[12800/16000] [L1: 0.0044] 15.6+0.1s +[14400/16000] [L1: 0.0044] 15.9+0.0s +[16000/16000] [L1: 0.0044] 17.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.993 (Best: 44.040 @epoch 216) +Forward: 9.12s + +Saving... +Total: 9.62s + +[Epoch 236] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.2+0.9s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0045] 17.7+0.1s +[6400/16000] [L1: 0.0044] 17.2+0.1s +[8000/16000] [L1: 0.0044] 15.9+0.1s +[9600/16000] [L1: 0.0044] 17.0+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0045] 16.8+0.1s +[14400/16000] [L1: 0.0044] 16.9+0.1s +[16000/16000] [L1: 0.0045] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.948 (Best: 44.040 @epoch 216) +Forward: 9.17s + +Saving... +Total: 9.72s + +[Epoch 237] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.8+1.0s +[3200/16000] [L1: 0.0045] 16.2+0.1s +[4800/16000] [L1: 0.0044] 15.9+0.1s +[6400/16000] [L1: 0.0044] 16.6+0.1s +[8000/16000] [L1: 0.0044] 16.4+0.1s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 15.9+0.0s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 17.0+0.1s +[16000/16000] [L1: 0.0044] 15.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.972 (Best: 44.040 @epoch 216) +Forward: 9.13s + +Saving... +Total: 9.59s + +[Epoch 238] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.9+1.0s +[3200/16000] [L1: 0.0044] 17.2+0.1s +[4800/16000] [L1: 0.0044] 16.4+0.1s +[6400/16000] [L1: 0.0044] 17.0+0.1s +[8000/16000] [L1: 0.0044] 16.5+0.1s +[9600/16000] [L1: 0.0044] 15.7+0.1s +[11200/16000] [L1: 0.0044] 16.1+0.1s +[12800/16000] [L1: 0.0044] 17.1+0.1s +[14400/16000] [L1: 0.0044] 15.2+0.1s +[16000/16000] [L1: 0.0044] 17.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.904 (Best: 44.040 @epoch 216) +Forward: 9.25s + +Saving... +Total: 9.74s + +[Epoch 239] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.4+0.9s +[3200/16000] [L1: 0.0045] 16.7+0.1s +[4800/16000] [L1: 0.0044] 15.7+0.0s +[6400/16000] [L1: 0.0045] 16.8+0.0s +[8000/16000] [L1: 0.0044] 16.5+0.0s +[9600/16000] [L1: 0.0044] 16.3+0.1s +[11200/16000] [L1: 0.0044] 16.0+0.1s +[12800/16000] [L1: 0.0044] 16.1+0.0s +[14400/16000] [L1: 0.0044] 15.2+0.0s +[16000/16000] [L1: 0.0044] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.020 (Best: 44.040 @epoch 216) +Forward: 9.13s + +Saving... +Total: 9.71s + +[Epoch 240] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.6+1.1s +[3200/16000] [L1: 0.0043] 15.9+0.1s +[4800/16000] [L1: 0.0043] 16.3+0.1s +[6400/16000] [L1: 0.0043] 17.2+0.1s +[8000/16000] [L1: 0.0043] 16.2+0.1s +[9600/16000] [L1: 0.0043] 16.4+0.1s +[11200/16000] [L1: 0.0044] 16.2+0.1s +[12800/16000] [L1: 0.0044] 16.2+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.025 (Best: 44.040 @epoch 216) +Forward: 9.23s + +Saving... +Total: 9.74s + +[Epoch 241] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.3+1.0s +[3200/16000] [L1: 0.0045] 16.5+0.1s +[4800/16000] [L1: 0.0044] 15.7+0.0s +[6400/16000] [L1: 0.0045] 15.9+0.0s +[8000/16000] [L1: 0.0045] 15.8+0.0s +[9600/16000] [L1: 0.0044] 16.1+0.0s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.0s +[16000/16000] [L1: 0.0044] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.010 (Best: 44.040 @epoch 216) +Forward: 9.11s + +Saving... +Total: 9.58s + +[Epoch 242] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.6+1.0s +[3200/16000] [L1: 0.0044] 15.0+0.0s +[4800/16000] [L1: 0.0044] 16.7+0.1s +[6400/16000] [L1: 0.0044] 16.7+0.1s +[8000/16000] [L1: 0.0044] 16.4+0.1s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 16.7+0.1s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0044] 16.9+0.1s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.988 (Best: 44.040 @epoch 216) +Forward: 9.20s + +Saving... +Total: 9.69s + +[Epoch 243] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.5+1.1s +[3200/16000] [L1: 0.0045] 16.5+0.1s +[4800/16000] [L1: 0.0044] 16.8+0.1s +[6400/16000] [L1: 0.0044] 16.7+0.1s +[8000/16000] [L1: 0.0044] 17.1+0.1s +[9600/16000] [L1: 0.0044] 16.5+0.1s +[11200/16000] [L1: 0.0044] 16.7+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.010 (Best: 44.040 @epoch 216) +Forward: 9.24s + +Saving... +Total: 9.67s + +[Epoch 244] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.2+1.0s +[3200/16000] [L1: 0.0044] 15.7+0.0s +[4800/16000] [L1: 0.0044] 16.2+0.1s +[6400/16000] [L1: 0.0043] 16.1+0.1s +[8000/16000] [L1: 0.0044] 15.9+0.0s +[9600/16000] [L1: 0.0044] 16.1+0.1s +[11200/16000] [L1: 0.0044] 16.2+0.0s +[12800/16000] [L1: 0.0044] 17.2+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.039 (Best: 44.040 @epoch 216) +Forward: 9.11s + +Saving... +Total: 9.59s + +[Epoch 245] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.6+0.9s +[3200/16000] [L1: 0.0044] 15.4+0.0s +[4800/16000] [L1: 0.0044] 17.0+0.1s +[6400/16000] [L1: 0.0044] 16.0+0.0s +[8000/16000] [L1: 0.0044] 15.9+0.1s +[9600/16000] [L1: 0.0044] 15.8+0.0s +[11200/16000] [L1: 0.0044] 15.2+0.0s +[12800/16000] [L1: 0.0044] 15.7+0.0s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.006 (Best: 44.040 @epoch 216) +Forward: 9.23s + +Saving... +Total: 9.74s + +[Epoch 246] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 17.0+0.9s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0044] 16.7+0.1s +[6400/16000] [L1: 0.0044] 17.2+0.1s +[8000/16000] [L1: 0.0044] 16.5+0.1s +[9600/16000] [L1: 0.0044] 17.2+0.1s +[11200/16000] [L1: 0.0044] 16.2+0.0s +[12800/16000] [L1: 0.0044] 16.2+0.0s +[14400/16000] [L1: 0.0044] 15.7+0.0s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.032 (Best: 44.040 @epoch 216) +Forward: 9.22s + +Saving... +Total: 9.73s + +[Epoch 247] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.5+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0044] 16.9+0.1s +[6400/16000] [L1: 0.0044] 16.7+0.1s +[8000/16000] [L1: 0.0044] 16.0+0.1s +[9600/16000] [L1: 0.0044] 15.9+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 14.3+0.0s +[14400/16000] [L1: 0.0044] 14.8+0.0s +[16000/16000] [L1: 0.0044] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.990 (Best: 44.040 @epoch 216) +Forward: 9.16s + +Saving... +Total: 9.77s + +[Epoch 248] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.4+1.1s +[3200/16000] [L1: 0.0045] 16.2+0.1s +[4800/16000] [L1: 0.0045] 16.1+0.0s +[6400/16000] [L1: 0.0044] 16.9+0.1s +[8000/16000] [L1: 0.0044] 16.1+0.0s +[9600/16000] [L1: 0.0044] 15.5+0.0s +[11200/16000] [L1: 0.0044] 14.6+0.0s +[12800/16000] [L1: 0.0044] 15.1+0.0s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.011 (Best: 44.040 @epoch 216) +Forward: 9.22s + +Saving... +Total: 9.72s + +[Epoch 249] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.4+1.0s +[3200/16000] [L1: 0.0043] 16.4+0.1s +[4800/16000] [L1: 0.0043] 16.4+0.1s +[6400/16000] [L1: 0.0043] 16.6+0.1s +[8000/16000] [L1: 0.0044] 17.1+0.1s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 15.9+0.1s +[14400/16000] [L1: 0.0044] 16.7+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.036 (Best: 44.040 @epoch 216) +Forward: 9.23s + +Saving... +Total: 9.81s + +[Epoch 250] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+1.0s +[3200/16000] [L1: 0.0044] 16.3+0.1s +[4800/16000] [L1: 0.0044] 15.8+0.1s +[6400/16000] [L1: 0.0044] 16.8+0.1s +[8000/16000] [L1: 0.0044] 17.0+0.1s +[9600/16000] [L1: 0.0044] 16.3+0.1s +[11200/16000] [L1: 0.0044] 16.7+0.1s +[12800/16000] [L1: 0.0044] 16.1+0.1s +[14400/16000] [L1: 0.0044] 16.1+0.1s +[16000/16000] [L1: 0.0044] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.981 (Best: 44.040 @epoch 216) +Forward: 9.27s + +Saving... +Total: 9.76s + +[Epoch 251] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 14.7+0.9s +[3200/16000] [L1: 0.0044] 16.4+0.1s +[4800/16000] [L1: 0.0044] 17.0+0.1s +[6400/16000] [L1: 0.0044] 17.0+0.1s +[8000/16000] [L1: 0.0044] 16.9+0.1s +[9600/16000] [L1: 0.0044] 17.3+0.1s +[11200/16000] [L1: 0.0044] 17.1+0.1s +[12800/16000] [L1: 0.0044] 16.7+0.1s +[14400/16000] [L1: 0.0044] 16.9+0.1s +[16000/16000] [L1: 0.0044] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.954 (Best: 44.040 @epoch 216) +Forward: 9.29s + +Saving... +Total: 9.81s + +[Epoch 252] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.1+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0044] 15.9+0.0s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 15.6+0.0s +[9600/16000] [L1: 0.0044] 16.7+0.0s +[11200/16000] [L1: 0.0044] 16.1+0.0s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 16.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.007 (Best: 44.040 @epoch 216) +Forward: 9.19s + +Saving... +Total: 9.67s + +[Epoch 253] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.9+1.0s +[3200/16000] [L1: 0.0044] 16.0+0.1s +[4800/16000] [L1: 0.0044] 16.3+0.0s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 16.1+0.1s +[9600/16000] [L1: 0.0044] 16.0+0.1s +[11200/16000] [L1: 0.0044] 17.1+0.1s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.0s +[16000/16000] [L1: 0.0044] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.033 (Best: 44.040 @epoch 216) +Forward: 9.22s + +Saving... +Total: 9.81s + +[Epoch 254] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0046] 16.7+1.1s +[3200/16000] [L1: 0.0046] 17.0+0.1s +[4800/16000] [L1: 0.0045] 17.0+0.1s +[6400/16000] [L1: 0.0045] 17.0+0.1s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 15.8+0.1s +[11200/16000] [L1: 0.0044] 14.7+0.0s +[12800/16000] [L1: 0.0044] 17.0+0.1s +[14400/16000] [L1: 0.0044] 16.1+0.1s +[16000/16000] [L1: 0.0044] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.968 (Best: 44.040 @epoch 216) +Forward: 9.22s + +Saving... +Total: 9.64s + +[Epoch 255] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 17.2+1.0s +[3200/16000] [L1: 0.0043] 15.8+0.0s +[4800/16000] [L1: 0.0043] 16.1+0.1s +[6400/16000] [L1: 0.0044] 16.0+0.0s +[8000/16000] [L1: 0.0043] 15.6+0.0s +[9600/16000] [L1: 0.0044] 15.9+0.0s +[11200/16000] [L1: 0.0044] 15.5+0.0s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0044] 16.1+0.1s +[16000/16000] [L1: 0.0044] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.983 (Best: 44.040 @epoch 216) +Forward: 9.22s + +Saving... +Total: 9.75s + +[Epoch 256] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.0+0.9s +[3200/16000] [L1: 0.0044] 15.8+0.1s +[4800/16000] [L1: 0.0043] 16.0+0.0s +[6400/16000] [L1: 0.0043] 15.3+0.0s +[8000/16000] [L1: 0.0044] 16.5+0.0s +[9600/16000] [L1: 0.0044] 15.7+0.0s +[11200/16000] [L1: 0.0044] 15.9+0.0s +[12800/16000] [L1: 0.0044] 15.8+0.0s +[14400/16000] [L1: 0.0044] 15.6+0.0s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.020 (Best: 44.040 @epoch 216) +Forward: 9.24s + +Saving... +Total: 9.74s + +[Epoch 257] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.6+1.0s +[3200/16000] [L1: 0.0044] 17.4+0.1s +[4800/16000] [L1: 0.0044] 16.5+0.1s +[6400/16000] [L1: 0.0045] 16.0+0.1s +[8000/16000] [L1: 0.0044] 16.6+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.3+0.0s +[14400/16000] [L1: 0.0044] 16.9+0.0s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.032 (Best: 44.040 @epoch 216) +Forward: 9.34s + +Saving... +Total: 9.89s + +[Epoch 258] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 17.3+1.0s +[3200/16000] [L1: 0.0044] 17.3+0.1s +[4800/16000] [L1: 0.0044] 16.8+0.1s +[6400/16000] [L1: 0.0044] 17.2+0.1s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 17.0+0.1s +[12800/16000] [L1: 0.0044] 16.4+0.1s +[14400/16000] [L1: 0.0044] 14.9+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.023 (Best: 44.040 @epoch 216) +Forward: 9.26s + +Saving... +Total: 9.78s + +[Epoch 259] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.4+1.0s +[3200/16000] [L1: 0.0044] 16.3+0.1s +[4800/16000] [L1: 0.0044] 17.1+0.1s +[6400/16000] [L1: 0.0044] 16.6+0.1s +[8000/16000] [L1: 0.0044] 16.5+0.1s +[9600/16000] [L1: 0.0044] 16.9+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 16.0+0.1s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.022 (Best: 44.040 @epoch 216) +Forward: 9.37s + +Saving... +Total: 9.94s + +[Epoch 260] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+1.1s +[3200/16000] [L1: 0.0045] 16.6+0.0s +[4800/16000] [L1: 0.0044] 16.5+0.1s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 16.2+0.1s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 16.1+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.018 (Best: 44.040 @epoch 216) +Forward: 9.26s + +Saving... +Total: 9.74s + +[Epoch 261] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.4+0.9s +[3200/16000] [L1: 0.0044] 15.9+0.0s +[4800/16000] [L1: 0.0044] 16.1+0.0s +[6400/16000] [L1: 0.0044] 16.7+0.1s +[8000/16000] [L1: 0.0044] 16.1+0.1s +[9600/16000] [L1: 0.0044] 14.9+0.0s +[11200/16000] [L1: 0.0044] 15.5+0.0s +[12800/16000] [L1: 0.0044] 16.3+0.0s +[14400/16000] [L1: 0.0044] 16.2+0.1s +[16000/16000] [L1: 0.0044] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.044 (Best: 44.044 @epoch 261) +Forward: 9.25s + +Saving... +Total: 9.71s + +[Epoch 262] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.0+0.9s +[3200/16000] [L1: 0.0043] 16.5+0.1s +[4800/16000] [L1: 0.0043] 15.7+0.0s +[6400/16000] [L1: 0.0044] 16.7+0.0s +[8000/16000] [L1: 0.0044] 16.4+0.1s +[9600/16000] [L1: 0.0044] 16.5+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 17.2+0.1s +[14400/16000] [L1: 0.0044] 17.0+0.1s +[16000/16000] [L1: 0.0044] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.013 (Best: 44.044 @epoch 261) +Forward: 9.49s + +Saving... +Total: 9.99s + +[Epoch 263] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+1.0s +[3200/16000] [L1: 0.0044] 16.0+0.1s +[4800/16000] [L1: 0.0044] 15.5+0.0s +[6400/16000] [L1: 0.0044] 16.5+0.1s +[8000/16000] [L1: 0.0044] 16.6+0.1s +[9600/16000] [L1: 0.0044] 16.9+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.0s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.035 (Best: 44.044 @epoch 261) +Forward: 9.12s + +Saving... +Total: 9.66s + +[Epoch 264] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.6+1.0s +[3200/16000] [L1: 0.0044] 16.4+0.1s +[4800/16000] [L1: 0.0044] 15.8+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 16.6+0.1s +[9600/16000] [L1: 0.0044] 15.7+0.1s +[11200/16000] [L1: 0.0044] 16.1+0.1s +[12800/16000] [L1: 0.0044] 16.7+0.1s +[14400/16000] [L1: 0.0044] 17.0+0.1s +[16000/16000] [L1: 0.0044] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.043 (Best: 44.044 @epoch 261) +Forward: 9.11s + +Saving... +Total: 10.17s + +[Epoch 265] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.7+1.1s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0044] 16.7+0.1s +[6400/16000] [L1: 0.0044] 16.8+0.1s +[8000/16000] [L1: 0.0044] 15.4+0.0s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 17.3+0.1s +[12800/16000] [L1: 0.0044] 17.0+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.957 (Best: 44.044 @epoch 261) +Forward: 9.13s + +Saving... +Total: 9.66s + +[Epoch 266] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.9+1.0s +[3200/16000] [L1: 0.0044] 15.8+0.1s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0044] 15.2+0.0s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 15.4+0.0s +[11200/16000] [L1: 0.0044] 16.0+0.1s +[12800/16000] [L1: 0.0044] 15.2+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.005 (Best: 44.044 @epoch 261) +Forward: 9.21s + +Saving... +Total: 9.69s + +[Epoch 267] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.5+1.1s +[3200/16000] [L1: 0.0044] 16.1+0.0s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0044] 15.6+0.1s +[8000/16000] [L1: 0.0044] 16.8+0.0s +[9600/16000] [L1: 0.0044] 15.9+0.0s +[11200/16000] [L1: 0.0044] 15.9+0.0s +[12800/16000] [L1: 0.0044] 15.9+0.0s +[14400/16000] [L1: 0.0044] 16.5+0.0s +[16000/16000] [L1: 0.0044] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.991 (Best: 44.044 @epoch 261) +Forward: 9.34s + +Saving... +Total: 9.89s + +[Epoch 268] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.1+1.0s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 16.2+0.1s +[6400/16000] [L1: 0.0044] 16.0+0.1s +[8000/16000] [L1: 0.0044] 15.8+0.1s +[9600/16000] [L1: 0.0044] 16.3+0.1s +[11200/16000] [L1: 0.0043] 16.4+0.1s +[12800/16000] [L1: 0.0044] 16.1+0.1s +[14400/16000] [L1: 0.0043] 15.3+0.1s +[16000/16000] [L1: 0.0043] 16.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.028 (Best: 44.044 @epoch 261) +Forward: 9.30s + +Saving... +Total: 9.77s + +[Epoch 269] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.1+0.9s +[3200/16000] [L1: 0.0044] 16.4+0.0s +[4800/16000] [L1: 0.0044] 15.9+0.0s +[6400/16000] [L1: 0.0044] 15.6+0.0s +[8000/16000] [L1: 0.0044] 17.0+0.1s +[9600/16000] [L1: 0.0045] 15.7+0.0s +[11200/16000] [L1: 0.0045] 15.7+0.0s +[12800/16000] [L1: 0.0045] 15.7+0.0s +[14400/16000] [L1: 0.0045] 15.8+0.0s +[16000/16000] [L1: 0.0045] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.982 (Best: 44.044 @epoch 261) +Forward: 9.15s + +Saving... +Total: 9.64s + +[Epoch 270] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.3+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0044] 15.6+0.0s +[6400/16000] [L1: 0.0044] 15.7+0.1s +[8000/16000] [L1: 0.0044] 15.7+0.0s +[9600/16000] [L1: 0.0044] 16.2+0.0s +[11200/16000] [L1: 0.0044] 17.4+0.1s +[12800/16000] [L1: 0.0044] 17.2+0.1s +[14400/16000] [L1: 0.0044] 16.4+0.1s +[16000/16000] [L1: 0.0044] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.969 (Best: 44.044 @epoch 261) +Forward: 9.39s + +Saving... +Total: 9.91s + +[Epoch 271] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.2+1.0s +[3200/16000] [L1: 0.0044] 15.3+0.1s +[4800/16000] [L1: 0.0044] 17.2+0.1s +[6400/16000] [L1: 0.0044] 16.9+0.1s +[8000/16000] [L1: 0.0044] 17.1+0.1s +[9600/16000] [L1: 0.0044] 17.2+0.1s +[11200/16000] [L1: 0.0044] 17.0+0.1s +[12800/16000] [L1: 0.0044] 16.4+0.1s +[14400/16000] [L1: 0.0044] 17.1+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.954 (Best: 44.044 @epoch 261) +Forward: 9.21s + +Saving... +Total: 9.79s + +[Epoch 272] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 17.2+1.0s +[3200/16000] [L1: 0.0044] 17.1+0.1s +[4800/16000] [L1: 0.0043] 15.8+0.0s +[6400/16000] [L1: 0.0043] 16.1+0.1s +[8000/16000] [L1: 0.0043] 16.2+0.0s +[9600/16000] [L1: 0.0043] 15.9+0.1s +[11200/16000] [L1: 0.0043] 16.0+0.0s +[12800/16000] [L1: 0.0043] 14.9+0.0s +[14400/16000] [L1: 0.0043] 15.0+0.0s +[16000/16000] [L1: 0.0043] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.028 (Best: 44.044 @epoch 261) +Forward: 9.21s + +Saving... +Total: 9.84s + +[Epoch 273] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.7+1.2s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0044] 16.4+0.0s +[6400/16000] [L1: 0.0044] 16.7+0.1s +[8000/16000] [L1: 0.0044] 16.0+0.1s +[9600/16000] [L1: 0.0044] 16.4+0.1s +[11200/16000] [L1: 0.0044] 15.9+0.1s +[12800/16000] [L1: 0.0044] 16.4+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.991 (Best: 44.044 @epoch 261) +Forward: 9.35s + +Saving... +Total: 9.86s + +[Epoch 274] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.9+1.2s +[3200/16000] [L1: 0.0043] 16.7+0.1s +[4800/16000] [L1: 0.0043] 17.3+0.1s +[6400/16000] [L1: 0.0043] 16.9+0.1s +[8000/16000] [L1: 0.0044] 16.5+0.1s +[9600/16000] [L1: 0.0044] 15.4+0.1s +[11200/16000] [L1: 0.0044] 16.0+0.0s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.973 (Best: 44.044 @epoch 261) +Forward: 9.22s + +Saving... +Total: 9.73s + +[Epoch 275] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.7+1.0s +[3200/16000] [L1: 0.0043] 16.3+0.0s +[4800/16000] [L1: 0.0044] 16.1+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 15.9+0.1s +[9600/16000] [L1: 0.0044] 15.9+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.043 (Best: 44.044 @epoch 261) +Forward: 9.21s + +Saving... +Total: 9.69s + +[Epoch 276] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.5+1.0s +[3200/16000] [L1: 0.0044] 15.2+0.0s +[4800/16000] [L1: 0.0044] 15.7+0.0s +[6400/16000] [L1: 0.0043] 16.6+0.1s +[8000/16000] [L1: 0.0044] 16.4+0.0s +[9600/16000] [L1: 0.0044] 16.2+0.0s +[11200/16000] [L1: 0.0044] 16.7+0.0s +[12800/16000] [L1: 0.0044] 16.0+0.0s +[14400/16000] [L1: 0.0044] 16.5+0.0s +[16000/16000] [L1: 0.0044] 17.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.018 (Best: 44.044 @epoch 261) +Forward: 9.21s + +Saving... +Total: 9.68s + +[Epoch 277] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.4+0.9s +[3200/16000] [L1: 0.0044] 15.8+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.1s +[6400/16000] [L1: 0.0044] 17.2+0.1s +[8000/16000] [L1: 0.0044] 16.1+0.1s +[9600/16000] [L1: 0.0044] 15.9+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.7+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0044] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.050 (Best: 44.050 @epoch 277) +Forward: 9.19s + +Saving... +Total: 9.74s + +[Epoch 278] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.0+1.0s +[3200/16000] [L1: 0.0042] 15.7+0.0s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0043] 15.5+0.0s +[8000/16000] [L1: 0.0043] 16.7+0.1s +[9600/16000] [L1: 0.0043] 15.0+0.0s +[11200/16000] [L1: 0.0043] 16.6+0.0s +[12800/16000] [L1: 0.0043] 16.8+0.0s +[14400/16000] [L1: 0.0043] 16.1+0.0s +[16000/16000] [L1: 0.0043] 17.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.042 (Best: 44.050 @epoch 277) +Forward: 9.19s + +Saving... +Total: 9.70s + +[Epoch 279] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.3+1.0s +[3200/16000] [L1: 0.0044] 15.9+0.1s +[4800/16000] [L1: 0.0044] 17.1+0.1s +[6400/16000] [L1: 0.0045] 16.1+0.0s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 16.3+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 15.9+0.1s +[14400/16000] [L1: 0.0044] 16.7+0.1s +[16000/16000] [L1: 0.0044] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.056 (Best: 44.056 @epoch 279) +Forward: 9.16s + +Saving... +Total: 9.69s + +[Epoch 280] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.6+1.0s +[3200/16000] [L1: 0.0044] 17.1+0.1s +[4800/16000] [L1: 0.0044] 15.6+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 15.8+0.1s +[9600/16000] [L1: 0.0044] 17.2+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0044] 17.4+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.014 (Best: 44.056 @epoch 279) +Forward: 9.22s + +Saving... +Total: 9.73s + +[Epoch 281] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.8+1.0s +[3200/16000] [L1: 0.0044] 14.8+0.1s +[4800/16000] [L1: 0.0043] 16.8+0.1s +[6400/16000] [L1: 0.0043] 17.4+0.1s +[8000/16000] [L1: 0.0043] 16.2+0.1s +[9600/16000] [L1: 0.0043] 17.1+0.1s +[11200/16000] [L1: 0.0043] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.031 (Best: 44.056 @epoch 279) +Forward: 9.31s + +Saving... +Total: 9.84s + +[Epoch 282] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 17.0+1.0s +[3200/16000] [L1: 0.0043] 16.4+0.1s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0044] 16.6+0.1s +[8000/16000] [L1: 0.0044] 17.0+0.1s +[9600/16000] [L1: 0.0044] 17.0+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 17.0+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0044] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.016 (Best: 44.056 @epoch 279) +Forward: 9.22s + +Saving... +Total: 9.82s + +[Epoch 283] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.6+1.0s +[3200/16000] [L1: 0.0043] 16.4+0.1s +[4800/16000] [L1: 0.0044] 16.4+0.1s +[6400/16000] [L1: 0.0044] 16.2+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 16.9+0.1s +[11200/16000] [L1: 0.0044] 16.2+0.1s +[12800/16000] [L1: 0.0044] 14.8+0.0s +[14400/16000] [L1: 0.0044] 16.2+0.1s +[16000/16000] [L1: 0.0044] 17.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.025 (Best: 44.056 @epoch 279) +Forward: 9.30s + +Saving... +Total: 9.80s + +[Epoch 284] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.3+1.0s +[3200/16000] [L1: 0.0043] 16.2+0.1s +[4800/16000] [L1: 0.0044] 16.0+0.1s +[6400/16000] [L1: 0.0043] 17.3+0.1s +[8000/16000] [L1: 0.0044] 17.5+0.1s +[9600/16000] [L1: 0.0044] 17.1+0.1s +[11200/16000] [L1: 0.0043] 16.2+0.1s +[12800/16000] [L1: 0.0043] 17.4+0.1s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.008 (Best: 44.056 @epoch 279) +Forward: 9.14s + +Saving... +Total: 9.66s + +[Epoch 285] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 17.0+1.0s +[3200/16000] [L1: 0.0043] 16.9+0.1s +[4800/16000] [L1: 0.0043] 15.8+0.1s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0044] 16.9+0.1s +[9600/16000] [L1: 0.0044] 16.8+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.990 (Best: 44.056 @epoch 279) +Forward: 9.19s + +Saving... +Total: 9.69s + +[Epoch 286] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 14.4+0.9s +[3200/16000] [L1: 0.0044] 15.3+0.0s +[4800/16000] [L1: 0.0044] 15.2+0.1s +[6400/16000] [L1: 0.0044] 16.0+0.0s +[8000/16000] [L1: 0.0044] 17.1+0.1s +[9600/16000] [L1: 0.0044] 14.5+0.0s +[11200/16000] [L1: 0.0044] 15.8+0.0s +[12800/16000] [L1: 0.0044] 15.4+0.0s +[14400/16000] [L1: 0.0044] 14.9+0.0s +[16000/16000] [L1: 0.0043] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.952 (Best: 44.056 @epoch 279) +Forward: 9.13s + +Saving... +Total: 9.61s + +[Epoch 287] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.9+0.9s +[3200/16000] [L1: 0.0044] 17.2+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.1s +[6400/16000] [L1: 0.0044] 17.1+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 15.6+0.0s +[11200/16000] [L1: 0.0043] 16.0+0.0s +[12800/16000] [L1: 0.0043] 16.2+0.1s +[14400/16000] [L1: 0.0043] 16.2+0.1s +[16000/16000] [L1: 0.0043] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.044 (Best: 44.056 @epoch 279) +Forward: 9.17s + +Saving... +Total: 9.70s + +[Epoch 288] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.3+1.0s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0043] 16.0+0.1s +[6400/16000] [L1: 0.0043] 16.0+0.0s +[8000/16000] [L1: 0.0044] 15.8+0.0s +[9600/16000] [L1: 0.0044] 15.6+0.0s +[11200/16000] [L1: 0.0044] 16.0+0.0s +[12800/16000] [L1: 0.0044] 15.5+0.0s +[14400/16000] [L1: 0.0044] 16.4+0.1s +[16000/16000] [L1: 0.0044] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.039 (Best: 44.056 @epoch 279) +Forward: 9.18s + +Saving... +Total: 9.68s + +[Epoch 289] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.7+1.0s +[3200/16000] [L1: 0.0043] 16.4+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 16.0+0.0s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.7+0.1s +[14400/16000] [L1: 0.0043] 16.2+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.947 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.77s + +[Epoch 290] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.6+1.0s +[3200/16000] [L1: 0.0044] 16.3+0.1s +[4800/16000] [L1: 0.0043] 16.6+0.1s +[6400/16000] [L1: 0.0043] 16.3+0.0s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 16.1+0.1s +[11200/16000] [L1: 0.0044] 16.9+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 17.0+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.013 (Best: 44.056 @epoch 279) +Forward: 9.29s + +Saving... +Total: 9.77s + +[Epoch 291] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.9+0.9s +[3200/16000] [L1: 0.0044] 15.6+0.0s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0044] 16.5+0.0s +[8000/16000] [L1: 0.0044] 16.4+0.0s +[9600/16000] [L1: 0.0043] 15.8+0.0s +[11200/16000] [L1: 0.0043] 17.0+0.0s +[12800/16000] [L1: 0.0044] 16.6+0.0s +[14400/16000] [L1: 0.0044] 16.3+0.1s +[16000/16000] [L1: 0.0043] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.983 (Best: 44.056 @epoch 279) +Forward: 9.10s + +Saving... +Total: 9.52s + +[Epoch 292] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.8+0.9s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0043] 15.9+0.1s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0044] 15.2+0.0s +[9600/16000] [L1: 0.0044] 14.7+0.0s +[11200/16000] [L1: 0.0044] 15.9+0.0s +[12800/16000] [L1: 0.0044] 16.8+0.0s +[14400/16000] [L1: 0.0044] 16.3+0.1s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.000 (Best: 44.056 @epoch 279) +Forward: 9.19s + +Saving... +Total: 9.74s + +[Epoch 293] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.8+1.0s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.0s +[6400/16000] [L1: 0.0044] 16.2+0.0s +[8000/16000] [L1: 0.0044] 15.1+0.0s +[9600/16000] [L1: 0.0045] 15.8+0.0s +[11200/16000] [L1: 0.0044] 16.2+0.0s +[12800/16000] [L1: 0.0044] 16.7+0.1s +[14400/16000] [L1: 0.0044] 15.4+0.0s +[16000/16000] [L1: 0.0044] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.006 (Best: 44.056 @epoch 279) +Forward: 9.36s + +Saving... +Total: 9.96s + +[Epoch 294] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.2+1.1s +[3200/16000] [L1: 0.0044] 17.3+0.1s +[4800/16000] [L1: 0.0044] 16.9+0.0s +[6400/16000] [L1: 0.0044] 16.8+0.1s +[8000/16000] [L1: 0.0044] 16.6+0.1s +[9600/16000] [L1: 0.0044] 16.5+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.992 (Best: 44.056 @epoch 279) +Forward: 9.19s + +Saving... +Total: 9.70s + +[Epoch 295] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.3+1.0s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 15.6+0.1s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0044] 15.8+0.1s +[14400/16000] [L1: 0.0044] 16.9+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.002 (Best: 44.056 @epoch 279) +Forward: 9.15s + +Saving... +Total: 9.53s + +[Epoch 296] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0046] 16.5+0.9s +[3200/16000] [L1: 0.0046] 16.6+0.1s +[4800/16000] [L1: 0.0045] 16.3+0.1s +[6400/16000] [L1: 0.0045] 16.7+0.1s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 17.0+0.1s +[11200/16000] [L1: 0.0044] 16.3+0.1s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0044] 17.9+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.027 (Best: 44.056 @epoch 279) +Forward: 9.28s + +Saving... +Total: 9.76s + +[Epoch 297] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 15.9+1.0s +[3200/16000] [L1: 0.0043] 16.7+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0043] 16.7+0.1s +[8000/16000] [L1: 0.0043] 17.0+0.1s +[9600/16000] [L1: 0.0043] 16.3+0.1s +[11200/16000] [L1: 0.0044] 15.7+0.1s +[12800/16000] [L1: 0.0044] 16.0+0.0s +[14400/16000] [L1: 0.0044] 15.3+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.008 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.77s + +[Epoch 298] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.4+1.0s +[3200/16000] [L1: 0.0044] 16.7+0.1s +[4800/16000] [L1: 0.0044] 16.5+0.1s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 16.9+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.1+0.1s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0044] 17.0+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.994 (Best: 44.056 @epoch 279) +Forward: 9.28s + +Saving... +Total: 9.83s + +[Epoch 299] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.9+1.0s +[3200/16000] [L1: 0.0044] 15.7+0.1s +[4800/16000] [L1: 0.0044] 16.5+0.1s +[6400/16000] [L1: 0.0044] 16.8+0.1s +[8000/16000] [L1: 0.0044] 17.1+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 15.4+0.0s +[14400/16000] [L1: 0.0044] 16.7+0.0s +[16000/16000] [L1: 0.0044] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.011 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.64s + +[Epoch 300] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.1+1.2s +[3200/16000] [L1: 0.0044] 17.0+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.1s +[6400/16000] [L1: 0.0044] 16.8+0.1s +[8000/16000] [L1: 0.0043] 16.6+0.1s +[9600/16000] [L1: 0.0043] 17.3+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 17.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.001 (Best: 44.056 @epoch 279) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 301] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 17.0+0.9s +[3200/16000] [L1: 0.0043] 16.7+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 16.3+0.1s +[8000/16000] [L1: 0.0043] 16.9+0.1s +[9600/16000] [L1: 0.0043] 16.7+0.1s +[11200/16000] [L1: 0.0043] 16.5+0.1s +[12800/16000] [L1: 0.0043] 16.4+0.0s +[14400/16000] [L1: 0.0043] 16.6+0.1s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.992 (Best: 44.056 @epoch 279) +Forward: 9.19s + +Saving... +Total: 9.72s + +[Epoch 302] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 14.5+1.1s +[3200/16000] [L1: 0.0044] 16.3+0.1s +[4800/16000] [L1: 0.0044] 16.4+0.1s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0044] 16.9+0.0s +[9600/16000] [L1: 0.0044] 16.3+0.1s +[11200/16000] [L1: 0.0044] 16.0+0.1s +[12800/16000] [L1: 0.0044] 15.2+0.0s +[14400/16000] [L1: 0.0044] 15.4+0.0s +[16000/16000] [L1: 0.0044] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.015 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.82s + +[Epoch 303] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.0+0.9s +[3200/16000] [L1: 0.0044] 16.3+0.0s +[4800/16000] [L1: 0.0044] 16.3+0.1s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0043] 16.8+0.1s +[9600/16000] [L1: 0.0044] 15.7+0.1s +[11200/16000] [L1: 0.0044] 16.4+0.0s +[12800/16000] [L1: 0.0044] 15.9+0.1s +[14400/16000] [L1: 0.0044] 16.8+0.0s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.041 (Best: 44.056 @epoch 279) +Forward: 9.37s + +Saving... +Total: 9.87s + +[Epoch 304] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.1+1.1s +[3200/16000] [L1: 0.0045] 15.9+0.0s +[4800/16000] [L1: 0.0045] 15.5+0.0s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0044] 17.0+0.1s +[9600/16000] [L1: 0.0044] 16.8+0.1s +[11200/16000] [L1: 0.0044] 16.1+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 16.3+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.055 (Best: 44.056 @epoch 279) +Forward: 9.21s + +Saving... +Total: 9.83s + +[Epoch 305] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.9+1.2s +[3200/16000] [L1: 0.0042] 16.5+0.0s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0043] 16.2+0.0s +[8000/16000] [L1: 0.0043] 17.3+0.1s +[9600/16000] [L1: 0.0043] 16.2+0.1s +[11200/16000] [L1: 0.0043] 16.0+0.0s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 15.3+0.0s +[16000/16000] [L1: 0.0043] 17.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.033 (Best: 44.056 @epoch 279) +Forward: 9.35s + +Saving... +Total: 9.73s + +[Epoch 306] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.1+0.9s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 16.7+0.1s +[6400/16000] [L1: 0.0044] 16.0+0.1s +[8000/16000] [L1: 0.0044] 16.8+0.1s +[9600/16000] [L1: 0.0044] 16.0+0.1s +[11200/16000] [L1: 0.0044] 15.8+0.1s +[12800/16000] [L1: 0.0044] 17.0+0.1s +[14400/16000] [L1: 0.0044] 16.7+0.1s +[16000/16000] [L1: 0.0044] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.978 (Best: 44.056 @epoch 279) +Forward: 9.38s + +Saving... +Total: 9.93s + +[Epoch 307] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.6+0.9s +[3200/16000] [L1: 0.0044] 15.8+0.1s +[4800/16000] [L1: 0.0044] 16.3+0.1s +[6400/16000] [L1: 0.0044] 16.0+0.1s +[8000/16000] [L1: 0.0044] 16.8+0.1s +[9600/16000] [L1: 0.0044] 15.4+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.2+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0044] 17.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.047 (Best: 44.056 @epoch 279) +Forward: 9.09s + +Saving... +Total: 9.67s + +[Epoch 308] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.1+1.0s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 15.9+0.0s +[8000/16000] [L1: 0.0044] 17.7+0.1s +[9600/16000] [L1: 0.0044] 16.9+0.1s +[11200/16000] [L1: 0.0044] 16.4+0.1s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.041 (Best: 44.056 @epoch 279) +Forward: 9.31s + +Saving... +Total: 9.90s + +[Epoch 309] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.1+1.0s +[3200/16000] [L1: 0.0044] 15.8+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.0s +[6400/16000] [L1: 0.0044] 16.0+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0043] 16.6+0.1s +[11200/16000] [L1: 0.0043] 16.6+0.0s +[12800/16000] [L1: 0.0043] 16.5+0.1s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.015 (Best: 44.056 @epoch 279) +Forward: 9.20s + +Saving... +Total: 9.72s + +[Epoch 310] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.7+1.0s +[3200/16000] [L1: 0.0042] 17.2+0.1s +[4800/16000] [L1: 0.0043] 16.6+0.0s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 14.9+0.0s +[9600/16000] [L1: 0.0044] 16.1+0.0s +[11200/16000] [L1: 0.0043] 16.3+0.1s +[12800/16000] [L1: 0.0043] 14.6+0.0s +[14400/16000] [L1: 0.0043] 16.3+0.0s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.024 (Best: 44.056 @epoch 279) +Forward: 9.28s + +Saving... +Total: 9.86s + +[Epoch 311] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.3+1.1s +[3200/16000] [L1: 0.0043] 16.9+0.1s +[4800/16000] [L1: 0.0043] 16.2+0.1s +[6400/16000] [L1: 0.0043] 15.4+0.0s +[8000/16000] [L1: 0.0043] 16.1+0.1s +[9600/16000] [L1: 0.0043] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.2+0.1s +[12800/16000] [L1: 0.0044] 15.8+0.0s +[14400/16000] [L1: 0.0044] 16.0+0.0s +[16000/16000] [L1: 0.0044] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.034 (Best: 44.056 @epoch 279) +Forward: 9.39s + +Saving... +Total: 9.78s + +[Epoch 312] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.6+0.9s +[3200/16000] [L1: 0.0043] 16.4+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.0s +[6400/16000] [L1: 0.0043] 16.9+0.1s +[8000/16000] [L1: 0.0043] 16.1+0.1s +[9600/16000] [L1: 0.0043] 16.0+0.1s +[11200/16000] [L1: 0.0043] 16.2+0.1s +[12800/16000] [L1: 0.0043] 17.1+0.1s +[14400/16000] [L1: 0.0044] 16.4+0.1s +[16000/16000] [L1: 0.0044] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.035 (Best: 44.056 @epoch 279) +Forward: 9.26s + +Saving... +Total: 9.77s + +[Epoch 313] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.9+1.0s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0044] 15.9+0.1s +[6400/16000] [L1: 0.0044] 16.2+0.1s +[8000/16000] [L1: 0.0043] 16.9+0.1s +[9600/16000] [L1: 0.0044] 17.0+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0044] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.912 (Best: 44.056 @epoch 279) +Forward: 9.23s + +Saving... +Total: 9.62s + +[Epoch 314] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.4+1.0s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0044] 17.2+0.1s +[6400/16000] [L1: 0.0044] 17.4+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 16.1+0.1s +[11200/16000] [L1: 0.0044] 16.1+0.1s +[12800/16000] [L1: 0.0044] 15.8+0.1s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.015 (Best: 44.056 @epoch 279) +Forward: 9.23s + +Saving... +Total: 9.71s + +[Epoch 315] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 14.8+1.0s +[3200/16000] [L1: 0.0043] 15.1+0.0s +[4800/16000] [L1: 0.0043] 16.5+0.0s +[6400/16000] [L1: 0.0043] 16.1+0.0s +[8000/16000] [L1: 0.0043] 16.2+0.0s +[9600/16000] [L1: 0.0043] 15.9+0.0s +[11200/16000] [L1: 0.0043] 15.6+0.0s +[12800/16000] [L1: 0.0043] 16.3+0.1s +[14400/16000] [L1: 0.0043] 16.9+0.1s +[16000/16000] [L1: 0.0043] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.998 (Best: 44.056 @epoch 279) +Forward: 9.18s + +Saving... +Total: 9.64s + +[Epoch 316] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+0.9s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0044] 16.6+0.1s +[6400/16000] [L1: 0.0044] 15.6+0.0s +[8000/16000] [L1: 0.0044] 15.4+0.0s +[9600/16000] [L1: 0.0044] 16.0+0.0s +[11200/16000] [L1: 0.0044] 16.7+0.0s +[12800/16000] [L1: 0.0043] 15.9+0.0s +[14400/16000] [L1: 0.0043] 16.4+0.0s +[16000/16000] [L1: 0.0044] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.001 (Best: 44.056 @epoch 279) +Forward: 9.32s + +Saving... +Total: 9.87s + +[Epoch 317] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.0+1.0s +[3200/16000] [L1: 0.0044] 17.3+0.1s +[4800/16000] [L1: 0.0044] 16.3+0.1s +[6400/16000] [L1: 0.0044] 15.7+0.0s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 16.8+0.1s +[11200/16000] [L1: 0.0044] 17.6+0.1s +[12800/16000] [L1: 0.0044] 17.7+0.1s +[14400/16000] [L1: 0.0044] 17.6+0.1s +[16000/16000] [L1: 0.0044] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.997 (Best: 44.056 @epoch 279) +Forward: 9.13s + +Saving... +Total: 9.69s + +[Epoch 318] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.1+1.0s +[3200/16000] [L1: 0.0043] 16.0+0.0s +[4800/16000] [L1: 0.0043] 16.6+0.1s +[6400/16000] [L1: 0.0043] 15.9+0.0s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 17.2+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 17.1+0.1s +[14400/16000] [L1: 0.0044] 17.3+0.1s +[16000/16000] [L1: 0.0044] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.013 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.63s + +[Epoch 319] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.0+1.0s +[3200/16000] [L1: 0.0043] 16.1+0.1s +[4800/16000] [L1: 0.0043] 16.6+0.1s +[6400/16000] [L1: 0.0044] 16.1+0.1s +[8000/16000] [L1: 0.0044] 16.1+0.1s +[9600/16000] [L1: 0.0044] 16.2+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.1+0.1s +[14400/16000] [L1: 0.0044] 17.0+0.1s +[16000/16000] [L1: 0.0044] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.017 (Best: 44.056 @epoch 279) +Forward: 9.21s + +Saving... +Total: 9.76s + +[Epoch 320] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.5+1.0s +[3200/16000] [L1: 0.0043] 16.3+0.1s +[4800/16000] [L1: 0.0044] 15.4+0.0s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 16.0+0.0s +[9600/16000] [L1: 0.0044] 17.2+0.1s +[11200/16000] [L1: 0.0044] 17.1+0.1s +[12800/16000] [L1: 0.0044] 16.0+0.1s +[14400/16000] [L1: 0.0044] 15.6+0.1s +[16000/16000] [L1: 0.0044] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.002 (Best: 44.056 @epoch 279) +Forward: 9.13s + +Saving... +Total: 10.41s + +[Epoch 321] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.9+1.1s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0044] 17.5+0.1s +[6400/16000] [L1: 0.0044] 16.2+0.1s +[8000/16000] [L1: 0.0044] 17.2+0.1s +[9600/16000] [L1: 0.0044] 16.1+0.1s +[11200/16000] [L1: 0.0044] 17.0+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0044] 15.8+0.1s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.997 (Best: 44.056 @epoch 279) +Forward: 9.33s + +Saving... +Total: 9.91s + +[Epoch 322] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 17.4+1.0s +[3200/16000] [L1: 0.0043] 17.0+0.1s +[4800/16000] [L1: 0.0043] 16.3+0.1s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0043] 16.6+0.1s +[9600/16000] [L1: 0.0043] 17.4+0.1s +[11200/16000] [L1: 0.0044] 16.4+0.1s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0044] 16.4+0.1s +[16000/16000] [L1: 0.0044] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.017 (Best: 44.056 @epoch 279) +Forward: 9.13s + +Saving... +Total: 9.64s + +[Epoch 323] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.2+1.1s +[3200/16000] [L1: 0.0044] 14.9+0.0s +[4800/16000] [L1: 0.0043] 16.4+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.0s +[8000/16000] [L1: 0.0044] 16.9+0.1s +[9600/16000] [L1: 0.0044] 15.8+0.0s +[11200/16000] [L1: 0.0044] 15.7+0.0s +[12800/16000] [L1: 0.0044] 16.2+0.0s +[14400/16000] [L1: 0.0044] 15.8+0.0s +[16000/16000] [L1: 0.0043] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.980 (Best: 44.056 @epoch 279) +Forward: 9.30s + +Saving... +Total: 9.72s + +[Epoch 324] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.0+1.0s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0043] 15.8+0.1s +[6400/16000] [L1: 0.0043] 15.7+0.0s +[8000/16000] [L1: 0.0043] 16.4+0.1s +[9600/16000] [L1: 0.0044] 16.8+0.0s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.0+0.0s +[14400/16000] [L1: 0.0044] 16.4+0.0s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.997 (Best: 44.056 @epoch 279) +Forward: 9.26s + +Saving... +Total: 9.66s + +[Epoch 325] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 17.4+1.0s +[3200/16000] [L1: 0.0043] 16.5+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0043] 15.9+0.1s +[8000/16000] [L1: 0.0043] 17.1+0.1s +[9600/16000] [L1: 0.0043] 16.4+0.1s +[11200/16000] [L1: 0.0043] 17.4+0.1s +[12800/16000] [L1: 0.0043] 16.9+0.1s +[14400/16000] [L1: 0.0043] 16.3+0.1s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.033 (Best: 44.056 @epoch 279) +Forward: 9.18s + +Saving... +Total: 9.74s + +[Epoch 326] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 16.9+0.1s +[8000/16000] [L1: 0.0044] 17.4+0.1s +[9600/16000] [L1: 0.0044] 17.0+0.1s +[11200/16000] [L1: 0.0044] 17.0+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.036 (Best: 44.056 @epoch 279) +Forward: 9.46s + +Saving... +Total: 9.97s + +[Epoch 327] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.0+1.1s +[3200/16000] [L1: 0.0044] 16.8+0.1s +[4800/16000] [L1: 0.0044] 16.4+0.1s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0044] 17.2+0.1s +[9600/16000] [L1: 0.0043] 16.5+0.1s +[11200/16000] [L1: 0.0043] 15.6+0.0s +[12800/16000] [L1: 0.0043] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.043 (Best: 44.056 @epoch 279) +Forward: 9.34s + +Saving... +Total: 9.83s + +[Epoch 328] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.4+0.9s +[3200/16000] [L1: 0.0043] 16.5+0.1s +[4800/16000] [L1: 0.0043] 15.6+0.1s +[6400/16000] [L1: 0.0043] 16.7+0.1s +[8000/16000] [L1: 0.0043] 14.3+0.0s +[9600/16000] [L1: 0.0043] 15.7+0.0s +[11200/16000] [L1: 0.0043] 14.8+0.0s +[12800/16000] [L1: 0.0043] 16.3+0.1s +[14400/16000] [L1: 0.0043] 16.4+0.1s +[16000/16000] [L1: 0.0043] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.020 (Best: 44.056 @epoch 279) +Forward: 9.15s + +Saving... +Total: 9.67s + +[Epoch 329] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.1+1.0s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0044] 17.1+0.1s +[8000/16000] [L1: 0.0044] 17.4+0.1s +[9600/16000] [L1: 0.0044] 17.5+0.0s +[11200/16000] [L1: 0.0044] 17.9+0.1s +[12800/16000] [L1: 0.0044] 17.1+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 16.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.986 (Best: 44.056 @epoch 279) +Forward: 9.21s + +Saving... +Total: 9.61s + +[Epoch 330] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.9+1.0s +[3200/16000] [L1: 0.0044] 15.0+0.0s +[4800/16000] [L1: 0.0044] 16.1+0.0s +[6400/16000] [L1: 0.0044] 16.3+0.0s +[8000/16000] [L1: 0.0044] 16.5+0.0s +[9600/16000] [L1: 0.0044] 16.3+0.0s +[11200/16000] [L1: 0.0044] 14.8+0.0s +[12800/16000] [L1: 0.0043] 15.0+0.0s +[14400/16000] [L1: 0.0044] 16.9+0.1s +[16000/16000] [L1: 0.0044] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.997 (Best: 44.056 @epoch 279) +Forward: 9.30s + +Saving... +Total: 9.77s + +[Epoch 331] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 15.8+1.0s +[3200/16000] [L1: 0.0043] 16.5+0.0s +[4800/16000] [L1: 0.0043] 15.0+0.0s +[6400/16000] [L1: 0.0043] 16.0+0.1s +[8000/16000] [L1: 0.0043] 16.2+0.1s +[9600/16000] [L1: 0.0043] 16.2+0.0s +[11200/16000] [L1: 0.0043] 16.0+0.1s +[12800/16000] [L1: 0.0044] 16.3+0.0s +[14400/16000] [L1: 0.0044] 16.9+0.1s +[16000/16000] [L1: 0.0044] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.037 (Best: 44.056 @epoch 279) +Forward: 9.18s + +Saving... +Total: 9.69s + +[Epoch 332] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 14.9+1.1s +[3200/16000] [L1: 0.0043] 15.0+0.0s +[4800/16000] [L1: 0.0043] 15.7+0.0s +[6400/16000] [L1: 0.0043] 16.6+0.0s +[8000/16000] [L1: 0.0043] 17.0+0.1s +[9600/16000] [L1: 0.0044] 15.0+0.0s +[11200/16000] [L1: 0.0044] 16.6+0.0s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0044] 16.1+0.0s +[16000/16000] [L1: 0.0044] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.983 (Best: 44.056 @epoch 279) +Forward: 9.19s + +Saving... +Total: 9.77s + +[Epoch 333] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.3+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0044] 17.0+0.1s +[6400/16000] [L1: 0.0044] 16.9+0.1s +[8000/16000] [L1: 0.0044] 15.6+0.1s +[9600/16000] [L1: 0.0044] 15.1+0.0s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.1+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.004 (Best: 44.056 @epoch 279) +Forward: 9.09s + +Saving... +Total: 9.68s + +[Epoch 334] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 17.1+1.1s +[3200/16000] [L1: 0.0044] 17.3+0.1s +[4800/16000] [L1: 0.0044] 16.8+0.0s +[6400/16000] [L1: 0.0044] 16.7+0.1s +[8000/16000] [L1: 0.0044] 16.2+0.1s +[9600/16000] [L1: 0.0044] 16.8+0.1s +[11200/16000] [L1: 0.0044] 16.4+0.1s +[12800/16000] [L1: 0.0044] 14.9+0.0s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.040 (Best: 44.056 @epoch 279) +Forward: 9.11s + +Saving... +Total: 9.62s + +[Epoch 335] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.0+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0043] 16.3+0.0s +[6400/16000] [L1: 0.0043] 17.2+0.1s +[8000/16000] [L1: 0.0043] 15.1+0.0s +[9600/16000] [L1: 0.0043] 16.1+0.0s +[11200/16000] [L1: 0.0043] 17.0+0.1s +[12800/16000] [L1: 0.0043] 17.0+0.1s +[14400/16000] [L1: 0.0043] 15.4+0.0s +[16000/16000] [L1: 0.0043] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.997 (Best: 44.056 @epoch 279) +Forward: 9.30s + +Saving... +Total: 9.82s + +[Epoch 336] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.6+1.0s +[3200/16000] [L1: 0.0043] 17.2+0.1s +[4800/16000] [L1: 0.0043] 17.5+0.1s +[6400/16000] [L1: 0.0044] 17.1+0.1s +[8000/16000] [L1: 0.0044] 17.1+0.1s +[9600/16000] [L1: 0.0044] 17.4+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.0s +[12800/16000] [L1: 0.0044] 16.1+0.0s +[14400/16000] [L1: 0.0044] 15.7+0.1s +[16000/16000] [L1: 0.0044] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.008 (Best: 44.056 @epoch 279) +Forward: 9.18s + +Saving... +Total: 9.67s + +[Epoch 337] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.1+0.9s +[3200/16000] [L1: 0.0044] 15.5+0.0s +[4800/16000] [L1: 0.0043] 15.3+0.0s +[6400/16000] [L1: 0.0043] 16.0+0.0s +[8000/16000] [L1: 0.0043] 16.7+0.0s +[9600/16000] [L1: 0.0043] 16.5+0.0s +[11200/16000] [L1: 0.0043] 16.1+0.0s +[12800/16000] [L1: 0.0043] 16.1+0.1s +[14400/16000] [L1: 0.0043] 15.7+0.0s +[16000/16000] [L1: 0.0043] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.878 (Best: 44.056 @epoch 279) +Forward: 9.27s + +Saving... +Total: 9.76s + +[Epoch 338] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.7+1.2s +[3200/16000] [L1: 0.0044] 17.2+0.1s +[4800/16000] [L1: 0.0044] 17.1+0.1s +[6400/16000] [L1: 0.0044] 16.8+0.1s +[8000/16000] [L1: 0.0043] 16.4+0.1s +[9600/16000] [L1: 0.0043] 16.1+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 17.1+0.1s +[14400/16000] [L1: 0.0044] 17.3+0.1s +[16000/16000] [L1: 0.0043] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.049 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.83s + +[Epoch 339] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.4+1.0s +[3200/16000] [L1: 0.0044] 15.3+0.0s +[4800/16000] [L1: 0.0044] 15.5+0.0s +[6400/16000] [L1: 0.0044] 15.0+0.0s +[8000/16000] [L1: 0.0044] 15.0+0.0s +[9600/16000] [L1: 0.0044] 15.9+0.0s +[11200/16000] [L1: 0.0044] 15.1+0.0s +[12800/16000] [L1: 0.0044] 15.7+0.0s +[14400/16000] [L1: 0.0044] 16.1+0.0s +[16000/16000] [L1: 0.0044] 17.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.015 (Best: 44.056 @epoch 279) +Forward: 9.16s + +Saving... +Total: 9.59s + +[Epoch 340] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.2+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.0s +[4800/16000] [L1: 0.0044] 16.5+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 16.0+0.0s +[9600/16000] [L1: 0.0044] 17.2+0.1s +[11200/16000] [L1: 0.0044] 17.2+0.1s +[12800/16000] [L1: 0.0044] 15.9+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.981 (Best: 44.056 @epoch 279) +Forward: 9.25s + +Saving... +Total: 9.81s + +[Epoch 341] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.5+0.9s +[3200/16000] [L1: 0.0045] 16.3+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.1s +[6400/16000] [L1: 0.0044] 17.0+0.1s +[8000/16000] [L1: 0.0044] 16.3+0.1s +[9600/16000] [L1: 0.0044] 16.3+0.0s +[11200/16000] [L1: 0.0044] 17.0+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0043] 16.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.040 (Best: 44.056 @epoch 279) +Forward: 9.34s + +Saving... +Total: 9.86s + +[Epoch 342] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.0+0.9s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 16.6+0.0s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0044] 15.6+0.0s +[9600/16000] [L1: 0.0044] 16.1+0.0s +[11200/16000] [L1: 0.0044] 16.8+0.1s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0044] 17.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.966 (Best: 44.056 @epoch 279) +Forward: 9.29s + +Saving... +Total: 9.94s + +[Epoch 343] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.9+1.0s +[3200/16000] [L1: 0.0043] 17.4+0.1s +[4800/16000] [L1: 0.0043] 15.4+0.0s +[6400/16000] [L1: 0.0043] 15.4+0.0s +[8000/16000] [L1: 0.0043] 16.5+0.1s +[9600/16000] [L1: 0.0043] 16.7+0.1s +[11200/16000] [L1: 0.0043] 16.7+0.1s +[12800/16000] [L1: 0.0043] 16.3+0.1s +[14400/16000] [L1: 0.0043] 16.7+0.1s +[16000/16000] [L1: 0.0043] 16.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.998 (Best: 44.056 @epoch 279) +Forward: 9.21s + +Saving... +Total: 9.74s + +[Epoch 344] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.1+1.0s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.1s +[6400/16000] [L1: 0.0044] 16.5+0.1s +[8000/16000] [L1: 0.0044] 15.8+0.0s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.7+0.0s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0043] 16.3+0.1s +[16000/16000] [L1: 0.0044] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.022 (Best: 44.056 @epoch 279) +Forward: 9.23s + +Saving... +Total: 9.87s + +[Epoch 345] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.3+1.0s +[3200/16000] [L1: 0.0043] 15.9+0.1s +[4800/16000] [L1: 0.0043] 16.7+0.1s +[6400/16000] [L1: 0.0044] 15.7+0.1s +[8000/16000] [L1: 0.0043] 16.5+0.1s +[9600/16000] [L1: 0.0044] 16.9+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0044] 15.9+0.0s +[16000/16000] [L1: 0.0044] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.013 (Best: 44.056 @epoch 279) +Forward: 9.27s + +Saving... +Total: 9.77s + +[Epoch 346] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.4+1.0s +[3200/16000] [L1: 0.0044] 17.0+0.1s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0045] 15.0+0.0s +[8000/16000] [L1: 0.0044] 14.7+0.0s +[9600/16000] [L1: 0.0044] 15.0+0.0s +[11200/16000] [L1: 0.0044] 14.6+0.0s +[12800/16000] [L1: 0.0044] 14.6+0.0s +[14400/16000] [L1: 0.0044] 16.0+0.0s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.974 (Best: 44.056 @epoch 279) +Forward: 9.21s + +Saving... +Total: 9.72s + +[Epoch 347] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+1.1s +[3200/16000] [L1: 0.0044] 16.2+0.1s +[4800/16000] [L1: 0.0044] 17.3+0.1s +[6400/16000] [L1: 0.0044] 17.2+0.1s +[8000/16000] [L1: 0.0044] 16.1+0.0s +[9600/16000] [L1: 0.0044] 15.0+0.0s +[11200/16000] [L1: 0.0044] 16.7+0.1s +[12800/16000] [L1: 0.0044] 14.7+0.0s +[14400/16000] [L1: 0.0044] 14.8+0.0s +[16000/16000] [L1: 0.0044] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.016 (Best: 44.056 @epoch 279) +Forward: 9.28s + +Saving... +Total: 9.80s + +[Epoch 348] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.2+1.0s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0043] 15.5+0.1s +[6400/16000] [L1: 0.0044] 15.6+0.0s +[8000/16000] [L1: 0.0044] 16.9+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.4+0.0s +[12800/16000] [L1: 0.0044] 16.1+0.0s +[14400/16000] [L1: 0.0043] 16.8+0.0s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.049 (Best: 44.056 @epoch 279) +Forward: 9.27s + +Saving... +Total: 9.66s + +[Epoch 349] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 15.9+1.0s +[3200/16000] [L1: 0.0042] 14.8+0.0s +[4800/16000] [L1: 0.0043] 16.4+0.0s +[6400/16000] [L1: 0.0043] 16.1+0.1s +[8000/16000] [L1: 0.0043] 16.2+0.0s +[9600/16000] [L1: 0.0043] 16.5+0.0s +[11200/16000] [L1: 0.0043] 16.8+0.1s +[12800/16000] [L1: 0.0043] 15.7+0.0s +[14400/16000] [L1: 0.0043] 15.6+0.0s +[16000/16000] [L1: 0.0043] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.970 (Best: 44.056 @epoch 279) +Forward: 9.18s + +Saving... +Total: 9.76s + +[Epoch 350] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 17.2+1.1s +[3200/16000] [L1: 0.0044] 16.3+0.0s +[4800/16000] [L1: 0.0044] 16.7+0.0s +[6400/16000] [L1: 0.0044] 17.0+0.0s +[8000/16000] [L1: 0.0044] 15.0+0.0s +[9600/16000] [L1: 0.0044] 15.2+0.0s +[11200/16000] [L1: 0.0044] 16.3+0.0s +[12800/16000] [L1: 0.0044] 15.7+0.0s +[14400/16000] [L1: 0.0044] 17.2+0.1s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.964 (Best: 44.056 @epoch 279) +Forward: 9.30s + +Saving... +Total: 9.74s + +[Epoch 351] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.4+0.9s +[3200/16000] [L1: 0.0044] 16.0+0.0s +[4800/16000] [L1: 0.0044] 16.2+0.0s +[6400/16000] [L1: 0.0044] 17.0+0.1s +[8000/16000] [L1: 0.0044] 16.0+0.0s +[9600/16000] [L1: 0.0044] 16.0+0.0s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.4+0.1s +[14400/16000] [L1: 0.0044] 16.5+0.1s +[16000/16000] [L1: 0.0043] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.023 (Best: 44.056 @epoch 279) +Forward: 9.29s + +Saving... +Total: 9.80s + +[Epoch 352] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.7+1.0s +[3200/16000] [L1: 0.0044] 15.7+0.0s +[4800/16000] [L1: 0.0044] 16.2+0.0s +[6400/16000] [L1: 0.0043] 15.5+0.0s +[8000/16000] [L1: 0.0043] 15.6+0.0s +[9600/16000] [L1: 0.0043] 16.9+0.1s +[11200/16000] [L1: 0.0043] 16.6+0.1s +[12800/16000] [L1: 0.0044] 15.8+0.0s +[14400/16000] [L1: 0.0044] 15.6+0.0s +[16000/16000] [L1: 0.0044] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.018 (Best: 44.056 @epoch 279) +Forward: 9.38s + +Saving... +Total: 9.92s + +[Epoch 353] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.9+1.0s +[3200/16000] [L1: 0.0044] 16.5+0.1s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0044] 15.1+0.0s +[8000/16000] [L1: 0.0044] 15.2+0.0s +[9600/16000] [L1: 0.0043] 15.8+0.0s +[11200/16000] [L1: 0.0043] 15.5+0.0s +[12800/16000] [L1: 0.0044] 15.6+0.0s +[14400/16000] [L1: 0.0044] 16.4+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.992 (Best: 44.056 @epoch 279) +Forward: 9.25s + +Saving... +Total: 9.77s + +[Epoch 354] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 17.6+1.0s +[3200/16000] [L1: 0.0043] 17.1+0.0s +[4800/16000] [L1: 0.0043] 14.7+0.0s +[6400/16000] [L1: 0.0043] 16.1+0.0s +[8000/16000] [L1: 0.0043] 17.1+0.0s +[9600/16000] [L1: 0.0043] 16.7+0.0s +[11200/16000] [L1: 0.0043] 15.9+0.0s +[12800/16000] [L1: 0.0043] 16.6+0.0s +[14400/16000] [L1: 0.0043] 16.9+0.0s +[16000/16000] [L1: 0.0043] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.001 (Best: 44.056 @epoch 279) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 355] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.7+1.1s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0044] 16.5+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.0s +[8000/16000] [L1: 0.0044] 15.3+0.0s +[9600/16000] [L1: 0.0044] 16.5+0.1s +[11200/16000] [L1: 0.0044] 15.5+0.0s +[12800/16000] [L1: 0.0044] 16.0+0.0s +[14400/16000] [L1: 0.0044] 15.4+0.0s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.017 (Best: 44.056 @epoch 279) +Forward: 9.14s + +Saving... +Total: 9.70s + +[Epoch 356] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.7+1.0s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0043] 16.3+0.1s +[6400/16000] [L1: 0.0044] 16.2+0.1s +[8000/16000] [L1: 0.0044] 16.6+0.1s +[9600/16000] [L1: 0.0044] 15.9+0.1s +[11200/16000] [L1: 0.0044] 16.2+0.1s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.009 (Best: 44.056 @epoch 279) +Forward: 9.18s + +Saving... +Total: 9.57s + +[Epoch 357] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.1+0.9s +[3200/16000] [L1: 0.0044] 13.4+0.0s +[4800/16000] [L1: 0.0044] 16.6+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 16.6+0.1s +[9600/16000] [L1: 0.0043] 14.8+0.0s +[11200/16000] [L1: 0.0044] 14.5+0.0s +[12800/16000] [L1: 0.0044] 14.5+0.0s +[14400/16000] [L1: 0.0044] 14.8+0.0s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.043 (Best: 44.056 @epoch 279) +Forward: 9.23s + +Saving... +Total: 9.75s + +[Epoch 358] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.9+1.0s +[3200/16000] [L1: 0.0043] 16.1+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.0s +[6400/16000] [L1: 0.0044] 16.3+0.0s +[8000/16000] [L1: 0.0044] 17.3+0.0s +[9600/16000] [L1: 0.0043] 16.1+0.1s +[11200/16000] [L1: 0.0044] 15.2+0.0s +[12800/16000] [L1: 0.0044] 16.5+0.1s +[14400/16000] [L1: 0.0044] 15.8+0.0s +[16000/16000] [L1: 0.0044] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.983 (Best: 44.056 @epoch 279) +Forward: 9.28s + +Saving... +Total: 9.84s + +[Epoch 359] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+1.0s +[3200/16000] [L1: 0.0044] 16.6+0.1s +[4800/16000] [L1: 0.0044] 17.0+0.0s +[6400/16000] [L1: 0.0044] 16.0+0.1s +[8000/16000] [L1: 0.0044] 15.0+0.0s +[9600/16000] [L1: 0.0044] 15.7+0.0s +[11200/16000] [L1: 0.0044] 16.1+0.1s +[12800/16000] [L1: 0.0044] 15.9+0.0s +[14400/16000] [L1: 0.0044] 16.6+0.0s +[16000/16000] [L1: 0.0044] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.967 (Best: 44.056 @epoch 279) +Forward: 9.22s + +Saving... +Total: 9.75s + +[Epoch 360] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 14.8+1.0s +[3200/16000] [L1: 0.0042] 14.0+0.0s +[4800/16000] [L1: 0.0042] 14.9+0.0s +[6400/16000] [L1: 0.0043] 16.0+0.0s +[8000/16000] [L1: 0.0043] 16.2+0.0s +[9600/16000] [L1: 0.0043] 14.9+0.0s +[11200/16000] [L1: 0.0043] 17.2+0.0s +[12800/16000] [L1: 0.0043] 15.1+0.0s +[14400/16000] [L1: 0.0043] 15.1+0.0s +[16000/16000] [L1: 0.0043] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.028 (Best: 44.056 @epoch 279) +Forward: 9.20s + +Saving... +Total: 9.71s + +[Epoch 361] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.9+1.0s +[3200/16000] [L1: 0.0043] 15.3+0.0s +[4800/16000] [L1: 0.0043] 15.7+0.0s +[6400/16000] [L1: 0.0043] 16.5+0.1s +[8000/16000] [L1: 0.0043] 14.8+0.0s +[9600/16000] [L1: 0.0043] 15.7+0.0s +[11200/16000] [L1: 0.0043] 15.7+0.0s +[12800/16000] [L1: 0.0044] 15.5+0.0s +[14400/16000] [L1: 0.0044] 17.0+0.0s +[16000/16000] [L1: 0.0044] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.995 (Best: 44.056 @epoch 279) +Forward: 9.27s + +Saving... +Total: 9.85s + +[Epoch 362] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.1+1.2s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.0s +[6400/16000] [L1: 0.0043] 16.1+0.0s +[8000/16000] [L1: 0.0043] 15.5+0.0s +[9600/16000] [L1: 0.0043] 16.2+0.0s +[11200/16000] [L1: 0.0043] 16.3+0.0s +[12800/16000] [L1: 0.0043] 16.3+0.0s +[14400/16000] [L1: 0.0043] 16.2+0.1s +[16000/16000] [L1: 0.0043] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.984 (Best: 44.056 @epoch 279) +Forward: 9.23s + +Saving... +Total: 9.82s + +[Epoch 363] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.7+1.1s +[3200/16000] [L1: 0.0044] 15.9+0.1s +[4800/16000] [L1: 0.0044] 17.1+0.1s +[6400/16000] [L1: 0.0043] 16.6+0.1s +[8000/16000] [L1: 0.0043] 16.1+0.0s +[9600/16000] [L1: 0.0043] 14.9+0.0s +[11200/16000] [L1: 0.0043] 16.0+0.0s +[12800/16000] [L1: 0.0043] 16.1+0.0s +[14400/16000] [L1: 0.0043] 16.1+0.0s +[16000/16000] [L1: 0.0043] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.958 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.75s + +[Epoch 364] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.3+1.0s +[3200/16000] [L1: 0.0044] 15.7+0.1s +[4800/16000] [L1: 0.0043] 15.1+0.0s +[6400/16000] [L1: 0.0043] 16.0+0.0s +[8000/16000] [L1: 0.0043] 15.5+0.0s +[9600/16000] [L1: 0.0043] 16.2+0.0s +[11200/16000] [L1: 0.0044] 16.2+0.1s +[12800/16000] [L1: 0.0043] 16.3+0.1s +[14400/16000] [L1: 0.0044] 16.9+0.1s +[16000/16000] [L1: 0.0044] 16.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.020 (Best: 44.056 @epoch 279) +Forward: 9.14s + +Saving... +Total: 9.65s + +[Epoch 365] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.4+0.9s +[3200/16000] [L1: 0.0044] 16.3+0.1s +[4800/16000] [L1: 0.0044] 15.7+0.0s +[6400/16000] [L1: 0.0044] 15.8+0.0s +[8000/16000] [L1: 0.0043] 16.7+0.1s +[9600/16000] [L1: 0.0044] 16.7+0.1s +[11200/16000] [L1: 0.0044] 16.8+0.0s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0043] 17.3+0.1s +[16000/16000] [L1: 0.0043] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.033 (Best: 44.056 @epoch 279) +Forward: 9.37s + +Saving... +Total: 9.88s + +[Epoch 366] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 14.7+1.0s +[3200/16000] [L1: 0.0044] 14.9+0.0s +[4800/16000] [L1: 0.0044] 15.2+0.0s +[6400/16000] [L1: 0.0044] 16.0+0.0s +[8000/16000] [L1: 0.0044] 17.1+0.0s +[9600/16000] [L1: 0.0044] 16.1+0.0s +[11200/16000] [L1: 0.0044] 15.9+0.1s +[12800/16000] [L1: 0.0044] 15.4+0.0s +[14400/16000] [L1: 0.0044] 16.8+0.1s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.040 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.69s + +[Epoch 367] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 15.8+0.9s +[3200/16000] [L1: 0.0044] 16.4+0.1s +[4800/16000] [L1: 0.0044] 17.1+0.1s +[6400/16000] [L1: 0.0043] 16.7+0.1s +[8000/16000] [L1: 0.0044] 17.4+0.1s +[9600/16000] [L1: 0.0044] 16.6+0.1s +[11200/16000] [L1: 0.0044] 17.0+0.1s +[12800/16000] [L1: 0.0044] 17.0+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.1s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.923 (Best: 44.056 @epoch 279) +Forward: 9.26s + +Saving... +Total: 9.77s + +[Epoch 368] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.8+0.9s +[3200/16000] [L1: 0.0044] 17.2+0.1s +[4800/16000] [L1: 0.0044] 16.8+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0043] 16.6+0.1s +[9600/16000] [L1: 0.0043] 16.7+0.1s +[11200/16000] [L1: 0.0043] 16.9+0.1s +[12800/16000] [L1: 0.0043] 16.7+0.1s +[14400/16000] [L1: 0.0043] 16.6+0.1s +[16000/16000] [L1: 0.0043] 16.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.994 (Best: 44.056 @epoch 279) +Forward: 9.33s + +Saving... +Total: 9.91s + +[Epoch 369] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.3+1.0s +[3200/16000] [L1: 0.0045] 15.9+0.1s +[4800/16000] [L1: 0.0044] 16.7+0.1s +[6400/16000] [L1: 0.0044] 16.5+0.1s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 17.3+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.9+0.1s +[14400/16000] [L1: 0.0044] 16.2+0.1s +[16000/16000] [L1: 0.0044] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.012 (Best: 44.056 @epoch 279) +Forward: 9.24s + +Saving... +Total: 9.75s + +[Epoch 370] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 17.1+0.9s +[3200/16000] [L1: 0.0044] 16.8+0.1s +[4800/16000] [L1: 0.0043] 16.7+0.1s +[6400/16000] [L1: 0.0044] 15.9+0.1s +[8000/16000] [L1: 0.0044] 15.9+0.1s +[9600/16000] [L1: 0.0044] 16.0+0.1s +[11200/16000] [L1: 0.0044] 16.5+0.1s +[12800/16000] [L1: 0.0044] 16.8+0.1s +[14400/16000] [L1: 0.0043] 16.8+0.0s +[16000/16000] [L1: 0.0043] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.995 (Best: 44.056 @epoch 279) +Forward: 9.29s + +Saving... +Total: 9.81s + +[Epoch 371] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.3+1.1s +[3200/16000] [L1: 0.0042] 16.3+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0043] 16.6+0.1s +[9600/16000] [L1: 0.0043] 17.0+0.1s +[11200/16000] [L1: 0.0043] 16.8+0.1s +[12800/16000] [L1: 0.0043] 17.2+0.1s +[14400/16000] [L1: 0.0043] 16.6+0.1s +[16000/16000] [L1: 0.0043] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.041 (Best: 44.056 @epoch 279) +Forward: 9.50s + +Saving... +Total: 10.02s + +[Epoch 372] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0041] 15.8+1.0s +[3200/16000] [L1: 0.0042] 15.6+0.0s +[4800/16000] [L1: 0.0043] 15.9+0.0s +[6400/16000] [L1: 0.0042] 15.2+0.0s +[8000/16000] [L1: 0.0043] 15.0+0.0s +[9600/16000] [L1: 0.0043] 15.1+0.0s +[11200/16000] [L1: 0.0043] 15.1+0.0s +[12800/16000] [L1: 0.0043] 16.1+0.0s +[14400/16000] [L1: 0.0043] 16.0+0.0s +[16000/16000] [L1: 0.0043] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.998 (Best: 44.056 @epoch 279) +Forward: 9.09s + +Saving... +Total: 9.50s + +[Epoch 373] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.9+0.9s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0043] 17.1+0.1s +[6400/16000] [L1: 0.0044] 16.3+0.1s +[8000/16000] [L1: 0.0043] 16.8+0.1s +[9600/16000] [L1: 0.0043] 16.6+0.1s +[11200/16000] [L1: 0.0043] 16.7+0.1s +[12800/16000] [L1: 0.0043] 17.3+0.1s +[14400/16000] [L1: 0.0043] 16.0+0.1s +[16000/16000] [L1: 0.0043] 16.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.990 (Best: 44.056 @epoch 279) +Forward: 9.21s + +Saving... +Total: 9.73s + +[Epoch 374] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.4+1.0s +[3200/16000] [L1: 0.0043] 16.9+0.1s +[4800/16000] [L1: 0.0044] 16.1+0.1s +[6400/16000] [L1: 0.0043] 15.6+0.0s +[8000/16000] [L1: 0.0043] 16.9+0.1s +[9600/16000] [L1: 0.0043] 17.1+0.1s +[11200/16000] [L1: 0.0043] 16.0+0.0s +[12800/16000] [L1: 0.0043] 16.2+0.1s +[14400/16000] [L1: 0.0043] 16.2+0.0s +[16000/16000] [L1: 0.0043] 16.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.063 (Best: 44.063 @epoch 374) +Forward: 9.35s + +Saving... +Total: 9.78s + +[Epoch 375] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.2+1.0s +[3200/16000] [L1: 0.0044] 16.4+0.0s +[4800/16000] [L1: 0.0043] 17.4+0.1s +[6400/16000] [L1: 0.0043] 16.4+0.1s +[8000/16000] [L1: 0.0043] 18.0+0.1s +[9600/16000] [L1: 0.0043] 17.4+0.1s +[11200/16000] [L1: 0.0043] 16.7+0.0s +[12800/16000] [L1: 0.0043] 16.1+0.0s +[14400/16000] [L1: 0.0043] 15.2+0.0s +[16000/16000] [L1: 0.0043] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.034 (Best: 44.063 @epoch 374) +Forward: 9.16s + +Saving... +Total: 9.69s + +[Epoch 376] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 17.1+1.0s +[3200/16000] [L1: 0.0044] 16.3+0.1s +[4800/16000] [L1: 0.0044] 16.8+0.1s +[6400/16000] [L1: 0.0044] 17.1+0.1s +[8000/16000] [L1: 0.0043] 17.1+0.1s +[9600/16000] [L1: 0.0043] 16.8+0.1s +[11200/16000] [L1: 0.0043] 17.0+0.1s +[12800/16000] [L1: 0.0043] 16.0+0.1s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.061 (Best: 44.063 @epoch 374) +Forward: 9.27s + +Saving... +Total: 9.75s + +[Epoch 377] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.6+1.0s +[3200/16000] [L1: 0.0042] 15.9+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0043] 16.4+0.1s +[8000/16000] [L1: 0.0043] 15.8+0.0s +[9600/16000] [L1: 0.0044] 16.8+0.1s +[11200/16000] [L1: 0.0043] 16.4+0.0s +[12800/16000] [L1: 0.0043] 16.1+0.0s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.038 (Best: 44.063 @epoch 374) +Forward: 9.16s + +Saving... +Total: 9.57s + +[Epoch 378] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+0.9s +[3200/16000] [L1: 0.0044] 15.9+0.0s +[4800/16000] [L1: 0.0044] 15.6+0.0s +[6400/16000] [L1: 0.0044] 15.6+0.0s +[8000/16000] [L1: 0.0044] 15.9+0.1s +[9600/16000] [L1: 0.0044] 15.1+0.0s +[11200/16000] [L1: 0.0044] 15.5+0.0s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0044] 15.9+0.0s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.041 (Best: 44.063 @epoch 374) +Forward: 9.21s + +Saving... +Total: 9.63s + +[Epoch 379] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.0+1.0s +[3200/16000] [L1: 0.0043] 16.5+0.1s +[4800/16000] [L1: 0.0044] 16.6+0.1s +[6400/16000] [L1: 0.0043] 16.6+0.0s +[8000/16000] [L1: 0.0043] 16.0+0.0s +[9600/16000] [L1: 0.0044] 14.7+0.0s +[11200/16000] [L1: 0.0044] 15.7+0.0s +[12800/16000] [L1: 0.0044] 16.3+0.1s +[14400/16000] [L1: 0.0044] 16.6+0.0s +[16000/16000] [L1: 0.0044] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.037 (Best: 44.063 @epoch 374) +Forward: 9.25s + +Saving... +Total: 9.78s + +[Epoch 380] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.4+1.0s +[3200/16000] [L1: 0.0044] 15.5+0.1s +[4800/16000] [L1: 0.0044] 14.1+0.0s +[6400/16000] [L1: 0.0044] 15.3+0.0s +[8000/16000] [L1: 0.0044] 16.6+0.0s +[9600/16000] [L1: 0.0044] 16.8+0.1s +[11200/16000] [L1: 0.0043] 16.6+0.0s +[12800/16000] [L1: 0.0044] 15.4+0.0s +[14400/16000] [L1: 0.0044] 16.3+0.1s +[16000/16000] [L1: 0.0043] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.980 (Best: 44.063 @epoch 374) +Forward: 9.30s + +Saving... +Total: 9.85s + +[Epoch 381] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.1+1.1s +[3200/16000] [L1: 0.0044] 16.0+0.0s +[4800/16000] [L1: 0.0043] 15.8+0.0s +[6400/16000] [L1: 0.0044] 15.7+0.0s +[8000/16000] [L1: 0.0043] 17.1+0.1s +[9600/16000] [L1: 0.0043] 16.9+0.1s +[11200/16000] [L1: 0.0043] 15.7+0.0s +[12800/16000] [L1: 0.0043] 15.3+0.0s +[14400/16000] [L1: 0.0043] 15.7+0.0s +[16000/16000] [L1: 0.0043] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.977 (Best: 44.063 @epoch 374) +Forward: 9.16s + +Saving... +Total: 9.74s + +[Epoch 382] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.8+1.0s +[3200/16000] [L1: 0.0043] 17.0+0.1s +[4800/16000] [L1: 0.0043] 16.3+0.1s +[6400/16000] [L1: 0.0044] 16.6+0.1s +[8000/16000] [L1: 0.0044] 16.7+0.1s +[9600/16000] [L1: 0.0044] 16.3+0.1s +[11200/16000] [L1: 0.0044] 16.9+0.1s +[12800/16000] [L1: 0.0044] 16.4+0.1s +[14400/16000] [L1: 0.0044] 16.0+0.1s +[16000/16000] [L1: 0.0044] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.896 (Best: 44.063 @epoch 374) +Forward: 9.30s + +Saving... +Total: 9.73s + +[Epoch 383] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.6+1.1s +[3200/16000] [L1: 0.0044] 16.4+0.1s +[4800/16000] [L1: 0.0043] 17.1+0.1s +[6400/16000] [L1: 0.0043] 16.2+0.1s +[8000/16000] [L1: 0.0043] 16.7+0.1s +[9600/16000] [L1: 0.0043] 17.1+0.1s +[11200/16000] [L1: 0.0043] 16.2+0.1s +[12800/16000] [L1: 0.0043] 16.1+0.1s +[14400/16000] [L1: 0.0043] 16.4+0.1s +[16000/16000] [L1: 0.0043] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.019 (Best: 44.063 @epoch 374) +Forward: 9.14s + +Saving... +Total: 9.62s + +[Epoch 384] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 15.8+1.0s +[3200/16000] [L1: 0.0043] 15.0+0.0s +[4800/16000] [L1: 0.0043] 15.2+0.0s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0043] 16.4+0.1s +[9600/16000] [L1: 0.0043] 15.9+0.1s +[11200/16000] [L1: 0.0044] 15.9+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.1s +[14400/16000] [L1: 0.0044] 16.4+0.1s +[16000/16000] [L1: 0.0043] 17.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.970 (Best: 44.063 @epoch 374) +Forward: 9.14s + +Saving... +Total: 9.64s + +[Epoch 385] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.6+1.0s +[3200/16000] [L1: 0.0044] 16.2+0.0s +[4800/16000] [L1: 0.0044] 16.3+0.0s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0043] 15.7+0.0s +[9600/16000] [L1: 0.0043] 16.1+0.0s +[11200/16000] [L1: 0.0044] 16.6+0.0s +[12800/16000] [L1: 0.0044] 16.6+0.0s +[14400/16000] [L1: 0.0044] 16.2+0.0s +[16000/16000] [L1: 0.0044] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.053 (Best: 44.063 @epoch 374) +Forward: 9.05s + +Saving... +Total: 9.51s + +[Epoch 386] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.7+1.0s +[3200/16000] [L1: 0.0044] 16.5+0.1s +[4800/16000] [L1: 0.0044] 15.5+0.0s +[6400/16000] [L1: 0.0044] 16.6+0.1s +[8000/16000] [L1: 0.0044] 16.4+0.0s +[9600/16000] [L1: 0.0044] 16.5+0.0s +[11200/16000] [L1: 0.0044] 15.9+0.1s +[12800/16000] [L1: 0.0044] 16.6+0.0s +[14400/16000] [L1: 0.0044] 16.0+0.0s +[16000/16000] [L1: 0.0044] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.063 (Best: 44.063 @epoch 374) +Forward: 9.46s + +Saving... +Total: 10.01s + +[Epoch 387] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.9+1.0s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0043] 16.7+0.1s +[6400/16000] [L1: 0.0044] 17.1+0.1s +[8000/16000] [L1: 0.0043] 16.6+0.1s +[9600/16000] [L1: 0.0043] 16.4+0.1s +[11200/16000] [L1: 0.0043] 16.3+0.0s +[12800/16000] [L1: 0.0043] 16.5+0.1s +[14400/16000] [L1: 0.0043] 16.4+0.1s +[16000/16000] [L1: 0.0043] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.046 (Best: 44.063 @epoch 374) +Forward: 9.22s + +Saving... +Total: 9.81s + +[Epoch 388] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.7+1.1s +[3200/16000] [L1: 0.0043] 15.8+0.0s +[4800/16000] [L1: 0.0043] 15.7+0.0s +[6400/16000] [L1: 0.0044] 16.2+0.0s +[8000/16000] [L1: 0.0044] 16.0+0.1s +[9600/16000] [L1: 0.0043] 15.3+0.0s +[11200/16000] [L1: 0.0043] 14.9+0.0s +[12800/16000] [L1: 0.0044] 14.4+0.0s +[14400/16000] [L1: 0.0044] 16.6+0.0s +[16000/16000] [L1: 0.0044] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.010 (Best: 44.063 @epoch 374) +Forward: 9.25s + +Saving... +Total: 9.80s + +[Epoch 389] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.7+1.1s +[3200/16000] [L1: 0.0043] 15.9+0.1s +[4800/16000] [L1: 0.0043] 16.8+0.1s +[6400/16000] [L1: 0.0043] 15.8+0.1s +[8000/16000] [L1: 0.0043] 15.9+0.1s +[9600/16000] [L1: 0.0043] 16.6+0.1s +[11200/16000] [L1: 0.0044] 16.7+0.1s +[12800/16000] [L1: 0.0044] 15.7+0.0s +[14400/16000] [L1: 0.0043] 16.2+0.1s +[16000/16000] [L1: 0.0043] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.015 (Best: 44.063 @epoch 374) +Forward: 9.56s + +Saving... +Total: 10.07s + +[Epoch 390] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.9+1.1s +[3200/16000] [L1: 0.0042] 16.5+0.1s +[4800/16000] [L1: 0.0043] 16.2+0.1s +[6400/16000] [L1: 0.0043] 16.9+0.1s +[8000/16000] [L1: 0.0043] 16.1+0.1s +[9600/16000] [L1: 0.0043] 16.9+0.1s +[11200/16000] [L1: 0.0043] 16.7+0.1s +[12800/16000] [L1: 0.0043] 16.9+0.1s +[14400/16000] [L1: 0.0043] 16.6+0.1s +[16000/16000] [L1: 0.0043] 16.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.979 (Best: 44.063 @epoch 374) +Forward: 9.20s + +Saving... +Total: 9.76s + +[Epoch 391] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.9+1.2s +[3200/16000] [L1: 0.0044] 16.2+0.0s +[4800/16000] [L1: 0.0043] 16.1+0.0s +[6400/16000] [L1: 0.0043] 16.1+0.1s +[8000/16000] [L1: 0.0043] 16.2+0.0s +[9600/16000] [L1: 0.0043] 16.3+0.0s +[11200/16000] [L1: 0.0043] 16.9+0.1s +[12800/16000] [L1: 0.0043] 16.7+0.1s +[14400/16000] [L1: 0.0043] 16.2+0.0s +[16000/16000] [L1: 0.0043] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.068 (Best: 44.068 @epoch 391) +Forward: 9.32s + +Saving... +Total: 9.89s + +[Epoch 392] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0042] 16.9+1.0s +[3200/16000] [L1: 0.0044] 16.9+0.1s +[4800/16000] [L1: 0.0043] 16.6+0.1s +[6400/16000] [L1: 0.0044] 16.4+0.1s +[8000/16000] [L1: 0.0044] 15.9+0.0s +[9600/16000] [L1: 0.0044] 15.7+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0043] 16.8+0.1s +[14400/16000] [L1: 0.0043] 16.9+0.1s +[16000/16000] [L1: 0.0043] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.003 (Best: 44.068 @epoch 391) +Forward: 9.10s + +Saving... +Total: 9.71s + +[Epoch 393] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.1+1.1s +[3200/16000] [L1: 0.0043] 15.9+0.1s +[4800/16000] [L1: 0.0043] 16.9+0.1s +[6400/16000] [L1: 0.0042] 15.8+0.1s +[8000/16000] [L1: 0.0042] 16.5+0.1s +[9600/16000] [L1: 0.0043] 15.8+0.1s +[11200/16000] [L1: 0.0043] 16.8+0.1s +[12800/16000] [L1: 0.0043] 17.0+0.1s +[14400/16000] [L1: 0.0043] 17.3+0.1s +[16000/16000] [L1: 0.0043] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.021 (Best: 44.068 @epoch 391) +Forward: 9.26s + +Saving... +Total: 9.80s + +[Epoch 394] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 16.2+1.0s +[3200/16000] [L1: 0.0045] 16.0+0.0s +[4800/16000] [L1: 0.0044] 15.2+0.0s +[6400/16000] [L1: 0.0044] 14.8+0.0s +[8000/16000] [L1: 0.0044] 16.0+0.0s +[9600/16000] [L1: 0.0044] 15.9+0.0s +[11200/16000] [L1: 0.0044] 16.1+0.0s +[12800/16000] [L1: 0.0044] 16.0+0.0s +[14400/16000] [L1: 0.0044] 14.5+0.0s +[16000/16000] [L1: 0.0044] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.017 (Best: 44.068 @epoch 391) +Forward: 9.12s + +Saving... +Total: 9.71s + +[Epoch 395] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.5+1.1s +[3200/16000] [L1: 0.0045] 16.6+0.1s +[4800/16000] [L1: 0.0044] 16.7+0.1s +[6400/16000] [L1: 0.0044] 16.9+0.1s +[8000/16000] [L1: 0.0044] 16.8+0.1s +[9600/16000] [L1: 0.0044] 17.1+0.1s +[11200/16000] [L1: 0.0044] 16.6+0.1s +[12800/16000] [L1: 0.0044] 17.2+0.1s +[14400/16000] [L1: 0.0044] 17.1+0.1s +[16000/16000] [L1: 0.0044] 16.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.068 (Best: 44.068 @epoch 391) +Forward: 9.28s + +Saving... +Total: 9.86s + +[Epoch 396] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 16.8+1.0s +[3200/16000] [L1: 0.0043] 16.3+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0043] 17.0+0.1s +[9600/16000] [L1: 0.0043] 16.5+0.1s +[11200/16000] [L1: 0.0043] 16.3+0.1s +[12800/16000] [L1: 0.0043] 16.1+0.1s +[14400/16000] [L1: 0.0043] 16.7+0.1s +[16000/16000] [L1: 0.0043] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.016 (Best: 44.068 @epoch 391) +Forward: 9.25s + +Saving... +Total: 9.77s + +[Epoch 397] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 17.4+1.0s +[3200/16000] [L1: 0.0043] 17.3+0.1s +[4800/16000] [L1: 0.0043] 17.2+0.1s +[6400/16000] [L1: 0.0043] 16.1+0.1s +[8000/16000] [L1: 0.0044] 16.8+0.1s +[9600/16000] [L1: 0.0044] 15.7+0.1s +[11200/16000] [L1: 0.0044] 17.3+0.1s +[12800/16000] [L1: 0.0043] 13.4+0.0s +[14400/16000] [L1: 0.0043] 16.2+0.1s +[16000/16000] [L1: 0.0043] 17.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.028 (Best: 44.068 @epoch 391) +Forward: 9.17s + +Saving... +Total: 9.71s + +[Epoch 398] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.7+1.0s +[3200/16000] [L1: 0.0044] 17.2+0.1s +[4800/16000] [L1: 0.0043] 15.8+0.0s +[6400/16000] [L1: 0.0044] 15.8+0.0s +[8000/16000] [L1: 0.0044] 16.7+0.0s +[9600/16000] [L1: 0.0044] 14.3+0.0s +[11200/16000] [L1: 0.0044] 15.2+0.0s +[12800/16000] [L1: 0.0043] 15.6+0.0s +[14400/16000] [L1: 0.0044] 16.3+0.1s +[16000/16000] [L1: 0.0044] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.938 (Best: 44.068 @epoch 391) +Forward: 9.18s + +Saving... +Total: 9.61s + +[Epoch 399] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 16.4+1.1s +[3200/16000] [L1: 0.0043] 16.5+0.0s +[4800/16000] [L1: 0.0043] 16.7+0.1s +[6400/16000] [L1: 0.0043] 17.1+0.1s +[8000/16000] [L1: 0.0043] 15.6+0.0s +[9600/16000] [L1: 0.0043] 16.1+0.1s +[11200/16000] [L1: 0.0043] 15.0+0.0s +[12800/16000] [L1: 0.0043] 16.3+0.1s +[14400/16000] [L1: 0.0043] 15.1+0.0s +[16000/16000] [L1: 0.0043] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.055 (Best: 44.068 @epoch 391) +Forward: 9.30s + +Saving... +Total: 9.77s + +[Epoch 400] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.2+0.9s +[3200/16000] [L1: 0.0043] 16.0+0.0s +[4800/16000] [L1: 0.0043] 15.9+0.0s +[6400/16000] [L1: 0.0044] 16.1+0.0s +[8000/16000] [L1: 0.0043] 15.7+0.0s +[9600/16000] [L1: 0.0043] 16.7+0.1s +[11200/16000] [L1: 0.0043] 16.9+0.1s +[12800/16000] [L1: 0.0043] 16.4+0.1s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.984 (Best: 44.068 @epoch 391) +Forward: 9.15s + +Saving... +Total: 9.67s + +[Epoch 401] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0042] 16.0+1.1s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0043] 16.4+0.1s +[6400/16000] [L1: 0.0043] 16.8+0.1s +[8000/16000] [L1: 0.0043] 16.2+0.1s +[9600/16000] [L1: 0.0043] 17.0+0.1s +[11200/16000] [L1: 0.0043] 16.9+0.1s +[12800/16000] [L1: 0.0043] 17.0+0.1s +[14400/16000] [L1: 0.0043] 16.1+0.1s +[16000/16000] [L1: 0.0043] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.024 (Best: 44.068 @epoch 391) +Forward: 9.26s + +Saving... +Total: 9.78s + +[Epoch 402] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 15.5+0.9s +[3200/16000] [L1: 0.0042] 16.9+0.1s +[4800/16000] [L1: 0.0042] 17.1+0.1s +[6400/16000] [L1: 0.0042] 16.5+0.1s +[8000/16000] [L1: 0.0042] 16.9+0.1s +[9600/16000] [L1: 0.0043] 16.7+0.0s +[11200/16000] [L1: 0.0043] 15.9+0.1s +[12800/16000] [L1: 0.0043] 14.8+0.0s +[14400/16000] [L1: 0.0043] 16.3+0.1s +[16000/16000] [L1: 0.0043] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.997 (Best: 44.068 @epoch 391) +Forward: 9.24s + +Saving... +Total: 9.71s + +[Epoch 403] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 15.8+0.9s +[3200/16000] [L1: 0.0042] 16.1+0.0s +[4800/16000] [L1: 0.0042] 16.1+0.1s +[6400/16000] [L1: 0.0042] 16.6+0.1s +[8000/16000] [L1: 0.0042] 15.4+0.1s +[9600/16000] [L1: 0.0043] 16.0+0.0s +[11200/16000] [L1: 0.0043] 15.6+0.0s +[12800/16000] [L1: 0.0043] 16.3+0.1s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.097 (Best: 44.097 @epoch 403) +Forward: 9.16s + +Saving... +Total: 9.63s + +[Epoch 404] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.3+1.0s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0043] 16.8+0.1s +[6400/16000] [L1: 0.0043] 16.8+0.1s +[8000/16000] [L1: 0.0043] 16.8+0.1s +[9600/16000] [L1: 0.0043] 16.9+0.1s +[11200/16000] [L1: 0.0043] 16.5+0.1s +[12800/16000] [L1: 0.0043] 16.8+0.1s +[14400/16000] [L1: 0.0043] 17.2+0.1s +[16000/16000] [L1: 0.0043] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.083 (Best: 44.097 @epoch 403) +Forward: 9.30s + +Saving... +Total: 9.83s + +[Epoch 405] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.9+1.0s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0043] 15.5+0.0s +[6400/16000] [L1: 0.0043] 15.0+0.0s +[8000/16000] [L1: 0.0043] 15.7+0.0s +[9600/16000] [L1: 0.0043] 15.6+0.0s +[11200/16000] [L1: 0.0043] 15.4+0.0s +[12800/16000] [L1: 0.0043] 15.4+0.0s +[14400/16000] [L1: 0.0042] 14.9+0.0s +[16000/16000] [L1: 0.0042] 17.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.065 (Best: 44.097 @epoch 403) +Forward: 9.33s + +Saving... +Total: 9.84s + +[Epoch 406] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 15.4+1.0s +[3200/16000] [L1: 0.0043] 16.4+0.1s +[4800/16000] [L1: 0.0042] 14.8+0.0s +[6400/16000] [L1: 0.0042] 15.7+0.1s +[8000/16000] [L1: 0.0042] 16.8+0.1s +[9600/16000] [L1: 0.0042] 15.9+0.0s +[11200/16000] [L1: 0.0042] 16.5+0.1s +[12800/16000] [L1: 0.0043] 15.7+0.0s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.085 (Best: 44.097 @epoch 403) +Forward: 9.21s + +Saving... +Total: 9.74s + +[Epoch 407] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.0+1.0s +[3200/16000] [L1: 0.0042] 16.9+0.0s +[4800/16000] [L1: 0.0042] 16.3+0.1s +[6400/16000] [L1: 0.0042] 15.7+0.0s +[8000/16000] [L1: 0.0042] 17.5+0.1s +[9600/16000] [L1: 0.0042] 16.8+0.0s +[11200/16000] [L1: 0.0042] 16.1+0.0s +[12800/16000] [L1: 0.0042] 15.9+0.0s +[14400/16000] [L1: 0.0042] 15.4+0.0s +[16000/16000] [L1: 0.0042] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.028 (Best: 44.097 @epoch 403) +Forward: 9.44s + +Saving... +Total: 9.97s + +[Epoch 408] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 15.9+1.2s +[3200/16000] [L1: 0.0043] 17.2+0.1s +[4800/16000] [L1: 0.0042] 15.8+0.1s +[6400/16000] [L1: 0.0042] 15.6+0.1s +[8000/16000] [L1: 0.0042] 16.1+0.0s +[9600/16000] [L1: 0.0042] 16.9+0.1s +[11200/16000] [L1: 0.0042] 16.1+0.1s +[12800/16000] [L1: 0.0042] 16.8+0.1s +[14400/16000] [L1: 0.0042] 16.4+0.0s +[16000/16000] [L1: 0.0042] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.061 (Best: 44.097 @epoch 403) +Forward: 9.27s + +Saving... +Total: 9.84s + +[Epoch 409] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.2+1.2s +[3200/16000] [L1: 0.0043] 17.0+0.1s +[4800/16000] [L1: 0.0042] 16.7+0.1s +[6400/16000] [L1: 0.0042] 16.9+0.1s +[8000/16000] [L1: 0.0042] 16.8+0.1s +[9600/16000] [L1: 0.0042] 16.8+0.1s +[11200/16000] [L1: 0.0042] 16.7+0.1s +[12800/16000] [L1: 0.0042] 16.6+0.1s +[14400/16000] [L1: 0.0042] 16.4+0.1s +[16000/16000] [L1: 0.0042] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.038 (Best: 44.097 @epoch 403) +Forward: 9.36s + +Saving... +Total: 9.87s + +[Epoch 410] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.4+1.0s +[3200/16000] [L1: 0.0042] 16.1+0.1s +[4800/16000] [L1: 0.0042] 16.7+0.1s +[6400/16000] [L1: 0.0042] 15.8+0.0s +[8000/16000] [L1: 0.0042] 16.9+0.1s +[9600/16000] [L1: 0.0043] 16.7+0.0s +[11200/16000] [L1: 0.0043] 16.4+0.0s +[12800/16000] [L1: 0.0043] 15.7+0.0s +[14400/16000] [L1: 0.0043] 14.0+0.0s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.077 (Best: 44.097 @epoch 403) +Forward: 9.25s + +Saving... +Total: 9.75s + +[Epoch 411] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.7+1.0s +[3200/16000] [L1: 0.0043] 16.0+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 16.2+0.1s +[8000/16000] [L1: 0.0043] 16.9+0.1s +[9600/16000] [L1: 0.0043] 17.3+0.1s +[11200/16000] [L1: 0.0043] 16.5+0.1s +[12800/16000] [L1: 0.0043] 16.7+0.1s +[14400/16000] [L1: 0.0043] 16.9+0.1s +[16000/16000] [L1: 0.0043] 16.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.069 (Best: 44.097 @epoch 403) +Forward: 9.29s + +Saving... +Total: 9.82s + +[Epoch 412] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 17.3+1.1s +[3200/16000] [L1: 0.0043] 15.9+0.1s +[4800/16000] [L1: 0.0043] 16.6+0.1s +[6400/16000] [L1: 0.0043] 17.2+0.1s +[8000/16000] [L1: 0.0043] 16.0+0.0s +[9600/16000] [L1: 0.0043] 15.6+0.0s +[11200/16000] [L1: 0.0043] 15.8+0.0s +[12800/16000] [L1: 0.0043] 16.6+0.1s +[14400/16000] [L1: 0.0043] 16.0+0.0s +[16000/16000] [L1: 0.0043] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.054 (Best: 44.097 @epoch 403) +Forward: 9.32s + +Saving... +Total: 9.74s + +[Epoch 413] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.9+1.0s +[3200/16000] [L1: 0.0043] 16.7+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 16.8+0.1s +[8000/16000] [L1: 0.0043] 16.3+0.1s +[9600/16000] [L1: 0.0043] 16.4+0.1s +[11200/16000] [L1: 0.0043] 16.4+0.1s +[12800/16000] [L1: 0.0043] 16.4+0.1s +[14400/16000] [L1: 0.0043] 16.1+0.1s +[16000/16000] [L1: 0.0043] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 43.992 (Best: 44.097 @epoch 403) +Forward: 9.25s + +Saving... +Total: 9.75s + +[Epoch 414] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.1+1.0s +[3200/16000] [L1: 0.0043] 16.3+0.1s +[4800/16000] [L1: 0.0043] 15.4+0.0s +[6400/16000] [L1: 0.0042] 16.7+0.1s +[8000/16000] [L1: 0.0043] 16.4+0.1s +[9600/16000] [L1: 0.0042] 15.6+0.0s +[11200/16000] [L1: 0.0043] 15.7+0.0s +[12800/16000] [L1: 0.0043] 16.3+0.0s +[14400/16000] [L1: 0.0043] 16.8+0.1s +[16000/16000] [L1: 0.0043] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.030 (Best: 44.097 @epoch 403) +Forward: 9.38s + +Saving... +Total: 9.88s + +[Epoch 415] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.2+1.0s +[3200/16000] [L1: 0.0043] 15.4+0.0s +[4800/16000] [L1: 0.0043] 15.1+0.0s +[6400/16000] [L1: 0.0043] 15.8+0.0s +[8000/16000] [L1: 0.0043] 16.0+0.0s +[9600/16000] [L1: 0.0043] 15.6+0.0s +[11200/16000] [L1: 0.0043] 16.9+0.1s +[12800/16000] [L1: 0.0043] 15.9+0.0s +[14400/16000] [L1: 0.0043] 15.5+0.0s +[16000/16000] [L1: 0.0043] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.051 (Best: 44.097 @epoch 403) +Forward: 9.13s + +Saving... +Total: 9.64s + +[Epoch 416] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 14.8+1.0s +[3200/16000] [L1: 0.0042] 16.1+0.1s +[4800/16000] [L1: 0.0042] 15.1+0.1s +[6400/16000] [L1: 0.0042] 15.5+0.0s +[8000/16000] [L1: 0.0042] 17.0+0.1s +[9600/16000] [L1: 0.0042] 16.6+0.1s +[11200/16000] [L1: 0.0042] 15.7+0.0s +[12800/16000] [L1: 0.0042] 15.9+0.0s +[14400/16000] [L1: 0.0043] 16.1+0.0s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.033 (Best: 44.097 @epoch 403) +Forward: 9.30s + +Saving... +Total: 9.83s + +[Epoch 417] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 17.0+1.0s +[3200/16000] [L1: 0.0042] 16.6+0.1s +[4800/16000] [L1: 0.0043] 17.2+0.1s +[6400/16000] [L1: 0.0042] 15.8+0.1s +[8000/16000] [L1: 0.0042] 16.3+0.1s +[9600/16000] [L1: 0.0042] 15.2+0.0s +[11200/16000] [L1: 0.0042] 15.7+0.0s +[12800/16000] [L1: 0.0043] 16.2+0.1s +[14400/16000] [L1: 0.0043] 16.9+0.1s +[16000/16000] [L1: 0.0043] 17.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.037 (Best: 44.097 @epoch 403) +Forward: 9.25s + +Saving... +Total: 9.64s + +[Epoch 418] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.6+1.0s +[3200/16000] [L1: 0.0043] 16.9+0.1s +[4800/16000] [L1: 0.0043] 16.4+0.1s +[6400/16000] [L1: 0.0043] 16.4+0.1s +[8000/16000] [L1: 0.0043] 15.6+0.0s +[9600/16000] [L1: 0.0043] 15.8+0.0s +[11200/16000] [L1: 0.0043] 15.9+0.1s +[12800/16000] [L1: 0.0043] 16.6+0.1s +[14400/16000] [L1: 0.0043] 15.6+0.0s +[16000/16000] [L1: 0.0043] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.041 (Best: 44.097 @epoch 403) +Forward: 9.23s + +Saving... +Total: 9.65s + +[Epoch 419] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0044] 16.6+1.2s +[3200/16000] [L1: 0.0043] 17.3+0.1s +[4800/16000] [L1: 0.0043] 17.2+0.1s +[6400/16000] [L1: 0.0043] 16.5+0.1s +[8000/16000] [L1: 0.0043] 17.2+0.1s +[9600/16000] [L1: 0.0043] 16.5+0.1s +[11200/16000] [L1: 0.0043] 16.7+0.1s +[12800/16000] [L1: 0.0043] 16.7+0.1s +[14400/16000] [L1: 0.0043] 16.7+0.1s +[16000/16000] [L1: 0.0043] 17.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.080 (Best: 44.097 @epoch 403) +Forward: 9.29s + +Saving... +Total: 9.84s + +[Epoch 420] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 17.1+1.1s +[3200/16000] [L1: 0.0043] 16.1+0.1s +[4800/16000] [L1: 0.0043] 16.2+0.1s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0043] 16.1+0.1s +[9600/16000] [L1: 0.0043] 15.9+0.0s +[11200/16000] [L1: 0.0043] 15.6+0.0s +[12800/16000] [L1: 0.0042] 16.3+0.0s +[14400/16000] [L1: 0.0042] 16.2+0.1s +[16000/16000] [L1: 0.0043] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.062 (Best: 44.097 @epoch 403) +Forward: 9.18s + +Saving... +Total: 9.68s + +[Epoch 421] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0044] 15.7+1.0s +[3200/16000] [L1: 0.0043] 15.8+0.0s +[4800/16000] [L1: 0.0043] 15.6+0.0s +[6400/16000] [L1: 0.0043] 16.7+0.1s +[8000/16000] [L1: 0.0043] 16.3+0.1s +[9600/16000] [L1: 0.0043] 15.6+0.0s +[11200/16000] [L1: 0.0043] 15.8+0.0s +[12800/16000] [L1: 0.0043] 15.7+0.0s +[14400/16000] [L1: 0.0042] 16.2+0.1s +[16000/16000] [L1: 0.0042] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.037 (Best: 44.097 @epoch 403) +Forward: 9.25s + +Saving... +Total: 9.72s + +[Epoch 422] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.5+1.0s +[3200/16000] [L1: 0.0042] 16.6+0.1s +[4800/16000] [L1: 0.0042] 17.3+0.1s +[6400/16000] [L1: 0.0042] 15.9+0.0s +[8000/16000] [L1: 0.0042] 16.0+0.0s +[9600/16000] [L1: 0.0042] 15.5+0.0s +[11200/16000] [L1: 0.0042] 14.7+0.0s +[12800/16000] [L1: 0.0042] 16.1+0.0s +[14400/16000] [L1: 0.0042] 16.9+0.0s +[16000/16000] [L1: 0.0042] 16.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.079 (Best: 44.097 @epoch 403) +Forward: 9.30s + +Saving... +Total: 9.84s + +[Epoch 423] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0044] 15.4+1.0s +[3200/16000] [L1: 0.0044] 16.2+0.0s +[4800/16000] [L1: 0.0044] 17.1+0.1s +[6400/16000] [L1: 0.0044] 17.4+0.1s +[8000/16000] [L1: 0.0043] 16.1+0.0s +[9600/16000] [L1: 0.0043] 16.2+0.0s +[11200/16000] [L1: 0.0043] 16.5+0.1s +[12800/16000] [L1: 0.0043] 16.1+0.1s +[14400/16000] [L1: 0.0043] 16.9+0.1s +[16000/16000] [L1: 0.0043] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.017 (Best: 44.097 @epoch 403) +Forward: 9.25s + +Saving... +Total: 9.79s + +[Epoch 424] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.9+1.1s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0043] 17.4+0.1s +[9600/16000] [L1: 0.0043] 15.6+0.1s +[11200/16000] [L1: 0.0043] 16.3+0.1s +[12800/16000] [L1: 0.0043] 17.0+0.1s +[14400/16000] [L1: 0.0043] 16.9+0.1s +[16000/16000] [L1: 0.0043] 16.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.031 (Best: 44.097 @epoch 403) +Forward: 9.27s + +Saving... +Total: 9.86s + +[Epoch 425] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0045] 17.2+1.0s +[3200/16000] [L1: 0.0044] 17.2+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0043] 16.4+0.1s +[8000/16000] [L1: 0.0043] 16.4+0.1s +[9600/16000] [L1: 0.0043] 17.5+0.1s +[11200/16000] [L1: 0.0043] 16.5+0.1s +[12800/16000] [L1: 0.0042] 16.9+0.1s +[14400/16000] [L1: 0.0042] 16.2+0.1s +[16000/16000] [L1: 0.0042] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.057 (Best: 44.097 @epoch 403) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 426] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.1+1.2s +[3200/16000] [L1: 0.0043] 16.0+0.1s +[4800/16000] [L1: 0.0043] 16.1+0.1s +[6400/16000] [L1: 0.0043] 16.3+0.1s +[8000/16000] [L1: 0.0043] 16.0+0.1s +[9600/16000] [L1: 0.0042] 16.5+0.1s +[11200/16000] [L1: 0.0042] 16.9+0.1s +[12800/16000] [L1: 0.0042] 16.0+0.0s +[14400/16000] [L1: 0.0042] 15.5+0.1s +[16000/16000] [L1: 0.0042] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.006 (Best: 44.097 @epoch 403) +Forward: 9.26s + +Saving... +Total: 9.75s + +[Epoch 427] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.7+1.1s +[3200/16000] [L1: 0.0043] 16.1+0.0s +[4800/16000] [L1: 0.0042] 16.0+0.1s +[6400/16000] [L1: 0.0042] 17.0+0.1s +[8000/16000] [L1: 0.0043] 16.8+0.1s +[9600/16000] [L1: 0.0043] 17.4+0.1s +[11200/16000] [L1: 0.0043] 17.0+0.1s +[12800/16000] [L1: 0.0042] 15.7+0.1s +[14400/16000] [L1: 0.0043] 15.6+0.0s +[16000/16000] [L1: 0.0043] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.042 (Best: 44.097 @epoch 403) +Forward: 9.32s + +Saving... +Total: 9.71s + +[Epoch 428] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.8+1.1s +[3200/16000] [L1: 0.0042] 17.0+0.1s +[4800/16000] [L1: 0.0043] 16.5+0.1s +[6400/16000] [L1: 0.0042] 16.0+0.0s +[8000/16000] [L1: 0.0043] 15.9+0.1s +[9600/16000] [L1: 0.0043] 16.3+0.1s +[11200/16000] [L1: 0.0043] 15.4+0.1s +[12800/16000] [L1: 0.0043] 16.1+0.0s +[14400/16000] [L1: 0.0043] 17.1+0.0s +[16000/16000] [L1: 0.0043] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.052 (Best: 44.097 @epoch 403) +Forward: 9.15s + +Saving... +Total: 9.69s + +[Epoch 429] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.0+1.1s +[3200/16000] [L1: 0.0042] 16.6+0.1s +[4800/16000] [L1: 0.0043] 16.1+0.1s +[6400/16000] [L1: 0.0043] 17.0+0.1s +[8000/16000] [L1: 0.0043] 17.5+0.1s +[9600/16000] [L1: 0.0043] 16.3+0.1s +[11200/16000] [L1: 0.0043] 15.7+0.1s +[12800/16000] [L1: 0.0043] 16.1+0.1s +[14400/16000] [L1: 0.0043] 16.5+0.1s +[16000/16000] [L1: 0.0043] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.074 (Best: 44.097 @epoch 403) +Forward: 9.31s + +Saving... +Total: 9.79s + +[Epoch 430] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 14.9+1.0s +[3200/16000] [L1: 0.0042] 16.9+0.1s +[4800/16000] [L1: 0.0042] 16.7+0.1s +[6400/16000] [L1: 0.0042] 17.0+0.1s +[8000/16000] [L1: 0.0042] 15.7+0.1s +[9600/16000] [L1: 0.0042] 16.0+0.1s +[11200/16000] [L1: 0.0043] 16.2+0.1s +[12800/16000] [L1: 0.0043] 16.5+0.1s +[14400/16000] [L1: 0.0042] 16.8+0.1s +[16000/16000] [L1: 0.0042] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.004 (Best: 44.097 @epoch 403) +Forward: 9.31s + +Saving... +Total: 9.89s + +[Epoch 431] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 15.4+1.0s +[3200/16000] [L1: 0.0043] 16.1+0.0s +[4800/16000] [L1: 0.0042] 16.7+0.0s +[6400/16000] [L1: 0.0042] 16.3+0.1s +[8000/16000] [L1: 0.0042] 16.2+0.1s +[9600/16000] [L1: 0.0042] 16.3+0.1s +[11200/16000] [L1: 0.0042] 15.8+0.0s +[12800/16000] [L1: 0.0042] 15.9+0.0s +[14400/16000] [L1: 0.0042] 16.6+0.0s +[16000/16000] [L1: 0.0043] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.029 (Best: 44.097 @epoch 403) +Forward: 9.26s + +Saving... +Total: 9.73s + +[Epoch 432] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0041] 16.8+1.0s +[3200/16000] [L1: 0.0042] 16.9+0.1s +[4800/16000] [L1: 0.0043] 15.9+0.0s +[6400/16000] [L1: 0.0043] 16.8+0.1s +[8000/16000] [L1: 0.0043] 16.8+0.1s +[9600/16000] [L1: 0.0043] 16.3+0.0s +[11200/16000] [L1: 0.0043] 17.6+0.1s +[12800/16000] [L1: 0.0043] 16.8+0.0s +[14400/16000] [L1: 0.0043] 16.7+0.0s +[16000/16000] [L1: 0.0043] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.049 (Best: 44.097 @epoch 403) +Forward: 9.21s + +Saving... +Total: 9.71s + +[Epoch 433] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.3+1.3s +[3200/16000] [L1: 0.0042] 16.0+0.1s +[4800/16000] [L1: 0.0042] 15.7+0.0s +[6400/16000] [L1: 0.0042] 16.4+0.0s +[8000/16000] [L1: 0.0042] 15.3+0.1s +[9600/16000] [L1: 0.0042] 16.8+0.1s +[11200/16000] [L1: 0.0042] 16.6+0.1s +[12800/16000] [L1: 0.0042] 15.9+0.1s +[14400/16000] [L1: 0.0042] 16.6+0.1s +[16000/16000] [L1: 0.0042] 17.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.037 (Best: 44.097 @epoch 403) +Forward: 9.27s + +Saving... +Total: 9.73s + +[Epoch 434] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.6+1.1s +[3200/16000] [L1: 0.0043] 15.9+0.0s +[4800/16000] [L1: 0.0042] 16.4+0.1s +[6400/16000] [L1: 0.0043] 16.5+0.1s +[8000/16000] [L1: 0.0043] 17.4+0.1s +[9600/16000] [L1: 0.0043] 16.2+0.1s +[11200/16000] [L1: 0.0043] 16.2+0.1s +[12800/16000] [L1: 0.0043] 16.6+0.1s +[14400/16000] [L1: 0.0043] 16.2+0.1s +[16000/16000] [L1: 0.0043] 16.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.082 (Best: 44.097 @epoch 403) +Forward: 9.32s + +Saving... +Total: 9.89s + +[Epoch 435] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.8+1.1s +[3200/16000] [L1: 0.0043] 16.3+0.1s +[4800/16000] [L1: 0.0043] 16.4+0.1s +[6400/16000] [L1: 0.0043] 16.5+0.1s +[8000/16000] [L1: 0.0043] 17.1+0.1s +[9600/16000] [L1: 0.0043] 15.7+0.0s +[11200/16000] [L1: 0.0043] 16.5+0.1s +[12800/16000] [L1: 0.0043] 16.8+0.1s +[14400/16000] [L1: 0.0043] 16.1+0.0s +[16000/16000] [L1: 0.0043] 16.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.993 (Best: 44.097 @epoch 403) +Forward: 9.37s + +Saving... +Total: 9.88s + +[Epoch 436] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0041] 15.7+1.0s +[3200/16000] [L1: 0.0041] 15.1+0.1s +[4800/16000] [L1: 0.0041] 16.2+0.0s +[6400/16000] [L1: 0.0042] 16.8+0.1s +[8000/16000] [L1: 0.0042] 16.8+0.1s +[9600/16000] [L1: 0.0042] 16.6+0.1s +[11200/16000] [L1: 0.0042] 16.7+0.1s +[12800/16000] [L1: 0.0042] 16.9+0.1s +[14400/16000] [L1: 0.0043] 15.7+0.0s +[16000/16000] [L1: 0.0043] 17.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.031 (Best: 44.097 @epoch 403) +Forward: 9.25s + +Saving... +Total: 9.77s + +[Epoch 437] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 15.2+1.0s +[3200/16000] [L1: 0.0042] 16.1+0.1s +[4800/16000] [L1: 0.0042] 16.7+0.1s +[6400/16000] [L1: 0.0042] 15.4+0.0s +[8000/16000] [L1: 0.0042] 15.7+0.0s +[9600/16000] [L1: 0.0042] 16.1+0.1s +[11200/16000] [L1: 0.0042] 15.6+0.0s +[12800/16000] [L1: 0.0042] 16.8+0.1s +[14400/16000] [L1: 0.0042] 16.2+0.1s +[16000/16000] [L1: 0.0042] 16.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.035 (Best: 44.097 @epoch 403) +Forward: 9.17s + +Saving... +Total: 9.72s + +[Epoch 438] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 15.9+1.1s +[3200/16000] [L1: 0.0043] 16.8+0.1s +[4800/16000] [L1: 0.0043] 15.6+0.0s +[6400/16000] [L1: 0.0042] 17.1+0.1s +[8000/16000] [L1: 0.0042] 16.8+0.0s +[9600/16000] [L1: 0.0042] 17.0+0.1s +[11200/16000] [L1: 0.0042] 16.7+0.0s +[12800/16000] [L1: 0.0042] 16.1+0.0s +[14400/16000] [L1: 0.0042] 16.6+0.0s +[16000/16000] [L1: 0.0042] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.058 (Best: 44.097 @epoch 403) +Forward: 9.16s + +Saving... +Total: 9.73s + +[Epoch 439] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 15.9+1.0s +[3200/16000] [L1: 0.0043] 16.2+0.0s +[4800/16000] [L1: 0.0043] 16.8+0.1s +[6400/16000] [L1: 0.0043] 16.6+0.1s +[8000/16000] [L1: 0.0043] 17.1+0.1s +[9600/16000] [L1: 0.0043] 16.8+0.1s +[11200/16000] [L1: 0.0043] 16.7+0.1s +[12800/16000] [L1: 0.0043] 16.0+0.1s +[14400/16000] [L1: 0.0043] 16.5+0.1s +[16000/16000] [L1: 0.0043] 16.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.028 (Best: 44.097 @epoch 403) +Forward: 9.16s + +Saving... +Total: 9.61s + +[Epoch 440] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.1+0.9s +[3200/16000] [L1: 0.0042] 16.0+0.0s +[4800/16000] [L1: 0.0042] 15.4+0.0s +[6400/16000] [L1: 0.0042] 15.4+0.0s +[8000/16000] [L1: 0.0042] 16.9+0.1s +[9600/16000] [L1: 0.0042] 16.2+0.0s +[11200/16000] [L1: 0.0042] 16.5+0.1s +[12800/16000] [L1: 0.0042] 16.7+0.1s +[14400/16000] [L1: 0.0042] 15.8+0.1s +[16000/16000] [L1: 0.0042] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.030 (Best: 44.097 @epoch 403) +Forward: 9.24s + +Saving... +Total: 9.73s + +[Epoch 441] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0044] 15.4+1.1s +[3200/16000] [L1: 0.0044] 16.8+0.1s +[4800/16000] [L1: 0.0044] 16.6+0.1s +[6400/16000] [L1: 0.0043] 16.9+0.1s +[8000/16000] [L1: 0.0043] 16.7+0.1s +[9600/16000] [L1: 0.0043] 16.0+0.1s +[11200/16000] [L1: 0.0043] 15.6+0.0s +[12800/16000] [L1: 0.0043] 16.2+0.1s +[14400/16000] [L1: 0.0043] 15.6+0.0s +[16000/16000] [L1: 0.0043] 16.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.074 (Best: 44.097 @epoch 403) +Forward: 9.33s + +Saving... +Total: 9.82s + +[Epoch 442] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 15.8+1.0s +[3200/16000] [L1: 0.0042] 15.9+0.0s +[4800/16000] [L1: 0.0042] 15.9+0.0s +[6400/16000] [L1: 0.0042] 16.6+0.0s +[8000/16000] [L1: 0.0042] 15.0+0.0s +[9600/16000] [L1: 0.0042] 15.1+0.0s +[11200/16000] [L1: 0.0042] 15.6+0.0s +[12800/16000] [L1: 0.0042] 16.6+0.0s +[14400/16000] [L1: 0.0042] 15.5+0.0s +[16000/16000] [L1: 0.0042] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.032 (Best: 44.097 @epoch 403) +Forward: 9.42s + +Saving... +Total: 9.91s + +[Epoch 443] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0041] 16.2+1.1s +[3200/16000] [L1: 0.0042] 16.7+0.0s +[4800/16000] [L1: 0.0043] 17.0+0.1s +[6400/16000] [L1: 0.0043] 17.1+0.1s +[8000/16000] [L1: 0.0043] 17.1+0.1s +[9600/16000] [L1: 0.0043] 16.0+0.1s +[11200/16000] [L1: 0.0043] 16.6+0.1s +[12800/16000] [L1: 0.0043] 16.3+0.0s +[14400/16000] [L1: 0.0043] 15.6+0.0s +[16000/16000] [L1: 0.0043] 16.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.016 (Best: 44.097 @epoch 403) +Forward: 9.28s + +Saving... +Total: 9.79s + +[Epoch 444] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.3+1.1s +[3200/16000] [L1: 0.0042] 15.4+0.0s +[4800/16000] [L1: 0.0042] 16.1+0.1s +[6400/16000] [L1: 0.0042] 16.2+0.1s +[8000/16000] [L1: 0.0042] 16.3+0.1s +[9600/16000] [L1: 0.0042] 16.0+0.1s +[11200/16000] [L1: 0.0042] 16.6+0.1s +[12800/16000] [L1: 0.0042] 16.4+0.1s +[14400/16000] [L1: 0.0043] 16.5+0.1s +[16000/16000] [L1: 0.0043] 16.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.057 (Best: 44.097 @epoch 403) +Forward: 9.26s + +Saving... +Total: 9.74s + +[Epoch 445] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.6+1.1s +[3200/16000] [L1: 0.0043] 17.3+0.1s +[4800/16000] [L1: 0.0042] 16.7+0.1s +[6400/16000] [L1: 0.0043] 15.9+0.0s +[8000/16000] [L1: 0.0042] 16.2+0.1s +[9600/16000] [L1: 0.0042] 15.9+0.0s +[11200/16000] [L1: 0.0042] 15.6+0.0s +[12800/16000] [L1: 0.0042] 16.0+0.1s +[14400/16000] [L1: 0.0042] 16.8+0.0s +[16000/16000] [L1: 0.0042] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.028 (Best: 44.097 @epoch 403) +Forward: 9.16s + +Saving... +Total: 9.69s + +[Epoch 446] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.8+1.0s +[3200/16000] [L1: 0.0043] 16.6+0.1s +[4800/16000] [L1: 0.0042] 16.2+0.1s +[6400/16000] [L1: 0.0042] 16.1+0.1s +[8000/16000] [L1: 0.0042] 16.5+0.1s +[9600/16000] [L1: 0.0042] 17.0+0.1s +[11200/16000] [L1: 0.0042] 16.1+0.1s +[12800/16000] [L1: 0.0042] 16.1+0.1s +[14400/16000] [L1: 0.0042] 16.7+0.1s +[16000/16000] [L1: 0.0042] 16.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.055 (Best: 44.097 @epoch 403) +Forward: 9.22s + +Saving... +Total: 9.74s + +[Epoch 447] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 17.3+1.0s +[3200/16000] [L1: 0.0043] 16.9+0.1s +[4800/16000] [L1: 0.0042] 16.7+0.1s +[6400/16000] [L1: 0.0042] 17.1+0.1s +[8000/16000] [L1: 0.0042] 16.2+0.1s +[9600/16000] [L1: 0.0043] 16.4+0.1s +[11200/16000] [L1: 0.0042] 16.3+0.1s +[12800/16000] [L1: 0.0043] 16.8+0.1s +[14400/16000] [L1: 0.0042] 16.8+0.1s +[16000/16000] [L1: 0.0042] 17.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.111 (Best: 44.111 @epoch 447) +Forward: 9.19s + +Saving... +Total: 9.80s + +[Epoch 448] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 15.4+1.1s +[3200/16000] [L1: 0.0043] 15.1+0.0s +[4800/16000] [L1: 0.0043] 16.1+0.0s +[6400/16000] [L1: 0.0043] 15.1+0.0s +[8000/16000] [L1: 0.0043] 15.1+0.0s +[9600/16000] [L1: 0.0043] 16.0+0.0s +[11200/16000] [L1: 0.0043] 16.0+0.0s +[12800/16000] [L1: 0.0043] 16.5+0.0s +[14400/16000] [L1: 0.0043] 16.2+0.0s +[16000/16000] [L1: 0.0043] 17.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.061 (Best: 44.111 @epoch 447) +Forward: 9.24s + +Saving... +Total: 9.68s + +[Epoch 449] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 15.9+0.9s +[3200/16000] [L1: 0.0043] 16.3+0.1s +[4800/16000] [L1: 0.0043] 16.6+0.1s +[6400/16000] [L1: 0.0042] 17.1+0.1s +[8000/16000] [L1: 0.0042] 16.7+0.1s +[9600/16000] [L1: 0.0042] 16.5+0.0s +[11200/16000] [L1: 0.0043] 17.1+0.1s +[12800/16000] [L1: 0.0043] 17.7+0.1s +[14400/16000] [L1: 0.0043] 17.2+0.1s +[16000/16000] [L1: 0.0043] 17.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.070 (Best: 44.111 @epoch 447) +Forward: 9.24s + +Saving... +Total: 9.73s + +[Epoch 450] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.2+1.0s +[3200/16000] [L1: 0.0043] 15.3+0.0s +[4800/16000] [L1: 0.0043] 17.1+0.1s +[6400/16000] [L1: 0.0043] 16.5+0.1s +[8000/16000] [L1: 0.0043] 16.6+0.1s +[9600/16000] [L1: 0.0043] 17.4+0.1s +[11200/16000] [L1: 0.0043] 15.8+0.1s +[12800/16000] [L1: 0.0043] 15.7+0.0s +[14400/16000] [L1: 0.0043] 17.2+0.1s +[16000/16000] [L1: 0.0043] 17.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.035 (Best: 44.111 @epoch 447) +Forward: 9.26s + +Saving... +Total: 9.76s + +[Epoch 451] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 17.1+1.1s +[3200/16000] [L1: 0.0041] 17.0+0.1s +[4800/16000] [L1: 0.0041] 16.1+0.0s +[6400/16000] [L1: 0.0042] 16.3+0.1s +[8000/16000] [L1: 0.0042] 16.0+0.1s +[9600/16000] [L1: 0.0043] 16.7+0.0s +[11200/16000] [L1: 0.0042] 15.8+0.0s +[12800/16000] [L1: 0.0043] 15.7+0.0s +[14400/16000] [L1: 0.0043] 15.6+0.0s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 43.993 (Best: 44.111 @epoch 447) +Forward: 9.33s + +Saving... +Total: 9.84s + +[Epoch 452] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0042] 16.8+1.0s +[3200/16000] [L1: 0.0042] 16.7+0.1s +[4800/16000] [L1: 0.0043] 17.2+0.1s +[6400/16000] [L1: 0.0043] 16.7+0.1s +[8000/16000] [L1: 0.0043] 16.8+0.1s +[9600/16000] [L1: 0.0043] 16.1+0.1s +[11200/16000] [L1: 0.0043] 17.1+0.1s +[12800/16000] [L1: 0.0042] 16.2+0.1s +[14400/16000] [L1: 0.0043] 16.3+0.1s +[16000/16000] [L1: 0.0043] 16.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.029 (Best: 44.111 @epoch 447) +Forward: 9.32s + +Saving... +Total: 9.80s + +[Epoch 453] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 16.2+0.9s +[3200/16000] [L1: 0.0043] 16.0+0.1s +[4800/16000] [L1: 0.0043] 16.4+0.1s +[6400/16000] [L1: 0.0043] 17.2+0.1s +[8000/16000] [L1: 0.0043] 16.3+0.0s +[9600/16000] [L1: 0.0043] 15.7+0.1s +[11200/16000] [L1: 0.0043] 16.1+0.1s +[12800/16000] [L1: 0.0043] 15.5+0.0s +[14400/16000] [L1: 0.0043] 15.7+0.0s +[16000/16000] [L1: 0.0043] 16.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 44.036 (Best: 44.111 @epoch 447) +Forward: 9.24s + +Saving... +Total: 9.75s + +[Epoch 454] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0043] 15.3+1.0s +[3200/16000] [L1: 0.0043] 16.0+0.1s +[4800/16000] [L1: 0.0042] 16.5+0.1s +[6400/16000] [L1: 0.0043] 17.1+0.1s +[8000/16000] [L1: 0.0043] 17.8+0.1s +[9600/16000] [L1: 0.0043] 16.3+0.1s +[11200/16000] [L1: 0.0043] 16.8+0.1s +[12800/16000] [L1: 0.0043] 15.1+0.0s +[14400/16000] [L1: 0.0043] 16.4+0.1s +[16000/16000] [L1: 0.0043] 16.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 44.016 (Best: 44.111 @epoch 447) +Forward: 9.17s + +Saving... +Total: 9.54s + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/loss.pt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..0fa84de77f3d033ff542b374829e18a7126d2448 --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d67cad1450dda1e43cb5e987137317cb0e47e3cb9d1946c3da85c8ffb664e036 +size 559 diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/loss_L1.pdf b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1/loss_L1.pdf new file mode 100644 index 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file mode 100644 index 0000000000000000000000000000000000000000..fdcb5dce793f1c4391afb551d46a9eab36c50919 --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/config.txt @@ -0,0 +1,330 @@ +2020-11-09-16:36:26 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1_new +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-16:42:57 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 3 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1_new +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-16:44:52 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 3 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1_new +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-16:47:01 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 3 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1_new +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-17:22:25 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 3 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R1_new +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/log.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a65e3fa8ccd186864109aeca1fa908a39761d4d --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/log.txt @@ -0,0 +1,1465 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2083] 38.9+1.1s +[3200/16000] [L1: 0.1420] 33.6+0.0s +[4800/16000] [L1: 0.1138] 33.3+0.0s +[6400/16000] [L1: 0.0979] 33.4+0.0s +[8000/16000] [L1: 0.0871] 33.1+0.0s +[9600/16000] [L1: 0.0788] 33.3+0.0s +[11200/16000] [L1: 0.0723] 32.6+0.0s +[12800/16000] [L1: 0.0673] 33.6+0.0s +[14400/16000] [L1: 0.0630] 33.4+0.0s +[16000/16000] [L1: 0.0593] 32.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.942 (Best: 29.942 @epoch 1) +Forward: 10.93s + +Saving... +Total: 12.50s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0249] 34.3+0.8s +[3200/16000] [L1: 0.0245] 33.1+0.0s +[4800/16000] [L1: 0.0242] 33.2+0.0s +[6400/16000] [L1: 0.0234] 33.6+0.0s +[8000/16000] [L1: 0.0229] 33.3+0.0s +[9600/16000] [L1: 0.0224] 33.9+0.0s +[11200/16000] [L1: 0.0219] 33.5+0.0s +[12800/16000] [L1: 0.0214] 32.7+0.0s +[14400/16000] [L1: 0.0211] 33.6+0.0s +[16000/16000] [L1: 0.0207] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.844 (Best: 32.844 @epoch 2) +Forward: 10.79s + +Saving... +Total: 11.30s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0164] 33.9+1.3s +[3200/16000] [L1: 0.0166] 33.4+0.0s +[4800/16000] [L1: 0.0166] 33.5+0.0s +[6400/16000] [L1: 0.0165] 33.8+0.0s +[8000/16000] [L1: 0.0164] 33.1+0.0s +[9600/16000] [L1: 0.0162] 33.6+0.0s +[11200/16000] [L1: 0.0158] 33.6+0.0s +[12800/16000] [L1: 0.0157] 33.4+0.0s +[14400/16000] [L1: 0.0156] 33.1+0.0s +[16000/16000] [L1: 0.0155] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.020 (Best: 34.020 @epoch 3) +Forward: 10.67s + +Saving... +Total: 11.32s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0134] 33.3+1.2s +[3200/16000] [L1: 0.0138] 33.4+0.0s +[4800/16000] [L1: 0.0136] 33.2+0.0s +[6400/16000] [L1: 0.0133] 32.6+0.0s +[8000/16000] [L1: 0.0131] 32.7+0.0s +[9600/16000] [L1: 0.0130] 32.8+0.0s +[11200/16000] [L1: 0.0130] 33.1+0.0s +[12800/16000] [L1: 0.0129] 33.3+0.0s +[14400/16000] [L1: 0.0129] 32.8+0.0s +[16000/16000] [L1: 0.0128] 32.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.083 (Best: 34.083 @epoch 4) +Forward: 10.68s + +Saving... +Total: 11.18s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0122] 33.2+1.3s +[3200/16000] [L1: 0.0117] 33.0+0.0s +[4800/16000] [L1: 0.0118] 33.0+0.0s +[6400/16000] [L1: 0.0119] 32.7+0.0s +[8000/16000] [L1: 0.0118] 33.1+0.0s +[9600/16000] [L1: 0.0119] 33.1+0.0s +[11200/16000] [L1: 0.0117] 32.8+0.0s +[12800/16000] [L1: 0.0117] 32.9+0.0s +[14400/16000] [L1: 0.0116] 32.8+0.0s +[16000/16000] [L1: 0.0115] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.296 (Best: 35.296 @epoch 5) +Forward: 10.70s + +Saving... +Total: 11.23s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0112] 33.5+1.2s +[3200/16000] [L1: 0.0109] 33.2+0.0s +[4800/16000] [L1: 0.0107] 33.6+0.0s +[6400/16000] [L1: 0.0107] 33.1+0.0s +[8000/16000] [L1: 0.0107] 33.1+0.0s +[9600/16000] [L1: 0.0106] 33.6+0.0s +[11200/16000] [L1: 0.0106] 32.5+0.0s +[12800/16000] [L1: 0.0106] 33.2+0.0s +[14400/16000] [L1: 0.0105] 33.2+0.0s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2084] 14.7+0.7s +[3200/16000] [L1: 0.1423] 9.8+0.0s +[4800/16000] [L1: 0.1140] 9.6+0.0s +[6400/16000] [L1: 0.0974] 9.7+0.0s +[8000/16000] [L1: 0.0866] 9.7+0.1s +[9600/16000] [L1: 0.0783] 9.5+0.0s +[11200/16000] [L1: 0.0717] 9.3+0.0s +[12800/16000] [L1: 0.0669] 9.4+0.0s +[14400/16000] [L1: 0.0627] 9.4+0.0s +[16000/16000] [L1: 0.0591] 9.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.385 (Best: 30.385 @epoch 1) +Forward: 11.12s + +Saving... +Total: 11.72s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0245] 9.7+1.1s +[3200/16000] [L1: 0.0239] 9.1+0.0s +[4800/16000] [L1: 0.0236] 9.0+0.0s +[6400/16000] [L1: 0.0231] 9.1+0.0s +[8000/16000] [L1: 0.0226] 9.2+0.0s +[9600/16000] [L1: 0.0221] 9.2+0.0s +[11200/16000] [L1: 0.0219] 9.3+0.0s +[12800/16000] [L1: 0.0215] 9.3+0.0s +[14400/16000] [L1: 0.0211] 9.2+0.0s +[16000/16000] [L1: 0.0207] 9.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.638 (Best: 33.638 @epoch 2) +Forward: 11.00s + +Saving... +Total: 11.52s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0167] 9.2+1.5s +[3200/16000] [L1: 0.0165] 9.3+0.0s +[4800/16000] [L1: 0.0162] 9.4+0.0s +[6400/16000] [L1: 0.0163] 9.1+0.0s +[8000/16000] [L1: 0.0160] 9.1+0.0s +[9600/16000] [L1: 0.0160] 9.1+0.0s +[11200/16000] [L1: 0.0158] 8.8+0.0s +[12800/16000] [L1: 0.0156] 8.9+0.0s +[14400/16000] [L1: 0.0156] 8.7+0.0s +[16000/16000] [L1: 0.0155] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.513 (Best: 33.638 @epoch 2) +Forward: 11.02s + +Saving... +Total: 11.49s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0130] 8.9+1.1s +[3200/16000] [L1: 0.0133] 9.3+0.0s +[4800/16000] [L1: 0.0135] 8.9+0.0s +[6400/16000] [L1: 0.0134] 8.9+0.0s +[8000/16000] [L1: 0.0132] 8.8+0.0s +[9600/16000] [L1: 0.0131] 8.7+0.0s +[11200/16000] [L1: 0.0130] 9.0+0.0s +[12800/16000] [L1: 0.0129] 8.9+0.0s +[14400/16000] [L1: 0.0129] 8.9+0.0s +[16000/16000] [L1: 0.0128] 9.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.904 (Best: 34.904 @epoch 4) +Forward: 11.07s + +Saving... +Total: 11.59s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0126] 9.6+1.4s +[3200/16000] [L1: 0.0123] 9.7+0.0s +[4800/16000] [L1: 0.0123] 9.5+0.1s +[6400/16000] [L1: 0.0123] 9.6+0.1s +[8000/16000] [L1: 0.0120] 9.1+0.0s +[9600/16000] [L1: 0.0120] 9.0+0.0s +[11200/16000] [L1: 0.0119] 9.3+0.0s +[12800/16000] [L1: 0.0119] 9.2+0.0s +[14400/16000] [L1: 0.0118] 9.2+0.0s +[16000/16000] [L1: 0.0117] 9.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.233 (Best: 35.233 @epoch 5) +Forward: 11.02s + +Saving... +Total: 11.55s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0111] 9.5+1.2s +[3200/16000] [L1: 0.0111] 9.1+0.0s +[4800/16000] [L1: 0.0108] 9.0+0.0s +[6400/16000] [L1: 0.0108] 9.0+0.0s +[8000/16000] [L1: 0.0107] 8.9+0.0s +[9600/16000] [L1: 0.0107] 8.7+0.0s +[11200/16000] [L1: 0.0107] 9.2+0.0s +[12800/16000] [L1: 0.0107] 8.9+0.0s +[14400/16000] [L1: 0.0107] 9.0+0.0s +[16000/16000] [L1: 0.0107] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.577 (Best: 35.577 @epoch 6) +Forward: 11.02s + +Saving... +Total: 11.50s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0100] 9.4+1.3s +[3200/16000] [L1: 0.0102] 9.4+0.0s +[4800/16000] [L1: 0.0101] 8.9+0.0s +[6400/16000] [L1: 0.0101] 9.2+0.0s +[8000/16000] [L1: 0.0101] 9.2+0.0s +[9600/16000] [L1: 0.0101] 8.8+0.0s +[11200/16000] [L1: 0.0100] 9.0+0.0s +[12800/16000] [L1: 0.0099] 9.3+0.0s +[14400/16000] [L1: 0.0100] 9.1+0.0s +[16000/16000] [L1: 0.0099] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.893 (Best: 35.577 @epoch 6) +Forward: 11.04s + +Saving... +Total: 11.51s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0098] 9.8+1.5s +[3200/16000] [L1: 0.0097] 9.8+0.1s +[4800/16000] [L1: 0.0098] 9.5+0.1s +[6400/16000] [L1: 0.0097] 9.6+0.1s +[8000/16000] [L1: 0.0097] 9.7+0.1s +[9600/16000] [L1: 0.0096] 9.6+0.0s +[11200/16000] [L1: 0.0096] 9.5+0.0s +[12800/16000] [L1: 0.0096] 9.7+0.0s +[14400/16000] [L1: 0.0096] 9.5+0.0s +[16000/16000] [L1: 0.0096] 9.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.840 (Best: 35.840 @epoch 8) +Forward: 10.84s + +Saving... +Total: 11.34s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0091] 9.4+1.6s +[3200/16000] [L1: 0.0093] 8.9+0.0s +[4800/16000] [L1: 0.0091] 8.8+0.0s +[6400/16000] [L1: 0.0093] 9.1+0.0s +[8000/16000] [L1: 0.0092] 9.0+0.0s +[9600/16000] [L1: 0.0092] 9.0+0.0s +[11200/16000] [L1: 0.0092] 8.8+0.0s +[12800/16000] [L1: 0.0092] 9.1+0.0s +[14400/16000] [L1: 0.0092] 9.0+0.0s +[16000/16000] [L1: 0.0092] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.249 (Best: 36.249 @epoch 9) +Forward: 10.98s + +Saving... +Total: 11.50s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0092] 9.9+1.4s +[3200/16000] [L1: 0.0090] 8.9+0.0s +[4800/16000] [L1: 0.0089] 9.3+0.0s +[6400/16000] [L1: 0.0089] 9.2+0.0s +[8000/16000] [L1: 0.0089] 9.1+0.0s +[9600/16000] [L1: 0.0088] 9.2+0.0s +[11200/16000] [L1: 0.0088] 9.2+0.0s +[12800/16000] [L1: 0.0089] 9.1+0.0s +[14400/16000] [L1: 0.0089] 9.3+0.0s +[16000/16000] [L1: 0.0088] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.265 (Best: 36.265 @epoch 10) +Forward: 10.96s + +Saving... +Total: 11.43s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0083] 9.8+1.5s +[3200/16000] [L1: 0.0082] 9.3+0.0s +[4800/16000] [L1: 0.0083] 9.2+0.0s +[6400/16000] [L1: 0.0084] 9.1+0.0s +[8000/16000] [L1: 0.0085] 9.0+0.0s +[9600/16000] [L1: 0.0085] 9.1+0.0s +[11200/16000] [L1: 0.0084] 9.0+0.0s +[12800/16000] [L1: 0.0084] 9.0+0.0s +[14400/16000] [L1: 0.0084] 9.2+0.0s +[16000/16000] [L1: 0.0085] 8.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.632 (Best: 36.632 @epoch 11) +Forward: 11.02s + +Saving... +Total: 11.50s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0081] 9.7+1.4s +[3200/16000] [L1: 0.0083] 9.3+0.0s +[4800/16000] [L1: 0.0083] 9.3+0.0s +[6400/16000] [L1: 0.0084] 9.2+0.0s +[8000/16000] [L1: 0.0083] 9.1+0.0s +[9600/16000] [L1: 0.0083] 9.2+0.0s +[11200/16000] [L1: 0.0082] 9.0+0.0s +[12800/16000] [L1: 0.0082] 9.2+0.0s +[14400/16000] [L1: 0.0082] 9.1+0.0s +[16000/16000] [L1: 0.0082] 8.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.307 (Best: 36.632 @epoch 11) +Forward: 11.12s + +Saving... +Total: 11.56s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0081] 9.6+1.4s +[3200/16000] [L1: 0.0080] 9.0+0.0s +[4800/16000] [L1: 0.0080] 9.1+0.0s +[6400/16000] [L1: 0.0079] 9.1+0.0s +[8000/16000] [L1: 0.0079] 9.0+0.0s +[9600/16000] [L1: 0.0080] 9.2+0.0s +[11200/16000] [L1: 0.0080] 9.0+0.0s +[12800/16000] [L1: 0.0080] 9.1+0.0s +[14400/16000] [L1: 0.0080] 9.2+0.0s +[16000/16000] [L1: 0.0079] 8.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.729 (Best: 36.729 @epoch 13) +Forward: 11.02s + +Saving... +Total: 11.55s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0077] 9.6+1.4s +[3200/16000] [L1: 0.0078] 9.1+0.0s +[4800/16000] [L1: 0.0078] 9.0+0.0s +[6400/16000] [L1: 0.0077] 9.1+0.0s +[8000/16000] [L1: 0.0077] 9.1+0.0s +[9600/16000] [L1: 0.0077] 9.2+0.0s +[11200/16000] [L1: 0.0077] 8.9+0.0s +[12800/16000] [L1: 0.0077] 8.9+0.0s +[14400/16000] [L1: 0.0077] 9.1+0.0s +[16000/16000] [L1: 0.0077] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.395 (Best: 37.395 @epoch 14) +Forward: 11.07s + +Saving... +Total: 11.57s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0074] 9.5+1.7s +[3200/16000] [L1: 0.0075] 9.1+0.0s +[4800/16000] [L1: 0.0075] 9.2+0.0s +[6400/16000] [L1: 0.0075] 9.2+0.0s +[8000/16000] [L1: 0.0074] 9.3+0.0s +[9600/16000] [L1: 0.0075] 9.3+0.0s +[11200/16000] [L1: 0.0074] 9.3+0.0s +[12800/16000] [L1: 0.0074] 9.2+0.0s +[14400/16000] [L1: 0.0074] 9.3+0.0s +[16000/16000] [L1: 0.0074] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.579 (Best: 37.579 @epoch 15) +Forward: 10.88s + +Saving... +Total: 11.43s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0076] 9.6+1.3s +[3200/16000] [L1: 0.0075] 8.9+0.0s +[4800/16000] [L1: 0.0074] 8.9+0.0s +[6400/16000] [L1: 0.0074] 9.0+0.0s +[8000/16000] [L1: 0.0075] 8.8+0.0s +[9600/16000] [L1: 0.0074] 9.0+0.0s +[11200/16000] [L1: 0.0074] 9.0+0.0s +[12800/16000] [L1: 0.0073] 9.0+0.0s +[14400/16000] [L1: 0.0073] 9.1+0.0s +[16000/16000] [L1: 0.0072] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.826 (Best: 37.826 @epoch 16) +Forward: 11.03s + +Saving... +Total: 12.20s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0218] 9.7+1.3s +[3200/16000] [L1: 0.0165] 9.0+0.0s +[4800/16000] [L1: 0.0135] 9.0+0.0s +[6400/16000] [L1: 0.0120] 9.1+0.0s +[8000/16000] [L1: 0.0111] 8.9+0.0s +[9600/16000] [L1: 0.0105] 8.9+0.0s +[11200/16000] [L1: 0.0100] 9.2+0.0s +[12800/16000] [L1: 0.0097] 8.9+0.0s +[14400/16000] [L1: 0.0094] 9.0+0.0s +[16000/16000] [L1: 0.0091] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.837 (Best: 37.837 @epoch 17) +Forward: 11.04s + +Saving... +Total: 11.59s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0069] 9.3+1.8s +[3200/16000] [L1: 0.0070] 8.9+0.0s +[4800/16000] [L1: 0.0069] 9.0+0.0s +[6400/16000] [L1: 0.0069] 8.9+0.0s +[8000/16000] [L1: 0.0069] 8.9+0.0s +[9600/16000] [L1: 0.0069] 8.8+0.0s +[11200/16000] [L1: 0.0068] 8.9+0.0s +[12800/16000] [L1: 0.0069] 8.5+0.0s +[14400/16000] [L1: 0.0069] 9.1+0.0s +[16000/16000] [L1: 0.0069] 8.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.275 (Best: 38.275 @epoch 18) +Forward: 10.91s + +Saving... +Total: 11.41s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0068] 9.6+1.4s +[3200/16000] [L1: 0.0068] 9.2+0.0s +[4800/16000] [L1: 0.0068] 9.0+0.0s +[6400/16000] [L1: 0.0068] 9.1+0.0s +[8000/16000] [L1: 0.0068] 8.8+0.0s +[9600/16000] [L1: 0.0068] 9.2+0.0s +[11200/16000] [L1: 0.0068] 9.1+0.0s +[12800/16000] [L1: 0.0068] 9.1+0.0s +[14400/16000] [L1: 0.0068] 9.1+0.0s +[16000/16000] [L1: 0.0068] 8.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.332 (Best: 38.332 @epoch 19) +Forward: 10.91s + +Saving... +Total: 11.41s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0069] 9.7+1.3s +[3200/16000] [L1: 0.0068] 9.1+0.0s +[4800/16000] [L1: 0.0069] 9.1+0.0s +[6400/16000] [L1: 0.0069] 9.0+0.0s +[8000/16000] [L1: 0.0068] 8.8+0.0s +[9600/16000] [L1: 0.0068] 9.0+0.0s +[11200/16000] [L1: 0.0068] 8.6+0.0s +[12800/16000] [L1: 0.0067] 9.0+0.0s +[14400/16000] [L1: 0.0067] 8.9+0.0s +[16000/16000] [L1: 0.0067] 8.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.603 (Best: 38.603 @epoch 20) +Forward: 10.90s + +Saving... +Total: 11.38s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0066] 9.7+1.6s +[3200/16000] [L1: 0.0066] 9.4+0.1s +[4800/16000] [L1: 0.0067] 9.2+0.0s +[6400/16000] [L1: 0.0067] 9.2+0.0s +[8000/16000] [L1: 0.0067] 9.3+0.0s +[9600/16000] [L1: 0.0067] 9.3+0.0s +[11200/16000] [L1: 0.0067] 9.3+0.0s +[12800/16000] [L1: 0.0067] 9.2+0.0s +[14400/16000] [L1: 0.0067] 9.1+0.0s +[16000/16000] [L1: 0.0066] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.492 (Best: 38.603 @epoch 20) +Forward: 10.84s + +Saving... +Total: 11.31s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0066] 9.6+1.6s +[3200/16000] [L1: 0.0066] 9.4+0.0s +[4800/16000] [L1: 0.0066] 8.9+0.0s +[6400/16000] [L1: 0.0066] 9.3+0.0s +[8000/16000] [L1: 0.0066] 9.2+0.0s +[9600/16000] [L1: 0.0066] 9.0+0.0s +[11200/16000] [L1: 0.0066] 9.4+0.0s +[12800/16000] [L1: 0.0065] 9.0+0.0s +[14400/16000] [L1: 0.0065] 9.0+0.0s +[16000/16000] [L1: 0.0065] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.754 (Best: 38.754 @epoch 22) +Forward: 10.94s + +Saving... +Total: 11.39s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0065] 9.3+1.7s +[3200/16000] [L1: 0.0065] 9.2+0.0s +[4800/16000] [L1: 0.0065] 9.1+0.0s +[6400/16000] [L1: 0.0065] 9.2+0.0s +[8000/16000] [L1: 0.0065] 9.1+0.0s +[9600/16000] [L1: 0.0065] 9.2+0.0s +[11200/16000] [L1: 0.0065] 9.1+0.0s +[12800/16000] [L1: 0.0064] 9.2+0.0s +[14400/16000] [L1: 0.0064] 9.1+0.0s +[16000/16000] [L1: 0.0064] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.901 (Best: 38.901 @epoch 23) +Forward: 10.94s + +Saving... +Total: 11.37s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0064] 9.8+1.5s +[3200/16000] [L1: 0.0063] 9.2+0.0s +[4800/16000] [L1: 0.0063] 9.0+0.0s +[6400/16000] [L1: 0.0063] 9.1+0.0s +[8000/16000] [L1: 0.0063] 9.2+0.0s +[9600/16000] [L1: 0.0063] 9.0+0.0s +[11200/16000] [L1: 0.0063] 9.1+0.0s +[12800/16000] [L1: 0.0063] 9.3+0.0s +[14400/16000] [L1: 0.0063] 9.2+0.0s +[16000/16000] [L1: 0.0063] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.945 (Best: 38.945 @epoch 24) +Forward: 10.93s + +Saving... +Total: 11.38s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0063] 9.6+1.2s +[3200/16000] [L1: 0.0062] 9.0+0.1s +[4800/16000] [L1: 0.0063] 9.2+0.0s +[6400/16000] [L1: 0.0063] 9.1+0.0s +[8000/16000] [L1: 0.0063] 9.1+0.0s +[9600/16000] [L1: 0.0063] 9.2+0.0s +[11200/16000] [L1: 0.0063] 9.0+0.0s +[12800/16000] [L1: 0.0063] 9.2+0.0s +[14400/16000] [L1: 0.0063] 9.1+0.0s +[16000/16000] [L1: 0.0063] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.135 (Best: 39.135 @epoch 25) +Forward: 10.89s + +Saving... +Total: 11.34s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0061] 9.9+1.6s +[3200/16000] [L1: 0.0112] 9.0+0.0s +[4800/16000] [L1: 0.0107] 9.2+0.0s +[6400/16000] [L1: 0.0096] 9.2+0.0s +[8000/16000] [L1: 0.0089] 9.3+0.0s +[9600/16000] [L1: 0.0084] 9.1+0.0s +[11200/16000] [L1: 0.0081] 9.3+0.0s +[12800/16000] [L1: 0.0078] 9.3+0.0s +[14400/16000] [L1: 0.0077] 9.2+0.0s +[16000/16000] [L1: 0.0075] 8.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.198 (Best: 39.198 @epoch 26) +Forward: 10.98s + +Saving... +Total: 11.43s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 9.9+1.4s +[3200/16000] [L1: 0.0059] 9.3+0.0s +[4800/16000] [L1: 0.0059] 9.4+0.0s +[6400/16000] [L1: 0.0059] 9.4+0.0s +[8000/16000] [L1: 0.0060] 9.5+0.0s +[9600/16000] [L1: 0.0060] 9.1+0.0s +[11200/16000] [L1: 0.0060] 9.2+0.0s +[12800/16000] [L1: 0.0060] 9.1+0.0s +[14400/16000] [L1: 0.0060] 9.2+0.0s +[16000/16000] [L1: 0.0060] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.341 (Best: 39.341 @epoch 27) +Forward: 10.97s + +Saving... +Total: 11.44s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0062] 9.3+1.5s +[3200/16000] [L1: 0.0061] 9.4+0.0s +[4800/16000] [L1: 0.0061] 9.1+0.0s +[6400/16000] [L1: 0.0061] 9.0+0.0s +[8000/16000] [L1: 0.0060] 9.2+0.0s +[9600/16000] [L1: 0.0060] 9.1+0.0s +[11200/16000] [L1: 0.0060] 9.2+0.0s +[12800/16000] [L1: 0.0060] 8.9+0.0s +[14400/16000] [L1: 0.0060] 8.9+0.0s +[16000/16000] [L1: 0.0060] 8.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.200 (Best: 39.341 @epoch 27) +Forward: 10.88s + +Saving... +Total: 11.33s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 9.5+1.8s +[3200/16000] [L1: 0.0058] 9.1+0.0s +[4800/16000] [L1: 0.0058] 8.8+0.0s +[6400/16000] [L1: 0.0058] 9.2+0.0s +[8000/16000] [L1: 0.0058] 9.1+0.0s +[9600/16000] [L1: 0.0059] 9.0+0.0s +[11200/16000] [L1: 0.0059] 9.3+0.0s +[12800/16000] [L1: 0.0059] 9.2+0.0s +[14400/16000] [L1: 0.0059] 9.0+0.0s +[16000/16000] [L1: 0.0059] 8.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.645 (Best: 39.645 @epoch 29) +Forward: 10.99s + +Saving... +Total: 11.47s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 9.8+1.4s +[3200/16000] [L1: 0.0058] 9.2+0.0s +[4800/16000] [L1: 0.0058] 9.1+0.0s +[6400/16000] [L1: 0.0058] 9.0+0.0s +[8000/16000] [L1: 0.0058] 9.1+0.0s +[9600/16000] [L1: 0.0058] 9.0+0.0s +[11200/16000] [L1: 0.0058] 9.1+0.0s +[12800/16000] [L1: 0.0058] 9.2+0.0s +[14400/16000] [L1: 0.0058] 9.2+0.0s +[16000/16000] [L1: 0.0058] 8.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.455 (Best: 39.645 @epoch 29) +Forward: 10.91s + +Saving... +Total: 11.35s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 9.5+1.6s +[3200/16000] [L1: 0.0058] 9.0+0.0s +[4800/16000] [L1: 0.0058] 9.1+0.0s +[6400/16000] [L1: 0.0059] 9.0+0.0s +[8000/16000] [L1: 0.0058] 9.0+0.0s +[9600/16000] [L1: 0.0058] 9.1+0.0s +[11200/16000] [L1: 0.0059] 9.1+0.0s +[12800/16000] [L1: 0.0059] 9.1+0.0s +[14400/16000] [L1: 0.0059] 9.0+0.0s +[16000/16000] [L1: 0.0059] 8.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.725 (Best: 39.725 @epoch 31) +Forward: 10.89s + +Saving... +Total: 11.38s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0058] 9.6+1.4s +[3200/16000] [L1: 0.0058] 9.2+0.0s +[4800/16000] [L1: 0.0059] 9.0+0.0s +[6400/16000] [L1: 0.0058] 8.9+0.0s +[8000/16000] [L1: 0.0058] 9.2+0.0s +[9600/16000] [L1: 0.0058] 9.2+0.0s +[11200/16000] [L1: 0.0058] 9.2+0.0s +[12800/16000] [L1: 0.0058] 9.1+0.0s +[14400/16000] [L1: 0.0058] 9.1+0.0s +[16000/16000] [L1: 0.0058] 8.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.658 (Best: 39.725 @epoch 31) +Forward: 11.05s + +Saving... +Total: 11.51s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0058] 10.1+1.3s +[3200/16000] [L1: 0.0058] 8.9+0.0s +[4800/16000] [L1: 0.0058] 9.2+0.0s +[6400/16000] [L1: 0.0057] 9.2+0.0s +[8000/16000] [L1: 0.0057] 9.1+0.0s +[9600/16000] [L1: 0.0057] 9.1+0.0s +[11200/16000] [L1: 0.0057] 9.2+0.0s +[12800/16000] [L1: 0.0057] 9.2+0.0s +[14400/16000] [L1: 0.0057] 9.2+0.0s +[16000/16000] [L1: 0.0057] 8.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.685 (Best: 39.725 @epoch 31) +Forward: 10.96s + +Saving... +Total: 11.44s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 9.9+1.7s +[3200/16000] [L1: 0.0056] 9.2+0.0s +[4800/16000] [L1: 0.0057] 9.2+0.0s +[6400/16000] [L1: 0.0057] 9.2+0.0s +[8000/16000] [L1: 0.0057] 9.0+0.0s +[9600/16000] [L1: 0.0057] 9.1+0.0s +[11200/16000] [L1: 0.0057] 9.1+0.0s +[12800/16000] [L1: 0.0057] 9.2+0.0s +[14400/16000] [L1: 0.0057] 9.1+0.0s +[16000/16000] [L1: 0.0057] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.794 (Best: 39.794 @epoch 34) +Forward: 11.02s + +Saving... +Total: 11.50s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 9.6+1.5s +[3200/16000] [L1: 0.0056] 9.0+0.0s +[4800/16000] [L1: 0.0055] 9.2+0.0s +[6400/16000] [L1: 0.0055] 9.1+0.0s +[8000/16000] [L1: 0.0056] 9.2+0.0s +[9600/16000] [L1: 0.0056] 9.1+0.0s +[11200/16000] [L1: 0.0056] 9.2+0.0s +[12800/16000] [L1: 0.0056] 8.9+0.0s +[14400/16000] [L1: 0.0056] 8.6+0.0s +[16000/16000] [L1: 0.0056] 8.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.721 (Best: 39.794 @epoch 34) +Forward: 11.01s + +Saving... +Total: 11.50s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 10.1+1.2s +[3200/16000] [L1: 0.0055] 9.1+0.0s +[4800/16000] [L1: 0.0055] 9.0+0.0s +[6400/16000] [L1: 0.0055] 9.4+0.0s +[8000/16000] [L1: 0.0055] 9.2+0.0s +[9600/16000] [L1: 0.0056] 9.3+0.0s +[11200/16000] [L1: 0.0056] 9.3+0.0s +[12800/16000] [L1: 0.0056] 9.0+0.0s +[14400/16000] [L1: 0.0056] 9.1+0.0s +[16000/16000] [L1: 0.0056] 8.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.921 (Best: 39.921 @epoch 36) +Forward: 10.99s + +Saving... +Total: 11.53s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 9.9+1.4s +[3200/16000] [L1: 0.0056] 9.2+0.0s +[4800/16000] [L1: 0.0055] 9.2+0.0s +[6400/16000] [L1: 0.0056] 9.2+0.0s +[8000/16000] [L1: 0.0055] 9.2+0.0s +[9600/16000] [L1: 0.0055] 9.3+0.0s +[11200/16000] [L1: 0.0055] 9.2+0.0s +[12800/16000] [L1: 0.0055] 8.9+0.0s +[14400/16000] [L1: 0.0055] 9.0+0.0s +[16000/16000] [L1: 0.0055] 9.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.095 (Best: 40.095 @epoch 37) +Forward: 11.00s + +Saving... +Total: 11.50s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 9.7+1.7s +[3200/16000] [L1: 0.0056] 9.1+0.0s diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/loss.pt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/loss.pt new file mode 100644 index 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sha256:fa9287a9a97d8bd028925085a1a943f6e49ed32fc2b9b9b97dc49969494d3a3d +size 888 diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/test_DIV2K.pdf b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/test_DIV2K.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4a753c4645a87eace018ce607b08facd71296541 Binary files /dev/null and b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R1_new/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4/config.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..0ab750a588cfd96932979ca9223f00d13ca9fc16 --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4/config.txt @@ -0,0 +1,455 @@ +2020-11-06-15:27:17 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-15:30:52 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-15:32:29 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-15:33:59 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-15:35:11 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-15:47:05 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-15:50:15 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4/log.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..7cfc954d937d36b154b6b16609f5cbb3536747af --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4/log.txt @@ -0,0 +1,1220 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.1592] 53.1+0.7s +[3200/16000] [L1: 2.1102] 49.0+0.1s +[4800/16000] [L1: 1.6227] 50.3+0.1s +[6400/16000] [L1: 1.3362] 52.4+0.1s +[8000/16000] [L1: 1.1366] 53.0+0.1s +[9600/16000] [L1: 0.9951] 52.7+0.1s +[11200/16000] [L1: 0.8875] 53.0+0.1s +[12800/16000] [L1: 0.8039] 53.1+0.1s +[14400/16000] [L1: 0.7366] 53.0+0.1s +[16000/16000] [L1: 0.6820] 53.0+0.1s + +Evaluation: +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.0808] 57.1+0.7s +[3200/16000] [L1: 2.0652] 51.8+0.1s +[4800/16000] [L1: 1.5855] 55.5+0.1s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.1288] 56.4+0.6s +[3200/16000] [L1: 2.0813] 54.2+0.1s +[4800/16000] [L1: 1.5813] 53.9+0.1s +[6400/16000] [L1: 1.2954] 56.0+0.1s +[8000/16000] [L1: 1.1018] 54.9+0.1s +[9600/16000] [L1: 0.9645] 55.8+0.1s +[11200/16000] [L1: 0.8599] 55.9+0.1s +[12800/16000] [L1: 0.7794] 56.0+0.1s +[14400/16000] [L1: 0.7134] 56.3+0.1s +[16000/16000] [L1: 0.6599] 55.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.714 (Best: 13.714 @epoch 1) +Forward: 33.95s + +Saving... +Total: 34.92s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1574] 51.0+0.9s +[3200/16000] [L1: 0.1554] 54.6+0.1s +[4800/16000] [L1: 0.1514] 55.8+0.1s +[6400/16000] [L1: 0.1483] 55.5+0.1s +[8000/16000] [L1: 0.1458] 55.0+0.1s +[9600/16000] [L1: 0.1433] 55.6+0.1s +[11200/16000] [L1: 0.1426] 56.3+0.1s +[12800/16000] [L1: 0.1393] 55.6+0.1s +[14400/16000] [L1: 0.1369] 55.6+0.1s +[16000/16000] [L1: 0.1344] 55.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 22.216 (Best: 22.216 @epoch 2) +Forward: 33.80s + +Saving... +Total: 34.36s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1138] 50.5+0.8s +[3200/16000] [L1: 0.1180] 54.5+0.1s +[4800/16000] [L1: 0.1170] 54.0+0.0s +[6400/16000] [L1: 0.1130] 54.0+0.1s +[8000/16000] [L1: 0.1104] 54.9+0.1s +[9600/16000] [L1: 0.1094] 55.5+0.1s +[11200/16000] [L1: 0.1074] 53.7+0.0s +[12800/16000] [L1: 0.1061] 54.8+0.0s +[14400/16000] [L1: 0.1057] 52.6+0.0s +[16000/16000] [L1: 0.1044] 54.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.171 (Best: 22.216 @epoch 2) +Forward: 34.13s + +Saving... +Total: 34.65s + +[Epoch 4] Learning 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/data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4_RWEIGHT +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-16:36:59 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4_RWEIGHT +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-18:09:02 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4_RWEIGHT +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/log.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd4ac1be004041424ffbad3040c4c40b6a9ae979 --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/log.txt @@ -0,0 +1,2869 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 8.0541] 53.8+1.0s +[3200/16000] [L1: 5.2042] 51.9+0.1s +[4800/16000] [L1: 3.9985] 53.1+0.1s +[6400/16000] [L1: 3.3315] 54.3+0.0s +[8000/16000] [L1: 2.8747] 54.5+0.0s +[9600/16000] [L1: 2.5458] 53.2+0.1s +[11200/16000] [L1: 2.2950] 53.8+0.0s +[12800/16000] [L1: 2.0979] 54.1+0.1s +[14400/16000] [L1: 1.9371] 53.1+0.0s +[16000/16000] [L1: 1.8017] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.914 (Best: 12.914 @epoch 1) +Forward: 34.78s + +Saving... +Total: 35.52s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.5305] 51.7+0.8s +[3200/16000] [L1: 0.5115] 54.1+0.1s +[4800/16000] [L1: 0.4928] 54.0+0.0s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 10.7202] 55.9+0.7s +[3200/16000] [L1: 7.1312] 51.6+0.1s +[4800/16000] [L1: 5.6803] 54.0+0.0s +[6400/16000] [L1: 4.7180] 55.0+0.1s +[8000/16000] [L1: 4.0556] 54.5+0.1s +[9600/16000] [L1: 3.5799] 53.1+0.0s +[11200/16000] [L1: 3.2194] 54.1+0.0s +[12800/16000] [L1: 2.9349] 54.3+0.0s +[14400/16000] [L1: 2.7043] 54.4+0.0s +[16000/16000] [L1: 2.5137] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.284 (Best: 12.284 @epoch 1) +Forward: 35.18s + +Saving... +Total: 35.85s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.7078] 51.1+0.8s +[3200/16000] [L1: 0.6798] 54.5+0.1s +[4800/16000] [L1: 0.6617] 56.2+0.1s +[6400/16000] [L1: 0.6491] 54.2+0.1s +[8000/16000] [L1: 0.6282] 53.8+0.1s +[9600/16000] [L1: 0.6108] 54.0+0.0s +[11200/16000] [L1: 0.5956] 54.2+0.0s +[12800/16000] [L1: 0.5797] 53.9+0.0s +[14400/16000] [L1: 0.5650] 53.8+0.1s +[16000/16000] [L1: 0.5521] 54.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 16.852 (Best: 16.852 @epoch 2) +Forward: 34.35s + +Saving... +Total: 34.97s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.4243] 51.7+1.0s +[3200/16000] [L1: 0.4179] 54.0+0.1s +[4800/16000] [L1: 0.4072] 54.7+0.1s +[6400/16000] [L1: 0.3971] 54.3+0.1s +[8000/16000] [L1: 0.3861] 54.6+0.1s +[9600/16000] [L1: 0.3774] 54.0+0.1s +[11200/16000] [L1: 0.3747] 54.2+0.1s +[12800/16000] [L1: 0.3756] 54.9+0.1s +[14400/16000] [L1: 0.3685] 55.2+0.1s +[16000/16000] [L1: 0.3600] 54.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 19.669 (Best: 19.669 @epoch 3) +Forward: 34.24s + +Saving... +Total: 34.85s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2775] 51.3+1.1s +[3200/16000] [L1: 0.2728] 53.2+0.1s +[4800/16000] [L1: 0.2698] 54.7+0.1s +[6400/16000] [L1: 0.2642] 54.2+0.1s +[8000/16000] [L1: 0.2672] 53.8+0.1s +[9600/16000] [L1: 0.2644] 54.0+0.1s +[11200/16000] [L1: 0.2602] 54.9+0.1s +[12800/16000] [L1: 0.2563] 53.1+0.1s +[14400/16000] [L1: 0.2527] 54.7+0.1s +[16000/16000] [L1: 0.2492] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.923 (Best: 22.923 @epoch 4) +Forward: 34.51s + +Saving... +Total: 35.05s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2112] 51.7+0.9s +[3200/16000] [L1: 0.2087] 54.0+0.1s +[4800/16000] [L1: 0.2066] 55.0+0.1s +[6400/16000] [L1: 0.2082] 53.9+0.1s +[8000/16000] [L1: 0.2067] 53.9+0.0s +[9600/16000] [L1: 0.2047] 53.2+0.0s +[11200/16000] [L1: 0.2017] 54.0+0.1s +[12800/16000] [L1: 0.2005] 54.3+0.1s +[14400/16000] [L1: 0.1981] 54.2+0.1s +[16000/16000] [L1: 0.1961] 53.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 25.726 (Best: 25.726 @epoch 5) +Forward: 34.57s + +Saving... +Total: 35.15s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1845] 51.0+0.9s +[3200/16000] [L1: 0.1792] 53.5+0.1s +[4800/16000] [L1: 0.1751] 54.2+0.1s +[6400/16000] [L1: 0.1733] 55.0+0.1s +[8000/16000] [L1: 0.1728] 55.0+0.1s +[9600/16000] [L1: 0.1701] 53.1+0.1s +[11200/16000] [L1: 0.1689] 54.7+0.1s +[12800/16000] [L1: 0.1681] 55.3+0.1s +[14400/16000] [L1: 0.1671] 55.3+0.1s +[16000/16000] [L1: 0.1656] 55.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 18.958 (Best: 25.726 @epoch 5) +Forward: 34.27s + +Saving... +Total: 34.78s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1568] 51.3+0.8s +[3200/16000] [L1: 0.1541] 54.2+0.1s +[4800/16000] [L1: 0.1500] 54.6+0.1s +[6400/16000] [L1: 0.1485] 54.4+0.1s +[8000/16000] [L1: 0.1463] 55.4+0.1s +[9600/16000] [L1: 0.1456] 53.7+0.1s +[11200/16000] [L1: 0.1439] 55.2+0.1s +[12800/16000] [L1: 0.1418] 55.1+0.1s +[14400/16000] [L1: 0.1412] 55.0+0.1s +[16000/16000] [L1: 0.1399] 54.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 18.857 (Best: 25.726 @epoch 5) +Forward: 34.73s + +Saving... +Total: 35.21s + +[Epoch 8] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 10.8206] 56.5+1.1s +[3200/16000] [L1: 7.2091] 50.4+0.1s +[4800/16000] [L1: 5.5825] 52.7+0.1s +[6400/16000] [L1: 4.6546] 52.2+0.0s +[8000/16000] [L1: 4.0252] 55.1+0.0s +[9600/16000] [L1: 3.5693] 54.8+0.0s +[11200/16000] [L1: 3.2193] 53.3+0.0s +[12800/16000] [L1: 2.9413] 54.1+0.0s +[14400/16000] [L1: 2.7139] 53.0+0.0s +[16000/16000] [L1: 2.5239] 50.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.878 (Best: 10.878 @epoch 1) +Forward: 35.22s + +Saving... +Total: 36.17s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.7112] 50.2+1.0s +[3200/16000] [L1: 0.6924] 52.4+0.0s +[4800/16000] [L1: 0.6667] 52.9+0.0s +[6400/16000] [L1: 0.6456] 53.0+0.0s +[8000/16000] [L1: 0.6251] 53.6+0.0s +[9600/16000] [L1: 0.6055] 55.1+0.0s +[11200/16000] [L1: 0.5883] 51.4+0.0s +[12800/16000] [L1: 0.5717] 52.1+0.0s +[14400/16000] [L1: 0.5563] 55.0+0.1s +[16000/16000] [L1: 0.5415] 51.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.345 (Best: 14.345 @epoch 2) +Forward: 34.86s + +Saving... +Total: 35.38s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3891] 49.8+0.8s +[3200/16000] [L1: 0.3885] 54.6+0.1s +[4800/16000] [L1: 0.3786] 54.8+0.1s +[6400/16000] [L1: 0.3711] 54.7+0.1s +[8000/16000] [L1: 0.3617] 54.2+0.0s +[9600/16000] [L1: 0.3537] 54.1+0.0s +[11200/16000] [L1: 0.3461] 53.9+0.0s +[12800/16000] [L1: 0.3394] 52.7+0.0s +[14400/16000] [L1: 0.6586] 54.8+0.0s +[16000/16000] [L1: 0.8616] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 9.684 (Best: 14.345 @epoch 2) +Forward: 34.96s + +Saving... +Total: 35.52s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.7532] 50.9+0.9s +[3200/16000] [L1: 0.6867] 55.3+0.1s +[4800/16000] [L1: 0.6442] 52.9+0.0s +[6400/16000] [L1: 0.6232] 53.2+0.0s +[8000/16000] [L1: 0.6047] 54.4+0.1s +[9600/16000] [L1: 0.5900] 54.9+0.0s +[11200/16000] [L1: 0.5778] 54.5+0.0s +[12800/16000] [L1: 0.5673] 55.4+0.0s +[14400/16000] [L1: 0.5592] 54.8+0.0s +[16000/16000] [L1: 0.5505] 52.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.554 (Best: 15.554 @epoch 4) +Forward: 34.92s + +Saving... +Total: 35.43s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.4567] 52.1+0.8s +[3200/16000] [L1: 0.4517] 54.7+0.1s +[4800/16000] [L1: 0.4403] 55.0+0.0s +[6400/16000] [L1: 0.4345] 54.9+0.0s +[8000/16000] [L1: 0.4285] 53.8+0.0s +[9600/16000] [L1: 0.4271] 54.4+0.1s +[11200/16000] [L1: 0.4215] 54.5+0.1s +[12800/16000] [L1: 0.4184] 53.1+0.0s +[14400/16000] [L1: 0.4134] 54.4+0.0s +[16000/16000] [L1: 0.4067] 54.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.249 (Best: 21.249 @epoch 5) +Forward: 34.76s + +Saving... +Total: 35.41s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3527] 51.0+0.9s +[3200/16000] [L1: 0.3506] 53.6+0.1s +[4800/16000] [L1: 0.3460] 52.6+0.0s +[6400/16000] [L1: 0.3417] 51.2+0.0s +[8000/16000] [L1: 0.3367] 54.3+0.0s +[9600/16000] [L1: 0.3328] 54.7+0.0s +[11200/16000] [L1: 0.3290] 53.4+0.0s +[12800/16000] [L1: 0.3237] 53.4+0.1s +[14400/16000] [L1: 0.3220] 53.8+0.0s +[16000/16000] [L1: 0.3191] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.980 (Best: 21.249 @epoch 5) +Forward: 35.01s + +Saving... +Total: 35.45s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2690] 51.6+0.8s +[3200/16000] [L1: 0.2629] 51.8+0.0s +[4800/16000] [L1: 0.2672] 55.5+0.1s +[6400/16000] [L1: 0.2630] 54.1+0.1s +[8000/16000] [L1: 0.2591] 53.2+0.0s +[9600/16000] [L1: 0.2562] 53.8+0.0s +[11200/16000] [L1: 0.2510] 54.2+0.0s +[12800/16000] [L1: 0.2469] 53.7+0.0s +[14400/16000] [L1: 0.2435] 55.5+0.1s +[16000/16000] [L1: 0.2396] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.232 (Best: 21.249 @epoch 5) +Forward: 34.99s + +Saving... +Total: 35.45s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2041] 52.8+0.8s +[3200/16000] [L1: 0.1992] 54.0+0.1s +[4800/16000] [L1: 0.1984] 54.6+0.0s +[6400/16000] [L1: 0.1949] 54.0+0.0s +[8000/16000] [L1: 0.1935] 54.4+0.0s +[9600/16000] [L1: 0.1928] 54.4+0.0s +[11200/16000] [L1: 0.1893] 54.9+0.1s +[12800/16000] [L1: 0.1899] 52.7+0.0s +[14400/16000] [L1: 0.1891] 53.3+0.0s +[16000/16000] [L1: 0.1887] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.870 (Best: 21.249 @epoch 5) +Forward: 34.82s + +Saving... +Total: 35.32s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1673] 51.6+1.0s +[3200/16000] [L1: 0.1648] 53.7+0.1s +[4800/16000] [L1: 0.1620] 52.9+0.0s +[6400/16000] [L1: 0.1621] 54.4+0.1s +[8000/16000] [L1: 0.1611] 55.1+0.1s +[9600/16000] [L1: 0.1613] 54.0+0.0s +[11200/16000] [L1: 0.1604] 53.2+0.0s +[12800/16000] [L1: 0.1687] 52.9+0.0s +[14400/16000] [L1: 0.1689] 52.8+0.0s +[16000/16000] [L1: 0.1679] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.529 (Best: 25.529 @epoch 9) +Forward: 34.96s + +Saving... +Total: 35.60s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1575] 51.6+1.1s +[3200/16000] [L1: 0.1568] 54.2+0.1s +[4800/16000] [L1: 0.1525] 55.2+0.1s +[6400/16000] [L1: 0.1501] 55.0+0.0s +[8000/16000] [L1: 0.1518] 55.3+0.1s +[9600/16000] [L1: 0.1505] 55.0+0.1s +[11200/16000] [L1: 0.1491] 53.9+0.1s +[12800/16000] [L1: 0.1473] 52.7+0.0s +[14400/16000] [L1: 0.1478] 53.9+0.1s +[16000/16000] [L1: 0.1481] 55.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 23.831 (Best: 25.529 @epoch 9) +Forward: 34.77s + +Saving... +Total: 35.20s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1379] 52.1+0.8s +[3200/16000] [L1: 0.1334] 53.8+0.0s +[4800/16000] [L1: 0.1333] 54.9+0.1s +[6400/16000] [L1: 0.1345] 53.7+0.0s +[8000/16000] [L1: 0.1343] 55.0+0.1s +[9600/16000] [L1: 0.1345] 53.6+0.1s +[11200/16000] [L1: 0.1341] 54.9+0.1s +[12800/16000] [L1: 0.1339] 53.9+0.0s +[14400/16000] [L1: 0.1346] 54.1+0.0s +[16000/16000] [L1: 0.1338] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.613 (Best: 28.613 @epoch 11) +Forward: 34.92s + +Saving... +Total: 35.46s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1696] 52.1+1.0s +[3200/16000] [L1: 0.1530] 54.1+0.0s +[4800/16000] [L1: 0.1476] 53.5+0.0s +[6400/16000] [L1: 0.1424] 53.2+0.0s +[8000/16000] [L1: 0.1386] 54.7+0.1s +[9600/16000] [L1: 0.1353] 54.4+0.1s +[11200/16000] [L1: 0.1331] 54.7+0.1s +[12800/16000] [L1: 0.1306] 52.9+0.0s +[14400/16000] [L1: 0.1287] 53.9+0.0s +[16000/16000] [L1: 0.1268] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.331 (Best: 29.331 @epoch 12) +Forward: 34.93s + +Saving... +Total: 35.56s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1108] 52.5+1.0s +[3200/16000] [L1: 0.1073] 52.8+0.0s +[4800/16000] [L1: 0.1063] 53.5+0.0s +[6400/16000] [L1: 0.1052] 52.9+0.0s +[8000/16000] [L1: 0.1055] 55.7+0.1s +[9600/16000] [L1: 0.1046] 53.4+0.1s +[11200/16000] [L1: 0.1036] 55.1+0.1s +[12800/16000] [L1: 0.1025] 55.1+0.0s +[14400/16000] [L1: 0.1013] 53.1+0.0s +[16000/16000] [L1: 0.0999] 53.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.501 (Best: 30.501 @epoch 13) +Forward: 34.90s + +Saving... +Total: 35.45s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0817] 52.2+1.0s +[3200/16000] [L1: 0.0785] 53.4+0.0s +[4800/16000] [L1: 0.0821] 55.0+0.1s +[6400/16000] [L1: 0.0826] 55.1+0.1s +[8000/16000] [L1: 0.0819] 54.8+0.1s +[9600/16000] [L1: 0.0805] 54.2+0.1s +[11200/16000] [L1: 0.0807] 53.8+0.0s +[12800/16000] [L1: 0.0800] 54.2+0.0s +[14400/16000] [L1: 0.0803] 53.0+0.0s +[16000/16000] [L1: 0.0792] 52.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.997 (Best: 30.501 @epoch 13) +Forward: 34.94s + +Saving... +Total: 35.47s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0741] 52.4+0.9s +[3200/16000] [L1: 0.0735] 55.6+0.1s +[4800/16000] [L1: 0.0726] 54.0+0.1s +[6400/16000] [L1: 0.0715] 53.0+0.0s +[8000/16000] [L1: 0.0721] 53.6+0.0s +[9600/16000] [L1: 0.0727] 55.2+0.1s +[11200/16000] [L1: 0.0719] 54.5+0.1s +[12800/16000] [L1: 0.0709] 54.4+0.0s +[14400/16000] [L1: 0.0701] 53.7+0.0s +[16000/16000] [L1: 0.0697] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.701 (Best: 30.701 @epoch 15) +Forward: 34.82s + +Saving... +Total: 35.39s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0666] 52.3+0.9s +[3200/16000] [L1: 0.0684] 52.9+0.0s +[4800/16000] [L1: 0.0666] 54.5+0.0s +[6400/16000] [L1: 0.0661] 55.4+0.1s +[8000/16000] [L1: 0.0668] 55.2+0.1s +[9600/16000] [L1: 0.0667] 53.9+0.1s +[11200/16000] [L1: 0.0668] 51.8+0.0s +[12800/16000] [L1: 0.0664] 53.3+0.0s +[14400/16000] [L1: 0.0661] 53.2+0.0s +[16000/16000] [L1: 0.0659] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.257 (Best: 30.701 @epoch 15) +Forward: 34.93s + +Saving... +Total: 35.33s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0672] 52.6+0.8s +[3200/16000] [L1: 0.0639] 54.6+0.1s +[4800/16000] [L1: 0.0623] 55.3+0.0s +[6400/16000] [L1: 0.0615] 54.6+0.1s +[8000/16000] [L1: 0.0618] 54.4+0.1s +[9600/16000] [L1: 0.0621] 55.6+0.1s +[11200/16000] [L1: 0.0616] 53.9+0.1s +[12800/16000] [L1: 0.0622] 54.2+0.0s +[14400/16000] [L1: 0.0619] 55.5+0.0s +[16000/16000] [L1: 0.0615] 55.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.476 (Best: 30.701 @epoch 15) +Forward: 34.79s + +Saving... +Total: 35.22s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0581] 52.4+0.8s +[3200/16000] [L1: 0.0605] 52.9+0.1s +[4800/16000] [L1: 0.0612] 54.2+0.1s +[6400/16000] [L1: 0.0613] 53.9+0.1s +[8000/16000] [L1: 0.0613] 52.8+0.0s +[9600/16000] [L1: 0.0607] 53.6+0.0s +[11200/16000] [L1: 0.0602] 54.2+0.1s +[12800/16000] [L1: 0.0604] 54.4+0.0s +[14400/16000] [L1: 0.0603] 54.7+0.1s +[16000/16000] [L1: 0.0600] 55.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.362 (Best: 30.701 @epoch 15) +Forward: 34.61s + +Saving... +Total: 35.04s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0625] 52.0+1.1s +[3200/16000] [L1: 0.0612] 54.2+0.1s +[4800/16000] [L1: 0.0598] 53.9+0.1s +[6400/16000] [L1: 0.0588] 55.1+0.1s +[8000/16000] [L1: 0.0580] 52.3+0.0s +[9600/16000] [L1: 0.0574] 54.8+0.0s +[11200/16000] [L1: 0.0568] 54.1+0.0s +[12800/16000] [L1: 0.0563] 54.3+0.0s +[14400/16000] [L1: 0.0572] 53.1+0.0s +[16000/16000] [L1: 0.0572] 55.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 18.223 (Best: 30.701 @epoch 15) +Forward: 34.71s + +Saving... +Total: 35.85s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0595] 51.6+0.8s +[3200/16000] [L1: 0.0562] 54.8+0.1s +[4800/16000] [L1: 0.0560] 54.4+0.1s +[6400/16000] [L1: 0.0561] 54.4+0.0s +[8000/16000] [L1: 0.0559] 54.1+0.1s +[9600/16000] [L1: 0.0570] 54.0+0.1s +[11200/16000] [L1: 0.0563] 54.4+0.0s +[12800/16000] [L1: 0.0557] 53.4+0.0s +[14400/16000] [L1: 0.0554] 53.7+0.0s +[16000/16000] [L1: 0.0556] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.629 (Best: 30.701 @epoch 15) +Forward: 34.85s + +Saving... +Total: 35.35s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0558] 52.5+0.8s +[3200/16000] [L1: 0.0566] 54.0+0.1s +[4800/16000] [L1: 0.0564] 54.8+0.1s +[6400/16000] [L1: 0.0587] 52.1+0.0s +[8000/16000] [L1: 0.0588] 53.3+0.0s +[9600/16000] [L1: 0.0591] 55.7+0.1s +[11200/16000] [L1: 0.0586] 55.4+0.1s +[12800/16000] [L1: 0.0581] 53.8+0.0s +[14400/16000] [L1: 0.0573] 54.6+0.1s +[16000/16000] [L1: 0.0570] 54.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.764 (Best: 30.701 @epoch 15) +Forward: 34.83s + +Saving... +Total: 35.33s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0486] 52.5+0.9s +[3200/16000] [L1: 0.0510] 55.2+0.1s +[4800/16000] [L1: 0.0513] 55.1+0.1s +[6400/16000] [L1: 0.0517] 53.8+0.0s +[8000/16000] [L1: 0.0525] 55.3+0.1s +[9600/16000] [L1: 0.0525] 55.2+0.1s +[11200/16000] [L1: 0.0523] 53.7+0.0s +[12800/16000] [L1: 0.0526] 54.3+0.0s +[14400/16000] [L1: 0.0524] 51.2+0.0s +[16000/16000] [L1: 0.0520] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.476 (Best: 30.701 @epoch 15) +Forward: 34.92s + +Saving... +Total: 35.45s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0514] 52.4+1.0s +[3200/16000] [L1: 0.0542] 53.1+0.0s +[4800/16000] [L1: 0.0541] 54.0+0.0s +[6400/16000] [L1: 0.0528] 55.1+0.1s +[8000/16000] [L1: 0.0521] 55.4+0.1s +[9600/16000] [L1: 0.0546] 54.1+0.1s +[11200/16000] [L1: 0.0553] 54.3+0.0s +[12800/16000] [L1: 0.0549] 54.4+0.0s +[14400/16000] [L1: 0.0545] 54.5+0.0s +[16000/16000] [L1: 0.0538] 53.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.862 (Best: 30.701 @epoch 15) +Forward: 34.83s + +Saving... +Total: 35.30s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0476] 51.9+0.9s +[3200/16000] [L1: 0.0488] 55.3+0.1s +[4800/16000] [L1: 0.0501] 55.0+0.1s +[6400/16000] [L1: 0.0494] 54.1+0.1s +[8000/16000] [L1: 0.0490] 54.4+0.1s +[9600/16000] [L1: 0.0486] 54.8+0.1s +[11200/16000] [L1: 0.0486] 54.5+0.1s +[12800/16000] [L1: 0.0486] 55.6+0.1s +[14400/16000] [L1: 0.0484] 53.9+0.1s +[16000/16000] [L1: 0.0488] 55.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.669 (Best: 30.701 @epoch 15) +Forward: 34.72s + +Saving... +Total: 35.14s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0492] 50.1+0.9s +[3200/16000] [L1: 0.0486] 53.1+0.1s +[4800/16000] [L1: 0.0500] 55.6+0.1s +[6400/16000] [L1: 0.0504] 51.9+0.0s +[8000/16000] [L1: 0.0508] 55.2+0.1s +[9600/16000] [L1: 0.0503] 55.1+0.1s +[11200/16000] [L1: 0.0501] 53.2+0.0s +[12800/16000] [L1: 0.0497] 52.2+0.0s +[14400/16000] [L1: 0.0496] 53.6+0.0s +[16000/16000] [L1: 0.0493] 54.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.068 (Best: 30.701 @epoch 15) +Forward: 34.45s + +Saving... +Total: 34.83s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0503] 51.1+0.9s +[3200/16000] [L1: 0.0482] 53.4+0.0s +[4800/16000] [L1: 0.0485] 54.6+0.1s +[6400/16000] [L1: 0.0482] 53.9+0.0s +[8000/16000] [L1: 0.0474] 54.7+0.1s +[9600/16000] [L1: 0.0479] 55.5+0.1s +[11200/16000] [L1: 0.0500] 55.4+0.1s +[12800/16000] [L1: 0.0513] 53.2+0.1s +[14400/16000] [L1: 0.0508] 54.6+0.1s +[16000/16000] [L1: 0.0504] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.122 (Best: 30.701 @epoch 15) +Forward: 34.52s + +Saving... +Total: 35.01s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0459] 51.1+0.9s +[3200/16000] [L1: 0.0459] 53.0+0.1s +[4800/16000] [L1: 0.0448] 53.0+0.1s +[6400/16000] [L1: 0.0452] 54.2+0.1s +[8000/16000] [L1: 0.0455] 53.1+0.0s +[9600/16000] [L1: 0.0460] 53.7+0.1s +[11200/16000] [L1: 0.0457] 53.9+0.1s +[12800/16000] [L1: 0.0455] 53.6+0.0s +[14400/16000] [L1: 0.0454] 53.3+0.0s +[16000/16000] [L1: 0.0459] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.227 (Best: 30.701 @epoch 15) +Forward: 34.41s + +Saving... +Total: 34.82s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0468] 51.4+1.0s +[3200/16000] [L1: 0.0453] 53.5+0.1s +[4800/16000] [L1: 0.0455] 54.1+0.1s +[6400/16000] [L1: 0.0465] 55.8+0.1s +[8000/16000] [L1: 0.0461] 54.2+0.0s +[9600/16000] [L1: 0.0457] 55.8+0.1s +[11200/16000] [L1: 0.0466] 53.9+0.1s +[12800/16000] [L1: 0.0465] 54.2+0.1s +[14400/16000] [L1: 0.0459] 54.1+0.1s +[16000/16000] [L1: 0.0457] 54.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.030 (Best: 30.701 @epoch 15) +Forward: 34.29s + +Saving... +Total: 34.67s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0419] 51.7+0.9s +[3200/16000] [L1: 0.0435] 53.5+0.1s +[4800/16000] [L1: 0.0441] 53.5+0.0s +[6400/16000] [L1: 0.0437] 54.5+0.1s +[8000/16000] [L1: 0.0432] 55.7+0.1s +[9600/16000] [L1: 0.0433] 53.4+0.0s +[11200/16000] [L1: 0.0430] 54.1+0.0s +[12800/16000] [L1: 0.0445] 55.0+0.0s +[14400/16000] [L1: 0.0441] 54.2+0.0s +[16000/16000] [L1: 0.0438] 54.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.049 (Best: 30.701 @epoch 15) +Forward: 34.30s + +Saving... +Total: 34.80s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0395] 50.7+0.9s +[3200/16000] [L1: 0.0410] 54.3+0.1s +[4800/16000] [L1: 0.0427] 54.9+0.1s +[6400/16000] [L1: 0.0420] 54.7+0.1s +[8000/16000] [L1: 0.0415] 53.2+0.0s +[9600/16000] [L1: 0.0411] 52.7+0.0s +[11200/16000] [L1: 0.0408] 53.3+0.0s +[12800/16000] [L1: 0.0404] 55.0+0.1s +[14400/16000] [L1: 0.0406] 55.8+0.1s +[16000/16000] [L1: 0.0409] 55.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.339 (Best: 30.701 @epoch 15) +Forward: 33.93s + +Saving... +Total: 34.44s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0421] 51.6+0.9s +[3200/16000] [L1: 0.0412] 54.1+0.1s +[4800/16000] [L1: 0.0406] 55.3+0.1s +[6400/16000] [L1: 0.0415] 54.2+0.0s +[8000/16000] [L1: 0.0418] 53.5+0.0s +[9600/16000] [L1: 0.0413] 55.0+0.1s +[11200/16000] [L1: 0.0408] 54.8+0.1s +[12800/16000] [L1: 0.0407] 54.0+0.1s +[14400/16000] [L1: 0.0404] 54.2+0.1s +[16000/16000] [L1: 0.0401] 54.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 11.659 (Best: 30.701 @epoch 15) +Forward: 33.95s + +Saving... +Total: 34.41s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0497] 50.4+0.9s +[3200/16000] [L1: 0.0463] 54.2+0.1s +[4800/16000] [L1: 0.0445] 53.7+0.1s +[6400/16000] [L1: 0.0440] 55.0+0.0s +[8000/16000] [L1: 0.0439] 55.0+0.1s +[9600/16000] [L1: 0.0426] 54.9+0.0s +[11200/16000] [L1: 0.0424] 55.7+0.1s +[12800/16000] [L1: 0.0422] 51.6+0.0s +[14400/16000] [L1: 0.0420] 52.3+0.0s +[16000/16000] [L1: 0.0417] 53.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.973 (Best: 30.701 @epoch 15) +Forward: 34.37s + +Saving... +Total: 34.93s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0373] 51.4+0.9s +[3200/16000] [L1: 0.0373] 53.0+0.1s +[4800/16000] [L1: 0.0372] 53.8+0.1s +[6400/16000] [L1: 0.0375] 54.2+0.1s +[8000/16000] [L1: 0.0375] 54.0+0.1s +[9600/16000] [L1: 0.0374] 52.5+0.0s +[11200/16000] [L1: 0.0381] 54.5+0.0s +[12800/16000] [L1: 0.0381] 54.9+0.0s +[14400/16000] [L1: 0.0376] 54.7+0.0s +[16000/16000] [L1: 0.0374] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.223 (Best: 30.701 @epoch 15) +Forward: 33.99s + +Saving... +Total: 34.45s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0372] 51.6+0.9s +[3200/16000] [L1: 0.0407] 53.4+0.1s +[4800/16000] [L1: 0.0428] 55.3+0.1s +[6400/16000] [L1: 0.0413] 51.8+0.0s +[8000/16000] [L1: 0.0401] 55.4+0.1s +[9600/16000] [L1: 0.0394] 54.6+0.0s +[11200/16000] [L1: 0.0390] 54.1+0.0s +[12800/16000] [L1: 0.0395] 53.9+0.0s +[14400/16000] [L1: 0.0391] 54.6+0.0s +[16000/16000] [L1: 0.0389] 54.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.127 (Best: 30.701 @epoch 15) +Forward: 33.74s + +Saving... +Total: 34.16s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0379] 51.2+0.8s +[3200/16000] [L1: 0.0359] 50.9+0.0s +[4800/16000] [L1: 0.0362] 54.6+0.1s +[6400/16000] [L1: 0.0362] 55.9+0.1s +[8000/16000] [L1: 0.0364] 55.5+0.1s +[9600/16000] [L1: 0.0368] 54.0+0.0s +[11200/16000] [L1: 0.0368] 53.6+0.0s +[12800/16000] [L1: 0.0369] 53.3+0.0s +[14400/16000] [L1: 0.0368] 53.8+0.0s +[16000/16000] [L1: 0.0366] 54.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.317 (Best: 30.701 @epoch 15) +Forward: 33.85s + +Saving... +Total: 34.29s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0391] 51.1+0.9s +[3200/16000] [L1: 0.0392] 54.1+0.1s +[4800/16000] [L1: 0.0381] 55.1+0.1s +[6400/16000] [L1: 0.0378] 52.3+0.0s +[8000/16000] [L1: 0.0368] 54.2+0.0s +[9600/16000] [L1: 0.0362] 54.8+0.1s +[11200/16000] [L1: 0.0362] 55.2+0.1s +[12800/16000] [L1: 0.0363] 55.1+0.1s +[14400/16000] [L1: 0.0362] 53.7+0.1s +[16000/16000] [L1: 0.0362] 54.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.293 (Best: 30.701 @epoch 15) +Forward: 33.86s + +Saving... +Total: 34.40s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0338] 51.3+0.9s +[3200/16000] [L1: 0.0339] 52.2+0.1s +[4800/16000] [L1: 0.0348] 52.0+0.0s +[6400/16000] [L1: 0.0372] 54.0+0.1s +[8000/16000] [L1: 0.0365] 52.1+0.0s +[9600/16000] [L1: 0.0371] 55.2+0.1s +[11200/16000] [L1: 0.0377] 54.3+0.1s +[12800/16000] [L1: 0.0379] 55.0+0.1s +[14400/16000] [L1: 0.0375] 54.6+0.1s +[16000/16000] [L1: 0.0371] 51.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.621 (Best: 30.701 @epoch 15) +Forward: 34.20s + +Saving... +Total: 34.70s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0334] 51.5+0.9s +[3200/16000] [L1: 0.0338] 53.6+0.1s +[4800/16000] [L1: 0.0335] 53.5+0.1s +[6400/16000] [L1: 0.0340] 55.6+0.1s +[8000/16000] [L1: 0.0352] 53.7+0.0s +[9600/16000] [L1: 0.0354] 53.4+0.0s +[11200/16000] [L1: 0.0350] 55.9+0.1s +[12800/16000] [L1: 0.0354] 52.8+0.0s +[14400/16000] [L1: 0.0354] 54.0+0.1s +[16000/16000] [L1: 0.0358] 55.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 11.953 (Best: 30.701 @epoch 15) +Forward: 33.59s + +Saving... +Total: 34.05s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0371] 49.9+0.8s +[3200/16000] [L1: 0.0357] 52.5+0.0s +[4800/16000] [L1: 0.0344] 55.0+0.1s +[6400/16000] [L1: 0.0338] 53.7+0.0s +[8000/16000] [L1: 0.0340] 55.0+0.0s +[9600/16000] [L1: 0.0335] 51.7+0.0s +[11200/16000] [L1: 0.0331] 51.1+0.0s +[12800/16000] [L1: 0.0331] 53.4+0.0s +[14400/16000] [L1: 0.0332] 54.2+0.1s +[16000/16000] [L1: 0.0331] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.935 (Best: 30.701 @epoch 15) +Forward: 33.65s + +Saving... +Total: 34.12s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0375] 51.7+0.9s +[3200/16000] [L1: 0.0376] 54.6+0.1s +[4800/16000] [L1: 0.0388] 53.7+0.1s +[6400/16000] [L1: 0.0383] 53.8+0.0s +[8000/16000] [L1: 0.0370] 55.4+0.1s +[9600/16000] [L1: 0.0361] 54.0+0.1s +[11200/16000] [L1: 0.0356] 54.2+0.1s +[12800/16000] [L1: 0.0353] 53.2+0.1s +[14400/16000] [L1: 0.0352] 55.2+0.1s +[16000/16000] [L1: 0.0350] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.069 (Best: 30.701 @epoch 15) +Forward: 33.56s + +Saving... +Total: 34.10s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0347] 50.9+0.9s +[3200/16000] [L1: 0.0341] 53.4+0.1s +[4800/16000] [L1: 0.0345] 54.8+0.1s +[6400/16000] [L1: 0.0337] 53.9+0.1s +[8000/16000] [L1: 0.0337] 54.9+0.1s +[9600/16000] [L1: 0.0348] 54.1+0.0s +[11200/16000] [L1: 0.0348] 53.3+0.0s +[12800/16000] [L1: 0.0346] 54.2+0.1s +[14400/16000] [L1: 0.0347] 51.9+0.0s +[16000/16000] [L1: 0.0345] 52.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.127 (Best: 30.701 @epoch 15) +Forward: 33.93s + +Saving... +Total: 34.34s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0335] 51.2+0.9s +[3200/16000] [L1: 0.0337] 53.0+0.1s +[4800/16000] [L1: 0.0332] 51.4+0.0s +[6400/16000] [L1: 0.0328] 53.0+0.0s +[8000/16000] [L1: 0.0330] 55.0+0.1s +[9600/16000] [L1: 0.0338] 54.2+0.0s +[11200/16000] [L1: 0.0342] 51.8+0.0s +[12800/16000] [L1: 0.0342] 55.3+0.1s +[14400/16000] [L1: 0.0343] 54.2+0.1s +[16000/16000] [L1: 0.0341] 53.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.811 (Best: 30.701 @epoch 15) +Forward: 33.52s + +Saving... +Total: 33.98s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0338] 52.0+0.9s +[3200/16000] [L1: 0.0326] 52.7+0.1s +[4800/16000] [L1: 0.0333] 54.5+0.1s +[6400/16000] [L1: 0.0327] 54.4+0.1s +[8000/16000] [L1: 0.0340] 53.9+0.1s +[9600/16000] [L1: 0.0339] 53.7+0.1s +[11200/16000] [L1: 0.0335] 54.7+0.0s +[12800/16000] [L1: 0.0341] 55.1+0.1s +[14400/16000] [L1: 0.0342] 54.4+0.1s +[16000/16000] [L1: 0.0338] 53.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.552 (Best: 30.701 @epoch 15) +Forward: 33.68s + +Saving... +Total: 34.11s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0328] 51.5+1.0s +[3200/16000] [L1: 0.0328] 54.6+0.1s +[4800/16000] [L1: 0.0326] 54.9+0.1s +[6400/16000] [L1: 0.0326] 54.9+0.1s +[8000/16000] [L1: 0.0334] 55.5+0.1s +[9600/16000] [L1: 0.0336] 54.6+0.0s +[11200/16000] [L1: 0.0335] 55.1+0.0s +[12800/16000] [L1: 0.0331] 55.3+0.1s +[14400/16000] [L1: 0.0328] 53.8+0.0s +[16000/16000] [L1: 0.0326] 53.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.126 (Best: 30.701 @epoch 15) +Forward: 33.61s + +Saving... +Total: 34.06s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0315] 51.4+0.9s +[3200/16000] [L1: 0.0329] 54.2+0.1s +[4800/16000] [L1: 0.0365] 54.4+0.0s +[6400/16000] [L1: 0.0356] 55.7+0.1s +[8000/16000] [L1: 0.0352] 54.3+0.0s +[9600/16000] [L1: 0.0344] 54.6+0.0s +[11200/16000] [L1: 0.0338] 55.5+0.1s +[12800/16000] [L1: 0.0332] 55.3+0.0s +[14400/16000] [L1: 0.0332] 53.3+0.0s +[16000/16000] [L1: 0.0333] 54.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 11.758 (Best: 30.701 @epoch 15) +Forward: 33.62s + +Saving... +Total: 34.17s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0328] 51.3+0.9s +[3200/16000] [L1: 0.0318] 54.1+0.1s +[4800/16000] [L1: 0.0362] 54.1+0.1s +[6400/16000] [L1: 0.0361] 54.9+0.1s +[8000/16000] [L1: 0.0352] 54.4+0.1s +[9600/16000] [L1: 0.0344] 54.6+0.1s +[11200/16000] [L1: 0.0337] 54.0+0.0s +[12800/16000] [L1: 0.0334] 54.0+0.0s +[14400/16000] [L1: 0.0331] 53.7+0.0s +[16000/16000] [L1: 0.0328] 54.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.660 (Best: 30.701 @epoch 15) +Forward: 33.96s + +Saving... +Total: 34.41s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0301] 52.0+0.9s +[3200/16000] [L1: 0.0329] 54.6+0.1s +[4800/16000] [L1: 0.0321] 53.9+0.1s +[6400/16000] [L1: 0.0315] 54.1+0.1s +[8000/16000] [L1: 0.0314] 55.7+0.1s +[9600/16000] [L1: 0.0313] 54.8+0.1s +[11200/16000] [L1: 0.0312] 53.7+0.0s +[12800/16000] [L1: 0.0312] 53.2+0.0s +[14400/16000] [L1: 0.0311] 54.7+0.0s +[16000/16000] [L1: 0.0313] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.048 (Best: 30.701 @epoch 15) +Forward: 34.15s + +Saving... +Total: 34.54s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0321] 51.1+0.9s +[3200/16000] [L1: 0.0327] 54.0+0.1s +[4800/16000] [L1: 0.0319] 53.5+0.1s +[6400/16000] [L1: 0.0314] 55.3+0.1s +[8000/16000] [L1: 0.0312] 54.8+0.1s +[9600/16000] [L1: 0.0307] 52.0+0.0s +[11200/16000] [L1: 0.0306] 54.1+0.0s +[12800/16000] [L1: 0.0310] 54.5+0.1s +[14400/16000] [L1: 0.0311] 53.5+0.0s +[16000/16000] [L1: 0.0312] 52.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.766 (Best: 30.701 @epoch 15) +Forward: 34.36s + +Saving... +Total: 34.79s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0392] 51.7+0.8s +[3200/16000] [L1: 0.0413] 54.6+0.1s +[4800/16000] [L1: 0.0383] 54.6+0.1s +[6400/16000] [L1: 0.0360] 54.5+0.1s +[8000/16000] [L1: 0.0346] 54.6+0.1s +[9600/16000] [L1: 0.0338] 52.2+0.0s +[11200/16000] [L1: 0.0333] 55.2+0.0s +[12800/16000] [L1: 0.0329] 54.8+0.1s +[14400/16000] [L1: 0.0328] 55.0+0.1s +[16000/16000] [L1: 0.0324] 53.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 12.003 (Best: 30.701 @epoch 15) +Forward: 34.12s + +Saving... +Total: 34.55s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0301] 51.3+1.0s +[3200/16000] [L1: 0.0293] 53.4+0.0s +[4800/16000] [L1: 0.0296] 54.5+0.1s +[6400/16000] [L1: 0.0295] 54.7+0.1s +[8000/16000] [L1: 0.0303] 54.5+0.1s +[9600/16000] [L1: 0.0313] 55.1+0.0s +[11200/16000] [L1: 0.0312] 54.2+0.1s +[12800/16000] [L1: 0.0312] 53.9+0.1s +[14400/16000] [L1: 0.0310] 54.2+0.1s +[16000/16000] [L1: 0.0311] 55.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.191 (Best: 30.701 @epoch 15) +Forward: 34.07s + +Saving... +Total: 34.48s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0326] 51.0+0.8s +[3200/16000] [L1: 0.0316] 54.5+0.1s +[4800/16000] [L1: 0.0308] 54.1+0.1s +[6400/16000] [L1: 0.0303] 55.1+0.1s +[8000/16000] [L1: 0.0312] 53.8+0.0s +[9600/16000] [L1: 0.0319] 53.5+0.0s +[11200/16000] [L1: 0.0314] 55.5+0.1s +[12800/16000] [L1: 0.0316] 54.5+0.1s +[14400/16000] [L1: 0.0321] 55.7+0.1s +[16000/16000] [L1: 0.0318] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.252 (Best: 30.701 @epoch 15) +Forward: 34.23s + +Saving... +Total: 34.75s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0304] 52.0+1.0s +[3200/16000] [L1: 0.0311] 53.9+0.1s +[4800/16000] [L1: 0.0319] 55.1+0.1s +[6400/16000] [L1: 0.0316] 53.4+0.0s +[8000/16000] [L1: 0.0308] 55.3+0.1s +[9600/16000] [L1: 0.0304] 55.2+0.1s +[11200/16000] [L1: 0.0303] 54.6+0.1s +[12800/16000] [L1: 0.0311] 54.0+0.1s +[14400/16000] [L1: 0.0321] 53.4+0.1s +[16000/16000] [L1: 0.0323] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.794 (Best: 30.701 @epoch 15) +Forward: 34.31s + +Saving... +Total: 34.73s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0312] 51.1+1.0s +[3200/16000] [L1: 0.0301] 54.3+0.1s +[4800/16000] [L1: 0.0299] 55.2+0.1s +[6400/16000] [L1: 0.0305] 55.8+0.1s +[8000/16000] [L1: 0.0300] 55.4+0.1s +[9600/16000] [L1: 0.0301] 55.0+0.1s +[11200/16000] [L1: 0.0297] 51.5+0.0s +[12800/16000] [L1: 0.0295] 54.7+0.1s +[14400/16000] [L1: 0.0294] 52.3+0.0s +[16000/16000] [L1: 0.0294] 51.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.316 (Best: 30.701 @epoch 15) +Forward: 34.74s + +Saving... +Total: 35.26s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0270] 49.3+1.0s +[3200/16000] [L1: 0.0285] 53.7+0.1s +[4800/16000] [L1: 0.0285] 53.1+0.0s +[6400/16000] [L1: 0.0285] 54.6+0.1s +[8000/16000] [L1: 0.0286] 54.7+0.1s +[9600/16000] [L1: 0.0287] 54.1+0.1s +[11200/16000] [L1: 0.0293] 55.1+0.1s +[12800/16000] [L1: 0.0301] 54.5+0.1s +[14400/16000] [L1: 0.0299] 55.6+0.1s +[16000/16000] [L1: 0.0297] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.833 (Best: 30.701 @epoch 15) +Forward: 34.23s + +Saving... +Total: 34.86s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0295] 50.6+0.9s +[3200/16000] [L1: 0.0293] 52.8+0.0s +[4800/16000] [L1: 0.0301] 54.1+0.0s +[6400/16000] [L1: 0.0300] 55.5+0.1s +[8000/16000] [L1: 0.0304] 54.0+0.1s +[9600/16000] [L1: 0.0301] 54.9+0.1s +[11200/16000] [L1: 0.0301] 54.7+0.1s +[12800/16000] [L1: 0.0300] 53.2+0.0s +[14400/16000] [L1: 0.0300] 52.7+0.0s +[16000/16000] [L1: 0.0318] 53.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.421 (Best: 30.701 @epoch 15) +Forward: 34.39s + +Saving... +Total: 34.83s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0325] 51.8+0.9s +[3200/16000] [L1: 0.0306] 54.8+0.1s +[4800/16000] [L1: 0.0297] 53.9+0.0s +[6400/16000] [L1: 0.0319] 52.8+0.0s +[8000/16000] [L1: 0.0316] 54.7+0.0s +[9600/16000] [L1: 0.0319] 54.1+0.1s +[11200/16000] [L1: 0.0319] 55.5+0.1s +[12800/16000] [L1: 0.0316] 54.9+0.1s +[14400/16000] [L1: 0.0319] 54.2+0.1s +[16000/16000] [L1: 0.0315] 55.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 12.004 (Best: 30.701 @epoch 15) +Forward: 34.09s + +Saving... +Total: 34.61s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0272] 51.0+1.0s +[3200/16000] [L1: 0.0285] 54.6+0.1s +[4800/16000] [L1: 0.0283] 54.3+0.1s +[6400/16000] [L1: 0.0318] 54.6+0.0s +[8000/16000] [L1: 0.0341] 55.1+0.1s +[9600/16000] [L1: 0.0340] 55.5+0.1s +[11200/16000] [L1: 0.0337] 54.6+0.1s +[12800/16000] [L1: 0.0333] 54.8+0.1s +[14400/16000] [L1: 0.0331] 53.8+0.0s +[16000/16000] [L1: 0.0327] 53.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.629 (Best: 30.701 @epoch 15) +Forward: 34.82s + +Saving... +Total: 35.30s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0290] 52.0+0.9s +[3200/16000] [L1: 0.0287] 54.5+0.1s +[4800/16000] [L1: 0.0282] 56.0+0.1s +[6400/16000] [L1: 0.0281] 55.1+0.1s +[8000/16000] [L1: 0.0283] 53.0+0.0s +[9600/16000] [L1: 0.0282] 56.0+0.1s +[11200/16000] [L1: 0.0283] 55.0+0.0s +[12800/16000] [L1: 0.0283] 54.5+0.1s +[14400/16000] [L1: 0.0287] 54.3+0.1s +[16000/16000] [L1: 0.0289] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.474 (Best: 30.701 @epoch 15) +Forward: 34.76s + +Saving... +Total: 35.60s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0295] 51.3+0.9s +[3200/16000] [L1: 0.0288] 54.1+0.1s +[4800/16000] [L1: 0.0282] 53.0+0.1s +[6400/16000] [L1: 0.0281] 53.6+0.0s +[8000/16000] [L1: 0.0284] 52.2+0.0s +[9600/16000] [L1: 0.0282] 53.6+0.0s +[11200/16000] [L1: 0.0282] 54.7+0.1s +[12800/16000] [L1: 0.0291] 55.4+0.1s +[14400/16000] [L1: 0.0291] 55.5+0.1s +[16000/16000] [L1: 0.0292] 55.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.697 (Best: 30.701 @epoch 15) +Forward: 34.16s + +Saving... +Total: 34.64s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0271] 51.7+0.9s +[3200/16000] [L1: 0.0295] 54.6+0.1s +[4800/16000] [L1: 0.0295] 53.3+0.1s +[6400/16000] [L1: 0.0299] 53.6+0.1s +[8000/16000] [L1: 0.0295] 55.4+0.1s +[9600/16000] [L1: 0.0294] 54.5+0.1s +[11200/16000] [L1: 0.0291] 54.1+0.0s +[12800/16000] [L1: 0.0291] 53.2+0.1s +[14400/16000] [L1: 0.0292] 54.2+0.0s +[16000/16000] [L1: 0.0290] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.784 (Best: 30.701 @epoch 15) +Forward: 34.51s + +Saving... +Total: 35.03s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0295] 52.3+0.9s +[3200/16000] [L1: 0.0293] 53.5+0.1s +[4800/16000] [L1: 0.0293] 54.5+0.1s +[6400/16000] [L1: 0.0292] 51.4+0.0s +[8000/16000] [L1: 0.0290] 52.4+0.0s +[9600/16000] [L1: 0.0295] 54.9+0.1s +[11200/16000] [L1: 0.0297] 54.9+0.1s +[12800/16000] [L1: 0.0297] 52.2+0.1s +[14400/16000] [L1: 0.0296] 50.6+0.0s +[16000/16000] [L1: 0.0293] 54.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.620 (Best: 30.701 @epoch 15) +Forward: 34.44s + +Saving... +Total: 34.99s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0263] 51.3+0.9s +[3200/16000] [L1: 0.0271] 54.9+0.1s +[4800/16000] [L1: 0.0317] 54.0+0.1s +[6400/16000] [L1: 0.0311] 54.7+0.1s +[8000/16000] [L1: 0.0305] 54.5+0.1s +[9600/16000] [L1: 0.0301] 53.2+0.0s +[11200/16000] [L1: 0.0296] 55.4+0.1s +[12800/16000] [L1: 0.0295] 53.3+0.1s +[14400/16000] [L1: 0.0295] 53.9+0.0s +[16000/16000] [L1: 0.0296] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.450 (Best: 30.701 @epoch 15) +Forward: 34.41s + +Saving... +Total: 34.88s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0312] 51.4+1.0s +[3200/16000] [L1: 0.0344] 53.0+0.1s +[4800/16000] [L1: 0.0322] 54.0+0.1s +[6400/16000] [L1: 0.0311] 53.6+0.1s +[8000/16000] [L1: 0.0304] 55.0+0.1s +[9600/16000] [L1: 0.0299] 53.7+0.1s +[11200/16000] [L1: 0.0296] 53.8+0.0s +[12800/16000] [L1: 0.0294] 55.0+0.1s +[14400/16000] [L1: 0.0291] 54.1+0.1s +[16000/16000] [L1: 0.0288] 53.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.202 (Best: 30.701 @epoch 15) +Forward: 34.33s + +Saving... +Total: 34.72s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0284] 50.0+0.9s +[3200/16000] [L1: 0.0284] 54.9+0.1s +[4800/16000] [L1: 0.0279] 53.4+0.1s +[6400/16000] [L1: 0.0277] 53.1+0.1s +[8000/16000] [L1: 0.0286] 53.3+0.0s +[9600/16000] [L1: 0.0292] 52.8+0.1s +[11200/16000] [L1: 0.0289] 53.5+0.1s +[12800/16000] [L1: 0.0288] 54.0+0.1s +[14400/16000] [L1: 0.0287] 55.2+0.1s +[16000/16000] [L1: 0.0286] 52.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.280 (Best: 30.701 @epoch 15) +Forward: 34.23s + +Saving... +Total: 34.72s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0271] 51.3+1.0s +[3200/16000] [L1: 0.0281] 53.2+0.0s +[4800/16000] [L1: 0.0278] 53.0+0.0s +[6400/16000] [L1: 0.0277] 52.6+0.1s +[8000/16000] [L1: 0.0277] 53.8+0.0s +[9600/16000] [L1: 0.0279] 56.0+0.1s +[11200/16000] [L1: 0.0279] 55.4+0.1s +[12800/16000] [L1: 0.0280] 55.2+0.1s +[14400/16000] [L1: 0.0280] 54.1+0.1s +[16000/16000] [L1: 0.0282] 54.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.924 (Best: 30.701 @epoch 15) +Forward: 34.09s + +Saving... +Total: 34.49s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0277] 51.3+1.0s +[3200/16000] [L1: 0.0286] 55.0+0.1s +[4800/16000] [L1: 0.0283] 54.2+0.1s +[6400/16000] [L1: 0.0282] 54.2+0.0s +[8000/16000] [L1: 0.0285] 55.9+0.1s +[9600/16000] [L1: 0.0290] 55.2+0.0s +[11200/16000] [L1: 0.0290] 54.7+0.1s +[12800/16000] [L1: 0.0288] 54.5+0.1s +[14400/16000] [L1: 0.0285] 54.8+0.1s +[16000/16000] [L1: 0.0283] 55.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.674 (Best: 30.701 @epoch 15) +Forward: 33.94s + +Saving... +Total: 34.41s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0279] 50.9+1.0s +[3200/16000] [L1: 0.0284] 53.3+0.0s +[4800/16000] [L1: 0.0280] 54.5+0.1s +[6400/16000] [L1: 0.0308] 54.9+0.1s +[8000/16000] [L1: 0.0303] 54.3+0.1s +[9600/16000] [L1: 0.0297] 55.6+0.1s +[11200/16000] [L1: 0.0295] 54.5+0.1s +[12800/16000] [L1: 0.0289] 54.6+0.1s +[14400/16000] [L1: 0.0291] 52.5+0.0s +[16000/16000] [L1: 0.0291] 53.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.513 (Best: 30.701 @epoch 15) +Forward: 34.50s + +Saving... +Total: 35.18s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0301] 49.7+1.0s +[3200/16000] [L1: 0.0295] 52.9+0.0s +[4800/16000] [L1: 0.0286] 54.8+0.1s +[6400/16000] [L1: 0.0287] 54.8+0.0s +[8000/16000] [L1: 0.0285] 52.7+0.0s +[9600/16000] [L1: 0.0283] 54.8+0.1s +[11200/16000] [L1: 0.0280] 53.7+0.1s +[12800/16000] [L1: 0.0279] 54.0+0.0s +[14400/16000] [L1: 0.0279] 52.0+0.0s +[16000/16000] [L1: 0.0279] 54.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 11.682 (Best: 30.701 @epoch 15) +Forward: 34.09s + +Saving... +Total: 34.52s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0324] 50.3+0.9s +[3200/16000] [L1: 0.0324] 55.0+0.1s +[4800/16000] [L1: 0.0309] 54.8+0.1s +[6400/16000] [L1: 0.0302] 54.7+0.1s +[8000/16000] [L1: 0.0294] 55.0+0.0s +[9600/16000] [L1: 0.0293] 54.0+0.0s +[11200/16000] [L1: 0.0292] 54.8+0.1s +[12800/16000] [L1: 0.0293] 52.9+0.0s +[14400/16000] [L1: 0.0295] 54.2+0.0s +[16000/16000] [L1: 0.0292] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.818 (Best: 30.701 @epoch 15) +Forward: 34.09s + +Saving... +Total: 34.50s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0292] 51.6+0.8s +[3200/16000] [L1: 0.0283] 54.1+0.1s +[4800/16000] [L1: 0.0274] 53.6+0.0s +[6400/16000] [L1: 0.0432] 55.5+0.1s +[8000/16000] [L1: 0.0427] 53.0+0.0s +[9600/16000] [L1: 0.0411] 54.1+0.1s +[11200/16000] [L1: 0.0395] 54.1+0.1s +[12800/16000] [L1: 0.0380] 52.9+0.0s +[14400/16000] [L1: 0.0368] 53.6+0.0s +[16000/16000] [L1: 0.0359] 53.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.296 (Best: 30.701 @epoch 15) +Forward: 34.13s + +Saving... +Total: 34.62s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0293] 51.8+0.9s +[3200/16000] [L1: 0.0284] 53.4+0.0s +[4800/16000] [L1: 0.0275] 54.5+0.1s +[6400/16000] [L1: 0.0273] 55.1+0.1s +[8000/16000] [L1: 0.0276] 55.6+0.1s +[9600/16000] [L1: 0.0274] 55.7+0.1s +[11200/16000] [L1: 0.0275] 53.2+0.1s +[12800/16000] [L1: 0.0274] 54.2+0.1s +[14400/16000] [L1: 0.0272] 55.0+0.1s +[16000/16000] [L1: 0.0271] 53.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.261 (Best: 30.701 @epoch 15) +Forward: 33.90s + +Saving... +Total: 34.48s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0279] 51.9+0.9s +[3200/16000] [L1: 0.0270] 54.8+0.1s +[4800/16000] [L1: 0.0268] 55.5+0.1s +[6400/16000] [L1: 0.0272] 54.2+0.0s +[8000/16000] [L1: 0.0272] 53.6+0.0s +[9600/16000] [L1: 0.0269] 54.4+0.0s +[11200/16000] [L1: 0.0267] 54.5+0.1s +[12800/16000] [L1: 0.0265] 55.9+0.1s +[14400/16000] [L1: 0.0268] 54.4+0.1s +[16000/16000] [L1: 0.0269] 54.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.745 (Best: 30.701 @epoch 15) +Forward: 33.97s + +Saving... +Total: 34.46s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0280] 50.4+0.8s +[3200/16000] [L1: 0.0279] 55.3+0.1s +[4800/16000] [L1: 0.0287] 54.7+0.0s +[6400/16000] [L1: 0.0282] 53.5+0.0s +[8000/16000] [L1: 0.0298] 55.8+0.1s +[9600/16000] [L1: 0.0300] 55.3+0.1s +[11200/16000] [L1: 0.0294] 54.3+0.0s +[12800/16000] [L1: 0.0290] 52.2+0.0s +[14400/16000] [L1: 0.0287] 51.5+0.0s +[16000/16000] [L1: 0.0285] 53.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 12.994 (Best: 30.701 @epoch 15) +Forward: 34.01s + +Saving... +Total: 34.43s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0269] 49.8+0.9s +[3200/16000] [L1: 0.0266] 52.2+0.0s +[4800/16000] [L1: 0.0264] 52.8+0.0s +[6400/16000] [L1: 0.0265] 55.5+0.1s +[8000/16000] [L1: 0.0277] 53.0+0.0s +[9600/16000] [L1: 0.0287] 53.7+0.0s +[11200/16000] [L1: 0.0285] 53.0+0.0s +[12800/16000] [L1: 0.0282] 55.4+0.1s +[14400/16000] [L1: 0.0279] 53.9+0.0s +[16000/16000] [L1: 0.0287] 53.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.843 (Best: 30.701 @epoch 15) +Forward: 33.83s + +Saving... +Total: 34.34s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0270] 51.4+0.9s +[3200/16000] [L1: 0.0277] 54.4+0.1s +[4800/16000] [L1: 0.0334] 54.5+0.0s +[6400/16000] [L1: 0.0350] 53.6+0.0s +[8000/16000] [L1: 0.0340] 53.8+0.1s +[9600/16000] [L1: 0.0333] 54.9+0.1s +[11200/16000] [L1: 0.0322] 53.2+0.0s +[12800/16000] [L1: 0.0314] 53.1+0.0s +[14400/16000] [L1: 0.0308] 54.2+0.1s +[16000/16000] [L1: 0.0303] 55.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.608 (Best: 30.701 @epoch 15) +Forward: 33.88s + +Saving... +Total: 34.49s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0248] 51.7+0.9s +[3200/16000] [L1: 0.0261] 53.3+0.0s +[4800/16000] [L1: 0.0270] 54.8+0.1s +[6400/16000] [L1: 0.0268] 54.0+0.1s +[8000/16000] [L1: 0.0276] 55.9+0.1s +[9600/16000] [L1: 0.0282] 54.4+0.1s +[11200/16000] [L1: 0.0282] 55.1+0.1s +[12800/16000] [L1: 0.0285] 53.3+0.1s +[14400/16000] [L1: 0.0288] 53.7+0.1s +[16000/16000] [L1: 0.0285] 53.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.804 (Best: 30.701 @epoch 15) +Forward: 33.99s + +Saving... +Total: 34.45s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0261] 51.9+0.9s +[3200/16000] [L1: 0.0262] 54.0+0.1s +[4800/16000] [L1: 0.0265] 53.7+0.1s +[6400/16000] [L1: 0.0262] 54.3+0.0s +[8000/16000] [L1: 0.0261] 54.9+0.1s +[9600/16000] [L1: 0.0262] 54.2+0.1s +[11200/16000] [L1: 0.0266] 54.1+0.1s +[12800/16000] [L1: 0.0266] 53.6+0.1s +[14400/16000] [L1: 0.0266] 54.4+0.1s +[16000/16000] [L1: 0.0264] 53.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.560 (Best: 30.701 @epoch 15) +Forward: 34.13s + +Saving... +Total: 34.64s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0276] 50.1+0.9s +[3200/16000] [L1: 0.0268] 53.6+0.1s +[4800/16000] [L1: 0.0262] 53.5+0.0s +[6400/16000] [L1: 0.0260] 54.5+0.1s +[8000/16000] [L1: 0.0259] 54.1+0.0s +[9600/16000] [L1: 0.0261] 51.7+0.0s +[11200/16000] [L1: 0.0260] 52.9+0.1s +[12800/16000] [L1: 0.0264] 53.6+0.0s +[14400/16000] [L1: 0.0265] 54.6+0.1s +[16000/16000] [L1: 0.0266] 54.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.046 (Best: 30.701 @epoch 15) +Forward: 33.72s + +Saving... +Total: 34.68s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0259] 51.1+0.9s +[3200/16000] [L1: 0.0255] 54.7+0.1s +[4800/16000] [L1: 0.0257] 54.3+0.1s +[6400/16000] [L1: 0.0254] 53.2+0.0s +[8000/16000] [L1: 0.0257] 55.5+0.1s +[9600/16000] [L1: 0.0307] 54.5+0.1s +[11200/16000] [L1: 0.0324] 54.2+0.0s +[12800/16000] [L1: 0.0322] 51.9+0.0s +[14400/16000] [L1: 0.0317] 53.8+0.0s +[16000/16000] [L1: 0.0312] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.357 (Best: 30.701 @epoch 15) +Forward: 33.90s + +Saving... +Total: 34.37s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0266] 51.0+0.9s +[3200/16000] [L1: 0.0271] 54.1+0.1s +[4800/16000] [L1: 0.0269] 53.6+0.1s +[6400/16000] [L1: 0.0267] 54.0+0.1s +[8000/16000] [L1: 0.0269] 51.9+0.0s +[9600/16000] [L1: 0.0266] 54.7+0.1s +[11200/16000] [L1: 0.0271] 55.3+0.1s +[12800/16000] [L1: 0.0270] 53.2+0.0s +[14400/16000] [L1: 0.0269] 54.0+0.1s +[16000/16000] [L1: 0.0275] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.860 (Best: 30.701 @epoch 15) +Forward: 33.63s + +Saving... +Total: 34.19s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0275] 52.4+0.9s +[3200/16000] [L1: 0.0268] 54.7+0.1s +[4800/16000] [L1: 0.0260] 54.5+0.1s +[6400/16000] [L1: 0.0257] 54.5+0.1s +[8000/16000] [L1: 0.0255] 54.5+0.1s +[9600/16000] [L1: 0.0255] 54.2+0.1s +[11200/16000] [L1: 0.0256] 54.7+0.0s +[12800/16000] [L1: 0.0256] 52.8+0.0s +[14400/16000] [L1: 0.0259] 55.7+0.1s +[16000/16000] [L1: 0.0259] 52.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.224 (Best: 30.701 @epoch 15) +Forward: 34.10s + +Saving... +Total: 34.80s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0275] 52.1+1.0s +[3200/16000] [L1: 0.0282] 52.5+0.1s +[4800/16000] [L1: 0.0284] 54.6+0.1s +[6400/16000] [L1: 0.0283] 53.3+0.1s +[8000/16000] [L1: 0.0283] 51.7+0.0s +[9600/16000] [L1: 0.0288] 53.3+0.0s +[11200/16000] [L1: 0.0286] 53.9+0.1s +[12800/16000] [L1: 0.0289] 55.3+0.1s +[14400/16000] [L1: 0.0284] 54.0+0.1s +[16000/16000] [L1: 0.0282] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.882 (Best: 30.701 @epoch 15) +Forward: 33.63s + +Saving... +Total: 34.17s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0260] 50.1+1.1s +[3200/16000] [L1: 0.0253] 52.9+0.1s +[4800/16000] [L1: 0.0259] 52.8+0.0s +[6400/16000] [L1: 0.0258] 53.0+0.0s +[8000/16000] [L1: 0.0262] 53.9+0.0s +[9600/16000] [L1: 0.0268] 52.2+0.0s +[11200/16000] [L1: 0.0268] 51.8+0.0s +[12800/16000] [L1: 0.0267] 55.3+0.1s +[14400/16000] [L1: 0.0268] 54.7+0.0s +[16000/16000] [L1: 0.0266] 53.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.745 (Best: 30.701 @epoch 15) +Forward: 33.75s + +Saving... +Total: 34.30s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0276] 52.0+1.0s +[3200/16000] [L1: 0.0268] 54.8+0.1s +[4800/16000] [L1: 0.0263] 55.5+0.1s +[6400/16000] [L1: 0.0261] 55.2+0.1s +[8000/16000] [L1: 0.0266] 55.8+0.1s +[9600/16000] [L1: 0.0269] 54.1+0.1s +[11200/16000] [L1: 0.0272] 55.1+0.1s +[12800/16000] [L1: 0.0285] 54.8+0.1s +[14400/16000] [L1: 0.0289] 52.8+0.0s +[16000/16000] [L1: 0.0288] 51.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.316 (Best: 30.701 @epoch 15) +Forward: 34.18s + +Saving... +Total: 34.62s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0267] 52.3+1.0s +[3200/16000] [L1: 0.0288] 54.5+0.1s +[4800/16000] [L1: 0.0281] 54.9+0.1s +[6400/16000] [L1: 0.0276] 52.7+0.1s +[8000/16000] [L1: 0.0270] 54.8+0.1s +[9600/16000] [L1: 0.0267] 52.3+0.0s +[11200/16000] [L1: 0.0265] 54.4+0.1s +[12800/16000] [L1: 0.0266] 52.4+0.1s +[14400/16000] [L1: 0.0265] 54.0+0.1s +[16000/16000] [L1: 0.0265] 52.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.392 (Best: 30.701 @epoch 15) +Forward: 33.94s + +Saving... +Total: 34.35s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0251] 51.7+0.9s +[3200/16000] [L1: 0.0258] 52.5+0.0s +[4800/16000] [L1: 0.0257] 53.7+0.0s +[6400/16000] [L1: 0.0283] 52.8+0.0s +[8000/16000] [L1: 0.0302] 54.4+0.0s +[9600/16000] [L1: 0.0301] 54.5+0.0s +[11200/16000] [L1: 0.0303] 53.0+0.0s +[12800/16000] [L1: 0.0303] 54.0+0.0s +[14400/16000] [L1: 0.0297] 52.6+0.1s +[16000/16000] [L1: 0.0293] 54.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.149 (Best: 30.701 @epoch 15) +Forward: 33.73s + +Saving... +Total: 34.12s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0277] 51.3+0.8s +[3200/16000] [L1: 0.0258] 54.1+0.1s +[4800/16000] [L1: 0.0256] 54.8+0.1s +[6400/16000] [L1: 0.0267] 54.3+0.1s +[8000/16000] [L1: 0.0271] 54.2+0.1s +[9600/16000] [L1: 0.0267] 53.9+0.0s +[11200/16000] [L1: 0.0264] 54.4+0.1s +[12800/16000] [L1: 0.0263] 53.7+0.0s +[14400/16000] [L1: 0.0262] 53.3+0.0s +[16000/16000] [L1: 0.0260] 53.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.772 (Best: 30.701 @epoch 15) +Forward: 33.78s + +Saving... +Total: 34.23s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0244] 50.5+1.0s +[3200/16000] [L1: 0.0247] 54.0+0.1s +[4800/16000] [L1: 0.0255] 53.7+0.1s +[6400/16000] [L1: 0.0255] 51.9+0.0s +[8000/16000] [L1: 0.0261] 52.7+0.0s +[9600/16000] [L1: 0.0262] 52.5+0.0s +[11200/16000] [L1: 0.0262] 54.3+0.0s +[12800/16000] [L1: 0.0261] 53.8+0.0s +[14400/16000] [L1: 0.0260] 53.7+0.1s +[16000/16000] [L1: 0.0260] 51.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.911 (Best: 30.701 @epoch 15) +Forward: 33.91s + +Saving... +Total: 34.46s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0243] 51.3+0.9s +[3200/16000] [L1: 0.0264] 53.4+0.1s +[4800/16000] [L1: 0.0274] 54.4+0.1s +[6400/16000] [L1: 0.0269] 55.0+0.1s +[8000/16000] [L1: 0.0269] 54.4+0.1s +[9600/16000] [L1: 0.0267] 55.2+0.1s +[11200/16000] [L1: 0.0268] 54.9+0.0s +[12800/16000] [L1: 0.0269] 55.2+0.0s +[14400/16000] [L1: 0.0271] 55.0+0.1s +[16000/16000] [L1: 0.0271] 55.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.468 (Best: 30.701 @epoch 15) +Forward: 33.47s + +Saving... +Total: 33.92s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0264] 52.3+0.9s +[3200/16000] [L1: 0.0267] 54.8+0.1s +[4800/16000] [L1: 0.0267] 54.3+0.1s +[6400/16000] [L1: 0.0263] 55.8+0.1s +[8000/16000] [L1: 0.0258] 54.5+0.1s +[9600/16000] [L1: 0.0258] 52.1+0.0s +[11200/16000] [L1: 0.0256] 53.1+0.0s +[12800/16000] [L1: 0.0262] 55.0+0.1s +[14400/16000] [L1: 0.0262] 53.6+0.0s +[16000/16000] [L1: 0.0263] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.413 (Best: 30.701 @epoch 15) +Forward: 33.65s + +Saving... +Total: 34.05s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0253] 49.7+1.0s +[3200/16000] [L1: 0.0256] 54.4+0.1s +[4800/16000] [L1: 0.0272] 54.4+0.1s +[6400/16000] [L1: 0.0278] 54.0+0.1s +[8000/16000] [L1: 0.0275] 55.3+0.1s +[9600/16000] [L1: 0.0271] 54.7+0.0s +[11200/16000] [L1: 0.0268] 54.8+0.1s +[12800/16000] [L1: 0.0266] 55.1+0.1s +[14400/16000] [L1: 0.0265] 53.6+0.0s +[16000/16000] [L1: 0.0266] 54.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.080 (Best: 30.701 @epoch 15) +Forward: 33.77s + +Saving... +Total: 34.15s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0259] 51.5+0.8s +[3200/16000] [L1: 0.0251] 53.8+0.1s +[4800/16000] [L1: 0.0264] 53.6+0.1s +[6400/16000] [L1: 0.0269] 55.0+0.1s +[8000/16000] [L1: 0.0270] 53.2+0.0s +[9600/16000] [L1: 0.0276] 54.7+0.1s +[11200/16000] [L1: 0.0273] 54.1+0.1s +[12800/16000] [L1: 0.0270] 52.6+0.0s +[14400/16000] [L1: 0.0268] 52.0+0.0s +[16000/16000] [L1: 0.0268] 55.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.903 (Best: 30.701 @epoch 15) +Forward: 33.80s + +Saving... +Total: 34.21s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0258] 51.1+1.0s +[3200/16000] [L1: 0.0253] 53.8+0.1s +[4800/16000] [L1: 0.0253] 54.1+0.1s +[6400/16000] [L1: 0.0251] 54.9+0.1s +[8000/16000] [L1: 0.0252] 53.5+0.0s +[9600/16000] [L1: 0.0254] 55.4+0.1s +[11200/16000] [L1: 0.0267] 54.3+0.1s +[12800/16000] [L1: 0.0272] 53.7+0.0s +[14400/16000] [L1: 0.0274] 51.5+0.0s +[16000/16000] [L1: 0.0301] 52.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.346 (Best: 30.701 @epoch 15) +Forward: 34.38s + +Saving... +Total: 34.91s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0268] 48.7+0.9s +[3200/16000] [L1: 0.0263] 51.8+0.1s +[4800/16000] [L1: 0.0265] 55.6+0.1s +[6400/16000] [L1: 0.0261] 55.0+0.1s +[8000/16000] [L1: 0.0259] 55.4+0.1s +[9600/16000] [L1: 0.0258] 53.5+0.0s +[11200/16000] [L1: 0.0256] 54.2+0.0s +[12800/16000] [L1: 0.0257] 54.9+0.0s +[14400/16000] [L1: 0.0257] 52.4+0.0s +[16000/16000] [L1: 0.0255] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.116 (Best: 30.701 @epoch 15) +Forward: 33.76s + +Saving... +Total: 34.22s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0259] 51.8+0.9s +[3200/16000] [L1: 0.0256] 53.1+0.0s +[4800/16000] [L1: 0.0257] 54.8+0.1s +[6400/16000] [L1: 0.0257] 53.7+0.1s +[8000/16000] [L1: 0.0254] 53.7+0.1s +[9600/16000] [L1: 0.0252] 55.2+0.1s +[11200/16000] [L1: 0.0259] 53.4+0.0s +[12800/16000] [L1: 0.0261] 51.8+0.0s +[14400/16000] [L1: 0.0261] 54.5+0.0s +[16000/16000] [L1: 0.0260] 55.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.972 (Best: 30.701 @epoch 15) +Forward: 33.65s + +Saving... +Total: 34.06s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0242] 49.9+0.9s +[3200/16000] [L1: 0.0247] 52.9+0.1s +[4800/16000] [L1: 0.0246] 55.8+0.1s +[6400/16000] [L1: 0.0260] 54.0+0.1s +[8000/16000] [L1: 0.0260] 53.9+0.1s +[9600/16000] [L1: 0.0258] 54.6+0.1s +[11200/16000] [L1: 0.0257] 55.2+0.1s +[12800/16000] [L1: 0.0262] 54.9+0.1s +[14400/16000] [L1: 0.0261] 53.5+0.0s +[16000/16000] [L1: 0.0259] 53.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.524 (Best: 30.701 @epoch 15) +Forward: 33.68s + +Saving... +Total: 34.09s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0229] 51.1+0.9s +[3200/16000] [L1: 0.0266] 53.9+0.0s +[4800/16000] [L1: 0.0281] 53.8+0.0s +[6400/16000] [L1: 0.0275] 55.4+0.1s +[8000/16000] [L1: 0.0270] 54.6+0.1s +[9600/16000] [L1: 0.0266] 54.1+0.1s +[11200/16000] [L1: 0.0271] 54.0+0.1s +[12800/16000] [L1: 0.0268] 55.4+0.1s +[14400/16000] [L1: 0.0265] 53.9+0.0s +[16000/16000] [L1: 0.0264] 51.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.876 (Best: 30.701 @epoch 15) +Forward: 33.76s + +Saving... +Total: 34.38s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0232] 50.5+1.1s +[3200/16000] [L1: 0.0237] 52.6+0.0s +[4800/16000] [L1: 0.0241] 55.2+0.1s +[6400/16000] [L1: 0.0243] 54.9+0.1s +[8000/16000] [L1: 0.0242] 54.5+0.1s +[9600/16000] [L1: 0.0243] 53.4+0.0s +[11200/16000] [L1: 0.0247] 53.7+0.0s +[12800/16000] [L1: 0.0247] 54.1+0.0s +[14400/16000] [L1: 0.0247] 54.8+0.0s +[16000/16000] [L1: 0.0247] 55.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.494 (Best: 30.701 @epoch 15) +Forward: 33.24s + +Saving... +Total: 33.67s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0251] 51.5+0.9s +[3200/16000] [L1: 0.0249] 53.4+0.0s +[4800/16000] [L1: 0.0252] 54.7+0.0s +[6400/16000] [L1: 0.0256] 52.9+0.0s +[8000/16000] [L1: 0.0254] 55.8+0.1s +[9600/16000] [L1: 0.0277] 55.2+0.0s +[11200/16000] [L1: 0.0277] 55.2+0.1s +[12800/16000] [L1: 0.0276] 54.3+0.1s +[14400/16000] [L1: 0.0276] 51.5+0.0s +[16000/16000] [L1: 0.0274] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.481 (Best: 30.701 @epoch 15) +Forward: 33.71s + +Saving... +Total: 34.20s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0248] 51.7+0.9s +[3200/16000] [L1: 0.0246] 54.2+0.1s +[4800/16000] [L1: 0.0246] 53.3+0.1s +[6400/16000] [L1: 0.0246] 54.0+0.0s +[8000/16000] [L1: 0.0248] 53.5+0.0s +[9600/16000] [L1: 0.0261] 53.6+0.1s +[11200/16000] [L1: 0.0268] 54.3+0.1s +[12800/16000] [L1: 0.0267] 51.6+0.0s +[14400/16000] [L1: 0.0264] 53.4+0.0s +[16000/16000] [L1: 0.0262] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.084 (Best: 30.701 @epoch 15) +Forward: 33.55s + +Saving... +Total: 34.05s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0257] 51.1+0.9s +[3200/16000] [L1: 0.0254] 54.5+0.1s +[4800/16000] [L1: 0.0253] 53.9+0.0s +[6400/16000] [L1: 0.0251] 52.4+0.0s +[8000/16000] [L1: 0.0251] 52.3+0.0s +[9600/16000] [L1: 0.0251] 55.2+0.1s +[11200/16000] [L1: 0.0250] 55.4+0.1s +[12800/16000] [L1: 0.0251] 54.2+0.1s +[14400/16000] [L1: 0.0251] 54.4+0.1s +[16000/16000] [L1: 0.0251] 55.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.079 (Best: 30.701 @epoch 15) +Forward: 33.44s + +Saving... +Total: 33.83s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0243] 49.9+0.9s +[3200/16000] [L1: 0.0245] 53.2+0.0s +[4800/16000] [L1: 0.0251] 54.6+0.1s +[6400/16000] [L1: 0.0253] 54.7+0.1s +[8000/16000] [L1: 0.0253] 53.4+0.0s +[9600/16000] [L1: 0.0252] 54.7+0.1s +[11200/16000] [L1: 0.0251] 53.6+0.1s +[12800/16000] [L1: 0.0255] 53.0+0.0s +[14400/16000] [L1: 0.0269] 52.3+0.0s +[16000/16000] [L1: 0.0272] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.417 (Best: 30.701 @epoch 15) +Forward: 33.86s + +Saving... +Total: 34.42s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0262] 49.9+0.9s +[3200/16000] [L1: 0.0262] 54.0+0.1s +[4800/16000] [L1: 0.0260] 53.4+0.1s +[6400/16000] [L1: 0.0256] 53.1+0.0s +[8000/16000] [L1: 0.0252] 53.9+0.0s +[9600/16000] [L1: 0.0255] 54.6+0.1s +[11200/16000] [L1: 0.0256] 52.8+0.1s +[12800/16000] [L1: 0.0254] 55.7+0.1s +[14400/16000] [L1: 0.0254] 55.2+0.1s +[16000/16000] [L1: 0.0258] 54.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.173 (Best: 30.701 @epoch 15) +Forward: 33.45s + +Saving... +Total: 33.93s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0253] 51.2+1.0s +[3200/16000] [L1: 0.0251] 53.9+0.0s +[4800/16000] [L1: 0.0247] 53.3+0.0s +[6400/16000] [L1: 0.0253] 53.6+0.0s +[8000/16000] [L1: 0.0251] 54.9+0.1s +[9600/16000] [L1: 0.0249] 54.6+0.1s +[11200/16000] [L1: 0.0279] 55.4+0.1s +[12800/16000] [L1: 0.0283] 55.7+0.1s +[14400/16000] [L1: 0.0283] 53.1+0.0s +[16000/16000] [L1: 0.0280] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.692 (Best: 30.701 @epoch 15) +Forward: 33.53s + +Saving... +Total: 33.93s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0245] 51.2+0.9s +[3200/16000] [L1: 0.0246] 51.7+0.0s +[4800/16000] [L1: 0.0254] 54.2+0.1s +[6400/16000] [L1: 0.0262] 53.8+0.0s +[8000/16000] [L1: 0.0262] 55.5+0.1s +[9600/16000] [L1: 0.0258] 54.9+0.1s +[11200/16000] [L1: 0.0257] 54.0+0.1s +[12800/16000] [L1: 0.0257] 54.0+0.1s +[14400/16000] [L1: 0.0258] 51.7+0.0s +[16000/16000] [L1: 0.0257] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.640 (Best: 30.701 @epoch 15) +Forward: 33.64s + +Saving... +Total: 34.02s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0263] 51.5+0.9s +[3200/16000] [L1: 0.0249] 54.0+0.1s +[4800/16000] [L1: 0.0249] 54.1+0.1s +[6400/16000] [L1: 0.0248] 53.1+0.0s +[8000/16000] [L1: 0.0252] 54.9+0.1s +[9600/16000] [L1: 0.0267] 55.0+0.1s +[11200/16000] [L1: 0.0274] 51.4+0.0s +[12800/16000] [L1: 0.0273] 52.1+0.0s +[14400/16000] [L1: 0.0270] 53.8+0.1s +[16000/16000] [L1: 0.0268] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.022 (Best: 30.701 @epoch 15) +Forward: 33.33s + +Saving... +Total: 33.80s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0242] 51.6+1.0s +[3200/16000] [L1: 0.0245] 53.7+0.1s +[4800/16000] [L1: 0.0251] 53.7+0.1s +[6400/16000] [L1: 0.0248] 54.8+0.1s +[8000/16000] [L1: 0.0250] 54.2+0.1s +[9600/16000] [L1: 0.0250] 54.9+0.1s +[11200/16000] [L1: 0.0249] 55.1+0.1s +[12800/16000] [L1: 0.0251] 51.1+0.0s +[14400/16000] [L1: 0.0251] 53.4+0.1s +[16000/16000] [L1: 0.0250] 54.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.016 (Best: 30.701 @epoch 15) +Forward: 33.59s + +Saving... +Total: 34.11s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0231] 52.0+1.1s +[3200/16000] [L1: 0.0235] 54.4+0.1s +[4800/16000] [L1: 0.0236] 54.6+0.1s +[6400/16000] [L1: 0.0239] 53.9+0.1s +[8000/16000] [L1: 0.0239] 54.2+0.0s +[9600/16000] [L1: 0.0239] 55.1+0.1s +[11200/16000] [L1: 0.0241] 54.4+0.1s +[12800/16000] [L1: 0.0243] 55.8+0.1s +[14400/16000] [L1: 0.0244] 53.0+0.0s +[16000/16000] [L1: 0.0246] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.075 (Best: 30.701 @epoch 15) +Forward: 33.65s + +Saving... +Total: 34.05s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0278] 52.1+0.9s +[3200/16000] [L1: 0.0261] 52.9+0.1s +[4800/16000] [L1: 0.0259] 54.2+0.1s +[6400/16000] [L1: 0.0277] 55.3+0.1s +[8000/16000] [L1: 0.0271] 54.4+0.0s +[9600/16000] [L1: 0.0268] 51.6+0.0s +[11200/16000] [L1: 0.0266] 52.0+0.0s +[12800/16000] [L1: 0.0263] 53.2+0.0s +[14400/16000] [L1: 0.0260] 55.5+0.1s +[16000/16000] [L1: 0.0257] 51.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.861 (Best: 30.701 @epoch 15) +Forward: 34.02s + +Saving... +Total: 34.48s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0239] 51.5+1.1s +[3200/16000] [L1: 0.0240] 53.8+0.1s +[4800/16000] [L1: 0.0243] 53.2+0.0s +[6400/16000] [L1: 0.0241] 55.4+0.1s +[8000/16000] [L1: 0.0239] 54.9+0.1s +[9600/16000] [L1: 0.0242] 55.3+0.1s +[11200/16000] [L1: 0.0244] 53.8+0.1s +[12800/16000] [L1: 0.0246] 53.7+0.1s +[14400/16000] [L1: 0.0246] 53.4+0.1s +[16000/16000] [L1: 0.0245] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.253 (Best: 30.701 @epoch 15) +Forward: 33.82s + +Saving... +Total: 34.25s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0242] 52.0+0.9s +[3200/16000] [L1: 0.0243] 53.2+0.0s +[4800/16000] [L1: 0.0245] 54.6+0.1s +[6400/16000] [L1: 0.0263] 53.1+0.1s +[8000/16000] [L1: 0.0262] 53.5+0.1s +[9600/16000] [L1: 0.0259] 52.0+0.0s +[11200/16000] [L1: 0.0256] 51.6+0.0s +[12800/16000] [L1: 0.0254] 54.1+0.0s +[14400/16000] [L1: 0.0254] 55.2+0.1s +[16000/16000] [L1: 0.0253] 55.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.196 (Best: 30.701 @epoch 15) +Forward: 33.93s + +Saving... +Total: 34.31s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0249] 51.5+1.0s +[3200/16000] [L1: 0.0252] 54.0+0.1s +[4800/16000] [L1: 0.0241] 55.1+0.1s +[6400/16000] [L1: 0.0238] 54.9+0.1s +[8000/16000] [L1: 0.0240] 53.0+0.0s +[9600/16000] [L1: 0.0240] 54.4+0.0s +[11200/16000] [L1: 0.0240] 54.1+0.0s +[12800/16000] [L1: 0.0241] 55.4+0.1s +[14400/16000] [L1: 0.0242] 54.5+0.1s +[16000/16000] [L1: 0.0244] 54.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.435 (Best: 30.701 @epoch 15) +Forward: 34.02s + +Saving... +Total: 34.56s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0296] 52.4+0.9s +[3200/16000] [L1: 0.0276] 55.0+0.1s +[4800/16000] [L1: 0.0265] 53.1+0.0s +[6400/16000] [L1: 0.0266] 53.4+0.0s +[8000/16000] [L1: 0.0262] 53.4+0.0s +[9600/16000] [L1: 0.0257] 55.6+0.1s +[11200/16000] [L1: 0.0254] 54.7+0.1s +[12800/16000] [L1: 0.0253] 54.8+0.0s +[14400/16000] [L1: 0.0252] 54.0+0.0s +[16000/16000] [L1: 0.0253] 53.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.831 (Best: 30.701 @epoch 15) +Forward: 34.27s + +Saving... +Total: 34.76s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0236] 50.0+0.9s +[3200/16000] [L1: 0.0235] 53.7+0.1s +[4800/16000] [L1: 0.0236] 54.8+0.1s +[6400/16000] [L1: 0.0238] 54.9+0.1s +[8000/16000] [L1: 0.0240] 54.4+0.0s +[9600/16000] [L1: 0.0240] 54.7+0.1s +[11200/16000] [L1: 0.0242] 53.2+0.0s +[12800/16000] [L1: 0.0242] 55.7+0.1s +[14400/16000] [L1: 0.0241] 55.7+0.1s +[16000/16000] [L1: 0.0241] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.910 (Best: 30.701 @epoch 15) +Forward: 34.37s + +Saving... +Total: 34.78s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0881] 51.8+0.9s +[3200/16000] [L1: 0.0676] 54.2+0.1s +[4800/16000] [L1: 0.0557] 54.4+0.1s +[6400/16000] [L1: 0.0494] 53.9+0.1s +[8000/16000] [L1: 0.0454] 55.2+0.1s +[9600/16000] [L1: 0.0426] 54.4+0.1s +[11200/16000] [L1: 0.0404] 55.8+0.1s +[12800/16000] [L1: 0.0385] 51.5+0.0s +[14400/16000] [L1: 0.0379] 52.7+0.0s +[16000/16000] [L1: 0.0366] 55.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 10.734 (Best: 30.701 @epoch 15) +Forward: 34.35s + +Saving... +Total: 34.81s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0255] 51.5+0.9s +[3200/16000] [L1: 0.0257] 54.0+0.0s +[4800/16000] [L1: 0.0255] 53.7+0.1s +[6400/16000] [L1: 0.0253] 54.1+0.1s +[8000/16000] [L1: 0.0258] 54.3+0.1s +[9600/16000] [L1: 0.0257] 54.7+0.1s +[11200/16000] [L1: 0.0255] 54.3+0.0s +[12800/16000] [L1: 0.0255] 53.5+0.0s +[14400/16000] [L1: 0.0254] 53.2+0.0s +[16000/16000] [L1: 0.0253] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.169 (Best: 30.701 @epoch 15) +Forward: 34.44s + +Saving... +Total: 34.86s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0248] 52.4+1.1s +[3200/16000] [L1: 0.0247] 53.7+0.1s +[4800/16000] [L1: 0.0245] 54.5+0.0s +[6400/16000] [L1: 0.0248] 54.8+0.1s +[8000/16000] [L1: 0.0248] 55.1+0.1s +[9600/16000] [L1: 0.0249] 53.8+0.0s +[11200/16000] [L1: 0.0255] 56.0+0.1s +[12800/16000] [L1: 0.0254] 55.2+0.1s +[14400/16000] [L1: 0.0253] 53.4+0.1s +[16000/16000] [L1: 0.0251] 55.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.350 (Best: 30.701 @epoch 15) +Forward: 34.31s + +Saving... +Total: 34.82s + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/loss.pt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..2cb8aeac7a3784a1b17d156d419747cef2ed44bf --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48da99bdddb436cdb4a093ba3a3efbe02ec42ba0bd17f6415b4f6645eb17b79f +size 559 diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/loss_L1.pdf b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/loss_L1.pdf new file mode 100644 index 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b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_RWEIGHT/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach/config.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..55d2a490e5f5790138092df48969fad3bebe80bd --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach/config.txt @@ -0,0 +1,65 @@ +2020-11-06-15:50:29 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4_detach +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach/log.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..e5babcb43c80200b56d0e7c061d69b1f071a89cf --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach/log.txt @@ -0,0 +1,222 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 2.8150] 55.8+0.6s +[3200/16000] [L1: 1.8449] 49.2+0.0s +[4800/16000] [L1: 1.3789] 49.9+0.0s +[6400/16000] [L1: 1.1197] 52.3+0.1s +[8000/16000] [L1: 0.9496] 51.7+0.0s +[9600/16000] [L1: 0.8289] 53.0+0.0s +[11200/16000] [L1: 0.7403] 52.9+0.0s +[12800/16000] [L1: 0.6707] 53.4+0.0s +[14400/16000] [L1: 0.6155] 54.2+0.0s +[16000/16000] [L1: 0.5705] 53.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.500 (Best: 15.500 @epoch 1) +Forward: 35.19s + +Saving... +Total: 36.31s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1450] 52.6+0.7s +[3200/16000] [L1: 0.1446] 52.8+0.1s +[4800/16000] [L1: 0.1463] 53.8+0.0s +[6400/16000] [L1: 0.1453] 52.1+0.0s +[8000/16000] [L1: 0.1418] 52.1+0.0s +[9600/16000] [L1: 0.1389] 53.0+0.0s +[11200/16000] [L1: 0.1366] 53.6+0.0s +[12800/16000] [L1: 0.1336] 54.4+0.0s +[14400/16000] [L1: 0.1313] 53.0+0.0s +[16000/16000] [L1: 0.1299] 52.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.549 (Best: 20.549 @epoch 2) +Forward: 34.85s + +Saving... +Total: 35.50s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1028] 52.4+0.8s +[3200/16000] [L1: 0.1063] 53.8+0.1s +[4800/16000] [L1: 0.1058] 54.3+0.1s +[6400/16000] [L1: 0.1048] 54.5+0.1s +[8000/16000] [L1: 0.1033] 54.8+0.1s +[9600/16000] [L1: 0.1021] 54.4+0.1s +[11200/16000] [L1: 0.1019] 54.4+0.1s +[12800/16000] [L1: 0.1023] 54.5+0.1s +[14400/16000] [L1: 0.1029] 53.7+0.1s +[16000/16000] [L1: 0.1026] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 18.853 (Best: 20.549 @epoch 2) +Forward: 34.88s + +Saving... +Total: 35.38s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0877] 51.5+0.7s diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach/loss.pt 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+n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-16:36:59 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-18:09:02 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: True +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/log.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..5c794bb0de5d4a1cb70cb0aad99a5b57bdfda31d --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/log.txt @@ -0,0 +1,2889 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.1894] 58.1+0.7s +[3200/16000] [L1: 1.9718] 51.3+0.1s +[4800/16000] [L1: 1.4864] 51.4+0.1s +[6400/16000] [L1: 1.2363] 53.9+0.1s +[8000/16000] [L1: 1.0762] 53.3+0.0s +[9600/16000] [L1: 0.9671] 52.7+0.0s +[11200/16000] [L1: 0.8799] 52.2+0.0s +[12800/16000] [L1: 0.8159] 52.0+0.0s +[14400/16000] [L1: 0.7639] 52.3+0.0s +[16000/16000] [L1: 0.7204] 52.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.411 (Best: 14.411 @epoch 1) +Forward: 35.40s + +Saving... +Total: 36.21s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3306] 51.1+0.9s +[3200/16000] [L1: 0.3165] 53.0+0.1s +[4800/16000] [L1: 0.3140] 54.8+0.1s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.4751] 55.6+0.7s +[3200/16000] [L1: 2.1487] 51.6+0.1s +[4800/16000] [L1: 1.6266] 52.4+0.1s +[6400/16000] [L1: 1.3495] 52.7+0.1s +[8000/16000] [L1: 1.1753] 53.2+0.1s +[9600/16000] [L1: 1.0521] 53.9+0.1s +[11200/16000] [L1: 0.9592] 53.5+0.1s +[12800/16000] [L1: 0.8901] 53.5+0.0s +[14400/16000] [L1: 0.8353] 53.7+0.1s +[16000/16000] [L1: 0.7904] 53.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.680 (Best: 14.680 @epoch 1) +Forward: 35.46s + +Saving... +Total: 36.26s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3680] 52.0+0.9s +[3200/16000] [L1: 0.3596] 52.4+0.0s +[4800/16000] [L1: 0.3561] 54.1+0.1s +[6400/16000] [L1: 0.3517] 52.8+0.1s +[8000/16000] [L1: 0.3470] 53.7+0.1s +[9600/16000] [L1: 0.3448] 53.5+0.0s +[11200/16000] [L1: 0.3400] 53.2+0.1s +[12800/16000] [L1: 0.3372] 53.3+0.1s +[14400/16000] [L1: 0.3351] 53.4+0.1s +[16000/16000] [L1: 0.3327] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.317 (Best: 17.317 @epoch 2) +Forward: 35.03s + +Saving... +Total: 35.67s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3171] 51.6+0.9s +[3200/16000] [L1: 0.3106] 52.2+0.1s +[4800/16000] [L1: 0.3049] 54.0+0.1s +[6400/16000] [L1: 0.3060] 53.5+0.1s +[8000/16000] [L1: 0.3057] 53.6+0.1s +[9600/16000] [L1: 0.3085] 53.0+0.0s +[11200/16000] [L1: 0.3084] 54.0+0.1s +[12800/16000] [L1: 0.3091] 53.6+0.0s +[14400/16000] [L1: 0.3065] 53.5+0.0s +[16000/16000] [L1: 0.3035] 53.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.775 (Best: 17.317 @epoch 2) +Forward: 35.19s + +Saving... +Total: 35.79s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2698] 51.4+0.8s +[3200/16000] [L1: 0.2728] 52.7+0.1s +[4800/16000] [L1: 0.2765] 54.0+0.1s +[6400/16000] [L1: 0.2707] 54.2+0.1s +[8000/16000] [L1: 0.2659] 53.6+0.1s +[9600/16000] [L1: 0.2609] 53.7+0.1s +[11200/16000] [L1: 0.2576] 54.0+0.1s +[12800/16000] [L1: 0.2567] 53.2+0.1s +[14400/16000] [L1: 0.2529] 53.6+0.1s +[16000/16000] [L1: 0.2495] 54.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 21.198 (Best: 21.198 @epoch 4) +Forward: 35.19s + +Saving... +Total: 35.84s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2138] 53.4+1.0s +[3200/16000] [L1: 0.2107] 53.7+0.1s +[4800/16000] [L1: 0.2045] 54.9+0.1s +[6400/16000] [L1: 0.2044] 54.6+0.1s +[8000/16000] [L1: 0.2020] 54.9+0.1s +[9600/16000] [L1: 0.1965] 53.7+0.1s +[11200/16000] [L1: 0.1915] 54.0+0.1s +[12800/16000] [L1: 0.1885] 52.5+0.0s +[14400/16000] [L1: 0.1850] 53.7+0.1s +[16000/16000] [L1: 0.1802] 53.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.120 (Best: 21.198 @epoch 4) +Forward: 35.10s + +Saving... +Total: 35.79s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1377] 52.2+0.8s +[3200/16000] [L1: 0.1359] 53.0+0.1s +[4800/16000] [L1: 0.1326] 53.3+0.1s +[6400/16000] [L1: 0.1317] 54.2+0.1s +[8000/16000] [L1: 0.1292] 53.4+0.1s +[9600/16000] [L1: 0.1257] 53.4+0.1s +[11200/16000] [L1: 0.1245] 53.7+0.1s +[12800/16000] [L1: 0.1230] 53.3+0.0s +[14400/16000] [L1: 0.1218] 54.0+0.0s +[16000/16000] [L1: 0.1203] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.319 (Best: 21.198 @epoch 4) +Forward: 35.15s + +Saving... +Total: 35.77s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1070] 52.1+0.9s +[3200/16000] [L1: 0.1085] 52.3+0.0s +[4800/16000] [L1: 0.1090] 52.8+0.1s +[6400/16000] [L1: 0.1078] 53.8+0.1s +[8000/16000] [L1: 0.1057] 51.8+0.0s +[9600/16000] [L1: 0.1045] 53.1+0.1s +[11200/16000] [L1: 0.1033] 53.8+0.0s +[12800/16000] [L1: 0.1030] 53.4+0.0s +[14400/16000] [L1: 0.1038] 53.1+0.0s +[16000/16000] [L1: 0.1062] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 18.128 (Best: 21.198 @epoch 4) +Forward: 35.32s + +Saving... +Total: 35.85s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1969] 52.1+0.9s +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.4751] 55.3+0.7s +[3200/16000] [L1: 2.1487] 51.7+0.1s +[4800/16000] [L1: 1.6266] 50.3+0.0s +[6400/16000] [L1: 1.3495] 54.1+0.0s +[8000/16000] [L1: 1.1753] 54.6+0.1s +[9600/16000] [L1: 1.0521] 53.5+0.1s +[11200/16000] [L1: 0.9592] 54.0+0.0s +[12800/16000] [L1: 0.8901] 53.3+0.0s +[14400/16000] [L1: 0.8353] 52.8+0.0s +[16000/16000] [L1: 0.7904] 52.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.680 (Best: 14.680 @epoch 1) +Forward: 35.60s + +Saving... +Total: 36.61s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3680] 53.9+1.0s +[3200/16000] [L1: 0.3596] 53.9+0.0s +[4800/16000] [L1: 0.3561] 55.7+0.1s +[6400/16000] [L1: 0.3517] 54.3+0.1s +[8000/16000] [L1: 0.3470] 54.5+0.1s +[9600/16000] [L1: 0.3448] 54.2+0.0s +[11200/16000] [L1: 0.3400] 52.9+0.0s +[12800/16000] [L1: 0.3372] 54.6+0.1s +[14400/16000] [L1: 0.3351] 54.3+0.1s +[16000/16000] [L1: 0.3327] 53.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.317 (Best: 17.317 @epoch 2) +Forward: 35.26s + +Saving... +Total: 35.73s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3171] 53.6+0.9s +[3200/16000] [L1: 0.3106] 53.9+0.1s +[4800/16000] [L1: 0.3049] 55.1+0.1s +[6400/16000] [L1: 0.3060] 55.3+0.0s +[8000/16000] [L1: 0.3057] 53.9+0.1s +[9600/16000] [L1: 0.3085] 54.9+0.1s +[11200/16000] [L1: 0.3084] 53.4+0.0s +[12800/16000] [L1: 0.3091] 53.2+0.0s +[14400/16000] [L1: 0.3065] 52.8+0.0s +[16000/16000] [L1: 0.3035] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.775 (Best: 17.317 @epoch 2) +Forward: 35.24s + +Saving... +Total: 35.84s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2698] 52.6+0.9s +[3200/16000] [L1: 0.2728] 53.7+0.1s +[4800/16000] [L1: 0.2765] 54.3+0.1s +[6400/16000] [L1: 0.2707] 55.5+0.1s +[8000/16000] [L1: 0.2659] 53.9+0.0s +[9600/16000] [L1: 0.2609] 53.5+0.0s +[11200/16000] [L1: 0.2576] 53.1+0.1s +[12800/16000] [L1: 0.2567] 53.2+0.1s +[14400/16000] [L1: 0.2529] 54.3+0.1s +[16000/16000] [L1: 0.2495] 53.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.198 (Best: 21.198 @epoch 4) +Forward: 35.22s + +Saving... +Total: 35.79s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2138] 50.9+1.0s +[3200/16000] [L1: 0.2107] 51.9+0.0s +[4800/16000] [L1: 0.2045] 55.1+0.1s +[6400/16000] [L1: 0.2044] 53.4+0.0s +[8000/16000] [L1: 0.2020] 53.4+0.0s +[9600/16000] [L1: 0.1965] 53.2+0.0s +[11200/16000] [L1: 0.1915] 55.1+0.1s +[12800/16000] [L1: 0.1885] 55.1+0.1s +[14400/16000] [L1: 0.1850] 53.0+0.0s +[16000/16000] [L1: 0.1802] 52.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.120 (Best: 21.198 @epoch 4) +Forward: 35.25s + +Saving... +Total: 35.87s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1377] 52.9+0.8s +[3200/16000] [L1: 0.1359] 54.0+0.1s +[4800/16000] [L1: 0.1326] 54.9+0.1s +[6400/16000] [L1: 0.1317] 54.4+0.1s +[8000/16000] [L1: 0.1292] 53.5+0.0s +[9600/16000] [L1: 0.1257] 54.5+0.0s +[11200/16000] [L1: 0.1245] 53.2+0.0s +[12800/16000] [L1: 0.1230] 54.6+0.1s +[14400/16000] [L1: 0.1218] 55.7+0.1s +[16000/16000] [L1: 0.1203] 55.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 17.319 (Best: 21.198 @epoch 4) +Forward: 35.33s + +Saving... +Total: 35.84s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1070] 54.0+1.0s +[3200/16000] [L1: 0.1085] 52.5+0.0s +[4800/16000] [L1: 0.1090] 54.1+0.0s +[6400/16000] [L1: 0.1078] 55.6+0.1s +[8000/16000] [L1: 0.1057] 55.1+0.0s +[9600/16000] [L1: 0.1045] 54.8+0.1s +[11200/16000] [L1: 0.1033] 54.7+0.1s +[12800/16000] [L1: 0.1030] 55.0+0.0s +[14400/16000] [L1: 0.1038] 55.3+0.0s +[16000/16000] [L1: 0.1062] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 18.128 (Best: 21.198 @epoch 4) +Forward: 35.26s + +Saving... +Total: 35.76s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1969] 52.3+0.8s +[3200/16000] [L1: 0.2046] 51.1+0.0s +[4800/16000] [L1: 0.2154] 55.6+0.1s +[6400/16000] [L1: 0.2187] 55.0+0.1s +[8000/16000] [L1: 0.2094] 55.3+0.0s +[9600/16000] [L1: 0.2084] 55.0+0.1s +[11200/16000] [L1: 0.2046] 53.7+0.0s +[12800/16000] [L1: 0.2051] 54.1+0.1s +[14400/16000] [L1: 0.2023] 55.2+0.1s +[16000/16000] [L1: 0.2037] 55.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.661 (Best: 21.661 @epoch 8) +Forward: 35.14s + +Saving... +Total: 35.62s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1804] 53.0+0.8s +[3200/16000] [L1: 0.1772] 53.2+0.0s +[4800/16000] [L1: 0.1754] 54.4+0.0s +[6400/16000] [L1: 0.1786] 54.9+0.1s +[8000/16000] [L1: 0.1732] 54.0+0.0s +[9600/16000] [L1: 0.1687] 55.5+0.0s +[11200/16000] [L1: 0.1644] 54.7+0.1s +[12800/16000] [L1: 0.1600] 54.8+0.1s +[14400/16000] [L1: 0.1563] 53.1+0.0s +[16000/16000] [L1: 0.1593] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 19.647 (Best: 21.661 @epoch 8) +Forward: 35.21s + +Saving... +Total: 35.85s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2611] 52.5+1.0s +[3200/16000] [L1: 0.2327] 52.5+0.0s +[4800/16000] [L1: 0.2097] 53.9+0.0s +[6400/16000] [L1: 0.2055] 56.1+0.0s +[8000/16000] [L1: 0.1957] 54.1+0.0s +[9600/16000] [L1: 0.1866] 55.4+0.0s +[11200/16000] [L1: 0.1833] 53.9+0.0s +[12800/16000] [L1: 0.1820] 54.2+0.1s +[14400/16000] [L1: 0.1777] 54.8+0.0s +[16000/16000] [L1: 0.1730] 55.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.469 (Best: 21.661 @epoch 8) +Forward: 35.21s + +Saving... +Total: 35.60s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1194] 52.9+0.9s +[3200/16000] [L1: 0.1267] 53.0+0.1s +[4800/16000] [L1: 0.1304] 54.9+0.1s +[6400/16000] [L1: 0.1317] 54.9+0.1s +[8000/16000] [L1: 0.1352] 55.1+0.1s +[9600/16000] [L1: 0.1385] 55.2+0.0s +[11200/16000] [L1: 0.1509] 55.3+0.0s +[12800/16000] [L1: 0.1560] 53.8+0.0s +[14400/16000] [L1: 0.1518] 54.5+0.1s +[16000/16000] [L1: 0.1465] 54.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 19.870 (Best: 21.661 @epoch 8) +Forward: 35.07s + +Saving... +Total: 35.49s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1730] 53.3+0.8s +[3200/16000] [L1: 0.2075] 54.6+0.0s +[4800/16000] [L1: 0.2042] 55.3+0.1s +[6400/16000] [L1: 0.2043] 55.1+0.1s +[8000/16000] [L1: 0.1890] 52.5+0.0s +[9600/16000] [L1: 0.1863] 52.0+0.0s +[11200/16000] [L1: 0.1836] 53.1+0.0s +[12800/16000] [L1: 0.1776] 55.1+0.0s +[14400/16000] [L1: 0.1747] 54.0+0.0s +[16000/16000] [L1: 0.1692] 54.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.660 (Best: 21.661 @epoch 8) +Forward: 35.21s + +Saving... +Total: 35.68s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1152] 52.8+0.9s +[3200/16000] [L1: 0.0994] 53.7+0.1s +[4800/16000] [L1: 0.1236] 55.4+0.1s +[6400/16000] [L1: 0.1327] 53.7+0.1s +[8000/16000] [L1: 0.1328] 54.2+0.1s +[9600/16000] [L1: 0.1307] 54.6+0.1s +[11200/16000] [L1: 0.1273] 54.8+0.1s +[12800/16000] [L1: 0.1308] 53.0+0.0s +[14400/16000] [L1: 0.1364] 51.6+0.0s +[16000/16000] [L1: 0.1441] 52.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.911 (Best: 21.661 @epoch 8) +Forward: 35.29s + +Saving... +Total: 35.97s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1661] 53.2+1.0s +[3200/16000] [L1: 0.1614] 53.5+0.1s +[4800/16000] [L1: 0.1494] 53.4+0.0s +[6400/16000] [L1: 0.1400] 54.5+0.0s +[8000/16000] [L1: 0.1383] 55.0+0.0s +[9600/16000] [L1: 0.1304] 55.0+0.1s +[11200/16000] [L1: 0.1267] 54.6+0.1s +[12800/16000] [L1: 0.1250] 52.7+0.0s +[14400/16000] [L1: 0.1229] 53.9+0.0s +[16000/16000] [L1: 0.1223] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.258 (Best: 21.661 @epoch 8) +Forward: 35.37s + +Saving... +Total: 35.90s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1693] 52.0+0.9s +[3200/16000] [L1: 0.1530] 53.4+0.1s +[4800/16000] [L1: 0.1329] 53.3+0.0s +[6400/16000] [L1: 0.1265] 54.1+0.0s +[8000/16000] [L1: 0.1251] 53.5+0.1s +[9600/16000] [L1: 0.1284] 53.2+0.0s +[11200/16000] [L1: 0.1332] 53.2+0.0s +[12800/16000] [L1: 0.1326] 53.8+0.0s +[14400/16000] [L1: 0.1296] 53.2+0.0s +[16000/16000] [L1: 0.1251] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.940 (Best: 21.661 @epoch 8) +Forward: 35.28s + +Saving... +Total: 35.86s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1137] 51.8+1.0s +[3200/16000] [L1: 0.1043] 52.4+0.1s +[4800/16000] [L1: 0.1138] 53.5+0.0s +[6400/16000] [L1: 0.1127] 54.7+0.1s +[8000/16000] [L1: 0.1099] 54.9+0.0s +[9600/16000] [L1: 0.1099] 55.0+0.1s +[11200/16000] [L1: 0.1129] 55.5+0.0s +[12800/16000] [L1: 0.1186] 53.3+0.0s +[14400/16000] [L1: 0.1212] 54.6+0.0s +[16000/16000] [L1: 0.1195] 52.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.378 (Best: 21.661 @epoch 8) +Forward: 35.33s + +Saving... +Total: 35.81s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1020] 52.5+0.9s +[3200/16000] [L1: 0.1270] 54.4+0.1s +[4800/16000] [L1: 0.1216] 54.6+0.1s +[6400/16000] [L1: 0.1133] 53.8+0.0s +[8000/16000] [L1: 0.1135] 55.2+0.1s +[9600/16000] [L1: 0.1194] 55.5+0.1s +[11200/16000] [L1: 0.1206] 54.6+0.1s +[12800/16000] [L1: 0.1175] 54.9+0.1s +[14400/16000] [L1: 0.1199] 54.5+0.0s +[16000/16000] [L1: 0.1193] 54.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 15.434 (Best: 21.661 @epoch 8) +Forward: 35.20s + +Saving... +Total: 35.70s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1387] 53.2+0.9s +[3200/16000] [L1: 0.1437] 52.9+0.0s +[4800/16000] [L1: 0.1288] 52.3+0.0s +[6400/16000] [L1: 0.1203] 52.1+0.0s +[8000/16000] [L1: 0.1197] 53.3+0.0s +[9600/16000] [L1: 0.1188] 54.4+0.1s +[11200/16000] [L1: 0.1161] 55.0+0.1s +[12800/16000] [L1: 0.1120] 53.3+0.0s +[14400/16000] [L1: 0.1077] 53.2+0.0s +[16000/16000] [L1: 0.1050] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.168 (Best: 21.661 @epoch 8) +Forward: 35.18s + +Saving... +Total: 35.68s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0895] 53.1+0.9s +[3200/16000] [L1: 0.1054] 51.9+0.0s +[4800/16000] [L1: 0.1005] 54.8+0.1s +[6400/16000] [L1: 0.1042] 55.1+0.1s +[8000/16000] [L1: 0.1170] 54.5+0.1s +[9600/16000] [L1: 0.1261] 54.6+0.0s +[11200/16000] [L1: 0.1281] 55.1+0.1s +[12800/16000] [L1: 0.1214] 54.8+0.1s +[14400/16000] [L1: 0.1151] 54.2+0.0s +[16000/16000] [L1: 0.1127] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.241 (Best: 21.661 @epoch 8) +Forward: 35.18s + +Saving... +Total: 35.75s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1179] 53.1+1.0s +[3200/16000] [L1: 0.1072] 52.1+0.0s +[4800/16000] [L1: 0.0953] 55.2+0.0s +[6400/16000] [L1: 0.0868] 52.4+0.0s +[8000/16000] [L1: 0.0872] 52.4+0.0s +[9600/16000] [L1: 0.1067] 52.6+0.0s +[11200/16000] [L1: 0.1167] 52.4+0.0s +[12800/16000] [L1: 0.1135] 53.2+0.0s +[14400/16000] [L1: 0.1091] 52.6+0.0s +[16000/16000] [L1: 0.1063] 52.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.764 (Best: 21.661 @epoch 8) +Forward: 35.21s + +Saving... +Total: 35.69s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0677] 53.2+0.9s +[3200/16000] [L1: 0.1348] 52.0+0.0s +[4800/16000] [L1: 0.1359] 54.3+0.0s +[6400/16000] [L1: 0.1247] 53.2+0.0s +[8000/16000] [L1: 0.1144] 55.6+0.1s +[9600/16000] [L1: 0.1191] 55.4+0.1s +[11200/16000] [L1: 0.1246] 55.3+0.1s +[12800/16000] [L1: 0.1199] 52.9+0.1s +[14400/16000] [L1: 0.1176] 55.0+0.1s +[16000/16000] [L1: 0.1155] 55.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 12.994 (Best: 21.661 @epoch 8) +Forward: 35.11s + +Saving... +Total: 35.85s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1381] 49.4+1.0s +[3200/16000] [L1: 0.1091] 50.9+0.0s +[4800/16000] [L1: 0.1065] 50.9+0.0s +[6400/16000] [L1: 0.1114] 55.3+0.1s +[8000/16000] [L1: 0.1112] 53.3+0.0s +[9600/16000] [L1: 0.1152] 50.6+0.0s +[11200/16000] [L1: 0.1175] 51.7+0.0s +[12800/16000] [L1: 0.1187] 52.6+0.0s +[14400/16000] [L1: 0.1155] 53.2+0.1s +[16000/16000] [L1: 0.1127] 54.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.348 (Best: 21.661 @epoch 8) +Forward: 35.14s + +Saving... +Total: 35.56s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1211] 52.4+1.0s +[3200/16000] [L1: 0.1160] 53.4+0.0s +[4800/16000] [L1: 0.1100] 53.2+0.0s +[6400/16000] [L1: 0.1116] 53.4+0.1s +[8000/16000] [L1: 0.1103] 54.6+0.1s +[9600/16000] [L1: 0.1045] 53.3+0.1s +[11200/16000] [L1: 0.0996] 53.4+0.1s +[12800/16000] [L1: 0.0996] 54.7+0.1s +[14400/16000] [L1: 0.0988] 53.4+0.1s +[16000/16000] [L1: 0.1036] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.545 (Best: 21.661 @epoch 8) +Forward: 35.39s + +Saving... +Total: 35.79s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1107] 52.5+1.0s +[3200/16000] [L1: 0.1009] 51.6+0.0s +[4800/16000] [L1: 0.1055] 52.9+0.0s +[6400/16000] [L1: 0.1110] 52.6+0.0s +[8000/16000] [L1: 0.1100] 52.5+0.0s +[9600/16000] [L1: 0.1073] 52.5+0.0s +[11200/16000] [L1: 0.1022] 53.3+0.0s +[12800/16000] [L1: 0.1007] 53.5+0.0s +[14400/16000] [L1: 0.1004] 52.6+0.0s +[16000/16000] [L1: 0.0993] 51.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.397 (Best: 21.661 @epoch 8) +Forward: 35.32s + +Saving... +Total: 35.78s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1117] 53.0+1.0s +[3200/16000] [L1: 0.0966] 52.1+0.1s +[4800/16000] [L1: 0.0952] 54.8+0.1s +[6400/16000] [L1: 0.1042] 54.4+0.1s +[8000/16000] [L1: 0.0999] 54.2+0.1s +[9600/16000] [L1: 0.1057] 53.8+0.1s +[11200/16000] [L1: 0.1036] 52.2+0.0s +[12800/16000] [L1: 0.1005] 52.1+0.0s +[14400/16000] [L1: 0.0995] 55.1+0.1s +[16000/16000] [L1: 0.0986] 55.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.434 (Best: 21.661 @epoch 8) +Forward: 35.04s + +Saving... +Total: 35.52s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1064] 51.8+0.9s +[3200/16000] [L1: 0.1165] 52.0+0.1s +[4800/16000] [L1: 0.0998] 53.7+0.1s +[6400/16000] [L1: 0.0917] 54.3+0.1s +[8000/16000] [L1: 0.0935] 54.4+0.1s +[9600/16000] [L1: 0.0954] 54.5+0.0s +[11200/16000] [L1: 0.0944] 53.9+0.1s +[12800/16000] [L1: 0.0926] 53.3+0.1s +[14400/16000] [L1: 0.0917] 52.6+0.0s +[16000/16000] [L1: 0.0935] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.847 (Best: 21.661 @epoch 8) +Forward: 35.01s + +Saving... +Total: 35.46s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0950] 50.7+0.9s +[3200/16000] [L1: 0.1144] 51.5+0.1s +[4800/16000] [L1: 0.1090] 52.7+0.0s +[6400/16000] [L1: 0.1000] 54.5+0.1s +[8000/16000] [L1: 0.0964] 53.2+0.1s +[9600/16000] [L1: 0.0922] 52.7+0.0s +[11200/16000] [L1: 0.0948] 52.6+0.0s +[12800/16000] [L1: 0.0923] 53.9+0.1s +[14400/16000] [L1: 0.0938] 54.6+0.1s +[16000/16000] [L1: 0.0948] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.009 (Best: 21.661 @epoch 8) +Forward: 35.11s + +Saving... +Total: 35.85s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1048] 50.9+0.9s +[3200/16000] [L1: 0.1028] 50.9+0.0s +[4800/16000] [L1: 0.1103] 53.4+0.1s +[6400/16000] [L1: 0.1036] 53.0+0.0s +[8000/16000] [L1: 0.0973] 54.1+0.1s +[9600/16000] [L1: 0.0898] 54.7+0.1s +[11200/16000] [L1: 0.0847] 54.6+0.1s +[12800/16000] [L1: 0.0822] 54.7+0.1s +[14400/16000] [L1: 0.0837] 54.5+0.1s +[16000/16000] [L1: 0.0897] 53.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.470 (Best: 21.661 @epoch 8) +Forward: 35.13s + +Saving... +Total: 35.62s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1277] 52.4+0.9s +[3200/16000] [L1: 0.1139] 52.6+0.1s +[4800/16000] [L1: 0.1173] 54.4+0.1s +[6400/16000] [L1: 0.1210] 54.7+0.1s +[8000/16000] [L1: 0.1130] 54.6+0.1s +[9600/16000] [L1: 0.1116] 54.6+0.1s +[11200/16000] [L1: 0.1061] 54.9+0.1s +[12800/16000] [L1: 0.1151] 53.7+0.0s +[14400/16000] [L1: 0.1178] 54.5+0.0s +[16000/16000] [L1: 0.1175] 53.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.650 (Best: 21.661 @epoch 8) +Forward: 35.06s + +Saving... +Total: 35.61s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1166] 53.1+1.1s +[3200/16000] [L1: 0.1152] 53.1+0.1s +[4800/16000] [L1: 0.1014] 54.4+0.1s +[6400/16000] [L1: 0.0982] 54.3+0.1s +[8000/16000] [L1: 0.1008] 53.0+0.1s +[9600/16000] [L1: 0.1032] 53.3+0.1s +[11200/16000] [L1: 0.1066] 53.8+0.1s +[12800/16000] [L1: 0.1069] 55.6+0.1s +[14400/16000] [L1: 0.1106] 54.5+0.1s +[16000/16000] [L1: 0.1110] 54.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 11.741 (Best: 21.661 @epoch 8) +Forward: 35.02s + +Saving... +Total: 35.48s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1014] 52.0+0.9s +[3200/16000] [L1: 0.1022] 51.7+0.0s +[4800/16000] [L1: 0.1101] 52.4+0.0s +[6400/16000] [L1: 0.1103] 52.1+0.0s +[8000/16000] [L1: 0.1017] 52.6+0.0s +[9600/16000] [L1: 0.1066] 53.4+0.0s +[11200/16000] [L1: 0.1094] 53.1+0.0s +[12800/16000] [L1: 0.1072] 52.3+0.0s +[14400/16000] [L1: 0.1064] 52.7+0.0s +[16000/16000] [L1: 0.1077] 54.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.386 (Best: 21.661 @epoch 8) +Forward: 34.89s + +Saving... +Total: 35.42s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1207] 52.7+1.0s +[3200/16000] [L1: 0.1076] 53.2+0.1s +[4800/16000] [L1: 0.0979] 54.6+0.1s +[6400/16000] [L1: 0.0985] 54.2+0.1s +[8000/16000] [L1: 0.1030] 54.5+0.1s +[9600/16000] [L1: 0.1036] 54.3+0.1s +[11200/16000] [L1: 0.1033] 52.9+0.0s +[12800/16000] [L1: 0.1026] 52.5+0.0s +[14400/16000] [L1: 0.0989] 53.4+0.0s +[16000/16000] [L1: 0.1001] 54.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.174 (Best: 21.661 @epoch 8) +Forward: 35.08s + +Saving... +Total: 35.55s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1261] 52.0+0.8s +[3200/16000] [L1: 0.1201] 53.3+0.1s +[4800/16000] [L1: 0.1070] 52.9+0.1s +[6400/16000] [L1: 0.1035] 52.5+0.0s +[8000/16000] [L1: 0.0959] 53.0+0.0s +[9600/16000] [L1: 0.0905] 53.2+0.0s +[11200/16000] [L1: 0.0915] 53.5+0.1s +[12800/16000] [L1: 0.0915] 53.4+0.1s +[14400/16000] [L1: 0.0883] 53.2+0.1s +[16000/16000] [L1: 0.0870] 52.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.734 (Best: 21.661 @epoch 8) +Forward: 35.04s + +Saving... +Total: 35.56s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0973] 52.8+0.9s +[3200/16000] [L1: 0.0972] 52.8+0.1s +[4800/16000] [L1: 0.0918] 54.3+0.1s +[6400/16000] [L1: 0.0917] 54.8+0.1s +[8000/16000] [L1: 0.0912] 54.4+0.1s +[9600/16000] [L1: 0.0908] 54.8+0.0s +[11200/16000] [L1: 0.0946] 54.8+0.1s +[12800/16000] [L1: 0.0925] 54.4+0.0s +[14400/16000] [L1: 0.0947] 54.3+0.0s +[16000/16000] [L1: 0.0942] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.286 (Best: 21.661 @epoch 8) +Forward: 34.90s + +Saving... +Total: 35.43s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0914] 52.4+0.9s +[3200/16000] [L1: 0.0809] 53.4+0.1s +[4800/16000] [L1: 0.0720] 52.8+0.0s +[6400/16000] [L1: 0.0739] 53.3+0.1s +[8000/16000] [L1: 0.0818] 53.9+0.0s +[9600/16000] [L1: 0.0843] 52.6+0.0s +[11200/16000] [L1: 0.0825] 52.3+0.0s +[12800/16000] [L1: 0.0833] 53.5+0.0s +[14400/16000] [L1: 0.0839] 54.3+0.0s +[16000/16000] [L1: 0.0847] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.515 (Best: 21.661 @epoch 8) +Forward: 34.93s + +Saving... +Total: 35.47s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0837] 52.4+1.0s +[3200/16000] [L1: 0.0859] 51.5+0.0s +[4800/16000] [L1: 0.0786] 53.3+0.1s +[6400/16000] [L1: 0.0722] 53.6+0.1s +[8000/16000] [L1: 0.0740] 52.1+0.0s +[9600/16000] [L1: 0.0790] 54.0+0.1s +[11200/16000] [L1: 0.0769] 52.2+0.0s +[12800/16000] [L1: 0.0767] 52.3+0.0s +[14400/16000] [L1: 0.0758] 51.5+0.0s +[16000/16000] [L1: 0.0782] 51.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.444 (Best: 21.661 @epoch 8) +Forward: 34.94s + +Saving... +Total: 35.58s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0746] 52.1+1.0s +[3200/16000] [L1: 0.0842] 51.6+0.0s +[4800/16000] [L1: 0.0823] 53.4+0.0s +[6400/16000] [L1: 0.0830] 52.0+0.0s +[8000/16000] [L1: 0.0819] 52.9+0.1s +[9600/16000] [L1: 0.0850] 53.5+0.1s +[11200/16000] [L1: 0.0878] 52.2+0.1s +[12800/16000] [L1: 0.0869] 53.7+0.1s +[14400/16000] [L1: 0.0845] 53.8+0.1s +[16000/16000] [L1: 0.0850] 52.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.022 (Best: 21.661 @epoch 8) +Forward: 34.94s + +Saving... +Total: 35.49s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0936] 51.4+0.9s +[3200/16000] [L1: 0.0830] 53.0+0.1s +[4800/16000] [L1: 0.0781] 52.9+0.1s +[6400/16000] [L1: 0.0775] 53.3+0.1s +[8000/16000] [L1: 0.0749] 53.0+0.0s +[9600/16000] [L1: 0.0745] 51.8+0.0s +[11200/16000] [L1: 0.0759] 52.5+0.0s +[12800/16000] [L1: 0.0798] 51.8+0.0s +[14400/16000] [L1: 0.0804] 52.8+0.0s +[16000/16000] [L1: 0.0793] 53.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.446 (Best: 21.661 @epoch 8) +Forward: 34.70s + +Saving... +Total: 35.25s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0647] 52.1+1.0s +[3200/16000] [L1: 0.0607] 51.7+0.1s +[4800/16000] [L1: 0.0626] 51.6+0.0s +[6400/16000] [L1: 0.0680] 54.1+0.1s +[8000/16000] [L1: 0.0709] 54.0+0.0s +[9600/16000] [L1: 0.0680] 53.0+0.0s +[11200/16000] [L1: 0.0674] 53.2+0.1s +[12800/16000] [L1: 0.0718] 51.9+0.0s +[14400/16000] [L1: 0.0714] 54.2+0.1s +[16000/16000] [L1: 0.0729] 53.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.983 (Best: 21.661 @epoch 8) +Forward: 34.65s + +Saving... +Total: 35.10s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0747] 52.8+0.9s +[3200/16000] [L1: 0.0727] 52.1+0.1s +[4800/16000] [L1: 0.0839] 53.4+0.1s +[6400/16000] [L1: 0.0823] 52.4+0.0s +[8000/16000] [L1: 0.0785] 52.0+0.0s +[9600/16000] [L1: 0.0769] 51.8+0.0s +[11200/16000] [L1: 0.0749] 52.1+0.0s +[12800/16000] [L1: 0.0764] 51.7+0.0s +[14400/16000] [L1: 0.0789] 51.9+0.0s +[16000/16000] [L1: 0.0789] 51.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.208 (Best: 21.661 @epoch 8) +Forward: 34.91s + +Saving... +Total: 35.43s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0876] 52.5+1.0s +[3200/16000] [L1: 0.0874] 52.1+0.1s +[4800/16000] [L1: 0.0778] 51.6+0.1s +[6400/16000] [L1: 0.0780] 53.1+0.0s +[8000/16000] [L1: 0.0822] 52.3+0.0s +[9600/16000] [L1: 0.0810] 53.5+0.0s +[11200/16000] [L1: 0.0796] 54.1+0.1s +[12800/16000] [L1: 0.0805] 55.3+0.1s +[14400/16000] [L1: 0.0810] 54.4+0.1s +[16000/16000] [L1: 0.0802] 54.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.255 (Best: 21.661 @epoch 8) +Forward: 34.39s + +Saving... +Total: 34.95s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0770] 52.4+0.9s +[3200/16000] [L1: 0.0683] 52.4+0.1s +[4800/16000] [L1: 0.0626] 54.7+0.1s +[6400/16000] [L1: 0.0585] 53.0+0.1s +[8000/16000] [L1: 0.0560] 53.6+0.1s +[9600/16000] [L1: 0.0547] 53.2+0.1s +[11200/16000] [L1: 0.0590] 53.9+0.1s +[12800/16000] [L1: 0.0635] 53.7+0.1s +[14400/16000] [L1: 0.0678] 53.1+0.1s +[16000/16000] [L1: 0.0688] 52.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.879 (Best: 21.661 @epoch 8) +Forward: 34.86s + +Saving... +Total: 35.32s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0794] 52.7+1.1s +[3200/16000] [L1: 0.0874] 52.7+0.1s +[4800/16000] [L1: 0.0835] 53.6+0.1s +[6400/16000] [L1: 0.0756] 52.8+0.0s +[8000/16000] [L1: 0.0745] 51.2+0.0s +[9600/16000] [L1: 0.0756] 50.9+0.0s +[11200/16000] [L1: 0.0785] 53.1+0.0s +[12800/16000] [L1: 0.0804] 53.4+0.0s +[14400/16000] [L1: 0.0819] 53.9+0.1s +[16000/16000] [L1: 0.0803] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.945 (Best: 21.661 @epoch 8) +Forward: 34.49s + +Saving... +Total: 35.02s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0518] 51.0+0.9s +[3200/16000] [L1: 0.0713] 51.6+0.1s +[4800/16000] [L1: 0.0697] 52.9+0.1s +[6400/16000] [L1: 0.0737] 53.8+0.1s +[8000/16000] [L1: 0.0715] 54.7+0.1s +[9600/16000] [L1: 0.0717] 53.5+0.1s +[11200/16000] [L1: 0.0756] 52.5+0.0s +[12800/16000] [L1: 0.0748] 51.5+0.0s +[14400/16000] [L1: 0.0755] 51.6+0.0s +[16000/16000] [L1: 0.0778] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.880 (Best: 21.661 @epoch 8) +Forward: 34.76s + +Saving... +Total: 35.19s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0710] 52.3+1.0s +[3200/16000] [L1: 0.0770] 52.8+0.1s +[4800/16000] [L1: 0.0832] 54.3+0.1s +[6400/16000] [L1: 0.0797] 52.7+0.0s +[8000/16000] [L1: 0.0828] 52.2+0.0s +[9600/16000] [L1: 0.0817] 52.3+0.0s +[11200/16000] [L1: 0.0773] 53.0+0.1s +[12800/16000] [L1: 0.0728] 54.4+0.1s +[14400/16000] [L1: 0.0692] 54.8+0.1s +[16000/16000] [L1: 0.0666] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.890 (Best: 21.661 @epoch 8) +Forward: 34.61s + +Saving... +Total: 35.00s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0466] 51.1+0.9s +[3200/16000] [L1: 0.0575] 50.5+0.0s +[4800/16000] [L1: 0.0703] 53.9+0.1s +[6400/16000] [L1: 0.0723] 53.7+0.1s +[8000/16000] [L1: 0.0730] 54.1+0.1s +[9600/16000] [L1: 0.0771] 54.5+0.1s +[11200/16000] [L1: 0.0732] 54.3+0.1s +[12800/16000] [L1: 0.0708] 53.0+0.0s +[14400/16000] [L1: 0.0702] 53.6+0.1s +[16000/16000] [L1: 0.0674] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.398 (Best: 21.661 @epoch 8) +Forward: 35.00s + +Saving... +Total: 35.40s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0645] 52.6+0.9s +[3200/16000] [L1: 0.0796] 53.0+0.1s +[4800/16000] [L1: 0.0844] 55.0+0.1s +[6400/16000] [L1: 0.0831] 52.6+0.1s +[8000/16000] [L1: 0.0813] 53.8+0.1s +[9600/16000] [L1: 0.0815] 52.2+0.1s +[11200/16000] [L1: 0.0795] 53.6+0.1s +[12800/16000] [L1: 0.0787] 52.7+0.0s +[14400/16000] [L1: 0.0799] 53.1+0.1s +[16000/16000] [L1: 0.0792] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.423 (Best: 21.661 @epoch 8) +Forward: 34.96s + +Saving... +Total: 35.66s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0859] 51.3+1.0s +[3200/16000] [L1: 0.0828] 52.8+0.1s +[4800/16000] [L1: 0.0804] 53.0+0.0s +[6400/16000] [L1: 0.0773] 52.3+0.0s +[8000/16000] [L1: 0.0778] 52.7+0.0s +[9600/16000] [L1: 0.0805] 54.2+0.1s +[11200/16000] [L1: 0.0828] 53.1+0.0s +[12800/16000] [L1: 0.0815] 52.4+0.0s +[14400/16000] [L1: 0.0803] 51.6+0.0s +[16000/16000] [L1: 0.0810] 53.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.286 (Best: 21.661 @epoch 8) +Forward: 35.12s + +Saving... +Total: 35.50s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0742] 50.4+0.9s +[3200/16000] [L1: 0.0678] 51.0+0.0s +[4800/16000] [L1: 0.0695] 53.3+0.0s +[6400/16000] [L1: 0.0773] 50.2+0.0s +[8000/16000] [L1: 0.0773] 50.0+0.0s +[9600/16000] [L1: 0.0789] 50.2+0.0s +[11200/16000] [L1: 0.0791] 53.4+0.0s +[12800/16000] [L1: 0.0768] 53.6+0.0s +[14400/16000] [L1: 0.0796] 52.8+0.0s +[16000/16000] [L1: 0.0797] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.298 (Best: 21.661 @epoch 8) +Forward: 35.12s + +Saving... +Total: 35.57s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0607] 52.9+1.0s +[3200/16000] [L1: 0.0601] 52.2+0.0s +[4800/16000] [L1: 0.0637] 51.7+0.0s +[6400/16000] [L1: 0.0728] 54.3+0.1s +[8000/16000] [L1: 0.0755] 54.1+0.1s +[9600/16000] [L1: 0.0742] 53.1+0.0s +[11200/16000] [L1: 0.0714] 53.6+0.1s +[12800/16000] [L1: 0.0701] 54.6+0.1s +[14400/16000] [L1: 0.0738] 52.4+0.0s +[16000/16000] [L1: 0.0754] 54.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.114 (Best: 21.661 @epoch 8) +Forward: 35.04s + +Saving... +Total: 35.55s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0735] 52.8+1.0s +[3200/16000] [L1: 0.0898] 53.5+0.1s +[4800/16000] [L1: 0.0804] 54.0+0.1s +[6400/16000] [L1: 0.0772] 53.6+0.0s +[8000/16000] [L1: 0.0711] 52.1+0.0s +[9600/16000] [L1: 0.0662] 54.6+0.1s +[11200/16000] [L1: 0.0640] 54.3+0.1s +[12800/16000] [L1: 0.0629] 54.8+0.1s +[14400/16000] [L1: 0.0641] 54.3+0.1s +[16000/16000] [L1: 0.0673] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.935 (Best: 21.661 @epoch 8) +Forward: 35.07s + +Saving... +Total: 35.57s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0538] 52.5+0.9s +[3200/16000] [L1: 0.0534] 52.9+0.1s +[4800/16000] [L1: 0.0608] 54.0+0.1s +[6400/16000] [L1: 0.0705] 53.3+0.0s +[8000/16000] [L1: 0.0726] 54.4+0.1s +[9600/16000] [L1: 0.0756] 54.1+0.0s +[11200/16000] [L1: 0.0765] 51.6+0.0s +[12800/16000] [L1: 0.0760] 51.9+0.0s +[14400/16000] [L1: 0.0749] 51.9+0.0s +[16000/16000] [L1: 0.0758] 53.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.302 (Best: 21.661 @epoch 8) +Forward: 35.12s + +Saving... +Total: 35.51s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0708] 51.4+0.9s +[3200/16000] [L1: 0.0746] 52.0+0.0s +[4800/16000] [L1: 0.0691] 52.3+0.0s +[6400/16000] [L1: 0.0703] 52.4+0.0s +[8000/16000] [L1: 0.0711] 53.7+0.0s +[9600/16000] [L1: 0.0728] 53.6+0.0s +[11200/16000] [L1: 0.0745] 53.6+0.1s +[12800/16000] [L1: 0.0722] 54.3+0.1s +[14400/16000] [L1: 0.0723] 54.7+0.1s +[16000/16000] [L1: 0.0719] 53.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.078 (Best: 21.661 @epoch 8) +Forward: 35.06s + +Saving... +Total: 35.53s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0655] 52.8+1.0s +[3200/16000] [L1: 0.0723] 52.8+0.1s +[4800/16000] [L1: 0.0675] 54.2+0.1s +[6400/16000] [L1: 0.0623] 54.9+0.1s +[8000/16000] [L1: 0.0617] 55.1+0.0s +[9600/16000] [L1: 0.0645] 51.8+0.1s +[11200/16000] [L1: 0.0663] 52.9+0.0s +[12800/16000] [L1: 0.0676] 55.1+0.1s +[14400/16000] [L1: 0.0684] 54.3+0.1s +[16000/16000] [L1: 0.0679] 54.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.541 (Best: 21.661 @epoch 8) +Forward: 35.23s + +Saving... +Total: 35.63s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0721] 52.1+1.0s +[3200/16000] [L1: 0.0727] 51.4+0.0s +[4800/16000] [L1: 0.0660] 53.9+0.0s +[6400/16000] [L1: 0.0662] 53.1+0.0s +[8000/16000] [L1: 0.0726] 54.7+0.1s +[9600/16000] [L1: 0.0736] 54.7+0.1s +[11200/16000] [L1: 0.0737] 54.1+0.1s +[12800/16000] [L1: 0.0719] 53.7+0.0s +[14400/16000] [L1: 0.0701] 52.4+0.0s +[16000/16000] [L1: 0.0705] 51.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.027 (Best: 21.661 @epoch 8) +Forward: 35.17s + +Saving... +Total: 35.66s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0714] 52.8+0.9s +[3200/16000] [L1: 0.0746] 54.7+0.1s +[4800/16000] [L1: 0.0800] 54.1+0.1s +[6400/16000] [L1: 0.0816] 51.9+0.1s +[8000/16000] [L1: 0.0789] 54.2+0.1s +[9600/16000] [L1: 0.0769] 54.2+0.1s +[11200/16000] [L1: 0.0736] 54.7+0.1s +[12800/16000] [L1: 0.0722] 54.3+0.1s +[14400/16000] [L1: 0.0744] 54.8+0.1s +[16000/16000] [L1: 0.0755] 54.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.529 (Best: 21.661 @epoch 8) +Forward: 35.08s + +Saving... +Total: 35.61s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0667] 52.0+0.9s +[3200/16000] [L1: 0.0742] 53.0+0.1s +[4800/16000] [L1: 0.0741] 54.7+0.1s +[6400/16000] [L1: 0.0738] 54.3+0.1s +[8000/16000] [L1: 0.0742] 54.2+0.1s +[9600/16000] [L1: 0.0753] 53.6+0.1s +[11200/16000] [L1: 0.0722] 54.2+0.1s +[12800/16000] [L1: 0.0720] 52.8+0.0s +[14400/16000] [L1: 0.0747] 52.8+0.0s +[16000/16000] [L1: 0.0758] 52.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.379 (Best: 21.661 @epoch 8) +Forward: 35.29s + +Saving... +Total: 35.75s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0801] 52.8+1.0s +[3200/16000] [L1: 0.0754] 53.3+0.1s +[4800/16000] [L1: 0.0737] 54.6+0.1s +[6400/16000] [L1: 0.0707] 54.8+0.1s +[8000/16000] [L1: 0.0728] 53.2+0.1s +[9600/16000] [L1: 0.0759] 52.1+0.0s +[11200/16000] [L1: 0.0774] 55.1+0.1s +[12800/16000] [L1: 0.0770] 54.1+0.1s +[14400/16000] [L1: 0.0753] 54.5+0.1s +[16000/16000] [L1: 0.0746] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.924 (Best: 21.661 @epoch 8) +Forward: 35.11s + +Saving... +Total: 35.60s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0552] 52.8+0.9s +[3200/16000] [L1: 0.0667] 53.2+0.1s +[4800/16000] [L1: 0.0725] 54.2+0.1s +[6400/16000] [L1: 0.0769] 54.9+0.1s +[8000/16000] [L1: 0.0792] 52.7+0.0s +[9600/16000] [L1: 0.0787] 53.5+0.0s +[11200/16000] [L1: 0.0763] 54.2+0.1s +[12800/16000] [L1: 0.0731] 54.6+0.0s +[14400/16000] [L1: 0.0702] 54.6+0.1s +[16000/16000] [L1: 0.0686] 52.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.471 (Best: 21.661 @epoch 8) +Forward: 35.23s + +Saving... +Total: 35.65s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0924] 52.9+0.9s +[3200/16000] [L1: 0.0829] 52.7+0.0s +[4800/16000] [L1: 0.0845] 53.9+0.1s +[6400/16000] [L1: 0.0840] 53.8+0.1s +[8000/16000] [L1: 0.0823] 51.8+0.0s +[9600/16000] [L1: 0.0809] 53.2+0.0s +[11200/16000] [L1: 0.0797] 54.6+0.0s +[12800/16000] [L1: 0.0764] 53.5+0.0s +[14400/16000] [L1: 0.0739] 54.0+0.1s +[16000/16000] [L1: 0.0714] 53.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.073 (Best: 21.661 @epoch 8) +Forward: 35.33s + +Saving... +Total: 35.87s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0471] 53.3+0.9s +[3200/16000] [L1: 0.0443] 52.2+0.1s +[4800/16000] [L1: 0.0449] 54.0+0.1s +[6400/16000] [L1: 0.0492] 54.3+0.1s +[8000/16000] [L1: 0.0500] 53.2+0.1s +[9600/16000] [L1: 0.0543] 53.2+0.1s +[11200/16000] [L1: 0.0561] 54.1+0.1s +[12800/16000] [L1: 0.0595] 53.5+0.1s +[14400/16000] [L1: 0.0604] 52.0+0.0s +[16000/16000] [L1: 0.0620] 53.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.695 (Best: 21.661 @epoch 8) +Forward: 35.24s + +Saving... +Total: 35.65s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0544] 53.4+0.9s +[3200/16000] [L1: 0.0575] 52.9+0.1s +[4800/16000] [L1: 0.0569] 54.5+0.1s +[6400/16000] [L1: 0.0562] 51.6+0.0s +[8000/16000] [L1: 0.0584] 53.1+0.0s +[9600/16000] [L1: 0.0615] 53.2+0.0s +[11200/16000] [L1: 0.0644] 53.0+0.0s +[12800/16000] [L1: 0.0665] 53.6+0.1s +[14400/16000] [L1: 0.0672] 54.4+0.1s +[16000/16000] [L1: 0.0681] 54.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.804 (Best: 21.661 @epoch 8) +Forward: 35.09s + +Saving... +Total: 35.55s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0935] 51.0+0.9s +[3200/16000] [L1: 0.0838] 52.4+0.1s +[4800/16000] [L1: 0.0762] 54.3+0.1s +[6400/16000] [L1: 0.0767] 53.9+0.1s +[8000/16000] [L1: 0.0763] 52.1+0.0s +[9600/16000] [L1: 0.0779] 52.4+0.1s +[11200/16000] [L1: 0.0784] 52.0+0.0s +[12800/16000] [L1: 0.0778] 52.7+0.0s +[14400/16000] [L1: 0.0763] 54.6+0.1s +[16000/16000] [L1: 0.0769] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.083 (Best: 21.661 @epoch 8) +Forward: 35.17s + +Saving... +Total: 35.70s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0758] 50.3+1.0s +[3200/16000] [L1: 0.0759] 52.6+0.0s +[4800/16000] [L1: 0.0682] 53.9+0.0s +[6400/16000] [L1: 0.0743] 54.1+0.1s +[8000/16000] [L1: 0.0751] 53.9+0.0s +[9600/16000] [L1: 0.0744] 52.6+0.0s +[11200/16000] [L1: 0.0753] 51.9+0.0s +[12800/16000] [L1: 0.0787] 53.7+0.1s +[14400/16000] [L1: 0.0766] 53.5+0.0s +[16000/16000] [L1: 0.0735] 54.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.368 (Best: 21.661 @epoch 8) +Forward: 34.97s + +Saving... +Total: 35.56s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0534] 53.4+0.9s +[3200/16000] [L1: 0.0688] 53.2+0.1s +[4800/16000] [L1: 0.0777] 52.5+0.1s +[6400/16000] [L1: 0.0721] 54.7+0.1s +[8000/16000] [L1: 0.0711] 54.4+0.1s +[9600/16000] [L1: 0.0703] 53.1+0.0s +[11200/16000] [L1: 0.0697] 54.8+0.1s +[12800/16000] [L1: 0.0719] 52.8+0.0s +[14400/16000] [L1: 0.0728] 53.7+0.1s +[16000/16000] [L1: 0.0716] 53.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.682 (Best: 21.661 @epoch 8) +Forward: 35.10s + +Saving... +Total: 35.69s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0749] 51.1+1.0s +[3200/16000] [L1: 0.0744] 52.7+0.0s +[4800/16000] [L1: 0.0775] 54.7+0.1s +[6400/16000] [L1: 0.0805] 54.2+0.1s +[8000/16000] [L1: 0.0818] 51.7+0.0s +[9600/16000] [L1: 0.0839] 51.9+0.0s +[11200/16000] [L1: 0.0831] 52.3+0.0s +[12800/16000] [L1: 0.0795] 51.9+0.0s +[14400/16000] [L1: 0.0771] 52.1+0.0s +[16000/16000] [L1: 0.0765] 52.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.839 (Best: 21.661 @epoch 8) +Forward: 35.21s + +Saving... +Total: 35.62s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0883] 51.7+0.9s +[3200/16000] [L1: 0.0937] 52.2+0.0s +[4800/16000] [L1: 0.0907] 54.6+0.0s +[6400/16000] [L1: 0.0887] 54.1+0.1s +[8000/16000] [L1: 0.0873] 52.8+0.1s +[9600/16000] [L1: 0.0817] 51.4+0.0s +[11200/16000] [L1: 0.0767] 53.8+0.1s +[12800/16000] [L1: 0.0739] 54.2+0.1s +[14400/16000] [L1: 0.0724] 51.9+0.0s +[16000/16000] [L1: 0.0700] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.155 (Best: 21.661 @epoch 8) +Forward: 35.05s + +Saving... +Total: 35.71s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0405] 54.0+1.0s +[3200/16000] [L1: 0.0393] 53.5+0.1s +[4800/16000] [L1: 0.0402] 52.2+0.0s +[6400/16000] [L1: 0.0420] 52.0+0.0s +[8000/16000] [L1: 0.0412] 52.7+0.1s +[9600/16000] [L1: 0.0411] 53.1+0.1s +[11200/16000] [L1: 0.0418] 52.6+0.0s +[12800/16000] [L1: 0.0425] 53.2+0.1s +[14400/16000] [L1: 0.0427] 53.5+0.1s +[16000/16000] [L1: 0.0433] 52.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.732 (Best: 21.661 @epoch 8) +Forward: 35.26s + +Saving... +Total: 35.77s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0419] 52.9+1.0s +[3200/16000] [L1: 0.0457] 53.6+0.1s +[4800/16000] [L1: 0.0456] 54.4+0.1s +[6400/16000] [L1: 0.0437] 53.7+0.1s +[8000/16000] [L1: 0.0427] 54.0+0.1s +[9600/16000] [L1: 0.0449] 53.0+0.0s +[11200/16000] [L1: 0.0458] 52.8+0.0s +[12800/16000] [L1: 0.0516] 52.6+0.0s +[14400/16000] [L1: 0.0566] 52.4+0.0s +[16000/16000] [L1: 0.0601] 52.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.153 (Best: 21.661 @epoch 8) +Forward: 35.02s + +Saving... +Total: 35.43s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0908] 52.0+1.0s +[3200/16000] [L1: 0.0861] 53.9+0.1s +[4800/16000] [L1: 0.0873] 54.7+0.1s +[6400/16000] [L1: 0.0851] 54.6+0.1s +[8000/16000] [L1: 0.0830] 54.2+0.1s +[9600/16000] [L1: 0.0877] 53.0+0.1s +[11200/16000] [L1: 0.0861] 53.2+0.1s +[12800/16000] [L1: 0.0852] 54.0+0.1s +[14400/16000] [L1: 0.0837] 52.1+0.0s +[16000/16000] [L1: 0.0834] 52.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.307 (Best: 21.661 @epoch 8) +Forward: 35.04s + +Saving... +Total: 35.50s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0882] 52.8+0.9s +[3200/16000] [L1: 0.0786] 53.3+0.1s +[4800/16000] [L1: 0.0751] 53.0+0.0s +[6400/16000] [L1: 0.0727] 52.9+0.0s +[8000/16000] [L1: 0.0737] 53.3+0.1s +[9600/16000] [L1: 0.0755] 53.3+0.1s +[11200/16000] [L1: 0.0746] 53.7+0.1s +[12800/16000] [L1: 0.0743] 54.1+0.1s +[14400/16000] [L1: 0.0725] 53.7+0.1s +[16000/16000] [L1: 0.0735] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.918 (Best: 21.661 @epoch 8) +Forward: 35.02s + +Saving... +Total: 35.52s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0788] 51.1+0.9s +[3200/16000] [L1: 0.0653] 52.0+0.0s +[4800/16000] [L1: 0.0613] 52.3+0.0s +[6400/16000] [L1: 0.0657] 53.1+0.0s +[8000/16000] [L1: 0.0704] 52.7+0.1s +[9600/16000] [L1: 0.0664] 53.7+0.1s +[11200/16000] [L1: 0.0634] 54.3+0.1s +[12800/16000] [L1: 0.0624] 54.2+0.0s +[14400/16000] [L1: 0.0625] 53.1+0.0s +[16000/16000] [L1: 0.0617] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.768 (Best: 21.661 @epoch 8) +Forward: 35.04s + +Saving... +Total: 35.52s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0494] 51.7+0.9s +[3200/16000] [L1: 0.0444] 52.9+0.1s +[4800/16000] [L1: 0.0437] 54.3+0.1s +[6400/16000] [L1: 0.0458] 54.2+0.1s +[8000/16000] [L1: 0.0474] 53.5+0.0s +[9600/16000] [L1: 0.0472] 54.0+0.1s +[11200/16000] [L1: 0.0452] 53.3+0.0s +[12800/16000] [L1: 0.0438] 52.8+0.1s +[14400/16000] [L1: 0.0445] 54.2+0.1s +[16000/16000] [L1: 0.0456] 53.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.867 (Best: 21.661 @epoch 8) +Forward: 34.85s + +Saving... +Total: 35.36s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0489] 50.0+1.1s +[3200/16000] [L1: 0.0671] 50.4+0.1s +[4800/16000] [L1: 0.0754] 51.9+0.0s +[6400/16000] [L1: 0.0778] 51.7+0.0s +[8000/16000] [L1: 0.0781] 54.4+0.0s +[9600/16000] [L1: 0.0787] 53.5+0.1s +[11200/16000] [L1: 0.0781] 52.3+0.0s +[12800/16000] [L1: 0.0750] 52.5+0.0s +[14400/16000] [L1: 0.0747] 53.2+0.0s +[16000/16000] [L1: 0.0768] 52.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.792 (Best: 21.661 @epoch 8) +Forward: 35.04s + +Saving... +Total: 35.59s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0745] 52.7+1.2s +[3200/16000] [L1: 0.0850] 52.8+0.1s +[4800/16000] [L1: 0.0832] 54.3+0.1s +[6400/16000] [L1: 0.0855] 52.9+0.0s +[8000/16000] [L1: 0.0833] 53.6+0.0s +[9600/16000] [L1: 0.0813] 53.2+0.1s +[11200/16000] [L1: 0.0823] 52.7+0.1s +[12800/16000] [L1: 0.0840] 51.8+0.0s +[14400/16000] [L1: 0.0825] 53.3+0.1s +[16000/16000] [L1: 0.0819] 53.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.168 (Best: 21.661 @epoch 8) +Forward: 34.99s + +Saving... +Total: 35.40s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0498] 52.5+1.0s +[3200/16000] [L1: 0.0582] 52.1+0.1s +[4800/16000] [L1: 0.0578] 53.7+0.1s +[6400/16000] [L1: 0.0559] 54.2+0.1s +[8000/16000] [L1: 0.0556] 54.6+0.1s +[9600/16000] [L1: 0.0598] 54.1+0.1s +[11200/16000] [L1: 0.0647] 53.1+0.0s +[12800/16000] [L1: 0.0673] 53.7+0.0s +[14400/16000] [L1: 0.0676] 52.7+0.0s +[16000/16000] [L1: 0.0679] 52.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.913 (Best: 21.661 @epoch 8) +Forward: 34.94s + +Saving... +Total: 35.54s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0689] 52.6+1.0s +[3200/16000] [L1: 0.0668] 52.2+0.1s +[4800/16000] [L1: 0.0712] 53.9+0.1s +[6400/16000] [L1: 0.0736] 51.6+0.0s +[8000/16000] [L1: 0.0703] 52.9+0.0s +[9600/16000] [L1: 0.0719] 53.1+0.0s +[11200/16000] [L1: 0.0737] 52.6+0.0s +[12800/16000] [L1: 0.0724] 54.0+0.1s +[14400/16000] [L1: 0.0695] 52.8+0.0s +[16000/16000] [L1: 0.0695] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.812 (Best: 21.661 @epoch 8) +Forward: 34.96s + +Saving... +Total: 35.48s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0925] 52.3+1.0s +[3200/16000] [L1: 0.0817] 53.1+0.1s +[4800/16000] [L1: 0.0762] 53.0+0.1s +[6400/16000] [L1: 0.0692] 53.2+0.1s +[8000/16000] [L1: 0.0686] 53.4+0.1s +[9600/16000] [L1: 0.0712] 54.2+0.1s +[11200/16000] [L1: 0.0722] 53.9+0.1s +[12800/16000] [L1: 0.0724] 53.7+0.1s +[14400/16000] [L1: 0.0728] 52.9+0.1s +[16000/16000] [L1: 0.0731] 51.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.600 (Best: 21.661 @epoch 8) +Forward: 35.00s + +Saving... +Total: 35.44s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0902] 51.8+0.9s +[3200/16000] [L1: 0.0806] 51.6+0.1s +[4800/16000] [L1: 0.0757] 54.7+0.1s +[6400/16000] [L1: 0.0719] 53.1+0.1s +[8000/16000] [L1: 0.0718] 52.5+0.0s +[9600/16000] [L1: 0.0768] 54.5+0.1s +[11200/16000] [L1: 0.0759] 53.1+0.1s +[12800/16000] [L1: 0.0736] 52.2+0.0s +[14400/16000] [L1: 0.0726] 53.7+0.1s +[16000/16000] [L1: 0.0699] 54.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.412 (Best: 21.661 @epoch 8) +Forward: 34.88s + +Saving... +Total: 35.34s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0679] 50.6+0.9s +[3200/16000] [L1: 0.0653] 53.2+0.1s +[4800/16000] [L1: 0.0697] 52.9+0.0s +[6400/16000] [L1: 0.0692] 53.3+0.0s +[8000/16000] [L1: 0.0680] 54.2+0.0s +[9600/16000] [L1: 0.0656] 53.4+0.0s +[11200/16000] [L1: 0.0635] 53.2+0.0s +[12800/16000] [L1: 0.0622] 53.6+0.1s +[14400/16000] [L1: 0.0623] 54.4+0.1s +[16000/16000] [L1: 0.0644] 52.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.935 (Best: 21.661 @epoch 8) +Forward: 35.00s + +Saving... +Total: 35.60s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0802] 51.2+0.9s +[3200/16000] [L1: 0.0755] 52.2+0.1s +[4800/16000] [L1: 0.0769] 54.2+0.0s +[6400/16000] [L1: 0.0775] 52.4+0.0s +[8000/16000] [L1: 0.0743] 52.3+0.0s +[9600/16000] [L1: 0.0755] 52.4+0.0s +[11200/16000] [L1: 0.0765] 54.1+0.1s +[12800/16000] [L1: 0.0763] 54.1+0.1s +[14400/16000] [L1: 0.0762] 54.1+0.1s +[16000/16000] [L1: 0.0758] 55.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.734 (Best: 21.661 @epoch 8) +Forward: 34.55s + +Saving... +Total: 35.05s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0798] 50.5+0.9s +[3200/16000] [L1: 0.0866] 51.4+0.0s +[4800/16000] [L1: 0.0851] 54.7+0.1s +[6400/16000] [L1: 0.0833] 53.8+0.1s +[8000/16000] [L1: 0.0823] 51.5+0.0s +[9600/16000] [L1: 0.0832] 53.0+0.1s +[11200/16000] [L1: 0.0856] 52.8+0.1s +[12800/16000] [L1: 0.0855] 54.2+0.1s +[14400/16000] [L1: 0.0860] 54.2+0.1s +[16000/16000] [L1: 0.0861] 53.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.557 (Best: 21.661 @epoch 8) +Forward: 34.85s + +Saving... +Total: 35.38s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0668] 53.0+0.9s +[3200/16000] [L1: 0.0678] 53.4+0.1s +[4800/16000] [L1: 0.0694] 53.9+0.1s +[6400/16000] [L1: 0.0690] 53.7+0.0s +[8000/16000] [L1: 0.0699] 54.1+0.0s +[9600/16000] [L1: 0.0714] 55.0+0.1s +[11200/16000] [L1: 0.0732] 54.4+0.1s +[12800/16000] [L1: 0.0715] 52.2+0.1s +[14400/16000] [L1: 0.0715] 52.2+0.0s +[16000/16000] [L1: 0.0696] 54.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.744 (Best: 21.661 @epoch 8) +Forward: 34.72s + +Saving... +Total: 35.11s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0661] 52.3+0.9s +[3200/16000] [L1: 0.0747] 52.9+0.1s +[4800/16000] [L1: 0.0715] 54.3+0.1s +[6400/16000] [L1: 0.0733] 54.2+0.1s +[8000/16000] [L1: 0.0743] 54.0+0.1s +[9600/16000] [L1: 0.0757] 53.9+0.1s +[11200/16000] [L1: 0.0766] 52.6+0.0s +[12800/16000] [L1: 0.0769] 53.9+0.1s +[14400/16000] [L1: 0.0774] 52.6+0.0s +[16000/16000] [L1: 0.0763] 52.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.930 (Best: 21.661 @epoch 8) +Forward: 35.09s + +Saving... +Total: 35.62s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0879] 53.3+1.2s +[3200/16000] [L1: 0.0866] 51.7+0.1s +[4800/16000] [L1: 0.0748] 53.4+0.1s +[6400/16000] [L1: 0.0742] 53.0+0.1s +[8000/16000] [L1: 0.0752] 53.4+0.0s +[9600/16000] [L1: 0.0739] 53.0+0.0s +[11200/16000] [L1: 0.0694] 53.0+0.0s +[12800/16000] [L1: 0.0688] 53.7+0.1s +[14400/16000] [L1: 0.0702] 53.9+0.1s +[16000/16000] [L1: 0.0710] 54.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.791 (Best: 21.661 @epoch 8) +Forward: 34.69s + +Saving... +Total: 35.15s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0741] 51.6+0.9s +[3200/16000] [L1: 0.0669] 49.9+0.0s +[4800/16000] [L1: 0.0682] 50.5+0.0s +[6400/16000] [L1: 0.0706] 50.8+0.0s +[8000/16000] [L1: 0.0725] 52.0+0.0s +[9600/16000] [L1: 0.0724] 53.8+0.1s +[11200/16000] [L1: 0.0712] 53.7+0.0s +[12800/16000] [L1: 0.0683] 53.7+0.1s +[14400/16000] [L1: 0.0664] 54.2+0.1s +[16000/16000] [L1: 0.0675] 53.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 15.149 (Best: 21.661 @epoch 8) +Forward: 34.76s + +Saving... +Total: 35.21s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0723] 51.0+1.1s +[3200/16000] [L1: 0.0774] 51.5+0.1s +[4800/16000] [L1: 0.0834] 54.6+0.1s +[6400/16000] [L1: 0.0805] 55.3+0.1s +[8000/16000] [L1: 0.0767] 53.3+0.1s +[9600/16000] [L1: 0.0763] 53.1+0.1s +[11200/16000] [L1: 0.0776] 53.8+0.1s +[12800/16000] [L1: 0.0782] 53.9+0.1s +[14400/16000] [L1: 0.0786] 53.0+0.1s +[16000/16000] [L1: 0.0781] 53.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.144 (Best: 21.661 @epoch 8) +Forward: 34.76s + +Saving... +Total: 35.21s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0872] 51.1+0.9s +[3200/16000] [L1: 0.0826] 51.7+0.1s +[4800/16000] [L1: 0.0842] 53.3+0.1s +[6400/16000] [L1: 0.0834] 53.2+0.0s +[8000/16000] [L1: 0.0798] 52.7+0.0s +[9600/16000] [L1: 0.0777] 54.8+0.1s +[11200/16000] [L1: 0.0781] 53.4+0.0s +[12800/16000] [L1: 0.0744] 51.6+0.0s +[14400/16000] [L1: 0.0736] 51.9+0.0s +[16000/16000] [L1: 0.0740] 52.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.747 (Best: 21.661 @epoch 8) +Forward: 34.96s + +Saving... +Total: 35.34s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0833] 53.0+0.9s +[3200/16000] [L1: 0.0803] 52.4+0.1s +[4800/16000] [L1: 0.0851] 54.0+0.1s +[6400/16000] [L1: 0.0830] 53.4+0.1s +[8000/16000] [L1: 0.0828] 51.9+0.0s +[9600/16000] [L1: 0.0817] 52.0+0.0s +[11200/16000] [L1: 0.0788] 52.9+0.0s +[12800/16000] [L1: 0.0793] 52.3+0.0s +[14400/16000] [L1: 0.0792] 53.9+0.1s +[16000/16000] [L1: 0.0801] 52.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.870 (Best: 21.661 @epoch 8) +Forward: 34.98s + +Saving... +Total: 35.48s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0583] 53.5+1.1s +[3200/16000] [L1: 0.0641] 52.7+0.1s +[4800/16000] [L1: 0.0692] 54.7+0.1s +[6400/16000] [L1: 0.0690] 55.0+0.1s +[8000/16000] [L1: 0.0693] 54.9+0.1s +[9600/16000] [L1: 0.0695] 54.5+0.1s +[11200/16000] [L1: 0.0691] 53.8+0.1s +[12800/16000] [L1: 0.0711] 53.2+0.1s +[14400/16000] [L1: 0.0727] 52.9+0.1s +[16000/16000] [L1: 0.0723] 52.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.709 (Best: 21.661 @epoch 8) +Forward: 35.14s + +Saving... +Total: 35.58s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0785] 51.7+0.9s +[3200/16000] [L1: 0.0714] 50.7+0.0s +[4800/16000] [L1: 0.0707] 53.2+0.1s +[6400/16000] [L1: 0.0763] 52.1+0.0s +[8000/16000] [L1: 0.0735] 53.5+0.1s +[9600/16000] [L1: 0.0737] 53.2+0.1s +[11200/16000] [L1: 0.0726] 54.4+0.1s +[12800/16000] [L1: 0.0736] 53.2+0.0s +[14400/16000] [L1: 0.0741] 52.3+0.0s +[16000/16000] [L1: 0.0751] 51.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.601 (Best: 21.661 @epoch 8) +Forward: 34.83s + +Saving... +Total: 35.23s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0652] 51.6+0.9s +[3200/16000] [L1: 0.0543] 50.9+0.0s +[4800/16000] [L1: 0.0520] 51.5+0.0s +[6400/16000] [L1: 0.0533] 51.9+0.0s +[8000/16000] [L1: 0.0588] 51.8+0.0s +[9600/16000] [L1: 0.0618] 51.6+0.0s +[11200/16000] [L1: 0.0626] 53.4+0.0s +[12800/16000] [L1: 0.0633] 54.1+0.1s +[14400/16000] [L1: 0.0641] 53.5+0.1s +[16000/16000] [L1: 0.0664] 51.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.771 (Best: 21.661 @epoch 8) +Forward: 34.94s + +Saving... +Total: 35.35s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0749] 51.4+0.8s +[3200/16000] [L1: 0.0788] 50.3+0.1s +[4800/16000] [L1: 0.0743] 51.2+0.0s +[6400/16000] [L1: 0.0726] 51.3+0.0s +[8000/16000] [L1: 0.0731] 54.8+0.1s +[9600/16000] [L1: 0.0716] 54.0+0.0s +[11200/16000] [L1: 0.0719] 52.4+0.0s +[12800/16000] [L1: 0.0731] 51.9+0.0s +[14400/16000] [L1: 0.0734] 51.1+0.0s +[16000/16000] [L1: 0.0741] 52.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.015 (Best: 21.661 @epoch 8) +Forward: 34.88s + +Saving... +Total: 35.27s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0710] 51.8+0.9s +[3200/16000] [L1: 0.0726] 53.0+0.1s +[4800/16000] [L1: 0.0724] 54.2+0.1s +[6400/16000] [L1: 0.0720] 54.5+0.1s +[8000/16000] [L1: 0.0720] 53.6+0.1s +[9600/16000] [L1: 0.0725] 53.9+0.1s +[11200/16000] [L1: 0.0703] 54.3+0.1s +[12800/16000] [L1: 0.0714] 53.8+0.1s +[14400/16000] [L1: 0.0712] 51.6+0.0s +[16000/16000] [L1: 0.0696] 51.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.450 (Best: 21.661 @epoch 8) +Forward: 34.95s + +Saving... +Total: 35.57s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0735] 52.1+1.1s +[3200/16000] [L1: 0.0695] 52.1+0.1s +[4800/16000] [L1: 0.0628] 53.1+0.0s +[6400/16000] [L1: 0.0658] 53.1+0.0s +[8000/16000] [L1: 0.0701] 53.7+0.0s +[9600/16000] [L1: 0.0697] 52.3+0.0s +[11200/16000] [L1: 0.0725] 52.0+0.0s +[12800/16000] [L1: 0.0726] 51.8+0.0s +[14400/16000] [L1: 0.0744] 52.1+0.0s +[16000/16000] [L1: 0.0758] 53.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.925 (Best: 21.661 @epoch 8) +Forward: 34.86s + +Saving... +Total: 35.41s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0661] 52.6+1.0s +[3200/16000] [L1: 0.0606] 52.4+0.1s +[4800/16000] [L1: 0.0684] 51.6+0.0s +[6400/16000] [L1: 0.0648] 53.5+0.1s +[8000/16000] [L1: 0.0636] 52.4+0.0s +[9600/16000] [L1: 0.0631] 52.5+0.0s +[11200/16000] [L1: 0.0655] 54.3+0.0s +[12800/16000] [L1: 0.0647] 53.1+0.1s +[14400/16000] [L1: 0.0639] 53.8+0.1s +[16000/16000] [L1: 0.0648] 54.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.071 (Best: 21.661 @epoch 8) +Forward: 34.66s + +Saving... +Total: 35.12s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0636] 51.8+0.9s +[3200/16000] [L1: 0.0671] 49.6+0.0s +[4800/16000] [L1: 0.0648] 52.5+0.0s +[6400/16000] [L1: 0.0684] 52.3+0.0s +[8000/16000] [L1: 0.0679] 53.0+0.0s +[9600/16000] [L1: 0.0675] 52.2+0.0s +[11200/16000] [L1: 0.0652] 52.3+0.1s +[12800/16000] [L1: 0.0659] 52.7+0.0s +[14400/16000] [L1: 0.0642] 53.3+0.1s +[16000/16000] [L1: 0.0631] 54.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.201 (Best: 21.661 @epoch 8) +Forward: 34.55s + +Saving... +Total: 34.94s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0613] 50.6+0.9s +[3200/16000] [L1: 0.0718] 50.5+0.0s +[4800/16000] [L1: 0.0746] 51.6+0.0s +[6400/16000] [L1: 0.0683] 52.0+0.0s +[8000/16000] [L1: 0.0695] 54.3+0.1s +[9600/16000] [L1: 0.0689] 54.3+0.1s +[11200/16000] [L1: 0.0686] 53.3+0.0s +[12800/16000] [L1: 0.0680] 53.9+0.1s +[14400/16000] [L1: 0.0705] 52.5+0.0s +[16000/16000] [L1: 0.0704] 53.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.528 (Best: 21.661 @epoch 8) +Forward: 34.73s + +Saving... +Total: 35.12s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0737] 53.0+0.9s +[3200/16000] [L1: 0.0786] 53.0+0.1s +[4800/16000] [L1: 0.0721] 53.3+0.1s +[6400/16000] [L1: 0.0748] 52.6+0.0s +[8000/16000] [L1: 0.0694] 53.2+0.1s +[9600/16000] [L1: 0.0670] 51.8+0.0s +[11200/16000] [L1: 0.0676] 51.3+0.0s +[12800/16000] [L1: 0.0683] 53.3+0.0s +[14400/16000] [L1: 0.0680] 54.0+0.1s +[16000/16000] [L1: 0.0685] 53.3+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.827 (Best: 21.661 @epoch 8) +Forward: 34.59s + +Saving... +Total: 35.08s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0635] 52.9+0.9s +[3200/16000] [L1: 0.0754] 52.9+0.1s +[4800/16000] [L1: 0.0684] 53.7+0.1s +[6400/16000] [L1: 0.0673] 54.1+0.1s +[8000/16000] [L1: 0.0673] 53.1+0.1s +[9600/16000] [L1: 0.0689] 52.2+0.0s +[11200/16000] [L1: 0.0694] 54.1+0.1s +[12800/16000] [L1: 0.0697] 53.4+0.1s +[14400/16000] [L1: 0.0698] 53.6+0.1s +[16000/16000] [L1: 0.0684] 52.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.165 (Best: 21.661 @epoch 8) +Forward: 34.78s + +Saving... +Total: 35.29s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0637] 52.0+1.1s +[3200/16000] [L1: 0.0603] 53.2+0.1s +[4800/16000] [L1: 0.0603] 54.0+0.1s +[6400/16000] [L1: 0.0577] 53.1+0.1s +[8000/16000] [L1: 0.0568] 53.1+0.1s +[9600/16000] [L1: 0.0575] 53.0+0.1s +[11200/16000] [L1: 0.0600] 53.6+0.1s +[12800/16000] [L1: 0.0623] 52.7+0.1s +[14400/16000] [L1: 0.0631] 52.8+0.0s +[16000/16000] [L1: 0.0640] 52.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.186 (Best: 21.661 @epoch 8) +Forward: 34.77s + +Saving... +Total: 35.18s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0599] 52.2+1.0s +[3200/16000] [L1: 0.0611] 52.4+0.1s +[4800/16000] [L1: 0.0594] 53.9+0.1s +[6400/16000] [L1: 0.0604] 53.5+0.1s +[8000/16000] [L1: 0.0619] 51.8+0.1s +[9600/16000] [L1: 0.0593] 51.5+0.0s +[11200/16000] [L1: 0.0621] 51.2+0.0s +[12800/16000] [L1: 0.0617] 52.7+0.0s +[14400/16000] [L1: 0.0615] 53.6+0.1s +[16000/16000] [L1: 0.0605] 52.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.253 (Best: 21.661 @epoch 8) +Forward: 34.65s + +Saving... +Total: 35.11s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0547] 52.9+0.9s +[3200/16000] [L1: 0.0609] 52.6+0.1s +[4800/16000] [L1: 0.0585] 53.9+0.1s +[6400/16000] [L1: 0.0591] 54.3+0.1s +[8000/16000] [L1: 0.0620] 53.7+0.0s +[9600/16000] [L1: 0.0606] 53.4+0.1s +[11200/16000] [L1: 0.0597] 54.0+0.1s +[12800/16000] [L1: 0.0591] 53.1+0.1s +[14400/16000] [L1: 0.0582] 53.4+0.1s +[16000/16000] [L1: 0.0584] 53.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.636 (Best: 21.661 @epoch 8) +Forward: 34.49s + +Saving... +Total: 34.96s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0686] 53.0+0.9s +[3200/16000] [L1: 0.0647] 52.8+0.1s +[4800/16000] [L1: 0.0650] 52.5+0.0s +[6400/16000] [L1: 0.0647] 53.6+0.0s +[8000/16000] [L1: 0.0659] 52.5+0.0s +[9600/16000] [L1: 0.0664] 53.2+0.0s +[11200/16000] [L1: 0.0660] 54.7+0.1s +[12800/16000] [L1: 0.0673] 54.3+0.1s +[14400/16000] [L1: 0.0675] 54.6+0.1s +[16000/16000] [L1: 0.0672] 54.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 15.325 (Best: 21.661 @epoch 8) +Forward: 34.28s + +Saving... +Total: 34.81s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0740] 51.9+1.0s +[3200/16000] [L1: 0.0618] 52.2+0.1s +[4800/16000] [L1: 0.0578] 51.8+0.0s +[6400/16000] [L1: 0.0611] 53.1+0.1s +[8000/16000] [L1: 0.0579] 52.8+0.1s +[9600/16000] [L1: 0.0577] 52.4+0.0s +[11200/16000] [L1: 0.0575] 51.7+0.0s +[12800/16000] [L1: 0.0586] 51.8+0.0s +[14400/16000] [L1: 0.0604] 51.9+0.1s +[16000/16000] [L1: 0.0608] 54.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.053 (Best: 21.661 @epoch 8) +Forward: 34.51s + +Saving... +Total: 35.00s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0573] 51.5+1.1s +[3200/16000] [L1: 0.0583] 52.2+0.1s +[4800/16000] [L1: 0.0540] 54.0+0.1s +[6400/16000] [L1: 0.0539] 54.0+0.1s +[8000/16000] [L1: 0.0594] 53.4+0.1s +[9600/16000] [L1: 0.0583] 53.9+0.0s +[11200/16000] [L1: 0.0585] 54.2+0.1s +[12800/16000] [L1: 0.0593] 52.7+0.0s +[14400/16000] [L1: 0.0607] 52.3+0.1s +[16000/16000] [L1: 0.0604] 52.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 14.348 (Best: 21.661 @epoch 8) +Forward: 34.60s + +Saving... +Total: 35.08s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0729] 52.5+0.9s +[3200/16000] [L1: 0.0639] 52.8+0.1s +[4800/16000] [L1: 0.0626] 54.0+0.1s +[6400/16000] [L1: 0.0592] 52.2+0.1s +[8000/16000] [L1: 0.0618] 52.2+0.1s +[9600/16000] [L1: 0.0627] 53.7+0.1s +[11200/16000] [L1: 0.0603] 53.8+0.1s +[12800/16000] [L1: 0.0586] 54.0+0.1s +[14400/16000] [L1: 0.0584] 53.1+0.1s +[16000/16000] [L1: 0.0578] 51.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.409 (Best: 21.661 @epoch 8) +Forward: 34.72s + +Saving... +Total: 35.28s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0403] 52.8+0.9s +[3200/16000] [L1: 0.0421] 53.0+0.1s +[4800/16000] [L1: 0.0419] 52.7+0.1s +[6400/16000] [L1: 0.0432] 52.9+0.0s +[8000/16000] [L1: 0.0465] 52.9+0.1s +[9600/16000] [L1: 0.0507] 52.2+0.0s +[11200/16000] [L1: 0.0531] 52.6+0.0s +[12800/16000] [L1: 0.0541] 53.8+0.1s +[14400/16000] [L1: 0.0534] 53.9+0.1s +[16000/16000] [L1: 0.0525] 53.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 15.067 (Best: 21.661 @epoch 8) +Forward: 34.49s + +Saving... +Total: 35.02s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0625] 52.2+1.0s +[3200/16000] [L1: 0.0737] 52.4+0.1s +[4800/16000] [L1: 0.0724] 54.0+0.1s +[6400/16000] [L1: 0.0668] 54.2+0.1s +[8000/16000] [L1: 0.0658] 54.5+0.1s +[9600/16000] [L1: 0.0658] 54.1+0.1s +[11200/16000] [L1: 0.0638] 54.4+0.1s +[12800/16000] [L1: 0.0607] 53.7+0.1s +[14400/16000] [L1: 0.0589] 52.3+0.0s +[16000/16000] [L1: 0.0583] 52.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.386 (Best: 21.661 @epoch 8) +Forward: 34.82s + +Saving... +Total: 35.24s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0516] 52.8+0.9s +[3200/16000] [L1: 0.0530] 51.5+0.1s +[4800/16000] [L1: 0.0592] 54.4+0.1s +[6400/16000] [L1: 0.0552] 53.8+0.1s +[8000/16000] [L1: 0.0548] 53.1+0.0s +[9600/16000] [L1: 0.0587] 52.0+0.0s +[11200/16000] [L1: 0.0570] 52.8+0.0s +[12800/16000] [L1: 0.0567] 51.3+0.0s +[14400/16000] [L1: 0.0592] 51.5+0.0s +[16000/16000] [L1: 0.0598] 51.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.358 (Best: 21.661 @epoch 8) +Forward: 35.05s + +Saving... +Total: 35.58s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0795] 53.3+1.1s +[3200/16000] [L1: 0.0763] 53.1+0.1s +[4800/16000] [L1: 0.0733] 52.7+0.1s +[6400/16000] [L1: 0.0696] 53.0+0.1s +[8000/16000] [L1: 0.0656] 53.5+0.1s +[9600/16000] [L1: 0.0634] 51.9+0.0s +[11200/16000] [L1: 0.0628] 51.3+0.0s +[12800/16000] [L1: 0.0641] 52.3+0.0s +[14400/16000] [L1: 0.0656] 53.3+0.0s +[16000/16000] [L1: 0.0651] 54.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 15.970 (Best: 21.661 @epoch 8) +Forward: 34.80s + +Saving... +Total: 35.43s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0519] 53.3+1.0s +[3200/16000] [L1: 0.0535] 52.0+0.1s +[4800/16000] [L1: 0.0553] 53.3+0.1s +[6400/16000] [L1: 0.0613] 53.9+0.1s +[8000/16000] [L1: 0.0657] 54.4+0.1s +[9600/16000] [L1: 0.0642] 54.5+0.1s +[11200/16000] [L1: 0.0631] 52.5+0.0s +[12800/16000] [L1: 0.0620] 54.1+0.1s +[14400/16000] [L1: 0.0613] 53.7+0.1s +[16000/16000] [L1: 0.0610] 52.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.205 (Best: 21.661 @epoch 8) +Forward: 35.01s + +Saving... +Total: 35.49s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0520] 51.3+1.0s +[3200/16000] [L1: 0.0509] 53.0+0.1s +[4800/16000] [L1: 0.0528] 54.3+0.1s +[6400/16000] [L1: 0.0526] 54.5+0.1s +[8000/16000] [L1: 0.0543] 54.5+0.1s +[9600/16000] [L1: 0.0544] 54.2+0.1s +[11200/16000] [L1: 0.0524] 54.3+0.0s +[12800/16000] [L1: 0.0513] 53.3+0.0s +[14400/16000] [L1: 0.0502] 54.7+0.1s +[16000/16000] [L1: 0.0519] 53.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.133 (Best: 21.661 @epoch 8) +Forward: 34.96s + +Saving... +Total: 35.35s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0757] 49.3+0.9s +[3200/16000] [L1: 0.0657] 50.0+0.0s +[4800/16000] [L1: 0.0594] 52.8+0.0s +[6400/16000] [L1: 0.0557] 53.5+0.1s +[8000/16000] [L1: 0.0578] 53.3+0.0s +[9600/16000] [L1: 0.0583] 53.4+0.1s +[11200/16000] [L1: 0.0612] 53.3+0.0s +[12800/16000] [L1: 0.0626] 52.9+0.0s +[14400/16000] [L1: 0.0650] 51.8+0.0s +[16000/16000] [L1: 0.0656] 52.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.193 (Best: 21.661 @epoch 8) +Forward: 35.12s + +Saving... +Total: 35.51s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0647] 52.7+0.9s +[3200/16000] [L1: 0.0699] 52.6+0.1s +[4800/16000] [L1: 0.0737] 54.6+0.1s +[6400/16000] [L1: 0.0726] 55.0+0.1s +[8000/16000] [L1: 0.0708] 53.9+0.1s +[9600/16000] [L1: 0.0683] 53.3+0.1s +[11200/16000] [L1: 0.0694] 54.5+0.1s +[12800/16000] [L1: 0.0699] 54.2+0.1s +[14400/16000] [L1: 0.0726] 54.2+0.1s +[16000/16000] [L1: 0.0714] 53.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.605 (Best: 21.661 @epoch 8) +Forward: 34.99s + +Saving... +Total: 35.63s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0449] 52.8+0.9s +[3200/16000] [L1: 0.0518] 51.5+0.0s +[4800/16000] [L1: 0.0515] 53.0+0.0s +[6400/16000] [L1: 0.0547] 53.4+0.0s +[8000/16000] [L1: 0.0569] 54.7+0.1s +[9600/16000] [L1: 0.0562] 55.3+0.1s +[11200/16000] [L1: 0.0550] 55.3+0.1s +[12800/16000] [L1: 0.0562] 54.3+0.1s +[14400/16000] [L1: 0.0548] 54.3+0.0s +[16000/16000] [L1: 0.0530] 55.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.914 (Best: 21.661 @epoch 8) +Forward: 35.07s + +Saving... +Total: 35.47s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0403] 53.0+0.9s +[3200/16000] [L1: 0.0577] 53.0+0.1s +[4800/16000] [L1: 0.0605] 52.6+0.1s +[6400/16000] [L1: 0.0557] 54.8+0.1s +[8000/16000] [L1: 0.0521] 52.3+0.0s +[9600/16000] [L1: 0.0500] 53.6+0.0s +[11200/16000] [L1: 0.0521] 53.1+0.0s +[12800/16000] [L1: 0.0535] 53.9+0.0s +[14400/16000] [L1: 0.0536] 54.7+0.1s +[16000/16000] [L1: 0.0531] 54.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 13.568 (Best: 21.661 @epoch 8) +Forward: 35.09s + +Saving... +Total: 35.50s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0717] 51.6+0.8s +[3200/16000] [L1: 0.0591] 53.2+0.1s +[4800/16000] [L1: 0.0521] 52.9+0.0s +[6400/16000] [L1: 0.0569] 52.8+0.0s +[8000/16000] [L1: 0.0617] 52.7+0.0s +[9600/16000] [L1: 0.0613] 52.7+0.0s +[11200/16000] [L1: 0.0631] 52.9+0.0s +[12800/16000] [L1: 0.0610] 53.2+0.1s +[14400/16000] [L1: 0.0588] 52.8+0.0s +[16000/16000] [L1: 0.0612] 52.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.830 (Best: 21.661 @epoch 8) +Forward: 35.09s + +Saving... +Total: 35.68s + diff --git 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a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/psnr_log.pt b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/psnr_log.pt new file mode 100644 index 0000000000000000000000000000000000000000..966fa435d22e7c2796ebfdeef1923717f923edb2 --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/psnr_log.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f11cd6752c04209bc6fc9ff54eeccca000282e1a8c586f24e6eb01eb52962e40 +size 1208 diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/test_DIV2K.pdf b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/test_DIV2K.pdf new file mode 100644 index 0000000000000000000000000000000000000000..95473f4e520b0f30aa20d47826dd76e0bdb0755d Binary files /dev/null and b/Demosaic/experiment/LAMBDA_DEMOSAIC20_R4_detach_RWEIGHT/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/config.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..3808bdab8e57473c118974a971d47566e7a2ff5f --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/config.txt @@ -0,0 +1,195 @@ +2020-11-06-00:02:27 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 80 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC80_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-00:12:27 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 80 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC80_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-12:19:34 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 4 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 80 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: LAMBDA_DEMOSAIC80_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/log.txt b/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..4c88d8504e89b8e009806edda3f10013d2a673c0 --- /dev/null +++ b/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/log.txt @@ -0,0 +1,1243 @@ +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (22): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (23): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (24): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (25): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (26): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (27): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (28): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (29): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (30): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (31): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (32): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (33): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (34): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (35): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (36): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (37): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (38): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (39): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (40): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (41): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (42): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (43): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (44): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (45): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (46): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (47): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (48): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (49): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (50): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (51): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (52): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (53): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (54): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (55): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (56): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (57): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (58): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (59): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (60): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (61): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (62): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (63): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (64): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (65): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (66): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (67): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (68): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (69): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (70): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (71): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (72): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (73): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (74): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (75): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (76): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (77): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (78): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (79): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (80): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (81): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (22): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (23): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (24): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (25): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (26): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (27): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (28): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (29): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (30): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (31): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (32): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (33): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (34): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (35): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (36): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (37): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (38): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (39): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (40): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (41): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (42): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (43): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (44): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (45): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (46): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (47): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (48): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (49): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (50): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (51): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (52): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (53): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (54): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (55): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (56): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (57): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (58): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (59): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (60): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (61): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (62): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (63): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (64): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (65): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (66): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (67): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (68): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (69): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (70): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (71): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (72): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (73): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (74): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (75): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (76): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (77): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (78): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (79): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (80): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (81): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.4549] 46.1+0.6s +[3200/16000] [L1: 0.2821] 39.6+0.1s +[4800/16000] [L1: 0.2133] 38.7+0.1s +[6400/16000] [L1: 0.1759] 39.5+0.1s +[8000/16000] [L1: 0.1517] 39.1+0.1s +[9600/16000] [L1: 0.1347] 39.0+0.1s +[11200/16000] [L1: 0.1222] 40.0+0.1s +[12800/16000] [L1: 0.1121] 39.6+0.1s +[14400/16000] [L1: 0.1039] 39.7+0.1s +[16000/16000] [L1: 0.0970] 39.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 26.937 (Best: 26.937 @epoch 1) +Forward: 15.04s + +Saving... +Total: 16.43s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0320] 38.7+1.1s +[3200/16000] [L1: 0.0324] 39.1+0.1s +[4800/16000] [L1: 0.0324] 38.9+0.1s +[6400/16000] [L1: 0.0311] 39.4+0.1s +[8000/16000] [L1: 0.0305] 39.4+0.1s +[9600/16000] [L1: 0.0299] 39.8+0.1s +[11200/16000] [L1: 0.0289] 38.2+0.0s +[12800/16000] [L1: 0.0282] 38.4+0.0s +[14400/16000] [L1: 0.0275] 36.6+0.0s +[16000/16000] [L1: 0.0269] 37.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.075 (Best: 32.075 @epoch 2) +Forward: 14.73s + +Saving... +Total: 15.97s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0225] 39.0+1.0s +[3200/16000] [L1: 0.0213] 39.4+0.1s +[4800/16000] [L1: 0.0208] 38.2+0.1s +[6400/16000] [L1: 0.0204] 39.6+0.1s +[8000/16000] [L1: 0.0202] 38.8+0.1s +[9600/16000] [L1: 0.0200] 37.8+0.1s +[11200/16000] [L1: 0.0197] 38.5+0.1s +[12800/16000] [L1: 0.0192] 38.8+0.0s +[14400/16000] [L1: 0.0189] 37.7+0.1s +[16000/16000] [L1: 0.0186] 38.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.785 (Best: 32.785 @epoch 3) +Forward: 14.74s + +Saving... +Total: 15.81s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0172] 39.8+1.0s +[3200/16000] [L1: 0.0166] 39.5+0.1s +[4800/16000] [L1: 0.0165] 38.3+0.1s +[6400/16000] [L1: 0.0160] 39.0+0.1s +[8000/16000] [L1: 0.0158] 39.4+0.1s +[9600/16000] [L1: 0.0155] 38.7+0.0s +[11200/16000] [L1: 0.0154] 38.6+0.0s +[12800/16000] [L1: 0.0152] 38.4+0.1s +[14400/16000] [L1: 0.0150] 38.6+0.1s +[16000/16000] [L1: 0.0149] 38.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.005 (Best: 34.005 @epoch 4) +Forward: 14.82s + +Saving... +Total: 15.75s + diff --git a/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/loss.pt b/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..0fa84de77f3d033ff542b374829e18a7126d2448 --- /dev/null +++ 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b/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/test_DIV2K.pdf new file mode 100644 index 0000000000000000000000000000000000000000..a8c65d0f3cd3fc74321bee03e89cbcf79e8ab6d5 Binary files /dev/null and b/Demosaic/experiment/LAMBDA_DEMOSAIC80_R1/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/PP_Evaluate_Demosaic_RGB_PSNR_SSIM.m b/Demosaic/experiment/PP_Evaluate_Demosaic_RGB_PSNR_SSIM.m new file mode 100644 index 0000000000000000000000000000000000000000..a13d84fa3fb623feb1110ed26c695db5a30f4873 --- /dev/null +++ b/Demosaic/experiment/PP_Evaluate_Demosaic_RGB_PSNR_SSIM.m @@ -0,0 +1,285 @@ +function PP_Evaluate_Demosaic_RGB_PSNR_SSIM() +clear all; close all; clc +tic +%% set path +path_HR = 'HQ'; +methods = {'Test_RNAN'}; +method_name = 'RNAN'; + +sigma_current = 1; +dataset = {'Kodak24', 'CBSD68', 'McMaster18', 'Urban100'}; +ext = {'*.jpg', '*.png', '*.bmp'}; +num_method = length(methods); +num_set = length(dataset); + + +for idx_method = 1:num_method + + for idx_set = 1%:num_set + fprintf('**********************\n'); + fprintf('Method_%d: %s; Set: %s\n', idx_method, methods{idx_method}, dataset{idx_set}); + for sigma = sigma_current + filepaths = []; + for idx_ext = 1:length(ext) + filepaths = cat(1, filepaths, dir(fullfile(path_HR, dataset{idx_set}, ['N', num2str(sigma)], ext{idx_ext}))); + end + PSNR_all = zeros(1, length(filepaths)); + SSIM_all = zeros(1, length(filepaths)); + for idx_im = 1:length(filepaths) + name_HR = filepaths(idx_im).name; + name_SR = strrep(name_HR, 'HQ', method_name); + im_HR = imread(fullfile(path_HR, dataset{idx_set}, ['N', num2str(sigma)], name_HR)); + im_SR = imread(fullfile([methods{idx_method}, '/results'], dataset{idx_set}, ['N', num2str(sigma)], name_SR)); + im_HR = double(im_HR); + im_SR = double(im_SR); + % calculate PSNR, SSIM + PSNR_all(idx_im) = csnr(im_HR, im_SR, 0, 0); + [~, SSIM_all(idx_im)] = Cal_PSNRSSIM(im_HR, im_SR, 0, 0); + fprintf('x%d %d %s: PSNR= %f SSIM= %f\n', sigma, idx_im, name_HR, PSNR_all(idx_im), SSIM_all(idx_im)); + + end + fprintf('--------Mean--------\n'); + fprintf('x%d: PSNR= %f, SSIM= %f \n', sigma, mean(PSNR_all), mean(SSIM_all)); + end + end +end + +end +function s=csnr(A,B,row,col) + +[n,m,ch]=size(A); + +if ch==1 + e=A-B; + e=e(row+1:n-row,col+1:m-col); + me=mean(mean(e.^2)); + s=10*log10(255^2/me); +else + e=A-B; + e=e(row+1:n-row,col+1:m-col,:); + e1=e(:,:,1);e2=e(:,:,2);e3=e(:,:,3); + me1=mean(mean(e1.^2)); + me2=mean(mean(e2.^2)); + me3=mean(mean(e3.^2)); + mse=(me1+me2+me3)/3; + s = 10*log10(255^2/mse); +% s(1)=10*log10(255^2/me1); +% s(2)=10*log10(255^2/me2); +% s(3)=10*log10(255^2/me3); +end +end + +function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col) + + +[n,m,ch]=size(B); +A = A(row+1:n-row,col+1:m-col,:); +B = B(row+1:n-row,col+1:m-col,:); +A=double(A); % Ground-truth +B=double(B); % + +e=A(:)-B(:); +mse=mean(e.^2); +psnr_cur=10*log10(255^2/mse); + +if ch==1 + [ssim_cur, ~] = ssim_index(A, B); +else + ssim_cur = (ssim_index(A(:,:,1), B(:,:,1)) + ssim_index(A(:,:,2), B(:,:,2)) + ssim_index(A(:,:,3), B(:,:,3)))/3; +end +end + +function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L) + +%======================================================================== +%SSIM Index, Version 1.0 +%Copyright(c) 2003 Zhou Wang +%All Rights Reserved. +% +%The author is with Howard Hughes Medical Institute, and Laboratory +%for Computational Vision at Center for Neural Science and Courant +%Institute of Mathematical Sciences, New York University. +% +%---------------------------------------------------------------------- +%Permission to use, copy, or modify this software and its documentation +%for educational and research purposes only and without fee is hereby +%granted, provided that this copyright notice and the original authors' +%names appear on all copies and supporting documentation. This program +%shall not be used, rewritten, or adapted as the basis of a commercial +%software or hardware product without first obtaining permission of the +%authors. The authors make no representations about the suitability of +%this software for any purpose. It is provided "as is" without express +%or implied warranty. +%---------------------------------------------------------------------- +% +%This is an implementation of the algorithm for calculating the +%Structural SIMilarity (SSIM) index between two images. Please refer +%to the following paper: +% +%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image +%quality assessment: From error measurement to structural similarity" +%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004. +% +%Kindly report any suggestions or corrections to zhouwang@ieee.org +% +%---------------------------------------------------------------------- +% +%Input : (1) img1: the first image being compared +% (2) img2: the second image being compared +% (3) K: constants in the SSIM index formula (see the above +% reference). defualt value: K = [0.01 0.03] +% (4) window: local window for statistics (see the above +% reference). default widnow is Gaussian given by +% window = fspecial('gaussian', 11, 1.5); +% (5) L: dynamic range of the images. default: L = 255 +% +%Output: (1) mssim: the mean SSIM index value between 2 images. +% If one of the images being compared is regarded as +% perfect quality, then mssim can be considered as the +% quality measure of the other image. +% If img1 = img2, then mssim = 1. +% (2) ssim_map: the SSIM index map of the test image. The map +% has a smaller size than the input images. The actual size: +% size(img1) - size(window) + 1. +% +%Default Usage: +% Given 2 test images img1 and img2, whose dynamic range is 0-255 +% +% [mssim ssim_map] = ssim_index(img1, img2); +% +%Advanced Usage: +% User defined parameters. For example +% +% K = [0.05 0.05]; +% window = ones(8); +% L = 100; +% [mssim ssim_map] = ssim_index(img1, img2, K, window, L); +% +%See the results: +% +% mssim %Gives the mssim value +% imshow(max(0, ssim_map).^4) %Shows the SSIM index map +% +%======================================================================== + + +if (nargin < 2 || nargin > 5) + ssim_index = -Inf; + ssim_map = -Inf; + return; +end + +if (size(img1) ~= size(img2)) + ssim_index = -Inf; + ssim_map = -Inf; + return; +end + +[M N] = size(img1); + +if (nargin == 2) + if ((M < 11) || (N < 11)) + ssim_index = -Inf; + ssim_map = -Inf; + return + end + window = fspecial('gaussian', 11, 1.5); % + K(1) = 0.01; % default settings + K(2) = 0.03; % + L = 255; % +end + +if (nargin == 3) + if ((M < 11) || (N < 11)) + ssim_index = -Inf; + ssim_map = -Inf; + return + end + window = fspecial('gaussian', 11, 1.5); + L = 255; + if (length(K) == 2) + if (K(1) < 0 || K(2) < 0) + ssim_index = -Inf; + ssim_map = -Inf; + return; + end + else + ssim_index = -Inf; + ssim_map = -Inf; + return; + end +end + +if (nargin == 4) + [H W] = size(window); + if ((H*W) < 4 || (H > M) || (W > N)) + ssim_index = -Inf; + ssim_map = -Inf; + return + end + L = 255; + if (length(K) == 2) + if (K(1) < 0 || K(2) < 0) + ssim_index = -Inf; + ssim_map = -Inf; + return; + end + else + ssim_index = -Inf; + ssim_map = -Inf; + return; + end +end + +if (nargin == 5) + [H W] = size(window); + if ((H*W) < 4 || (H > M) || (W > N)) + ssim_index = -Inf; + ssim_map = -Inf; + return + end + if (length(K) == 2) + if (K(1) < 0 || K(2) < 0) + ssim_index = -Inf; + ssim_map = -Inf; + return; + end + else + ssim_index = -Inf; + ssim_map = -Inf; + return; + end +end + +C1 = (K(1)*L)^2; +C2 = (K(2)*L)^2; +window = window/sum(sum(window)); +img1 = double(img1); +img2 = double(img2); + +mu1 = filter2(window, img1, 'valid'); +mu2 = filter2(window, img2, 'valid'); +mu1_sq = mu1.*mu1; +mu2_sq = mu2.*mu2; +mu1_mu2 = mu1.*mu2; +sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq; +sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq; +sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2; + +if (C1 > 0 & C2 > 0) + ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2)); +else + numerator1 = 2*mu1_mu2 + C1; + numerator2 = 2*sigma12 + C2; + denominator1 = mu1_sq + mu2_sq + C1; + denominator2 = sigma1_sq + sigma2_sq + C2; + ssim_map = ones(size(mu1)); + index = (denominator1.*denominator2 > 0); + ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index)); + index = (denominator1 ~= 0) & (denominator2 == 0); + ssim_map(index) = numerator1(index)./denominator1(index); +end + +mssim = mean2(ssim_map); + +end \ No newline at end of file diff --git a/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/config.txt b/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..7efd8aebb53257b7e2ea016cdf6d6762608586dc --- /dev/null +++ b/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/config.txt @@ -0,0 +1,68 @@ +2020-11-11-00:36:13 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETSINGLE +act: relu +pre_train: . +extend: . +n_resblocks: 10 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +normalization: instance +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +decay_gamma: 0.8 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFTSINGLE_DEMOSAIC20_R1 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/log.txt b/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..3ac1ada966c9a5cc8ac599d62275e66868d78c73 --- /dev/null +++ b/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/log.txt @@ -0,0 +1,4153 @@ +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) + (norm_v): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1048] 19.3+0.7s +[3200/16000] [L1: 0.0751] 15.0+0.1s +[4800/16000] [L1: 0.0610] 14.3+0.0s +[6400/16000] [L1: 0.0528] 14.0+0.0s +[8000/16000] [L1: 0.0475] 14.5+0.0s +[9600/16000] [L1: 0.0431] 15.0+0.1s +[11200/16000] [L1: 0.0401] 15.2+0.1s +[12800/16000] [L1: 0.0377] 14.6+0.0s +[14400/16000] [L1: 0.0355] 14.6+0.1s +[16000/16000] [L1: 0.0337] 14.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.105 (Best: 31.105 @epoch 1) +Forward: 9.51s + +Saving... +Total: 10.27s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0170] 14.2+0.7s +[3200/16000] [L1: 0.0174] 14.9+0.0s +[4800/16000] [L1: 0.0169] 13.8+0.0s +[6400/16000] [L1: 0.0166] 14.3+0.0s +[8000/16000] [L1: 0.0163] 14.8+0.0s +[9600/16000] [L1: 0.0160] 14.4+0.0s +[11200/16000] [L1: 0.0157] 14.4+0.0s +[12800/16000] [L1: 0.0157] 14.1+0.0s +[14400/16000] [L1: 0.0155] 14.1+0.0s +[16000/16000] [L1: 0.0153] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.587 (Best: 32.587 @epoch 2) +Forward: 9.39s + +Saving... +Total: 10.15s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 15.2+0.8s +[3200/16000] [L1: 0.0136] 14.9+0.0s +[4800/16000] [L1: 0.0134] 14.2+0.0s +[6400/16000] [L1: 0.0133] 14.8+0.0s +[8000/16000] [L1: 0.0130] 15.2+0.1s +[9600/16000] [L1: 0.0129] 15.7+0.0s +[11200/16000] [L1: 0.0129] 15.2+0.0s +[12800/16000] [L1: 0.0128] 15.3+0.0s +[14400/16000] [L1: 0.0128] 14.6+0.0s +[16000/16000] [L1: 0.0127] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.897 (Best: 33.897 @epoch 3) +Forward: 9.43s + +Saving... +Total: 10.13s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0120] 15.7+0.8s +[3200/16000] [L1: 0.0116] 13.9+0.0s +[4800/16000] [L1: 0.0118] 14.7+0.0s +[6400/16000] [L1: 0.0118] 15.2+0.0s +[8000/16000] [L1: 0.0116] 15.8+0.1s +[9600/16000] [L1: 0.0117] 14.1+0.0s +[11200/16000] [L1: 0.0116] 14.3+0.0s +[12800/16000] [L1: 0.0114] 14.5+0.0s +[14400/16000] [L1: 0.0113] 15.1+0.0s +[16000/16000] [L1: 0.0113] 14.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.298 (Best: 35.298 @epoch 4) +Forward: 9.33s + +Saving... +Total: 9.89s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0106] 14.9+1.0s +[3200/16000] [L1: 0.0107] 15.3+0.1s +[4800/16000] [L1: 0.0108] 15.0+0.1s +[6400/16000] [L1: 0.0109] 15.8+0.1s +[8000/16000] [L1: 0.0109] 14.8+0.0s +[9600/16000] [L1: 0.0108] 15.7+0.0s +[11200/16000] [L1: 0.0110] 14.9+0.0s +[12800/16000] [L1: 0.0109] 13.9+0.0s +[14400/16000] [L1: 0.0109] 15.5+0.0s +[16000/16000] [L1: 0.0108] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.137 (Best: 35.298 @epoch 4) +Forward: 9.28s + +Saving... +Total: 9.78s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0104] 15.2+0.8s +[3200/16000] [L1: 0.0106] 15.7+0.1s +[4800/16000] [L1: 0.0107] 15.3+0.1s +[6400/16000] [L1: 0.0108] 15.7+0.1s +[8000/16000] [L1: 0.0106] 14.7+0.0s +[9600/16000] [L1: 0.0104] 15.0+0.0s +[11200/16000] [L1: 0.0104] 14.3+0.0s +[12800/16000] [L1: 0.0105] 13.9+0.0s +[14400/16000] [L1: 0.0104] 14.7+0.0s +[16000/16000] [L1: 0.0103] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.994 (Best: 35.994 @epoch 6) +Forward: 9.28s + +Saving... +Total: 10.02s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0100] 14.4+0.8s +[3200/16000] [L1: 0.0101] 14.4+0.0s +[4800/16000] [L1: 0.0099] 14.8+0.0s +[6400/16000] [L1: 0.0099] 14.8+0.0s +[8000/16000] [L1: 0.0097] 14.8+0.0s +[9600/16000] [L1: 0.0097] 15.2+0.0s +[11200/16000] [L1: 0.0097] 15.5+0.0s +[12800/16000] [L1: 0.0098] 14.5+0.0s +[14400/16000] [L1: 0.0098] 15.0+0.0s +[16000/16000] [L1: 0.0097] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.089 (Best: 36.089 @epoch 7) +Forward: 9.33s + +Saving... +Total: 9.81s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0093] 14.7+0.9s +[3200/16000] [L1: 0.0096] 16.0+0.0s +[4800/16000] [L1: 0.0095] 15.9+0.1s +[6400/16000] [L1: 0.0095] 15.9+0.0s +[8000/16000] [L1: 0.0094] 14.7+0.0s +[9600/16000] [L1: 0.0095] 15.4+0.0s +[11200/16000] [L1: 0.0096] 15.0+0.0s +[12800/16000] [L1: 0.0095] 15.3+0.0s +[14400/16000] [L1: 0.0095] 15.8+0.0s +[16000/16000] [L1: 0.0095] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.482 (Best: 36.482 @epoch 8) +Forward: 9.51s + +Saving... +Total: 10.13s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0088] 15.1+0.8s +[3200/16000] [L1: 0.0088] 14.2+0.0s +[4800/16000] [L1: 0.0090] 15.7+0.0s +[6400/16000] [L1: 0.0091] 15.4+0.0s +[8000/16000] [L1: 0.0092] 13.7+0.0s +[9600/16000] [L1: 0.0092] 14.9+0.0s +[11200/16000] [L1: 0.0091] 15.4+0.1s +[12800/16000] [L1: 0.0091] 15.1+0.0s +[14400/16000] [L1: 0.0091] 14.1+0.0s +[16000/16000] [L1: 0.0091] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.703 (Best: 36.703 @epoch 9) +Forward: 9.46s + +Saving... +Total: 10.01s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0091] 15.8+0.7s +[3200/16000] [L1: 0.0090] 15.6+0.0s +[4800/16000] [L1: 0.0088] 15.3+0.0s +[6400/16000] [L1: 0.0088] 13.7+0.0s +[8000/16000] [L1: 0.0089] 15.2+0.0s +[9600/16000] [L1: 0.0089] 15.9+0.1s +[11200/16000] [L1: 0.0089] 16.0+0.1s +[12800/16000] [L1: 0.0089] 15.5+0.0s +[14400/16000] [L1: 0.0089] 15.7+0.0s +[16000/16000] [L1: 0.0089] 14.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.007 (Best: 37.007 @epoch 10) +Forward: 9.43s + +Saving... +Total: 9.89s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0091] 15.7+0.8s +[3200/16000] [L1: 0.0088] 15.6+0.1s +[4800/16000] [L1: 0.0088] 15.5+0.1s +[6400/16000] [L1: 0.0089] 15.5+0.1s +[8000/16000] [L1: 0.0088] 15.3+0.1s +[9600/16000] [L1: 0.0088] 15.6+0.1s +[11200/16000] [L1: 0.0088] 15.3+0.1s +[12800/16000] [L1: 0.0088] 15.1+0.0s +[14400/16000] [L1: 0.0088] 15.2+0.0s +[16000/16000] [L1: 0.0088] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.552 (Best: 37.007 @epoch 10) +Forward: 9.46s + +Saving... +Total: 9.92s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0089] 15.5+0.9s +[3200/16000] [L1: 0.0086] 14.7+0.0s +[4800/16000] [L1: 0.0086] 15.4+0.0s +[6400/16000] [L1: 0.0085] 13.8+0.0s +[8000/16000] [L1: 0.0084] 14.9+0.0s +[9600/16000] [L1: 0.0084] 14.7+0.0s +[11200/16000] [L1: 0.0084] 15.6+0.0s +[12800/16000] [L1: 0.0083] 15.6+0.0s +[14400/16000] [L1: 0.0084] 15.1+0.0s +[16000/16000] [L1: 0.0084] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.518 (Best: 37.518 @epoch 12) +Forward: 9.50s + +Saving... +Total: 9.93s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0079] 14.8+0.8s +[3200/16000] [L1: 0.0084] 14.5+0.0s +[4800/16000] [L1: 0.0083] 15.7+0.0s +[6400/16000] [L1: 0.0083] 15.4+0.1s +[8000/16000] [L1: 0.0083] 13.7+0.0s +[9600/16000] [L1: 0.0082] 14.0+0.0s +[11200/16000] [L1: 0.0083] 15.5+0.0s +[12800/16000] [L1: 0.0083] 15.7+0.0s +[14400/16000] [L1: 0.0083] 15.6+0.0s +[16000/16000] [L1: 0.0082] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.767 (Best: 37.767 @epoch 13) +Forward: 9.38s + +Saving... +Total: 9.90s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0082] 14.7+0.9s +[3200/16000] [L1: 0.0079] 14.2+0.0s +[4800/16000] [L1: 0.0079] 14.6+0.0s +[6400/16000] [L1: 0.0078] 14.7+0.0s +[8000/16000] [L1: 0.0078] 14.5+0.0s +[9600/16000] [L1: 0.0077] 15.7+0.1s +[11200/16000] [L1: 0.0078] 15.4+0.0s +[12800/16000] [L1: 0.0078] 15.6+0.0s +[14400/16000] [L1: 0.0078] 14.9+0.0s +[16000/16000] [L1: 0.0078] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.866 (Best: 37.866 @epoch 14) +Forward: 9.27s + +Saving... +Total: 9.82s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0078] 15.4+0.8s +[3200/16000] [L1: 0.0080] 15.3+0.0s +[4800/16000] [L1: 0.0079] 14.4+0.0s +[6400/16000] [L1: 0.0078] 15.2+0.0s +[8000/16000] [L1: 0.0079] 15.5+0.0s +[9600/16000] [L1: 0.0079] 14.7+0.0s +[11200/16000] [L1: 0.0078] 14.8+0.0s +[12800/16000] [L1: 0.0078] 14.0+0.0s +[14400/16000] [L1: 0.0078] 15.9+0.0s +[16000/16000] [L1: 0.0078] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.085 (Best: 38.085 @epoch 15) +Forward: 9.37s + +Saving... +Total: 9.82s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0075] 15.6+0.7s +[3200/16000] [L1: 0.0074] 13.9+0.0s +[4800/16000] [L1: 0.0074] 15.5+0.0s +[6400/16000] [L1: 0.0080] 15.4+0.0s +[8000/16000] [L1: 0.0082] 14.5+0.0s +[9600/16000] [L1: 0.0082] 14.9+0.0s +[11200/16000] [L1: 0.0081] 14.7+0.0s +[12800/16000] [L1: 0.0080] 15.8+0.0s +[14400/16000] [L1: 0.0079] 15.4+0.0s +[16000/16000] [L1: 0.0079] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.206 (Best: 38.206 @epoch 16) +Forward: 9.47s + +Saving... +Total: 9.91s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0080] 15.8+0.8s +[3200/16000] [L1: 0.0078] 14.5+0.0s +[4800/16000] [L1: 0.0076] 14.0+0.0s +[6400/16000] [L1: 0.0076] 14.4+0.0s +[8000/16000] [L1: 0.0076] 14.7+0.0s +[9600/16000] [L1: 0.0076] 15.4+0.1s +[11200/16000] [L1: 0.0076] 14.5+0.0s +[12800/16000] [L1: 0.0075] 15.6+0.1s +[14400/16000] [L1: 0.0075] 15.6+0.0s +[16000/16000] [L1: 0.0075] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.166 (Best: 38.206 @epoch 16) +Forward: 9.29s + +Saving... +Total: 9.82s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0075] 15.1+0.7s +[3200/16000] [L1: 0.0075] 15.8+0.1s +[4800/16000] [L1: 0.0075] 14.4+0.0s +[6400/16000] [L1: 0.0076] 14.2+0.0s +[8000/16000] [L1: 0.0076] 14.9+0.1s +[9600/16000] [L1: 0.0075] 15.9+0.1s +[11200/16000] [L1: 0.0075] 15.7+0.1s +[12800/16000] [L1: 0.0075] 13.9+0.0s +[14400/16000] [L1: 0.0075] 14.8+0.0s +[16000/16000] [L1: 0.0075] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.590 (Best: 38.590 @epoch 18) +Forward: 9.40s + +Saving... +Total: 9.96s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0074] 15.5+0.8s +[3200/16000] [L1: 0.0072] 15.0+0.1s +[4800/16000] [L1: 0.0072] 16.0+0.1s +[6400/16000] [L1: 0.0073] 14.7+0.0s +[8000/16000] [L1: 0.0072] 13.9+0.0s +[9600/16000] [L1: 0.0073] 14.5+0.0s +[11200/16000] [L1: 0.0073] 14.6+0.0s +[12800/16000] [L1: 0.0074] 15.8+0.0s +[14400/16000] [L1: 0.0074] 15.8+0.1s +[16000/16000] [L1: 0.0074] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.724 (Best: 38.724 @epoch 19) +Forward: 9.36s + +Saving... +Total: 9.78s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0068] 16.0+0.7s +[3200/16000] [L1: 0.0069] 15.3+0.1s +[4800/16000] [L1: 0.0070] 14.7+0.1s +[6400/16000] [L1: 0.0071] 14.7+0.1s +[8000/16000] [L1: 0.0071] 14.5+0.0s +[9600/16000] [L1: 0.0071] 14.9+0.0s +[11200/16000] [L1: 0.0071] 14.4+0.0s +[12800/16000] [L1: 0.0071] 15.6+0.0s +[14400/16000] [L1: 0.0071] 15.5+0.0s +[16000/16000] [L1: 0.0071] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.848 (Best: 38.848 @epoch 20) +Forward: 9.23s + +Saving... +Total: 9.69s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0073] 15.3+0.9s +[3200/16000] [L1: 0.0071] 14.7+0.0s +[4800/16000] [L1: 0.0071] 15.5+0.1s +[6400/16000] [L1: 0.0071] 14.4+0.1s +[8000/16000] [L1: 0.0072] 15.5+0.1s +[9600/16000] [L1: 0.0071] 15.7+0.1s +[11200/16000] [L1: 0.0071] 15.2+0.1s +[12800/16000] [L1: 0.0071] 15.5+0.1s +[14400/16000] [L1: 0.0071] 15.3+0.1s +[16000/16000] [L1: 0.0072] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.860 (Best: 38.860 @epoch 21) +Forward: 9.33s + +Saving... +Total: 9.84s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0067] 15.0+0.8s +[3200/16000] [L1: 0.0070] 15.5+0.1s +[4800/16000] [L1: 0.0071] 15.7+0.1s +[6400/16000] [L1: 0.0070] 14.9+0.0s +[8000/16000] [L1: 0.0070] 15.1+0.1s +[9600/16000] [L1: 0.0070] 14.1+0.0s +[11200/16000] [L1: 0.0070] 14.0+0.0s +[12800/16000] [L1: 0.0070] 15.6+0.1s +[14400/16000] [L1: 0.0070] 14.9+0.1s +[16000/16000] [L1: 0.0070] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.919 (Best: 38.919 @epoch 22) +Forward: 9.54s + +Saving... +Total: 10.08s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0069] 14.7+0.8s +[3200/16000] [L1: 0.0069] 15.1+0.1s +[4800/16000] [L1: 0.0072] 14.6+0.0s +[6400/16000] [L1: 0.0072] 14.8+0.1s +[8000/16000] [L1: 0.0071] 15.2+0.1s +[9600/16000] [L1: 0.0071] 15.3+0.1s +[11200/16000] [L1: 0.0071] 15.5+0.1s +[12800/16000] [L1: 0.0070] 14.0+0.0s +[14400/16000] [L1: 0.0070] 14.7+0.0s +[16000/16000] [L1: 0.0070] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.851 (Best: 38.919 @epoch 22) +Forward: 9.29s + +Saving... +Total: 9.75s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0069] 15.7+0.8s +[3200/16000] [L1: 0.0070] 15.2+0.1s +[4800/16000] [L1: 0.0068] 15.4+0.1s +[6400/16000] [L1: 0.0067] 15.0+0.0s +[8000/16000] [L1: 0.0068] 14.9+0.0s +[9600/16000] [L1: 0.0067] 15.1+0.0s +[11200/16000] [L1: 0.0067] 14.8+0.0s +[12800/16000] [L1: 0.0068] 15.4+0.0s +[14400/16000] [L1: 0.0067] 14.6+0.0s +[16000/16000] [L1: 0.0068] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.985 (Best: 38.985 @epoch 24) +Forward: 9.38s + +Saving... +Total: 9.86s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0066] 14.0+0.7s +[3200/16000] [L1: 0.0066] 14.9+0.0s +[4800/16000] [L1: 0.0066] 15.2+0.1s +[6400/16000] [L1: 0.0065] 15.4+0.1s +[8000/16000] [L1: 0.0066] 14.8+0.0s +[9600/16000] [L1: 0.0066] 15.4+0.0s +[11200/16000] [L1: 0.0066] 15.4+0.0s +[12800/16000] [L1: 0.0067] 14.6+0.0s +[14400/16000] [L1: 0.0067] 15.8+0.0s +[16000/16000] [L1: 0.0067] 14.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.081 (Best: 39.081 @epoch 25) +Forward: 9.36s + +Saving... +Total: 9.81s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0069] 15.4+0.8s +[3200/16000] [L1: 0.0067] 15.5+0.0s +[4800/16000] [L1: 0.0067] 14.4+0.0s +[6400/16000] [L1: 0.0066] 14.6+0.0s +[8000/16000] [L1: 0.0066] 14.6+0.0s +[9600/16000] [L1: 0.0067] 15.3+0.1s +[11200/16000] [L1: 0.0067] 15.7+0.1s +[12800/16000] [L1: 0.0067] 14.4+0.0s +[14400/16000] [L1: 0.0067] 14.9+0.0s +[16000/16000] [L1: 0.0067] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.121 (Best: 39.121 @epoch 26) +Forward: 9.33s + +Saving... +Total: 9.85s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0066] 15.5+0.7s +[3200/16000] [L1: 0.0067] 15.7+0.1s +[4800/16000] [L1: 0.0067] 15.9+0.1s +[6400/16000] [L1: 0.0066] 15.5+0.1s +[8000/16000] [L1: 0.0066] 15.1+0.1s +[9600/16000] [L1: 0.0067] 14.9+0.0s +[11200/16000] [L1: 0.0066] 14.8+0.0s +[12800/16000] [L1: 0.0066] 14.9+0.0s +[14400/16000] [L1: 0.0066] 15.7+0.0s +[16000/16000] [L1: 0.0066] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.238 (Best: 39.238 @epoch 27) +Forward: 9.35s + +Saving... +Total: 10.01s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0065] 14.9+0.9s +[3200/16000] [L1: 0.0065] 15.4+0.1s +[4800/16000] [L1: 0.0064] 14.1+0.0s +[6400/16000] [L1: 0.0064] 15.6+0.0s +[8000/16000] [L1: 0.0064] 15.2+0.1s +[9600/16000] [L1: 0.0064] 15.4+0.0s +[11200/16000] [L1: 0.0064] 14.8+0.0s +[12800/16000] [L1: 0.0064] 15.0+0.1s +[14400/16000] [L1: 0.0064] 14.8+0.0s +[16000/16000] [L1: 0.0064] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.416 (Best: 39.416 @epoch 28) +Forward: 9.32s + +Saving... +Total: 9.87s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0067] 15.9+0.9s +[3200/16000] [L1: 0.0066] 13.9+0.0s +[4800/16000] [L1: 0.0065] 14.3+0.0s +[6400/16000] [L1: 0.0064] 14.6+0.0s +[8000/16000] [L1: 0.0064] 15.6+0.1s +[9600/16000] [L1: 0.0064] 14.6+0.0s +[11200/16000] [L1: 0.0064] 15.0+0.1s +[12800/16000] [L1: 0.0064] 15.2+0.1s +[14400/16000] [L1: 0.0064] 15.3+0.0s +[16000/16000] [L1: 0.0064] 14.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.340 (Best: 39.416 @epoch 28) +Forward: 9.42s + +Saving... +Total: 9.87s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0066] 15.0+0.7s +[3200/16000] [L1: 0.0066] 14.6+0.0s +[4800/16000] [L1: 0.0065] 15.0+0.1s +[6400/16000] [L1: 0.0065] 14.4+0.0s +[8000/16000] [L1: 0.0066] 15.6+0.1s +[9600/16000] [L1: 0.0066] 14.8+0.1s +[11200/16000] [L1: 0.0065] 14.8+0.1s +[12800/16000] [L1: 0.0065] 15.1+0.0s +[14400/16000] [L1: 0.0065] 14.1+0.0s +[16000/16000] [L1: 0.0065] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.372 (Best: 39.416 @epoch 28) +Forward: 9.34s + +Saving... +Total: 9.84s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0064] 14.6+0.8s +[3200/16000] [L1: 0.0063] 15.5+0.1s +[4800/16000] [L1: 0.0064] 14.5+0.0s +[6400/16000] [L1: 0.0064] 15.2+0.1s +[8000/16000] [L1: 0.0064] 15.4+0.1s +[9600/16000] [L1: 0.0064] 14.2+0.0s +[11200/16000] [L1: 0.0064] 15.6+0.1s +[12800/16000] [L1: 0.0064] 15.4+0.0s +[14400/16000] [L1: 0.0064] 14.7+0.1s +[16000/16000] [L1: 0.0064] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.355 (Best: 39.416 @epoch 28) +Forward: 9.45s + +Saving... +Total: 10.06s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0064] 14.7+0.8s +[3200/16000] [L1: 0.0063] 15.1+0.0s +[4800/16000] [L1: 0.0063] 14.7+0.1s +[6400/16000] [L1: 0.0063] 14.6+0.1s +[8000/16000] [L1: 0.0063] 14.8+0.0s +[9600/16000] [L1: 0.0063] 15.3+0.1s +[11200/16000] [L1: 0.0063] 14.4+0.0s +[12800/16000] [L1: 0.0063] 14.9+0.0s +[14400/16000] [L1: 0.0063] 15.5+0.0s +[16000/16000] [L1: 0.0063] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.550 (Best: 39.550 @epoch 32) +Forward: 9.42s + +Saving... +Total: 9.86s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 14.5+0.7s +[3200/16000] [L1: 0.0062] 15.0+0.1s +[4800/16000] [L1: 0.0062] 15.5+0.1s +[6400/16000] [L1: 0.0062] 15.3+0.0s +[8000/16000] [L1: 0.0063] 15.0+0.0s +[9600/16000] [L1: 0.0100] 15.0+0.0s +[11200/16000] [L1: 0.0108] 14.8+0.1s +[12800/16000] [L1: 0.0106] 14.9+0.1s +[14400/16000] [L1: 0.0103] 15.5+0.1s +[16000/16000] [L1: 0.0101] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 37.018 (Best: 39.550 @epoch 32) +Forward: 9.40s + +Saving... +Total: 9.86s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0076] 14.1+0.7s +[3200/16000] [L1: 0.0076] 15.4+0.1s +[4800/16000] [L1: 0.0075] 14.9+0.1s +[6400/16000] [L1: 0.0075] 15.2+0.0s +[8000/16000] [L1: 0.0074] 15.3+0.1s +[9600/16000] [L1: 0.0073] 14.9+0.1s +[11200/16000] [L1: 0.0072] 15.3+0.1s +[12800/16000] [L1: 0.0072] 14.9+0.0s +[14400/16000] [L1: 0.0071] 15.4+0.0s +[16000/16000] [L1: 0.0071] 14.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.780 (Best: 39.550 @epoch 32) +Forward: 9.50s + +Saving... +Total: 9.95s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0065] 15.1+0.8s +[3200/16000] [L1: 0.0066] 15.4+0.1s +[4800/16000] [L1: 0.0066] 15.7+0.1s +[6400/16000] [L1: 0.0066] 15.9+0.1s +[8000/16000] [L1: 0.0066] 15.4+0.1s +[9600/16000] [L1: 0.0067] 14.7+0.1s +[11200/16000] [L1: 0.0066] 15.6+0.0s +[12800/16000] [L1: 0.0066] 15.1+0.0s +[14400/16000] [L1: 0.0066] 15.4+0.0s +[16000/16000] [L1: 0.0065] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.216 (Best: 39.550 @epoch 32) +Forward: 9.31s + +Saving... +Total: 9.79s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0065] 15.3+0.9s +[3200/16000] [L1: 0.0065] 14.8+0.0s +[4800/16000] [L1: 0.0065] 14.0+0.0s +[6400/16000] [L1: 0.0064] 14.3+0.0s +[8000/16000] [L1: 0.0064] 14.7+0.0s +[9600/16000] [L1: 0.0065] 14.7+0.0s +[11200/16000] [L1: 0.0064] 14.7+0.0s +[12800/16000] [L1: 0.0064] 15.3+0.1s +[14400/16000] [L1: 0.0064] 15.5+0.1s +[16000/16000] [L1: 0.0064] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.439 (Best: 39.550 @epoch 32) +Forward: 9.30s + +Saving... +Total: 9.79s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0064] 14.9+0.9s +[3200/16000] [L1: 0.0063] 14.9+0.0s +[4800/16000] [L1: 0.0063] 14.9+0.0s +[6400/16000] [L1: 0.0063] 15.6+0.0s +[8000/16000] [L1: 0.0063] 15.7+0.1s +[9600/16000] [L1: 0.0063] 15.9+0.1s +[11200/16000] [L1: 0.0063] 15.2+0.1s +[12800/16000] [L1: 0.0063] 15.3+0.1s +[14400/16000] [L1: 0.0063] 15.2+0.1s +[16000/16000] [L1: 0.0063] 15.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.538 (Best: 39.550 @epoch 32) +Forward: 9.29s + +Saving... +Total: 9.75s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0063] 15.8+0.8s +[3200/16000] [L1: 0.0062] 14.5+0.0s +[4800/16000] [L1: 0.0062] 15.1+0.0s +[6400/16000] [L1: 0.0063] 15.5+0.1s +[8000/16000] [L1: 0.0063] 15.9+0.1s +[9600/16000] [L1: 0.0062] 15.4+0.0s +[11200/16000] [L1: 0.0062] 15.6+0.1s +[12800/16000] [L1: 0.0062] 15.3+0.1s +[14400/16000] [L1: 0.0062] 15.3+0.1s +[16000/16000] [L1: 0.0062] 14.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.348 (Best: 39.550 @epoch 32) +Forward: 9.36s + +Saving... +Total: 9.85s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0064] 15.3+0.8s +[3200/16000] [L1: 0.0063] 15.8+0.1s +[4800/16000] [L1: 0.0064] 14.6+0.1s +[6400/16000] [L1: 0.0063] 14.3+0.0s +[8000/16000] [L1: 0.0063] 15.1+0.0s +[9600/16000] [L1: 0.0063] 14.8+0.1s +[11200/16000] [L1: 0.0063] 14.9+0.0s +[12800/16000] [L1: 0.0063] 15.9+0.1s +[14400/16000] [L1: 0.0062] 15.7+0.1s +[16000/16000] [L1: 0.0062] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.610 (Best: 39.610 @epoch 39) +Forward: 9.31s + +Saving... +Total: 9.83s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0063] 14.8+0.8s +[3200/16000] [L1: 0.0062] 14.0+0.0s +[4800/16000] [L1: 0.0062] 15.0+0.0s +[6400/16000] [L1: 0.0062] 14.6+0.0s +[8000/16000] [L1: 0.0062] 14.4+0.0s +[9600/16000] [L1: 0.0061] 14.4+0.0s +[11200/16000] [L1: 0.0061] 15.4+0.0s +[12800/16000] [L1: 0.0061] 15.3+0.0s +[14400/16000] [L1: 0.0061] 15.3+0.0s +[16000/16000] [L1: 0.0061] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.768 (Best: 39.768 @epoch 40) +Forward: 9.35s + +Saving... +Total: 9.93s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0061] 15.0+0.7s +[3200/16000] [L1: 0.0062] 14.6+0.1s +[4800/16000] [L1: 0.0062] 15.6+0.1s +[6400/16000] [L1: 0.0061] 15.4+0.1s +[8000/16000] [L1: 0.0062] 14.9+0.0s +[9600/16000] [L1: 0.0061] 15.4+0.0s +[11200/16000] [L1: 0.0061] 14.1+0.0s +[12800/16000] [L1: 0.0062] 14.7+0.0s +[14400/16000] [L1: 0.0062] 15.9+0.0s +[16000/16000] [L1: 0.0062] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.718 (Best: 39.768 @epoch 40) +Forward: 9.33s + +Saving... +Total: 9.84s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0062] 14.9+0.7s +[3200/16000] [L1: 0.0062] 15.7+0.1s +[4800/16000] [L1: 0.0061] 15.9+0.1s +[6400/16000] [L1: 0.0062] 14.5+0.1s +[8000/16000] [L1: 0.0062] 16.0+0.1s +[9600/16000] [L1: 0.0062] 15.8+0.1s +[11200/16000] [L1: 0.0062] 15.2+0.0s +[12800/16000] [L1: 0.0062] 15.0+0.0s +[14400/16000] [L1: 0.0061] 15.0+0.0s +[16000/16000] [L1: 0.0061] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.808 (Best: 39.808 @epoch 42) +Forward: 9.28s + +Saving... +Total: 9.74s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0061] 15.4+0.7s +[3200/16000] [L1: 0.0060] 14.9+0.1s +[4800/16000] [L1: 0.0060] 14.6+0.0s +[6400/16000] [L1: 0.0060] 15.1+0.0s +[8000/16000] [L1: 0.0061] 14.6+0.0s +[9600/16000] [L1: 0.0061] 15.3+0.0s +[11200/16000] [L1: 0.0061] 14.0+0.0s +[12800/16000] [L1: 0.0061] 13.9+0.0s +[14400/16000] [L1: 0.0061] 14.6+0.0s +[16000/16000] [L1: 0.0061] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.830 (Best: 39.830 @epoch 43) +Forward: 9.38s + +Saving... +Total: 9.83s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0060] 14.8+0.7s +[3200/16000] [L1: 0.0060] 14.2+0.0s +[4800/16000] [L1: 0.0061] 13.9+0.0s +[6400/16000] [L1: 0.0060] 15.1+0.1s +[8000/16000] [L1: 0.0060] 14.2+0.0s +[9600/16000] [L1: 0.0060] 15.8+0.1s +[11200/16000] [L1: 0.0060] 14.5+0.0s +[12800/16000] [L1: 0.0060] 14.9+0.0s +[14400/16000] [L1: 0.0060] 15.6+0.1s +[16000/16000] [L1: 0.0060] 15.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.626 (Best: 39.830 @epoch 43) +Forward: 9.50s + +Saving... +Total: 10.05s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 15.6+0.8s +[3200/16000] [L1: 0.0059] 15.6+0.1s +[4800/16000] [L1: 0.0060] 14.9+0.1s +[6400/16000] [L1: 0.0061] 14.4+0.0s +[8000/16000] [L1: 0.0060] 15.1+0.1s +[9600/16000] [L1: 0.0060] 14.9+0.0s +[11200/16000] [L1: 0.0060] 15.9+0.0s +[12800/16000] [L1: 0.0060] 15.4+0.0s +[14400/16000] [L1: 0.0068] 15.1+0.0s +[16000/16000] [L1: 0.0076] 13.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.506 (Best: 39.830 @epoch 43) +Forward: 9.42s + +Saving... +Total: 9.84s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0074] 15.7+0.8s +[3200/16000] [L1: 0.0071] 14.9+0.0s +[4800/16000] [L1: 0.0069] 14.2+0.0s +[6400/16000] [L1: 0.0067] 14.0+0.0s +[8000/16000] [L1: 0.0066] 15.3+0.1s +[9600/16000] [L1: 0.0066] 15.3+0.0s +[11200/16000] [L1: 0.0065] 15.7+0.0s +[12800/16000] [L1: 0.0065] 14.7+0.0s +[14400/16000] [L1: 0.0064] 14.9+0.0s +[16000/16000] [L1: 0.0064] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.722 (Best: 39.830 @epoch 43) +Forward: 9.37s + +Saving... +Total: 9.80s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 15.5+0.9s +[3200/16000] [L1: 0.0060] 15.7+0.1s +[4800/16000] [L1: 0.0059] 15.7+0.1s +[6400/16000] [L1: 0.0060] 15.0+0.1s +[8000/16000] [L1: 0.0060] 14.8+0.0s +[9600/16000] [L1: 0.0060] 15.6+0.1s +[11200/16000] [L1: 0.0060] 16.1+0.1s +[12800/16000] [L1: 0.0060] 15.5+0.1s +[14400/16000] [L1: 0.0060] 15.7+0.1s +[16000/16000] [L1: 0.0060] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.832 (Best: 39.832 @epoch 47) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 14.3+0.7s +[3200/16000] [L1: 0.0060] 14.6+0.0s +[4800/16000] [L1: 0.0059] 15.3+0.0s +[6400/16000] [L1: 0.0059] 14.4+0.0s +[8000/16000] [L1: 0.0059] 15.0+0.0s +[9600/16000] [L1: 0.0059] 15.1+0.0s +[11200/16000] [L1: 0.0059] 14.8+0.0s +[12800/16000] [L1: 0.0059] 15.7+0.1s +[14400/16000] [L1: 0.0059] 15.2+0.0s +[16000/16000] [L1: 0.0059] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.472 (Best: 39.832 @epoch 47) +Forward: 9.37s + +Saving... +Total: 9.86s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 15.5+0.8s +[3200/16000] [L1: 0.0059] 14.4+0.0s +[4800/16000] [L1: 0.0060] 15.0+0.0s +[6400/16000] [L1: 0.0059] 15.6+0.1s +[8000/16000] [L1: 0.0059] 15.8+0.1s +[9600/16000] [L1: 0.0059] 15.4+0.1s +[11200/16000] [L1: 0.0059] 16.0+0.1s +[12800/16000] [L1: 0.0059] 15.7+0.1s +[14400/16000] [L1: 0.0059] 15.6+0.0s +[16000/16000] [L1: 0.0059] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.695 (Best: 39.832 @epoch 47) +Forward: 9.28s + +Saving... +Total: 9.79s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0062] 15.7+0.8s +[3200/16000] [L1: 0.0060] 14.7+0.0s +[4800/16000] [L1: 0.0060] 14.8+0.1s +[6400/16000] [L1: 0.0060] 14.6+0.0s +[8000/16000] [L1: 0.0059] 15.3+0.1s +[9600/16000] [L1: 0.0059] 14.4+0.0s +[11200/16000] [L1: 0.0059] 14.9+0.0s +[12800/16000] [L1: 0.0059] 15.0+0.0s +[14400/16000] [L1: 0.0059] 14.8+0.0s +[16000/16000] [L1: 0.0059] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.871 (Best: 39.871 @epoch 50) +Forward: 9.23s + +Saving... +Total: 9.66s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0060] 15.5+0.9s +[3200/16000] [L1: 0.0060] 14.5+0.1s +[4800/16000] [L1: 0.0060] 14.6+0.1s +[6400/16000] [L1: 0.0060] 14.3+0.1s +[8000/16000] [L1: 0.0060] 14.6+0.1s +[9600/16000] [L1: 0.0060] 15.2+0.1s +[11200/16000] [L1: 0.0059] 15.6+0.0s +[12800/16000] [L1: 0.0059] 15.4+0.1s +[14400/16000] [L1: 0.0059] 15.2+0.1s +[16000/16000] [L1: 0.0059] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.871 (Best: 39.871 @epoch 50) +Forward: 9.41s + +Saving... +Total: 9.84s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0058] 15.8+0.8s +[3200/16000] [L1: 0.0059] 15.4+0.1s +[4800/16000] [L1: 0.0059] 14.8+0.0s +[6400/16000] [L1: 0.0059] 14.7+0.0s +[8000/16000] [L1: 0.0059] 14.4+0.0s +[9600/16000] [L1: 0.0059] 15.7+0.0s +[11200/16000] [L1: 0.0059] 15.9+0.1s +[12800/16000] [L1: 0.0058] 15.4+0.0s +[14400/16000] [L1: 0.0058] 14.6+0.0s +[16000/16000] [L1: 0.0058] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.854 (Best: 39.871 @epoch 50) +Forward: 9.43s + +Saving... +Total: 9.87s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0058] 14.4+0.8s +[3200/16000] [L1: 0.0058] 15.5+0.1s +[4800/16000] [L1: 0.0059] 14.6+0.1s +[6400/16000] [L1: 0.0059] 14.3+0.0s +[8000/16000] [L1: 0.0059] 15.2+0.0s +[9600/16000] [L1: 0.0059] 14.5+0.0s +[11200/16000] [L1: 0.0059] 15.0+0.1s +[12800/16000] [L1: 0.0059] 15.2+0.1s +[14400/16000] [L1: 0.0059] 14.6+0.1s +[16000/16000] [L1: 0.0059] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.003 (Best: 40.003 @epoch 53) +Forward: 9.44s + +Saving... +Total: 9.90s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0060] 15.6+0.7s +[3200/16000] [L1: 0.0058] 15.0+0.1s +[4800/16000] [L1: 0.0058] 14.3+0.0s +[6400/16000] [L1: 0.0058] 13.9+0.0s +[8000/16000] [L1: 0.0058] 15.4+0.1s +[9600/16000] [L1: 0.0058] 15.1+0.1s +[11200/16000] [L1: 0.0058] 14.4+0.0s +[12800/16000] [L1: 0.0058] 14.5+0.0s +[14400/16000] [L1: 0.0058] 14.4+0.0s +[16000/16000] [L1: 0.0058] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.772 (Best: 40.003 @epoch 53) +Forward: 9.32s + +Saving... +Total: 9.74s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.0+0.8s +[3200/16000] [L1: 0.0058] 14.7+0.0s +[4800/16000] [L1: 0.0058] 15.5+0.0s +[6400/16000] [L1: 0.0058] 15.6+0.1s +[8000/16000] [L1: 0.0058] 14.1+0.0s +[9600/16000] [L1: 0.0058] 15.8+0.0s +[11200/16000] [L1: 0.0057] 15.7+0.1s +[12800/16000] [L1: 0.0057] 15.4+0.0s +[14400/16000] [L1: 0.0057] 16.0+0.0s +[16000/16000] [L1: 0.0058] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.855 (Best: 40.003 @epoch 53) +Forward: 9.39s + +Saving... +Total: 9.83s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 14.9+0.8s +[3200/16000] [L1: 0.0058] 15.1+0.1s +[4800/16000] [L1: 0.0058] 15.8+0.1s +[6400/16000] [L1: 0.0058] 14.3+0.0s +[8000/16000] [L1: 0.0059] 15.6+0.1s +[9600/16000] [L1: 0.0058] 14.5+0.0s +[11200/16000] [L1: 0.0058] 14.8+0.0s +[12800/16000] [L1: 0.0058] 14.4+0.0s +[14400/16000] [L1: 0.0057] 15.9+0.1s +[16000/16000] [L1: 0.0058] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.043 (Best: 40.043 @epoch 56) +Forward: 9.27s + +Saving... +Total: 9.78s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.7+0.9s +[3200/16000] [L1: 0.0056] 15.4+0.1s +[4800/16000] [L1: 0.0056] 15.2+0.1s +[6400/16000] [L1: 0.0056] 15.7+0.1s +[8000/16000] [L1: 0.0057] 15.7+0.1s +[9600/16000] [L1: 0.0057] 15.2+0.1s +[11200/16000] [L1: 0.0057] 14.8+0.1s +[12800/16000] [L1: 0.0057] 14.8+0.0s +[14400/16000] [L1: 0.0057] 15.2+0.0s +[16000/16000] [L1: 0.0057] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.017 (Best: 40.043 @epoch 56) +Forward: 9.45s + +Saving... +Total: 9.89s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.2+0.7s +[3200/16000] [L1: 0.0056] 14.5+0.1s +[4800/16000] [L1: 0.0057] 14.9+0.0s +[6400/16000] [L1: 0.0057] 15.6+0.1s +[8000/16000] [L1: 0.0057] 15.6+0.1s +[9600/16000] [L1: 0.0057] 15.1+0.1s +[11200/16000] [L1: 0.0057] 15.6+0.0s +[12800/16000] [L1: 0.0057] 14.4+0.0s +[14400/16000] [L1: 0.0057] 15.0+0.0s +[16000/16000] [L1: 0.0057] 14.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.077 (Best: 40.077 @epoch 58) +Forward: 9.28s + +Saving... +Total: 9.85s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.0+0.8s +[3200/16000] [L1: 0.0057] 14.4+0.0s +[4800/16000] [L1: 0.0057] 14.1+0.0s +[6400/16000] [L1: 0.0056] 14.7+0.0s +[8000/16000] [L1: 0.0056] 16.1+0.1s +[9600/16000] [L1: 0.0056] 15.4+0.0s +[11200/16000] [L1: 0.0056] 15.1+0.0s +[12800/16000] [L1: 0.0056] 15.4+0.0s +[14400/16000] [L1: 0.0056] 15.1+0.0s +[16000/16000] [L1: 0.0056] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.021 (Best: 40.077 @epoch 58) +Forward: 9.54s + +Saving... +Total: 10.01s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 14.9+0.9s +[3200/16000] [L1: 0.0056] 14.5+0.0s +[4800/16000] [L1: 0.0056] 14.9+0.1s +[6400/16000] [L1: 0.0056] 15.2+0.0s +[8000/16000] [L1: 0.0057] 15.0+0.1s +[9600/16000] [L1: 0.0057] 15.4+0.1s +[11200/16000] [L1: 0.0057] 14.7+0.1s +[12800/16000] [L1: 0.0057] 15.2+0.0s +[14400/16000] [L1: 0.0057] 15.2+0.0s +[16000/16000] [L1: 0.0057] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.843 (Best: 40.077 @epoch 58) +Forward: 9.45s + +Saving... +Total: 9.89s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0058] 15.0+0.8s +[3200/16000] [L1: 0.0057] 15.3+0.1s +[4800/16000] [L1: 0.0056] 14.6+0.0s +[6400/16000] [L1: 0.0056] 15.6+0.1s +[8000/16000] [L1: 0.0056] 14.9+0.1s +[9600/16000] [L1: 0.0056] 15.7+0.1s +[11200/16000] [L1: 0.0056] 15.9+0.1s +[12800/16000] [L1: 0.0056] 16.0+0.1s +[14400/16000] [L1: 0.0056] 14.9+0.0s +[16000/16000] [L1: 0.0056] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.923 (Best: 40.077 @epoch 58) +Forward: 9.26s + +Saving... +Total: 9.69s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.6+0.7s +[3200/16000] [L1: 0.0055] 15.7+0.1s +[4800/16000] [L1: 0.0055] 15.2+0.1s +[6400/16000] [L1: 0.0095] 14.7+0.0s +[8000/16000] [L1: 0.0094] 15.0+0.1s +[9600/16000] [L1: 0.0090] 15.2+0.0s +[11200/16000] [L1: 0.0086] 14.2+0.0s +[12800/16000] [L1: 0.0083] 14.5+0.0s +[14400/16000] [L1: 0.0081] 15.5+0.1s +[16000/16000] [L1: 0.0079] 16.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.522 (Best: 40.077 @epoch 58) +Forward: 9.36s + +Saving... +Total: 10.33s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0059] 14.9+0.9s +[3200/16000] [L1: 0.0059] 15.7+0.1s +[4800/16000] [L1: 0.0059] 15.6+0.1s +[6400/16000] [L1: 0.0058] 14.3+0.0s +[8000/16000] [L1: 0.0058] 15.1+0.0s +[9600/16000] [L1: 0.0058] 15.2+0.0s +[11200/16000] [L1: 0.0058] 16.1+0.1s +[12800/16000] [L1: 0.0058] 15.1+0.0s +[14400/16000] [L1: 0.0058] 14.9+0.0s +[16000/16000] [L1: 0.0058] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.832 (Best: 40.077 @epoch 58) +Forward: 9.32s + +Saving... +Total: 9.81s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.3+0.9s +[3200/16000] [L1: 0.0057] 15.6+0.1s +[4800/16000] [L1: 0.0058] 14.9+0.1s +[6400/16000] [L1: 0.0058] 16.1+0.1s +[8000/16000] [L1: 0.0058] 14.3+0.0s +[9600/16000] [L1: 0.0058] 15.9+0.1s +[11200/16000] [L1: 0.0057] 15.4+0.1s +[12800/16000] [L1: 0.0057] 15.2+0.1s +[14400/16000] [L1: 0.0057] 14.7+0.1s +[16000/16000] [L1: 0.0057] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.949 (Best: 40.077 @epoch 58) +Forward: 9.26s + +Saving... +Total: 9.74s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.1+0.9s +[3200/16000] [L1: 0.0057] 14.8+0.0s +[4800/16000] [L1: 0.0056] 15.1+0.1s +[6400/16000] [L1: 0.0056] 15.5+0.1s +[8000/16000] [L1: 0.0056] 15.1+0.1s +[9600/16000] [L1: 0.0056] 14.8+0.0s +[11200/16000] [L1: 0.0056] 15.7+0.1s +[12800/16000] [L1: 0.0056] 15.3+0.1s +[14400/16000] [L1: 0.0056] 15.6+0.1s +[16000/16000] [L1: 0.0056] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.147 (Best: 40.147 @epoch 65) +Forward: 9.28s + +Saving... +Total: 9.76s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 15.1+0.8s +[3200/16000] [L1: 0.0056] 14.4+0.1s +[4800/16000] [L1: 0.0056] 15.8+0.1s +[6400/16000] [L1: 0.0055] 14.8+0.1s +[8000/16000] [L1: 0.0055] 15.2+0.0s +[9600/16000] [L1: 0.0056] 14.6+0.0s +[11200/16000] [L1: 0.0056] 15.7+0.1s +[12800/16000] [L1: 0.0056] 15.6+0.1s +[14400/16000] [L1: 0.0056] 15.6+0.1s +[16000/16000] [L1: 0.0056] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.157 (Best: 40.157 @epoch 66) +Forward: 9.29s + +Saving... +Total: 9.74s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 13.9+0.7s +[3200/16000] [L1: 0.0056] 14.0+0.0s +[4800/16000] [L1: 0.0057] 15.4+0.1s +[6400/16000] [L1: 0.0057] 15.9+0.1s +[8000/16000] [L1: 0.0056] 14.7+0.0s +[9600/16000] [L1: 0.0056] 15.5+0.1s +[11200/16000] [L1: 0.0056] 14.2+0.0s +[12800/16000] [L1: 0.0056] 14.8+0.0s +[14400/16000] [L1: 0.0056] 15.3+0.0s +[16000/16000] [L1: 0.0056] 14.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.823 (Best: 40.157 @epoch 66) +Forward: 9.37s + +Saving... +Total: 9.89s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 14.9+0.8s +[3200/16000] [L1: 0.0057] 14.8+0.1s +[4800/16000] [L1: 0.0056] 14.5+0.0s +[6400/16000] [L1: 0.0056] 15.2+0.1s +[8000/16000] [L1: 0.0056] 13.8+0.0s +[9600/16000] [L1: 0.0056] 14.0+0.0s +[11200/16000] [L1: 0.0056] 15.5+0.0s +[12800/16000] [L1: 0.0056] 14.4+0.0s +[14400/16000] [L1: 0.0056] 14.3+0.0s +[16000/16000] [L1: 0.0056] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.941 (Best: 40.157 @epoch 66) +Forward: 9.36s + +Saving... +Total: 9.84s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.9+0.7s +[3200/16000] [L1: 0.0056] 14.7+0.1s +[4800/16000] [L1: 0.0056] 14.9+0.1s +[6400/16000] [L1: 0.0056] 14.9+0.1s +[8000/16000] [L1: 0.0056] 15.7+0.1s +[9600/16000] [L1: 0.0056] 15.5+0.1s +[11200/16000] [L1: 0.0056] 15.6+0.1s +[12800/16000] [L1: 0.0056] 14.9+0.0s +[14400/16000] [L1: 0.0056] 14.9+0.1s +[16000/16000] [L1: 0.0055] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.135 (Best: 40.157 @epoch 66) +Forward: 9.44s + +Saving... +Total: 9.92s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.5+0.8s +[3200/16000] [L1: 0.0053] 14.8+0.1s +[4800/16000] [L1: 0.0055] 15.1+0.1s +[6400/16000] [L1: 0.0055] 13.8+0.0s +[8000/16000] [L1: 0.0055] 14.9+0.1s +[9600/16000] [L1: 0.0055] 14.6+0.1s +[11200/16000] [L1: 0.0055] 15.5+0.1s +[12800/16000] [L1: 0.0055] 16.0+0.1s +[14400/16000] [L1: 0.0085] 15.6+0.1s +[16000/16000] [L1: 0.0086] 15.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 37.023 (Best: 40.157 @epoch 66) +Forward: 9.47s + +Saving... +Total: 10.00s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0078] 14.9+1.0s +[3200/16000] [L1: 0.0072] 15.7+0.1s +[4800/16000] [L1: 0.0069] 15.1+0.1s +[6400/16000] [L1: 0.0067] 15.5+0.1s +[8000/16000] [L1: 0.0066] 15.3+0.0s +[9600/16000] [L1: 0.0065] 15.8+0.1s +[11200/16000] [L1: 0.0064] 15.7+0.1s +[12800/16000] [L1: 0.0064] 15.3+0.1s +[14400/16000] [L1: 0.0063] 15.2+0.1s +[16000/16000] [L1: 0.0062] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.912 (Best: 40.157 @epoch 66) +Forward: 9.34s + +Saving... +Total: 9.79s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.6+0.7s +[3200/16000] [L1: 0.0056] 15.3+0.1s +[4800/16000] [L1: 0.0056] 14.7+0.0s +[6400/16000] [L1: 0.0056] 15.5+0.1s +[8000/16000] [L1: 0.0056] 15.8+0.1s +[9600/16000] [L1: 0.0057] 15.3+0.1s +[11200/16000] [L1: 0.0057] 14.4+0.0s +[12800/16000] [L1: 0.0057] 14.4+0.0s +[14400/16000] [L1: 0.0057] 14.4+0.0s +[16000/16000] [L1: 0.0057] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.161 (Best: 40.161 @epoch 72) +Forward: 9.55s + +Saving... +Total: 9.99s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.7+0.9s +[3200/16000] [L1: 0.0056] 15.7+0.1s +[4800/16000] [L1: 0.0056] 14.0+0.0s +[6400/16000] [L1: 0.0056] 14.8+0.0s +[8000/16000] [L1: 0.0056] 16.0+0.1s +[9600/16000] [L1: 0.0056] 15.7+0.1s +[11200/16000] [L1: 0.0056] 14.7+0.0s +[12800/16000] [L1: 0.0056] 14.6+0.0s +[14400/16000] [L1: 0.0056] 16.0+0.1s +[16000/16000] [L1: 0.0055] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.143 (Best: 40.161 @epoch 72) +Forward: 9.31s + +Saving... +Total: 9.74s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 14.7+0.8s +[3200/16000] [L1: 0.0056] 15.6+0.1s +[4800/16000] [L1: 0.0056] 15.6+0.1s +[6400/16000] [L1: 0.0056] 14.8+0.1s +[8000/16000] [L1: 0.0056] 15.9+0.1s +[9600/16000] [L1: 0.0056] 15.5+0.1s +[11200/16000] [L1: 0.0056] 15.2+0.1s +[12800/16000] [L1: 0.0056] 15.5+0.1s +[14400/16000] [L1: 0.0056] 15.7+0.1s +[16000/16000] [L1: 0.0056] 15.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 40.115 (Best: 40.161 @epoch 72) +Forward: 9.34s + +Saving... +Total: 9.84s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 15.7+0.8s +[3200/16000] [L1: 0.0056] 15.8+0.1s +[4800/16000] [L1: 0.0055] 14.6+0.0s +[6400/16000] [L1: 0.0056] 14.5+0.0s +[8000/16000] [L1: 0.0056] 15.3+0.1s +[9600/16000] [L1: 0.0056] 15.2+0.0s +[11200/16000] [L1: 0.0056] 14.2+0.0s +[12800/16000] [L1: 0.0056] 14.5+0.0s +[14400/16000] [L1: 0.0055] 15.5+0.1s +[16000/16000] [L1: 0.0055] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.278 (Best: 40.278 @epoch 75) +Forward: 9.42s + +Saving... +Total: 9.94s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 14.9+0.8s +[3200/16000] [L1: 0.0055] 13.9+0.0s +[4800/16000] [L1: 0.0055] 15.8+0.1s +[6400/16000] [L1: 0.0055] 15.4+0.1s +[8000/16000] [L1: 0.0055] 15.3+0.1s +[9600/16000] [L1: 0.0055] 15.2+0.0s +[11200/16000] [L1: 0.0055] 14.4+0.0s +[12800/16000] [L1: 0.0055] 14.6+0.0s +[14400/16000] [L1: 0.0055] 15.0+0.0s +[16000/16000] [L1: 0.0055] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.225 (Best: 40.278 @epoch 75) +Forward: 9.35s + +Saving... +Total: 9.90s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 16.0+0.8s +[3200/16000] [L1: 0.0055] 15.3+0.1s +[4800/16000] [L1: 0.0055] 15.8+0.1s +[6400/16000] [L1: 0.0054] 14.6+0.0s +[8000/16000] [L1: 0.0054] 14.7+0.0s +[9600/16000] [L1: 0.0054] 14.6+0.0s +[11200/16000] [L1: 0.0055] 15.7+0.1s +[12800/16000] [L1: 0.0055] 16.0+0.1s +[14400/16000] [L1: 0.0055] 15.7+0.0s +[16000/16000] [L1: 0.0055] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.257 (Best: 40.278 @epoch 75) +Forward: 9.42s + +Saving... +Total: 9.87s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 14.2+0.9s +[3200/16000] [L1: 0.0055] 15.5+0.1s +[4800/16000] [L1: 0.0056] 15.3+0.1s +[6400/16000] [L1: 0.0056] 15.1+0.1s +[8000/16000] [L1: 0.0055] 15.3+0.0s +[9600/16000] [L1: 0.0055] 14.8+0.0s +[11200/16000] [L1: 0.0055] 15.1+0.0s +[12800/16000] [L1: 0.0055] 15.1+0.0s +[14400/16000] [L1: 0.0055] 14.9+0.0s +[16000/16000] [L1: 0.0055] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.236 (Best: 40.278 @epoch 75) +Forward: 9.31s + +Saving... +Total: 9.87s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.8+0.8s +[3200/16000] [L1: 0.0054] 15.1+0.1s +[4800/16000] [L1: 0.0054] 15.1+0.0s +[6400/16000] [L1: 0.0054] 15.0+0.0s +[8000/16000] [L1: 0.0054] 15.6+0.1s +[9600/16000] [L1: 0.0055] 15.5+0.0s +[11200/16000] [L1: 0.0055] 14.1+0.0s +[12800/16000] [L1: 0.0055] 15.2+0.0s +[14400/16000] [L1: 0.0055] 15.7+0.1s +[16000/16000] [L1: 0.0055] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.247 (Best: 40.278 @epoch 75) +Forward: 9.37s + +Saving... +Total: 9.81s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.5+0.8s +[3200/16000] [L1: 0.0053] 15.8+0.1s +[4800/16000] [L1: 0.0054] 15.4+0.1s +[6400/16000] [L1: 0.0053] 15.2+0.1s +[8000/16000] [L1: 0.0053] 15.3+0.1s +[9600/16000] [L1: 0.0054] 15.0+0.0s +[11200/16000] [L1: 0.0054] 15.2+0.1s +[12800/16000] [L1: 0.0054] 14.9+0.1s +[14400/16000] [L1: 0.0054] 15.9+0.1s +[16000/16000] [L1: 0.0054] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 40.298 (Best: 40.298 @epoch 80) +Forward: 9.47s + +Saving... +Total: 10.06s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 14.7+0.8s +[3200/16000] [L1: 0.0055] 13.9+0.0s +[4800/16000] [L1: 0.0055] 15.7+0.1s +[6400/16000] [L1: 0.0055] 15.3+0.1s +[8000/16000] [L1: 0.0055] 15.2+0.0s +[9600/16000] [L1: 0.0054] 15.5+0.1s +[11200/16000] [L1: 0.0054] 15.9+0.0s +[12800/16000] [L1: 0.0054] 15.6+0.0s +[14400/16000] [L1: 0.0054] 15.2+0.0s +[16000/16000] [L1: 0.0054] 14.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.210 (Best: 40.298 @epoch 80) +Forward: 9.50s + +Saving... +Total: 10.03s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.7+0.9s +[3200/16000] [L1: 0.0054] 14.7+0.1s +[4800/16000] [L1: 0.0054] 13.9+0.0s +[6400/16000] [L1: 0.0054] 15.1+0.0s +[8000/16000] [L1: 0.0054] 14.6+0.0s +[9600/16000] [L1: 0.0054] 15.7+0.0s +[11200/16000] [L1: 0.0054] 15.0+0.0s +[12800/16000] [L1: 0.0063] 14.7+0.0s +[14400/16000] [L1: 0.0064] 14.8+0.0s +[16000/16000] [L1: 0.0064] 16.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.924 (Best: 40.298 @epoch 80) +Forward: 9.30s + +Saving... +Total: 9.76s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0060] 15.8+0.8s +[3200/16000] [L1: 0.0059] 14.3+0.0s +[4800/16000] [L1: 0.0058] 15.6+0.1s +[6400/16000] [L1: 0.0058] 15.0+0.0s +[8000/16000] [L1: 0.0057] 15.0+0.0s +[9600/16000] [L1: 0.0057] 14.7+0.0s +[11200/16000] [L1: 0.0057] 14.4+0.0s +[12800/16000] [L1: 0.0057] 15.1+0.1s +[14400/16000] [L1: 0.0056] 14.1+0.0s +[16000/16000] [L1: 0.0056] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.035 (Best: 40.298 @epoch 80) +Forward: 9.51s + +Saving... +Total: 9.96s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 14.9+0.8s +[3200/16000] [L1: 0.0055] 14.5+0.1s +[4800/16000] [L1: 0.0055] 15.4+0.1s +[6400/16000] [L1: 0.0055] 15.4+0.1s +[8000/16000] [L1: 0.0055] 14.3+0.0s +[9600/16000] [L1: 0.0055] 13.9+0.0s +[11200/16000] [L1: 0.0055] 15.1+0.0s +[12800/16000] [L1: 0.0055] 14.2+0.0s +[14400/16000] [L1: 0.0055] 15.8+0.1s +[16000/16000] [L1: 0.0055] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.458 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.73s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.5+0.9s +[3200/16000] [L1: 0.0055] 15.8+0.1s +[4800/16000] [L1: 0.0054] 15.6+0.1s +[6400/16000] [L1: 0.0054] 15.1+0.1s +[8000/16000] [L1: 0.0054] 15.2+0.1s +[9600/16000] [L1: 0.0054] 15.7+0.1s +[11200/16000] [L1: 0.0054] 15.5+0.0s +[12800/16000] [L1: 0.0054] 14.7+0.0s +[14400/16000] [L1: 0.0054] 15.1+0.0s +[16000/16000] [L1: 0.0054] 14.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.669 (Best: 40.298 @epoch 80) +Forward: 9.36s + +Saving... +Total: 9.78s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0056] 15.6+0.7s +[3200/16000] [L1: 0.0056] 15.8+0.1s +[4800/16000] [L1: 0.0055] 15.5+0.0s +[6400/16000] [L1: 0.0055] 15.8+0.1s +[8000/16000] [L1: 0.0055] 14.9+0.1s +[9600/16000] [L1: 0.0054] 15.0+0.0s +[11200/16000] [L1: 0.0055] 14.6+0.0s +[12800/16000] [L1: 0.0055] 15.0+0.0s +[14400/16000] [L1: 0.0055] 15.6+0.1s +[16000/16000] [L1: 0.0055] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.905 (Best: 40.298 @epoch 80) +Forward: 9.27s + +Saving... +Total: 9.74s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 15.4+1.6s +[3200/16000] [L1: 0.0054] 15.2+0.1s +[4800/16000] [L1: 0.0054] 14.6+0.0s +[6400/16000] [L1: 0.0054] 15.3+0.0s +[8000/16000] [L1: 0.0054] 15.1+0.1s +[9600/16000] [L1: 0.0054] 15.0+0.0s +[11200/16000] [L1: 0.0054] 15.1+0.0s +[12800/16000] [L1: 0.0054] 14.9+0.1s +[14400/16000] [L1: 0.0054] 14.0+0.0s +[16000/16000] [L1: 0.0054] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.838 (Best: 40.298 @epoch 80) +Forward: 9.46s + +Saving... +Total: 9.97s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0057] 15.6+0.9s +[3200/16000] [L1: 0.0056] 15.3+0.1s +[4800/16000] [L1: 0.0054] 15.6+0.1s +[6400/16000] [L1: 0.0054] 13.9+0.0s +[8000/16000] [L1: 0.0054] 15.0+0.0s +[9600/16000] [L1: 0.0054] 15.7+0.1s +[11200/16000] [L1: 0.0054] 15.0+0.0s +[12800/16000] [L1: 0.0054] 15.2+0.1s +[14400/16000] [L1: 0.0054] 14.5+0.0s +[16000/16000] [L1: 0.0054] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.562 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.74s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 16.0+0.9s +[3200/16000] [L1: 0.0055] 14.8+0.1s +[4800/16000] [L1: 0.0054] 15.7+0.1s +[6400/16000] [L1: 0.0054] 15.1+0.1s +[8000/16000] [L1: 0.0054] 15.5+0.1s +[9600/16000] [L1: 0.0054] 15.3+0.1s +[11200/16000] [L1: 0.0054] 15.3+0.1s +[12800/16000] [L1: 0.0054] 15.3+0.1s +[14400/16000] [L1: 0.0054] 14.9+0.0s +[16000/16000] [L1: 0.0054] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.594 (Best: 40.298 @epoch 80) +Forward: 9.36s + +Saving... +Total: 9.83s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.0+0.8s +[3200/16000] [L1: 0.0053] 15.6+0.1s +[4800/16000] [L1: 0.0053] 15.0+0.1s +[6400/16000] [L1: 0.0054] 14.5+0.0s +[8000/16000] [L1: 0.0053] 14.9+0.0s +[9600/16000] [L1: 0.0054] 15.3+0.1s +[11200/16000] [L1: 0.0054] 15.6+0.0s +[12800/16000] [L1: 0.0054] 15.8+0.1s +[14400/16000] [L1: 0.0054] 15.9+0.1s +[16000/16000] [L1: 0.0054] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.425 (Best: 40.298 @epoch 80) +Forward: 9.51s + +Saving... +Total: 10.05s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 15.0+0.8s +[3200/16000] [L1: 0.0053] 15.1+0.1s +[4800/16000] [L1: 0.0053] 15.0+0.1s +[6400/16000] [L1: 0.0053] 14.5+0.0s +[8000/16000] [L1: 0.0053] 13.9+0.0s +[9600/16000] [L1: 0.0053] 15.2+0.1s +[11200/16000] [L1: 0.0053] 14.9+0.0s +[12800/16000] [L1: 0.0053] 15.6+0.1s +[14400/16000] [L1: 0.0053] 14.8+0.0s +[16000/16000] [L1: 0.0053] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.233 (Best: 40.298 @epoch 80) +Forward: 9.48s + +Saving... +Total: 9.95s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 14.3+0.8s +[3200/16000] [L1: 0.0053] 14.1+0.0s +[4800/16000] [L1: 0.0053] 15.4+0.1s +[6400/16000] [L1: 0.0053] 14.4+0.0s +[8000/16000] [L1: 0.0054] 15.4+0.1s +[9600/16000] [L1: 0.0054] 15.8+0.0s +[11200/16000] [L1: 0.0054] 15.2+0.0s +[12800/16000] [L1: 0.0054] 15.8+0.1s +[14400/16000] [L1: 0.0054] 14.9+0.0s +[16000/16000] [L1: 0.0054] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.718 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.70s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0055] 16.1+0.8s +[3200/16000] [L1: 0.0054] 15.4+0.0s +[4800/16000] [L1: 0.0054] 13.6+0.0s +[6400/16000] [L1: 0.0054] 13.8+0.0s +[8000/16000] [L1: 0.0053] 14.2+0.0s +[9600/16000] [L1: 0.0053] 15.1+0.0s +[11200/16000] [L1: 0.0054] 14.0+0.0s +[12800/16000] [L1: 0.0054] 14.9+0.0s +[14400/16000] [L1: 0.0054] 14.6+0.0s +[16000/16000] [L1: 0.0054] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.007 (Best: 40.298 @epoch 80) +Forward: 9.43s + +Saving... +Total: 9.85s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.0+0.8s +[3200/16000] [L1: 0.0054] 14.9+0.0s +[4800/16000] [L1: 0.0053] 14.6+0.0s +[6400/16000] [L1: 0.0053] 15.4+0.0s +[8000/16000] [L1: 0.0053] 14.9+0.0s +[9600/16000] [L1: 0.0053] 15.8+0.0s +[11200/16000] [L1: 0.0053] 14.3+0.0s +[12800/16000] [L1: 0.0053] 15.4+0.0s +[14400/16000] [L1: 0.0053] 15.0+0.0s +[16000/16000] [L1: 0.0053] 15.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 38.841 (Best: 40.298 @epoch 80) +Forward: 9.42s + +Saving... +Total: 9.82s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 15.4+0.8s +[3200/16000] [L1: 0.0053] 15.4+0.1s +[4800/16000] [L1: 0.0053] 14.9+0.0s +[6400/16000] [L1: 0.0053] 15.0+0.0s +[8000/16000] [L1: 0.0053] 15.0+0.0s +[9600/16000] [L1: 0.0053] 15.5+0.1s +[11200/16000] [L1: 0.0053] 15.1+0.0s +[12800/16000] [L1: 0.0053] 15.3+0.1s +[14400/16000] [L1: 0.0053] 15.2+0.1s +[16000/16000] [L1: 0.0053] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.818 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.71s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.7+0.9s +[3200/16000] [L1: 0.0053] 15.6+0.1s +[4800/16000] [L1: 0.0053] 15.2+0.1s +[6400/16000] [L1: 0.0053] 14.6+0.0s +[8000/16000] [L1: 0.0053] 15.8+0.1s +[9600/16000] [L1: 0.0053] 15.9+0.1s +[11200/16000] [L1: 0.0053] 15.9+0.1s +[12800/16000] [L1: 0.0053] 15.5+0.1s +[14400/16000] [L1: 0.0053] 14.0+0.0s +[16000/16000] [L1: 0.0053] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.606 (Best: 40.298 @epoch 80) +Forward: 9.30s + +Saving... +Total: 9.68s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 16.1+0.8s +[3200/16000] [L1: 0.0051] 15.8+0.1s +[4800/16000] [L1: 0.0052] 15.5+0.0s +[6400/16000] [L1: 0.0052] 14.7+0.0s +[8000/16000] [L1: 0.0052] 14.9+0.0s +[9600/16000] [L1: 0.0052] 15.3+0.0s +[11200/16000] [L1: 0.0053] 14.1+0.0s +[12800/16000] [L1: 0.0053] 15.7+0.0s +[14400/16000] [L1: 0.0053] 15.5+0.0s +[16000/16000] [L1: 0.0053] 13.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.709 (Best: 40.298 @epoch 80) +Forward: 9.21s + +Saving... +Total: 9.63s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 14.5+0.8s +[3200/16000] [L1: 0.0052] 14.7+0.0s +[4800/16000] [L1: 0.0052] 13.9+0.0s +[6400/16000] [L1: 0.0053] 14.7+0.0s +[8000/16000] [L1: 0.0053] 16.0+0.1s +[9600/16000] [L1: 0.0053] 16.0+0.1s +[11200/16000] [L1: 0.0053] 14.5+0.0s +[12800/16000] [L1: 0.0053] 13.8+0.0s +[14400/16000] [L1: 0.0053] 13.9+0.0s +[16000/16000] [L1: 0.0053] 16.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.459 (Best: 40.298 @epoch 80) +Forward: 9.30s + +Saving... +Total: 10.21s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.2+0.8s +[3200/16000] [L1: 0.0053] 15.8+0.1s +[4800/16000] [L1: 0.0053] 15.9+0.1s +[6400/16000] [L1: 0.0053] 15.6+0.1s +[8000/16000] [L1: 0.0053] 14.0+0.0s +[9600/16000] [L1: 0.0053] 13.9+0.0s +[11200/16000] [L1: 0.0053] 14.9+0.0s +[12800/16000] [L1: 0.0053] 15.5+0.1s +[14400/16000] [L1: 0.0053] 14.8+0.0s +[16000/16000] [L1: 0.0053] 14.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.529 (Best: 40.298 @epoch 80) +Forward: 9.42s + +Saving... +Total: 9.88s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 14.5+0.8s +[3200/16000] [L1: 0.0054] 14.1+0.0s +[4800/16000] [L1: 0.0054] 14.6+0.0s +[6400/16000] [L1: 0.0053] 16.2+0.1s +[8000/16000] [L1: 0.0053] 15.4+0.1s +[9600/16000] [L1: 0.0053] 15.3+0.0s +[11200/16000] [L1: 0.0053] 15.9+0.1s +[12800/16000] [L1: 0.0053] 16.0+0.0s +[14400/16000] [L1: 0.0053] 15.0+0.0s +[16000/16000] [L1: 0.0053] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.681 (Best: 40.298 @epoch 80) +Forward: 9.26s + +Saving... +Total: 9.69s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.7+0.8s +[3200/16000] [L1: 0.0053] 15.9+0.1s +[4800/16000] [L1: 0.0053] 15.8+0.1s +[6400/16000] [L1: 0.0052] 15.8+0.1s +[8000/16000] [L1: 0.0053] 15.0+0.1s +[9600/16000] [L1: 0.0053] 14.8+0.0s +[11200/16000] [L1: 0.0052] 15.5+0.0s +[12800/16000] [L1: 0.0052] 15.9+0.0s +[14400/16000] [L1: 0.0052] 16.1+0.0s +[16000/16000] [L1: 0.0052] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.738 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.76s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 15.2+0.8s +[3200/16000] [L1: 0.0052] 15.4+0.1s +[4800/16000] [L1: 0.0051] 15.6+0.1s +[6400/16000] [L1: 0.0052] 15.8+0.1s +[8000/16000] [L1: 0.0052] 15.5+0.1s +[9600/16000] [L1: 0.0052] 15.2+0.1s +[11200/16000] [L1: 0.0052] 16.1+0.1s +[12800/16000] [L1: 0.0052] 15.3+0.0s +[14400/16000] [L1: 0.0052] 15.2+0.0s +[16000/16000] [L1: 0.0052] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.257 (Best: 40.298 @epoch 80) +Forward: 9.27s + +Saving... +Total: 9.70s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.6+0.8s +[3200/16000] [L1: 0.0053] 15.3+0.1s +[4800/16000] [L1: 0.0052] 14.8+0.1s +[6400/16000] [L1: 0.0052] 15.1+0.1s +[8000/16000] [L1: 0.0052] 14.9+0.1s +[9600/16000] [L1: 0.0053] 15.6+0.1s +[11200/16000] [L1: 0.0053] 15.5+0.0s +[12800/16000] [L1: 0.0053] 15.7+0.1s +[14400/16000] [L1: 0.0052] 15.4+0.0s +[16000/16000] [L1: 0.0052] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.537 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.82s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 15.6+0.8s +[3200/16000] [L1: 0.0052] 15.2+0.1s +[4800/16000] [L1: 0.0052] 15.3+0.1s +[6400/16000] [L1: 0.0052] 15.7+0.0s +[8000/16000] [L1: 0.0052] 16.0+0.1s +[9600/16000] [L1: 0.0052] 15.8+0.0s +[11200/16000] [L1: 0.0052] 15.8+0.1s +[12800/16000] [L1: 0.0052] 15.7+0.1s +[14400/16000] [L1: 0.0052] 15.7+0.0s +[16000/16000] [L1: 0.0052] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.339 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.79s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0054] 15.8+1.0s +[3200/16000] [L1: 0.0053] 14.8+0.0s +[4800/16000] [L1: 0.0052] 14.5+0.0s +[6400/16000] [L1: 0.0052] 14.0+0.0s +[8000/16000] [L1: 0.0053] 15.1+0.0s +[9600/16000] [L1: 0.0052] 16.0+0.1s +[11200/16000] [L1: 0.0052] 16.1+0.0s +[12800/16000] [L1: 0.0052] 16.1+0.1s +[14400/16000] [L1: 0.0052] 15.8+0.1s +[16000/16000] [L1: 0.0052] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.547 (Best: 40.298 @epoch 80) +Forward: 9.26s + +Saving... +Total: 9.76s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 14.8+0.9s +[3200/16000] [L1: 0.0051] 15.3+0.0s +[4800/16000] [L1: 0.0051] 15.1+0.1s +[6400/16000] [L1: 0.0051] 16.0+0.0s +[8000/16000] [L1: 0.0052] 14.6+0.0s +[9600/16000] [L1: 0.0052] 13.9+0.0s +[11200/16000] [L1: 0.0052] 13.9+0.0s +[12800/16000] [L1: 0.0052] 15.0+0.0s +[14400/16000] [L1: 0.0052] 14.3+0.0s +[16000/16000] [L1: 0.0052] 14.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.290 (Best: 40.298 @epoch 80) +Forward: 9.37s + +Saving... +Total: 9.87s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 15.9+0.9s +[3200/16000] [L1: 0.0052] 14.4+0.0s +[4800/16000] [L1: 0.0053] 14.7+0.0s +[6400/16000] [L1: 0.0053] 15.7+0.1s +[8000/16000] [L1: 0.0053] 14.7+0.0s +[9600/16000] [L1: 0.0053] 15.4+0.0s +[11200/16000] [L1: 0.0053] 14.6+0.1s +[12800/16000] [L1: 0.0053] 15.1+0.0s +[14400/16000] [L1: 0.0052] 14.2+0.0s +[16000/16000] [L1: 0.0053] 13.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.938 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.75s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.2+0.8s +[3200/16000] [L1: 0.0051] 15.0+0.0s +[4800/16000] [L1: 0.0051] 13.9+0.0s +[6400/16000] [L1: 0.0052] 15.2+0.1s +[8000/16000] [L1: 0.0051] 14.0+0.0s +[9600/16000] [L1: 0.0051] 15.3+0.0s +[11200/16000] [L1: 0.0051] 14.3+0.0s +[12800/16000] [L1: 0.0051] 16.0+0.0s +[14400/16000] [L1: 0.0051] 15.8+0.0s +[16000/16000] [L1: 0.0051] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.956 (Best: 40.298 @epoch 80) +Forward: 9.28s + +Saving... +Total: 9.86s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.9+0.8s +[3200/16000] [L1: 0.0052] 15.6+0.1s +[4800/16000] [L1: 0.0052] 13.9+0.0s +[6400/16000] [L1: 0.0052] 15.0+0.0s +[8000/16000] [L1: 0.0052] 15.4+0.0s +[9600/16000] [L1: 0.0052] 15.2+0.0s +[11200/16000] [L1: 0.0052] 15.2+0.0s +[12800/16000] [L1: 0.0052] 14.4+0.0s +[14400/16000] [L1: 0.0052] 15.5+0.0s +[16000/16000] [L1: 0.0052] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.036 (Best: 40.298 @epoch 80) +Forward: 9.37s + +Saving... +Total: 9.84s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 14.7+0.8s +[3200/16000] [L1: 0.0051] 14.2+0.0s +[4800/16000] [L1: 0.0052] 13.5+0.0s +[6400/16000] [L1: 0.0052] 15.7+0.0s +[8000/16000] [L1: 0.0052] 15.8+0.0s +[9600/16000] [L1: 0.0052] 15.9+0.0s +[11200/16000] [L1: 0.0052] 14.3+0.0s +[12800/16000] [L1: 0.0051] 15.3+0.0s +[14400/16000] [L1: 0.0051] 15.9+0.0s +[16000/16000] [L1: 0.0051] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.419 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.70s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.8+0.7s +[3200/16000] [L1: 0.0052] 14.0+0.0s +[4800/16000] [L1: 0.0052] 13.9+0.0s +[6400/16000] [L1: 0.0051] 15.3+0.0s +[8000/16000] [L1: 0.0052] 14.5+0.0s +[9600/16000] [L1: 0.0052] 15.6+0.0s +[11200/16000] [L1: 0.0052] 14.6+0.0s +[12800/16000] [L1: 0.0052] 15.8+0.0s +[14400/16000] [L1: 0.0052] 15.8+0.0s +[16000/16000] [L1: 0.0052] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.491 (Best: 40.298 @epoch 80) +Forward: 9.58s + +Saving... +Total: 10.04s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 14.1+0.8s +[3200/16000] [L1: 0.0052] 16.1+0.1s +[4800/16000] [L1: 0.0051] 14.5+0.0s +[6400/16000] [L1: 0.0052] 15.4+0.1s +[8000/16000] [L1: 0.0052] 14.6+0.1s +[9600/16000] [L1: 0.0052] 15.9+0.1s +[11200/16000] [L1: 0.0052] 15.8+0.1s +[12800/16000] [L1: 0.0052] 13.7+0.0s +[14400/16000] [L1: 0.0052] 13.8+0.0s +[16000/16000] [L1: 0.0051] 13.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.823 (Best: 40.298 @epoch 80) +Forward: 9.26s + +Saving... +Total: 9.69s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.0+0.7s +[3200/16000] [L1: 0.0051] 15.2+0.1s +[4800/16000] [L1: 0.0052] 14.5+0.0s +[6400/16000] [L1: 0.0052] 13.8+0.0s +[8000/16000] [L1: 0.0052] 14.0+0.0s +[9600/16000] [L1: 0.0052] 15.5+0.0s +[11200/16000] [L1: 0.0052] 16.2+0.1s +[12800/16000] [L1: 0.0052] 15.1+0.0s +[14400/16000] [L1: 0.0052] 15.5+0.0s +[16000/16000] [L1: 0.0052] 14.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.573 (Best: 40.298 @epoch 80) +Forward: 9.22s + +Saving... +Total: 9.67s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 15.6+0.8s +[3200/16000] [L1: 0.0051] 15.7+0.1s +[4800/16000] [L1: 0.0051] 14.9+0.1s +[6400/16000] [L1: 0.0051] 15.5+0.1s +[8000/16000] [L1: 0.0052] 14.9+0.0s +[9600/16000] [L1: 0.0051] 15.5+0.0s +[11200/16000] [L1: 0.0051] 15.5+0.0s +[12800/16000] [L1: 0.0051] 15.2+0.0s +[14400/16000] [L1: 0.0051] 15.4+0.1s +[16000/16000] [L1: 0.0051] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.903 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.78s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.4+0.7s +[3200/16000] [L1: 0.0050] 15.6+0.0s +[4800/16000] [L1: 0.0050] 15.4+0.0s +[6400/16000] [L1: 0.0050] 14.4+0.0s +[8000/16000] [L1: 0.0050] 14.6+0.0s +[9600/16000] [L1: 0.0050] 15.8+0.0s +[11200/16000] [L1: 0.0050] 15.2+0.1s +[12800/16000] [L1: 0.0050] 15.3+0.0s +[14400/16000] [L1: 0.0051] 14.8+0.0s +[16000/16000] [L1: 0.0051] 14.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.046 (Best: 40.298 @epoch 80) +Forward: 9.50s + +Saving... +Total: 9.93s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.1+0.7s +[3200/16000] [L1: 0.0051] 15.8+0.1s +[4800/16000] [L1: 0.0050] 15.1+0.0s +[6400/16000] [L1: 0.0051] 15.4+0.0s +[8000/16000] [L1: 0.0051] 15.8+0.0s +[9600/16000] [L1: 0.0051] 15.8+0.0s +[11200/16000] [L1: 0.0051] 14.0+0.0s +[12800/16000] [L1: 0.0051] 16.0+0.0s +[14400/16000] [L1: 0.0051] 14.4+0.0s +[16000/16000] [L1: 0.0051] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.825 (Best: 40.298 @epoch 80) +Forward: 9.25s + +Saving... +Total: 9.74s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 14.2+0.8s +[3200/16000] [L1: 0.0051] 14.8+0.1s +[4800/16000] [L1: 0.0051] 14.8+0.0s +[6400/16000] [L1: 0.0050] 14.0+0.0s +[8000/16000] [L1: 0.0050] 14.0+0.0s +[9600/16000] [L1: 0.0050] 14.1+0.0s +[11200/16000] [L1: 0.0051] 15.5+0.0s +[12800/16000] [L1: 0.0051] 14.5+0.0s +[14400/16000] [L1: 0.0051] 16.0+0.1s +[16000/16000] [L1: 0.0051] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.479 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.82s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0053] 15.8+0.9s +[3200/16000] [L1: 0.0053] 15.6+0.1s +[4800/16000] [L1: 0.0053] 15.4+0.0s +[6400/16000] [L1: 0.0052] 14.7+0.0s +[8000/16000] [L1: 0.0052] 15.8+0.0s +[9600/16000] [L1: 0.0052] 15.7+0.0s +[11200/16000] [L1: 0.0052] 15.8+0.0s +[12800/16000] [L1: 0.0051] 15.5+0.0s +[14400/16000] [L1: 0.0051] 15.5+0.0s +[16000/16000] [L1: 0.0051] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.177 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.81s + +[Epoch 119] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.1+0.7s +[3200/16000] [L1: 0.0050] 15.2+0.1s +[4800/16000] [L1: 0.0050] 15.6+0.1s +[6400/16000] [L1: 0.0050] 15.0+0.1s +[8000/16000] [L1: 0.0050] 13.9+0.0s +[9600/16000] [L1: 0.0050] 14.0+0.0s +[11200/16000] [L1: 0.0051] 15.4+0.0s +[12800/16000] [L1: 0.0051] 16.0+0.1s +[14400/16000] [L1: 0.0051] 14.5+0.0s +[16000/16000] [L1: 0.0051] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.362 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.82s + +[Epoch 120] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.0+0.9s +[3200/16000] [L1: 0.0049] 15.2+0.1s +[4800/16000] [L1: 0.0050] 15.8+0.1s +[6400/16000] [L1: 0.0050] 15.9+0.1s +[8000/16000] [L1: 0.0050] 15.2+0.0s +[9600/16000] [L1: 0.0050] 14.2+0.0s +[11200/16000] [L1: 0.0051] 13.9+0.0s +[12800/16000] [L1: 0.0051] 15.4+0.0s +[14400/16000] [L1: 0.0050] 16.0+0.0s +[16000/16000] [L1: 0.0050] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.314 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.75s + +[Epoch 121] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 14.3+0.7s +[3200/16000] [L1: 0.0050] 14.6+0.1s +[4800/16000] [L1: 0.0050] 15.0+0.0s +[6400/16000] [L1: 0.0051] 14.2+0.0s +[8000/16000] [L1: 0.0051] 15.3+0.0s +[9600/16000] [L1: 0.0051] 15.6+0.0s +[11200/16000] [L1: 0.0051] 15.1+0.0s +[12800/16000] [L1: 0.0050] 15.5+0.0s +[14400/16000] [L1: 0.0050] 13.7+0.0s +[16000/16000] [L1: 0.0050] 14.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.744 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.79s + +[Epoch 122] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.9+0.8s +[3200/16000] [L1: 0.0050] 15.6+0.1s +[4800/16000] [L1: 0.0051] 15.1+0.1s +[6400/16000] [L1: 0.0051] 15.8+0.1s +[8000/16000] [L1: 0.0050] 14.8+0.1s +[9600/16000] [L1: 0.0050] 16.0+0.1s +[11200/16000] [L1: 0.0051] 16.0+0.1s +[12800/16000] [L1: 0.0051] 14.9+0.0s +[14400/16000] [L1: 0.0051] 15.1+0.1s +[16000/16000] [L1: 0.0051] 14.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.419 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.87s + +[Epoch 123] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.5+1.0s +[3200/16000] [L1: 0.0050] 14.0+0.1s +[4800/16000] [L1: 0.0050] 15.3+0.1s +[6400/16000] [L1: 0.0050] 15.6+0.0s +[8000/16000] [L1: 0.0050] 15.7+0.1s +[9600/16000] [L1: 0.0050] 15.7+0.1s +[11200/16000] [L1: 0.0050] 16.0+0.1s +[12800/16000] [L1: 0.0050] 15.0+0.0s +[14400/16000] [L1: 0.0050] 14.2+0.0s +[16000/16000] [L1: 0.0050] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.205 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.82s + +[Epoch 124] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.5+0.7s +[3200/16000] [L1: 0.0050] 15.6+0.1s +[4800/16000] [L1: 0.0050] 15.8+0.1s +[6400/16000] [L1: 0.0050] 15.9+0.1s +[8000/16000] [L1: 0.0050] 15.7+0.0s +[9600/16000] [L1: 0.0050] 15.4+0.0s +[11200/16000] [L1: 0.0050] 15.6+0.1s +[12800/16000] [L1: 0.0050] 13.7+0.0s +[14400/16000] [L1: 0.0050] 14.4+0.0s +[16000/16000] [L1: 0.0050] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.658 (Best: 40.298 @epoch 80) +Forward: 9.28s + +Saving... +Total: 9.72s + +[Epoch 125] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.5+0.8s +[3200/16000] [L1: 0.0050] 14.6+0.0s +[4800/16000] [L1: 0.0050] 15.5+0.1s +[6400/16000] [L1: 0.0050] 14.9+0.0s +[8000/16000] [L1: 0.0050] 14.2+0.0s +[9600/16000] [L1: 0.0050] 15.6+0.1s +[11200/16000] [L1: 0.0050] 14.9+0.0s +[12800/16000] [L1: 0.0050] 14.0+0.0s +[14400/16000] [L1: 0.0050] 14.9+0.0s +[16000/16000] [L1: 0.0050] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.321 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 126] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 14.3+0.9s +[3200/16000] [L1: 0.0050] 15.6+0.1s +[4800/16000] [L1: 0.0051] 14.6+0.0s +[6400/16000] [L1: 0.0051] 15.2+0.1s +[8000/16000] [L1: 0.0051] 15.5+0.0s +[9600/16000] [L1: 0.0050] 13.9+0.0s +[11200/16000] [L1: 0.0050] 14.6+0.1s +[12800/16000] [L1: 0.0050] 15.9+0.0s +[14400/16000] [L1: 0.0050] 15.4+0.0s +[16000/16000] [L1: 0.0051] 14.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.914 (Best: 40.298 @epoch 80) +Forward: 9.43s + +Saving... +Total: 9.99s + +[Epoch 127] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.8+0.8s +[3200/16000] [L1: 0.0050] 15.2+0.1s +[4800/16000] [L1: 0.0050] 15.3+0.1s +[6400/16000] [L1: 0.0051] 14.8+0.1s +[8000/16000] [L1: 0.0051] 15.1+0.1s +[9600/16000] [L1: 0.0050] 15.7+0.1s +[11200/16000] [L1: 0.0050] 16.0+0.1s +[12800/16000] [L1: 0.0050] 15.6+0.1s +[14400/16000] [L1: 0.0050] 15.0+0.1s +[16000/16000] [L1: 0.0050] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.964 (Best: 40.298 @epoch 80) +Forward: 9.45s + +Saving... +Total: 9.91s + +[Epoch 128] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.6+0.9s +[3200/16000] [L1: 0.0050] 15.0+0.1s +[4800/16000] [L1: 0.0050] 14.9+0.1s +[6400/16000] [L1: 0.0050] 14.5+0.0s +[8000/16000] [L1: 0.0050] 15.6+0.1s +[9600/16000] [L1: 0.0050] 15.6+0.1s +[11200/16000] [L1: 0.0050] 14.5+0.0s +[12800/16000] [L1: 0.0050] 14.8+0.0s +[14400/16000] [L1: 0.0050] 15.1+0.1s +[16000/16000] [L1: 0.0050] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.248 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.77s + +[Epoch 129] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.2+0.8s +[3200/16000] [L1: 0.0050] 15.1+0.0s +[4800/16000] [L1: 0.0050] 15.2+0.1s +[6400/16000] [L1: 0.0050] 14.9+0.0s +[8000/16000] [L1: 0.0049] 15.0+0.0s +[9600/16000] [L1: 0.0049] 15.4+0.0s +[11200/16000] [L1: 0.0050] 15.4+0.0s +[12800/16000] [L1: 0.0050] 15.0+0.0s +[14400/16000] [L1: 0.0050] 14.9+0.0s +[16000/16000] [L1: 0.0049] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.322 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.76s + +[Epoch 130] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.0+0.9s +[3200/16000] [L1: 0.0048] 15.5+0.1s +[4800/16000] [L1: 0.0049] 14.7+0.0s +[6400/16000] [L1: 0.0050] 14.3+0.0s +[8000/16000] [L1: 0.0050] 14.2+0.0s +[9600/16000] [L1: 0.0050] 15.8+0.1s +[11200/16000] [L1: 0.0050] 15.0+0.0s +[12800/16000] [L1: 0.0050] 15.1+0.0s +[14400/16000] [L1: 0.0050] 15.2+0.0s +[16000/16000] [L1: 0.0050] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.140 (Best: 40.298 @epoch 80) +Forward: 9.40s + +Saving... +Total: 9.92s + +[Epoch 131] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.6+0.9s +[3200/16000] [L1: 0.0049] 15.2+0.1s +[4800/16000] [L1: 0.0051] 15.4+0.1s +[6400/16000] [L1: 0.0051] 15.2+0.1s +[8000/16000] [L1: 0.0051] 14.5+0.1s +[9600/16000] [L1: 0.0051] 15.4+0.0s +[11200/16000] [L1: 0.0051] 15.2+0.0s +[12800/16000] [L1: 0.0050] 13.9+0.0s +[14400/16000] [L1: 0.0050] 15.1+0.0s +[16000/16000] [L1: 0.0050] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.023 (Best: 40.298 @epoch 80) +Forward: 9.38s + +Saving... +Total: 9.82s + +[Epoch 132] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 14.3+0.9s +[3200/16000] [L1: 0.0050] 15.4+0.1s +[4800/16000] [L1: 0.0050] 14.4+0.1s +[6400/16000] [L1: 0.0049] 14.6+0.0s +[8000/16000] [L1: 0.0050] 14.7+0.0s +[9600/16000] [L1: 0.0050] 15.4+0.0s +[11200/16000] [L1: 0.0050] 15.0+0.0s +[12800/16000] [L1: 0.0050] 14.9+0.0s +[14400/16000] [L1: 0.0050] 14.0+0.0s +[16000/16000] [L1: 0.0050] 14.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.234 (Best: 40.298 @epoch 80) +Forward: 9.51s + +Saving... +Total: 9.97s + +[Epoch 133] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.3+0.7s +[3200/16000] [L1: 0.0049] 14.1+0.0s +[4800/16000] [L1: 0.0049] 15.7+0.1s +[6400/16000] [L1: 0.0049] 15.4+0.0s +[8000/16000] [L1: 0.0049] 15.8+0.1s +[9600/16000] [L1: 0.0049] 15.7+0.1s +[11200/16000] [L1: 0.0049] 15.0+0.0s +[12800/16000] [L1: 0.0049] 15.2+0.0s +[14400/16000] [L1: 0.0049] 15.9+0.0s +[16000/16000] [L1: 0.0049] 14.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.646 (Best: 40.298 @epoch 80) +Forward: 9.26s + +Saving... +Total: 9.73s + +[Epoch 134] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.2+0.7s +[3200/16000] [L1: 0.0050] 15.8+0.1s +[4800/16000] [L1: 0.0050] 15.2+0.1s +[6400/16000] [L1: 0.0050] 14.1+0.0s +[8000/16000] [L1: 0.0049] 14.5+0.0s +[9600/16000] [L1: 0.0049] 15.9+0.1s +[11200/16000] [L1: 0.0049] 15.4+0.1s +[12800/16000] [L1: 0.0050] 15.5+0.1s +[14400/16000] [L1: 0.0050] 15.4+0.1s +[16000/16000] [L1: 0.0049] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.005 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 135] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 14.5+0.8s +[3200/16000] [L1: 0.0049] 14.7+0.1s +[4800/16000] [L1: 0.0050] 14.9+0.1s +[6400/16000] [L1: 0.0049] 15.4+0.1s +[8000/16000] [L1: 0.0050] 15.0+0.0s +[9600/16000] [L1: 0.0050] 14.8+0.0s +[11200/16000] [L1: 0.0050] 15.2+0.0s +[12800/16000] [L1: 0.0050] 14.8+0.0s +[14400/16000] [L1: 0.0050] 14.9+0.1s +[16000/16000] [L1: 0.0049] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.898 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.81s + +[Epoch 136] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.6+0.8s +[3200/16000] [L1: 0.0049] 15.4+0.1s +[4800/16000] [L1: 0.0049] 14.9+0.0s +[6400/16000] [L1: 0.0049] 15.4+0.1s +[8000/16000] [L1: 0.0049] 14.2+0.0s +[9600/16000] [L1: 0.0049] 14.1+0.0s +[11200/16000] [L1: 0.0049] 14.5+0.0s +[12800/16000] [L1: 0.0049] 14.8+0.0s +[14400/16000] [L1: 0.0049] 14.1+0.0s +[16000/16000] [L1: 0.0049] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.920 (Best: 40.298 @epoch 80) +Forward: 9.65s + +Saving... +Total: 10.17s + +[Epoch 137] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.4+0.9s +[3200/16000] [L1: 0.0049] 15.0+0.1s +[4800/16000] [L1: 0.0050] 14.7+0.1s +[6400/16000] [L1: 0.0050] 15.4+0.1s +[8000/16000] [L1: 0.0050] 14.9+0.1s +[9600/16000] [L1: 0.0050] 14.8+0.0s +[11200/16000] [L1: 0.0050] 15.7+0.1s +[12800/16000] [L1: 0.0050] 14.0+0.0s +[14400/16000] [L1: 0.0050] 14.6+0.0s +[16000/16000] [L1: 0.0050] 15.5+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.463 (Best: 40.298 @epoch 80) +Forward: 9.43s + +Saving... +Total: 9.89s + +[Epoch 138] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.4+0.7s +[3200/16000] [L1: 0.0050] 14.3+0.1s +[4800/16000] [L1: 0.0049] 14.7+0.0s +[6400/16000] [L1: 0.0049] 14.7+0.0s +[8000/16000] [L1: 0.0049] 14.8+0.0s +[9600/16000] [L1: 0.0049] 14.7+0.1s +[11200/16000] [L1: 0.0049] 14.2+0.0s +[12800/16000] [L1: 0.0049] 14.4+0.0s +[14400/16000] [L1: 0.0049] 14.1+0.0s +[16000/16000] [L1: 0.0050] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.803 (Best: 40.298 @epoch 80) +Forward: 9.38s + +Saving... +Total: 9.88s + +[Epoch 139] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 14.8+0.9s +[3200/16000] [L1: 0.0048] 14.0+0.0s +[4800/16000] [L1: 0.0048] 14.7+0.0s +[6400/16000] [L1: 0.0048] 15.2+0.1s +[8000/16000] [L1: 0.0048] 15.4+0.1s +[9600/16000] [L1: 0.0048] 14.8+0.0s +[11200/16000] [L1: 0.0049] 15.1+0.0s +[12800/16000] [L1: 0.0049] 14.3+0.0s +[14400/16000] [L1: 0.0049] 14.8+0.1s +[16000/16000] [L1: 0.0049] 15.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.163 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.78s + +[Epoch 140] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.8+0.7s +[3200/16000] [L1: 0.0050] 14.7+0.0s +[4800/16000] [L1: 0.0050] 14.5+0.1s +[6400/16000] [L1: 0.0050] 15.0+0.1s +[8000/16000] [L1: 0.0050] 15.6+0.1s +[9600/16000] [L1: 0.0050] 15.0+0.0s +[11200/16000] [L1: 0.0050] 14.6+0.0s +[12800/16000] [L1: 0.0050] 14.9+0.0s +[14400/16000] [L1: 0.0050] 14.9+0.0s +[16000/16000] [L1: 0.0050] 14.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.551 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.87s + +[Epoch 141] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 14.3+1.0s +[3200/16000] [L1: 0.0051] 15.2+0.1s +[4800/16000] [L1: 0.0050] 13.8+0.0s +[6400/16000] [L1: 0.0050] 15.8+0.1s +[8000/16000] [L1: 0.0050] 15.4+0.1s +[9600/16000] [L1: 0.0049] 15.2+0.1s +[11200/16000] [L1: 0.0049] 15.0+0.0s +[12800/16000] [L1: 0.0049] 14.7+0.0s +[14400/16000] [L1: 0.0049] 15.4+0.0s +[16000/16000] [L1: 0.0049] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.761 (Best: 40.298 @epoch 80) +Forward: 9.27s + +Saving... +Total: 9.72s + +[Epoch 142] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.6+0.8s +[3200/16000] [L1: 0.0049] 15.1+0.1s +[4800/16000] [L1: 0.0049] 14.9+0.1s +[6400/16000] [L1: 0.0049] 14.6+0.0s +[8000/16000] [L1: 0.0049] 15.9+0.1s +[9600/16000] [L1: 0.0049] 15.2+0.0s +[11200/16000] [L1: 0.0049] 14.6+0.0s +[12800/16000] [L1: 0.0049] 14.3+0.0s +[14400/16000] [L1: 0.0049] 14.4+0.0s +[16000/16000] [L1: 0.0049] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.672 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 143] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.2+0.8s +[3200/16000] [L1: 0.0049] 14.8+0.0s +[4800/16000] [L1: 0.0050] 15.5+0.1s +[6400/16000] [L1: 0.0049] 14.8+0.0s +[8000/16000] [L1: 0.0049] 15.4+0.1s +[9600/16000] [L1: 0.0050] 15.0+0.0s +[11200/16000] [L1: 0.0050] 16.0+0.1s +[12800/16000] [L1: 0.0050] 14.7+0.1s +[14400/16000] [L1: 0.0050] 15.1+0.1s +[16000/16000] [L1: 0.0050] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.386 (Best: 40.298 @epoch 80) +Forward: 9.36s + +Saving... +Total: 9.86s + +[Epoch 144] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.6+0.8s +[3200/16000] [L1: 0.0049] 14.0+0.1s +[4800/16000] [L1: 0.0050] 13.9+0.1s +[6400/16000] [L1: 0.0050] 15.0+0.1s +[8000/16000] [L1: 0.0050] 15.3+0.0s +[9600/16000] [L1: 0.0050] 15.7+0.1s +[11200/16000] [L1: 0.0049] 15.1+0.0s +[12800/16000] [L1: 0.0049] 14.9+0.0s +[14400/16000] [L1: 0.0049] 15.1+0.1s +[16000/16000] [L1: 0.0049] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.758 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.91s + +[Epoch 145] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.5+0.8s +[3200/16000] [L1: 0.0049] 15.1+0.1s +[4800/16000] [L1: 0.0049] 14.7+0.1s +[6400/16000] [L1: 0.0049] 15.5+0.1s +[8000/16000] [L1: 0.0050] 15.0+0.0s +[9600/16000] [L1: 0.0050] 14.2+0.0s +[11200/16000] [L1: 0.0050] 14.9+0.1s +[12800/16000] [L1: 0.0050] 13.9+0.0s +[14400/16000] [L1: 0.0050] 14.6+0.0s +[16000/16000] [L1: 0.0050] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.499 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.76s + +[Epoch 146] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 13.7+0.8s +[3200/16000] [L1: 0.0049] 14.5+0.1s +[4800/16000] [L1: 0.0049] 15.0+0.1s +[6400/16000] [L1: 0.0049] 15.7+0.1s +[8000/16000] [L1: 0.0049] 15.2+0.1s +[9600/16000] [L1: 0.0049] 15.3+0.0s +[11200/16000] [L1: 0.0049] 15.0+0.0s +[12800/16000] [L1: 0.0049] 15.1+0.1s +[14400/16000] [L1: 0.0049] 14.8+0.1s +[16000/16000] [L1: 0.0049] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.464 (Best: 40.298 @epoch 80) +Forward: 9.55s + +Saving... +Total: 10.06s + +[Epoch 147] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.2+0.8s +[3200/16000] [L1: 0.0049] 14.2+0.0s +[4800/16000] [L1: 0.0049] 15.9+0.1s +[6400/16000] [L1: 0.0049] 15.2+0.0s +[8000/16000] [L1: 0.0048] 14.6+0.0s +[9600/16000] [L1: 0.0049] 15.1+0.0s +[11200/16000] [L1: 0.0049] 15.8+0.0s +[12800/16000] [L1: 0.0049] 15.2+0.1s +[14400/16000] [L1: 0.0049] 14.8+0.0s +[16000/16000] [L1: 0.0049] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.128 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.85s + +[Epoch 148] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 14.4+0.8s +[3200/16000] [L1: 0.0049] 15.5+0.1s +[4800/16000] [L1: 0.0049] 15.3+0.1s +[6400/16000] [L1: 0.0050] 15.5+0.1s +[8000/16000] [L1: 0.0050] 15.3+0.0s +[9600/16000] [L1: 0.0050] 14.4+0.1s +[11200/16000] [L1: 0.0049] 15.0+0.0s +[12800/16000] [L1: 0.0049] 15.6+0.0s +[14400/16000] [L1: 0.0049] 15.7+0.0s +[16000/16000] [L1: 0.0049] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.646 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.82s + +[Epoch 149] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.3+0.9s +[3200/16000] [L1: 0.0049] 15.2+0.1s +[4800/16000] [L1: 0.0049] 15.4+0.1s +[6400/16000] [L1: 0.0049] 15.8+0.1s +[8000/16000] [L1: 0.0050] 14.0+0.0s +[9600/16000] [L1: 0.0050] 15.6+0.1s +[11200/16000] [L1: 0.0049] 15.8+0.1s +[12800/16000] [L1: 0.0049] 15.0+0.1s +[14400/16000] [L1: 0.0049] 14.9+0.1s +[16000/16000] [L1: 0.0049] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.421 (Best: 40.298 @epoch 80) +Forward: 9.28s + +Saving... +Total: 9.76s + +[Epoch 150] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.7+1.0s +[3200/16000] [L1: 0.0049] 15.5+0.1s +[4800/16000] [L1: 0.0049] 15.3+0.1s +[6400/16000] [L1: 0.0049] 15.8+0.1s +[8000/16000] [L1: 0.0049] 14.6+0.0s +[9600/16000] [L1: 0.0049] 15.6+0.0s +[11200/16000] [L1: 0.0049] 15.1+0.0s +[12800/16000] [L1: 0.0049] 15.1+0.0s +[14400/16000] [L1: 0.0049] 15.1+0.0s +[16000/16000] [L1: 0.0049] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.615 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.83s + +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.1+0.7s +[3200/16000] [L1: 0.0049] 14.2+0.1s +[4800/16000] [L1: 0.0049] 14.3+0.0s +[6400/16000] [L1: 0.0049] 14.6+0.1s +[8000/16000] [L1: 0.0049] 15.1+0.0s +[9600/16000] [L1: 0.0049] 14.8+0.1s +[11200/16000] [L1: 0.0049] 15.0+0.0s +[12800/16000] [L1: 0.0049] 15.2+0.1s +[14400/16000] [L1: 0.0049] 14.5+0.0s +[16000/16000] [L1: 0.0049] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.256 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.79s + +[Epoch 152] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.7+0.9s +[3200/16000] [L1: 0.0049] 15.0+0.1s +[4800/16000] [L1: 0.0049] 15.3+0.1s +[6400/16000] [L1: 0.0049] 15.6+0.1s +[8000/16000] [L1: 0.0049] 15.3+0.1s +[9600/16000] [L1: 0.0049] 14.5+0.0s +[11200/16000] [L1: 0.0049] 14.5+0.1s +[12800/16000] [L1: 0.0049] 14.9+0.0s +[14400/16000] [L1: 0.0049] 15.6+0.1s +[16000/16000] [L1: 0.0049] 15.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.295 (Best: 40.298 @epoch 80) +Forward: 9.46s + +Saving... +Total: 9.91s + +[Epoch 153] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.2+0.8s +[3200/16000] [L1: 0.0049] 14.8+0.0s +[4800/16000] [L1: 0.0049] 14.8+0.0s +[6400/16000] [L1: 0.0049] 15.5+0.1s +[8000/16000] [L1: 0.0049] 14.9+0.1s +[9600/16000] [L1: 0.0049] 14.5+0.1s +[11200/16000] [L1: 0.0049] 15.7+0.1s +[12800/16000] [L1: 0.0049] 14.7+0.1s +[14400/16000] [L1: 0.0049] 14.7+0.1s +[16000/16000] [L1: 0.0049] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.042 (Best: 40.298 @epoch 80) +Forward: 9.46s + +Saving... +Total: 10.05s + +[Epoch 154] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 14.8+0.7s +[3200/16000] [L1: 0.0049] 15.6+0.1s +[4800/16000] [L1: 0.0049] 15.8+0.1s +[6400/16000] [L1: 0.0049] 15.8+0.1s +[8000/16000] [L1: 0.0049] 14.5+0.1s +[9600/16000] [L1: 0.0049] 15.2+0.1s +[11200/16000] [L1: 0.0049] 15.0+0.1s +[12800/16000] [L1: 0.0049] 15.3+0.1s +[14400/16000] [L1: 0.0049] 15.3+0.0s +[16000/16000] [L1: 0.0049] 14.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.652 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.81s + +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.0+0.8s +[3200/16000] [L1: 0.0048] 14.3+0.1s +[4800/16000] [L1: 0.0049] 14.9+0.0s +[6400/16000] [L1: 0.0049] 14.6+0.0s +[8000/16000] [L1: 0.0049] 15.7+0.0s +[9600/16000] [L1: 0.0049] 15.4+0.0s +[11200/16000] [L1: 0.0049] 14.5+0.0s +[12800/16000] [L1: 0.0049] 14.4+0.0s +[14400/16000] [L1: 0.0049] 15.1+0.0s +[16000/16000] [L1: 0.0049] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.690 (Best: 40.298 @epoch 80) +Forward: 9.36s + +Saving... +Total: 9.82s + +[Epoch 156] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.3+0.7s +[3200/16000] [L1: 0.0049] 14.8+0.1s +[4800/16000] [L1: 0.0049] 14.9+0.0s +[6400/16000] [L1: 0.0049] 15.0+0.0s +[8000/16000] [L1: 0.0049] 14.6+0.0s +[9600/16000] [L1: 0.0049] 14.1+0.0s +[11200/16000] [L1: 0.0049] 14.5+0.0s +[12800/16000] [L1: 0.0049] 14.8+0.0s +[14400/16000] [L1: 0.0049] 14.5+0.0s +[16000/16000] [L1: 0.0049] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.448 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.81s + +[Epoch 157] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.3+0.8s +[3200/16000] [L1: 0.0048] 15.4+0.1s +[4800/16000] [L1: 0.0048] 14.7+0.1s +[6400/16000] [L1: 0.0048] 14.0+0.0s +[8000/16000] [L1: 0.0048] 14.1+0.0s +[9600/16000] [L1: 0.0048] 15.1+0.1s +[11200/16000] [L1: 0.0048] 15.6+0.1s +[12800/16000] [L1: 0.0048] 15.4+0.1s +[14400/16000] [L1: 0.0048] 14.3+0.0s +[16000/16000] [L1: 0.0048] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.988 (Best: 40.298 @epoch 80) +Forward: 9.50s + +Saving... +Total: 10.00s + +[Epoch 158] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.7+0.8s +[3200/16000] [L1: 0.0050] 15.5+0.1s +[4800/16000] [L1: 0.0049] 15.8+0.1s +[6400/16000] [L1: 0.0048] 15.5+0.1s +[8000/16000] [L1: 0.0049] 15.0+0.1s +[9600/16000] [L1: 0.0048] 14.5+0.0s +[11200/16000] [L1: 0.0049] 15.8+0.0s +[12800/16000] [L1: 0.0049] 15.2+0.0s +[14400/16000] [L1: 0.0048] 15.2+0.0s +[16000/16000] [L1: 0.0048] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.263 (Best: 40.298 @epoch 80) +Forward: 9.57s + +Saving... +Total: 10.18s + +[Epoch 159] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 13.9+0.8s +[3200/16000] [L1: 0.0048] 14.7+0.0s +[4800/16000] [L1: 0.0049] 15.2+0.1s +[6400/16000] [L1: 0.0048] 14.7+0.0s +[8000/16000] [L1: 0.0048] 15.0+0.1s +[9600/16000] [L1: 0.0048] 14.8+0.1s +[11200/16000] [L1: 0.0048] 14.8+0.1s +[12800/16000] [L1: 0.0049] 16.0+0.1s +[14400/16000] [L1: 0.0049] 14.7+0.1s +[16000/16000] [L1: 0.0048] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.115 (Best: 40.298 @epoch 80) +Forward: 9.38s + +Saving... +Total: 9.92s + +[Epoch 160] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 14.7+0.9s +[3200/16000] [L1: 0.0048] 15.2+0.0s +[4800/16000] [L1: 0.0048] 15.4+0.1s +[6400/16000] [L1: 0.0048] 14.5+0.0s +[8000/16000] [L1: 0.0049] 15.8+0.1s +[9600/16000] [L1: 0.0049] 14.8+0.1s +[11200/16000] [L1: 0.0049] 14.3+0.0s +[12800/16000] [L1: 0.0049] 14.7+0.0s +[14400/16000] [L1: 0.0049] 15.1+0.0s +[16000/16000] [L1: 0.0049] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.386 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.85s + +[Epoch 161] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.5+0.9s +[3200/16000] [L1: 0.0049] 15.1+0.1s +[4800/16000] [L1: 0.0049] 15.6+0.1s +[6400/16000] [L1: 0.0048] 15.3+0.1s +[8000/16000] [L1: 0.0049] 14.8+0.1s +[9600/16000] [L1: 0.0049] 15.6+0.1s +[11200/16000] [L1: 0.0049] 16.0+0.1s +[12800/16000] [L1: 0.0049] 15.7+0.1s +[14400/16000] [L1: 0.0049] 13.8+0.0s +[16000/16000] [L1: 0.0049] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.168 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.80s + +[Epoch 162] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.6+0.8s +[3200/16000] [L1: 0.0049] 15.7+0.1s +[4800/16000] [L1: 0.0049] 15.7+0.1s +[6400/16000] [L1: 0.0049] 15.7+0.1s +[8000/16000] [L1: 0.0049] 16.0+0.1s +[9600/16000] [L1: 0.0049] 15.7+0.1s +[11200/16000] [L1: 0.0049] 16.0+0.1s +[12800/16000] [L1: 0.0049] 15.3+0.1s +[14400/16000] [L1: 0.0049] 15.9+0.1s +[16000/16000] [L1: 0.0049] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.296 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.71s + +[Epoch 163] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 16.0+0.8s +[3200/16000] [L1: 0.0049] 15.9+0.1s +[4800/16000] [L1: 0.0049] 15.9+0.1s +[6400/16000] [L1: 0.0049] 15.0+0.1s +[8000/16000] [L1: 0.0049] 15.2+0.1s +[9600/16000] [L1: 0.0049] 14.5+0.0s +[11200/16000] [L1: 0.0049] 13.9+0.0s +[12800/16000] [L1: 0.0049] 15.2+0.0s +[14400/16000] [L1: 0.0049] 15.2+0.0s +[16000/16000] [L1: 0.0049] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.930 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.94s + +[Epoch 164] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.0+0.8s +[3200/16000] [L1: 0.0048] 14.9+0.1s +[4800/16000] [L1: 0.0048] 15.3+0.1s +[6400/16000] [L1: 0.0048] 16.0+0.1s +[8000/16000] [L1: 0.0048] 15.1+0.1s +[9600/16000] [L1: 0.0048] 15.9+0.1s +[11200/16000] [L1: 0.0048] 14.3+0.0s +[12800/16000] [L1: 0.0048] 13.8+0.0s +[14400/16000] [L1: 0.0048] 14.6+0.0s +[16000/16000] [L1: 0.0048] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.514 (Best: 40.298 @epoch 80) +Forward: 9.32s + +Saving... +Total: 9.71s + +[Epoch 165] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 16.0+0.8s +[3200/16000] [L1: 0.0047] 14.7+0.0s +[4800/16000] [L1: 0.0048] 15.6+0.1s +[6400/16000] [L1: 0.0048] 15.4+0.1s +[8000/16000] [L1: 0.0048] 15.7+0.1s +[9600/16000] [L1: 0.0048] 15.3+0.1s +[11200/16000] [L1: 0.0048] 15.8+0.1s +[12800/16000] [L1: 0.0048] 15.8+0.1s +[14400/16000] [L1: 0.0048] 15.8+0.0s +[16000/16000] [L1: 0.0048] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.073 (Best: 40.298 @epoch 80) +Forward: 9.38s + +Saving... +Total: 9.98s + +[Epoch 166] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0052] 16.0+0.8s +[3200/16000] [L1: 0.0050] 15.9+0.1s +[4800/16000] [L1: 0.0050] 15.5+0.1s +[6400/16000] [L1: 0.0049] 14.3+0.1s +[8000/16000] [L1: 0.0049] 15.3+0.1s +[9600/16000] [L1: 0.0049] 15.6+0.1s +[11200/16000] [L1: 0.0049] 14.5+0.0s +[12800/16000] [L1: 0.0049] 14.7+0.0s +[14400/16000] [L1: 0.0049] 16.0+0.1s +[16000/16000] [L1: 0.0049] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.548 (Best: 40.298 @epoch 80) +Forward: 9.39s + +Saving... +Total: 9.89s + +[Epoch 167] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 16.0+0.7s +[3200/16000] [L1: 0.0048] 15.6+0.1s +[4800/16000] [L1: 0.0048] 15.7+0.1s +[6400/16000] [L1: 0.0048] 15.1+0.1s +[8000/16000] [L1: 0.0049] 15.5+0.0s +[9600/16000] [L1: 0.0049] 15.2+0.0s +[11200/16000] [L1: 0.0048] 15.5+0.1s +[12800/16000] [L1: 0.0049] 15.6+0.1s +[14400/16000] [L1: 0.0048] 15.8+0.1s +[16000/16000] [L1: 0.0048] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.559 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.76s + +[Epoch 168] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.0+0.7s +[3200/16000] [L1: 0.0049] 14.6+0.1s +[4800/16000] [L1: 0.0048] 14.7+0.0s +[6400/16000] [L1: 0.0049] 14.7+0.0s +[8000/16000] [L1: 0.0048] 14.9+0.0s +[9600/16000] [L1: 0.0048] 14.5+0.0s +[11200/16000] [L1: 0.0048] 14.9+0.0s +[12800/16000] [L1: 0.0048] 14.6+0.0s +[14400/16000] [L1: 0.0048] 14.7+0.0s +[16000/16000] [L1: 0.0048] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.624 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.87s + +[Epoch 169] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 14.5+0.9s +[3200/16000] [L1: 0.0049] 14.9+0.1s +[4800/16000] [L1: 0.0049] 15.4+0.1s +[6400/16000] [L1: 0.0049] 14.5+0.0s +[8000/16000] [L1: 0.0049] 14.8+0.1s +[9600/16000] [L1: 0.0049] 15.0+0.1s +[11200/16000] [L1: 0.0049] 15.2+0.0s +[12800/16000] [L1: 0.0049] 14.7+0.0s +[14400/16000] [L1: 0.0049] 15.6+0.1s +[16000/16000] [L1: 0.0049] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.621 (Best: 40.298 @epoch 80) +Forward: 9.29s + +Saving... +Total: 9.85s + +[Epoch 170] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 16.0+0.8s +[3200/16000] [L1: 0.0049] 15.5+0.1s +[4800/16000] [L1: 0.0049] 14.6+0.1s +[6400/16000] [L1: 0.0049] 14.3+0.0s +[8000/16000] [L1: 0.0048] 14.7+0.0s +[9600/16000] [L1: 0.0048] 13.8+0.0s +[11200/16000] [L1: 0.0048] 14.1+0.0s +[12800/16000] [L1: 0.0048] 14.7+0.0s +[14400/16000] [L1: 0.0048] 14.2+0.0s +[16000/16000] [L1: 0.0048] 14.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.901 (Best: 40.298 @epoch 80) +Forward: 9.51s + +Saving... +Total: 10.02s + +[Epoch 171] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.2+0.8s +[3200/16000] [L1: 0.0048] 15.7+0.1s +[4800/16000] [L1: 0.0048] 15.1+0.0s +[6400/16000] [L1: 0.0048] 15.3+0.0s +[8000/16000] [L1: 0.0048] 13.8+0.0s +[9600/16000] [L1: 0.0048] 13.9+0.0s +[11200/16000] [L1: 0.0048] 14.0+0.0s +[12800/16000] [L1: 0.0048] 14.6+0.0s +[14400/16000] [L1: 0.0048] 15.7+0.0s +[16000/16000] [L1: 0.0048] 15.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.117 (Best: 40.298 @epoch 80) +Forward: 9.46s + +Saving... +Total: 9.99s + +[Epoch 172] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.2+0.8s +[3200/16000] [L1: 0.0048] 15.5+0.1s +[4800/16000] [L1: 0.0048] 14.6+0.0s +[6400/16000] [L1: 0.0048] 15.7+0.0s +[8000/16000] [L1: 0.0048] 15.7+0.1s +[9600/16000] [L1: 0.0048] 15.2+0.1s +[11200/16000] [L1: 0.0048] 15.8+0.1s +[12800/16000] [L1: 0.0048] 15.5+0.0s +[14400/16000] [L1: 0.0048] 14.9+0.1s +[16000/16000] [L1: 0.0048] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.210 (Best: 40.298 @epoch 80) +Forward: 9.53s + +Saving... +Total: 10.08s + +[Epoch 173] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0051] 15.4+0.7s +[3200/16000] [L1: 0.0050] 14.9+0.0s +[4800/16000] [L1: 0.0049] 14.8+0.0s +[6400/16000] [L1: 0.0049] 14.8+0.0s +[8000/16000] [L1: 0.0049] 15.9+0.1s +[9600/16000] [L1: 0.0049] 15.8+0.1s +[11200/16000] [L1: 0.0049] 14.0+0.0s +[12800/16000] [L1: 0.0049] 14.6+0.0s +[14400/16000] [L1: 0.0049] 14.9+0.0s +[16000/16000] [L1: 0.0049] 14.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.500 (Best: 40.298 @epoch 80) +Forward: 9.29s + +Saving... +Total: 9.77s + +[Epoch 174] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.4+0.7s +[3200/16000] [L1: 0.0049] 15.6+0.1s +[4800/16000] [L1: 0.0049] 15.2+0.1s +[6400/16000] [L1: 0.0048] 15.2+0.1s +[8000/16000] [L1: 0.0048] 14.0+0.0s +[9600/16000] [L1: 0.0049] 14.7+0.0s +[11200/16000] [L1: 0.0049] 14.1+0.0s +[12800/16000] [L1: 0.0048] 13.8+0.0s +[14400/16000] [L1: 0.0048] 15.6+0.0s +[16000/16000] [L1: 0.0048] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.243 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.78s + +[Epoch 175] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 14.5+0.7s +[3200/16000] [L1: 0.0048] 15.3+0.1s +[4800/16000] [L1: 0.0048] 15.5+0.1s +[6400/16000] [L1: 0.0048] 15.7+0.1s +[8000/16000] [L1: 0.0048] 15.6+0.0s +[9600/16000] [L1: 0.0048] 15.3+0.0s +[11200/16000] [L1: 0.0048] 15.1+0.0s +[12800/16000] [L1: 0.0048] 14.8+0.0s +[14400/16000] [L1: 0.0048] 15.4+0.0s +[16000/16000] [L1: 0.0048] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.395 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.69s + +[Epoch 176] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.0+0.7s +[3200/16000] [L1: 0.0047] 14.5+0.0s +[4800/16000] [L1: 0.0048] 14.5+0.0s +[6400/16000] [L1: 0.0048] 15.0+0.0s +[8000/16000] [L1: 0.0047] 14.4+0.0s +[9600/16000] [L1: 0.0047] 15.3+0.0s +[11200/16000] [L1: 0.0047] 15.0+0.0s +[12800/16000] [L1: 0.0048] 15.8+0.1s +[14400/16000] [L1: 0.0048] 15.8+0.1s +[16000/16000] [L1: 0.0048] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.083 (Best: 40.298 @epoch 80) +Forward: 9.50s + +Saving... +Total: 9.92s + +[Epoch 177] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.8+0.8s +[3200/16000] [L1: 0.0048] 15.5+0.1s +[4800/16000] [L1: 0.0048] 15.6+0.1s +[6400/16000] [L1: 0.0048] 15.3+0.1s +[8000/16000] [L1: 0.0048] 15.7+0.0s +[9600/16000] [L1: 0.0048] 15.3+0.0s +[11200/16000] [L1: 0.0048] 15.7+0.0s +[12800/16000] [L1: 0.0048] 14.3+0.0s +[14400/16000] [L1: 0.0048] 14.7+0.0s +[16000/16000] [L1: 0.0048] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.501 (Best: 40.298 @epoch 80) +Forward: 9.25s + +Saving... +Total: 9.82s + +[Epoch 178] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.8+0.9s +[3200/16000] [L1: 0.0048] 14.8+0.0s +[4800/16000] [L1: 0.0048] 15.7+0.1s +[6400/16000] [L1: 0.0048] 15.7+0.1s +[8000/16000] [L1: 0.0048] 14.3+0.0s +[9600/16000] [L1: 0.0048] 14.8+0.1s +[11200/16000] [L1: 0.0048] 15.2+0.0s +[12800/16000] [L1: 0.0048] 15.9+0.0s +[14400/16000] [L1: 0.0048] 15.7+0.1s +[16000/16000] [L1: 0.0048] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.994 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.79s + +[Epoch 179] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 14.1+0.8s +[3200/16000] [L1: 0.0050] 13.9+0.0s +[4800/16000] [L1: 0.0050] 14.2+0.0s +[6400/16000] [L1: 0.0050] 15.9+0.1s +[8000/16000] [L1: 0.0049] 16.1+0.1s +[9600/16000] [L1: 0.0049] 15.6+0.1s +[11200/16000] [L1: 0.0049] 15.3+0.0s +[12800/16000] [L1: 0.0049] 15.4+0.0s +[14400/16000] [L1: 0.0048] 15.4+0.1s +[16000/16000] [L1: 0.0048] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.236 (Best: 40.298 @epoch 80) +Forward: 9.24s + +Saving... +Total: 9.69s + +[Epoch 180] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.9+0.8s +[3200/16000] [L1: 0.0048] 15.1+0.1s +[4800/16000] [L1: 0.0048] 15.8+0.0s +[6400/16000] [L1: 0.0048] 15.2+0.0s +[8000/16000] [L1: 0.0048] 14.0+0.0s +[9600/16000] [L1: 0.0048] 15.3+0.0s +[11200/16000] [L1: 0.0048] 15.6+0.0s +[12800/16000] [L1: 0.0048] 15.2+0.0s +[14400/16000] [L1: 0.0048] 14.5+0.0s +[16000/16000] [L1: 0.0048] 13.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.121 (Best: 40.298 @epoch 80) +Forward: 9.35s + +Saving... +Total: 9.87s + +[Epoch 181] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 14.1+0.8s +[3200/16000] [L1: 0.0047] 14.5+0.1s +[4800/16000] [L1: 0.0047] 15.3+0.1s +[6400/16000] [L1: 0.0048] 15.7+0.1s +[8000/16000] [L1: 0.0048] 15.9+0.1s +[9600/16000] [L1: 0.0048] 14.9+0.1s +[11200/16000] [L1: 0.0048] 14.7+0.0s +[12800/16000] [L1: 0.0048] 15.9+0.1s +[14400/16000] [L1: 0.0048] 15.8+0.1s +[16000/16000] [L1: 0.0048] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.721 (Best: 40.298 @epoch 80) +Forward: 9.29s + +Saving... +Total: 9.81s + +[Epoch 182] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.8+0.9s +[3200/16000] [L1: 0.0047] 15.3+0.1s +[4800/16000] [L1: 0.0047] 15.5+0.1s +[6400/16000] [L1: 0.0047] 14.4+0.0s +[8000/16000] [L1: 0.0047] 15.2+0.1s +[9600/16000] [L1: 0.0047] 15.9+0.1s +[11200/16000] [L1: 0.0048] 15.1+0.0s +[12800/16000] [L1: 0.0048] 14.7+0.0s +[14400/16000] [L1: 0.0048] 14.7+0.0s +[16000/16000] [L1: 0.0048] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.834 (Best: 40.298 @epoch 80) +Forward: 9.37s + +Saving... +Total: 9.78s + +[Epoch 183] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.9+0.8s +[3200/16000] [L1: 0.0047] 15.0+0.1s +[4800/16000] [L1: 0.0047] 15.5+0.1s +[6400/16000] [L1: 0.0047] 14.7+0.0s +[8000/16000] [L1: 0.0047] 14.9+0.0s +[9600/16000] [L1: 0.0047] 14.5+0.0s +[11200/16000] [L1: 0.0047] 15.7+0.1s +[12800/16000] [L1: 0.0047] 15.6+0.1s +[14400/16000] [L1: 0.0047] 13.9+0.0s +[16000/16000] [L1: 0.0047] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.301 (Best: 40.298 @epoch 80) +Forward: 9.28s + +Saving... +Total: 9.75s + +[Epoch 184] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.4+0.7s +[3200/16000] [L1: 0.0049] 14.3+0.0s +[4800/16000] [L1: 0.0048] 13.5+0.0s +[6400/16000] [L1: 0.0048] 15.4+0.0s +[8000/16000] [L1: 0.0048] 15.5+0.0s +[9600/16000] [L1: 0.0048] 14.7+0.0s +[11200/16000] [L1: 0.0048] 15.8+0.1s +[12800/16000] [L1: 0.0048] 15.8+0.0s +[14400/16000] [L1: 0.0048] 15.8+0.0s +[16000/16000] [L1: 0.0048] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.222 (Best: 40.298 @epoch 80) +Forward: 9.25s + +Saving... +Total: 9.66s + +[Epoch 185] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.6+0.7s +[3200/16000] [L1: 0.0047] 15.7+0.1s +[4800/16000] [L1: 0.0048] 15.4+0.0s +[6400/16000] [L1: 0.0048] 14.4+0.0s +[8000/16000] [L1: 0.0048] 15.6+0.1s +[9600/16000] [L1: 0.0048] 15.1+0.0s +[11200/16000] [L1: 0.0048] 14.8+0.0s +[12800/16000] [L1: 0.0048] 15.3+0.0s +[14400/16000] [L1: 0.0048] 15.9+0.1s +[16000/16000] [L1: 0.0048] 15.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.546 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.72s + +[Epoch 186] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.0+0.7s +[3200/16000] [L1: 0.0049] 14.4+0.0s +[4800/16000] [L1: 0.0048] 14.9+0.0s +[6400/16000] [L1: 0.0048] 14.6+0.0s +[8000/16000] [L1: 0.0048] 14.3+0.0s +[9600/16000] [L1: 0.0048] 14.9+0.0s +[11200/16000] [L1: 0.0048] 15.9+0.0s +[12800/16000] [L1: 0.0048] 15.7+0.1s +[14400/16000] [L1: 0.0048] 15.6+0.0s +[16000/16000] [L1: 0.0048] 14.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.259 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.83s + +[Epoch 187] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 16.0+0.8s +[3200/16000] [L1: 0.0047] 14.7+0.0s +[4800/16000] [L1: 0.0047] 14.0+0.0s +[6400/16000] [L1: 0.0047] 14.4+0.0s +[8000/16000] [L1: 0.0047] 15.3+0.1s +[9600/16000] [L1: 0.0047] 16.0+0.1s +[11200/16000] [L1: 0.0047] 14.9+0.0s +[12800/16000] [L1: 0.0047] 14.0+0.0s +[14400/16000] [L1: 0.0047] 13.9+0.0s +[16000/16000] [L1: 0.0047] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.516 (Best: 40.298 @epoch 80) +Forward: 9.41s + +Saving... +Total: 9.81s + +[Epoch 188] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 14.0+0.8s +[3200/16000] [L1: 0.0048] 13.6+0.0s +[4800/16000] [L1: 0.0049] 15.2+0.0s +[6400/16000] [L1: 0.0049] 15.7+0.0s +[8000/16000] [L1: 0.0049] 15.8+0.1s +[9600/16000] [L1: 0.0049] 14.4+0.0s +[11200/16000] [L1: 0.0049] 15.6+0.0s +[12800/16000] [L1: 0.0049] 15.5+0.0s +[14400/16000] [L1: 0.0049] 15.8+0.1s +[16000/16000] [L1: 0.0048] 15.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.068 (Best: 40.298 @epoch 80) +Forward: 9.47s + +Saving... +Total: 9.87s + +[Epoch 189] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.5+0.7s +[3200/16000] [L1: 0.0048] 14.4+0.0s +[4800/16000] [L1: 0.0048] 14.5+0.0s +[6400/16000] [L1: 0.0048] 14.9+0.0s +[8000/16000] [L1: 0.0048] 16.1+0.1s +[9600/16000] [L1: 0.0048] 14.8+0.0s +[11200/16000] [L1: 0.0048] 15.3+0.1s +[12800/16000] [L1: 0.0048] 15.5+0.1s +[14400/16000] [L1: 0.0048] 14.7+0.0s +[16000/16000] [L1: 0.0048] 16.0+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.569 (Best: 40.298 @epoch 80) +Forward: 9.46s + +Saving... +Total: 10.03s + +[Epoch 190] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.6+0.8s +[3200/16000] [L1: 0.0049] 14.6+0.1s +[4800/16000] [L1: 0.0048] 14.5+0.1s +[6400/16000] [L1: 0.0048] 15.4+0.1s +[8000/16000] [L1: 0.0048] 15.5+0.0s +[9600/16000] [L1: 0.0048] 15.9+0.1s +[11200/16000] [L1: 0.0048] 14.4+0.0s +[12800/16000] [L1: 0.0048] 13.8+0.0s +[14400/16000] [L1: 0.0048] 14.5+0.0s +[16000/16000] [L1: 0.0048] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.162 (Best: 40.298 @epoch 80) +Forward: 9.51s + +Saving... +Total: 10.03s + +[Epoch 191] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 14.2+0.8s +[3200/16000] [L1: 0.0048] 13.9+0.0s +[4800/16000] [L1: 0.0048] 15.1+0.0s +[6400/16000] [L1: 0.0048] 15.9+0.1s +[8000/16000] [L1: 0.0048] 15.3+0.0s +[9600/16000] [L1: 0.0048] 15.9+0.1s +[11200/16000] [L1: 0.0048] 15.9+0.1s +[12800/16000] [L1: 0.0048] 16.0+0.1s +[14400/16000] [L1: 0.0048] 15.5+0.0s +[16000/16000] [L1: 0.0048] 15.8+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.392 (Best: 40.298 @epoch 80) +Forward: 9.50s + +Saving... +Total: 9.99s + +[Epoch 192] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0050] 15.3+1.2s +[3200/16000] [L1: 0.0049] 14.8+0.1s +[4800/16000] [L1: 0.0048] 15.5+0.1s +[6400/16000] [L1: 0.0048] 14.7+0.0s +[8000/16000] [L1: 0.0048] 14.8+0.0s +[9600/16000] [L1: 0.0048] 13.9+0.0s +[11200/16000] [L1: 0.0048] 13.9+0.0s +[12800/16000] [L1: 0.0048] 15.9+0.1s +[14400/16000] [L1: 0.0048] 16.0+0.1s +[16000/16000] [L1: 0.0048] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.309 (Best: 40.298 @epoch 80) +Forward: 9.46s + +Saving... +Total: 9.98s + +[Epoch 193] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0046] 15.7+0.8s +[3200/16000] [L1: 0.0047] 15.5+0.1s +[4800/16000] [L1: 0.0047] 14.9+0.1s +[6400/16000] [L1: 0.0048] 14.6+0.1s +[8000/16000] [L1: 0.0048] 15.8+0.1s +[9600/16000] [L1: 0.0047] 15.9+0.0s +[11200/16000] [L1: 0.0048] 15.6+0.1s +[12800/16000] [L1: 0.0048] 15.7+0.1s +[14400/16000] [L1: 0.0048] 15.6+0.0s +[16000/16000] [L1: 0.0047] 13.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.214 (Best: 40.298 @epoch 80) +Forward: 9.22s + +Saving... +Total: 9.67s + +[Epoch 194] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 14.5+0.7s +[3200/16000] [L1: 0.0048] 15.6+0.1s +[4800/16000] [L1: 0.0047] 15.5+0.1s +[6400/16000] [L1: 0.0047] 15.6+0.1s +[8000/16000] [L1: 0.0047] 15.7+0.1s +[9600/16000] [L1: 0.0048] 15.7+0.1s +[11200/16000] [L1: 0.0048] 15.9+0.1s +[12800/16000] [L1: 0.0048] 15.1+0.0s +[14400/16000] [L1: 0.0048] 14.8+0.0s +[16000/16000] [L1: 0.0048] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.525 (Best: 40.298 @epoch 80) +Forward: 9.44s + +Saving... +Total: 9.95s + +[Epoch 195] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.2+0.8s +[3200/16000] [L1: 0.0048] 15.3+0.0s +[4800/16000] [L1: 0.0048] 15.6+0.1s +[6400/16000] [L1: 0.0048] 14.5+0.0s +[8000/16000] [L1: 0.0048] 15.5+0.1s +[9600/16000] [L1: 0.0048] 15.6+0.1s +[11200/16000] [L1: 0.0048] 14.3+0.0s +[12800/16000] [L1: 0.0048] 15.4+0.1s +[14400/16000] [L1: 0.0048] 15.9+0.0s +[16000/16000] [L1: 0.0048] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.877 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.84s + +[Epoch 196] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.0+0.7s +[3200/16000] [L1: 0.0047] 15.7+0.1s +[4800/16000] [L1: 0.0047] 15.7+0.1s +[6400/16000] [L1: 0.0048] 15.5+0.0s +[8000/16000] [L1: 0.0048] 15.1+0.0s +[9600/16000] [L1: 0.0048] 15.9+0.0s +[11200/16000] [L1: 0.0048] 14.5+0.0s +[12800/16000] [L1: 0.0048] 13.9+0.0s +[14400/16000] [L1: 0.0048] 15.4+0.0s +[16000/16000] [L1: 0.0047] 15.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.357 (Best: 40.298 @epoch 80) +Forward: 9.25s + +Saving... +Total: 9.67s + +[Epoch 197] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0047] 15.4+0.9s +[3200/16000] [L1: 0.0047] 15.7+0.0s +[4800/16000] [L1: 0.0047] 15.2+0.0s +[6400/16000] [L1: 0.0048] 14.3+0.0s +[8000/16000] [L1: 0.0048] 13.8+0.0s +[9600/16000] [L1: 0.0048] 15.0+0.0s +[11200/16000] [L1: 0.0048] 16.0+0.1s +[12800/16000] [L1: 0.0048] 15.3+0.1s +[14400/16000] [L1: 0.0048] 15.3+0.0s +[16000/16000] [L1: 0.0048] 15.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.698 (Best: 40.298 @epoch 80) +Forward: 9.29s + +Saving... +Total: 9.77s + +[Epoch 198] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 15.9+0.9s +[3200/16000] [L1: 0.0048] 14.7+0.1s +[4800/16000] [L1: 0.0048] 15.4+0.1s +[6400/16000] [L1: 0.0048] 15.9+0.0s +[8000/16000] [L1: 0.0048] 15.8+0.0s +[9600/16000] [L1: 0.0048] 14.1+0.0s +[11200/16000] [L1: 0.0048] 13.8+0.0s +[12800/16000] [L1: 0.0048] 14.7+0.0s +[14400/16000] [L1: 0.0048] 16.0+0.1s +[16000/16000] [L1: 0.0048] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.760 (Best: 40.298 @epoch 80) +Forward: 9.57s + +Saving... +Total: 10.09s + +[Epoch 199] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0049] 14.9+0.8s +[3200/16000] [L1: 0.0047] 14.2+0.0s +[4800/16000] [L1: 0.0047] 14.7+0.0s +[6400/16000] [L1: 0.0047] 15.0+0.0s +[8000/16000] [L1: 0.0048] 14.8+0.1s +[9600/16000] [L1: 0.0048] 15.5+0.0s +[11200/16000] [L1: 0.0048] 14.9+0.1s +[12800/16000] [L1: 0.0048] 13.9+0.0s +[14400/16000] [L1: 0.0048] 15.7+0.0s +[16000/16000] [L1: 0.0048] 15.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.801 (Best: 40.298 @epoch 80) +Forward: 9.30s + +Saving... +Total: 9.83s + +[Epoch 200] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0048] 15.3+0.8s +[3200/16000] [L1: 0.0048] 15.4+0.1s +[4800/16000] [L1: 0.0047] 15.0+0.0s +[6400/16000] [L1: 0.0048] 14.7+0.0s +[8000/16000] [L1: 0.0047] 15.0+0.0s +[9600/16000] [L1: 0.0048] 15.2+0.0s +[11200/16000] [L1: 0.0048] 15.3+0.0s +[12800/16000] [L1: 0.0048] 13.8+0.0s +[14400/16000] [L1: 0.0048] 14.5+0.0s +[16000/16000] [L1: 0.0048] 13.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.062 (Best: 40.298 @epoch 80) +Forward: 9.26s + +Saving... +Total: 9.77s + +[Epoch 201] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0044] 15.4+0.8s +[3200/16000] [L1: 0.0045] 14.9+0.1s +[4800/16000] [L1: 0.0045] 13.9+0.0s +[6400/16000] [L1: 0.0045] 15.9+0.1s +[8000/16000] [L1: 0.0045] 14.4+0.0s +[9600/16000] [L1: 0.0045] 14.9+0.0s +[11200/16000] [L1: 0.0045] 15.0+0.0s +[12800/16000] [L1: 0.0046] 14.0+0.0s +[14400/16000] [L1: 0.0045] 15.7+0.0s +[16000/16000] [L1: 0.0046] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.118 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.86s + +[Epoch 202] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0047] 15.1+0.8s +[3200/16000] [L1: 0.0045] 15.2+0.1s +[4800/16000] [L1: 0.0046] 15.2+0.1s +[6400/16000] [L1: 0.0046] 15.6+0.1s +[8000/16000] [L1: 0.0046] 14.2+0.0s +[9600/16000] [L1: 0.0046] 14.4+0.0s +[11200/16000] [L1: 0.0046] 14.3+0.0s +[12800/16000] [L1: 0.0046] 14.0+0.0s +[14400/16000] [L1: 0.0046] 14.0+0.0s +[16000/16000] [L1: 0.0046] 14.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.142 (Best: 40.298 @epoch 80) +Forward: 9.26s + +Saving... +Total: 9.77s + +[Epoch 203] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0047] 16.0+0.8s +[3200/16000] [L1: 0.0046] 15.7+0.1s +[4800/16000] [L1: 0.0046] 14.8+0.1s +[6400/16000] [L1: 0.0046] 13.7+0.0s +[8000/16000] [L1: 0.0046] 14.2+0.0s +[9600/16000] [L1: 0.0046] 15.9+0.0s +[11200/16000] [L1: 0.0046] 15.5+0.0s +[12800/16000] [L1: 0.0046] 15.9+0.1s +[14400/16000] [L1: 0.0046] 15.9+0.1s +[16000/16000] [L1: 0.0046] 14.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.120 (Best: 40.298 @epoch 80) +Forward: 9.25s + +Saving... +Total: 10.10s + +[Epoch 204] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.9+0.7s +[3200/16000] [L1: 0.0045] 14.3+0.0s +[4800/16000] [L1: 0.0046] 14.8+0.0s +[6400/16000] [L1: 0.0046] 13.8+0.0s +[8000/16000] [L1: 0.0046] 14.0+0.0s +[9600/16000] [L1: 0.0045] 13.9+0.0s +[11200/16000] [L1: 0.0045] 14.2+0.0s +[12800/16000] [L1: 0.0046] 15.7+0.0s +[14400/16000] [L1: 0.0046] 15.8+0.0s +[16000/16000] [L1: 0.0046] 15.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.613 (Best: 40.298 @epoch 80) +Forward: 9.31s + +Saving... +Total: 9.82s + +[Epoch 205] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0047] 15.0+0.7s +[3200/16000] [L1: 0.0046] 14.9+0.1s +[4800/16000] [L1: 0.0046] 14.3+0.1s +[6400/16000] [L1: 0.0046] 14.5+0.1s +[8000/16000] [L1: 0.0046] 15.7+0.1s +[9600/16000] [L1: 0.0046] 13.8+0.0s +[11200/16000] [L1: 0.0046] 14.1+0.0s +[12800/16000] [L1: 0.0046] 14.4+0.0s +[14400/16000] [L1: 0.0046] 15.0+0.0s +[16000/16000] [L1: 0.0045] 14.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.142 (Best: 40.298 @epoch 80) +Forward: 9.34s + +Saving... +Total: 9.82s + +[Epoch 206] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 14.8+0.8s +[3200/16000] [L1: 0.0046] 14.7+0.1s +[4800/16000] [L1: 0.0046] 15.0+0.0s +[6400/16000] [L1: 0.0046] 15.8+0.1s +[8000/16000] [L1: 0.0046] 15.4+0.1s +[9600/16000] [L1: 0.0046] 15.6+0.1s +[11200/16000] [L1: 0.0046] 15.7+0.1s +[12800/16000] [L1: 0.0046] 15.5+0.0s +[14400/16000] [L1: 0.0046] 15.9+0.1s +[16000/16000] [L1: 0.0046] 14.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.838 (Best: 40.298 @epoch 80) +Forward: 9.36s + +Saving... +Total: 9.81s + +[Epoch 207] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.1+0.8s +[3200/16000] [L1: 0.0045] 15.4+0.1s +[4800/16000] [L1: 0.0045] 15.7+0.1s +[6400/16000] [L1: 0.0045] 14.3+0.0s +[8000/16000] [L1: 0.0045] 14.8+0.0s +[9600/16000] [L1: 0.0045] 13.9+0.0s +[11200/16000] [L1: 0.0045] 13.9+0.0s +[12800/16000] [L1: 0.0045] 13.8+0.0s +[14400/16000] [L1: 0.0045] 14.8+0.0s +[16000/16000] [L1: 0.0045] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.192 (Best: 40.298 @epoch 80) +Forward: 9.33s + +Saving... +Total: 9.87s + +[Epoch 208] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0045] 15.9+0.8s +[3200/16000] [L1: 0.0045] 15.8+0.0s +[4800/16000] [L1: 0.0045] 15.8+0.1s +[6400/16000] [L1: 0.0045] 15.7+0.0s +[8000/16000] [L1: 0.0045] 15.8+0.0s +[9600/16000] [L1: 0.0045] 16.0+0.1s +[11200/16000] [L1: 0.0045] 15.1+0.0s +[12800/16000] [L1: 0.0045] 15.4+0.1s +[14400/16000] [L1: 0.0045] 15.2+0.0s +[16000/16000] [L1: 0.0045] 15.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.411 (Best: 40.298 @epoch 80) +Forward: 9.26s + +Saving... +Total: 9.72s + +[Epoch 209] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0043] 15.8+0.9s +[3200/16000] [L1: 0.0044] 15.5+0.0s +[4800/16000] [L1: 0.0044] 15.8+0.0s +[6400/16000] [L1: 0.0045] 14.9+0.0s +[8000/16000] [L1: 0.0045] 15.4+0.1s +[9600/16000] [L1: 0.0045] 15.0+0.0s +[11200/16000] [L1: 0.0045] 16.0+0.1s +[12800/16000] [L1: 0.0045] 15.3+0.0s +[14400/16000] [L1: 0.0045] 15.7+0.0s +[16000/16000] [L1: 0.0045] 15.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.244 (Best: 40.298 @epoch 80) +Forward: 9.29s + +Saving... +Total: 9.78s + +[Epoch 210] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 15.8+0.7s +[3200/16000] [L1: 0.0044] 15.3+0.1s +[4800/16000] [L1: 0.0045] 14.7+0.1s +[6400/16000] [L1: 0.0045] 15.5+0.1s +[8000/16000] [L1: 0.0045] 15.8+0.1s +[9600/16000] [L1: 0.0045] 15.8+0.1s +[11200/16000] [L1: 0.0045] 15.6+0.0s +[12800/16000] [L1: 0.0045] 14.1+0.0s +[14400/16000] [L1: 0.0045] 15.9+0.1s +[16000/16000] [L1: 0.0045] 14.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.266 (Best: 40.298 @epoch 80) +Forward: 9.29s + +Saving... +Total: 9.77s + +[Epoch 211] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0044] 14.7+0.8s +[3200/16000] [L1: 0.0044] 14.6+0.1s +[4800/16000] [L1: 0.0045] 14.5+0.0s +[6400/16000] [L1: 0.0045] 15.8+0.1s +[8000/16000] [L1: 0.0046] 15.4+0.0s +[9600/16000] [L1: 0.0046] 14.9+0.0s +[11200/16000] [L1: 0.0046] 14.7+0.0s +[12800/16000] [L1: 0.0046] 15.3+0.0s +[14400/16000] [L1: 0.0046] 16.0+0.0s +[16000/16000] [L1: 0.0045] 15.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.685 (Best: 40.298 @epoch 80) +Forward: 9.37s + +Saving... +Total: 9.86s + diff --git a/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/loss.pt b/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..2cb8aeac7a3784a1b17d156d419747cef2ed44bf --- /dev/null +++ b/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48da99bdddb436cdb4a093ba3a3efbe02ec42ba0bd17f6415b4f6645eb17b79f +size 559 diff --git a/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/loss_L1.pdf 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b/Demosaic/experiment/RAFTSINGLE_DEMOSAIC20_R1/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/config.txt b/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..4dc8f8ca1319fef5409dd7491edb00e1dab44868 --- /dev/null +++ b/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/config.txt @@ -0,0 +1,264 @@ +2020-11-09-16:16:58 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: . +extend: . +n_resblocks: 10 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFTS_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-16:18:07 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: . +extend: . +n_resblocks: 10 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFTS_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-17:25:58 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: . +extend: . +n_resblocks: 10 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFTS_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-10-13:09:18 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: . +extend: . +n_resblocks: 10 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFTS_DEMOSAIC20_R4 +load: RAFTS_DEMOSAIC20_R4 +resume: -1 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/log.txt b/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..f7162f5ca066d35ecff90ce0f354009321816b58 --- /dev/null +++ b/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/log.txt @@ -0,0 +1,10214 @@ +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 2.6102] 74.0+0.6s +[3200/16000] [L1: 1.8447] 70.7+0.1s +[4800/16000] [L1: 1.4394] 70.5+0.0s +[6400/16000] [L1: 1.2074] 71.3+0.1s +[8000/16000] [L1: 1.0687] 70.6+0.0s +[9600/16000] [L1: 0.9598] 70.3+0.0s +[11200/16000] [L1: 0.8767] 70.7+0.0s +[12800/16000] [L1: 0.8111] 72.0+0.0s +[14400/16000] [L1: 0.7553] 70.5+0.0s +[16000/16000] [L1: 0.7084] 71.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.978 (Best: 10.978 @epoch 1) +Forward: 37.92s + +Saving... +Total: 38.92s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2814] 70.4+0.9s +[3200/16000] [L1: 0.2702] 71.3+0.0s +[4800/16000] [L1: 0.2625] 70.1+0.0s +[6400/16000] [L1: 0.2556] 71.6+0.0s +[8000/16000] [L1: 0.2475] 72.1+0.0s +[9600/16000] [L1: 0.2413] 71.6+0.0s +[11200/16000] [L1: 0.2359] 70.6+0.0s +[12800/16000] [L1: 0.2308] 71.3+0.0s +[14400/16000] [L1: 0.2256] 70.6+0.0s +[16000/16000] [L1: 0.2212] 69.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.817 (Best: 11.817 @epoch 2) +Forward: 37.66s + +Saving... +Total: 38.21s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1680] 70.3+1.0s +[3200/16000] [L1: 0.1668] 71.2+0.1s +[4800/16000] [L1: 0.1628] 71.5+0.0s +[6400/16000] [L1: 0.1610] 71.8+0.0s +[8000/16000] [L1: 0.1579] 71.1+0.0s +[9600/16000] [L1: 0.1554] 70.7+0.0s +[11200/16000] [L1: 0.1530] 71.3+0.0s +[12800/16000] [L1: 0.1507] 70.4+0.0s +[14400/16000] [L1: 0.1487] 69.9+0.0s +[16000/16000] [L1: 0.1468] 70.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.960 (Best: 12.960 @epoch 3) +Forward: 37.75s + +Saving... +Total: 38.29s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1266] 71.1+1.0s +[3200/16000] [L1: 0.1267] 70.0+0.0s +[4800/16000] [L1: 0.1236] 71.0+0.0s +[6400/16000] [L1: 0.1205] 71.9+0.0s +[8000/16000] [L1: 0.1188] 70.1+0.0s +[9600/16000] [L1: 0.1161] 70.4+0.0s +[11200/16000] [L1: 0.1146] 70.2+0.0s +[12800/16000] [L1: 0.1136] 70.1+0.0s +[14400/16000] [L1: 0.1119] 71.6+0.0s +[16000/16000] [L1: 0.1107] 71.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.973 (Best: 13.973 @epoch 4) +Forward: 37.73s + +Saving... +Total: 38.28s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0977] 71.5+0.8s +[3200/16000] [L1: 0.0974] 72.2+0.1s +[4800/16000] [L1: 0.0963] 72.2+0.1s +[6400/16000] [L1: 0.0946] 72.1+0.1s +[8000/16000] [L1: 0.0923] 72.0+0.0s +[9600/16000] [L1: 0.0911] 71.7+0.1s +[11200/16000] [L1: 0.0903] 71.4+0.0s +[12800/16000] [L1: 0.0899] 71.7+0.0s +[14400/16000] [L1: 0.0890] 71.6+0.0s +[16000/16000] [L1: 0.0884] 69.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.449 (Best: 15.449 @epoch 5) +Forward: 37.75s + +Saving... +Total: 38.31s + +[Epoch 6] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 2.8111] 28.1+0.7s +[3200/16000] [L1: 2.0863] 25.7+0.0s +[4800/16000] [L1: 1.6330] 25.8+0.0s +[6400/16000] [L1: 1.3711] 25.7+0.0s +[8000/16000] [L1: 1.1975] 26.0+0.0s +[9600/16000] [L1: 1.0734] 26.1+0.0s +[11200/16000] [L1: 0.9785] 26.1+0.0s +[12800/16000] [L1: 0.9039] 26.1+0.0s +[14400/16000] [L1: 0.8427] 26.2+0.0s +[16000/16000] [L1: 0.7905] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.489 (Best: 12.489 @epoch 1) +Forward: 39.05s + +Saving... +Total: 39.60s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3019] 26.6+0.9s +[3200/16000] [L1: 0.2952] 26.2+0.0s +[4800/16000] [L1: 0.2905] 26.2+0.0s +[6400/16000] [L1: 0.2814] 26.0+0.0s +[8000/16000] [L1: 0.2762] 25.8+0.0s +[9600/16000] [L1: 0.2694] 25.9+0.0s +[11200/16000] [L1: 0.2633] 25.9+0.0s +[12800/16000] [L1: 0.2570] 25.5+0.0s +[14400/16000] [L1: 0.2521] 25.7+0.0s +[16000/16000] [L1: 0.2478] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.054 (Best: 13.054 @epoch 2) +Forward: 38.23s + +Saving... +Total: 38.87s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1921] 25.7+0.9s +[3200/16000] [L1: 0.1892] 25.7+0.0s +[4800/16000] [L1: 0.1850] 25.8+0.0s +[6400/16000] [L1: 0.1834] 25.8+0.0s +[8000/16000] [L1: 0.1804] 25.8+0.0s +[9600/16000] [L1: 0.1777] 25.7+0.0s +[11200/16000] [L1: 0.1744] 25.8+0.0s +[12800/16000] [L1: 0.1715] 25.8+0.0s +[14400/16000] [L1: 0.1693] 25.8+0.0s +[16000/16000] [L1: 0.1676] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.949 (Best: 13.949 @epoch 3) +Forward: 38.09s + +Saving... +Total: 38.67s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1393] 26.0+0.9s +[3200/16000] [L1: 0.1376] 25.8+0.0s +[4800/16000] [L1: 0.1365] 25.8+0.0s +[6400/16000] [L1: 0.1347] 25.9+0.0s +[8000/16000] [L1: 0.1342] 25.8+0.0s +[9600/16000] [L1: 0.1322] 25.7+0.0s +[11200/16000] [L1: 0.1301] 25.7+0.0s +[12800/16000] [L1: 0.1289] 25.6+0.0s +[14400/16000] [L1: 0.1274] 25.7+0.0s +[16000/16000] [L1: 0.1262] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 14.777 (Best: 14.777 @epoch 4) +Forward: 38.17s + +Saving... +Total: 38.71s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1158] 25.8+0.9s +[3200/16000] [L1: 0.1135] 25.6+0.0s +[4800/16000] [L1: 0.1124] 25.8+0.0s +[6400/16000] [L1: 0.1097] 25.8+0.0s +[8000/16000] [L1: 0.1084] 26.1+0.0s +[9600/16000] [L1: 0.1074] 25.7+0.0s +[11200/16000] [L1: 0.1062] 25.9+0.0s +[12800/16000] [L1: 0.1046] 25.7+0.0s +[14400/16000] [L1: 0.1039] 25.6+0.0s +[16000/16000] [L1: 0.1026] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.943 (Best: 15.943 @epoch 5) +Forward: 38.24s + +Saving... +Total: 38.78s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0888] 25.9+0.9s +[3200/16000] [L1: 0.0874] 26.0+0.0s +[4800/16000] [L1: 0.0885] 25.9+0.0s +[6400/16000] [L1: 0.0869] 25.9+0.0s +[8000/16000] [L1: 0.0862] 25.6+0.0s +[9600/16000] [L1: 0.0846] 25.7+0.0s +[11200/16000] [L1: 0.0848] 25.8+0.0s +[12800/16000] [L1: 0.0843] 25.7+0.0s +[14400/16000] [L1: 0.0833] 25.9+0.0s +[16000/16000] [L1: 0.0829] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.501 (Best: 17.501 @epoch 6) +Forward: 38.22s + +Saving... +Total: 38.73s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0730] 25.4+1.1s +[3200/16000] [L1: 0.0736] 25.8+0.0s +[4800/16000] [L1: 0.0731] 25.8+0.0s +[6400/16000] [L1: 0.0720] 25.8+0.0s +[8000/16000] [L1: 0.0718] 26.0+0.0s +[9600/16000] [L1: 0.0716] 25.9+0.0s +[11200/16000] [L1: 0.0706] 25.9+0.0s +[12800/16000] [L1: 0.0698] 25.8+0.0s +[14400/16000] [L1: 0.0690] 25.7+0.0s +[16000/16000] [L1: 0.0682] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 18.876 (Best: 18.876 @epoch 7) +Forward: 38.09s + +Saving... +Total: 38.71s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0668] 25.7+0.8s +[3200/16000] [L1: 0.0637] 25.4+0.0s +[4800/16000] [L1: 0.0612] 25.7+0.0s +[6400/16000] [L1: 0.0603] 25.8+0.0s +[8000/16000] [L1: 0.0588] 25.8+0.0s +[9600/16000] [L1: 0.0590] 25.6+0.0s +[11200/16000] [L1: 0.0590] 25.4+0.0s +[12800/16000] [L1: 0.0584] 25.8+0.0s +[14400/16000] [L1: 0.0578] 25.7+0.0s +[16000/16000] [L1: 0.0572] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 19.897 (Best: 19.897 @epoch 8) +Forward: 38.16s + +Saving... +Total: 38.70s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0544] 25.8+0.9s +[3200/16000] [L1: 0.0520] 25.7+0.0s +[4800/16000] [L1: 0.0511] 25.8+0.0s +[6400/16000] [L1: 0.0510] 25.8+0.0s +[8000/16000] [L1: 0.0509] 26.0+0.0s +[9600/16000] [L1: 0.0504] 25.6+0.0s +[11200/16000] [L1: 0.0502] 25.2+0.0s +[12800/16000] [L1: 0.0497] 25.6+0.0s +[14400/16000] [L1: 0.0493] 25.6+0.0s +[16000/16000] [L1: 0.0491] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.536 (Best: 21.536 @epoch 9) +Forward: 38.29s + +Saving... +Total: 38.82s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0446] 25.6+0.9s +[3200/16000] [L1: 0.0452] 25.8+0.0s +[4800/16000] [L1: 0.0441] 25.9+0.0s +[6400/16000] [L1: 0.0441] 25.8+0.0s +[8000/16000] [L1: 0.0443] 26.0+0.0s +[9600/16000] [L1: 0.0441] 25.8+0.0s +[11200/16000] [L1: 0.0436] 25.6+0.0s +[12800/16000] [L1: 0.0431] 25.7+0.0s +[14400/16000] [L1: 0.0429] 25.7+0.0s +[16000/16000] [L1: 0.0428] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.965 (Best: 22.965 @epoch 10) +Forward: 38.33s + +Saving... +Total: 38.82s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0411] 25.7+0.9s +[3200/16000] [L1: 0.0413] 25.5+0.0s +[4800/16000] [L1: 0.0401] 25.7+0.0s +[6400/16000] [L1: 0.0391] 25.9+0.0s +[8000/16000] [L1: 0.0394] 25.8+0.0s +[9600/16000] [L1: 0.0391] 26.1+0.0s +[11200/16000] [L1: 0.0390] 26.1+0.0s +[12800/16000] [L1: 0.0391] 25.9+0.0s +[14400/16000] [L1: 0.0390] 25.8+0.0s +[16000/16000] [L1: 0.0388] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.187 (Best: 24.187 @epoch 11) +Forward: 38.18s + +Saving... +Total: 38.76s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0347] 25.7+1.0s +[3200/16000] [L1: 0.0358] 25.7+0.0s +[4800/16000] [L1: 0.0358] 25.9+0.0s +[6400/16000] [L1: 0.0362] 26.0+0.0s +[8000/16000] [L1: 0.0358] 25.8+0.0s +[9600/16000] [L1: 0.0357] 25.9+0.0s +[11200/16000] [L1: 0.0353] 25.7+0.0s +[12800/16000] [L1: 0.0354] 26.1+0.0s +[14400/16000] [L1: 0.0355] 26.0+0.0s +[16000/16000] [L1: 0.0356] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.890 (Best: 24.890 @epoch 12) +Forward: 38.28s + +Saving... +Total: 38.78s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0329] 25.6+0.9s +[3200/16000] [L1: 0.0338] 25.4+0.0s +[4800/16000] [L1: 0.0338] 25.7+0.0s +[6400/16000] [L1: 0.0336] 25.8+0.0s +[8000/16000] [L1: 0.0336] 25.7+0.0s +[9600/16000] [L1: 0.0338] 25.6+0.0s +[11200/16000] [L1: 0.0340] 25.8+0.0s +[12800/16000] [L1: 0.0338] 25.7+0.0s +[14400/16000] [L1: 0.0339] 25.7+0.0s +[16000/16000] [L1: 0.0338] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.629 (Best: 25.629 @epoch 13) +Forward: 38.28s + +Saving... +Total: 38.83s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0297] 26.0+0.9s +[3200/16000] [L1: 0.0314] 25.7+0.0s +[4800/16000] [L1: 0.0319] 25.9+0.0s +[6400/16000] [L1: 0.0316] 25.8+0.0s +[8000/16000] [L1: 0.0315] 25.8+0.0s +[9600/16000] [L1: 0.0315] 25.7+0.0s +[11200/16000] [L1: 0.0317] 26.1+0.0s +[12800/16000] [L1: 0.0316] 25.7+0.0s +[14400/16000] [L1: 0.0318] 25.9+0.0s +[16000/16000] [L1: 0.0321] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.270 (Best: 26.270 @epoch 14) +Forward: 38.18s + +Saving... +Total: 38.70s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0328] 25.4+0.9s +[3200/16000] [L1: 0.0322] 25.7+0.0s +[4800/16000] [L1: 0.0315] 25.9+0.0s +[6400/16000] [L1: 0.0313] 26.0+0.0s +[8000/16000] [L1: 0.0311] 25.9+0.0s +[9600/16000] [L1: 0.0310] 25.8+0.0s +[11200/16000] [L1: 0.0308] 25.9+0.0s +[12800/16000] [L1: 0.0309] 25.8+0.0s +[14400/16000] [L1: 0.0312] 25.5+0.0s +[16000/16000] [L1: 0.0312] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.112 (Best: 27.112 @epoch 15) +Forward: 38.23s + +Saving... +Total: 38.77s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0303] 25.6+0.9s +[3200/16000] [L1: 0.0299] 25.5+0.0s +[4800/16000] [L1: 0.0294] 25.8+0.0s +[6400/16000] [L1: 0.0296] 25.9+0.0s +[8000/16000] [L1: 0.0301] 25.7+0.0s +[9600/16000] [L1: 0.0299] 25.9+0.0s +[11200/16000] [L1: 0.0298] 25.9+0.0s +[12800/16000] [L1: 0.0297] 25.4+0.0s +[14400/16000] [L1: 0.0298] 25.5+0.0s +[16000/16000] [L1: 0.0297] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.284 (Best: 27.284 @epoch 16) +Forward: 38.11s + +Saving... +Total: 38.68s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0286] 25.7+1.0s +[3200/16000] [L1: 0.0289] 25.7+0.0s +[4800/16000] [L1: 0.0300] 25.9+0.0s +[6400/16000] [L1: 0.0300] 25.8+0.0s +[8000/16000] [L1: 0.0298] 25.5+0.0s +[9600/16000] [L1: 0.0298] 25.7+0.0s +[11200/16000] [L1: 0.0297] 25.7+0.0s +[12800/16000] [L1: 0.0295] 25.8+0.0s +[14400/16000] [L1: 0.0292] 26.0+0.0s +[16000/16000] [L1: 0.0290] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.402 (Best: 28.402 @epoch 17) +Forward: 38.00s + +Saving... +Total: 38.59s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0287] 25.8+0.9s +[3200/16000] [L1: 0.0290] 25.6+0.0s +[4800/16000] [L1: 0.0286] 25.5+0.0s +[6400/16000] [L1: 0.0283] 25.6+0.0s +[8000/16000] [L1: 0.0283] 25.5+0.0s +[9600/16000] [L1: 0.0285] 25.7+0.0s +[11200/16000] [L1: 0.0284] 25.8+0.0s +[12800/16000] [L1: 0.0285] 25.9+0.0s +[14400/16000] [L1: 0.0283] 25.7+0.0s +[16000/16000] [L1: 0.0283] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.218 (Best: 29.218 @epoch 18) +Forward: 38.13s + +Saving... +Total: 38.64s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0269] 25.8+0.9s +[3200/16000] [L1: 0.0270] 25.9+0.0s +[4800/16000] [L1: 0.0271] 26.0+0.0s +[6400/16000] [L1: 0.0268] 25.5+0.0s +[8000/16000] [L1: 0.0272] 25.7+0.0s +[9600/16000] [L1: 0.0273] 25.5+0.0s +[11200/16000] [L1: 0.0273] 25.4+0.0s +[12800/16000] [L1: 0.0274] 25.7+0.0s +[14400/16000] [L1: 0.0274] 25.7+0.0s +[16000/16000] [L1: 0.0273] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.179 (Best: 30.179 @epoch 19) +Forward: 38.22s + +Saving... +Total: 38.74s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0287] 25.8+0.9s +[3200/16000] [L1: 0.0281] 25.8+0.0s +[4800/16000] [L1: 0.0273] 26.0+0.0s +[6400/16000] [L1: 0.0269] 26.0+0.0s +[8000/16000] [L1: 0.0268] 25.9+0.0s +[9600/16000] [L1: 0.0268] 25.9+0.0s +[11200/16000] [L1: 0.0268] 25.7+0.0s +[12800/16000] [L1: 0.0268] 25.8+0.0s +[14400/16000] [L1: 0.0267] 25.8+0.0s +[16000/16000] [L1: 0.0267] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.717 (Best: 30.717 @epoch 20) +Forward: 38.20s + +Saving... +Total: 38.75s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0262] 25.7+1.0s +[3200/16000] [L1: 0.0264] 25.4+0.0s +[4800/16000] [L1: 0.0271] 25.4+0.0s +[6400/16000] [L1: 0.0270] 25.7+0.0s +[8000/16000] [L1: 0.0265] 26.0+0.0s +[9600/16000] [L1: 0.0263] 25.7+0.0s +[11200/16000] [L1: 0.0261] 25.8+0.0s +[12800/16000] [L1: 0.0260] 25.4+0.0s +[14400/16000] [L1: 0.0260] 25.7+0.0s +[16000/16000] [L1: 0.0260] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.346 (Best: 30.717 @epoch 20) +Forward: 38.20s + +Saving... +Total: 38.72s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0266] 25.8+0.9s +[3200/16000] [L1: 0.0256] 25.7+0.0s +[4800/16000] [L1: 0.0261] 25.7+0.0s +[6400/16000] [L1: 0.0261] 25.8+0.0s +[8000/16000] [L1: 0.0260] 26.0+0.0s +[9600/16000] [L1: 0.0258] 25.8+0.0s +[11200/16000] [L1: 0.0257] 26.0+0.0s +[12800/16000] [L1: 0.0257] 26.0+0.0s +[14400/16000] [L1: 0.0257] 26.0+0.0s +[16000/16000] [L1: 0.0257] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.920 (Best: 30.920 @epoch 22) +Forward: 38.08s + +Saving... +Total: 38.58s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0242] 25.7+0.9s +[3200/16000] [L1: 0.0246] 25.8+0.0s +[4800/16000] [L1: 0.0247] 25.8+0.0s +[6400/16000] [L1: 0.0247] 25.6+0.0s +[8000/16000] [L1: 0.0248] 25.6+0.0s +[9600/16000] [L1: 0.0249] 25.6+0.0s +[11200/16000] [L1: 0.0247] 25.5+0.0s +[12800/16000] [L1: 0.0245] 25.7+0.0s +[14400/16000] [L1: 0.0244] 25.6+0.0s +[16000/16000] [L1: 0.0244] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.693 (Best: 31.693 @epoch 23) +Forward: 38.15s + +Saving... +Total: 38.66s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0246] 25.9+0.9s +[3200/16000] [L1: 0.0242] 25.8+0.0s +[4800/16000] [L1: 0.0240] 25.6+0.0s +[6400/16000] [L1: 0.0241] 25.9+0.0s +[8000/16000] [L1: 0.0242] 26.1+0.0s +[9600/16000] [L1: 0.0241] 25.7+0.0s +[11200/16000] [L1: 0.0241] 25.8+0.0s +[12800/16000] [L1: 0.0243] 25.7+0.0s +[14400/16000] [L1: 0.0242] 25.5+0.0s +[16000/16000] [L1: 0.0240] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.071 (Best: 32.071 @epoch 24) +Forward: 38.17s + +Saving... +Total: 38.77s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0240] 25.7+0.9s +[3200/16000] [L1: 0.0240] 26.0+0.0s +[4800/16000] [L1: 0.0239] 26.1+0.0s +[6400/16000] [L1: 0.0238] 26.0+0.0s +[8000/16000] [L1: 0.0239] 25.8+0.0s +[9600/16000] [L1: 0.0237] 25.9+0.0s +[11200/16000] [L1: 0.0236] 25.8+0.0s +[12800/16000] [L1: 0.0235] 25.9+0.0s +[14400/16000] [L1: 0.0234] 25.8+0.0s +[16000/16000] [L1: 0.0233] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.125 (Best: 32.125 @epoch 25) +Forward: 38.07s + +Saving... +Total: 38.70s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0225] 25.7+0.9s +[3200/16000] [L1: 0.0231] 25.9+0.0s +[4800/16000] [L1: 0.0234] 25.7+0.0s +[6400/16000] [L1: 0.0233] 25.4+0.0s +[8000/16000] [L1: 0.0233] 25.9+0.0s +[9600/16000] [L1: 0.0231] 25.9+0.0s +[11200/16000] [L1: 0.0230] 25.7+0.0s +[12800/16000] [L1: 0.0228] 25.6+0.0s +[14400/16000] [L1: 0.0228] 25.8+0.0s +[16000/16000] [L1: 0.0227] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.783 (Best: 32.783 @epoch 26) +Forward: 38.14s + +Saving... +Total: 38.68s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0224] 25.5+0.9s +[3200/16000] [L1: 0.0224] 25.7+0.0s +[4800/16000] [L1: 0.0224] 25.7+0.0s +[6400/16000] [L1: 0.0225] 25.7+0.0s +[8000/16000] [L1: 0.0225] 25.6+0.0s +[9600/16000] [L1: 0.0225] 26.0+0.0s +[11200/16000] [L1: 0.0226] 25.7+0.0s +[12800/16000] [L1: 0.0225] 26.2+0.0s +[14400/16000] [L1: 0.0224] 26.0+0.0s +[16000/16000] [L1: 0.0224] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.611 (Best: 33.611 @epoch 27) +Forward: 38.22s + +Saving... +Total: 38.73s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0222] 25.7+0.9s +[3200/16000] [L1: 0.0230] 25.8+0.0s +[4800/16000] [L1: 0.0227] 26.0+0.0s +[6400/16000] [L1: 0.0226] 25.7+0.0s +[8000/16000] [L1: 0.0225] 25.5+0.0s +[9600/16000] [L1: 0.0225] 25.7+0.0s +[11200/16000] [L1: 0.0224] 25.6+0.0s +[12800/16000] [L1: 0.0224] 25.5+0.0s +[14400/16000] [L1: 0.0223] 25.6+0.0s +[16000/16000] [L1: 0.0221] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.283 (Best: 33.611 @epoch 27) +Forward: 38.15s + +Saving... +Total: 38.68s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0215] 25.5+0.9s +[3200/16000] [L1: 0.0216] 25.3+0.0s +[4800/16000] [L1: 0.0214] 25.5+0.0s +[6400/16000] [L1: 0.0214] 25.7+0.0s +[8000/16000] [L1: 0.0215] 25.7+0.0s +[9600/16000] [L1: 0.0214] 25.7+0.0s +[11200/16000] [L1: 0.0214] 25.8+0.0s +[12800/16000] [L1: 0.0215] 25.4+0.0s +[14400/16000] [L1: 0.0215] 25.9+0.0s +[16000/16000] [L1: 0.0215] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.769 (Best: 33.769 @epoch 29) +Forward: 38.21s + +Saving... +Total: 38.72s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0218] 25.7+1.0s +[3200/16000] [L1: 0.0213] 25.8+0.0s +[4800/16000] [L1: 0.0216] 25.8+0.0s +[6400/16000] [L1: 0.0215] 26.1+0.0s +[8000/16000] [L1: 0.0211] 25.9+0.0s +[9600/16000] [L1: 0.0212] 25.8+0.0s +[11200/16000] [L1: 0.0213] 25.9+0.0s +[12800/16000] [L1: 0.0213] 25.8+0.0s +[14400/16000] [L1: 0.0213] 25.6+0.0s +[16000/16000] [L1: 0.0213] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.678 (Best: 33.769 @epoch 29) +Forward: 38.23s + +Saving... +Total: 38.70s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0217] 25.8+0.9s +[3200/16000] [L1: 0.0213] 25.5+0.0s +[4800/16000] [L1: 0.0213] 25.7+0.0s +[6400/16000] [L1: 0.0211] 26.0+0.0s +[8000/16000] [L1: 0.0212] 26.1+0.0s +[9600/16000] [L1: 0.0213] 26.0+0.0s +[11200/16000] [L1: 0.0214] 25.7+0.0s +[12800/16000] [L1: 0.0214] 25.7+0.0s +[14400/16000] [L1: 0.0214] 25.8+0.0s +[16000/16000] [L1: 0.0212] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.956 (Best: 33.956 @epoch 31) +Forward: 38.24s + +Saving... +Total: 38.77s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0201] 25.8+0.9s +[3200/16000] [L1: 0.0211] 25.7+0.0s +[4800/16000] [L1: 0.0210] 25.7+0.0s +[6400/16000] [L1: 0.0209] 25.6+0.0s +[8000/16000] [L1: 0.0207] 25.7+0.0s +[9600/16000] [L1: 0.0205] 25.8+0.0s +[11200/16000] [L1: 0.0206] 25.6+0.0s +[12800/16000] [L1: 0.0206] 25.5+0.0s +[14400/16000] [L1: 0.0206] 25.8+0.0s +[16000/16000] [L1: 0.0206] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.144 (Best: 34.144 @epoch 32) +Forward: 38.04s + +Saving... +Total: 38.53s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0201] 25.7+0.9s +[3200/16000] [L1: 0.0200] 25.9+0.0s +[4800/16000] [L1: 0.0198] 25.6+0.0s +[6400/16000] [L1: 0.0201] 25.6+0.0s +[8000/16000] [L1: 0.0203] 25.8+0.0s +[9600/16000] [L1: 0.0203] 25.6+0.0s +[11200/16000] [L1: 0.0205] 25.7+0.0s +[12800/16000] [L1: 0.0205] 25.4+0.0s +[14400/16000] [L1: 0.0205] 25.6+0.0s +[16000/16000] [L1: 0.0204] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.133 (Best: 34.144 @epoch 32) +Forward: 38.04s + +Saving... +Total: 38.58s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0206] 25.7+0.8s +[3200/16000] [L1: 0.0206] 25.9+0.0s +[4800/16000] [L1: 0.0204] 25.6+0.0s +[6400/16000] [L1: 0.0205] 26.0+0.0s +[8000/16000] [L1: 0.0204] 25.6+0.0s +[9600/16000] [L1: 0.0203] 25.6+0.0s +[11200/16000] [L1: 0.0204] 25.4+0.0s +[12800/16000] [L1: 0.0204] 25.6+0.0s +[14400/16000] [L1: 0.0203] 25.7+0.0s +[16000/16000] [L1: 0.0202] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.148 (Best: 34.148 @epoch 34) +Forward: 38.25s + +Saving... +Total: 38.81s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0203] 26.0+0.9s +[3200/16000] [L1: 0.0201] 25.8+0.0s +[4800/16000] [L1: 0.0198] 26.2+0.0s +[6400/16000] [L1: 0.0200] 25.9+0.0s +[8000/16000] [L1: 0.0200] 26.2+0.0s +[9600/16000] [L1: 0.0199] 25.4+0.0s +[11200/16000] [L1: 0.0201] 25.5+0.0s +[12800/16000] [L1: 0.0201] 25.5+0.0s +[14400/16000] [L1: 0.0201] 25.6+0.0s +[16000/16000] [L1: 0.0201] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.592 (Best: 34.592 @epoch 35) +Forward: 38.33s + +Saving... +Total: 38.87s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0195] 25.8+0.9s +[3200/16000] [L1: 0.0197] 25.7+0.0s +[4800/16000] [L1: 0.0199] 25.8+0.0s +[6400/16000] [L1: 0.0197] 25.8+0.0s +[8000/16000] [L1: 0.0195] 26.0+0.0s +[9600/16000] [L1: 0.0195] 25.8+0.0s +[11200/16000] [L1: 0.0197] 25.6+0.0s +[12800/16000] [L1: 0.0196] 25.7+0.0s +[14400/16000] [L1: 0.0197] 26.0+0.0s +[16000/16000] [L1: 0.0196] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.230 (Best: 34.592 @epoch 35) +Forward: 38.09s + +Saving... +Total: 38.56s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0201] 25.6+0.9s +[3200/16000] [L1: 0.0197] 25.9+0.0s +[4800/16000] [L1: 0.0198] 25.7+0.0s +[6400/16000] [L1: 0.0198] 25.9+0.0s +[8000/16000] [L1: 0.0198] 25.7+0.0s +[9600/16000] [L1: 0.0200] 25.6+0.0s +[11200/16000] [L1: 0.0200] 25.4+0.0s +[12800/16000] [L1: 0.0199] 25.9+0.0s +[14400/16000] [L1: 0.0198] 25.6+0.0s +[16000/16000] [L1: 0.0199] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.712 (Best: 34.712 @epoch 37) +Forward: 38.05s + +Saving... +Total: 38.65s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0194] 25.6+0.9s +[3200/16000] [L1: 0.0193] 26.1+0.0s +[4800/16000] [L1: 0.0193] 25.7+0.0s +[6400/16000] [L1: 0.0194] 25.6+0.0s +[8000/16000] [L1: 0.0195] 25.6+0.0s +[9600/16000] [L1: 0.0197] 25.7+0.0s +[11200/16000] [L1: 0.0195] 26.0+0.0s +[12800/16000] [L1: 0.0195] 25.4+0.0s +[14400/16000] [L1: 0.0197] 25.5+0.0s +[16000/16000] [L1: 0.0197] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.740 (Best: 34.740 @epoch 38) +Forward: 38.29s + +Saving... +Total: 38.83s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0192] 25.9+1.0s +[3200/16000] [L1: 0.0190] 25.6+0.0s +[4800/16000] [L1: 0.0189] 25.7+0.0s +[6400/16000] [L1: 0.0192] 25.9+0.0s +[8000/16000] [L1: 0.0191] 25.9+0.0s +[9600/16000] [L1: 0.0192] 25.7+0.0s +[11200/16000] [L1: 0.0193] 25.5+0.0s +[12800/16000] [L1: 0.0192] 25.4+0.0s +[14400/16000] [L1: 0.0193] 25.7+0.0s +[16000/16000] [L1: 0.0193] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.595 (Best: 34.740 @epoch 38) +Forward: 38.26s + +Saving... +Total: 38.75s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0195] 26.1+1.0s +[3200/16000] [L1: 0.0192] 26.2+0.0s +[4800/16000] [L1: 0.0193] 26.0+0.0s +[6400/16000] [L1: 0.0192] 25.6+0.0s +[8000/16000] [L1: 0.0191] 25.6+0.0s +[9600/16000] [L1: 0.0191] 25.7+0.0s +[11200/16000] [L1: 0.0191] 25.5+0.0s +[12800/16000] [L1: 0.0191] 25.6+0.0s +[14400/16000] [L1: 0.0191] 25.4+0.0s +[16000/16000] [L1: 0.0190] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.822 (Best: 34.822 @epoch 40) +Forward: 38.19s + +Saving... +Total: 38.70s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0190] 25.9+1.0s +[3200/16000] [L1: 0.0186] 25.7+0.0s +[4800/16000] [L1: 0.0186] 25.8+0.0s +[6400/16000] [L1: 0.0188] 25.8+0.0s +[8000/16000] [L1: 0.0189] 26.2+0.0s +[9600/16000] [L1: 0.0188] 26.1+0.0s +[11200/16000] [L1: 0.0189] 25.6+0.0s +[12800/16000] [L1: 0.0189] 25.5+0.0s +[14400/16000] [L1: 0.0188] 25.9+0.0s +[16000/16000] [L1: 0.0189] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.121 (Best: 35.121 @epoch 41) +Forward: 38.23s + +Saving... +Total: 38.86s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0182] 25.7+1.0s +[3200/16000] [L1: 0.0188] 25.6+0.0s +[4800/16000] [L1: 0.0186] 25.7+0.0s +[6400/16000] [L1: 0.0184] 25.7+0.0s +[8000/16000] [L1: 0.0184] 25.6+0.0s +[9600/16000] [L1: 0.0184] 25.8+0.0s +[11200/16000] [L1: 0.0186] 25.8+0.0s +[12800/16000] [L1: 0.0186] 25.8+0.0s +[14400/16000] [L1: 0.0186] 25.8+0.0s +[16000/16000] [L1: 0.0188] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.489 (Best: 35.489 @epoch 42) +Forward: 38.27s + +Saving... +Total: 38.77s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0196] 25.8+1.0s +[3200/16000] [L1: 0.0187] 25.7+0.0s +[4800/16000] [L1: 0.0188] 25.8+0.0s +[6400/16000] [L1: 0.0188] 25.6+0.0s +[8000/16000] [L1: 0.0188] 25.7+0.0s +[9600/16000] [L1: 0.0189] 25.7+0.0s +[11200/16000] [L1: 0.0189] 25.7+0.0s +[12800/16000] [L1: 0.0189] 25.9+0.0s +[14400/16000] [L1: 0.0189] 25.6+0.0s +[16000/16000] [L1: 0.0188] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.399 (Best: 35.489 @epoch 42) +Forward: 38.19s + +Saving... +Total: 38.66s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0186] 25.4+0.9s +[3200/16000] [L1: 0.0187] 25.9+0.0s +[4800/16000] [L1: 0.0187] 25.9+0.0s +[6400/16000] [L1: 0.0187] 25.7+0.0s +[8000/16000] [L1: 0.0188] 26.1+0.0s +[9600/16000] [L1: 0.0187] 26.0+0.0s +[11200/16000] [L1: 0.0187] 25.6+0.0s +[12800/16000] [L1: 0.0186] 25.6+0.0s +[14400/16000] [L1: 0.0186] 25.6+0.0s +[16000/16000] [L1: 0.0186] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.637 (Best: 35.637 @epoch 44) +Forward: 38.15s + +Saving... +Total: 38.63s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0192] 59.8+0.9s +[3200/16000] [L1: 0.0186] 30.3+0.0s +[4800/16000] [L1: 0.0186] 59.1+0.0s +[6400/16000] [L1: 0.0184] 58.3+0.0s +[8000/16000] [L1: 0.0183] 58.5+0.0s +[9600/16000] [L1: 0.0183] 59.5+0.0s +[11200/16000] [L1: 0.0183] 59.7+0.0s +[12800/16000] [L1: 0.0183] 59.3+0.0s +[14400/16000] [L1: 0.0183] 59.6+0.0s +[16000/16000] [L1: 0.0184] 59.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.728 (Best: 35.728 @epoch 45) +Forward: 37.68s + +Saving... +Total: 38.23s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0186] 25.5+0.9s +[3200/16000] [L1: 0.0184] 25.6+0.0s +[4800/16000] [L1: 0.0185] 25.8+0.0s +[6400/16000] [L1: 0.0186] 25.8+0.0s +[8000/16000] [L1: 0.0185] 25.9+0.0s +[9600/16000] [L1: 0.0185] 25.8+0.0s +[11200/16000] [L1: 0.0185] 25.8+0.0s +[12800/16000] [L1: 0.0184] 26.0+0.0s +[14400/16000] [L1: 0.0184] 25.5+0.0s +[16000/16000] [L1: 0.0184] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.929 (Best: 35.929 @epoch 46) +Forward: 38.22s + +Saving... +Total: 38.71s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0183] 25.5+0.9s +[3200/16000] [L1: 0.0178] 25.7+0.0s +[4800/16000] [L1: 0.0178] 25.9+0.0s +[6400/16000] [L1: 0.0180] 25.8+0.0s +[8000/16000] [L1: 0.0181] 25.8+0.0s +[9600/16000] [L1: 0.0180] 25.8+0.0s +[11200/16000] [L1: 0.0181] 25.8+0.0s +[12800/16000] [L1: 0.0181] 26.2+0.0s +[14400/16000] [L1: 0.0182] 26.0+0.0s +[16000/16000] [L1: 0.0182] 26.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.126 (Best: 36.126 @epoch 47) +Forward: 38.28s + +Saving... +Total: 38.78s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0189] 25.7+1.0s +[3200/16000] [L1: 0.0178] 26.0+0.0s +[4800/16000] [L1: 0.0178] 26.0+0.0s +[6400/16000] [L1: 0.0179] 26.1+0.0s +[8000/16000] [L1: 0.0180] 26.1+0.0s +[9600/16000] [L1: 0.0180] 26.1+0.0s +[11200/16000] [L1: 0.0180] 25.9+0.0s +[12800/16000] [L1: 0.0180] 25.6+0.0s +[14400/16000] [L1: 0.0181] 25.5+0.0s +[16000/16000] [L1: 0.0182] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.799 (Best: 36.126 @epoch 47) +Forward: 38.23s + +Saving... +Total: 38.71s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0184] 25.5+0.9s +[3200/16000] [L1: 0.0181] 25.6+0.0s +[4800/16000] [L1: 0.0180] 25.8+0.0s +[6400/16000] [L1: 0.0179] 26.0+0.0s +[8000/16000] [L1: 0.0179] 25.8+0.0s +[9600/16000] [L1: 0.0177] 25.9+0.0s +[11200/16000] [L1: 0.0177] 25.8+0.0s +[12800/16000] [L1: 0.0178] 25.7+0.0s +[14400/16000] [L1: 0.0178] 25.6+0.0s +[16000/16000] [L1: 0.0178] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.698 (Best: 36.126 @epoch 47) +Forward: 38.23s + +Saving... +Total: 38.76s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0187] 25.7+0.9s +[3200/16000] [L1: 0.0184] 25.8+0.0s +[4800/16000] [L1: 0.0182] 25.9+0.0s +[6400/16000] [L1: 0.0182] 26.2+0.0s +[8000/16000] [L1: 0.0181] 25.9+0.0s +[9600/16000] [L1: 0.0180] 26.0+0.0s +[11200/16000] [L1: 0.0179] 25.9+0.0s +[12800/16000] [L1: 0.0179] 25.9+0.0s +[14400/16000] [L1: 0.0179] 25.7+0.0s +[16000/16000] [L1: 0.0179] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.930 (Best: 36.126 @epoch 47) +Forward: 38.22s + +Saving... +Total: 38.69s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 25.7+0.9s +[3200/16000] [L1: 0.0174] 25.8+0.0s +[4800/16000] [L1: 0.0176] 25.9+0.0s +[6400/16000] [L1: 0.0178] 26.0+0.0s +[8000/16000] [L1: 0.0177] 26.0+0.0s +[9600/16000] [L1: 0.0177] 25.8+0.0s +[11200/16000] [L1: 0.0177] 26.1+0.0s +[12800/16000] [L1: 0.0177] 25.9+0.0s +[14400/16000] [L1: 0.0177] 26.3+0.0s +[16000/16000] [L1: 0.0179] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.632 (Best: 36.126 @epoch 47) +Forward: 38.19s + +Saving... +Total: 38.83s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0975] 25.8+0.9s +[3200/16000] [L1: 0.0660] 25.8+0.0s +[4800/16000] [L1: 0.0540] 25.9+0.0s +[6400/16000] [L1: 0.0478] 26.0+0.0s +[8000/16000] [L1: 0.0437] 26.3+0.0s +[9600/16000] [L1: 0.0408] 25.9+0.0s +[11200/16000] [L1: 0.0387] 25.9+0.0s +[12800/16000] [L1: 0.0371] 26.0+0.0s +[14400/16000] [L1: 0.0358] 25.8+0.0s +[16000/16000] [L1: 0.0346] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.292 (Best: 36.292 @epoch 52) +Forward: 38.28s + +Saving... +Total: 38.80s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0253] 25.7+0.9s +[3200/16000] [L1: 0.0249] 25.9+0.0s +[4800/16000] [L1: 0.0248] 25.9+0.0s +[6400/16000] [L1: 0.0247] 25.7+0.0s +[8000/16000] [L1: 0.0245] 25.9+0.0s +[9600/16000] [L1: 0.0242] 25.8+0.0s +[11200/16000] [L1: 0.0238] 25.6+0.0s +[12800/16000] [L1: 0.0236] 25.8+0.0s +[14400/16000] [L1: 0.0235] 25.8+0.0s +[16000/16000] [L1: 0.0232] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.757 (Best: 37.757 @epoch 53) +Forward: 38.17s + +Saving... +Total: 38.78s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0210] 25.9+0.9s +[3200/16000] [L1: 0.0209] 26.0+0.0s +[4800/16000] [L1: 0.0210] 31.1+0.0s +[6400/16000] [L1: 0.0211] 59.6+0.0s +[8000/16000] [L1: 0.0210] 58.5+0.0s +[9600/16000] [L1: 0.0211] 59.4+0.0s +[11200/16000] [L1: 0.0210] 60.2+0.0s +[12800/16000] [L1: 0.0208] 59.3+0.0s +[14400/16000] [L1: 0.0207] 60.0+0.0s +[16000/16000] [L1: 0.0206] 59.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.402 (Best: 38.402 @epoch 54) +Forward: 37.78s + +Saving... +Total: 38.27s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0200] 43.0+0.9s +[3200/16000] [L1: 0.0194] 25.5+0.0s +[4800/16000] [L1: 0.0195] 25.5+0.0s +[6400/16000] [L1: 0.0193] 25.8+0.0s +[8000/16000] [L1: 0.0194] 25.5+0.0s +[9600/16000] [L1: 0.0196] 25.6+0.0s +[11200/16000] [L1: 0.0196] 25.6+0.0s +[12800/16000] [L1: 0.0195] 25.6+0.0s +[14400/16000] [L1: 0.0195] 25.6+0.0s +[16000/16000] [L1: 0.0194] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.965 (Best: 38.965 @epoch 55) +Forward: 38.33s + +Saving... +Total: 38.85s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0189] 25.6+0.9s +[3200/16000] [L1: 0.0183] 25.7+0.0s +[4800/16000] [L1: 0.0185] 26.0+0.0s +[6400/16000] [L1: 0.0185] 25.7+0.0s +[8000/16000] [L1: 0.0184] 25.7+0.0s +[9600/16000] [L1: 0.0182] 26.0+0.0s +[11200/16000] [L1: 0.0183] 44.4+0.0s +[12800/16000] [L1: 0.0183] 59.1+0.0s +[14400/16000] [L1: 0.0183] 58.2+0.0s +[16000/16000] [L1: 0.0183] 60.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.233 (Best: 39.233 @epoch 56) +Forward: 37.63s + +Saving... +Total: 38.16s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0183] 60.3+0.9s +[3200/16000] [L1: 0.0184] 60.1+0.0s +[4800/16000] [L1: 0.0183] 60.0+0.0s +[6400/16000] [L1: 0.0182] 59.5+0.0s +[8000/16000] [L1: 0.0182] 28.5+0.0s +[9600/16000] [L1: 0.0181] 25.8+0.0s +[11200/16000] [L1: 0.0181] 34.9+0.0s +[12800/16000] [L1: 0.0182] 57.4+0.0s +[14400/16000] [L1: 0.0182] 58.1+0.0s +[16000/16000] [L1: 0.0181] 59.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.129 (Best: 39.233 @epoch 56) +Forward: 37.52s + +Saving... +Total: 38.05s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0182] 60.3+1.0s +[3200/16000] [L1: 0.0183] 60.0+0.0s +[4800/16000] [L1: 0.0184] 60.2+0.0s +[6400/16000] [L1: 0.0184] 59.8+0.0s +[8000/16000] [L1: 0.0184] 37.9+0.0s +[9600/16000] [L1: 0.0182] 25.2+0.0s +[11200/16000] [L1: 0.0181] 25.5+0.0s +[12800/16000] [L1: 0.0179] 25.5+0.0s +[14400/16000] [L1: 0.0179] 25.6+0.0s +[16000/16000] [L1: 0.0178] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.001 (Best: 39.233 @epoch 56) +Forward: 38.22s + +Saving... +Total: 38.71s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 25.9+0.9s +[3200/16000] [L1: 0.0175] 25.7+0.0s +[4800/16000] [L1: 0.0178] 25.8+0.0s +[6400/16000] [L1: 0.0177] 25.6+0.0s +[8000/16000] [L1: 0.0178] 26.0+0.0s +[9600/16000] [L1: 0.0176] 25.9+0.0s +[11200/16000] [L1: 0.0177] 25.8+0.0s +[12800/16000] [L1: 0.0176] 25.8+0.0s +[14400/16000] [L1: 0.0176] 25.9+0.0s +[16000/16000] [L1: 0.0175] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.661 (Best: 39.661 @epoch 59) +Forward: 38.18s + +Saving... +Total: 38.70s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0176] 25.9+0.9s +[3200/16000] [L1: 0.0177] 25.9+0.0s +[4800/16000] [L1: 0.0176] 26.2+0.0s +[6400/16000] [L1: 0.0176] 25.8+0.0s +[8000/16000] [L1: 0.0175] 25.9+0.0s +[9600/16000] [L1: 0.0174] 25.7+0.0s +[11200/16000] [L1: 0.0174] 25.7+0.0s +[12800/16000] [L1: 0.0175] 25.6+0.0s +[14400/16000] [L1: 0.0175] 25.6+0.0s +[16000/16000] [L1: 0.0175] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.103 (Best: 39.661 @epoch 59) +Forward: 38.18s + +Saving... +Total: 38.68s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0175] 25.6+0.9s +[3200/16000] [L1: 0.0179] 25.7+0.0s +[4800/16000] [L1: 0.0179] 26.0+0.0s +[6400/16000] [L1: 0.0178] 25.6+0.0s +[8000/16000] [L1: 0.0177] 25.6+0.0s +[9600/16000] [L1: 0.0175] 25.8+0.0s +[11200/16000] [L1: 0.0173] 25.7+0.0s +[12800/16000] [L1: 0.0173] 25.6+0.0s +[14400/16000] [L1: 0.0173] 25.5+0.0s +[16000/16000] [L1: 0.0174] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.481 (Best: 39.661 @epoch 59) +Forward: 38.22s + +Saving... +Total: 38.72s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0160] 25.8+0.9s +[3200/16000] [L1: 0.0168] 25.5+0.0s +[4800/16000] [L1: 0.0170] 25.9+0.0s +[6400/16000] [L1: 0.0170] 26.1+0.0s +[8000/16000] [L1: 0.0170] 25.8+0.0s +[9600/16000] [L1: 0.0170] 26.0+0.0s +[11200/16000] [L1: 0.0170] 25.8+0.0s +[12800/16000] [L1: 0.0171] 25.9+0.0s +[14400/16000] [L1: 0.0171] 25.6+0.0s +[16000/16000] [L1: 0.0171] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.912 (Best: 39.912 @epoch 62) +Forward: 38.15s + +Saving... +Total: 38.69s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0174] 25.8+1.0s +[3200/16000] [L1: 0.0173] 25.8+0.0s +[4800/16000] [L1: 0.0173] 25.9+0.0s +[6400/16000] [L1: 0.0174] 26.0+0.0s +[8000/16000] [L1: 0.0173] 26.0+0.0s +[9600/16000] [L1: 0.0174] 26.1+0.0s +[11200/16000] [L1: 0.0173] 25.9+0.0s +[12800/16000] [L1: 0.0174] 26.0+0.0s +[14400/16000] [L1: 0.0174] 25.5+0.0s +[16000/16000] [L1: 0.0173] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.007 (Best: 40.007 @epoch 63) +Forward: 38.25s + +Saving... +Total: 38.98s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0171] 25.8+1.0s +[3200/16000] [L1: 0.0170] 26.1+0.0s +[4800/16000] [L1: 0.0171] 25.9+0.0s +[6400/16000] [L1: 0.0170] 25.9+0.0s +[8000/16000] [L1: 0.0171] 26.1+0.0s +[9600/16000] [L1: 0.0171] 25.8+0.0s +[11200/16000] [L1: 0.0170] 25.8+0.0s +[12800/16000] [L1: 0.0170] 25.9+0.0s +[14400/16000] [L1: 0.0170] 26.0+0.0s +[16000/16000] [L1: 0.0170] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.078 (Best: 40.078 @epoch 64) +Forward: 38.18s + +Saving... +Total: 38.81s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0166] 25.7+0.9s +[3200/16000] [L1: 0.0164] 26.1+0.0s +[4800/16000] [L1: 0.0164] 26.1+0.0s +[6400/16000] [L1: 0.0165] 26.1+0.0s +[8000/16000] [L1: 0.0165] 57.3+0.0s +[9600/16000] [L1: 0.0167] 58.4+0.0s +[11200/16000] [L1: 0.0168] 58.9+0.0s +[12800/16000] [L1: 0.0168] 59.7+0.0s +[14400/16000] [L1: 0.0168] 59.7+0.0s +[16000/16000] [L1: 0.0169] 59.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.064 (Best: 40.078 @epoch 64) +Forward: 37.77s + +Saving... +Total: 38.34s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 60.6+0.9s +[3200/16000] [L1: 0.0168] 50.2+0.0s +[4800/16000] [L1: 0.0169] 25.9+0.0s +[6400/16000] [L1: 0.0169] 25.7+0.0s +[8000/16000] [L1: 0.0170] 25.3+0.0s +[9600/16000] [L1: 0.0169] 25.5+0.0s +[11200/16000] [L1: 0.0169] 25.9+0.0s +[12800/16000] [L1: 0.0169] 25.9+0.0s +[14400/16000] [L1: 0.0169] 25.4+0.0s +[16000/16000] [L1: 0.0169] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.033 (Best: 40.078 @epoch 64) +Forward: 38.24s + +Saving... +Total: 38.71s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 25.6+1.0s +[3200/16000] [L1: 0.0167] 25.4+0.0s +[4800/16000] [L1: 0.0168] 25.7+0.0s +[6400/16000] [L1: 0.0166] 26.0+0.0s +[8000/16000] [L1: 0.0166] 25.7+0.0s +[9600/16000] [L1: 0.0165] 25.9+0.0s +[11200/16000] [L1: 0.0164] 25.4+0.0s +[12800/16000] [L1: 0.0165] 25.8+0.0s +[14400/16000] [L1: 0.0166] 25.9+0.0s +[16000/16000] [L1: 0.0166] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.149 (Best: 40.149 @epoch 67) +Forward: 38.12s + +Saving... +Total: 38.70s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0170] 25.9+0.9s +[3200/16000] [L1: 0.0170] 26.0+0.0s +[4800/16000] [L1: 0.0168] 25.9+0.0s +[6400/16000] [L1: 0.0167] 26.0+0.0s +[8000/16000] [L1: 0.0168] 25.9+0.0s +[9600/16000] [L1: 0.0169] 25.7+0.0s +[11200/16000] [L1: 0.0169] 25.8+0.0s +[12800/16000] [L1: 0.0168] 25.7+0.0s +[14400/16000] [L1: 0.0168] 25.9+0.0s +[16000/16000] [L1: 0.0167] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.046 (Best: 40.149 @epoch 67) +Forward: 38.25s + +Saving... +Total: 38.81s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0171] 25.7+0.9s +[3200/16000] [L1: 0.0168] 25.8+0.0s +[4800/16000] [L1: 0.0166] 26.1+0.0s +[6400/16000] [L1: 0.0170] 25.8+0.0s +[8000/16000] [L1: 0.0170] 25.9+0.0s +[9600/16000] [L1: 0.0169] 25.6+0.0s +[11200/16000] [L1: 0.0168] 25.7+0.0s +[12800/16000] [L1: 0.0168] 25.6+0.0s +[14400/16000] [L1: 0.0169] 25.9+0.0s +[16000/16000] [L1: 0.0169] 49.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.999 (Best: 40.149 @epoch 67) +Forward: 37.69s + +Saving... +Total: 38.14s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0165] 59.2+0.9s +[3200/16000] [L1: 0.0165] 60.1+0.0s +[4800/16000] [L1: 0.0167] 60.0+0.0s +[6400/16000] [L1: 0.0166] 59.8+0.0s +[8000/16000] [L1: 0.0165] 33.5+0.0s +[9600/16000] [L1: 0.0166] 25.6+0.0s +[11200/16000] [L1: 0.0164] 25.7+0.0s +[12800/16000] [L1: 0.0165] 25.7+0.0s +[14400/16000] [L1: 0.0165] 25.6+0.0s +[16000/16000] [L1: 0.0165] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.061 (Best: 40.149 @epoch 67) +Forward: 38.29s + +Saving... +Total: 38.81s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0163] 25.7+1.0s +[3200/16000] [L1: 0.0162] 25.9+0.0s +[4800/16000] [L1: 0.0165] 26.1+0.0s +[6400/16000] [L1: 0.0164] 26.1+0.0s +[8000/16000] [L1: 0.0163] 25.9+0.0s +[9600/16000] [L1: 0.0164] 26.0+0.0s +[11200/16000] [L1: 0.0164] 25.9+0.0s +[12800/16000] [L1: 0.0165] 26.2+0.0s +[14400/16000] [L1: 0.0164] 26.1+0.0s +[16000/16000] [L1: 0.0164] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.264 (Best: 40.264 @epoch 71) +Forward: 38.43s + +Saving... +Total: 39.16s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0171] 25.6+0.9s +[3200/16000] [L1: 0.0172] 25.5+0.0s +[4800/16000] [L1: 0.0170] 25.8+0.0s +[6400/16000] [L1: 0.0170] 25.9+0.0s +[8000/16000] [L1: 0.0170] 26.4+0.0s +[9600/16000] [L1: 0.0168] 25.9+0.0s +[11200/16000] [L1: 0.0168] 25.9+0.0s +[12800/16000] [L1: 0.0167] 25.8+0.0s +[14400/16000] [L1: 0.0167] 25.7+0.0s +[16000/16000] [L1: 0.0166] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.375 (Best: 40.375 @epoch 72) +Forward: 38.31s + +Saving... +Total: 38.84s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0169] 26.0+0.9s +[3200/16000] [L1: 0.0168] 25.8+0.0s +[4800/16000] [L1: 0.0164] 25.9+0.0s +[6400/16000] [L1: 0.0163] 26.1+0.0s +[8000/16000] [L1: 0.0164] 25.9+0.0s +[9600/16000] [L1: 0.0164] 25.8+0.0s +[11200/16000] [L1: 0.0165] 25.8+0.0s +[12800/16000] [L1: 0.0165] 25.9+0.0s +[14400/16000] [L1: 0.0164] 25.9+0.0s +[16000/16000] [L1: 0.0164] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.205 (Best: 40.375 @epoch 72) +Forward: 38.22s + +Saving... +Total: 38.69s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0167] 25.6+0.9s +[3200/16000] [L1: 0.0168] 25.9+0.0s +[4800/16000] [L1: 0.0169] 26.0+0.0s +[6400/16000] [L1: 0.0169] 26.0+0.0s +[8000/16000] [L1: 0.0169] 26.1+0.0s +[9600/16000] [L1: 0.0168] 26.0+0.0s +[11200/16000] [L1: 0.0167] 26.0+0.0s +[12800/16000] [L1: 0.0166] 25.9+0.0s +[14400/16000] [L1: 0.0165] 26.0+0.0s +[16000/16000] [L1: 0.0164] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.200 (Best: 40.375 @epoch 72) +Forward: 38.18s + +Saving... +Total: 38.66s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0169] 25.6+1.0s +[3200/16000] [L1: 0.0169] 25.7+0.0s +[4800/16000] [L1: 0.0167] 25.8+0.0s +[6400/16000] [L1: 0.0164] 26.0+0.0s +[8000/16000] [L1: 0.0164] 25.6+0.0s +[9600/16000] [L1: 0.0164] 25.7+0.0s +[11200/16000] [L1: 0.0163] 25.7+0.0s +[12800/16000] [L1: 0.0162] 25.9+0.0s +[14400/16000] [L1: 0.0163] 25.8+0.0s +[16000/16000] [L1: 0.0162] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.338 (Best: 40.375 @epoch 72) +Forward: 38.25s + +Saving... +Total: 38.75s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0169] 26.0+1.0s +[3200/16000] [L1: 0.0163] 25.9+0.0s +[4800/16000] [L1: 0.0160] 26.2+0.0s +[6400/16000] [L1: 0.0160] 26.0+0.0s +[8000/16000] [L1: 0.0160] 25.9+0.0s +[9600/16000] [L1: 0.0159] 25.8+0.0s +[11200/16000] [L1: 0.0160] 25.7+0.0s +[12800/16000] [L1: 0.0161] 25.9+0.0s +[14400/16000] [L1: 0.0160] 25.9+0.0s +[16000/16000] [L1: 0.0161] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.396 (Best: 40.396 @epoch 76) +Forward: 38.14s + +Saving... +Total: 38.68s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0159] 25.8+0.9s +[3200/16000] [L1: 0.0161] 25.9+0.0s +[4800/16000] [L1: 0.0162] 26.0+0.0s +[6400/16000] [L1: 0.0161] 26.1+0.0s +[8000/16000] [L1: 0.0162] 25.9+0.0s +[9600/16000] [L1: 0.0163] 26.4+0.0s +[11200/16000] [L1: 0.0163] 25.9+0.0s +[12800/16000] [L1: 0.0162] 25.8+0.0s +[14400/16000] [L1: 0.0162] 26.0+0.0s +[16000/16000] [L1: 0.0162] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.535 (Best: 40.535 @epoch 77) +Forward: 38.20s + +Saving... +Total: 38.77s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0157] 25.8+0.9s +[3200/16000] [L1: 0.0159] 25.8+0.0s +[4800/16000] [L1: 0.0158] 25.9+0.0s +[6400/16000] [L1: 0.0159] 26.0+0.0s +[8000/16000] [L1: 0.0159] 25.7+0.0s +[9600/16000] [L1: 0.0161] 26.1+0.0s +[11200/16000] [L1: 0.0160] 26.3+0.0s +[12800/16000] [L1: 0.0160] 26.0+0.0s +[14400/16000] [L1: 0.0160] 26.1+0.0s +[16000/16000] [L1: 0.0160] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.525 (Best: 40.535 @epoch 77) +Forward: 38.28s + +Saving... +Total: 38.83s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0163] 25.7+0.9s +[3200/16000] [L1: 0.0161] 25.8+0.0s +[4800/16000] [L1: 0.0163] 25.9+0.0s +[6400/16000] [L1: 0.0162] 26.1+0.0s +[8000/16000] [L1: 0.0162] 25.6+0.0s +[9600/16000] [L1: 0.0164] 25.7+0.0s +[11200/16000] [L1: 0.0163] 25.8+0.0s +[12800/16000] [L1: 0.0162] 25.8+0.0s +[14400/16000] [L1: 0.0162] 26.1+0.0s +[16000/16000] [L1: 0.0161] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.192 (Best: 40.535 @epoch 77) +Forward: 38.28s + +Saving... +Total: 38.81s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0168] 26.1+0.9s +[3200/16000] [L1: 0.0166] 25.8+0.0s +[4800/16000] [L1: 0.0164] 25.9+0.0s +[6400/16000] [L1: 0.0163] 26.2+0.0s +[8000/16000] [L1: 0.0162] 25.8+0.0s +[9600/16000] [L1: 0.0160] 25.6+0.0s +[11200/16000] [L1: 0.0161] 25.8+0.0s +[12800/16000] [L1: 0.0161] 25.7+0.0s +[14400/16000] [L1: 0.0161] 25.9+0.0s +[16000/16000] [L1: 0.0161] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.551 (Best: 40.551 @epoch 80) +Forward: 38.22s + +Saving... +Total: 38.76s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0159] 26.1+1.0s +[3200/16000] [L1: 0.0158] 26.3+0.0s +[4800/16000] [L1: 0.0159] 26.1+0.0s +[6400/16000] [L1: 0.0160] 26.0+0.0s +[8000/16000] [L1: 0.0160] 26.1+0.0s +[9600/16000] [L1: 0.0160] 26.1+0.0s +[11200/16000] [L1: 0.0160] 26.1+0.0s +[12800/16000] [L1: 0.0160] 25.8+0.0s +[14400/16000] [L1: 0.0160] 25.9+0.0s +[16000/16000] [L1: 0.0160] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.505 (Best: 40.551 @epoch 80) +Forward: 38.20s + +Saving... +Total: 38.70s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0163] 25.7+0.9s +[3200/16000] [L1: 0.0162] 25.8+0.0s +[4800/16000] [L1: 0.0160] 25.8+0.0s +[6400/16000] [L1: 0.0160] 26.3+0.0s +[8000/16000] [L1: 0.0159] 26.5+0.0s +[9600/16000] [L1: 0.0159] 26.2+0.0s +[11200/16000] [L1: 0.0159] 25.9+0.0s +[12800/16000] [L1: 0.0160] 25.8+0.0s +[14400/16000] [L1: 0.0160] 26.1+0.0s +[16000/16000] [L1: 0.0160] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.329 (Best: 40.551 @epoch 80) +Forward: 38.43s + +Saving... +Total: 38.99s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0161] 25.7+0.9s +[3200/16000] [L1: 0.0160] 25.8+0.0s +[4800/16000] [L1: 0.0162] 25.9+0.0s +[6400/16000] [L1: 0.0163] 26.2+0.0s +[8000/16000] [L1: 0.0163] 25.9+0.0s +[9600/16000] [L1: 0.0163] 25.8+0.0s +[11200/16000] [L1: 0.0163] 26.1+0.0s +[12800/16000] [L1: 0.0162] 26.3+0.0s +[14400/16000] [L1: 0.0161] 25.9+0.0s +[16000/16000] [L1: 0.0161] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.585 (Best: 40.585 @epoch 83) +Forward: 38.21s + +Saving... +Total: 38.71s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 25.7+1.3s +[3200/16000] [L1: 0.0152] 25.7+0.0s +[4800/16000] [L1: 0.0154] 26.1+0.0s +[6400/16000] [L1: 0.0155] 26.0+0.0s +[8000/16000] [L1: 0.0155] 25.8+0.0s +[9600/16000] [L1: 0.0156] 25.9+0.0s +[11200/16000] [L1: 0.0156] 25.9+0.0s +[12800/16000] [L1: 0.0156] 25.9+0.0s +[14400/16000] [L1: 0.0158] 25.9+0.0s +[16000/16000] [L1: 0.0158] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.554 (Best: 40.585 @epoch 83) +Forward: 38.66s + +Saving... +Total: 39.12s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0162] 25.9+0.9s +[3200/16000] [L1: 0.0162] 25.9+0.0s +[4800/16000] [L1: 0.0162] 26.0+0.0s +[6400/16000] [L1: 0.0162] 26.0+0.0s +[8000/16000] [L1: 0.0160] 26.1+0.0s +[9600/16000] [L1: 0.0160] 26.0+0.0s +[11200/16000] [L1: 0.0161] 25.7+0.0s +[12800/16000] [L1: 0.0161] 26.1+0.0s +[14400/16000] [L1: 0.0161] 25.9+0.0s +[16000/16000] [L1: 0.0160] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.689 (Best: 40.689 @epoch 85) +Forward: 38.33s + +Saving... +Total: 38.84s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0156] 25.9+1.0s +[3200/16000] [L1: 0.0158] 25.9+0.0s +[4800/16000] [L1: 0.0158] 25.9+0.0s +[6400/16000] [L1: 0.0159] 25.8+0.0s +[8000/16000] [L1: 0.0160] 26.0+0.0s +[9600/16000] [L1: 0.0161] 26.2+0.0s +[11200/16000] [L1: 0.0161] 26.0+0.0s +[12800/16000] [L1: 0.0160] 26.1+0.0s +[14400/16000] [L1: 0.0159] 25.8+0.0s +[16000/16000] [L1: 0.0159] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.602 (Best: 40.689 @epoch 85) +Forward: 38.27s + +Saving... +Total: 38.80s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0158] 25.9+0.9s +[3200/16000] [L1: 0.0160] 25.7+0.0s +[4800/16000] [L1: 0.0158] 26.2+0.0s +[6400/16000] [L1: 0.0157] 26.2+0.0s +[8000/16000] [L1: 0.0157] 26.2+0.0s +[9600/16000] [L1: 0.0157] 26.3+0.0s +[11200/16000] [L1: 0.0157] 25.9+0.0s +[12800/16000] [L1: 0.0157] 26.1+0.0s +[14400/16000] [L1: 0.0157] 25.9+0.0s +[16000/16000] [L1: 0.0157] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.455 (Best: 40.689 @epoch 85) +Forward: 38.30s + +Saving... +Total: 38.81s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0164] 25.9+0.9s +[3200/16000] [L1: 0.0160] 25.8+0.0s +[4800/16000] [L1: 0.0161] 25.9+0.0s +[6400/16000] [L1: 0.0158] 26.1+0.0s +[8000/16000] [L1: 0.0157] 25.8+0.0s +[9600/16000] [L1: 0.0158] 25.8+0.0s +[11200/16000] [L1: 0.0158] 25.8+0.0s +[12800/16000] [L1: 0.0158] 25.8+0.0s +[14400/16000] [L1: 0.0158] 25.9+0.0s +[16000/16000] [L1: 0.0158] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.744 (Best: 40.744 @epoch 88) +Forward: 38.25s + +Saving... +Total: 38.75s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0166] 25.7+0.9s +[3200/16000] [L1: 0.0161] 25.7+0.0s +[4800/16000] [L1: 0.0160] 26.1+0.0s +[6400/16000] [L1: 0.0159] 26.3+0.0s +[8000/16000] [L1: 0.0159] 26.0+0.0s +[9600/16000] [L1: 0.0159] 26.2+0.0s +[11200/16000] [L1: 0.0158] 25.9+0.0s +[12800/16000] [L1: 0.0158] 25.8+0.0s +[14400/16000] [L1: 0.0158] 25.8+0.0s +[16000/16000] [L1: 0.0158] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.669 (Best: 40.744 @epoch 88) +Forward: 38.45s + +Saving... +Total: 38.90s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0152] 25.6+0.9s +[3200/16000] [L1: 0.0153] 25.5+0.0s +[4800/16000] [L1: 0.0154] 25.8+0.0s +[6400/16000] [L1: 0.0155] 25.8+0.0s +[8000/16000] [L1: 0.0154] 25.9+0.0s +[9600/16000] [L1: 0.0155] 25.9+0.0s +[11200/16000] [L1: 0.0156] 25.8+0.0s +[12800/16000] [L1: 0.0156] 25.7+0.0s +[14400/16000] [L1: 0.0156] 25.7+0.0s +[16000/16000] [L1: 0.0157] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.737 (Best: 40.744 @epoch 88) +Forward: 38.27s + +Saving... +Total: 38.79s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0151] 26.0+0.9s +[3200/16000] [L1: 0.0155] 25.8+0.0s +[4800/16000] [L1: 0.0154] 25.9+0.0s +[6400/16000] [L1: 0.0153] 25.9+0.0s +[8000/16000] [L1: 0.0153] 26.0+0.0s +[9600/16000] [L1: 0.0154] 25.9+0.0s +[11200/16000] [L1: 0.0155] 25.8+0.0s +[12800/16000] [L1: 0.0155] 25.9+0.0s +[14400/16000] [L1: 0.0155] 25.9+0.0s +[16000/16000] [L1: 0.0156] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.521 (Best: 40.744 @epoch 88) +Forward: 38.26s + +Saving... +Total: 38.73s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0162] 26.0+0.9s +[3200/16000] [L1: 0.0160] 26.1+0.0s +[4800/16000] [L1: 0.0160] 26.2+0.0s +[6400/16000] [L1: 0.0159] 26.1+0.0s +[8000/16000] [L1: 0.0158] 26.1+0.0s +[9600/16000] [L1: 0.0159] 26.1+0.0s +[11200/16000] [L1: 0.0158] 25.9+0.0s +[12800/16000] [L1: 0.0158] 25.8+0.0s +[14400/16000] [L1: 0.0157] 25.9+0.0s +[16000/16000] [L1: 0.0157] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.711 (Best: 40.744 @epoch 88) +Forward: 38.25s + +Saving... +Total: 38.71s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 25.8+0.9s +[3200/16000] [L1: 0.0151] 25.7+0.0s +[4800/16000] [L1: 0.0153] 25.9+0.0s +[6400/16000] [L1: 0.0154] 25.8+0.0s +[8000/16000] [L1: 0.0156] 25.8+0.0s +[9600/16000] [L1: 0.0155] 25.9+0.0s +[11200/16000] [L1: 0.0155] 25.8+0.0s +[12800/16000] [L1: 0.0155] 25.8+0.0s +[14400/16000] [L1: 0.0155] 25.9+0.0s +[16000/16000] [L1: 0.0155] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.580 (Best: 40.744 @epoch 88) +Forward: 38.39s + +Saving... +Total: 38.83s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0152] 25.6+0.9s +[3200/16000] [L1: 0.0155] 25.8+0.0s +[4800/16000] [L1: 0.0157] 25.9+0.0s +[6400/16000] [L1: 0.0158] 26.0+0.0s +[8000/16000] [L1: 0.0157] 25.6+0.0s +[9600/16000] [L1: 0.0155] 25.7+0.0s +[11200/16000] [L1: 0.0154] 25.8+0.0s +[12800/16000] [L1: 0.0154] 25.9+0.0s +[14400/16000] [L1: 0.0154] 25.8+0.0s +[16000/16000] [L1: 0.0154] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.576 (Best: 40.744 @epoch 88) +Forward: 38.43s + +Saving... +Total: 38.94s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0153] 25.9+1.0s +[3200/16000] [L1: 0.0155] 25.8+0.0s +[4800/16000] [L1: 0.0155] 25.7+0.0s +[6400/16000] [L1: 0.0155] 26.1+0.0s +[8000/16000] [L1: 0.0154] 26.0+0.0s +[9600/16000] [L1: 0.0154] 26.1+0.0s +[11200/16000] [L1: 0.0155] 25.8+0.0s +[12800/16000] [L1: 0.0154] 26.1+0.0s +[14400/16000] [L1: 0.0154] 25.6+0.0s +[16000/16000] [L1: 0.0154] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.757 (Best: 40.757 @epoch 95) +Forward: 38.16s + +Saving... +Total: 38.74s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 25.9+0.9s +[3200/16000] [L1: 0.0153] 25.7+0.0s +[4800/16000] [L1: 0.0153] 25.9+0.0s +[6400/16000] [L1: 0.0155] 26.0+0.0s +[8000/16000] [L1: 0.0155] 26.0+0.0s +[9600/16000] [L1: 0.0155] 25.8+0.0s +[11200/16000] [L1: 0.0155] 26.2+0.0s +[12800/16000] [L1: 0.0155] 26.0+0.0s +[14400/16000] [L1: 0.0156] 25.9+0.0s +[16000/16000] [L1: 0.0156] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.259 (Best: 40.757 @epoch 95) +Forward: 38.30s + +Saving... +Total: 38.77s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0155] 25.7+0.9s +[3200/16000] [L1: 0.0156] 26.0+0.0s +[4800/16000] [L1: 0.0155] 26.0+0.0s +[6400/16000] [L1: 0.0155] 26.0+0.0s +[8000/16000] [L1: 0.0155] 25.9+0.0s +[9600/16000] [L1: 0.0155] 25.8+0.0s +[11200/16000] [L1: 0.0155] 25.8+0.0s +[12800/16000] [L1: 0.0155] 25.8+0.0s +[14400/16000] [L1: 0.0155] 25.9+0.0s +[16000/16000] [L1: 0.0155] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.770 (Best: 40.770 @epoch 97) +Forward: 38.37s + +Saving... +Total: 38.89s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0156] 25.8+0.9s +[3200/16000] [L1: 0.0153] 25.8+0.0s +[4800/16000] [L1: 0.0154] 26.1+0.0s +[6400/16000] [L1: 0.0154] 26.0+0.0s +[8000/16000] [L1: 0.0154] 26.0+0.0s +[9600/16000] [L1: 0.0153] 26.0+0.0s +[11200/16000] [L1: 0.0154] 26.0+0.0s +[12800/16000] [L1: 0.0154] 25.8+0.0s +[14400/16000] [L1: 0.0154] 25.8+0.0s +[16000/16000] [L1: 0.0154] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.809 (Best: 40.809 @epoch 98) +Forward: 38.20s + +Saving... +Total: 38.78s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0155] 25.7+0.9s +[3200/16000] [L1: 0.0156] 25.5+0.0s +[4800/16000] [L1: 0.0155] 25.9+0.0s +[6400/16000] [L1: 0.0153] 26.2+0.0s +[8000/16000] [L1: 0.0153] 26.1+0.0s +[9600/16000] [L1: 0.0154] 25.8+0.0s +[11200/16000] [L1: 0.0154] 25.7+0.0s +[12800/16000] [L1: 0.0155] 25.7+0.0s +[14400/16000] [L1: 0.0155] 25.9+0.0s +[16000/16000] [L1: 0.0154] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.508 (Best: 40.809 @epoch 98) +Forward: 38.41s + +Saving... +Total: 38.88s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0155] 25.8+0.9s +[3200/16000] [L1: 0.0156] 26.0+0.0s +[4800/16000] [L1: 0.0155] 25.7+0.0s +[6400/16000] [L1: 0.0155] 26.0+0.0s +[8000/16000] [L1: 0.0154] 26.1+0.0s +[9600/16000] [L1: 0.0153] 25.8+0.0s +[11200/16000] [L1: 0.0155] 25.9+0.0s +[12800/16000] [L1: 0.0155] 25.9+0.0s +[14400/16000] [L1: 0.0155] 25.9+0.0s +[16000/16000] [L1: 0.0155] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.692 (Best: 40.809 @epoch 98) +Forward: 38.37s + +Saving... +Total: 38.83s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0158] 25.8+0.9s +[3200/16000] [L1: 0.0157] 25.9+0.0s +[4800/16000] [L1: 0.0153] 26.0+0.0s +[6400/16000] [L1: 0.0152] 25.9+0.0s +[8000/16000] [L1: 0.0153] 25.9+0.0s +[9600/16000] [L1: 0.0154] 26.2+0.0s +[11200/16000] [L1: 0.0153] 25.9+0.0s +[12800/16000] [L1: 0.0153] 25.9+0.0s +[14400/16000] [L1: 0.0153] 25.7+0.0s +[16000/16000] [L1: 0.0153] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.790 (Best: 40.809 @epoch 98) +Forward: 38.23s + +Saving... +Total: 38.70s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0150] 25.8+0.9s +[3200/16000] [L1: 0.0153] 25.9+0.0s +[4800/16000] [L1: 0.0152] 26.2+0.0s +[6400/16000] [L1: 0.0154] 26.2+0.0s +[8000/16000] [L1: 0.0152] 25.9+0.0s +[9600/16000] [L1: 0.0152] 25.8+0.0s +[11200/16000] [L1: 0.0152] 25.8+0.0s +[12800/16000] [L1: 0.0151] 25.5+0.0s +[14400/16000] [L1: 0.0151] 25.5+0.0s +[16000/16000] [L1: 0.0151] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.771 (Best: 40.809 @epoch 98) +Forward: 38.42s + +Saving... +Total: 38.98s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 25.7+0.9s +[3200/16000] [L1: 0.0153] 25.7+0.0s +[4800/16000] [L1: 0.0155] 25.9+0.0s +[6400/16000] [L1: 0.0153] 26.1+0.0s +[8000/16000] [L1: 0.0153] 25.8+0.0s +[9600/16000] [L1: 0.0153] 26.2+0.0s +[11200/16000] [L1: 0.0153] 26.1+0.0s +[12800/16000] [L1: 0.0153] 25.9+0.0s +[14400/16000] [L1: 0.0153] 26.0+0.0s +[16000/16000] [L1: 0.0152] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.764 (Best: 40.809 @epoch 98) +Forward: 38.34s + +Saving... +Total: 38.84s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0151] 25.9+0.9s +[3200/16000] [L1: 0.0149] 25.7+0.0s +[4800/16000] [L1: 0.0148] 25.9+0.0s +[6400/16000] [L1: 0.0150] 26.1+0.0s +[8000/16000] [L1: 0.0151] 26.0+0.0s +[9600/16000] [L1: 0.0150] 25.8+0.0s +[11200/16000] [L1: 0.0150] 26.0+0.0s +[12800/16000] [L1: 0.0150] 25.8+0.0s +[14400/16000] [L1: 0.0150] 26.0+0.0s +[16000/16000] [L1: 0.0151] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.769 (Best: 40.809 @epoch 98) +Forward: 38.35s + +Saving... +Total: 38.86s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 26.0+0.9s +[3200/16000] [L1: 0.0154] 25.8+0.0s +[4800/16000] [L1: 0.0152] 26.1+0.0s +[6400/16000] [L1: 0.0152] 26.4+0.0s +[8000/16000] [L1: 0.0154] 25.9+0.0s +[9600/16000] [L1: 0.0153] 25.7+0.0s +[11200/16000] [L1: 0.0153] 25.7+0.0s +[12800/16000] [L1: 0.0152] 25.7+0.0s +[14400/16000] [L1: 0.0152] 25.5+0.0s +[16000/16000] [L1: 0.0151] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.898 (Best: 40.898 @epoch 105) +Forward: 38.28s + +Saving... +Total: 38.79s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0150] 25.8+1.0s +[3200/16000] [L1: 0.0152] 25.9+0.0s +[4800/16000] [L1: 0.0150] 26.2+0.0s +[6400/16000] [L1: 0.0150] 26.0+0.0s +[8000/16000] [L1: 0.0150] 26.3+0.0s +[9600/16000] [L1: 0.0150] 25.9+0.0s +[11200/16000] [L1: 0.0151] 25.9+0.0s +[12800/16000] [L1: 0.0151] 25.9+0.0s +[14400/16000] [L1: 0.0152] 25.6+0.0s +[16000/16000] [L1: 0.0152] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.900 (Best: 40.900 @epoch 106) +Forward: 38.25s + +Saving... +Total: 38.86s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0155] 25.9+0.9s +[3200/16000] [L1: 0.0155] 26.0+0.0s +[4800/16000] [L1: 0.0152] 25.9+0.0s +[6400/16000] [L1: 0.0152] 26.0+0.0s +[8000/16000] [L1: 0.0153] 25.8+0.0s +[9600/16000] [L1: 0.0153] 26.1+0.0s +[11200/16000] [L1: 0.0151] 26.2+0.0s +[12800/16000] [L1: 0.0151] 25.9+0.0s +[14400/16000] [L1: 0.0151] 25.6+0.0s +[16000/16000] [L1: 0.0151] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.840 (Best: 40.900 @epoch 106) +Forward: 38.44s + +Saving... +Total: 38.90s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0150] 25.7+0.9s +[3200/16000] [L1: 0.0148] 25.8+0.0s +[4800/16000] [L1: 0.0147] 25.6+0.0s +[6400/16000] [L1: 0.0148] 26.0+0.0s +[8000/16000] [L1: 0.0149] 26.0+0.0s +[9600/16000] [L1: 0.0150] 25.8+0.0s +[11200/16000] [L1: 0.0150] 26.0+0.0s +[12800/16000] [L1: 0.0149] 25.9+0.0s +[14400/16000] [L1: 0.0150] 25.7+0.0s +[16000/16000] [L1: 0.0151] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.802 (Best: 40.900 @epoch 106) +Forward: 38.37s + +Saving... +Total: 38.82s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0161] 25.5+0.9s +[3200/16000] [L1: 0.0153] 26.0+0.0s +[4800/16000] [L1: 0.0152] 26.1+0.0s +[6400/16000] [L1: 0.0152] 25.9+0.0s +[8000/16000] [L1: 0.0153] 25.8+0.0s +[9600/16000] [L1: 0.0153] 25.9+0.0s +[11200/16000] [L1: 0.0151] 26.1+0.0s +[12800/16000] [L1: 0.0151] 26.3+0.0s +[14400/16000] [L1: 0.0151] 25.9+0.0s +[16000/16000] [L1: 0.0151] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.643 (Best: 40.900 @epoch 106) +Forward: 38.15s + +Saving... +Total: 38.63s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 25.9+0.9s +[3200/16000] [L1: 0.0149] 26.1+0.0s +[4800/16000] [L1: 0.0150] 26.0+0.0s +[6400/16000] [L1: 0.0149] 26.0+0.0s +[8000/16000] [L1: 0.0150] 26.1+0.0s +[9600/16000] [L1: 0.0150] 26.1+0.0s +[11200/16000] [L1: 0.0150] 26.0+0.0s +[12800/16000] [L1: 0.0151] 25.9+0.0s +[14400/16000] [L1: 0.0151] 25.9+0.0s +[16000/16000] [L1: 0.0152] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.835 (Best: 40.900 @epoch 106) +Forward: 38.34s + +Saving... +Total: 38.93s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 25.5+0.9s +[3200/16000] [L1: 0.0153] 25.7+0.0s +[4800/16000] [L1: 0.0151] 26.0+0.0s +[6400/16000] [L1: 0.0150] 25.8+0.0s +[8000/16000] [L1: 0.0149] 25.8+0.0s +[9600/16000] [L1: 0.0149] 25.6+0.0s +[11200/16000] [L1: 0.0150] 26.0+0.0s +[12800/16000] [L1: 0.0150] 25.6+0.0s +[14400/16000] [L1: 0.0150] 25.9+0.0s +[16000/16000] [L1: 0.0149] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.938 (Best: 40.938 @epoch 111) +Forward: 38.25s + +Saving... +Total: 38.76s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0151] 25.9+1.0s +[3200/16000] [L1: 0.0149] 26.3+0.0s +[4800/16000] [L1: 0.0148] 25.9+0.0s +[6400/16000] [L1: 0.0149] 26.0+0.0s +[8000/16000] [L1: 0.0149] 25.8+0.0s +[9600/16000] [L1: 0.0149] 25.8+0.0s +[11200/16000] [L1: 0.0149] 25.9+0.0s +[12800/16000] [L1: 0.0149] 25.6+0.0s +[14400/16000] [L1: 0.0150] 26.0+0.0s +[16000/16000] [L1: 0.0150] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.926 (Best: 40.938 @epoch 111) +Forward: 38.31s + +Saving... +Total: 38.77s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0153] 25.9+0.9s +[3200/16000] [L1: 0.0150] 25.8+0.0s +[4800/16000] [L1: 0.0149] 25.9+0.0s +[6400/16000] [L1: 0.0149] 26.4+0.0s +[8000/16000] [L1: 0.0148] 26.3+0.0s +[9600/16000] [L1: 0.0150] 25.9+0.0s +[11200/16000] [L1: 0.0150] 26.0+0.0s +[12800/16000] [L1: 0.0149] 25.9+0.0s +[14400/16000] [L1: 0.0149] 25.6+0.0s +[16000/16000] [L1: 0.0149] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.930 (Best: 40.938 @epoch 111) +Forward: 38.23s + +Saving... +Total: 38.76s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 25.8+1.0s +[3200/16000] [L1: 0.0153] 25.9+0.0s +[4800/16000] [L1: 0.0153] 25.8+0.0s +[6400/16000] [L1: 0.0152] 26.0+0.0s +[8000/16000] [L1: 0.0150] 25.9+0.0s +[9600/16000] [L1: 0.0150] 26.1+0.0s +[11200/16000] [L1: 0.0150] 26.0+0.0s +[12800/16000] [L1: 0.0149] 25.9+0.0s +[14400/16000] [L1: 0.0149] 26.0+0.0s +[16000/16000] [L1: 0.0149] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.716 (Best: 40.938 @epoch 111) +Forward: 38.37s + +Saving... +Total: 38.85s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 25.6+0.9s +[3200/16000] [L1: 0.0149] 25.5+0.0s +[4800/16000] [L1: 0.0149] 25.5+0.0s +[6400/16000] [L1: 0.0148] 25.4+0.0s +[8000/16000] [L1: 0.0148] 25.6+0.0s +[9600/16000] [L1: 0.0149] 25.6+0.0s +[11200/16000] [L1: 0.0149] 25.5+0.0s +[12800/16000] [L1: 0.0148] 25.6+0.0s +[14400/16000] [L1: 0.0148] 25.9+0.0s +[16000/16000] [L1: 0.0148] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.705 (Best: 40.938 @epoch 111) +Forward: 38.14s + +Saving... +Total: 38.63s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0151] 25.8+1.1s +[3200/16000] [L1: 0.0146] 25.5+0.0s +[4800/16000] [L1: 0.0147] 25.7+0.0s +[6400/16000] [L1: 0.0148] 26.0+0.0s +[8000/16000] [L1: 0.0148] 25.8+0.0s +[9600/16000] [L1: 0.0149] 25.7+0.0s +[11200/16000] [L1: 0.0150] 25.8+0.0s +[12800/16000] [L1: 0.0150] 26.0+0.0s +[14400/16000] [L1: 0.0150] 25.7+0.0s +[16000/16000] [L1: 0.0150] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.924 (Best: 40.938 @epoch 111) +Forward: 38.19s + +Saving... +Total: 38.73s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0151] 26.2+0.9s +[3200/16000] [L1: 0.0151] 26.0+0.0s +[4800/16000] [L1: 0.0151] 25.8+0.0s +[6400/16000] [L1: 0.0149] 26.0+0.0s +[8000/16000] [L1: 0.0149] 25.6+0.0s +[9600/16000] [L1: 0.0149] 26.0+0.0s +[11200/16000] [L1: 0.0149] 26.0+0.0s +[12800/16000] [L1: 0.0149] 25.9+0.0s +[14400/16000] [L1: 0.0149] 25.6+0.0s +[16000/16000] [L1: 0.0150] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.806 (Best: 40.938 @epoch 111) +Forward: 38.24s + +Saving... +Total: 38.75s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0147] 25.7+0.9s +[3200/16000] [L1: 0.0153] 25.7+0.0s +[4800/16000] [L1: 0.0151] 26.1+0.0s +[6400/16000] [L1: 0.0149] 26.3+0.0s +[8000/16000] [L1: 0.0149] 25.7+0.0s +[9600/16000] [L1: 0.0150] 25.9+0.0s +[11200/16000] [L1: 0.0149] 25.7+0.0s +[12800/16000] [L1: 0.0149] 25.9+0.0s +[14400/16000] [L1: 0.0150] 25.9+0.0s +[16000/16000] [L1: 0.0150] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.720 (Best: 40.938 @epoch 111) +Forward: 38.31s + +Saving... +Total: 38.82s + +[Epoch 119] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 25.8+0.9s +[3200/16000] [L1: 0.0152] 25.9+0.0s +[4800/16000] [L1: 0.0151] 26.0+0.0s +[6400/16000] [L1: 0.0150] 26.0+0.0s +[8000/16000] [L1: 0.0150] 25.9+0.0s +[9600/16000] [L1: 0.0150] 26.2+0.0s +[11200/16000] [L1: 0.0149] 26.3+0.0s +[12800/16000] [L1: 0.0149] 25.8+0.0s +[14400/16000] [L1: 0.0149] 26.0+0.0s +[16000/16000] [L1: 0.0149] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.845 (Best: 40.938 @epoch 111) +Forward: 38.08s + +Saving... +Total: 38.54s + +[Epoch 120] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 26.0+1.0s +[3200/16000] [L1: 0.0148] 26.1+0.0s +[4800/16000] [L1: 0.0150] 25.8+0.0s +[6400/16000] [L1: 0.0151] 26.0+0.0s +[8000/16000] [L1: 0.0150] 25.9+0.0s +[9600/16000] [L1: 0.0150] 25.7+0.0s +[11200/16000] [L1: 0.0150] 25.7+0.0s +[12800/16000] [L1: 0.0149] 25.7+0.0s +[14400/16000] [L1: 0.0150] 26.0+0.0s +[16000/16000] [L1: 0.0150] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.962 (Best: 40.962 @epoch 120) +Forward: 38.12s + +Saving... +Total: 38.71s + +[Epoch 121] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0150] 25.6+0.9s +[3200/16000] [L1: 0.0149] 25.6+0.0s +[4800/16000] [L1: 0.0149] 25.9+0.0s +[6400/16000] [L1: 0.0148] 25.6+0.0s +[8000/16000] [L1: 0.0148] 25.7+0.0s +[9600/16000] [L1: 0.0149] 26.3+0.0s +[11200/16000] [L1: 0.0149] 25.8+0.0s +[12800/16000] [L1: 0.0149] 25.8+0.0s +[14400/16000] [L1: 0.0148] 25.8+0.0s +[16000/16000] [L1: 0.0149] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.799 (Best: 40.962 @epoch 120) +Forward: 38.10s + +Saving... +Total: 38.56s + +[Epoch 122] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0152] 25.9+0.9s +[3200/16000] [L1: 0.0150] 25.8+0.0s +[4800/16000] [L1: 0.0148] 26.1+0.0s +[6400/16000] [L1: 0.0146] 26.1+0.0s +[8000/16000] [L1: 0.0147] 25.9+0.0s +[9600/16000] [L1: 0.0148] 25.8+0.0s +[11200/16000] [L1: 0.0149] 25.9+0.0s +[12800/16000] [L1: 0.0148] 25.8+0.0s +[14400/16000] [L1: 0.0148] 25.9+0.0s +[16000/16000] [L1: 0.0148] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.013 (Best: 41.013 @epoch 122) +Forward: 38.11s + +Saving... +Total: 38.66s + +[Epoch 123] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 25.8+0.9s +[3200/16000] [L1: 0.0150] 25.7+0.0s +[4800/16000] [L1: 0.0150] 26.2+0.0s +[6400/16000] [L1: 0.0149] 26.3+0.0s +[8000/16000] [L1: 0.0149] 26.1+0.0s +[9600/16000] [L1: 0.0148] 26.0+0.0s +[11200/16000] [L1: 0.0149] 25.6+0.0s +[12800/16000] [L1: 0.0150] 25.8+0.0s +[14400/16000] [L1: 0.0149] 25.8+0.0s +[16000/16000] [L1: 0.0149] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.012 (Best: 41.013 @epoch 122) +Forward: 38.22s + +Saving... +Total: 38.71s + +[Epoch 124] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 25.7+1.0s +[3200/16000] [L1: 0.0146] 26.0+0.0s +[4800/16000] [L1: 0.0147] 25.8+0.0s +[6400/16000] [L1: 0.0147] 26.0+0.0s +[8000/16000] [L1: 0.0146] 25.7+0.0s +[9600/16000] [L1: 0.0146] 26.3+0.0s +[11200/16000] [L1: 0.0147] 26.0+0.0s +[12800/16000] [L1: 0.0147] 25.8+0.0s +[14400/16000] [L1: 0.0147] 25.6+0.0s +[16000/16000] [L1: 0.0147] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.746 (Best: 41.013 @epoch 122) +Forward: 38.25s + +Saving... +Total: 38.73s + +[Epoch 125] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 25.5+0.9s +[3200/16000] [L1: 0.0148] 25.7+0.0s +[4800/16000] [L1: 0.0147] 25.5+0.0s +[6400/16000] [L1: 0.0149] 25.8+0.0s +[8000/16000] [L1: 0.0149] 25.8+0.0s +[9600/16000] [L1: 0.0149] 25.6+0.0s +[11200/16000] [L1: 0.0148] 25.6+0.0s +[12800/16000] [L1: 0.0149] 25.6+0.0s +[14400/16000] [L1: 0.0149] 25.7+0.0s +[16000/16000] [L1: 0.0148] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.022 (Best: 41.022 @epoch 125) +Forward: 38.15s + +Saving... +Total: 38.69s + +[Epoch 126] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 25.7+0.9s +[3200/16000] [L1: 0.0146] 25.9+0.0s +[4800/16000] [L1: 0.0146] 25.7+0.0s +[6400/16000] [L1: 0.0146] 25.9+0.0s +[8000/16000] [L1: 0.0145] 25.4+0.0s +[9600/16000] [L1: 0.0145] 25.5+0.0s +[11200/16000] [L1: 0.0145] 25.4+0.0s +[12800/16000] [L1: 0.0144] 25.5+0.0s +[14400/16000] [L1: 0.0145] 25.6+0.0s +[16000/16000] [L1: 0.0145] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.961 (Best: 41.022 @epoch 125) +Forward: 38.11s + +Saving... +Total: 38.59s + +[Epoch 127] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 25.9+1.0s +[3200/16000] [L1: 0.0152] 25.7+0.0s +[4800/16000] [L1: 0.0150] 25.9+0.0s +[6400/16000] [L1: 0.0148] 25.8+0.0s +[8000/16000] [L1: 0.0147] 25.4+0.0s +[9600/16000] [L1: 0.0148] 25.5+0.0s +[11200/16000] [L1: 0.0148] 25.5+0.0s +[12800/16000] [L1: 0.0148] 25.6+0.0s +[14400/16000] [L1: 0.0148] 25.4+0.0s +[16000/16000] [L1: 0.0148] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.989 (Best: 41.022 @epoch 125) +Forward: 38.04s + +Saving... +Total: 38.51s + +[Epoch 128] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 25.8+1.1s +[3200/16000] [L1: 0.0150] 25.8+0.0s +[4800/16000] [L1: 0.0150] 26.2+0.0s +[6400/16000] [L1: 0.0150] 26.1+0.0s +[8000/16000] [L1: 0.0149] 25.8+0.0s +[9600/16000] [L1: 0.0148] 26.1+0.0s +[11200/16000] [L1: 0.0148] 25.7+0.0s +[12800/16000] [L1: 0.0148] 25.9+0.0s +[14400/16000] [L1: 0.0148] 25.6+0.0s +[16000/16000] [L1: 0.0148] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.898 (Best: 41.022 @epoch 125) +Forward: 38.22s + +Saving... +Total: 38.68s + +[Epoch 129] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 25.9+0.9s +[3200/16000] [L1: 0.0146] 26.0+0.0s +[4800/16000] [L1: 0.0148] 25.8+0.0s +[6400/16000] [L1: 0.0147] 25.9+0.0s +[8000/16000] [L1: 0.0146] 25.9+0.0s +[9600/16000] [L1: 0.0146] 25.9+0.0s +[11200/16000] [L1: 0.0146] 25.5+0.0s +[12800/16000] [L1: 0.0146] 25.5+0.0s +[14400/16000] [L1: 0.0146] 25.5+0.0s +[16000/16000] [L1: 0.0146] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.055 (Best: 41.055 @epoch 129) +Forward: 38.21s + +Saving... +Total: 38.73s + +[Epoch 130] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 25.9+1.0s +[3200/16000] [L1: 0.0146] 25.8+0.0s +[4800/16000] [L1: 0.0145] 25.6+0.0s +[6400/16000] [L1: 0.0145] 25.8+0.0s +[8000/16000] [L1: 0.0145] 25.8+0.0s +[9600/16000] [L1: 0.0145] 25.9+0.0s +[11200/16000] [L1: 0.0146] 25.5+0.0s +[12800/16000] [L1: 0.0145] 25.9+0.0s +[14400/16000] [L1: 0.0145] 25.8+0.0s +[16000/16000] [L1: 0.0146] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.933 (Best: 41.055 @epoch 129) +Forward: 38.17s + +Saving... +Total: 38.65s + +[Epoch 131] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 25.5+0.9s +[3200/16000] [L1: 0.0145] 25.9+0.0s +[4800/16000] [L1: 0.0144] 25.7+0.0s +[6400/16000] [L1: 0.0144] 25.9+0.0s +[8000/16000] [L1: 0.0144] 25.9+0.0s +[9600/16000] [L1: 0.0146] 25.7+0.0s +[11200/16000] [L1: 0.0147] 25.9+0.0s +[12800/16000] [L1: 0.0147] 25.8+0.0s +[14400/16000] [L1: 0.0147] 25.8+0.0s +[16000/16000] [L1: 0.0147] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.032 (Best: 41.055 @epoch 129) +Forward: 38.01s + +Saving... +Total: 38.46s + +[Epoch 132] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 26.0+1.3s +[3200/16000] [L1: 0.0143] 25.7+0.0s +[4800/16000] [L1: 0.0144] 25.9+0.0s +[6400/16000] [L1: 0.0146] 26.0+0.0s +[8000/16000] [L1: 0.0145] 25.8+0.0s +[9600/16000] [L1: 0.0146] 26.1+0.0s +[11200/16000] [L1: 0.0146] 25.8+0.0s +[12800/16000] [L1: 0.0145] 26.2+0.0s +[14400/16000] [L1: 0.0145] 25.9+0.0s +[16000/16000] [L1: 0.0145] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.105 (Best: 41.105 @epoch 132) +Forward: 38.02s + +Saving... +Total: 38.56s + +[Epoch 133] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 25.9+0.9s +[3200/16000] [L1: 0.0144] 26.2+0.0s +[4800/16000] [L1: 0.0145] 26.4+0.0s +[6400/16000] [L1: 0.0146] 25.9+0.0s +[8000/16000] [L1: 0.0146] 25.8+0.0s +[9600/16000] [L1: 0.0146] 25.8+0.0s +[11200/16000] [L1: 0.0146] 26.0+0.0s +[12800/16000] [L1: 0.0146] 25.9+0.0s +[14400/16000] [L1: 0.0146] 25.7+0.0s +[16000/16000] [L1: 0.0147] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.867 (Best: 41.105 @epoch 132) +Forward: 38.29s + +Saving... +Total: 38.78s + +[Epoch 134] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0147] 25.8+0.9s +[3200/16000] [L1: 0.0146] 26.0+0.1s +[4800/16000] [L1: 0.0145] 25.6+0.0s +[6400/16000] [L1: 0.0145] 26.0+0.0s +[8000/16000] [L1: 0.0145] 25.9+0.0s +[9600/16000] [L1: 0.0145] 26.0+0.0s +[11200/16000] [L1: 0.0146] 25.9+0.0s +[12800/16000] [L1: 0.0146] 25.6+0.0s +[14400/16000] [L1: 0.0146] 25.6+0.0s +[16000/16000] [L1: 0.0145] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.069 (Best: 41.105 @epoch 132) +Forward: 38.07s + +Saving... +Total: 38.58s + +[Epoch 135] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0152] 25.7+0.9s +[3200/16000] [L1: 0.0147] 25.8+0.0s +[4800/16000] [L1: 0.0146] 25.8+0.0s +[6400/16000] [L1: 0.0146] 26.0+0.0s +[8000/16000] [L1: 0.0147] 25.8+0.0s +[9600/16000] [L1: 0.0146] 26.1+0.0s +[11200/16000] [L1: 0.0146] 26.2+0.0s +[12800/16000] [L1: 0.0146] 26.1+0.0s +[14400/16000] [L1: 0.0146] 25.7+0.0s +[16000/16000] [L1: 0.0145] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.076 (Best: 41.105 @epoch 132) +Forward: 38.10s + +Saving... +Total: 38.61s + +[Epoch 136] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 25.7+1.0s +[3200/16000] [L1: 0.0142] 25.9+0.0s +[4800/16000] [L1: 0.0146] 25.6+0.0s +[6400/16000] [L1: 0.0147] 25.7+0.0s +[8000/16000] [L1: 0.0146] 25.9+0.0s +[9600/16000] [L1: 0.0146] 25.5+0.0s +[11200/16000] [L1: 0.0146] 26.0+0.0s +[12800/16000] [L1: 0.0146] 25.8+0.0s +[14400/16000] [L1: 0.0146] 25.6+0.0s +[16000/16000] [L1: 0.0146] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.053 (Best: 41.105 @epoch 132) +Forward: 38.18s + +Saving... +Total: 38.70s + +[Epoch 137] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 25.6+1.0s +[3200/16000] [L1: 0.0147] 25.9+0.0s +[4800/16000] [L1: 0.0146] 25.9+0.0s +[6400/16000] [L1: 0.0147] 26.1+0.0s +[8000/16000] [L1: 0.0147] 26.1+0.0s +[9600/16000] [L1: 0.0147] 25.7+0.0s +[11200/16000] [L1: 0.0145] 25.7+0.0s +[12800/16000] [L1: 0.0146] 25.9+0.0s +[14400/16000] [L1: 0.0145] 25.8+0.0s +[16000/16000] [L1: 0.0146] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.076 (Best: 41.105 @epoch 132) +Forward: 38.35s + +Saving... +Total: 38.86s + +[Epoch 138] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 26.0+1.0s +[3200/16000] [L1: 0.0144] 26.0+0.0s +[4800/16000] [L1: 0.0147] 25.9+0.0s +[6400/16000] [L1: 0.0146] 26.0+0.0s +[8000/16000] [L1: 0.0145] 25.9+0.0s +[9600/16000] [L1: 0.0144] 26.0+0.0s +[11200/16000] [L1: 0.0144] 26.0+0.0s +[12800/16000] [L1: 0.0145] 25.6+0.0s +[14400/16000] [L1: 0.0144] 25.7+0.0s +[16000/16000] [L1: 0.0145] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.934 (Best: 41.105 @epoch 132) +Forward: 38.06s + +Saving... +Total: 38.60s + +[Epoch 139] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 25.5+0.9s +[3200/16000] [L1: 0.0144] 25.7+0.0s +[4800/16000] [L1: 0.0143] 26.1+0.0s +[6400/16000] [L1: 0.0143] 26.4+0.0s +[8000/16000] [L1: 0.0144] 26.4+0.0s +[9600/16000] [L1: 0.0144] 25.8+0.0s +[11200/16000] [L1: 0.0145] 25.7+0.0s +[12800/16000] [L1: 0.0145] 25.9+0.0s +[14400/16000] [L1: 0.0145] 25.8+0.0s +[16000/16000] [L1: 0.0146] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.014 (Best: 41.105 @epoch 132) +Forward: 38.20s + +Saving... +Total: 38.69s + +[Epoch 140] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 25.5+1.4s +[3200/16000] [L1: 0.0144] 25.2+0.0s +[4800/16000] [L1: 0.0144] 25.5+0.0s +[6400/16000] [L1: 0.0144] 25.8+0.0s +[8000/16000] [L1: 0.0145] 25.7+0.0s +[9600/16000] [L1: 0.0145] 25.7+0.0s +[11200/16000] [L1: 0.0144] 25.6+0.0s +[12800/16000] [L1: 0.0144] 26.0+0.0s +[14400/16000] [L1: 0.0144] 25.6+0.0s +[16000/16000] [L1: 0.0145] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.137 (Best: 41.137 @epoch 140) +Forward: 38.23s + +Saving... +Total: 38.76s + +[Epoch 141] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 26.0+0.9s +[3200/16000] [L1: 0.0142] 25.9+0.0s +[4800/16000] [L1: 0.0143] 25.8+0.0s +[6400/16000] [L1: 0.0143] 25.8+0.0s +[8000/16000] [L1: 0.0142] 25.9+0.0s +[9600/16000] [L1: 0.0143] 25.9+0.0s +[11200/16000] [L1: 0.0144] 26.0+0.0s +[12800/16000] [L1: 0.0145] 25.7+0.0s +[14400/16000] [L1: 0.0145] 25.9+0.0s +[16000/16000] [L1: 0.0145] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.987 (Best: 41.137 @epoch 140) +Forward: 38.21s + +Saving... +Total: 38.71s + +[Epoch 142] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 25.7+0.9s +[3200/16000] [L1: 0.0148] 25.9+0.0s +[4800/16000] [L1: 0.0147] 25.8+0.0s +[6400/16000] [L1: 0.0147] 25.9+0.0s +[8000/16000] [L1: 0.0147] 25.8+0.0s +[9600/16000] [L1: 0.0145] 25.9+0.0s +[11200/16000] [L1: 0.0145] 25.6+0.0s +[12800/16000] [L1: 0.0145] 25.6+0.0s +[14400/16000] [L1: 0.0145] 25.9+0.0s +[16000/16000] [L1: 0.0145] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.114 (Best: 41.137 @epoch 140) +Forward: 38.36s + +Saving... +Total: 38.89s + +[Epoch 143] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 26.2+0.9s +[3200/16000] [L1: 0.0146] 25.8+0.0s +[4800/16000] [L1: 0.0146] 26.2+0.0s +[6400/16000] [L1: 0.0145] 26.2+0.0s +[8000/16000] [L1: 0.0145] 26.0+0.0s +[9600/16000] [L1: 0.0145] 26.2+0.0s +[11200/16000] [L1: 0.0145] 26.0+0.0s +[12800/16000] [L1: 0.0145] 25.9+0.0s +[14400/16000] [L1: 0.0145] 25.9+0.0s +[16000/16000] [L1: 0.0145] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.113 (Best: 41.137 @epoch 140) +Forward: 38.27s + +Saving... +Total: 38.76s + +[Epoch 144] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 25.7+1.0s +[3200/16000] [L1: 0.0146] 25.5+0.0s +[4800/16000] [L1: 0.0145] 25.9+0.0s +[6400/16000] [L1: 0.0147] 26.2+0.0s +[8000/16000] [L1: 0.0147] 25.9+0.0s +[9600/16000] [L1: 0.0146] 25.8+0.0s +[11200/16000] [L1: 0.0147] 25.9+0.0s +[12800/16000] [L1: 0.0147] 25.7+0.0s +[14400/16000] [L1: 0.0147] 25.8+0.0s +[16000/16000] [L1: 0.0146] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.056 (Best: 41.137 @epoch 140) +Forward: 38.43s + +Saving... +Total: 38.93s + +[Epoch 145] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 26.0+0.9s +[3200/16000] [L1: 0.0146] 25.8+0.0s +[4800/16000] [L1: 0.0145] 26.0+0.0s +[6400/16000] [L1: 0.0145] 26.2+0.0s +[8000/16000] [L1: 0.0144] 26.0+0.0s +[9600/16000] [L1: 0.0144] 26.1+0.0s +[11200/16000] [L1: 0.0144] 26.0+0.0s +[12800/16000] [L1: 0.0144] 26.0+0.0s +[14400/16000] [L1: 0.0144] 25.8+0.0s +[16000/16000] [L1: 0.0145] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.117 (Best: 41.137 @epoch 140) +Forward: 38.26s + +Saving... +Total: 38.79s + +[Epoch 146] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 25.4+0.9s +[3200/16000] [L1: 0.0144] 25.8+0.0s +[4800/16000] [L1: 0.0144] 25.8+0.0s +[6400/16000] [L1: 0.0144] 25.7+0.0s +[8000/16000] [L1: 0.0144] 26.0+0.0s +[9600/16000] [L1: 0.0144] 26.0+0.0s +[11200/16000] [L1: 0.0144] 25.8+0.0s +[12800/16000] [L1: 0.0144] 26.0+0.0s +[14400/16000] [L1: 0.0144] 26.0+0.0s +[16000/16000] [L1: 0.0144] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.090 (Best: 41.137 @epoch 140) +Forward: 38.38s + +Saving... +Total: 38.88s + +[Epoch 147] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 25.7+0.9s +[3200/16000] [L1: 0.0143] 25.7+0.0s +[4800/16000] [L1: 0.0143] 26.1+0.0s +[6400/16000] [L1: 0.0143] 26.1+0.0s +[8000/16000] [L1: 0.0145] 26.1+0.0s +[9600/16000] [L1: 0.0145] 26.0+0.0s +[11200/16000] [L1: 0.0145] 26.2+0.0s +[12800/16000] [L1: 0.0144] 26.2+0.0s +[14400/16000] [L1: 0.0144] 25.6+0.0s +[16000/16000] [L1: 0.0144] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.113 (Best: 41.137 @epoch 140) +Forward: 38.21s + +Saving... +Total: 38.68s + +[Epoch 148] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 25.8+1.0s +[3200/16000] [L1: 0.0146] 25.8+0.0s +[4800/16000] [L1: 0.0145] 26.1+0.0s +[6400/16000] [L1: 0.0144] 25.9+0.0s +[8000/16000] [L1: 0.0145] 26.1+0.0s +[9600/16000] [L1: 0.0145] 25.8+0.0s +[11200/16000] [L1: 0.0144] 26.0+0.0s +[12800/16000] [L1: 0.0144] 25.8+0.0s +[14400/16000] [L1: 0.0144] 25.9+0.0s +[16000/16000] [L1: 0.0144] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.919 (Best: 41.137 @epoch 140) +Forward: 38.27s + +Saving... +Total: 38.76s + +[Epoch 149] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 25.9+0.9s +[3200/16000] [L1: 0.0144] 25.7+0.0s +[4800/16000] [L1: 0.0144] 26.1+0.0s +[6400/16000] [L1: 0.0144] 26.1+0.0s +[8000/16000] [L1: 0.0143] 26.0+0.0s +[9600/16000] [L1: 0.0143] 26.1+0.0s +[11200/16000] [L1: 0.0143] 25.9+0.0s +[12800/16000] [L1: 0.0143] 25.9+0.0s +[14400/16000] [L1: 0.0143] 25.9+0.0s +[16000/16000] [L1: 0.0144] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.909 (Best: 41.137 @epoch 140) +Forward: 38.22s + +Saving... +Total: 38.69s + +[Epoch 150] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 25.8+0.9s +[3200/16000] [L1: 0.0143] 25.8+0.0s +[4800/16000] [L1: 0.0142] 25.7+0.0s +[6400/16000] [L1: 0.0142] 25.9+0.0s +[8000/16000] [L1: 0.0141] 25.8+0.0s +[9600/16000] [L1: 0.0142] 26.1+0.0s +[11200/16000] [L1: 0.0142] 25.8+0.0s +[12800/16000] [L1: 0.0142] 25.8+0.0s +[14400/16000] [L1: 0.0143] 25.8+0.0s +[16000/16000] [L1: 0.0143] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.147 (Best: 41.147 @epoch 150) +Forward: 38.18s + +Saving... +Total: 38.70s + +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 25.8+0.9s +[3200/16000] [L1: 0.0145] 25.7+0.0s +[4800/16000] [L1: 0.0144] 26.0+0.0s +[6400/16000] [L1: 0.0143] 25.8+0.0s +[8000/16000] [L1: 0.0143] 26.0+0.0s +[9600/16000] [L1: 0.0144] 25.9+0.0s +[11200/16000] [L1: 0.0144] 25.8+0.0s +[12800/16000] [L1: 0.0144] 26.0+0.0s +[14400/16000] [L1: 0.0144] 26.0+0.0s +[16000/16000] [L1: 0.0145] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.022 (Best: 41.147 @epoch 150) +Forward: 38.09s + +Saving... +Total: 38.59s + +[Epoch 152] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 25.7+1.0s +[3200/16000] [L1: 0.0144] 25.8+0.0s +[4800/16000] [L1: 0.0143] 25.8+0.0s +[6400/16000] [L1: 0.0142] 25.8+0.0s +[8000/16000] [L1: 0.0141] 25.6+0.0s +[9600/16000] [L1: 0.0141] 25.7+0.0s +[11200/16000] [L1: 0.0141] 25.9+0.0s +[12800/16000] [L1: 0.0142] 25.4+0.0s +[14400/16000] [L1: 0.0143] 25.8+0.0s +[16000/16000] [L1: 0.0143] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.959 (Best: 41.147 @epoch 150) +Forward: 38.10s + +Saving... +Total: 38.59s + +[Epoch 153] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 25.7+0.9s +[3200/16000] [L1: 0.0143] 25.5+0.0s +[4800/16000] [L1: 0.0141] 25.5+0.0s +[6400/16000] [L1: 0.0141] 25.5+0.0s +[8000/16000] [L1: 0.0141] 25.7+0.0s +[9600/16000] [L1: 0.0142] 25.6+0.0s +[11200/16000] [L1: 0.0142] 25.5+0.0s +[12800/16000] [L1: 0.0142] 25.8+0.0s +[14400/16000] [L1: 0.0143] 25.7+0.0s +[16000/16000] [L1: 0.0143] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.094 (Best: 41.147 @epoch 150) +Forward: 38.18s + +Saving... +Total: 38.83s + +[Epoch 154] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 25.9+0.9s +[3200/16000] [L1: 0.0143] 26.2+0.0s +[4800/16000] [L1: 0.0144] 26.0+0.0s +[6400/16000] [L1: 0.0143] 25.9+0.0s +[8000/16000] [L1: 0.0142] 25.7+0.0s +[9600/16000] [L1: 0.0142] 26.1+0.0s +[11200/16000] [L1: 0.0143] 25.6+0.0s +[12800/16000] [L1: 0.0144] 25.7+0.0s +[14400/16000] [L1: 0.0143] 25.5+0.0s +[16000/16000] [L1: 0.0144] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.181 (Best: 41.181 @epoch 154) +Forward: 38.31s + +Saving... +Total: 38.86s + +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 25.7+0.9s +[3200/16000] [L1: 0.0142] 25.5+0.0s +[4800/16000] [L1: 0.0141] 25.8+0.0s +[6400/16000] [L1: 0.0141] 26.1+0.0s +[8000/16000] [L1: 0.0140] 26.1+0.0s +[9600/16000] [L1: 0.0141] 26.2+0.0s +[11200/16000] [L1: 0.0141] 26.2+0.0s +[12800/16000] [L1: 0.0142] 25.8+0.0s +[14400/16000] [L1: 0.0142] 25.8+0.0s +[16000/16000] [L1: 0.0142] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.150 (Best: 41.181 @epoch 154) +Forward: 38.27s + +Saving... +Total: 38.77s + +[Epoch 156] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 25.6+1.0s +[3200/16000] [L1: 0.0142] 25.5+0.0s +[4800/16000] [L1: 0.0142] 25.7+0.0s +[6400/16000] [L1: 0.0142] 26.0+0.0s +[8000/16000] [L1: 0.0144] 25.7+0.0s +[9600/16000] [L1: 0.0144] 25.9+0.0s +[11200/16000] [L1: 0.0143] 25.7+0.0s +[12800/16000] [L1: 0.0143] 25.6+0.0s +[14400/16000] [L1: 0.0143] 25.4+0.0s +[16000/16000] [L1: 0.0143] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.143 (Best: 41.181 @epoch 154) +Forward: 38.04s + +Saving... +Total: 38.57s + +[Epoch 157] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 25.8+1.0s +[3200/16000] [L1: 0.0141] 25.5+0.0s +[4800/16000] [L1: 0.0143] 25.9+0.0s +[6400/16000] [L1: 0.0144] 25.6+0.0s +[8000/16000] [L1: 0.0143] 25.6+0.0s +[9600/16000] [L1: 0.0143] 25.8+0.0s +[11200/16000] [L1: 0.0142] 25.6+0.0s +[12800/16000] [L1: 0.0143] 25.6+0.0s +[14400/16000] [L1: 0.0143] 25.6+0.0s +[16000/16000] [L1: 0.0143] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.176 (Best: 41.181 @epoch 154) +Forward: 38.21s + +Saving... +Total: 38.70s + +[Epoch 158] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 25.5+0.9s +[3200/16000] [L1: 0.0146] 25.6+0.0s +[4800/16000] [L1: 0.0143] 25.5+0.0s +[6400/16000] [L1: 0.0143] 25.8+0.0s +[8000/16000] [L1: 0.0142] 25.7+0.0s +[9600/16000] [L1: 0.0141] 25.4+0.0s +[11200/16000] [L1: 0.0142] 25.5+0.0s +[12800/16000] [L1: 0.0142] 25.5+0.0s +[14400/16000] [L1: 0.0142] 25.6+0.0s +[16000/16000] [L1: 0.0142] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.123 (Best: 41.181 @epoch 154) +Forward: 38.06s + +Saving... +Total: 38.57s + +[Epoch 159] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 25.8+0.9s +[3200/16000] [L1: 0.0141] 25.7+0.0s +[4800/16000] [L1: 0.0140] 25.7+0.0s +[6400/16000] [L1: 0.0141] 25.9+0.0s +[8000/16000] [L1: 0.0141] 25.6+0.0s +[9600/16000] [L1: 0.0142] 25.7+0.0s +[11200/16000] [L1: 0.0143] 25.8+0.0s +[12800/16000] [L1: 0.0143] 25.8+0.0s +[14400/16000] [L1: 0.0143] 25.3+0.0s +[16000/16000] [L1: 0.0143] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.111 (Best: 41.181 @epoch 154) +Forward: 38.01s + +Saving... +Total: 38.54s + +[Epoch 160] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 25.8+1.0s +[3200/16000] [L1: 0.0143] 26.2+0.0s +[4800/16000] [L1: 0.0143] 26.0+0.0s +[6400/16000] [L1: 0.0142] 26.0+0.0s +[8000/16000] [L1: 0.0142] 25.9+0.0s +[9600/16000] [L1: 0.0142] 25.9+0.0s +[11200/16000] [L1: 0.0142] 25.8+0.0s +[12800/16000] [L1: 0.0142] 25.9+0.0s +[14400/16000] [L1: 0.0142] 25.9+0.0s +[16000/16000] [L1: 0.0143] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.226 (Best: 41.226 @epoch 160) +Forward: 38.09s + +Saving... +Total: 38.68s + +[Epoch 161] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 25.7+1.0s +[3200/16000] [L1: 0.0143] 25.4+0.0s +[4800/16000] [L1: 0.0141] 25.6+0.0s +[6400/16000] [L1: 0.0141] 25.9+0.0s +[8000/16000] [L1: 0.0142] 26.1+0.0s +[9600/16000] [L1: 0.0142] 26.0+0.0s +[11200/16000] [L1: 0.0142] 25.9+0.0s +[12800/16000] [L1: 0.0142] 25.7+0.0s +[14400/16000] [L1: 0.0142] 25.8+0.0s +[16000/16000] [L1: 0.0142] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.137 (Best: 41.226 @epoch 160) +Forward: 38.19s + +Saving... +Total: 38.69s + +[Epoch 162] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 25.8+1.0s +[3200/16000] [L1: 0.0146] 26.0+0.0s +[4800/16000] [L1: 0.0143] 25.8+0.0s +[6400/16000] [L1: 0.0143] 25.8+0.0s +[8000/16000] [L1: 0.0143] 25.7+0.0s +[9600/16000] [L1: 0.0143] 25.8+0.0s +[11200/16000] [L1: 0.0144] 25.8+0.0s +[12800/16000] [L1: 0.0144] 25.6+0.0s +[14400/16000] [L1: 0.0143] 25.8+0.0s +[16000/16000] [L1: 0.0143] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.154 (Best: 41.226 @epoch 160) +Forward: 38.21s + +Saving... +Total: 38.77s + +[Epoch 163] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 25.6+0.9s +[3200/16000] [L1: 0.0143] 25.6+0.0s +[4800/16000] [L1: 0.0142] 26.0+0.0s +[6400/16000] [L1: 0.0143] 25.6+0.0s +[8000/16000] [L1: 0.0142] 25.9+0.0s +[9600/16000] [L1: 0.0142] 25.5+0.0s +[11200/16000] [L1: 0.0142] 25.8+0.0s +[12800/16000] [L1: 0.0142] 25.6+0.0s +[14400/16000] [L1: 0.0142] 25.7+0.0s +[16000/16000] [L1: 0.0142] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.195 (Best: 41.226 @epoch 160) +Forward: 38.07s + +Saving... +Total: 38.58s + +[Epoch 164] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 25.7+1.0s +[3200/16000] [L1: 0.0145] 25.8+0.0s +[4800/16000] [L1: 0.0144] 25.7+0.0s +[6400/16000] [L1: 0.0144] 25.5+0.0s +[8000/16000] [L1: 0.0143] 25.5+0.0s +[9600/16000] [L1: 0.0143] 25.7+0.0s +[11200/16000] [L1: 0.0143] 25.6+0.0s +[12800/16000] [L1: 0.0143] 25.5+0.0s +[14400/16000] [L1: 0.0143] 25.8+0.0s +[16000/16000] [L1: 0.0143] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.200 (Best: 41.226 @epoch 160) +Forward: 38.01s + +Saving... +Total: 38.53s + +[Epoch 165] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 25.9+0.9s +[3200/16000] [L1: 0.0145] 25.8+0.0s +[4800/16000] [L1: 0.0146] 25.9+0.0s +[6400/16000] [L1: 0.0146] 25.8+0.0s +[8000/16000] [L1: 0.0146] 25.9+0.0s +[9600/16000] [L1: 0.0144] 25.8+0.0s +[11200/16000] [L1: 0.0143] 26.0+0.0s +[12800/16000] [L1: 0.0143] 25.9+0.0s +[14400/16000] [L1: 0.0143] 25.6+0.0s +[16000/16000] [L1: 0.0143] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.220 (Best: 41.226 @epoch 160) +Forward: 37.91s + +Saving... +Total: 38.51s + +[Epoch 166] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 25.8+1.0s +[3200/16000] [L1: 0.0141] 25.6+0.0s +[4800/16000] [L1: 0.0143] 25.8+0.0s +[6400/16000] [L1: 0.0143] 25.6+0.0s +[8000/16000] [L1: 0.0142] 25.7+0.0s +[9600/16000] [L1: 0.0142] 25.5+0.0s +[11200/16000] [L1: 0.0142] 25.7+0.0s +[12800/16000] [L1: 0.0143] 25.6+0.0s +[14400/16000] [L1: 0.0143] 25.7+0.0s +[16000/16000] [L1: 0.0143] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.228 (Best: 41.228 @epoch 166) +Forward: 38.14s + +Saving... +Total: 38.65s + +[Epoch 167] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 25.6+0.9s +[3200/16000] [L1: 0.0141] 25.4+0.0s +[4800/16000] [L1: 0.0141] 25.7+0.0s +[6400/16000] [L1: 0.0142] 25.8+0.0s +[8000/16000] [L1: 0.0142] 26.0+0.0s +[9600/16000] [L1: 0.0142] 26.1+0.0s +[11200/16000] [L1: 0.0142] 26.0+0.0s +[12800/16000] [L1: 0.0142] 25.7+0.0s +[14400/16000] [L1: 0.0142] 25.8+0.0s +[16000/16000] [L1: 0.0142] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.144 (Best: 41.228 @epoch 166) +Forward: 38.07s + +Saving... +Total: 38.55s + +[Epoch 168] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 25.5+0.9s +[3200/16000] [L1: 0.0142] 25.8+0.0s +[4800/16000] [L1: 0.0142] 25.9+0.0s +[6400/16000] [L1: 0.0143] 25.9+0.0s +[8000/16000] [L1: 0.0144] 25.9+0.0s +[9600/16000] [L1: 0.0144] 25.7+0.0s +[11200/16000] [L1: 0.0143] 25.9+0.0s +[12800/16000] [L1: 0.0143] 26.0+0.0s +[14400/16000] [L1: 0.0143] 26.0+0.0s +[16000/16000] [L1: 0.0143] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.207 (Best: 41.228 @epoch 166) +Forward: 38.18s + +Saving... +Total: 38.69s + +[Epoch 169] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0139] 25.8+0.9s +[3200/16000] [L1: 0.0140] 25.7+0.0s +[4800/16000] [L1: 0.0140] 25.5+0.0s +[6400/16000] [L1: 0.0140] 25.7+0.0s +[8000/16000] [L1: 0.0142] 25.9+0.0s +[9600/16000] [L1: 0.0142] 26.0+0.0s +[11200/16000] [L1: 0.0143] 25.5+0.0s +[12800/16000] [L1: 0.0143] 25.8+0.0s +[14400/16000] [L1: 0.0142] 25.6+0.0s +[16000/16000] [L1: 0.0143] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.190 (Best: 41.228 @epoch 166) +Forward: 38.01s + +Saving... +Total: 38.48s + +[Epoch 170] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0139] 26.0+0.9s +[3200/16000] [L1: 0.0142] 25.9+0.0s +[4800/16000] [L1: 0.0142] 25.5+0.0s +[6400/16000] [L1: 0.0143] 25.7+0.0s +[8000/16000] [L1: 0.0143] 26.1+0.0s +[9600/16000] [L1: 0.0143] 25.5+0.0s +[11200/16000] [L1: 0.0142] 25.6+0.0s +[12800/16000] [L1: 0.0142] 25.5+0.0s +[14400/16000] [L1: 0.0142] 25.6+0.0s +[16000/16000] [L1: 0.0142] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.182 (Best: 41.228 @epoch 166) +Forward: 38.05s + +Saving... +Total: 38.55s + +[Epoch 171] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0139] 25.7+0.9s +[3200/16000] [L1: 0.0139] 25.8+0.0s +[4800/16000] [L1: 0.0141] 26.0+0.0s +[6400/16000] [L1: 0.0140] 25.7+0.0s +[8000/16000] [L1: 0.0141] 25.6+0.0s +[9600/16000] [L1: 0.0141] 25.7+0.0s +[11200/16000] [L1: 0.0141] 25.7+0.0s +[12800/16000] [L1: 0.0141] 25.6+0.0s +[14400/16000] [L1: 0.0141] 25.7+0.0s +[16000/16000] [L1: 0.0142] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.116 (Best: 41.228 @epoch 166) +Forward: 38.18s + +Saving... +Total: 38.68s + +[Epoch 172] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 25.7+1.6s +[3200/16000] [L1: 0.0144] 25.7+0.0s +[4800/16000] [L1: 0.0142] 25.9+0.0s +[6400/16000] [L1: 0.0142] 25.8+0.0s +[8000/16000] [L1: 0.0143] 26.0+0.0s +[9600/16000] [L1: 0.0143] 26.2+0.0s +[11200/16000] [L1: 0.0142] 25.6+0.0s +[12800/16000] [L1: 0.0142] 25.7+0.0s +[14400/16000] [L1: 0.0142] 25.9+0.0s +[16000/16000] [L1: 0.0142] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.237 (Best: 41.237 @epoch 172) +Forward: 38.15s + +Saving... +Total: 38.71s + +[Epoch 173] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 25.5+1.0s +[3200/16000] [L1: 0.0141] 25.5+0.0s +[4800/16000] [L1: 0.0140] 25.9+0.0s +[6400/16000] [L1: 0.0141] 26.0+0.0s +[8000/16000] [L1: 0.0141] 25.6+0.0s +[9600/16000] [L1: 0.0142] 25.7+0.0s +[11200/16000] [L1: 0.0142] 25.7+0.0s +[12800/16000] [L1: 0.0142] 25.8+0.0s +[14400/16000] [L1: 0.0142] 25.7+0.0s +[16000/16000] [L1: 0.0142] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.183 (Best: 41.237 @epoch 172) +Forward: 38.04s + +Saving... +Total: 38.52s + +[Epoch 174] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 25.8+0.9s +[3200/16000] [L1: 0.0145] 25.7+0.0s +[4800/16000] [L1: 0.0146] 25.8+0.0s +[6400/16000] [L1: 0.0145] 25.8+0.0s +[8000/16000] [L1: 0.0144] 26.1+0.0s +[9600/16000] [L1: 0.0144] 25.8+0.0s +[11200/16000] [L1: 0.0143] 25.7+0.0s +[12800/16000] [L1: 0.0142] 25.8+0.0s +[14400/16000] [L1: 0.0142] 25.7+0.0s +[16000/16000] [L1: 0.0142] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.183 (Best: 41.237 @epoch 172) +Forward: 38.08s + +Saving... +Total: 38.60s + +[Epoch 175] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 25.6+0.9s +[3200/16000] [L1: 0.0140] 25.5+0.0s +[4800/16000] [L1: 0.0141] 25.9+0.0s +[6400/16000] [L1: 0.0142] 25.8+0.0s +[8000/16000] [L1: 0.0142] 25.8+0.0s +[9600/16000] [L1: 0.0142] 25.8+0.0s +[11200/16000] [L1: 0.0143] 26.1+0.0s +[12800/16000] [L1: 0.0143] 25.7+0.0s +[14400/16000] [L1: 0.0143] 25.8+0.0s +[16000/16000] [L1: 0.0142] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.219 (Best: 41.237 @epoch 172) +Forward: 38.10s + +Saving... +Total: 38.61s + +[Epoch 176] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0138] 26.1+1.4s +[3200/16000] [L1: 0.0140] 26.0+0.0s +[4800/16000] [L1: 0.0141] 25.8+0.0s +[6400/16000] [L1: 0.0142] 26.0+0.0s +[8000/16000] [L1: 0.0142] 25.7+0.0s +[9600/16000] [L1: 0.0140] 25.7+0.0s +[11200/16000] [L1: 0.0140] 25.8+0.0s +[12800/16000] [L1: 0.0140] 26.0+0.0s +[14400/16000] [L1: 0.0140] 25.9+0.0s +[16000/16000] [L1: 0.0140] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.287 (Best: 41.287 @epoch 176) +Forward: 38.19s + +Saving... +Total: 38.72s + +[Epoch 177] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 25.8+0.9s +[3200/16000] [L1: 0.0141] 25.7+0.0s +[4800/16000] [L1: 0.0142] 26.0+0.0s +[6400/16000] [L1: 0.0142] 25.8+0.0s +[8000/16000] [L1: 0.0142] 25.9+0.0s +[9600/16000] [L1: 0.0142] 26.0+0.0s +[11200/16000] [L1: 0.0142] 25.8+0.0s +[12800/16000] [L1: 0.0142] 33.4+0.0s +[14400/16000] [L1: 0.0142] 59.0+0.0s +[16000/16000] [L1: 0.0141] 57.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.217 (Best: 41.287 @epoch 176) +Forward: 37.69s + +Saving... +Total: 38.16s + +[Epoch 178] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0138] 59.8+0.9s +[3200/16000] [L1: 0.0137] 60.3+0.0s +[4800/16000] [L1: 0.0138] 59.7+0.0s +[6400/16000] [L1: 0.0139] 59.8+0.0s +[8000/16000] [L1: 0.0138] 59.3+0.0s +[9600/16000] [L1: 0.0139] 40.9+0.0s +[11200/16000] [L1: 0.0139] 25.6+0.0s +[12800/16000] [L1: 0.0139] 25.4+0.0s +[14400/16000] [L1: 0.0139] 25.3+0.0s +[16000/16000] [L1: 0.0139] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.246 (Best: 41.287 @epoch 176) +Forward: 38.17s + +Saving... +Total: 38.66s + +[Epoch 179] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 58.3+0.9s +[3200/16000] [L1: 0.0141] 58.6+0.0s +[4800/16000] [L1: 0.0141] 60.3+0.0s +[6400/16000] [L1: 0.0142] 59.8+0.0s +[8000/16000] [L1: 0.0141] 60.1+0.0s +[9600/16000] [L1: 0.0142] 59.9+0.0s +[11200/16000] [L1: 0.0142] 51.1+0.0s +[12800/16000] [L1: 0.0142] 26.0+0.0s +[14400/16000] [L1: 0.0143] 26.1+0.0s +[16000/16000] [L1: 0.0144] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.169 (Best: 41.287 @epoch 176) +Forward: 38.31s + +Saving... +Total: 38.91s + +[Epoch 180] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0139] 26.0+0.9s +[3200/16000] [L1: 0.0139] 25.8+0.0s +[4800/16000] [L1: 0.0139] 26.0+0.0s +[6400/16000] [L1: 0.0139] 26.2+0.0s +[8000/16000] [L1: 0.0139] 26.1+0.0s +[9600/16000] [L1: 0.0139] 26.2+0.0s +[11200/16000] [L1: 0.0140] 26.1+0.0s +[12800/16000] [L1: 0.0140] 26.1+0.0s +[14400/16000] [L1: 0.0140] 26.4+0.0s +[16000/16000] [L1: 0.0140] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.056 (Best: 41.287 @epoch 176) +Forward: 38.34s + +Saving... +Total: 38.86s + +[Epoch 181] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 25.9+1.0s +[3200/16000] [L1: 0.0140] 25.8+0.0s +[4800/16000] [L1: 0.0139] 26.0+0.0s +[6400/16000] [L1: 0.0140] 26.1+0.0s +[8000/16000] [L1: 0.0139] 25.7+0.0s +[9600/16000] [L1: 0.0140] 25.6+0.0s +[11200/16000] [L1: 0.0139] 25.6+0.0s +[12800/16000] [L1: 0.0140] 25.6+0.0s +[14400/16000] [L1: 0.0141] 25.6+0.0s +[16000/16000] [L1: 0.0140] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.321 (Best: 41.321 @epoch 181) +Forward: 38.08s + +Saving... +Total: 38.65s + +[Epoch 182] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0139] 25.7+0.9s +[3200/16000] [L1: 0.0138] 26.0+0.0s +[4800/16000] [L1: 0.0141] 25.9+0.0s +[6400/16000] [L1: 0.0141] 25.9+0.0s +[8000/16000] [L1: 0.0141] 26.1+0.0s +[9600/16000] [L1: 0.0142] 26.0+0.0s +[11200/16000] [L1: 0.0141] 26.2+0.0s +[12800/16000] [L1: 0.0141] 25.9+0.0s +[14400/16000] [L1: 0.0141] 25.9+0.0s +[16000/16000] [L1: 0.0142] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.285 (Best: 41.321 @epoch 181) +Forward: 38.03s + +Saving... +Total: 38.53s + +[Epoch 183] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 25.7+0.9s +[3200/16000] [L1: 0.0142] 25.9+0.0s +[4800/16000] [L1: 0.0143] 26.1+0.0s +[6400/16000] [L1: 0.0142] 26.3+0.0s +[8000/16000] [L1: 0.0140] 26.2+0.0s +[9600/16000] [L1: 0.0140] 26.0+0.0s +[11200/16000] [L1: 0.0141] 25.8+0.0s +[12800/16000] [L1: 0.0141] 25.9+0.0s +[14400/16000] [L1: 0.0141] 25.7+0.0s +[16000/16000] [L1: 0.0141] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.064 (Best: 41.321 @epoch 181) +Forward: 38.11s + +Saving... +Total: 38.66s + +[Epoch 184] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0136] 25.5+0.9s +[3200/16000] [L1: 0.0139] 25.8+0.0s +[4800/16000] [L1: 0.0140] 26.0+0.0s +[6400/16000] [L1: 0.0141] 26.0+0.0s +[8000/16000] [L1: 0.0140] 26.0+0.0s +[9600/16000] [L1: 0.0139] 25.7+0.0s +[11200/16000] [L1: 0.0139] 26.4+0.0s +[12800/16000] [L1: 0.0139] 25.9+0.0s +[14400/16000] [L1: 0.0139] 25.7+0.0s +[16000/16000] [L1: 0.0139] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.269 (Best: 41.321 @epoch 181) +Forward: 38.18s + +Saving... +Total: 38.70s + +[Epoch 185] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 25.8+1.0s +[3200/16000] [L1: 0.0140] 25.8+0.0s +[4800/16000] [L1: 0.0140] 25.8+0.0s +[6400/16000] [L1: 0.0139] 26.1+0.0s +[8000/16000] [L1: 0.0140] 25.9+0.0s +[9600/16000] [L1: 0.0139] 25.8+0.0s +[11200/16000] [L1: 0.0139] 25.7+0.0s +[12800/16000] [L1: 0.0139] 25.9+0.0s +[14400/16000] [L1: 0.0140] 25.9+0.0s +[16000/16000] [L1: 0.0140] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.209 (Best: 41.321 @epoch 181) +Forward: 38.28s + +Saving... +Total: 38.78s + +[Epoch 186] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0135] 25.8+0.9s +[3200/16000] [L1: 0.0137] 25.9+0.0s +[4800/16000] [L1: 0.0138] 25.7+0.0s +[6400/16000] [L1: 0.0138] 25.9+0.0s +[8000/16000] [L1: 0.0139] 25.7+0.0s +[9600/16000] [L1: 0.0140] 25.8+0.0s +[11200/16000] [L1: 0.0140] 25.6+0.0s +[12800/16000] [L1: 0.0140] 25.8+0.0s +[14400/16000] [L1: 0.0140] 26.1+0.0s +[16000/16000] [L1: 0.0140] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.233 (Best: 41.321 @epoch 181) +Forward: 38.18s + +Saving... +Total: 38.67s + +[Epoch 187] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 42.6+0.9s +[3200/16000] [L1: 0.0137] 59.2+0.0s +[4800/16000] [L1: 0.0138] 41.8+0.0s +[6400/16000] [L1: 0.0139] 25.9+0.0s +[8000/16000] [L1: 0.0139] 26.0+0.0s +[9600/16000] [L1: 0.0139] 25.8+0.0s +[11200/16000] [L1: 0.0139] 25.9+0.0s +[12800/16000] [L1: 0.0139] 25.7+0.0s +[14400/16000] [L1: 0.0139] 25.9+0.0s +[16000/16000] [L1: 0.0139] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.289 (Best: 41.321 @epoch 181) +Forward: 38.23s + +Saving... +Total: 38.86s + +[Epoch 188] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 25.6+0.9s +[3200/16000] [L1: 0.0141] 25.7+0.1s +[4800/16000] [L1: 0.0140] 26.0+0.0s +[6400/16000] [L1: 0.0141] 25.7+0.0s +[8000/16000] [L1: 0.0142] 25.9+0.0s +[9600/16000] [L1: 0.0142] 25.9+0.0s +[11200/16000] [L1: 0.0142] 26.2+0.0s +[12800/16000] [L1: 0.0141] 25.9+0.0s +[14400/16000] [L1: 0.0141] 26.0+0.0s +[16000/16000] [L1: 0.0141] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.035 (Best: 41.321 @epoch 181) +Forward: 38.51s + +Saving... +Total: 38.98s + +[Epoch 189] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 26.2+1.0s +[3200/16000] [L1: 0.0145] 26.0+0.1s +[4800/16000] [L1: 0.0144] 26.0+0.0s +[6400/16000] [L1: 0.0142] 26.1+0.0s +[8000/16000] [L1: 0.0141] 26.1+0.0s +[9600/16000] [L1: 0.0140] 26.2+0.0s +[11200/16000] [L1: 0.0140] 26.1+0.0s +[12800/16000] [L1: 0.0140] 26.3+0.0s +[14400/16000] [L1: 0.0140] 26.3+0.0s +[16000/16000] [L1: 0.0140] 26.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.207 (Best: 41.321 @epoch 181) +Forward: 38.58s + +Saving... +Total: 39.07s + +[Epoch 190] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 26.3+1.0s +[3200/16000] [L1: 0.0141] 25.9+0.0s +[4800/16000] [L1: 0.0141] 26.4+0.1s +[6400/16000] [L1: 0.0141] 26.2+0.0s +[8000/16000] [L1: 0.0142] 26.3+0.0s +[9600/16000] [L1: 0.0141] 26.3+0.0s +[11200/16000] [L1: 0.0141] 26.3+0.1s +[12800/16000] [L1: 0.0141] 26.2+0.0s +[14400/16000] [L1: 0.0141] 26.1+0.0s +[16000/16000] [L1: 0.0141] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.290 (Best: 41.321 @epoch 181) +Forward: 38.06s + +Saving... +Total: 38.71s + +[Epoch 191] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0136] 25.7+0.9s +[3200/16000] [L1: 0.0138] 26.0+0.0s +[4800/16000] [L1: 0.0137] 26.1+0.1s +[6400/16000] [L1: 0.0138] 26.2+0.1s +[8000/16000] [L1: 0.0139] 26.1+0.0s +[9600/16000] [L1: 0.0139] 26.1+0.0s +[11200/16000] [L1: 0.0139] 26.3+0.1s +[12800/16000] [L1: 0.0140] 26.0+0.0s +[14400/16000] [L1: 0.0140] 26.0+0.0s +[16000/16000] [L1: 0.0140] 26.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.321 (Best: 41.321 @epoch 181) +Forward: 38.12s + +Saving... +Total: 38.71s + +[Epoch 192] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0132] 25.8+0.9s +[3200/16000] [L1: 0.0135] 26.0+0.0s +[4800/16000] [L1: 0.0137] 26.1+0.0s +[6400/16000] [L1: 0.0137] 26.0+0.0s +[8000/16000] [L1: 0.0138] 25.9+0.0s +[9600/16000] [L1: 0.0137] 26.1+0.0s +[11200/16000] [L1: 0.0138] 26.1+0.0s +[12800/16000] [L1: 0.0139] 26.0+0.0s +[14400/16000] [L1: 0.0138] 26.0+0.0s +[16000/16000] [L1: 0.0139] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.242 (Best: 41.321 @epoch 181) +Forward: 38.01s + +Saving... +Total: 38.47s + +[Epoch 193] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 25.5+1.4s +[3200/16000] [L1: 0.0145] 25.7+0.0s +[4800/16000] [L1: 0.0143] 25.7+0.0s +[6400/16000] [L1: 0.0142] 26.2+0.0s +[8000/16000] [L1: 0.0141] 25.9+0.0s +[9600/16000] [L1: 0.0141] 26.0+0.0s +[11200/16000] [L1: 0.0141] 25.9+0.0s +[12800/16000] [L1: 0.0140] 26.1+0.0s +[14400/16000] [L1: 0.0141] 26.0+0.1s +[16000/16000] [L1: 0.0140] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.334 (Best: 41.334 @epoch 193) +Forward: 38.01s + +Saving... +Total: 38.58s + +[Epoch 194] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0139] 26.0+0.9s +[3200/16000] [L1: 0.0137] 25.8+0.0s +[4800/16000] [L1: 0.0138] 25.9+0.0s +[6400/16000] [L1: 0.0137] 26.1+0.0s +[8000/16000] [L1: 0.0139] 26.1+0.0s +[9600/16000] [L1: 0.0139] 26.2+0.0s +[11200/16000] [L1: 0.0139] 49.9+0.0s +[12800/16000] [L1: 0.0139] 58.7+0.0s +[14400/16000] [L1: 0.0139] 58.9+0.0s +[16000/16000] [L1: 0.0139] 60.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.219 (Best: 41.334 @epoch 193) +Forward: 37.68s + +Saving... +Total: 38.19s + +[Epoch 195] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0137] 60.4+1.2s +[3200/16000] [L1: 0.0136] 60.3+0.0s +[4800/16000] [L1: 0.0138] 44.6+0.0s +[6400/16000] [L1: 0.0138] 25.9+0.0s +[8000/16000] [L1: 0.0139] 25.8+0.0s +[9600/16000] [L1: 0.0138] 25.9+0.0s +[11200/16000] [L1: 0.0139] 25.8+0.0s +[12800/16000] [L1: 0.0139] 26.0+0.0s +[14400/16000] [L1: 0.0138] 25.9+0.0s +[16000/16000] [L1: 0.0138] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.154 (Best: 41.334 @epoch 193) +Forward: 38.28s + +Saving... +Total: 38.82s + +[Epoch 196] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0137] 25.7+0.9s +[3200/16000] [L1: 0.0136] 25.8+0.0s +[4800/16000] [L1: 0.0137] 26.1+0.1s +[6400/16000] [L1: 0.0137] 26.1+0.1s +[8000/16000] [L1: 0.0137] 26.2+0.0s +[9600/16000] [L1: 0.0136] 26.1+0.0s +[11200/16000] [L1: 0.0137] 26.0+0.0s +[12800/16000] [L1: 0.0138] 26.1+0.0s +[14400/16000] [L1: 0.0139] 26.1+0.0s +[16000/16000] [L1: 0.0139] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.312 (Best: 41.334 @epoch 193) +Forward: 38.07s + +Saving... +Total: 38.57s + +[Epoch 197] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0137] 25.6+1.1s +[3200/16000] [L1: 0.0139] 25.6+0.0s +[4800/16000] [L1: 0.0138] 25.8+0.0s +[6400/16000] [L1: 0.0138] 26.2+0.0s +[8000/16000] [L1: 0.0138] 26.0+0.0s +[9600/16000] [L1: 0.0138] 26.0+0.0s +[11200/16000] [L1: 0.0139] 26.1+0.0s +[12800/16000] [L1: 0.0139] 26.1+0.0s +[14400/16000] [L1: 0.0139] 26.2+0.0s +[16000/16000] [L1: 0.0139] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.237 (Best: 41.334 @epoch 193) +Forward: 38.12s + +Saving... +Total: 38.60s + +[Epoch 198] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 25.6+1.1s +[3200/16000] [L1: 0.0139] 25.8+0.0s +[4800/16000] [L1: 0.0141] 25.8+0.0s +[6400/16000] [L1: 0.0141] 25.9+0.0s +[8000/16000] [L1: 0.0141] 25.8+0.0s +[9600/16000] [L1: 0.0140] 26.2+0.1s +[11200/16000] [L1: 0.0140] 26.0+0.0s +[12800/16000] [L1: 0.0139] 26.0+0.0s +[14400/16000] [L1: 0.0140] 25.9+0.1s +[16000/16000] [L1: 0.0140] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.293 (Best: 41.334 @epoch 193) +Forward: 38.27s + +Saving... +Total: 38.78s + +[Epoch 199] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0136] 25.9+0.9s +[3200/16000] [L1: 0.0136] 25.7+0.0s +[4800/16000] [L1: 0.0137] 26.1+0.0s +[6400/16000] [L1: 0.0139] 26.0+0.0s +[8000/16000] [L1: 0.0140] 26.1+0.0s +[9600/16000] [L1: 0.0139] 26.0+0.0s +[11200/16000] [L1: 0.0139] 26.1+0.0s +[12800/16000] [L1: 0.0140] 41.9+0.0s +[14400/16000] [L1: 0.0139] 59.4+0.1s +[16000/16000] [L1: 0.0139] 58.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.223 (Best: 41.334 @epoch 193) +Forward: 37.62s + +Saving... +Total: 38.20s + +[Epoch 200] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 60.6+0.9s +[3200/16000] [L1: 0.0142] 60.5+0.1s +[4800/16000] [L1: 0.0140] 60.3+0.0s +[6400/16000] [L1: 0.0140] 52.7+0.0s +[8000/16000] [L1: 0.0141] 25.7+0.0s +[9600/16000] [L1: 0.0141] 25.7+0.0s +[11200/16000] [L1: 0.0141] 25.9+0.0s +[12800/16000] [L1: 0.0140] 25.7+0.0s +[14400/16000] [L1: 0.0140] 25.9+0.0s +[16000/16000] [L1: 0.0140] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.219 (Best: 41.334 @epoch 193) +Forward: 38.29s + +Saving... +Total: 38.78s + +[Epoch 201] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0137] 25.8+1.1s +[3200/16000] [L1: 0.0135] 25.9+0.0s +[4800/16000] [L1: 0.0135] 26.0+0.0s +[6400/16000] [L1: 0.0134] 26.2+0.0s +[8000/16000] [L1: 0.0133] 25.9+0.0s +[9600/16000] [L1: 0.0134] 26.0+0.0s +[11200/16000] [L1: 0.0133] 26.1+0.0s +[12800/16000] [L1: 0.0133] 26.2+0.0s +[14400/16000] [L1: 0.0133] 26.2+0.0s +[16000/16000] [L1: 0.0133] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.399 (Best: 41.399 @epoch 201) +Forward: 38.25s + +Saving... +Total: 38.81s + +[Epoch 202] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0132] 25.9+1.0s +[3200/16000] [L1: 0.0132] 25.9+0.1s +[4800/16000] [L1: 0.0132] 26.0+0.0s +[6400/16000] [L1: 0.0132] 26.2+0.0s +[8000/16000] [L1: 0.0132] 26.2+0.0s +[9600/16000] [L1: 0.0132] 26.1+0.0s +[11200/16000] [L1: 0.0132] 26.1+0.0s +[12800/16000] [L1: 0.0132] 26.3+0.0s +[14400/16000] [L1: 0.0133] 26.3+0.0s +[16000/16000] [L1: 0.0133] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.370 (Best: 41.399 @epoch 201) +Forward: 38.27s + +Saving... +Total: 38.74s + +[Epoch 203] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0135] 25.7+0.9s +[3200/16000] [L1: 0.0133] 26.0+0.1s +[4800/16000] [L1: 0.0131] 26.1+0.0s +[6400/16000] [L1: 0.0131] 26.4+0.0s +[8000/16000] [L1: 0.0131] 26.0+0.0s +[9600/16000] [L1: 0.0132] 26.2+0.0s +[11200/16000] [L1: 0.0132] 26.3+0.0s +[12800/16000] [L1: 0.0132] 26.2+0.0s +[14400/16000] [L1: 0.0132] 26.3+0.0s +[16000/16000] [L1: 0.0132] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.311 (Best: 41.399 @epoch 201) +Forward: 38.31s + +Saving... +Total: 38.90s + +[Epoch 204] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0130] 25.8+0.9s +[3200/16000] [L1: 0.0130] 25.8+0.0s +[4800/16000] [L1: 0.0131] 26.2+0.0s +[6400/16000] [L1: 0.0132] 26.3+0.1s +[8000/16000] [L1: 0.0132] 26.2+0.0s +[9600/16000] [L1: 0.0132] 26.7+0.0s +[11200/16000] [L1: 0.0132] 26.3+0.0s +[12800/16000] [L1: 0.0132] 26.2+0.0s +[14400/16000] [L1: 0.0131] 26.1+0.0s +[16000/16000] [L1: 0.0131] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.376 (Best: 41.399 @epoch 201) +Forward: 38.33s + +Saving... +Total: 38.87s + +[Epoch 205] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0132] 25.9+1.0s +[3200/16000] [L1: 0.0134] 26.0+0.0s +[4800/16000] [L1: 0.0133] 26.2+0.1s +[6400/16000] [L1: 0.0132] 26.3+0.0s +[8000/16000] [L1: 0.0133] 26.2+0.0s +[9600/16000] [L1: 0.0131] 26.2+0.0s +[11200/16000] [L1: 0.0132] 26.2+0.0s +[12800/16000] [L1: 0.0132] 25.9+0.0s +[14400/16000] [L1: 0.0132] 26.4+0.0s +[16000/16000] [L1: 0.0133] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.386 (Best: 41.399 @epoch 201) +Forward: 38.41s + +Saving... +Total: 38.92s + +[Epoch 206] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0140] 26.1+0.9s +[3200/16000] [L1: 0.0135] 25.7+0.0s +[4800/16000] [L1: 0.0136] 26.0+0.0s +[6400/16000] [L1: 0.0134] 26.0+0.0s +[8000/16000] [L1: 0.0134] 26.2+0.0s +[9600/16000] [L1: 0.0133] 26.3+0.0s +[11200/16000] [L1: 0.0134] 26.2+0.0s +[12800/16000] [L1: 0.0134] 26.2+0.0s +[14400/16000] [L1: 0.0134] 26.1+0.0s +[16000/16000] [L1: 0.0135] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.382 (Best: 41.399 @epoch 201) +Forward: 38.53s + +Saving... +Total: 39.00s + +[Epoch 207] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0133] 26.0+0.9s +[3200/16000] [L1: 0.0134] 26.1+0.0s +[4800/16000] [L1: 0.0133] 26.1+0.0s +[6400/16000] [L1: 0.0133] 26.2+0.0s +[8000/16000] [L1: 0.0132] 26.2+0.0s +[9600/16000] [L1: 0.0133] 26.2+0.0s +[11200/16000] [L1: 0.0133] 26.2+0.0s +[12800/16000] [L1: 0.0133] 26.1+0.0s +[14400/16000] [L1: 0.0133] 26.1+0.0s +[16000/16000] [L1: 0.0133] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.385 (Best: 41.399 @epoch 201) +Forward: 38.37s + +Saving... +Total: 38.89s + +[Epoch 208] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0136] 25.8+0.9s +[3200/16000] [L1: 0.0133] 25.8+0.1s +[4800/16000] [L1: 0.0133] 26.2+0.0s +[6400/16000] [L1: 0.0132] 26.5+0.0s +[8000/16000] [L1: 0.0132] 26.2+0.0s +[9600/16000] [L1: 0.0133] 26.2+0.0s +[11200/16000] [L1: 0.0133] 26.2+0.0s +[12800/16000] [L1: 0.0132] 26.1+0.0s +[14400/16000] [L1: 0.0132] 26.1+0.0s +[16000/16000] [L1: 0.0133] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.403 (Best: 41.403 @epoch 208) +Forward: 38.49s + +Saving... +Total: 42.31s + +[Epoch 209] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0128] 25.7+1.0s +[3200/16000] [L1: 0.0131] 25.9+0.0s +[4800/16000] [L1: 0.0132] 26.0+0.0s +[6400/16000] [L1: 0.0132] 26.2+0.0s +[8000/16000] [L1: 0.0132] 26.0+0.0s +[9600/16000] [L1: 0.0133] 26.3+0.0s +[11200/16000] [L1: 0.0132] 26.1+0.0s +[12800/16000] [L1: 0.0132] 26.0+0.0s +[14400/16000] [L1: 0.0132] 26.0+0.0s +[16000/16000] [L1: 0.0132] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.412 (Best: 41.412 @epoch 209) +Forward: 38.52s + +Saving... +Total: 39.03s + +[Epoch 210] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0134] 25.9+1.0s +[3200/16000] [L1: 0.0137] 26.2+0.0s +[4800/16000] [L1: 0.0134] 26.2+0.0s +[6400/16000] [L1: 0.0133] 26.3+0.0s +[8000/16000] [L1: 0.0133] 26.1+0.0s +[9600/16000] [L1: 0.0133] 26.4+0.0s +[11200/16000] [L1: 0.0133] 26.3+0.0s +[12800/16000] [L1: 0.0133] 26.2+0.0s +[14400/16000] [L1: 0.0133] 26.4+0.0s +[16000/16000] [L1: 0.0133] 26.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.375 (Best: 41.412 @epoch 209) +Forward: 38.41s + +Saving... +Total: 38.87s + +[Epoch 211] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0128] 25.6+1.0s +[3200/16000] [L1: 0.0132] 25.8+0.0s +[4800/16000] [L1: 0.0131] 26.1+0.0s +[6400/16000] [L1: 0.0132] 26.0+0.0s +[8000/16000] [L1: 0.0133] 26.0+0.0s +[9600/16000] [L1: 0.0133] 26.3+0.0s +[11200/16000] [L1: 0.0132] 26.2+0.0s +[12800/16000] [L1: 0.0133] 26.2+0.0s +[14400/16000] [L1: 0.0133] 26.2+0.0s +[16000/16000] [L1: 0.0132] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.408 (Best: 41.412 @epoch 209) +Forward: 38.50s + +Saving... +Total: 39.10s + +[Epoch 212] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0135] 26.0+0.9s +[3200/16000] [L1: 0.0133] 26.0+0.1s +[4800/16000] [L1: 0.0133] 26.1+0.0s +[6400/16000] [L1: 0.0132] 26.2+0.0s +[8000/16000] [L1: 0.0132] 26.2+0.0s +[9600/16000] [L1: 0.0132] 26.3+0.0s +[11200/16000] [L1: 0.0132] 26.3+0.0s +[12800/16000] [L1: 0.0132] 26.3+0.0s +[14400/16000] [L1: 0.0132] 26.2+0.0s +[16000/16000] [L1: 0.0132] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.397 (Best: 41.412 @epoch 209) +Forward: 38.24s + +Saving... +Total: 38.69s + +[Epoch 213] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0131] 26.0+0.9s +[3200/16000] [L1: 0.0129] 26.0+0.1s +[4800/16000] [L1: 0.0130] 26.1+0.0s +[6400/16000] [L1: 0.0130] 26.3+0.0s +[8000/16000] [L1: 0.0131] 26.3+0.0s +[9600/16000] [L1: 0.0131] 26.2+0.0s +[11200/16000] [L1: 0.0131] 26.3+0.0s +[12800/16000] [L1: 0.0131] 26.1+0.0s +[14400/16000] [L1: 0.0131] 26.3+0.0s +[16000/16000] [L1: 0.0131] 26.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.402 (Best: 41.412 @epoch 209) +Forward: 38.31s + +Saving... +Total: 38.79s + +[Epoch 214] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0130] 25.9+0.9s +[3200/16000] [L1: 0.0131] 26.2+0.0s +[4800/16000] [L1: 0.0132] 26.1+0.0s +[6400/16000] [L1: 0.0132] 26.3+0.1s +[8000/16000] [L1: 0.0133] 26.1+0.0s +[9600/16000] [L1: 0.0133] 26.3+0.0s +[11200/16000] [L1: 0.0133] 26.1+0.0s +[12800/16000] [L1: 0.0133] 26.2+0.0s +[14400/16000] [L1: 0.0133] 26.2+0.0s +[16000/16000] [L1: 0.0133] 26.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.416 (Best: 41.416 @epoch 214) +Forward: 38.29s + +Saving... +Total: 38.80s + +[Epoch 215] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0132] 26.0+1.0s +[3200/16000] [L1: 0.0132] 26.1+0.0s +[4800/16000] [L1: 0.0132] 26.2+0.0s +[6400/16000] [L1: 0.0132] 26.2+0.0s +[8000/16000] [L1: 0.0131] 26.2+0.0s +[9600/16000] [L1: 0.0132] 26.1+0.0s +[11200/16000] [L1: 0.0131] 26.2+0.0s +[12800/16000] [L1: 0.0131] 26.0+0.0s +[14400/16000] [L1: 0.0132] 26.2+0.0s +[16000/16000] [L1: 0.0132] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.385 (Best: 41.416 @epoch 214) +Forward: 38.36s + +Saving... +Total: 38.80s + +[Epoch 216] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0132] 25.9+1.0s +[3200/16000] [L1: 0.0132] 25.7+0.0s +[4800/16000] [L1: 0.0133] 26.0+0.0s +[6400/16000] [L1: 0.0132] 26.4+0.0s +[8000/16000] [L1: 0.0132] 26.0+0.0s +[9600/16000] [L1: 0.0132] 26.2+0.0s +[11200/16000] [L1: 0.0131] 26.1+0.0s +[12800/16000] [L1: 0.0132] 26.0+0.0s +[14400/16000] [L1: 0.0132] 25.8+0.0s +[16000/16000] [L1: 0.0132] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.404 (Best: 41.416 @epoch 214) +Forward: 38.44s + +Saving... +Total: 38.92s + +[Epoch 217] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0130] 26.1+0.9s +[3200/16000] [L1: 0.0131] 25.8+0.0s +[4800/16000] [L1: 0.0131] 25.9+0.0s +[6400/16000] [L1: 0.0131] 26.0+0.0s +[8000/16000] [L1: 0.0131] 26.1+0.0s +[9600/16000] [L1: 0.0132] 26.0+0.0s +[11200/16000] [L1: 0.0132] 26.0+0.0s +[12800/16000] [L1: 0.0132] 26.1+0.0s +[14400/16000] [L1: 0.0132] 25.8+0.0s +[16000/16000] [L1: 0.0132] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.413 (Best: 41.416 @epoch 214) +Forward: 38.23s + +Saving... +Total: 38.70s + +[Epoch 218] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0131] 26.2+0.9s +[3200/16000] [L1: 0.0132] 25.9+0.0s +[4800/16000] [L1: 0.0133] 26.2+0.0s +[6400/16000] [L1: 0.0133] 26.1+0.0s +[8000/16000] [L1: 0.0132] 26.4+0.0s +[9600/16000] [L1: 0.0132] 26.2+0.0s +[11200/16000] [L1: 0.0132] 26.3+0.0s +[12800/16000] [L1: 0.0132] 26.1+0.0s +[14400/16000] [L1: 0.0133] 26.0+0.0s +[16000/16000] [L1: 0.0133] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.406 (Best: 41.416 @epoch 214) +Forward: 38.36s + +Saving... +Total: 38.87s + +[Epoch 219] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0130] 26.0+0.9s +[3200/16000] [L1: 0.0128] 25.7+0.0s +[4800/16000] [L1: 0.0129] 26.1+0.0s +[6400/16000] [L1: 0.0129] 26.0+0.0s +[8000/16000] [L1: 0.0130] 26.4+0.0s +[9600/16000] [L1: 0.0130] 26.6+0.0s +[11200/16000] [L1: 0.0131] 26.5+0.0s +[12800/16000] [L1: 0.0132] 26.0+0.0s +[14400/16000] [L1: 0.0131] 26.1+0.0s +[16000/16000] [L1: 0.0131] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.364 (Best: 41.416 @epoch 214) +Forward: 38.38s + +Saving... +Total: 38.84s + +[Epoch 220] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0132] 26.0+1.0s +[3200/16000] [L1: 0.0131] 26.0+0.0s +[4800/16000] [L1: 0.0131] 26.1+0.0s +[6400/16000] [L1: 0.0131] 25.9+0.0s +[8000/16000] [L1: 0.0131] 26.1+0.0s +[9600/16000] [L1: 0.0130] 26.2+0.0s +[11200/16000] [L1: 0.0130] 26.2+0.0s +[12800/16000] [L1: 0.0130] 26.1+0.0s +[14400/16000] [L1: 0.0131] 26.3+0.0s +[16000/16000] [L1: 0.0131] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.437 (Best: 41.437 @epoch 220) +Forward: 38.34s + +Saving... +Total: 38.87s + +[Epoch 221] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0131] 25.9+0.9s +[3200/16000] [L1: 0.0130] 26.0+0.0s +[4800/16000] [L1: 0.0131] 26.1+0.0s +[6400/16000] [L1: 0.0131] 26.1+0.0s +[8000/16000] [L1: 0.0131] 26.6+0.0s +[9600/16000] [L1: 0.0131] 26.5+0.0s +[11200/16000] [L1: 0.0132] 26.2+0.0s +[12800/16000] [L1: 0.0131] 26.0+0.0s +[14400/16000] [L1: 0.0131] 25.9+0.0s +[16000/16000] [L1: 0.0132] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.398 (Best: 41.437 @epoch 220) +Forward: 38.26s + +Saving... +Total: 38.71s + +[Epoch 222] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0134] 26.0+0.9s +[3200/16000] [L1: 0.0135] 26.0+0.0s +[4800/16000] [L1: 0.0133] 26.2+0.0s +[6400/16000] [L1: 0.0133] 26.2+0.0s +[8000/16000] [L1: 0.0132] 26.1+0.0s +[9600/16000] [L1: 0.0132] 26.1+0.0s +[11200/16000] [L1: 0.0131] 26.4+0.0s +[12800/16000] [L1: 0.0132] 26.3+0.0s +[14400/16000] [L1: 0.0132] 26.3+0.0s +[16000/16000] [L1: 0.0132] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.417 (Best: 41.437 @epoch 220) +Forward: 38.36s + +Saving... +Total: 38.86s + +[Epoch 223] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0129] 25.7+0.9s +[3200/16000] [L1: 0.0132] 25.7+0.0s +[4800/16000] [L1: 0.0132] 30.3+0.0s +[6400/16000] [L1: 0.0133] 59.4+0.0s +[8000/16000] [L1: 0.0133] 59.0+0.0s +[9600/16000] [L1: 0.0133] 60.5+0.0s +[11200/16000] [L1: 0.0133] 60.5+0.0s +[12800/16000] [L1: 0.0133] 60.4+0.0s +[14400/16000] [L1: 0.0133] 60.0+0.0s +[16000/16000] [L1: 0.0132] 60.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.447 (Best: 41.447 @epoch 223) +Forward: 37.99s + +Saving... +Total: 38.50s + +[Epoch 224] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0132] 25.7+0.9s +[3200/16000] [L1: 0.0133] 26.0+0.1s +[4800/16000] [L1: 0.0132] 26.3+0.0s +[6400/16000] [L1: 0.0132] 26.3+0.0s +[8000/16000] [L1: 0.0131] 26.1+0.0s +[9600/16000] [L1: 0.0131] 26.1+0.0s +[11200/16000] [L1: 0.0131] 46.9+0.0s +[12800/16000] [L1: 0.0131] 60.1+0.0s +[14400/16000] [L1: 0.0131] 39.3+0.0s +[16000/16000] [L1: 0.0131] 54.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.449 (Best: 41.449 @epoch 224) +Forward: 37.96s + +Saving... +Total: 38.46s + +[Epoch 225] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0132] 61.2+1.0s +[3200/16000] [L1: 0.0131] 60.7+0.0s +[4800/16000] [L1: 0.0132] 60.6+0.0s +[6400/16000] [L1: 0.0133] 60.1+0.0s +[8000/16000] [L1: 0.0132] 60.2+0.0s +[9600/16000] [L1: 0.0131] 39.4+0.0s +[11200/16000] [L1: 0.0132] 25.8+0.0s +[12800/16000] [L1: 0.0132] 26.1+0.0s +[14400/16000] [L1: 0.0132] 26.2+0.0s +[16000/16000] [L1: 0.0132] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.425 (Best: 41.449 @epoch 224) +Forward: 38.58s + +Saving... +Total: 39.04s + +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 226] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0133] 28.0+8.1s +[3200/16000] [L1: 0.0132] 25.2+0.0s +[4800/16000] [L1: 0.0131] 25.5+0.0s +[6400/16000] [L1: 0.0131] 25.6+0.0s +[8000/16000] [L1: 0.0131] 25.5+0.0s +[9600/16000] [L1: 0.0131] 44.0+0.0s +[11200/16000] [L1: 0.0130] 60.1+0.0s +[12800/16000] [L1: 0.0130] 42.6+0.0s +[14400/16000] [L1: 0.0130] 25.7+0.0s +[16000/16000] [L1: 0.0129] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.460 (Best: 41.460 @epoch 226) +Forward: 38.76s + +Saving... +Total: 39.46s + +[Epoch 227] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 26.0+0.9s +[3200/16000] [L1: 0.0129] 25.9+0.0s +[4800/16000] [L1: 0.0130] 26.2+0.0s +[6400/16000] [L1: 0.0129] 26.2+0.0s +[8000/16000] [L1: 0.0130] 25.9+0.0s +[9600/16000] [L1: 0.0130] 25.8+0.0s +[11200/16000] [L1: 0.0129] 25.8+0.0s +[12800/16000] [L1: 0.0129] 25.8+0.0s +[14400/16000] [L1: 0.0128] 25.9+0.0s +[16000/16000] [L1: 0.0129] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.440 (Best: 41.460 @epoch 226) +Forward: 38.61s + +Saving... +Total: 39.08s + +[Epoch 228] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.9+0.9s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0126] 26.2+0.0s +[6400/16000] [L1: 0.0127] 26.2+0.0s +[8000/16000] [L1: 0.0127] 26.1+0.0s +[9600/16000] [L1: 0.0127] 26.0+0.0s +[11200/16000] [L1: 0.0128] 26.0+0.0s +[12800/16000] [L1: 0.0128] 25.8+0.0s +[14400/16000] [L1: 0.0128] 25.9+0.0s +[16000/16000] [L1: 0.0128] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.461 (Best: 41.461 @epoch 228) +Forward: 38.63s + +Saving... +Total: 39.25s + +[Epoch 229] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.9+0.9s +[3200/16000] [L1: 0.0132] 25.5+0.0s +[4800/16000] [L1: 0.0130] 25.9+0.0s +[6400/16000] [L1: 0.0129] 26.0+0.0s +[8000/16000] [L1: 0.0130] 26.0+0.0s +[9600/16000] [L1: 0.0130] 26.0+0.0s +[11200/16000] [L1: 0.0129] 25.7+0.0s +[12800/16000] [L1: 0.0129] 26.0+0.0s +[14400/16000] [L1: 0.0129] 26.0+0.0s +[16000/16000] [L1: 0.0129] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.415 (Best: 41.461 @epoch 228) +Forward: 38.71s + +Saving... +Total: 39.16s + +[Epoch 230] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0132] 26.2+0.9s +[3200/16000] [L1: 0.0131] 25.6+0.0s +[4800/16000] [L1: 0.0130] 25.9+0.0s +[6400/16000] [L1: 0.0131] 26.1+0.0s +[8000/16000] [L1: 0.0130] 26.2+0.0s +[9600/16000] [L1: 0.0130] 25.9+0.0s +[11200/16000] [L1: 0.0130] 25.8+0.0s +[12800/16000] [L1: 0.0130] 26.0+0.0s +[14400/16000] [L1: 0.0130] 25.9+0.0s +[16000/16000] [L1: 0.0130] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.428 (Best: 41.461 @epoch 228) +Forward: 38.65s + +Saving... +Total: 39.11s + +[Epoch 231] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 26.3+0.9s +[3200/16000] [L1: 0.0127] 25.6+0.0s +[4800/16000] [L1: 0.0128] 26.0+0.0s +[6400/16000] [L1: 0.0128] 26.2+0.0s +[8000/16000] [L1: 0.0129] 26.1+0.0s +[9600/16000] [L1: 0.0130] 25.9+0.0s +[11200/16000] [L1: 0.0130] 26.2+0.0s +[12800/16000] [L1: 0.0130] 26.1+0.0s +[14400/16000] [L1: 0.0130] 55.6+0.0s +[16000/16000] [L1: 0.0129] 59.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.448 (Best: 41.461 @epoch 228) +Forward: 38.09s + +Saving... +Total: 38.54s + +[Epoch 232] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0133] 61.5+0.9s +[3200/16000] [L1: 0.0132] 60.8+0.0s +[4800/16000] [L1: 0.0131] 60.9+0.0s +[6400/16000] [L1: 0.0131] 60.4+0.0s +[8000/16000] [L1: 0.0130] 43.9+0.0s +[9600/16000] [L1: 0.0130] 25.7+0.0s +[11200/16000] [L1: 0.0129] 54.0+0.0s +[12800/16000] [L1: 0.0129] 59.3+0.0s +[14400/16000] [L1: 0.0130] 59.1+0.0s +[16000/16000] [L1: 0.0130] 60.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.443 (Best: 41.461 @epoch 228) +Forward: 37.93s + +Saving... +Total: 38.51s + +[Epoch 233] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 61.1+1.0s +[3200/16000] [L1: 0.0129] 60.7+0.0s +[4800/16000] [L1: 0.0128] 60.8+0.0s +[6400/16000] [L1: 0.0128] 57.3+0.0s +[8000/16000] [L1: 0.0128] 25.5+0.0s +[9600/16000] [L1: 0.0128] 25.8+0.0s +[11200/16000] [L1: 0.0128] 25.7+0.0s +[12800/16000] [L1: 0.0128] 25.8+0.0s +[14400/16000] [L1: 0.0128] 25.9+0.0s +[16000/16000] [L1: 0.0128] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.472 (Best: 41.472 @epoch 233) +Forward: 38.81s + +Saving... +Total: 39.37s + +[Epoch 234] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.8+1.0s +[3200/16000] [L1: 0.0128] 25.8+0.0s +[4800/16000] [L1: 0.0129] 26.0+0.0s +[6400/16000] [L1: 0.0129] 26.0+0.0s +[8000/16000] [L1: 0.0129] 26.1+0.0s +[9600/16000] [L1: 0.0129] 26.2+0.0s +[11200/16000] [L1: 0.0129] 25.8+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.465 (Best: 41.472 @epoch 233) +Forward: 38.43s + +Saving... +Total: 38.91s + +[Epoch 235] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.6+0.9s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.5+0.0s +[9600/16000] [L1: 0.0128] 25.4+0.0s +[11200/16000] [L1: 0.0128] 25.4+0.0s +[12800/16000] [L1: 0.0128] 25.3+0.0s +[14400/16000] [L1: 0.0127] 25.3+0.0s +[16000/16000] [L1: 0.0127] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.463 (Best: 41.472 @epoch 233) +Forward: 37.94s + +Saving... +Total: 38.39s + +[Epoch 236] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.3+0.9s +[3200/16000] [L1: 0.0124] 25.2+0.0s +[4800/16000] [L1: 0.0126] 25.2+0.0s +[6400/16000] [L1: 0.0127] 25.4+0.0s +[8000/16000] [L1: 0.0128] 25.1+0.0s +[9600/16000] [L1: 0.0128] 25.3+0.0s +[11200/16000] [L1: 0.0129] 25.3+0.0s +[12800/16000] [L1: 0.0128] 25.3+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0128] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.470 (Best: 41.472 @epoch 233) +Forward: 38.24s + +Saving... +Total: 39.04s + +[Epoch 237] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.6+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0129] 25.5+0.0s +[9600/16000] [L1: 0.0129] 25.4+0.0s +[11200/16000] [L1: 0.0129] 25.5+0.0s +[12800/16000] [L1: 0.0129] 25.4+0.0s +[14400/16000] [L1: 0.0130] 25.4+0.0s +[16000/16000] [L1: 0.0129] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.485 (Best: 41.485 @epoch 237) +Forward: 38.13s + +Saving... +Total: 38.68s + +[Epoch 238] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.5+1.0s +[3200/16000] [L1: 0.0129] 25.5+0.0s +[4800/16000] [L1: 0.0127] 38.1+0.0s +[6400/16000] [L1: 0.0128] 58.9+0.0s +[8000/16000] [L1: 0.0129] 58.0+0.0s +[9600/16000] [L1: 0.0129] 59.3+0.0s +[11200/16000] [L1: 0.0129] 59.2+0.0s +[12800/16000] [L1: 0.0129] 59.3+0.0s +[14400/16000] [L1: 0.0129] 59.3+0.0s +[16000/16000] [L1: 0.0129] 59.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.465 (Best: 41.485 @epoch 237) +Forward: 37.62s + +Saving... +Total: 38.21s + +[Epoch 239] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.3+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0126] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.4+0.0s +[9600/16000] [L1: 0.0127] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.3+0.0s +[12800/16000] [L1: 0.0128] 25.3+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0129] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.442 (Best: 41.485 @epoch 237) +Forward: 38.06s + +Saving... +Total: 38.54s + +[Epoch 240] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0129] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0127] 57.9+0.0s +[8000/16000] [L1: 0.0127] 56.9+0.0s +[9600/16000] [L1: 0.0128] 59.5+0.0s +[11200/16000] [L1: 0.0128] 59.4+0.0s +[12800/16000] [L1: 0.0128] 59.0+0.0s +[14400/16000] [L1: 0.0128] 54.3+0.0s +[16000/16000] [L1: 0.0129] 46.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.433 (Best: 41.485 @epoch 237) +Forward: 37.57s + +Saving... +Total: 38.03s + +[Epoch 241] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 59.0+0.9s +[3200/16000] [L1: 0.0128] 59.8+0.0s +[4800/16000] [L1: 0.0127] 59.5+0.1s +[6400/16000] [L1: 0.0128] 41.5+0.0s +[8000/16000] [L1: 0.0128] 25.3+0.0s +[9600/16000] [L1: 0.0128] 25.4+0.0s +[11200/16000] [L1: 0.0128] 25.0+0.0s +[12800/16000] [L1: 0.0128] 25.0+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.472 (Best: 41.485 @epoch 237) +Forward: 38.06s + +Saving... +Total: 38.53s + +[Epoch 242] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.3+1.0s +[3200/16000] [L1: 0.0128] 25.1+0.0s +[4800/16000] [L1: 0.0130] 25.2+0.0s +[6400/16000] [L1: 0.0130] 25.4+0.0s +[8000/16000] [L1: 0.0130] 25.2+0.0s +[9600/16000] [L1: 0.0130] 25.1+0.0s +[11200/16000] [L1: 0.0130] 25.0+0.0s +[12800/16000] [L1: 0.0130] 25.2+0.0s +[14400/16000] [L1: 0.0129] 25.3+0.0s +[16000/16000] [L1: 0.0129] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.444 (Best: 41.485 @epoch 237) +Forward: 38.07s + +Saving... +Total: 38.54s + +[Epoch 243] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.2+0.9s +[3200/16000] [L1: 0.0129] 25.2+0.0s +[4800/16000] [L1: 0.0128] 25.4+0.0s +[6400/16000] [L1: 0.0129] 25.3+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0129] 25.6+0.0s +[11200/16000] [L1: 0.0129] 25.4+0.0s +[12800/16000] [L1: 0.0129] 25.4+0.0s +[14400/16000] [L1: 0.0129] 25.3+0.0s +[16000/16000] [L1: 0.0130] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.492 (Best: 41.492 @epoch 243) +Forward: 37.86s + +Saving... +Total: 38.41s + +[Epoch 244] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.5+1.0s +[3200/16000] [L1: 0.0128] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.3+0.0s +[6400/16000] [L1: 0.0127] 25.4+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.4+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0129] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.480 (Best: 41.492 @epoch 243) +Forward: 38.03s + +Saving... +Total: 38.58s + +[Epoch 245] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.1+1.0s +[3200/16000] [L1: 0.0128] 25.2+0.0s +[4800/16000] [L1: 0.0128] 25.4+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0127] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.3+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.457 (Best: 41.492 @epoch 243) +Forward: 37.91s + +Saving... +Total: 38.38s + +[Epoch 246] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.2+1.2s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0130] 25.3+0.0s +[6400/16000] [L1: 0.0130] 25.4+0.0s +[8000/16000] [L1: 0.0129] 25.1+0.0s +[9600/16000] [L1: 0.0128] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.3+0.0s +[12800/16000] [L1: 0.0128] 25.1+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 24.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.456 (Best: 41.492 @epoch 243) +Forward: 37.85s + +Saving... +Total: 38.31s + +[Epoch 247] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.2+1.0s +[3200/16000] [L1: 0.0126] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0129] 25.7+0.0s +[8000/16000] [L1: 0.0129] 25.2+0.0s +[9600/16000] [L1: 0.0129] 25.3+0.0s +[11200/16000] [L1: 0.0129] 25.5+0.0s +[12800/16000] [L1: 0.0129] 25.1+0.0s +[14400/16000] [L1: 0.0129] 25.2+0.0s +[16000/16000] [L1: 0.0129] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.457 (Best: 41.492 @epoch 243) +Forward: 38.04s + +Saving... +Total: 38.50s + +[Epoch 248] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.3+1.0s +[3200/16000] [L1: 0.0126] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.4+0.0s +[8000/16000] [L1: 0.0127] 25.3+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0128] 25.3+0.0s +[14400/16000] [L1: 0.0128] 25.3+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.511 (Best: 41.511 @epoch 248) +Forward: 38.09s + +Saving... +Total: 38.69s + +[Epoch 249] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.4+1.1s +[3200/16000] [L1: 0.0129] 25.2+0.0s +[4800/16000] [L1: 0.0128] 25.4+0.0s +[6400/16000] [L1: 0.0129] 25.3+0.0s +[8000/16000] [L1: 0.0128] 25.3+0.0s +[9600/16000] [L1: 0.0128] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.1+0.0s +[12800/16000] [L1: 0.0129] 25.4+0.0s +[14400/16000] [L1: 0.0129] 25.3+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.480 (Best: 41.511 @epoch 248) +Forward: 37.94s + +Saving... +Total: 38.43s + +[Epoch 250] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.3+0.9s +[3200/16000] [L1: 0.0129] 25.1+0.0s +[4800/16000] [L1: 0.0129] 25.2+0.0s +[6400/16000] [L1: 0.0129] 25.5+0.0s +[8000/16000] [L1: 0.0129] 25.7+0.0s +[9600/16000] [L1: 0.0129] 25.7+0.0s +[11200/16000] [L1: 0.0129] 25.4+0.0s +[12800/16000] [L1: 0.0129] 25.2+0.0s +[14400/16000] [L1: 0.0129] 25.4+0.0s +[16000/16000] [L1: 0.0128] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.432 (Best: 41.511 @epoch 248) +Forward: 38.02s + +Saving... +Total: 38.47s + +[Epoch 251] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.2+1.1s +[3200/16000] [L1: 0.0128] 25.1+0.0s +[4800/16000] [L1: 0.0129] 25.1+0.0s +[6400/16000] [L1: 0.0129] 25.1+0.0s +[8000/16000] [L1: 0.0129] 25.0+0.0s +[9600/16000] [L1: 0.0129] 25.3+0.0s +[11200/16000] [L1: 0.0129] 25.3+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.6+0.0s +[16000/16000] [L1: 0.0129] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.478 (Best: 41.511 @epoch 248) +Forward: 37.88s + +Saving... +Total: 38.36s + +[Epoch 252] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.3+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.3+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.2+0.0s +[9600/16000] [L1: 0.0128] 25.1+0.0s +[11200/16000] [L1: 0.0129] 25.4+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0129] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.439 (Best: 41.511 @epoch 248) +Forward: 38.06s + +Saving... +Total: 38.61s + +[Epoch 253] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.5+0.9s +[3200/16000] [L1: 0.0129] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.2+0.0s +[6400/16000] [L1: 0.0129] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.2+0.0s +[9600/16000] [L1: 0.0128] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.4+0.0s +[12800/16000] [L1: 0.0129] 25.3+0.0s +[14400/16000] [L1: 0.0128] 25.4+0.0s +[16000/16000] [L1: 0.0128] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.412 (Best: 41.511 @epoch 248) +Forward: 37.96s + +Saving... +Total: 38.44s + +[Epoch 254] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.4+1.0s +[3200/16000] [L1: 0.0128] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.4+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0129] 25.2+0.0s +[14400/16000] [L1: 0.0129] 25.2+0.0s +[16000/16000] [L1: 0.0128] 24.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.490 (Best: 41.511 @epoch 248) +Forward: 38.08s + +Saving... +Total: 38.55s + +[Epoch 255] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.4+0.9s +[3200/16000] [L1: 0.0126] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.3+0.0s +[8000/16000] [L1: 0.0126] 25.3+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.4+0.0s +[12800/16000] [L1: 0.0128] 25.1+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.459 (Best: 41.511 @epoch 248) +Forward: 37.87s + +Saving... +Total: 38.34s + +[Epoch 256] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.2+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.2+0.0s +[6400/16000] [L1: 0.0127] 25.3+0.0s +[8000/16000] [L1: 0.0128] 25.3+0.0s +[9600/16000] [L1: 0.0128] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0128] 25.1+0.0s +[14400/16000] [L1: 0.0128] 25.4+0.0s +[16000/16000] [L1: 0.0128] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.476 (Best: 41.511 @epoch 248) +Forward: 37.92s + +Saving... +Total: 38.55s + +[Epoch 257] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.3+0.9s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0129] 25.5+0.0s +[6400/16000] [L1: 0.0129] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.4+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0129] 25.1+0.0s +[16000/16000] [L1: 0.0129] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.443 (Best: 41.511 @epoch 248) +Forward: 37.98s + +Saving... +Total: 38.46s + +[Epoch 258] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.5+1.1s +[3200/16000] [L1: 0.0127] 25.7+0.0s +[4800/16000] [L1: 0.0125] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.2+0.0s +[9600/16000] [L1: 0.0127] 25.2+0.0s +[11200/16000] [L1: 0.0128] 25.1+0.0s +[12800/16000] [L1: 0.0128] 25.1+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0128] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.489 (Best: 41.511 @epoch 248) +Forward: 37.91s + +Saving... +Total: 38.36s + +[Epoch 259] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.5+0.9s +[3200/16000] [L1: 0.0129] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.5+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0129] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.467 (Best: 41.511 @epoch 248) +Forward: 37.91s + +Saving... +Total: 38.39s + +[Epoch 260] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.3+0.9s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.4+0.0s +[6400/16000] [L1: 0.0128] 25.3+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.3+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.490 (Best: 41.511 @epoch 248) +Forward: 38.09s + +Saving... +Total: 38.56s + +[Epoch 261] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.1+0.9s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.3+0.0s +[6400/16000] [L1: 0.0127] 25.0+0.0s +[8000/16000] [L1: 0.0127] 25.3+0.0s +[9600/16000] [L1: 0.0127] 25.4+0.0s +[11200/16000] [L1: 0.0127] 25.2+0.0s +[12800/16000] [L1: 0.0127] 25.3+0.0s +[14400/16000] [L1: 0.0127] 25.2+0.0s +[16000/16000] [L1: 0.0127] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.472 (Best: 41.511 @epoch 248) +Forward: 37.87s + +Saving... +Total: 38.40s + +[Epoch 262] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.4+1.2s +[3200/16000] [L1: 0.0130] 25.4+0.0s +[4800/16000] [L1: 0.0129] 25.2+0.0s +[6400/16000] [L1: 0.0129] 25.3+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0129] 25.1+0.0s +[11200/16000] [L1: 0.0129] 25.1+0.0s +[12800/16000] [L1: 0.0129] 25.0+0.0s +[14400/16000] [L1: 0.0129] 25.0+0.0s +[16000/16000] [L1: 0.0130] 24.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.459 (Best: 41.511 @epoch 248) +Forward: 38.06s + +Saving... +Total: 38.52s + +[Epoch 263] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.3+1.0s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.2+0.0s +[11200/16000] [L1: 0.0128] 25.4+0.0s +[12800/16000] [L1: 0.0128] 25.3+0.0s +[14400/16000] [L1: 0.0128] 25.4+0.0s +[16000/16000] [L1: 0.0129] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.478 (Best: 41.511 @epoch 248) +Forward: 37.96s + +Saving... +Total: 38.45s + +[Epoch 264] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.3+0.9s +[3200/16000] [L1: 0.0129] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.3+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.464 (Best: 41.511 @epoch 248) +Forward: 38.04s + +Saving... +Total: 38.50s + +[Epoch 265] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0132] 25.0+1.0s +[3200/16000] [L1: 0.0129] 25.3+0.0s +[4800/16000] [L1: 0.0129] 25.4+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.8+0.0s +[16000/16000] [L1: 0.0127] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.459 (Best: 41.511 @epoch 248) +Forward: 37.91s + +Saving... +Total: 38.37s + +[Epoch 266] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 24.9+1.0s +[3200/16000] [L1: 0.0126] 25.1+0.0s +[4800/16000] [L1: 0.0125] 25.2+0.0s +[6400/16000] [L1: 0.0127] 25.3+0.0s +[8000/16000] [L1: 0.0127] 25.2+0.0s +[9600/16000] [L1: 0.0127] 25.2+0.0s +[11200/16000] [L1: 0.0128] 25.1+0.0s +[12800/16000] [L1: 0.0128] 25.0+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 24.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.497 (Best: 41.511 @epoch 248) +Forward: 37.89s + +Saving... +Total: 38.38s + +[Epoch 267] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.8+0.9s +[3200/16000] [L1: 0.0129] 25.7+0.0s +[4800/16000] [L1: 0.0129] 25.3+0.0s +[6400/16000] [L1: 0.0128] 25.3+0.0s +[8000/16000] [L1: 0.0128] 25.3+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0128] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.472 (Best: 41.511 @epoch 248) +Forward: 37.96s + +Saving... +Total: 38.43s + +[Epoch 268] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.1+0.9s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0129] 25.4+0.0s +[11200/16000] [L1: 0.0129] 25.4+0.0s +[12800/16000] [L1: 0.0129] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.3+0.0s +[16000/16000] [L1: 0.0128] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.464 (Best: 41.511 @epoch 248) +Forward: 37.89s + +Saving... +Total: 38.36s + +[Epoch 269] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.5+1.0s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0129] 25.6+0.0s +[8000/16000] [L1: 0.0129] 25.7+0.0s +[9600/16000] [L1: 0.0129] 25.6+0.0s +[11200/16000] [L1: 0.0129] 25.6+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.5+0.0s +[16000/16000] [L1: 0.0129] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.484 (Best: 41.511 @epoch 248) +Forward: 37.86s + +Saving... +Total: 38.34s + +[Epoch 270] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.5+1.1s +[3200/16000] [L1: 0.0129] 25.2+0.0s +[4800/16000] [L1: 0.0129] 25.1+0.0s +[6400/16000] [L1: 0.0129] 25.4+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.458 (Best: 41.511 @epoch 248) +Forward: 37.94s + +Saving... +Total: 38.42s + +[Epoch 271] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+0.9s +[3200/16000] [L1: 0.0128] 25.3+0.0s +[4800/16000] [L1: 0.0129] 25.0+0.0s +[6400/16000] [L1: 0.0129] 25.3+0.0s +[8000/16000] [L1: 0.0128] 25.2+0.0s +[9600/16000] [L1: 0.0129] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0128] 25.1+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.467 (Best: 41.511 @epoch 248) +Forward: 37.95s + +Saving... +Total: 38.42s + +[Epoch 272] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.3+0.9s +[3200/16000] [L1: 0.0126] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0128] 25.0+0.0s +[9600/16000] [L1: 0.0128] 25.4+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.483 (Best: 41.511 @epoch 248) +Forward: 37.89s + +Saving... +Total: 38.37s + +[Epoch 273] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.4+1.1s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.4+0.0s +[16000/16000] [L1: 0.0128] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.487 (Best: 41.511 @epoch 248) +Forward: 37.93s + +Saving... +Total: 38.41s + +[Epoch 274] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0132] 25.3+1.0s +[3200/16000] [L1: 0.0131] 25.2+0.0s +[4800/16000] [L1: 0.0130] 25.4+0.0s +[6400/16000] [L1: 0.0129] 25.4+0.0s +[8000/16000] [L1: 0.0129] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.4+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.517 (Best: 41.517 @epoch 274) +Forward: 37.91s + +Saving... +Total: 38.47s + +[Epoch 275] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0133] 25.3+1.0s +[3200/16000] [L1: 0.0130] 25.2+0.0s +[4800/16000] [L1: 0.0129] 25.2+0.0s +[6400/16000] [L1: 0.0130] 25.3+0.0s +[8000/16000] [L1: 0.0130] 25.5+0.0s +[9600/16000] [L1: 0.0129] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.4+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0129] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.483 (Best: 41.517 @epoch 274) +Forward: 38.04s + +Saving... +Total: 38.54s + +[Epoch 276] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.5+0.9s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.4+0.0s +[6400/16000] [L1: 0.0129] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.4+0.0s +[9600/16000] [L1: 0.0128] 25.2+0.0s +[11200/16000] [L1: 0.0128] 25.2+0.0s +[12800/16000] [L1: 0.0128] 25.2+0.0s +[14400/16000] [L1: 0.0128] 25.1+0.0s +[16000/16000] [L1: 0.0128] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.444 (Best: 41.517 @epoch 274) +Forward: 37.95s + +Saving... +Total: 38.48s + +[Epoch 277] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.3+0.9s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0129] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.2+0.0s +[16000/16000] [L1: 0.0128] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.473 (Best: 41.517 @epoch 274) +Forward: 38.08s + +Saving... +Total: 38.53s + +[Epoch 278] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.2+1.1s +[3200/16000] [L1: 0.0129] 25.3+0.0s +[4800/16000] [L1: 0.0130] 25.2+0.0s +[6400/16000] [L1: 0.0130] 25.4+0.0s +[8000/16000] [L1: 0.0129] 25.2+0.0s +[9600/16000] [L1: 0.0129] 25.3+0.0s +[11200/16000] [L1: 0.0129] 25.3+0.0s +[12800/16000] [L1: 0.0129] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.3+0.0s +[16000/16000] [L1: 0.0128] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.487 (Best: 41.517 @epoch 274) +Forward: 37.96s + +Saving... +Total: 38.43s + +[Epoch 279] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.2+1.0s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.1+0.0s +[6400/16000] [L1: 0.0129] 24.9+0.0s +[8000/16000] [L1: 0.0129] 25.2+0.0s +[9600/16000] [L1: 0.0130] 25.2+0.0s +[11200/16000] [L1: 0.0130] 25.1+0.0s +[12800/16000] [L1: 0.0130] 25.2+0.0s +[14400/16000] [L1: 0.0129] 25.2+0.0s +[16000/16000] [L1: 0.0129] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.470 (Best: 41.517 @epoch 274) +Forward: 38.11s + +Saving... +Total: 38.58s + +[Epoch 280] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.2+0.0s +[8000/16000] [L1: 0.0128] 25.1+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0129] 25.4+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.6+0.0s +[16000/16000] [L1: 0.0129] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.477 (Best: 41.517 @epoch 274) +Forward: 38.17s + +Saving... +Total: 38.65s + +[Epoch 281] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.6+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0129] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.3+0.0s +[9600/16000] [L1: 0.0127] 25.4+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.3+0.0s +[14400/16000] [L1: 0.0128] 25.4+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.505 (Best: 41.517 @epoch 274) +Forward: 38.26s + +Saving... +Total: 38.74s + +[Epoch 282] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.0s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.9+0.0s +[11200/16000] [L1: 0.0128] 25.8+0.0s +[12800/16000] [L1: 0.0129] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.507 (Best: 41.517 @epoch 274) +Forward: 38.20s + +Saving... +Total: 38.70s + +[Epoch 283] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.0s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0130] 25.5+0.0s +[6400/16000] [L1: 0.0130] 25.7+0.0s +[8000/16000] [L1: 0.0130] 25.8+0.0s +[9600/16000] [L1: 0.0130] 25.6+0.0s +[11200/16000] [L1: 0.0129] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.472 (Best: 41.517 @epoch 274) +Forward: 38.22s + +Saving... +Total: 38.73s + +[Epoch 284] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.5+1.1s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0129] 25.5+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.467 (Best: 41.517 @epoch 274) +Forward: 38.34s + +Saving... +Total: 38.83s + +[Epoch 285] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.0s +[3200/16000] [L1: 0.0129] 25.5+0.0s +[4800/16000] [L1: 0.0129] 25.5+0.0s +[6400/16000] [L1: 0.0128] 26.0+0.0s +[8000/16000] [L1: 0.0129] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.8+0.0s +[12800/16000] [L1: 0.0128] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.453 (Best: 41.517 @epoch 274) +Forward: 38.41s + +Saving... +Total: 38.87s + +[Epoch 286] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.6+1.0s +[3200/16000] [L1: 0.0129] 25.6+0.0s +[4800/16000] [L1: 0.0129] 26.0+0.0s +[6400/16000] [L1: 0.0129] 26.2+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.9+0.0s +[11200/16000] [L1: 0.0126] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 26.0+0.0s +[16000/16000] [L1: 0.0128] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.431 (Best: 41.517 @epoch 274) +Forward: 38.41s + +Saving... +Total: 38.88s + +[Epoch 287] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0121] 25.4+1.0s +[3200/16000] [L1: 0.0124] 25.5+0.0s +[4800/16000] [L1: 0.0125] 25.7+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0126] 25.8+0.0s +[9600/16000] [L1: 0.0125] 25.9+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.4+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.465 (Best: 41.517 @epoch 274) +Forward: 38.42s + +Saving... +Total: 38.92s + +[Epoch 288] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.5+0.9s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0129] 25.5+0.0s +[6400/16000] [L1: 0.0129] 25.6+0.0s +[8000/16000] [L1: 0.0129] 25.4+0.0s +[9600/16000] [L1: 0.0129] 25.5+0.0s +[11200/16000] [L1: 0.0129] 25.5+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.4+0.0s +[16000/16000] [L1: 0.0129] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.480 (Best: 41.517 @epoch 274) +Forward: 38.19s + +Saving... +Total: 38.73s + +[Epoch 289] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.3+0.9s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0129] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.466 (Best: 41.517 @epoch 274) +Forward: 38.33s + +Saving... +Total: 38.83s + +[Epoch 290] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.5+1.0s +[3200/16000] [L1: 0.0126] 25.7+0.1s +[4800/16000] [L1: 0.0127] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0127] 25.9+0.0s +[9600/16000] [L1: 0.0127] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.8+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.507 (Best: 41.517 @epoch 274) +Forward: 38.47s + +Saving... +Total: 38.95s + +[Epoch 291] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.3+0.9s +[3200/16000] [L1: 0.0129] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.7+0.0s +[6400/16000] [L1: 0.0128] 26.0+0.0s +[8000/16000] [L1: 0.0129] 25.9+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0129] 25.8+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.9+0.0s +[16000/16000] [L1: 0.0129] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.500 (Best: 41.517 @epoch 274) +Forward: 38.35s + +Saving... +Total: 38.84s + +[Epoch 292] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.5+1.0s +[3200/16000] [L1: 0.0129] 25.8+0.0s +[4800/16000] [L1: 0.0129] 25.7+0.0s +[6400/16000] [L1: 0.0128] 26.0+0.0s +[8000/16000] [L1: 0.0128] 25.9+0.0s +[9600/16000] [L1: 0.0128] 26.0+0.0s +[11200/16000] [L1: 0.0127] 26.1+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0127] 25.4+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.456 (Best: 41.517 @epoch 274) +Forward: 38.28s + +Saving... +Total: 38.80s + +[Epoch 293] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0128] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0128] 25.7+0.0s +[8000/16000] [L1: 0.0128] 25.9+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.8+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.469 (Best: 41.517 @epoch 274) +Forward: 38.25s + +Saving... +Total: 38.72s + +[Epoch 294] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0132] 25.4+1.1s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0129] 25.5+0.0s +[6400/16000] [L1: 0.0130] 25.6+0.0s +[8000/16000] [L1: 0.0130] 25.4+0.0s +[9600/16000] [L1: 0.0130] 25.6+0.0s +[11200/16000] [L1: 0.0130] 25.4+0.0s +[12800/16000] [L1: 0.0130] 25.3+0.0s +[14400/16000] [L1: 0.0130] 25.6+0.0s +[16000/16000] [L1: 0.0130] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.469 (Best: 41.517 @epoch 274) +Forward: 38.42s + +Saving... +Total: 38.90s + +[Epoch 295] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.6+1.0s +[3200/16000] [L1: 0.0130] 25.8+0.0s +[4800/16000] [L1: 0.0130] 25.7+0.0s +[6400/16000] [L1: 0.0129] 25.7+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.8+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.478 (Best: 41.517 @epoch 274) +Forward: 38.36s + +Saving... +Total: 38.84s + +[Epoch 296] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.3+0.9s +[3200/16000] [L1: 0.0126] 25.2+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.492 (Best: 41.517 @epoch 274) +Forward: 38.23s + +Saving... +Total: 38.74s + +[Epoch 297] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0130] 25.5+0.0s +[4800/16000] [L1: 0.0129] 25.6+0.0s +[6400/16000] [L1: 0.0129] 25.8+0.0s +[8000/16000] [L1: 0.0130] 25.5+0.0s +[9600/16000] [L1: 0.0129] 25.6+0.0s +[11200/16000] [L1: 0.0129] 25.4+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.476 (Best: 41.517 @epoch 274) +Forward: 38.21s + +Saving... +Total: 38.70s + +[Epoch 298] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.3+1.0s +[3200/16000] [L1: 0.0130] 25.2+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0129] 25.6+0.0s +[9600/16000] [L1: 0.0129] 25.6+0.0s +[11200/16000] [L1: 0.0129] 25.7+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.7+0.0s +[16000/16000] [L1: 0.0129] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.500 (Best: 41.517 @epoch 274) +Forward: 38.27s + +Saving... +Total: 38.76s + +[Epoch 299] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.7+0.9s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0132] 25.7+0.0s +[6400/16000] [L1: 0.0130] 25.8+0.0s +[8000/16000] [L1: 0.0130] 25.6+0.0s +[9600/16000] [L1: 0.0129] 25.7+0.0s +[11200/16000] [L1: 0.0129] 25.6+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.516 (Best: 41.517 @epoch 274) +Forward: 38.32s + +Saving... +Total: 38.87s + +[Epoch 300] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.2+1.0s +[3200/16000] [L1: 0.0130] 25.6+0.0s +[4800/16000] [L1: 0.0129] 25.8+0.0s +[6400/16000] [L1: 0.0128] 25.7+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0129] 25.7+0.0s +[11200/16000] [L1: 0.0129] 25.3+0.0s +[12800/16000] [L1: 0.0129] 25.6+0.0s +[14400/16000] [L1: 0.0129] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.480 (Best: 41.517 @epoch 274) +Forward: 38.34s + +Saving... +Total: 38.81s + +[Epoch 301] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0126] 26.0+0.0s +[11200/16000] [L1: 0.0127] 26.0+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 25.4+0.0s +[16000/16000] [L1: 0.0127] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.475 (Best: 41.517 @epoch 274) +Forward: 38.28s + +Saving... +Total: 38.80s + +[Epoch 302] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.4+1.1s +[3200/16000] [L1: 0.0127] 25.6+0.0s +[4800/16000] [L1: 0.0127] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.452 (Best: 41.517 @epoch 274) +Forward: 38.22s + +Saving... +Total: 38.78s + +[Epoch 303] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.3+1.1s +[3200/16000] [L1: 0.0128] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.3+0.0s +[6400/16000] [L1: 0.0126] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.5+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.4+0.0s +[16000/16000] [L1: 0.0126] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.493 (Best: 41.517 @epoch 274) +Forward: 38.18s + +Saving... +Total: 38.68s + +[Epoch 304] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.5+1.0s +[3200/16000] [L1: 0.0128] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.476 (Best: 41.517 @epoch 274) +Forward: 38.33s + +Saving... +Total: 38.91s + +[Epoch 305] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0128] 26.0+0.0s +[8000/16000] [L1: 0.0127] 25.9+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.495 (Best: 41.517 @epoch 274) +Forward: 38.46s + +Saving... +Total: 38.97s + +[Epoch 306] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.4+1.0s +[3200/16000] [L1: 0.0130] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.493 (Best: 41.517 @epoch 274) +Forward: 38.32s + +Saving... +Total: 38.82s + +[Epoch 307] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+0.9s +[3200/16000] [L1: 0.0129] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.8+0.0s +[11200/16000] [L1: 0.0127] 25.9+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0128] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.484 (Best: 41.517 @epoch 274) +Forward: 38.26s + +Saving... +Total: 38.71s + +[Epoch 308] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0129] 25.4+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0129] 25.5+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0129] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.502 (Best: 41.517 @epoch 274) +Forward: 38.37s + +Saving... +Total: 38.89s + +[Epoch 309] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.5+0.9s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0126] 25.9+0.0s +[6400/16000] [L1: 0.0125] 25.7+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.5+0.0s +[11200/16000] [L1: 0.0126] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.3+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.512 (Best: 41.517 @epoch 274) +Forward: 38.28s + +Saving... +Total: 38.76s + +[Epoch 310] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.3+1.4s +[3200/16000] [L1: 0.0130] 25.5+0.0s +[4800/16000] [L1: 0.0130] 25.9+0.0s +[6400/16000] [L1: 0.0131] 25.8+0.0s +[8000/16000] [L1: 0.0130] 26.0+0.0s +[9600/16000] [L1: 0.0130] 25.9+0.0s +[11200/16000] [L1: 0.0129] 25.8+0.0s +[12800/16000] [L1: 0.0129] 25.6+0.0s +[14400/16000] [L1: 0.0129] 25.6+0.0s +[16000/16000] [L1: 0.0129] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.509 (Best: 41.517 @epoch 274) +Forward: 38.52s + +Saving... +Total: 38.99s + +[Epoch 311] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.5+1.0s +[3200/16000] [L1: 0.0128] 25.2+0.0s +[4800/16000] [L1: 0.0127] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.479 (Best: 41.517 @epoch 274) +Forward: 38.33s + +Saving... +Total: 38.82s + +[Epoch 312] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.3+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.8+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.468 (Best: 41.517 @epoch 274) +Forward: 38.47s + +Saving... +Total: 38.96s + +[Epoch 313] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.6+0.9s +[3200/16000] [L1: 0.0129] 25.4+0.0s +[4800/16000] [L1: 0.0130] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0128] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.511 (Best: 41.517 @epoch 274) +Forward: 38.22s + +Saving... +Total: 38.72s + +[Epoch 314] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.9+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.524 (Best: 41.524 @epoch 314) +Forward: 38.31s + +Saving... +Total: 38.84s + +[Epoch 315] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0123] 25.3+1.0s +[3200/16000] [L1: 0.0125] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.4+0.0s +[8000/16000] [L1: 0.0126] 25.3+0.0s +[9600/16000] [L1: 0.0127] 25.8+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.502 (Best: 41.524 @epoch 314) +Forward: 38.36s + +Saving... +Total: 38.84s + +[Epoch 316] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.2+1.0s +[3200/16000] [L1: 0.0129] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.8+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0128] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.489 (Best: 41.524 @epoch 314) +Forward: 38.23s + +Saving... +Total: 38.71s + +[Epoch 317] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.7+0.0s +[4800/16000] [L1: 0.0127] 25.9+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.4+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.495 (Best: 41.524 @epoch 314) +Forward: 38.18s + +Saving... +Total: 38.68s + +[Epoch 318] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.3+1.0s +[3200/16000] [L1: 0.0124] 25.7+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.523 (Best: 41.524 @epoch 314) +Forward: 38.29s + +Saving... +Total: 38.76s + +[Epoch 319] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.4+0.9s +[3200/16000] [L1: 0.0126] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.9+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.502 (Best: 41.524 @epoch 314) +Forward: 38.51s + +Saving... +Total: 38.99s + +[Epoch 320] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.4+0.0s +[6400/16000] [L1: 0.0129] 25.6+0.0s +[8000/16000] [L1: 0.0129] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.501 (Best: 41.524 @epoch 314) +Forward: 38.29s + +Saving... +Total: 38.77s + +[Epoch 321] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0123] 25.5+1.0s +[3200/16000] [L1: 0.0126] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.3+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.508 (Best: 41.524 @epoch 314) +Forward: 38.32s + +Saving... +Total: 38.80s + +[Epoch 322] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.5+1.0s +[3200/16000] [L1: 0.0127] 25.6+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.9+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.9+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.509 (Best: 41.524 @epoch 314) +Forward: 38.40s + +Saving... +Total: 38.86s + +[Epoch 323] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0125] 25.7+0.0s +[4800/16000] [L1: 0.0127] 26.0+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.8+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.538 (Best: 41.538 @epoch 323) +Forward: 38.49s + +Saving... +Total: 39.00s + +[Epoch 324] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.5+0.9s +[3200/16000] [L1: 0.0130] 25.4+0.0s +[4800/16000] [L1: 0.0129] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0128] 26.2+0.0s +[11200/16000] [L1: 0.0128] 25.7+0.0s +[12800/16000] [L1: 0.0128] 25.8+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.513 (Best: 41.538 @epoch 323) +Forward: 38.38s + +Saving... +Total: 38.87s + +[Epoch 325] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.2+0.9s +[3200/16000] [L1: 0.0129] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.9+0.0s +[12800/16000] [L1: 0.0127] 26.0+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.506 (Best: 41.538 @epoch 323) +Forward: 38.28s + +Saving... +Total: 39.07s + +[Epoch 326] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0132] 25.4+0.9s +[3200/16000] [L1: 0.0132] 25.4+0.0s +[4800/16000] [L1: 0.0129] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.9+0.0s +[8000/16000] [L1: 0.0129] 25.8+0.0s +[9600/16000] [L1: 0.0129] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.9+0.0s +[14400/16000] [L1: 0.0128] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.480 (Best: 41.538 @epoch 323) +Forward: 38.43s + +Saving... +Total: 38.92s + +[Epoch 327] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.5+0.9s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0129] 25.4+0.0s +[6400/16000] [L1: 0.0129] 25.5+0.0s +[8000/16000] [L1: 0.0128] 25.5+0.0s +[9600/16000] [L1: 0.0128] 25.3+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.469 (Best: 41.538 @epoch 323) +Forward: 38.51s + +Saving... +Total: 39.01s + +[Epoch 328] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.1+0.9s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0127] 25.5+0.0s +[9600/16000] [L1: 0.0127] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.462 (Best: 41.538 @epoch 323) +Forward: 38.43s + +Saving... +Total: 38.91s + +[Epoch 329] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.3+0.9s +[3200/16000] [L1: 0.0124] 25.3+0.0s +[4800/16000] [L1: 0.0124] 25.5+0.0s +[6400/16000] [L1: 0.0126] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.8+0.0s +[11200/16000] [L1: 0.0126] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.4+0.0s +[14400/16000] [L1: 0.0126] 25.3+0.0s +[16000/16000] [L1: 0.0127] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.497 (Best: 41.538 @epoch 323) +Forward: 38.34s + +Saving... +Total: 38.89s + +[Epoch 330] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.2+1.1s +[3200/16000] [L1: 0.0126] 25.1+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0126] 25.5+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.3+0.0s +[14400/16000] [L1: 0.0127] 25.4+0.0s +[16000/16000] [L1: 0.0126] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.517 (Best: 41.538 @epoch 323) +Forward: 38.18s + +Saving... +Total: 38.67s + +[Epoch 331] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.4+1.0s +[3200/16000] [L1: 0.0128] 25.0+0.0s +[4800/16000] [L1: 0.0127] 25.1+0.0s +[6400/16000] [L1: 0.0126] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.4+0.0s +[9600/16000] [L1: 0.0126] 25.4+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.482 (Best: 41.538 @epoch 323) +Forward: 38.34s + +Saving... +Total: 38.84s + +[Epoch 332] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.7+1.1s +[3200/16000] [L1: 0.0130] 25.7+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0127] 26.0+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.496 (Best: 41.538 @epoch 323) +Forward: 38.44s + +Saving... +Total: 38.92s + +[Epoch 333] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.2+0.9s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0125] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.5+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.4+0.0s +[14400/16000] [L1: 0.0126] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.549 (Best: 41.549 @epoch 333) +Forward: 38.26s + +Saving... +Total: 38.87s + +[Epoch 334] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0135] 25.5+0.9s +[3200/16000] [L1: 0.0130] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.7+0.0s +[8000/16000] [L1: 0.0129] 25.6+0.0s +[9600/16000] [L1: 0.0129] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.483 (Best: 41.549 @epoch 333) +Forward: 38.22s + +Saving... +Total: 38.86s + +[Epoch 335] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.2+1.1s +[3200/16000] [L1: 0.0128] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0126] 25.8+0.0s +[9600/16000] [L1: 0.0126] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.527 (Best: 41.549 @epoch 333) +Forward: 38.17s + +Saving... +Total: 38.70s + +[Epoch 336] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.4+0.9s +[3200/16000] [L1: 0.0130] 25.4+0.0s +[4800/16000] [L1: 0.0129] 25.3+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.5+0.0s +[9600/16000] [L1: 0.0127] 25.4+0.0s +[11200/16000] [L1: 0.0127] 25.4+0.0s +[12800/16000] [L1: 0.0127] 25.3+0.0s +[14400/16000] [L1: 0.0127] 25.4+0.0s +[16000/16000] [L1: 0.0127] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.510 (Best: 41.549 @epoch 333) +Forward: 38.27s + +Saving... +Total: 38.75s + +[Epoch 337] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.6+1.0s +[3200/16000] [L1: 0.0127] 25.9+0.0s +[4800/16000] [L1: 0.0127] 26.0+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.552 (Best: 41.552 @epoch 337) +Forward: 38.18s + +Saving... +Total: 38.69s + +[Epoch 338] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+0.9s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.8+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.537 (Best: 41.552 @epoch 337) +Forward: 38.26s + +Saving... +Total: 38.76s + +[Epoch 339] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.2+1.0s +[3200/16000] [L1: 0.0129] 25.2+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.5+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.462 (Best: 41.552 @epoch 337) +Forward: 38.27s + +Saving... +Total: 38.76s + +[Epoch 340] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.491 (Best: 41.552 @epoch 337) +Forward: 38.31s + +Saving... +Total: 38.85s + +[Epoch 341] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.7+0.9s +[3200/16000] [L1: 0.0125] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.8+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.474 (Best: 41.552 @epoch 337) +Forward: 38.45s + +Saving... +Total: 38.97s + +[Epoch 342] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+1.1s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0127] 26.1+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.459 (Best: 41.552 @epoch 337) +Forward: 38.23s + +Saving... +Total: 38.73s + +[Epoch 343] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.3+1.0s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.9+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.505 (Best: 41.552 @epoch 337) +Forward: 38.34s + +Saving... +Total: 38.87s + +[Epoch 344] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.3+0.9s +[3200/16000] [L1: 0.0130] 25.4+0.0s +[4800/16000] [L1: 0.0130] 25.6+0.0s +[6400/16000] [L1: 0.0129] 25.6+0.0s +[8000/16000] [L1: 0.0129] 25.6+0.0s +[9600/16000] [L1: 0.0129] 25.6+0.0s +[11200/16000] [L1: 0.0129] 25.5+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.539 (Best: 41.552 @epoch 337) +Forward: 38.39s + +Saving... +Total: 38.88s + +[Epoch 345] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.2+0.9s +[3200/16000] [L1: 0.0125] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.4+0.0s +[6400/16000] [L1: 0.0130] 25.5+0.0s +[8000/16000] [L1: 0.0129] 25.5+0.0s +[9600/16000] [L1: 0.0129] 25.4+0.0s +[11200/16000] [L1: 0.0129] 25.5+0.0s +[12800/16000] [L1: 0.0129] 25.3+0.0s +[14400/16000] [L1: 0.0129] 25.4+0.0s +[16000/16000] [L1: 0.0129] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.488 (Best: 41.552 @epoch 337) +Forward: 38.22s + +Saving... +Total: 38.77s + +[Epoch 346] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.5+0.9s +[3200/16000] [L1: 0.0128] 25.6+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.8+0.0s +[12800/16000] [L1: 0.0128] 25.8+0.0s +[14400/16000] [L1: 0.0127] 25.9+0.0s +[16000/16000] [L1: 0.0127] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.532 (Best: 41.552 @epoch 337) +Forward: 38.47s + +Saving... +Total: 38.97s + +[Epoch 347] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.2s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.556 (Best: 41.556 @epoch 347) +Forward: 38.45s + +Saving... +Total: 38.99s + +[Epoch 348] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.6+0.9s +[3200/16000] [L1: 0.0129] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0129] 25.5+0.0s +[14400/16000] [L1: 0.0129] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.537 (Best: 41.556 @epoch 347) +Forward: 38.29s + +Saving... +Total: 38.80s + +[Epoch 349] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.1+1.0s +[3200/16000] [L1: 0.0125] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.4+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.501 (Best: 41.556 @epoch 347) +Forward: 38.51s + +Saving... +Total: 39.05s + +[Epoch 350] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.8+1.0s +[3200/16000] [L1: 0.0127] 25.7+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0127] 26.0+0.0s +[12800/16000] [L1: 0.0128] 25.9+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0127] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.518 (Best: 41.556 @epoch 347) +Forward: 38.29s + +Saving... +Total: 38.86s + +[Epoch 351] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.4+1.0s +[3200/16000] [L1: 0.0128] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 25.6+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.509 (Best: 41.556 @epoch 347) +Forward: 38.32s + +Saving... +Total: 38.83s + +[Epoch 352] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.7+0.9s +[3200/16000] [L1: 0.0130] 25.3+0.0s +[4800/16000] [L1: 0.0130] 25.3+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.4+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.537 (Best: 41.556 @epoch 347) +Forward: 38.27s + +Saving... +Total: 38.75s + +[Epoch 353] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0129] 25.7+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 26.0+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 26.0+0.0s +[14400/16000] [L1: 0.0128] 25.9+0.0s +[16000/16000] [L1: 0.0128] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.525 (Best: 41.556 @epoch 347) +Forward: 38.28s + +Saving... +Total: 38.85s + +[Epoch 354] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.5+0.9s +[3200/16000] [L1: 0.0125] 25.2+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.3+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.8+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.525 (Best: 41.556 @epoch 347) +Forward: 38.25s + +Saving... +Total: 38.89s + +[Epoch 355] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.7+1.0s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.9+0.0s +[12800/16000] [L1: 0.0126] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.533 (Best: 41.556 @epoch 347) +Forward: 38.34s + +Saving... +Total: 38.85s + +[Epoch 356] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.6+1.0s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.9+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.483 (Best: 41.556 @epoch 347) +Forward: 38.34s + +Saving... +Total: 38.85s + +[Epoch 357] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.6+0.9s +[3200/16000] [L1: 0.0124] 25.5+0.0s +[4800/16000] [L1: 0.0125] 25.5+0.0s +[6400/16000] [L1: 0.0126] 25.4+0.0s +[8000/16000] [L1: 0.0126] 25.5+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.4+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.513 (Best: 41.556 @epoch 347) +Forward: 38.32s + +Saving... +Total: 38.81s + +[Epoch 358] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.4+1.0s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.456 (Best: 41.556 @epoch 347) +Forward: 38.20s + +Saving... +Total: 38.70s + +[Epoch 359] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.5+0.9s +[3200/16000] [L1: 0.0126] 25.3+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.4+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.501 (Best: 41.556 @epoch 347) +Forward: 38.26s + +Saving... +Total: 38.85s + +[Epoch 360] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0133] 25.5+1.5s +[3200/16000] [L1: 0.0128] 25.8+0.1s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0126] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.512 (Best: 41.556 @epoch 347) +Forward: 38.28s + +Saving... +Total: 38.78s + +[Epoch 361] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.4+1.0s +[3200/16000] [L1: 0.0126] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0129] 25.9+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.531 (Best: 41.556 @epoch 347) +Forward: 38.38s + +Saving... +Total: 38.89s + +[Epoch 362] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.3+1.0s +[3200/16000] [L1: 0.0125] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.4+0.0s +[6400/16000] [L1: 0.0126] 25.4+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.540 (Best: 41.556 @epoch 347) +Forward: 38.46s + +Saving... +Total: 38.98s + +[Epoch 363] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0123] 25.2+0.9s +[3200/16000] [L1: 0.0126] 25.0+0.0s +[4800/16000] [L1: 0.0128] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0127] 25.8+0.0s +[11200/16000] [L1: 0.0127] 25.8+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.4+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.500 (Best: 41.556 @epoch 347) +Forward: 38.23s + +Saving... +Total: 38.71s + +[Epoch 364] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.6+1.0s +[3200/16000] [L1: 0.0125] 25.6+0.0s +[4800/16000] [L1: 0.0124] 25.5+0.0s +[6400/16000] [L1: 0.0124] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.9+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.502 (Best: 41.556 @epoch 347) +Forward: 38.27s + +Saving... +Total: 38.81s + +[Epoch 365] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0123] 25.5+0.9s +[3200/16000] [L1: 0.0125] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0126] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.8+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.506 (Best: 41.556 @epoch 347) +Forward: 38.38s + +Saving... +Total: 38.90s + +[Epoch 366] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.3+0.9s +[3200/16000] [L1: 0.0125] 25.2+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0126] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.9+0.0s +[9600/16000] [L1: 0.0126] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 25.7+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.508 (Best: 41.556 @epoch 347) +Forward: 38.33s + +Saving... +Total: 38.87s + +[Epoch 367] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.6+0.9s +[3200/16000] [L1: 0.0129] 25.6+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.7+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.522 (Best: 41.556 @epoch 347) +Forward: 38.19s + +Saving... +Total: 38.67s + +[Epoch 368] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.6+1.0s +[3200/16000] [L1: 0.0126] 25.6+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0128] 26.0+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.8+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.9+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.523 (Best: 41.556 @epoch 347) +Forward: 38.22s + +Saving... +Total: 38.72s + +[Epoch 369] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.6+1.0s +[3200/16000] [L1: 0.0129] 25.4+0.0s +[4800/16000] [L1: 0.0129] 25.7+0.0s +[6400/16000] [L1: 0.0129] 25.7+0.0s +[8000/16000] [L1: 0.0130] 25.7+0.0s +[9600/16000] [L1: 0.0129] 25.7+0.0s +[11200/16000] [L1: 0.0129] 25.7+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.487 (Best: 41.556 @epoch 347) +Forward: 38.48s + +Saving... +Total: 38.98s + +[Epoch 370] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.3+0.9s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.8+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.491 (Best: 41.556 @epoch 347) +Forward: 38.43s + +Saving... +Total: 38.94s + +[Epoch 371] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.3+1.0s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0128] 25.4+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.518 (Best: 41.556 @epoch 347) +Forward: 38.48s + +Saving... +Total: 39.10s + +[Epoch 372] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.2s +[3200/16000] [L1: 0.0126] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0126] 25.7+0.0s +[8000/16000] [L1: 0.0128] 25.5+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.8+0.0s +[14400/16000] [L1: 0.0127] 25.9+0.0s +[16000/16000] [L1: 0.0126] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.522 (Best: 41.556 @epoch 347) +Forward: 38.25s + +Saving... +Total: 38.78s + +[Epoch 373] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.3+0.9s +[3200/16000] [L1: 0.0129] 25.3+0.0s +[4800/16000] [L1: 0.0128] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.525 (Best: 41.556 @epoch 347) +Forward: 38.30s + +Saving... +Total: 38.80s + +[Epoch 374] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.7+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.508 (Best: 41.556 @epoch 347) +Forward: 38.40s + +Saving... +Total: 38.93s + +[Epoch 375] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.5+1.1s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0126] 25.4+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.534 (Best: 41.556 @epoch 347) +Forward: 38.32s + +Saving... +Total: 38.81s + +[Epoch 376] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.2+0.9s +[3200/16000] [L1: 0.0128] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0126] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.4+0.0s +[9600/16000] [L1: 0.0127] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.507 (Best: 41.556 @epoch 347) +Forward: 38.28s + +Saving... +Total: 38.82s + +[Epoch 377] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.5+0.9s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0125] 25.8+0.0s +[11200/16000] [L1: 0.0125] 26.0+0.0s +[12800/16000] [L1: 0.0126] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.541 (Best: 41.556 @epoch 347) +Forward: 38.25s + +Saving... +Total: 38.75s + +[Epoch 378] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.1+0.9s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.4+0.0s +[8000/16000] [L1: 0.0126] 25.4+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.5+0.0s +[12800/16000] [L1: 0.0126] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.504 (Best: 41.556 @epoch 347) +Forward: 38.24s + +Saving... +Total: 38.75s + +[Epoch 379] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.6+0.9s +[3200/16000] [L1: 0.0129] 25.6+0.0s +[4800/16000] [L1: 0.0129] 26.0+0.0s +[6400/16000] [L1: 0.0128] 25.8+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.523 (Best: 41.556 @epoch 347) +Forward: 38.24s + +Saving... +Total: 38.77s + +[Epoch 380] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.2s +[3200/16000] [L1: 0.0128] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0126] 26.0+0.0s +[9600/16000] [L1: 0.0127] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.526 (Best: 41.556 @epoch 347) +Forward: 38.38s + +Saving... +Total: 38.88s + +[Epoch 381] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.3+1.0s +[3200/16000] [L1: 0.0127] 25.6+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0127] 25.8+0.0s +[16000/16000] [L1: 0.0127] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.544 (Best: 41.556 @epoch 347) +Forward: 38.47s + +Saving... +Total: 38.98s + +[Epoch 382] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.4+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.528 (Best: 41.556 @epoch 347) +Forward: 38.19s + +Saving... +Total: 38.77s + +[Epoch 383] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0131] 25.4+0.9s +[3200/16000] [L1: 0.0130] 25.2+0.0s +[4800/16000] [L1: 0.0129] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.4+0.0s +[9600/16000] [L1: 0.0127] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.4+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.490 (Best: 41.556 @epoch 347) +Forward: 38.48s + +Saving... +Total: 38.99s + +[Epoch 384] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.4+1.1s +[3200/16000] [L1: 0.0124] 25.7+0.0s +[4800/16000] [L1: 0.0124] 25.8+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.4+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.540 (Best: 41.556 @epoch 347) +Forward: 38.29s + +Saving... +Total: 38.83s + +[Epoch 385] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.8+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.3+0.0s +[14400/16000] [L1: 0.0126] 25.3+0.0s +[16000/16000] [L1: 0.0126] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.544 (Best: 41.556 @epoch 347) +Forward: 38.22s + +Saving... +Total: 38.72s + +[Epoch 386] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.6+1.0s +[3200/16000] [L1: 0.0128] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.4+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.531 (Best: 41.556 @epoch 347) +Forward: 38.12s + +Saving... +Total: 38.62s + +[Epoch 387] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0125] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.4+0.0s +[12800/16000] [L1: 0.0127] 25.3+0.0s +[14400/16000] [L1: 0.0128] 25.5+0.0s +[16000/16000] [L1: 0.0128] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.573 (Best: 41.573 @epoch 387) +Forward: 38.32s + +Saving... +Total: 38.85s + +[Epoch 388] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0129] 25.6+1.0s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0127] 26.0+0.0s +[14400/16000] [L1: 0.0127] 25.8+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.533 (Best: 41.573 @epoch 387) +Forward: 38.34s + +Saving... +Total: 38.87s + +[Epoch 389] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.5+1.1s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0128] 26.0+0.0s +[8000/16000] [L1: 0.0128] 25.9+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.3+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.527 (Best: 41.573 @epoch 387) +Forward: 38.26s + +Saving... +Total: 38.76s + +[Epoch 390] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0130] 25.3+1.0s +[3200/16000] [L1: 0.0130] 25.4+0.0s +[4800/16000] [L1: 0.0131] 25.5+0.0s +[6400/16000] [L1: 0.0131] 25.6+0.0s +[8000/16000] [L1: 0.0131] 25.4+0.0s +[9600/16000] [L1: 0.0130] 25.7+0.0s +[11200/16000] [L1: 0.0129] 25.5+0.0s +[12800/16000] [L1: 0.0128] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.525 (Best: 41.573 @epoch 387) +Forward: 38.24s + +Saving... +Total: 38.81s + +[Epoch 391] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0124] 25.4+0.9s +[3200/16000] [L1: 0.0125] 25.7+0.0s +[4800/16000] [L1: 0.0127] 25.8+0.0s +[6400/16000] [L1: 0.0127] 26.0+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0128] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.553 (Best: 41.573 @epoch 387) +Forward: 38.20s + +Saving... +Total: 38.70s + +[Epoch 392] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0128] 25.6+1.0s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.9+0.0s +[11200/16000] [L1: 0.0127] 25.8+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.540 (Best: 41.573 @epoch 387) +Forward: 38.46s + +Saving... +Total: 38.99s + +[Epoch 393] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.8+0.9s +[3200/16000] [L1: 0.0128] 25.9+0.1s +[4800/16000] [L1: 0.0128] 25.9+0.0s +[6400/16000] [L1: 0.0129] 25.5+0.0s +[8000/16000] [L1: 0.0129] 25.7+0.0s +[9600/16000] [L1: 0.0129] 25.4+0.0s +[11200/16000] [L1: 0.0128] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.530 (Best: 41.573 @epoch 387) +Forward: 38.24s + +Saving... +Total: 38.76s + +[Epoch 394] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0126] 25.3+1.0s +[3200/16000] [L1: 0.0127] 25.6+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0128] 25.8+0.0s +[9600/16000] [L1: 0.0128] 26.0+0.0s +[11200/16000] [L1: 0.0129] 25.8+0.0s +[12800/16000] [L1: 0.0129] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0128] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.552 (Best: 41.573 @epoch 387) +Forward: 38.40s + +Saving... +Total: 38.91s + +[Epoch 395] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0123] 25.5+0.9s +[3200/16000] [L1: 0.0126] 25.5+0.0s +[4800/16000] [L1: 0.0127] 26.0+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.509 (Best: 41.573 @epoch 387) +Forward: 38.28s + +Saving... +Total: 38.79s + +[Epoch 396] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0125] 25.4+1.0s +[3200/16000] [L1: 0.0125] 25.2+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0128] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.527 (Best: 41.573 @epoch 387) +Forward: 38.44s + +Saving... +Total: 38.92s + +[Epoch 397] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.6+1.0s +[3200/16000] [L1: 0.0128] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.4+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.6+0.0s +[14400/16000] [L1: 0.0126] 25.4+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.503 (Best: 41.573 @epoch 387) +Forward: 38.29s + +Saving... +Total: 38.79s + +[Epoch 398] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.3+1.0s +[3200/16000] [L1: 0.0127] 25.7+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0128] 25.5+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0128] 25.5+0.0s +[14400/16000] [L1: 0.0128] 25.6+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.529 (Best: 41.573 @epoch 387) +Forward: 38.29s + +Saving... +Total: 38.80s + +[Epoch 399] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.3+0.9s +[3200/16000] [L1: 0.0129] 25.4+0.0s +[4800/16000] [L1: 0.0130] 25.3+0.0s +[6400/16000] [L1: 0.0130] 25.4+0.0s +[8000/16000] [L1: 0.0129] 25.7+0.0s +[9600/16000] [L1: 0.0129] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.8+0.0s +[12800/16000] [L1: 0.0128] 25.7+0.0s +[14400/16000] [L1: 0.0128] 25.7+0.0s +[16000/16000] [L1: 0.0128] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.538 (Best: 41.573 @epoch 387) +Forward: 38.40s + +Saving... +Total: 38.96s + +[Epoch 400] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.0127] 25.6+1.0s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0127] 25.7+0.0s +[6400/16000] [L1: 0.0128] 25.9+0.0s +[8000/16000] [L1: 0.0128] 25.7+0.0s +[9600/16000] [L1: 0.0128] 25.6+0.0s +[11200/16000] [L1: 0.0128] 25.9+0.0s +[12800/16000] [L1: 0.0128] 25.9+0.0s +[14400/16000] [L1: 0.0128] 25.8+0.0s +[16000/16000] [L1: 0.0127] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.544 (Best: 41.573 @epoch 387) +Forward: 38.31s + +Saving... +Total: 38.81s + +[Epoch 401] Learning rate: 6.25e-6 +[1600/16000] [L1: 0.0123] 25.4+1.1s +[3200/16000] [L1: 0.0124] 25.2+0.0s +[4800/16000] [L1: 0.0125] 25.7+0.0s +[6400/16000] [L1: 0.0126] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0124] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.558 (Best: 41.573 @epoch 387) +Forward: 38.44s + +Saving... +Total: 38.95s + +[Epoch 402] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.7+0.9s +[3200/16000] [L1: 0.0126] 25.9+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0126] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0125] 25.8+0.0s +[11200/16000] [L1: 0.0125] 25.9+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.539 (Best: 41.573 @epoch 387) +Forward: 38.35s + +Saving... +Total: 38.93s + +[Epoch 403] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.3+1.0s +[3200/16000] [L1: 0.0124] 25.5+0.0s +[4800/16000] [L1: 0.0125] 25.7+0.0s +[6400/16000] [L1: 0.0124] 26.1+0.0s +[8000/16000] [L1: 0.0124] 25.6+0.0s +[9600/16000] [L1: 0.0124] 25.4+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0125] 25.6+0.0s +[14400/16000] [L1: 0.0125] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.557 (Best: 41.573 @epoch 387) +Forward: 38.25s + +Saving... +Total: 38.91s + +[Epoch 404] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.6+0.9s +[3200/16000] [L1: 0.0125] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0127] 25.7+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.535 (Best: 41.573 @epoch 387) +Forward: 38.23s + +Saving... +Total: 38.74s + +[Epoch 405] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.5+1.0s +[3200/16000] [L1: 0.0125] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.8+0.0s +[6400/16000] [L1: 0.0126] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.8+0.0s +[9600/16000] [L1: 0.0125] 25.5+0.0s +[11200/16000] [L1: 0.0125] 25.4+0.0s +[12800/16000] [L1: 0.0125] 25.4+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.564 (Best: 41.573 @epoch 387) +Forward: 38.18s + +Saving... +Total: 38.69s + +[Epoch 406] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.7+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0125] 25.9+0.0s +[9600/16000] [L1: 0.0125] 25.8+0.0s +[11200/16000] [L1: 0.0125] 25.9+0.0s +[12800/16000] [L1: 0.0125] 25.8+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.553 (Best: 41.573 @epoch 387) +Forward: 38.44s + +Saving... +Total: 39.01s + +[Epoch 407] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.8+0.9s +[3200/16000] [L1: 0.0124] 25.8+0.0s +[4800/16000] [L1: 0.0125] 25.9+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.5+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.5+0.0s +[12800/16000] [L1: 0.0127] 25.6+0.0s +[14400/16000] [L1: 0.0127] 25.5+0.0s +[16000/16000] [L1: 0.0127] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.560 (Best: 41.573 @epoch 387) +Forward: 38.48s + +Saving... +Total: 39.00s + +[Epoch 408] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 25.3+0.9s +[3200/16000] [L1: 0.0127] 25.0+0.0s +[4800/16000] [L1: 0.0127] 25.4+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.566 (Best: 41.573 @epoch 387) +Forward: 38.29s + +Saving... +Total: 38.83s + +[Epoch 409] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.6+0.9s +[3200/16000] [L1: 0.0124] 25.6+0.1s +[4800/16000] [L1: 0.0125] 25.9+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0125] 25.9+0.0s +[9600/16000] [L1: 0.0125] 25.9+0.0s +[11200/16000] [L1: 0.0124] 26.0+0.0s +[12800/16000] [L1: 0.0124] 26.0+0.0s +[14400/16000] [L1: 0.0124] 25.9+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.576 (Best: 41.576 @epoch 409) +Forward: 38.35s + +Saving... +Total: 38.91s + +[Epoch 410] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.4+0.9s +[3200/16000] [L1: 0.0125] 25.5+0.0s +[4800/16000] [L1: 0.0125] 25.5+0.0s +[6400/16000] [L1: 0.0124] 25.6+0.0s +[8000/16000] [L1: 0.0125] 25.5+0.0s +[9600/16000] [L1: 0.0124] 25.6+0.0s +[11200/16000] [L1: 0.0124] 25.6+0.0s +[12800/16000] [L1: 0.0124] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0124] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.558 (Best: 41.576 @epoch 409) +Forward: 38.30s + +Saving... +Total: 38.80s + +[Epoch 411] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.7+1.0s +[3200/16000] [L1: 0.0124] 25.5+0.0s +[4800/16000] [L1: 0.0125] 25.8+0.0s +[6400/16000] [L1: 0.0125] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.8+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 25.4+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.554 (Best: 41.576 @epoch 409) +Forward: 38.30s + +Saving... +Total: 38.77s + +[Epoch 412] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.2+1.0s +[3200/16000] [L1: 0.0123] 25.4+0.0s +[4800/16000] [L1: 0.0124] 25.6+0.0s +[6400/16000] [L1: 0.0124] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.5+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0125] 25.4+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.3+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.561 (Best: 41.576 @epoch 409) +Forward: 38.36s + +Saving... +Total: 38.85s + +[Epoch 413] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.5+0.9s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.7+0.0s +[6400/16000] [L1: 0.0126] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.4+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.547 (Best: 41.576 @epoch 409) +Forward: 38.33s + +Saving... +Total: 38.79s + +[Epoch 414] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.5+1.0s +[3200/16000] [L1: 0.0129] 25.5+0.0s +[4800/16000] [L1: 0.0129] 25.6+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.5+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.8+0.0s +[12800/16000] [L1: 0.0126] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.539 (Best: 41.576 @epoch 409) +Forward: 38.26s + +Saving... +Total: 38.78s + +[Epoch 415] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.5+0.9s +[3200/16000] [L1: 0.0126] 25.3+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0126] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.9+0.0s +[9600/16000] [L1: 0.0126] 26.1+0.0s +[11200/16000] [L1: 0.0125] 25.8+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.563 (Best: 41.576 @epoch 409) +Forward: 38.32s + +Saving... +Total: 38.77s + +[Epoch 416] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.7+0.9s +[3200/16000] [L1: 0.0124] 25.5+0.0s +[4800/16000] [L1: 0.0124] 25.7+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.5+0.0s +[11200/16000] [L1: 0.0126] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.560 (Best: 41.576 @epoch 409) +Forward: 38.49s + +Saving... +Total: 38.96s + +[Epoch 417] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0119] 25.5+1.0s +[3200/16000] [L1: 0.0121] 25.6+0.0s +[4800/16000] [L1: 0.0123] 26.2+0.0s +[6400/16000] [L1: 0.0123] 26.1+0.0s +[8000/16000] [L1: 0.0123] 25.9+0.0s +[9600/16000] [L1: 0.0123] 25.9+0.0s +[11200/16000] [L1: 0.0123] 25.9+0.0s +[12800/16000] [L1: 0.0123] 25.8+0.0s +[14400/16000] [L1: 0.0124] 25.7+0.0s +[16000/16000] [L1: 0.0124] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.537 (Best: 41.576 @epoch 409) +Forward: 38.36s + +Saving... +Total: 38.82s + +[Epoch 418] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.4+0.9s +[3200/16000] [L1: 0.0130] 25.5+0.0s +[4800/16000] [L1: 0.0128] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.7+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.9+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.560 (Best: 41.576 @epoch 409) +Forward: 38.22s + +Saving... +Total: 38.75s + +[Epoch 419] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.3+1.0s +[3200/16000] [L1: 0.0124] 25.6+0.0s +[4800/16000] [L1: 0.0124] 25.6+0.0s +[6400/16000] [L1: 0.0124] 25.7+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.9+0.0s +[16000/16000] [L1: 0.0125] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.555 (Best: 41.576 @epoch 409) +Forward: 38.25s + +Saving... +Total: 38.70s + +[Epoch 420] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.7+0.9s +[3200/16000] [L1: 0.0125] 25.4+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.5+0.0s +[11200/16000] [L1: 0.0125] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.538 (Best: 41.576 @epoch 409) +Forward: 38.24s + +Saving... +Total: 38.71s + +[Epoch 421] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.6+1.0s +[3200/16000] [L1: 0.0122] 25.8+0.0s +[4800/16000] [L1: 0.0123] 25.8+0.0s +[6400/16000] [L1: 0.0123] 25.8+0.0s +[8000/16000] [L1: 0.0123] 25.7+0.0s +[9600/16000] [L1: 0.0123] 25.6+0.0s +[11200/16000] [L1: 0.0124] 25.4+0.0s +[12800/16000] [L1: 0.0124] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.566 (Best: 41.576 @epoch 409) +Forward: 38.50s + +Saving... +Total: 38.95s + +[Epoch 422] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0121] 25.4+1.1s +[3200/16000] [L1: 0.0124] 25.2+0.0s +[4800/16000] [L1: 0.0124] 25.7+0.0s +[6400/16000] [L1: 0.0124] 26.0+0.0s +[8000/16000] [L1: 0.0124] 25.9+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.6+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.549 (Best: 41.576 @epoch 409) +Forward: 38.32s + +Saving... +Total: 38.89s + +[Epoch 423] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.6+0.9s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.8+0.0s +[6400/16000] [L1: 0.0127] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 26.2+0.0s +[14400/16000] [L1: 0.0126] 25.5+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.507 (Best: 41.576 @epoch 409) +Forward: 38.20s + +Saving... +Total: 38.66s + +[Epoch 424] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.6+1.0s +[3200/16000] [L1: 0.0123] 25.6+0.0s +[4800/16000] [L1: 0.0123] 25.7+0.0s +[6400/16000] [L1: 0.0124] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0124] 25.6+0.0s +[11200/16000] [L1: 0.0125] 25.5+0.0s +[12800/16000] [L1: 0.0125] 25.4+0.0s +[14400/16000] [L1: 0.0125] 25.7+0.0s +[16000/16000] [L1: 0.0125] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.535 (Best: 41.576 @epoch 409) +Forward: 38.22s + +Saving... +Total: 38.68s + +[Epoch 425] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.4+1.0s +[3200/16000] [L1: 0.0127] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0127] 25.9+0.0s +[8000/16000] [L1: 0.0127] 25.8+0.0s +[9600/16000] [L1: 0.0127] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.551 (Best: 41.576 @epoch 409) +Forward: 38.39s + +Saving... +Total: 38.84s + +[Epoch 426] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.7+0.9s +[3200/16000] [L1: 0.0128] 25.4+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0126] 25.9+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 25.6+0.0s +[14400/16000] [L1: 0.0126] 25.5+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.560 (Best: 41.576 @epoch 409) +Forward: 38.28s + +Saving... +Total: 38.82s + +[Epoch 427] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.5+1.0s +[3200/16000] [L1: 0.0125] 25.5+0.0s +[4800/16000] [L1: 0.0125] 25.4+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0124] 25.9+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0125] 25.8+0.0s +[14400/16000] [L1: 0.0126] 25.9+0.0s +[16000/16000] [L1: 0.0126] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.559 (Best: 41.576 @epoch 409) +Forward: 38.27s + +Saving... +Total: 38.73s + +[Epoch 428] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0124] 25.8+0.0s +[6400/16000] [L1: 0.0124] 25.7+0.0s +[8000/16000] [L1: 0.0124] 25.7+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0125] 25.6+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.528 (Best: 41.576 @epoch 409) +Forward: 38.30s + +Saving... +Total: 38.78s + +[Epoch 429] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.5+1.0s +[3200/16000] [L1: 0.0124] 25.7+0.0s +[4800/16000] [L1: 0.0124] 25.7+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0124] 25.5+0.0s +[16000/16000] [L1: 0.0124] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.555 (Best: 41.576 @epoch 409) +Forward: 38.17s + +Saving... +Total: 38.62s + +[Epoch 430] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.5+0.9s +[3200/16000] [L1: 0.0127] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.5+0.0s +[9600/16000] [L1: 0.0124] 25.5+0.0s +[11200/16000] [L1: 0.0125] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0125] 25.4+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.540 (Best: 41.576 @epoch 409) +Forward: 38.23s + +Saving... +Total: 38.73s + +[Epoch 431] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0131] 25.3+1.0s +[3200/16000] [L1: 0.0128] 25.6+0.0s +[4800/16000] [L1: 0.0128] 25.8+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0126] 25.9+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.5+0.0s +[14400/16000] [L1: 0.0127] 25.7+0.0s +[16000/16000] [L1: 0.0127] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.522 (Best: 41.576 @epoch 409) +Forward: 38.36s + +Saving... +Total: 38.82s + +[Epoch 432] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.3+0.9s +[3200/16000] [L1: 0.0126] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0125] 25.8+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.533 (Best: 41.576 @epoch 409) +Forward: 38.23s + +Saving... +Total: 38.70s + +[Epoch 433] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.3+0.9s +[3200/16000] [L1: 0.0125] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.6+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.4+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.551 (Best: 41.576 @epoch 409) +Forward: 38.26s + +Saving... +Total: 38.85s + +[Epoch 434] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.3+1.0s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0125] 25.6+0.0s +[6400/16000] [L1: 0.0125] 25.5+0.0s +[8000/16000] [L1: 0.0125] 25.5+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.4+0.0s +[12800/16000] [L1: 0.0125] 25.6+0.0s +[14400/16000] [L1: 0.0125] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.539 (Best: 41.576 @epoch 409) +Forward: 38.45s + +Saving... +Total: 38.99s + +[Epoch 435] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 25.6+0.9s +[3200/16000] [L1: 0.0130] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0126] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0126] 25.8+0.0s +[12800/16000] [L1: 0.0126] 25.5+0.0s +[14400/16000] [L1: 0.0126] 25.5+0.0s +[16000/16000] [L1: 0.0126] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.523 (Best: 41.576 @epoch 409) +Forward: 38.39s + +Saving... +Total: 38.86s + +[Epoch 436] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0121] 25.7+1.0s +[3200/16000] [L1: 0.0125] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.8+0.0s +[6400/16000] [L1: 0.0125] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.8+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0125] 25.4+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0125] 25.4+0.0s +[16000/16000] [L1: 0.0125] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.582 (Best: 41.582 @epoch 436) +Forward: 38.21s + +Saving... +Total: 38.73s + +[Epoch 437] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.2+1.1s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0126] 25.9+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.5+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0125] 25.6+0.0s +[14400/16000] [L1: 0.0125] 25.4+0.0s +[16000/16000] [L1: 0.0125] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.542 (Best: 41.582 @epoch 436) +Forward: 38.13s + +Saving... +Total: 38.60s + +[Epoch 438] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.4+0.9s +[3200/16000] [L1: 0.0123] 25.5+0.0s +[4800/16000] [L1: 0.0123] 25.4+0.0s +[6400/16000] [L1: 0.0124] 25.7+0.0s +[8000/16000] [L1: 0.0124] 25.6+0.0s +[9600/16000] [L1: 0.0124] 25.4+0.0s +[11200/16000] [L1: 0.0124] 25.5+0.0s +[12800/16000] [L1: 0.0124] 25.4+0.0s +[14400/16000] [L1: 0.0124] 25.6+0.0s +[16000/16000] [L1: 0.0124] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.547 (Best: 41.582 @epoch 436) +Forward: 38.25s + +Saving... +Total: 38.80s + +[Epoch 439] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.5+0.9s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0125] 25.2+0.0s +[6400/16000] [L1: 0.0125] 25.7+0.0s +[8000/16000] [L1: 0.0126] 25.5+0.0s +[9600/16000] [L1: 0.0126] 25.5+0.0s +[11200/16000] [L1: 0.0126] 25.6+0.0s +[12800/16000] [L1: 0.0126] 25.6+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.569 (Best: 41.582 @epoch 436) +Forward: 38.16s + +Saving... +Total: 38.62s + +[Epoch 440] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.5+1.0s +[3200/16000] [L1: 0.0126] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0125] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.3+0.0s +[14400/16000] [L1: 0.0125] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.552 (Best: 41.582 @epoch 436) +Forward: 38.20s + +Saving... +Total: 38.70s + +[Epoch 441] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.3+0.9s +[3200/16000] [L1: 0.0126] 25.3+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0125] 25.7+0.0s +[8000/16000] [L1: 0.0125] 26.0+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0125] 25.3+0.0s +[14400/16000] [L1: 0.0125] 25.3+0.0s +[16000/16000] [L1: 0.0125] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.553 (Best: 41.582 @epoch 436) +Forward: 38.38s + +Saving... +Total: 38.84s + +[Epoch 442] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.2+0.9s +[3200/16000] [L1: 0.0124] 24.9+0.0s +[4800/16000] [L1: 0.0124] 25.4+0.0s +[6400/16000] [L1: 0.0124] 25.5+0.0s +[8000/16000] [L1: 0.0125] 25.5+0.0s +[9600/16000] [L1: 0.0125] 25.4+0.0s +[11200/16000] [L1: 0.0125] 25.5+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.529 (Best: 41.582 @epoch 436) +Forward: 38.32s + +Saving... +Total: 38.87s + +[Epoch 443] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.4+1.0s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0126] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.8+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.7+0.0s +[16000/16000] [L1: 0.0126] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.549 (Best: 41.582 @epoch 436) +Forward: 38.23s + +Saving... +Total: 38.70s + +[Epoch 444] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.6+0.9s +[3200/16000] [L1: 0.0122] 25.2+0.0s +[4800/16000] [L1: 0.0122] 25.5+0.0s +[6400/16000] [L1: 0.0122] 25.6+0.0s +[8000/16000] [L1: 0.0123] 25.7+0.0s +[9600/16000] [L1: 0.0124] 25.2+0.0s +[11200/16000] [L1: 0.0124] 25.4+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0124] 25.4+0.0s +[16000/16000] [L1: 0.0124] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.551 (Best: 41.582 @epoch 436) +Forward: 38.22s + +Saving... +Total: 38.66s + +[Epoch 445] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.2+0.9s +[3200/16000] [L1: 0.0124] 25.5+0.0s +[4800/16000] [L1: 0.0124] 25.6+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0124] 25.6+0.0s +[9600/16000] [L1: 0.0123] 25.6+0.0s +[11200/16000] [L1: 0.0124] 25.6+0.0s +[12800/16000] [L1: 0.0123] 25.5+0.0s +[14400/16000] [L1: 0.0124] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.572 (Best: 41.582 @epoch 436) +Forward: 38.43s + +Saving... +Total: 38.89s + +[Epoch 446] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.3+0.9s +[3200/16000] [L1: 0.0123] 25.7+0.0s +[4800/16000] [L1: 0.0124] 25.7+0.0s +[6400/16000] [L1: 0.0124] 25.7+0.0s +[8000/16000] [L1: 0.0124] 25.8+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.8+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.575 (Best: 41.582 @epoch 436) +Forward: 38.32s + +Saving... +Total: 38.86s + +[Epoch 447] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0128] 25.6+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0125] 25.8+0.0s +[11200/16000] [L1: 0.0124] 25.8+0.0s +[12800/16000] [L1: 0.0125] 25.6+0.0s +[14400/16000] [L1: 0.0125] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.517 (Best: 41.582 @epoch 436) +Forward: 38.19s + +Saving... +Total: 38.67s + +[Epoch 448] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.6+0.9s +[3200/16000] [L1: 0.0126] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0127] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.6+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0126] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.575 (Best: 41.582 @epoch 436) +Forward: 38.29s + +Saving... +Total: 38.73s + +[Epoch 449] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.7+0.9s +[3200/16000] [L1: 0.0126] 25.8+0.0s +[4800/16000] [L1: 0.0125] 25.6+0.0s +[6400/16000] [L1: 0.0125] 25.7+0.0s +[8000/16000] [L1: 0.0125] 25.5+0.0s +[9600/16000] [L1: 0.0124] 25.5+0.0s +[11200/16000] [L1: 0.0124] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.4+0.0s +[14400/16000] [L1: 0.0125] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.559 (Best: 41.582 @epoch 436) +Forward: 38.35s + +Saving... +Total: 38.81s + +[Epoch 450] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.3+0.9s +[3200/16000] [L1: 0.0124] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.7+0.0s +[6400/16000] [L1: 0.0125] 26.0+0.0s +[8000/16000] [L1: 0.0125] 25.9+0.0s +[9600/16000] [L1: 0.0125] 25.4+0.0s +[11200/16000] [L1: 0.0125] 25.4+0.0s +[12800/16000] [L1: 0.0125] 25.5+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0125] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.545 (Best: 41.582 @epoch 436) +Forward: 38.39s + +Saving... +Total: 38.95s + +[Epoch 451] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.7+0.9s +[3200/16000] [L1: 0.0124] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.4+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0126] 25.8+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 25.7+0.0s +[14400/16000] [L1: 0.0126] 25.5+0.0s +[16000/16000] [L1: 0.0126] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.557 (Best: 41.582 @epoch 436) +Forward: 38.23s + +Saving... +Total: 38.69s + +[Epoch 452] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.6+0.9s +[3200/16000] [L1: 0.0126] 25.4+0.0s +[4800/16000] [L1: 0.0126] 25.5+0.0s +[6400/16000] [L1: 0.0126] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.4+0.0s +[11200/16000] [L1: 0.0125] 25.5+0.0s +[12800/16000] [L1: 0.0125] 25.6+0.0s +[14400/16000] [L1: 0.0126] 25.5+0.0s +[16000/16000] [L1: 0.0126] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.560 (Best: 41.582 @epoch 436) +Forward: 38.20s + +Saving... +Total: 38.67s + +[Epoch 453] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.1+1.0s +[3200/16000] [L1: 0.0128] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.6+0.0s +[6400/16000] [L1: 0.0128] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.4+0.0s +[11200/16000] [L1: 0.0126] 25.4+0.0s +[12800/16000] [L1: 0.0126] 25.3+0.0s +[14400/16000] [L1: 0.0125] 25.2+0.0s +[16000/16000] [L1: 0.0125] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.546 (Best: 41.582 @epoch 436) +Forward: 38.22s + +Saving... +Total: 38.68s + +[Epoch 454] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.5+1.0s +[3200/16000] [L1: 0.0125] 25.9+0.1s +[4800/16000] [L1: 0.0126] 26.2+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0126] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 25.5+0.0s +[12800/16000] [L1: 0.0126] 25.4+0.0s +[14400/16000] [L1: 0.0126] 25.6+0.0s +[16000/16000] [L1: 0.0126] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.536 (Best: 41.582 @epoch 436) +Forward: 38.40s + +Saving... +Total: 38.91s + +[Epoch 455] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.4+0.9s +[3200/16000] [L1: 0.0123] 25.7+0.0s +[4800/16000] [L1: 0.0123] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.6+0.0s +[14400/16000] [L1: 0.0126] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.536 (Best: 41.582 @epoch 436) +Forward: 38.21s + +Saving... +Total: 38.66s + +[Epoch 456] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 25.5+0.9s +[3200/16000] [L1: 0.0125] 25.4+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0126] 25.7+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.5+0.0s +[11200/16000] [L1: 0.0126] 25.7+0.0s +[12800/16000] [L1: 0.0126] 25.8+0.0s +[14400/16000] [L1: 0.0126] 25.9+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.570 (Best: 41.582 @epoch 436) +Forward: 37.97s + +Saving... +Total: 38.42s + +[Epoch 457] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 25.2+0.9s +[3200/16000] [L1: 0.0127] 25.3+0.0s +[4800/16000] [L1: 0.0127] 25.4+0.0s +[6400/16000] [L1: 0.0126] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0126] 25.4+0.0s +[11200/16000] [L1: 0.0126] 25.1+0.0s +[12800/16000] [L1: 0.0125] 25.3+0.0s +[14400/16000] [L1: 0.0125] 25.2+0.0s +[16000/16000] [L1: 0.0124] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.554 (Best: 41.582 @epoch 436) +Forward: 37.77s + +Saving... +Total: 38.25s + +[Epoch 458] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.6+0.9s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0125] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.5+0.0s +[8000/16000] [L1: 0.0126] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.4+0.0s +[11200/16000] [L1: 0.0125] 25.5+0.0s +[12800/16000] [L1: 0.0125] 25.2+0.0s +[14400/16000] [L1: 0.0125] 25.4+0.0s +[16000/16000] [L1: 0.0126] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.527 (Best: 41.582 @epoch 436) +Forward: 37.93s + +Saving... +Total: 38.47s + +[Epoch 459] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 25.3+0.9s +[3200/16000] [L1: 0.0126] 25.5+0.0s +[4800/16000] [L1: 0.0127] 25.9+0.0s +[6400/16000] [L1: 0.0126] 26.0+0.0s +[8000/16000] [L1: 0.0126] 25.5+0.0s +[9600/16000] [L1: 0.0125] 25.3+0.0s +[11200/16000] [L1: 0.0125] 25.5+0.0s +[12800/16000] [L1: 0.0125] 25.1+0.0s +[14400/16000] [L1: 0.0125] 25.2+0.0s +[16000/16000] [L1: 0.0125] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.551 (Best: 41.582 @epoch 436) +Forward: 37.85s + +Saving... +Total: 38.31s + +[Epoch 460] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 25.4+0.9s +[3200/16000] [L1: 0.0127] 25.2+0.0s +[4800/16000] [L1: 0.0126] 25.2+0.0s +[6400/16000] [L1: 0.0125] 25.1+0.0s +[8000/16000] [L1: 0.0125] 25.1+0.0s +[9600/16000] [L1: 0.0125] 25.1+0.0s +[11200/16000] [L1: 0.0125] 25.2+0.0s +[12800/16000] [L1: 0.0125] 25.1+0.0s +[14400/16000] [L1: 0.0125] 25.2+0.0s +[16000/16000] [L1: 0.0126] 24.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.535 (Best: 41.582 @epoch 436) +Forward: 37.92s + +Saving... +Total: 38.37s + +[Epoch 461] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.1+1.0s +[3200/16000] [L1: 0.0127] 25.2+0.0s +[4800/16000] [L1: 0.0125] 25.4+0.0s +[6400/16000] [L1: 0.0126] 25.0+0.0s +[8000/16000] [L1: 0.0127] 24.9+0.0s +[9600/16000] [L1: 0.0127] 25.2+0.0s +[11200/16000] [L1: 0.0126] 25.1+0.0s +[12800/16000] [L1: 0.0126] 25.1+0.0s +[14400/16000] [L1: 0.0127] 25.2+0.0s +[16000/16000] [L1: 0.0126] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.551 (Best: 41.582 @epoch 436) +Forward: 37.87s + +Saving... +Total: 38.35s + +[Epoch 462] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.2+1.0s +[3200/16000] [L1: 0.0126] 25.6+0.0s +[4800/16000] [L1: 0.0124] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0126] 25.5+0.0s +[9600/16000] [L1: 0.0126] 25.4+0.0s +[11200/16000] [L1: 0.0125] 25.5+0.0s +[12800/16000] [L1: 0.0126] 25.4+0.0s +[14400/16000] [L1: 0.0125] 25.5+0.0s +[16000/16000] [L1: 0.0126] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.526 (Best: 41.582 @epoch 436) +Forward: 37.89s + +Saving... +Total: 38.42s + +[Epoch 463] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.6+0.9s +[3200/16000] [L1: 0.0125] 25.6+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0125] 25.5+0.0s +[8000/16000] [L1: 0.0124] 25.6+0.0s +[9600/16000] [L1: 0.0124] 25.7+0.0s +[11200/16000] [L1: 0.0124] 25.6+0.0s +[12800/16000] [L1: 0.0124] 25.5+0.0s +[14400/16000] [L1: 0.0124] 25.3+0.0s +[16000/16000] [L1: 0.0124] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.554 (Best: 41.582 @epoch 436) +Forward: 37.96s + +Saving... +Total: 38.46s + +[Epoch 464] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.3+0.9s +[3200/16000] [L1: 0.0124] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.5+0.0s +[6400/16000] [L1: 0.0125] 25.6+0.0s +[8000/16000] [L1: 0.0125] 25.6+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.6+0.0s +[12800/16000] [L1: 0.0125] 25.3+0.0s +[14400/16000] [L1: 0.0125] 25.2+0.0s +[16000/16000] [L1: 0.0125] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.537 (Best: 41.582 @epoch 436) +Forward: 37.91s + +Saving... +Total: 38.38s + +[Epoch 465] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.4+1.0s +[3200/16000] [L1: 0.0123] 25.4+0.0s +[4800/16000] [L1: 0.0124] 25.4+0.0s +[6400/16000] [L1: 0.0124] 25.3+0.0s +[8000/16000] [L1: 0.0125] 25.4+0.0s +[9600/16000] [L1: 0.0125] 25.3+0.0s +[11200/16000] [L1: 0.0125] 25.3+0.0s +[12800/16000] [L1: 0.0125] 25.1+0.0s +[14400/16000] [L1: 0.0125] 25.4+0.0s +[16000/16000] [L1: 0.0125] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.583 (Best: 41.583 @epoch 465) +Forward: 37.85s + +Saving... +Total: 38.39s + +[Epoch 466] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.2+1.0s +[3200/16000] [L1: 0.0126] 25.1+0.0s +[4800/16000] [L1: 0.0124] 25.5+0.0s +[6400/16000] [L1: 0.0123] 25.3+0.0s +[8000/16000] [L1: 0.0123] 25.2+0.0s +[9600/16000] [L1: 0.0123] 25.1+0.0s +[11200/16000] [L1: 0.0123] 25.3+0.0s +[12800/16000] [L1: 0.0123] 25.2+0.0s +[14400/16000] [L1: 0.0123] 25.1+0.0s +[16000/16000] [L1: 0.0124] 25.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.555 (Best: 41.583 @epoch 465) +Forward: 38.04s + +Saving... +Total: 38.56s + +[Epoch 467] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.3+1.0s +[3200/16000] [L1: 0.0125] 25.3+0.0s +[4800/16000] [L1: 0.0124] 25.3+0.0s +[6400/16000] [L1: 0.0124] 25.4+0.0s +[8000/16000] [L1: 0.0124] 25.2+0.0s +[9600/16000] [L1: 0.0125] 25.3+0.0s +[11200/16000] [L1: 0.0125] 25.2+0.0s +[12800/16000] [L1: 0.0125] 25.4+0.0s +[14400/16000] [L1: 0.0125] 25.5+0.0s +[16000/16000] [L1: 0.0125] 25.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.562 (Best: 41.583 @epoch 465) +Forward: 37.92s + +Saving... +Total: 38.38s + +[Epoch 468] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.5+1.0s +[3200/16000] [L1: 0.0125] 25.5+0.0s +[4800/16000] [L1: 0.0126] 25.6+0.0s +[6400/16000] [L1: 0.0124] 25.7+0.0s +[8000/16000] [L1: 0.0124] 25.5+0.0s +[9600/16000] [L1: 0.0125] 25.3+0.0s +[11200/16000] [L1: 0.0125] 25.2+0.0s +[12800/16000] [L1: 0.0125] 25.3+0.0s +[14400/16000] [L1: 0.0125] 25.2+0.0s +[16000/16000] [L1: 0.0126] 25.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.589 (Best: 41.589 @epoch 468) +Forward: 37.95s + +Saving... +Total: 38.45s + +[Epoch 469] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 25.2+1.0s +[3200/16000] [L1: 0.0125] 25.4+0.0s +[4800/16000] [L1: 0.0125] 25.4+0.0s +[6400/16000] [L1: 0.0125] 25.3+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0124] 25.6+0.0s +[11200/16000] [L1: 0.0124] 25.1+0.0s +[12800/16000] [L1: 0.0124] 25.4+0.0s +[14400/16000] [L1: 0.0124] 25.4+0.0s +[16000/16000] [L1: 0.0125] 25.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.567 (Best: 41.589 @epoch 468) +Forward: 37.89s + +Saving... +Total: 38.36s + +[Epoch 470] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0128] 25.4+0.9s +[3200/16000] [L1: 0.0128] 25.1+0.0s +[4800/16000] [L1: 0.0127] 25.5+0.0s +[6400/16000] [L1: 0.0127] 25.6+0.0s +[8000/16000] [L1: 0.0127] 25.7+0.0s +[9600/16000] [L1: 0.0126] 25.7+0.0s +[11200/16000] [L1: 0.0126] 42.2+0.0s +[12800/16000] [L1: 0.0126] 57.5+0.0s +[14400/16000] [L1: 0.0126] 57.4+0.0s +[16000/16000] [L1: 0.0127] 58.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.584 (Best: 41.589 @epoch 468) +Forward: 37.72s + +Saving... +Total: 38.27s + +[Epoch 471] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 59.3+0.9s +[3200/16000] [L1: 0.0125] 59.4+0.0s +[4800/16000] [L1: 0.0126] 59.6+0.0s +[6400/16000] [L1: 0.0124] 54.8+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0125] 25.7+0.0s +[11200/16000] [L1: 0.0125] 25.8+0.0s +[12800/16000] [L1: 0.0125] 25.7+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.556 (Best: 41.589 @epoch 468) +Forward: 38.37s + +Saving... +Total: 38.85s + +[Epoch 472] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.9+1.0s +[3200/16000] [L1: 0.0125] 25.9+0.1s +[4800/16000] [L1: 0.0125] 25.9+0.0s +[6400/16000] [L1: 0.0126] 25.7+0.0s +[8000/16000] [L1: 0.0124] 26.0+0.0s +[9600/16000] [L1: 0.0125] 26.1+0.0s +[11200/16000] [L1: 0.0125] 26.2+0.0s +[12800/16000] [L1: 0.0125] 26.0+0.0s +[14400/16000] [L1: 0.0125] 26.0+0.0s +[16000/16000] [L1: 0.0125] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.561 (Best: 41.589 @epoch 468) +Forward: 38.52s + +Saving... +Total: 39.00s + +[Epoch 473] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0120] 25.8+0.9s +[3200/16000] [L1: 0.0122] 25.8+0.0s +[4800/16000] [L1: 0.0123] 26.0+0.0s +[6400/16000] [L1: 0.0124] 26.2+0.0s +[8000/16000] [L1: 0.0125] 26.2+0.0s +[9600/16000] [L1: 0.0125] 26.3+0.0s +[11200/16000] [L1: 0.0125] 26.1+0.0s +[12800/16000] [L1: 0.0125] 26.2+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.560 (Best: 41.589 @epoch 468) +Forward: 38.40s + +Saving... +Total: 38.88s + +[Epoch 474] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.9+1.0s +[3200/16000] [L1: 0.0127] 25.8+0.0s +[4800/16000] [L1: 0.0125] 26.0+0.0s +[6400/16000] [L1: 0.0126] 26.0+0.0s +[8000/16000] [L1: 0.0126] 26.0+0.0s +[9600/16000] [L1: 0.0126] 26.3+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 26.1+0.0s +[14400/16000] [L1: 0.0126] 26.1+0.0s +[16000/16000] [L1: 0.0126] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.569 (Best: 41.589 @epoch 468) +Forward: 38.33s + +Saving... +Total: 38.92s + +[Epoch 475] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.7+0.9s +[3200/16000] [L1: 0.0125] 25.7+0.0s +[4800/16000] [L1: 0.0126] 26.0+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0126] 25.9+0.0s +[9600/16000] [L1: 0.0126] 26.0+0.0s +[11200/16000] [L1: 0.0126] 26.2+0.0s +[12800/16000] [L1: 0.0127] 26.0+0.0s +[14400/16000] [L1: 0.0126] 26.1+0.0s +[16000/16000] [L1: 0.0126] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.525 (Best: 41.589 @epoch 468) +Forward: 38.49s + +Saving... +Total: 39.00s + +[Epoch 476] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.9+1.1s +[3200/16000] [L1: 0.0123] 25.9+0.1s +[4800/16000] [L1: 0.0124] 25.9+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 26.1+0.1s +[9600/16000] [L1: 0.0125] 26.2+0.1s +[11200/16000] [L1: 0.0125] 26.0+0.0s +[12800/16000] [L1: 0.0124] 26.1+0.0s +[14400/16000] [L1: 0.0124] 26.1+0.0s +[16000/16000] [L1: 0.0125] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.554 (Best: 41.589 @epoch 468) +Forward: 38.43s + +Saving... +Total: 38.96s + +[Epoch 477] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.6+1.1s +[3200/16000] [L1: 0.0124] 25.9+0.0s +[4800/16000] [L1: 0.0124] 25.8+0.0s +[6400/16000] [L1: 0.0125] 26.0+0.0s +[8000/16000] [L1: 0.0124] 25.9+0.0s +[9600/16000] [L1: 0.0125] 26.2+0.0s +[11200/16000] [L1: 0.0125] 25.9+0.0s +[12800/16000] [L1: 0.0124] 25.9+0.0s +[14400/16000] [L1: 0.0125] 26.0+0.0s +[16000/16000] [L1: 0.0125] 25.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.581 (Best: 41.589 @epoch 468) +Forward: 38.50s + +Saving... +Total: 38.98s + +[Epoch 478] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.9+1.0s +[3200/16000] [L1: 0.0124] 25.8+0.0s +[4800/16000] [L1: 0.0125] 26.1+0.0s +[6400/16000] [L1: 0.0126] 26.0+0.0s +[8000/16000] [L1: 0.0125] 25.9+0.0s +[9600/16000] [L1: 0.0125] 26.0+0.0s +[11200/16000] [L1: 0.0126] 26.0+0.0s +[12800/16000] [L1: 0.0125] 25.9+0.0s +[14400/16000] [L1: 0.0125] 26.1+0.0s +[16000/16000] [L1: 0.0125] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.533 (Best: 41.589 @epoch 468) +Forward: 38.43s + +Saving... +Total: 38.93s + +[Epoch 479] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.6+1.9s +[3200/16000] [L1: 0.0126] 25.6+0.0s +[4800/16000] [L1: 0.0127] 25.7+0.0s +[6400/16000] [L1: 0.0126] 26.1+0.0s +[8000/16000] [L1: 0.0125] 25.9+0.0s +[9600/16000] [L1: 0.0126] 25.6+0.0s +[11200/16000] [L1: 0.0125] 26.0+0.0s +[12800/16000] [L1: 0.0125] 26.0+0.0s +[14400/16000] [L1: 0.0125] 25.9+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.545 (Best: 41.589 @epoch 468) +Forward: 38.51s + +Saving... +Total: 39.00s + +[Epoch 480] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.6+0.9s +[3200/16000] [L1: 0.0125] 25.9+0.0s +[4800/16000] [L1: 0.0124] 25.7+0.0s +[6400/16000] [L1: 0.0124] 26.0+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0125] 25.9+0.0s +[11200/16000] [L1: 0.0124] 26.0+0.0s +[12800/16000] [L1: 0.0125] 26.2+0.0s +[14400/16000] [L1: 0.0125] 25.9+0.0s +[16000/16000] [L1: 0.0125] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.550 (Best: 41.589 @epoch 468) +Forward: 38.30s + +Saving... +Total: 38.80s + +[Epoch 481] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 26.0+0.9s +[3200/16000] [L1: 0.0126] 25.8+0.0s +[4800/16000] [L1: 0.0124] 26.1+0.0s +[6400/16000] [L1: 0.0124] 25.9+0.0s +[8000/16000] [L1: 0.0125] 26.0+0.0s +[9600/16000] [L1: 0.0125] 26.1+0.0s +[11200/16000] [L1: 0.0125] 26.0+0.0s +[12800/16000] [L1: 0.0125] 25.8+0.0s +[14400/16000] [L1: 0.0125] 26.1+0.0s +[16000/16000] [L1: 0.0126] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.572 (Best: 41.589 @epoch 468) +Forward: 38.57s + +Saving... +Total: 39.11s + +[Epoch 482] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.9+0.9s +[3200/16000] [L1: 0.0124] 26.0+0.0s +[4800/16000] [L1: 0.0124] 26.0+0.0s +[6400/16000] [L1: 0.0124] 26.3+0.0s +[8000/16000] [L1: 0.0124] 26.2+0.0s +[9600/16000] [L1: 0.0125] 25.9+0.0s +[11200/16000] [L1: 0.0124] 26.0+0.0s +[12800/16000] [L1: 0.0125] 26.1+0.0s +[14400/16000] [L1: 0.0125] 26.0+0.0s +[16000/16000] [L1: 0.0125] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.565 (Best: 41.589 @epoch 468) +Forward: 38.41s + +Saving... +Total: 38.92s + +[Epoch 483] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 25.9+1.0s +[3200/16000] [L1: 0.0125] 25.8+0.0s +[4800/16000] [L1: 0.0127] 25.8+0.0s +[6400/16000] [L1: 0.0125] 26.0+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0126] 26.0+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0126] 25.9+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.516 (Best: 41.589 @epoch 468) +Forward: 38.31s + +Saving... +Total: 38.80s + +[Epoch 484] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 26.1+1.0s +[3200/16000] [L1: 0.0128] 25.8+0.0s +[4800/16000] [L1: 0.0127] 26.0+0.0s +[6400/16000] [L1: 0.0126] 25.9+0.0s +[8000/16000] [L1: 0.0126] 26.0+0.0s +[9600/16000] [L1: 0.0126] 25.9+0.0s +[11200/16000] [L1: 0.0127] 26.0+0.0s +[12800/16000] [L1: 0.0127] 26.1+0.0s +[14400/16000] [L1: 0.0127] 26.1+0.0s +[16000/16000] [L1: 0.0127] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.536 (Best: 41.589 @epoch 468) +Forward: 38.43s + +Saving... +Total: 38.92s + +[Epoch 485] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.9+1.0s +[3200/16000] [L1: 0.0125] 25.8+0.0s +[4800/16000] [L1: 0.0124] 26.0+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0124] 25.8+0.0s +[9600/16000] [L1: 0.0124] 26.0+0.0s +[11200/16000] [L1: 0.0124] 26.2+0.0s +[12800/16000] [L1: 0.0125] 26.1+0.0s +[14400/16000] [L1: 0.0125] 26.0+0.0s +[16000/16000] [L1: 0.0125] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.528 (Best: 41.589 @epoch 468) +Forward: 38.36s + +Saving... +Total: 38.89s + +[Epoch 486] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.9+0.9s +[3200/16000] [L1: 0.0125] 25.9+0.0s +[4800/16000] [L1: 0.0126] 26.1+0.0s +[6400/16000] [L1: 0.0126] 26.0+0.0s +[8000/16000] [L1: 0.0125] 25.8+0.0s +[9600/16000] [L1: 0.0126] 26.1+0.0s +[11200/16000] [L1: 0.0126] 26.1+0.0s +[12800/16000] [L1: 0.0127] 25.8+0.0s +[14400/16000] [L1: 0.0126] 25.8+0.0s +[16000/16000] [L1: 0.0126] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.547 (Best: 41.589 @epoch 468) +Forward: 38.31s + +Saving... +Total: 38.85s + +[Epoch 487] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.6+1.3s +[3200/16000] [L1: 0.0125] 25.7+0.0s +[4800/16000] [L1: 0.0124] 26.0+0.0s +[6400/16000] [L1: 0.0124] 26.1+0.0s +[8000/16000] [L1: 0.0123] 25.8+0.0s +[9600/16000] [L1: 0.0124] 26.0+0.0s +[11200/16000] [L1: 0.0124] 26.1+0.0s +[12800/16000] [L1: 0.0125] 26.0+0.0s +[14400/16000] [L1: 0.0125] 26.1+0.0s +[16000/16000] [L1: 0.0125] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.527 (Best: 41.589 @epoch 468) +Forward: 38.36s + +Saving... +Total: 38.82s + +[Epoch 488] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 26.0+1.0s +[3200/16000] [L1: 0.0125] 26.0+0.0s +[4800/16000] [L1: 0.0126] 26.1+0.0s +[6400/16000] [L1: 0.0126] 26.1+0.0s +[8000/16000] [L1: 0.0126] 26.1+0.0s +[9600/16000] [L1: 0.0126] 26.1+0.0s +[11200/16000] [L1: 0.0125] 26.2+0.0s +[12800/16000] [L1: 0.0125] 26.2+0.0s +[14400/16000] [L1: 0.0125] 26.2+0.0s +[16000/16000] [L1: 0.0124] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.563 (Best: 41.589 @epoch 468) +Forward: 38.40s + +Saving... +Total: 38.86s + +[Epoch 489] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.9+1.0s +[3200/16000] [L1: 0.0125] 25.9+0.0s +[4800/16000] [L1: 0.0125] 26.0+0.0s +[6400/16000] [L1: 0.0127] 26.2+0.0s +[8000/16000] [L1: 0.0126] 26.2+0.0s +[9600/16000] [L1: 0.0125] 26.1+0.0s +[11200/16000] [L1: 0.0125] 26.2+0.0s +[12800/16000] [L1: 0.0124] 26.2+0.0s +[14400/16000] [L1: 0.0125] 26.0+0.0s +[16000/16000] [L1: 0.0125] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.561 (Best: 41.589 @epoch 468) +Forward: 38.36s + +Saving... +Total: 38.92s + +[Epoch 490] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 26.0+1.0s +[3200/16000] [L1: 0.0124] 25.9+0.0s +[4800/16000] [L1: 0.0124] 25.7+0.0s +[6400/16000] [L1: 0.0124] 26.1+0.0s +[8000/16000] [L1: 0.0125] 26.2+0.0s +[9600/16000] [L1: 0.0124] 26.1+0.0s +[11200/16000] [L1: 0.0124] 26.1+0.0s +[12800/16000] [L1: 0.0124] 26.1+0.0s +[14400/16000] [L1: 0.0124] 26.1+0.0s +[16000/16000] [L1: 0.0124] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.557 (Best: 41.589 @epoch 468) +Forward: 38.45s + +Saving... +Total: 38.94s + +[Epoch 491] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 26.0+1.0s +[3200/16000] [L1: 0.0123] 25.8+0.0s +[4800/16000] [L1: 0.0124] 25.9+0.0s +[6400/16000] [L1: 0.0124] 26.0+0.0s +[8000/16000] [L1: 0.0124] 26.1+0.0s +[9600/16000] [L1: 0.0124] 26.1+0.0s +[11200/16000] [L1: 0.0124] 26.1+0.0s +[12800/16000] [L1: 0.0124] 26.2+0.0s +[14400/16000] [L1: 0.0124] 26.1+0.0s +[16000/16000] [L1: 0.0124] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.558 (Best: 41.589 @epoch 468) +Forward: 38.49s + +Saving... +Total: 38.98s + +[Epoch 492] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.7+0.9s +[3200/16000] [L1: 0.0124] 26.0+0.0s +[4800/16000] [L1: 0.0125] 25.9+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0125] 26.1+0.0s +[9600/16000] [L1: 0.0126] 26.0+0.0s +[11200/16000] [L1: 0.0125] 26.0+0.0s +[12800/16000] [L1: 0.0125] 26.0+0.0s +[14400/16000] [L1: 0.0125] 25.9+0.0s +[16000/16000] [L1: 0.0124] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.573 (Best: 41.589 @epoch 468) +Forward: 38.57s + +Saving... +Total: 39.06s + +[Epoch 493] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 25.9+0.9s +[3200/16000] [L1: 0.0123] 25.9+0.0s +[4800/16000] [L1: 0.0124] 26.0+0.0s +[6400/16000] [L1: 0.0124] 26.1+0.0s +[8000/16000] [L1: 0.0124] 26.4+0.0s +[9600/16000] [L1: 0.0125] 26.2+0.0s +[11200/16000] [L1: 0.0124] 26.4+0.0s +[12800/16000] [L1: 0.0125] 26.0+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0124] 26.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.558 (Best: 41.589 @epoch 468) +Forward: 38.41s + +Saving... +Total: 39.00s + +[Epoch 494] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0120] 26.0+0.9s +[3200/16000] [L1: 0.0123] 26.1+0.1s +[4800/16000] [L1: 0.0123] 26.2+0.0s +[6400/16000] [L1: 0.0125] 26.4+0.1s +[8000/16000] [L1: 0.0125] 26.2+0.0s +[9600/16000] [L1: 0.0124] 26.2+0.0s +[11200/16000] [L1: 0.0124] 26.3+0.0s +[12800/16000] [L1: 0.0124] 26.1+0.0s +[14400/16000] [L1: 0.0125] 26.4+0.0s +[16000/16000] [L1: 0.0125] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.539 (Best: 41.589 @epoch 468) +Forward: 38.32s + +Saving... +Total: 38.80s + +[Epoch 495] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.7+1.0s +[3200/16000] [L1: 0.0125] 25.9+0.0s +[4800/16000] [L1: 0.0126] 25.8+0.0s +[6400/16000] [L1: 0.0126] 26.1+0.0s +[8000/16000] [L1: 0.0126] 25.9+0.0s +[9600/16000] [L1: 0.0125] 25.9+0.0s +[11200/16000] [L1: 0.0125] 25.9+0.0s +[12800/16000] [L1: 0.0125] 25.9+0.0s +[14400/16000] [L1: 0.0124] 26.1+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.575 (Best: 41.589 @epoch 468) +Forward: 38.42s + +Saving... +Total: 38.93s + +[Epoch 496] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0121] 26.0+0.9s +[3200/16000] [L1: 0.0124] 25.8+0.0s +[4800/16000] [L1: 0.0123] 26.0+0.0s +[6400/16000] [L1: 0.0124] 26.0+0.0s +[8000/16000] [L1: 0.0123] 26.3+0.0s +[9600/16000] [L1: 0.0123] 26.0+0.0s +[11200/16000] [L1: 0.0124] 26.0+0.0s +[12800/16000] [L1: 0.0124] 26.2+0.0s +[14400/16000] [L1: 0.0124] 26.3+0.0s +[16000/16000] [L1: 0.0125] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.532 (Best: 41.589 @epoch 468) +Forward: 38.34s + +Saving... +Total: 38.81s + +[Epoch 497] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0129] 25.8+0.9s +[3200/16000] [L1: 0.0126] 25.8+0.0s +[4800/16000] [L1: 0.0127] 25.7+0.0s +[6400/16000] [L1: 0.0125] 25.8+0.0s +[8000/16000] [L1: 0.0125] 25.7+0.0s +[9600/16000] [L1: 0.0125] 26.1+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0125] 25.9+0.0s +[14400/16000] [L1: 0.0125] 25.8+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.563 (Best: 41.589 @epoch 468) +Forward: 38.57s + +Saving... +Total: 39.04s + +[Epoch 498] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.8+0.9s +[3200/16000] [L1: 0.0123] 25.8+0.0s +[4800/16000] [L1: 0.0124] 26.2+0.0s +[6400/16000] [L1: 0.0125] 40.5+0.0s +[8000/16000] [L1: 0.0125] 34.9+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0124] 25.6+0.0s +[12800/16000] [L1: 0.0124] 26.0+0.0s +[14400/16000] [L1: 0.0124] 41.8+0.0s +[16000/16000] [L1: 0.0124] 58.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.547 (Best: 41.589 @epoch 468) +Forward: 37.65s + +Saving... +Total: 38.10s + +[Epoch 499] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 60.4+1.0s +[3200/16000] [L1: 0.0123] 59.9+0.0s +[4800/16000] [L1: 0.0125] 60.0+0.0s +[6400/16000] [L1: 0.0125] 60.3+0.0s +[8000/16000] [L1: 0.0125] 52.3+0.0s +[9600/16000] [L1: 0.0125] 25.6+0.0s +[11200/16000] [L1: 0.0125] 25.8+0.0s +[12800/16000] [L1: 0.0125] 25.9+0.0s +[14400/16000] [L1: 0.0125] 25.6+0.0s +[16000/16000] [L1: 0.0124] 25.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.560 (Best: 41.589 @epoch 468) +Forward: 38.36s + +Saving... +Total: 38.86s + +[Epoch 500] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 26.2+1.0s +[3200/16000] [L1: 0.0126] 25.8+0.0s +[4800/16000] [L1: 0.0125] 26.1+0.1s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0125] 26.1+0.0s +[9600/16000] [L1: 0.0125] 26.1+0.0s +[11200/16000] [L1: 0.0125] 25.9+0.0s +[12800/16000] [L1: 0.0125] 26.1+0.0s +[14400/16000] [L1: 0.0125] 26.3+0.0s +[16000/16000] [L1: 0.0125] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.516 (Best: 41.589 @epoch 468) +Forward: 38.41s + +Saving... +Total: 38.90s + +[Epoch 501] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0127] 26.0+1.0s +[3200/16000] [L1: 0.0125] 25.9+0.0s +[4800/16000] [L1: 0.0126] 25.7+0.0s +[6400/16000] [L1: 0.0125] 26.0+0.0s +[8000/16000] [L1: 0.0125] 26.1+0.0s +[9600/16000] [L1: 0.0125] 26.0+0.0s +[11200/16000] [L1: 0.0125] 25.9+0.0s +[12800/16000] [L1: 0.0125] 26.1+0.0s +[14400/16000] [L1: 0.0125] 25.9+0.0s +[16000/16000] [L1: 0.0125] 25.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.562 (Best: 41.589 @epoch 468) +Forward: 38.43s + +Saving... +Total: 38.92s + +[Epoch 502] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 26.0+1.0s +[3200/16000] [L1: 0.0126] 26.1+0.0s +[4800/16000] [L1: 0.0125] 26.2+0.0s +[6400/16000] [L1: 0.0125] 26.2+0.0s +[8000/16000] [L1: 0.0125] 26.0+0.0s +[9600/16000] [L1: 0.0124] 26.1+0.0s +[11200/16000] [L1: 0.0124] 26.3+0.0s +[12800/16000] [L1: 0.0124] 26.1+0.0s +[14400/16000] [L1: 0.0125] 26.2+0.0s +[16000/16000] [L1: 0.0125] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.559 (Best: 41.589 @epoch 468) +Forward: 38.41s + +Saving... +Total: 38.98s + +[Epoch 503] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0125] 26.1+0.9s +[3200/16000] [L1: 0.0125] 25.9+0.0s +[4800/16000] [L1: 0.0124] 25.9+0.0s +[6400/16000] [L1: 0.0125] 25.9+0.0s +[8000/16000] [L1: 0.0125] 26.3+0.0s +[9600/16000] [L1: 0.0125] 26.0+0.0s +[11200/16000] [L1: 0.0125] 26.1+0.0s +[12800/16000] [L1: 0.0125] 26.0+0.0s +[14400/16000] [L1: 0.0125] 25.9+0.0s +[16000/16000] [L1: 0.0124] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.555 (Best: 41.589 @epoch 468) +Forward: 38.39s + +Saving... +Total: 38.85s + +[Epoch 504] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 26.0+1.0s +[3200/16000] [L1: 0.0125] 25.7+0.0s +[4800/16000] [L1: 0.0125] 26.0+0.0s +[6400/16000] [L1: 0.0126] 26.1+0.0s +[8000/16000] [L1: 0.0125] 25.9+0.0s +[9600/16000] [L1: 0.0125] 26.0+0.0s +[11200/16000] [L1: 0.0126] 25.9+0.0s +[12800/16000] [L1: 0.0125] 26.3+0.0s +[14400/16000] [L1: 0.0125] 26.1+0.0s +[16000/16000] [L1: 0.0125] 25.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.552 (Best: 41.589 @epoch 468) +Forward: 38.42s + +Saving... +Total: 38.88s + +[Epoch 505] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0124] 25.8+0.9s +[3200/16000] [L1: 0.0125] 26.1+0.0s +[4800/16000] [L1: 0.0125] 26.1+0.0s +[6400/16000] [L1: 0.0126] 26.0+0.0s +[8000/16000] [L1: 0.0124] 26.2+0.0s +[9600/16000] [L1: 0.0124] 26.2+0.0s +[11200/16000] [L1: 0.0124] 26.2+0.0s +[12800/16000] [L1: 0.0125] 26.1+0.0s +[14400/16000] [L1: 0.0124] 26.1+0.0s +[16000/16000] [L1: 0.0125] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.594 (Best: 41.594 @epoch 505) +Forward: 38.45s + +Saving... +Total: 39.00s + +[Epoch 506] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0123] 25.8+1.0s +[3200/16000] [L1: 0.0124] 25.9+0.0s +[4800/16000] [L1: 0.0124] 25.9+0.0s +[6400/16000] [L1: 0.0123] 26.1+0.0s +[8000/16000] [L1: 0.0123] 26.0+0.0s +[9600/16000] [L1: 0.0123] 26.0+0.0s +[11200/16000] [L1: 0.0124] 26.1+0.0s +[12800/16000] [L1: 0.0124] 26.2+0.0s +[14400/16000] [L1: 0.0124] 26.0+0.0s +[16000/16000] [L1: 0.0124] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.567 (Best: 41.594 @epoch 505) +Forward: 38.37s + +Saving... +Total: 38.97s + +[Epoch 507] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 25.8+1.0s +[3200/16000] [L1: 0.0124] 25.8+0.0s +[4800/16000] [L1: 0.0123] 26.1+0.0s +[6400/16000] [L1: 0.0124] 26.0+0.0s +[8000/16000] [L1: 0.0125] 26.2+0.0s +[9600/16000] [L1: 0.0124] 26.1+0.0s +[11200/16000] [L1: 0.0125] 26.3+0.0s +[12800/16000] [L1: 0.0125] 26.3+0.0s +[14400/16000] [L1: 0.0126] 26.1+0.0s +[16000/16000] [L1: 0.0126] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.564 (Best: 41.594 @epoch 505) +Forward: 38.66s + +Saving... +Total: 39.12s + +[Epoch 508] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0122] 26.2+1.0s +[3200/16000] [L1: 0.0124] 26.3+0.0s +[4800/16000] [L1: 0.0124] 26.5+0.0s +[6400/16000] [L1: 0.0124] 26.5+0.0s +[8000/16000] [L1: 0.0125] 26.5+0.0s +[9600/16000] [L1: 0.0125] 26.5+0.0s +[11200/16000] [L1: 0.0125] 26.3+0.0s +[12800/16000] [L1: 0.0125] 26.2+0.0s +[14400/16000] [L1: 0.0125] 26.0+0.0s +[16000/16000] [L1: 0.0125] 25.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.571 (Best: 41.594 @epoch 505) +Forward: 38.41s + +Saving... +Total: 38.90s + +[Epoch 509] Learning rate: 1.25e-5 +[1600/16000] [L1: 0.0126] 26.0+0.9s +[3200/16000] [L1: 0.0124] 26.0+0.0s +[4800/16000] [L1: 0.0125] 25.7+0.0s +[6400/16000] [L1: 0.0124] 25.8+0.0s +[8000/16000] [L1: 0.0126] 25.9+0.0s +[9600/16000] [L1: 0.0125] 26.0+0.0s +[11200/16000] [L1: 0.0125] 26.0+0.0s +[12800/16000] [L1: 0.0125] 26.0+0.0s +[14400/16000] [L1: 0.0125] 25.9+0.0s +[16000/16000] [L1: 0.0125] 25.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.569 (Best: 41.594 @epoch 505) +Forward: 38.39s + +Saving... +Total: 38.85s + diff --git a/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/loss.pt b/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/loss.pt new file mode 100644 index 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--git a/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/test_DIV2K.pdf b/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/test_DIV2K.pdf new file mode 100644 index 0000000000000000000000000000000000000000..88189c74b82024e08edc528a7b90538414ed2dcd Binary files /dev/null and b/Demosaic/experiment/RAFTS_DEMOSAIC20_R4/test_DIV2K.pdf differ diff --git a/Demosaic/experiment/RAFTS_layer_DEMOSAIC20_R4/config.txt b/Demosaic/experiment/RAFTS_layer_DEMOSAIC20_R4/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..a37df2b5e3832699153386115f43bd1642774133 --- /dev/null +++ b/Demosaic/experiment/RAFTS_layer_DEMOSAIC20_R4/config.txt @@ -0,0 +1,68 @@ +2020-11-11-00:33:45 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETLAYER +act: relu +pre_train: . +extend: . +n_resblocks: 10 +recurrence: 5 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +normalization: instance +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.2 +decay_gamma: 0.8 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFTS_layer_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/RAFTS_layer_DEMOSAIC20_R4/log.txt b/Demosaic/experiment/RAFTS_layer_DEMOSAIC20_R4/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..647f8d5e2278257df8f8feb382c7e80c24aa44d4 --- /dev/null +++ b/Demosaic/experiment/RAFTS_layer_DEMOSAIC20_R4/log.txt @@ -0,0 +1,1157 @@ +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) + (norm_v): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 1.9416] 60.8+0.8s +[3200/16000] [L1: 1.3740] 57.6+0.1s +[4800/16000] [L1: 1.0867] 59.8+0.1s +[6400/16000] [L1: 0.9212] 59.4+0.0s +[8000/16000] [L1: 0.8097] 58.8+0.0s +[9600/16000] [L1: 0.7284] 59.5+0.0s +[11200/16000] [L1: 0.6659] 59.7+0.1s +[12800/16000] [L1: 0.6158] 58.6+0.0s +[14400/16000] [L1: 0.5746] 59.6+0.0s +[16000/16000] [L1: 0.5410] 58.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.078 (Best: 8.078 @epoch 1) +Forward: 42.83s + +Saving... +Total: 43.69s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2209] 59.8+1.0s +[3200/16000] [L1: 0.2146] 59.3+0.0s +[4800/16000] [L1: 0.2096] 59.3+0.1s +[6400/16000] [L1: 0.2059] 59.1+0.0s +[8000/16000] [L1: 0.2021] 59.7+0.1s +[9600/16000] [L1: 0.1979] 59.9+0.1s +[11200/16000] [L1: 0.1948] 60.4+0.1s +[12800/16000] [L1: 0.1917] 59.3+0.1s +[14400/16000] [L1: 0.1885] 59.2+0.0s +[16000/16000] [L1: 0.1855] 59.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.891 (Best: 13.891 @epoch 2) +Forward: 42.48s + +Saving... +Total: 43.06s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1474] 60.1+1.0s +[3200/16000] [L1: 0.1462] 58.7+0.0s +[4800/16000] [L1: 0.1443] 59.4+0.1s +[6400/16000] [L1: 0.1446] 58.5+0.0s +[8000/16000] [L1: 0.1416] 59.5+0.1s +[9600/16000] [L1: 0.1394] 58.2+0.0s +[11200/16000] [L1: 0.1379] 58.2+0.0s +[12800/16000] [L1: 0.1359] 59.4+0.0s +[14400/16000] [L1: 0.1341] 59.8+0.1s +[16000/16000] [L1: 0.1324] 58.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.917 (Best: 17.917 @epoch 3) +Forward: 42.74s + +Saving... +Total: 43.28s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1140] 59.7+0.8s +[3200/16000] [L1: 0.1130] 59.9+0.0s +[4800/16000] [L1: 0.1155] 59.6+0.1s +[6400/16000] [L1: 0.1138] 59.8+0.0s +[8000/16000] [L1: 0.1118] 60.0+0.0s +[9600/16000] [L1: 0.1102] 59.8+0.0s +[11200/16000] [L1: 0.1090] 59.1+0.0s +[12800/16000] [L1: 0.1088] 59.4+0.1s +[14400/16000] [L1: 0.1073] 59.2+0.0s +[16000/16000] [L1: 0.1062] 59.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.591 (Best: 21.591 @epoch 4) +Forward: 42.36s + +Saving... +Total: 42.93s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1000] 60.0+0.9s +[3200/16000] [L1: 0.0955] 58.8+0.0s +[4800/16000] [L1: 0.0924] 59.4+0.1s +[6400/16000] [L1: 0.0905] 59.9+0.0s +[8000/16000] [L1: 0.0900] 59.5+0.0s +[9600/16000] [L1: 0.0892] 58.7+0.0s +[11200/16000] [L1: 0.0891] 59.4+0.1s +[12800/16000] [L1: 0.0883] 60.1+0.1s +[14400/16000] [L1: 0.0874] 59.4+0.1s +[16000/16000] [L1: 0.0862] 59.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.355 (Best: 24.355 @epoch 5) +Forward: 42.51s + +Saving... +Total: 43.02s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0840] 59.3+0.9s +[3200/16000] [L1: 0.0788] 59.0+0.0s +[4800/16000] [L1: 0.0767] 59.8+0.1s +[6400/16000] [L1: 0.0763] 58.3+0.1s +[8000/16000] [L1: 0.0761] 59.3+0.0s +[9600/16000] [L1: 0.0760] 59.6+0.1s +[11200/16000] [L1: 0.0747] 58.9+0.0s +[12800/16000] [L1: 0.0739] 59.8+0.1s +[14400/16000] [L1: 0.0732] 58.9+0.1s +[16000/16000] [L1: 0.0725] 58.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.501 (Best: 26.501 @epoch 6) +Forward: 42.43s + +Saving... +Total: 43.19s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0645] 59.5+0.9s +[3200/16000] [L1: 0.0651] 58.5+0.1s +[4800/16000] [L1: 0.0661] 58.7+0.0s +[6400/16000] [L1: 0.0659] 59.0+0.0s +[8000/16000] [L1: 0.0650] 58.7+0.1s +[9600/16000] [L1: 0.0650] 59.4+0.0s +[11200/16000] [L1: 0.0637] 58.8+0.1s +[12800/16000] [L1: 0.0632] 59.7+0.0s +[14400/16000] [L1: 0.0631] 59.3+0.0s +[16000/16000] [L1: 0.0625] 58.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.771 (Best: 28.771 @epoch 7) +Forward: 42.41s + +Saving... +Total: 42.97s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0610] 59.5+0.9s +[3200/16000] [L1: 0.0595] 59.1+0.1s +[4800/16000] [L1: 0.0596] 59.3+0.1s +[6400/16000] [L1: 0.0579] 59.5+0.1s +[8000/16000] [L1: 0.0575] 59.4+0.0s +[9600/16000] [L1: 0.0565] 59.1+0.0s +[11200/16000] [L1: 0.0562] 59.7+0.1s +[12800/16000] [L1: 0.0557] 59.3+0.0s +[14400/16000] [L1: 0.0553] 59.5+0.1s +[16000/16000] [L1: 0.0545] 58.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.119 (Best: 30.119 @epoch 8) +Forward: 42.44s + +Saving... +Total: 43.14s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0498] 60.0+0.9s +[3200/16000] [L1: 0.0494] 59.2+0.0s +[4800/16000] [L1: 0.0503] 59.6+0.1s +[6400/16000] [L1: 0.0500] 58.8+0.0s +[8000/16000] [L1: 0.0503] 59.2+0.1s +[9600/16000] [L1: 0.0498] 59.0+0.1s +[11200/16000] [L1: 0.0488] 59.1+0.0s +[12800/16000] [L1: 0.0488] 59.1+0.0s +[14400/16000] [L1: 0.0484] 59.3+0.1s +[16000/16000] [L1: 0.0480] 59.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.497 (Best: 30.497 @epoch 9) +Forward: 42.51s + +Saving... +Total: 43.15s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0474] 59.7+1.0s +[3200/16000] [L1: 0.0456] 59.1+0.0s +[4800/16000] [L1: 0.0456] 58.7+0.1s +[6400/16000] [L1: 0.0446] 59.5+0.1s +[8000/16000] [L1: 0.0441] 59.2+0.0s +[9600/16000] [L1: 0.0441] 59.2+0.1s +[11200/16000] [L1: 0.0439] 59.3+0.1s +[12800/16000] [L1: 0.0438] 58.7+0.0s +[14400/16000] [L1: 0.0433] 58.6+0.1s +[16000/16000] [L1: 0.0430] 59.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.052 (Best: 33.052 @epoch 10) +Forward: 42.34s + +Saving... +Total: 42.92s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0413] 59.3+1.0s +[3200/16000] [L1: 0.0412] 58.8+0.0s +[4800/16000] [L1: 0.0416] 59.0+0.0s +[6400/16000] [L1: 0.0418] 59.1+0.1s +[8000/16000] [L1: 0.0420] 59.6+0.0s +[9600/16000] [L1: 0.0419] 59.1+0.0s +[11200/16000] [L1: 0.0417] 59.7+0.1s +[12800/16000] [L1: 0.0414] 59.4+0.0s +[14400/16000] [L1: 0.0413] 58.8+0.0s +[16000/16000] [L1: 0.0411] 59.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.592 (Best: 33.592 @epoch 11) +Forward: 42.32s + +Saving... +Total: 42.79s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0383] 59.7+1.1s +[3200/16000] [L1: 0.0382] 59.6+0.1s +[4800/16000] [L1: 0.0389] 58.2+0.0s +[6400/16000] [L1: 0.0391] 59.0+0.0s +[8000/16000] [L1: 0.0392] 59.5+0.0s +[9600/16000] [L1: 0.0389] 58.7+0.1s +[11200/16000] [L1: 0.0386] 58.6+0.0s +[12800/16000] [L1: 0.0383] 59.0+0.0s +[14400/16000] [L1: 0.0382] 60.2+0.1s +[16000/16000] [L1: 0.0380] 58.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.039 (Best: 34.039 @epoch 12) +Forward: 42.50s + +Saving... +Total: 43.02s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0363] 59.5+1.0s +[3200/16000] [L1: 0.0367] 58.7+0.1s +[4800/16000] [L1: 0.0364] 59.1+0.1s +[6400/16000] [L1: 0.0366] 59.6+0.1s +[8000/16000] [L1: 0.0364] 59.4+0.0s +[9600/16000] [L1: 0.0363] 58.9+0.0s +[11200/16000] [L1: 0.0361] 59.4+0.1s +[12800/16000] [L1: 0.0360] 59.6+0.1s +[14400/16000] [L1: 0.0359] 58.8+0.0s +[16000/16000] [L1: 0.0361] 59.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.405 (Best: 34.405 @epoch 13) +Forward: 42.65s + +Saving... +Total: 43.22s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0360] 60.5+1.0s +[3200/16000] [L1: 0.0358] 58.3+0.0s +[4800/16000] [L1: 0.0358] 58.3+0.1s +[6400/16000] [L1: 0.0357] 59.4+0.1s +[8000/16000] [L1: 0.0358] 59.5+0.1s +[9600/16000] [L1: 0.0357] 59.0+0.0s +[11200/16000] [L1: 0.0353] 59.4+0.1s +[12800/16000] [L1: 0.0352] 59.8+0.1s +[14400/16000] [L1: 0.0352] 58.8+0.0s +[16000/16000] [L1: 0.0351] 58.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.502 (Best: 35.502 @epoch 14) +Forward: 42.49s + +Saving... +Total: 42.94s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0356] 60.0+0.8s +[3200/16000] [L1: 0.0353] 59.1+0.1s +[4800/16000] [L1: 0.0343] 58.6+0.0s +[6400/16000] [L1: 0.0344] 59.5+0.1s +[8000/16000] [L1: 0.0346] 58.7+0.0s +[9600/16000] [L1: 0.0345] 58.9+0.1s +[11200/16000] [L1: 0.0347] 58.9+0.0s +[12800/16000] [L1: 0.0344] 58.7+0.1s +[14400/16000] [L1: 0.0345] 60.1+0.1s +[16000/16000] [L1: 0.0346] 59.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 35.552 (Best: 35.552 @epoch 15) +Forward: 42.55s + +Saving... +Total: 43.06s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0322] 59.6+0.9s +[3200/16000] [L1: 0.0334] 59.3+0.1s +[4800/16000] [L1: 0.0334] 58.9+0.0s +[6400/16000] [L1: 0.0330] 59.1+0.1s +[8000/16000] [L1: 0.0330] 59.5+0.0s +[9600/16000] [L1: 0.0331] 59.4+0.1s +[11200/16000] [L1: 0.0329] 59.9+0.1s +[12800/16000] [L1: 0.0328] 59.4+0.1s +[14400/16000] [L1: 0.0329] 59.4+0.0s +[16000/16000] [L1: 0.0329] 59.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 36.399 (Best: 36.399 @epoch 16) +Forward: 42.57s + +Saving... +Total: 43.15s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0316] 59.4+1.0s +[3200/16000] [L1: 0.0314] 59.3+0.1s +[4800/16000] [L1: 0.0315] 58.9+0.0s +[6400/16000] [L1: 0.0317] 59.4+0.0s +[8000/16000] [L1: 0.0318] 59.7+0.1s +[9600/16000] [L1: 0.0321] 59.1+0.0s +[11200/16000] [L1: 0.0321] 59.4+0.0s +[12800/16000] [L1: 0.0323] 59.2+0.1s +[14400/16000] [L1: 0.0322] 59.4+0.0s +[16000/16000] [L1: 0.0321] 58.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.908 (Best: 36.908 @epoch 17) +Forward: 42.47s + +Saving... +Total: 42.99s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0322] 60.4+1.0s +[3200/16000] [L1: 0.0310] 58.7+0.1s +[4800/16000] [L1: 0.0305] 58.9+0.1s +[6400/16000] [L1: 0.0306] 59.4+0.1s +[8000/16000] [L1: 0.0305] 59.2+0.1s +[9600/16000] [L1: 0.0304] 60.0+0.1s +[11200/16000] [L1: 0.0305] 59.1+0.1s +[12800/16000] [L1: 0.0305] 58.9+0.0s +[14400/16000] [L1: 0.0302] 59.4+0.0s +[16000/16000] [L1: 0.0300] 58.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.883 (Best: 36.908 @epoch 17) +Forward: 42.48s + +Saving... +Total: 42.94s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0291] 59.5+0.9s +[3200/16000] [L1: 0.0298] 58.5+0.1s +[4800/16000] [L1: 0.0297] 59.5+0.1s +[6400/16000] [L1: 0.0297] 59.3+0.0s +[8000/16000] [L1: 0.0295] 59.6+0.0s +[9600/16000] [L1: 0.0296] 60.3+0.1s +[11200/16000] [L1: 0.0293] 59.8+0.0s +[12800/16000] [L1: 0.0294] 59.9+0.1s +[14400/16000] [L1: 0.0293] 59.4+0.1s +[16000/16000] [L1: 0.0292] 58.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.307 (Best: 37.307 @epoch 19) +Forward: 42.38s + +Saving... +Total: 42.91s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0288] 59.7+1.1s +[3200/16000] [L1: 0.0294] 59.5+0.1s +[4800/16000] [L1: 0.0293] 59.1+0.1s +[6400/16000] [L1: 0.0295] 59.7+0.1s +[8000/16000] [L1: 0.0292] 59.1+0.1s +[9600/16000] [L1: 0.0291] 59.2+0.1s +[11200/16000] [L1: 0.0291] 58.5+0.0s +[12800/16000] [L1: 0.0289] 60.0+0.1s +[14400/16000] [L1: 0.0288] 59.0+0.1s +[16000/16000] [L1: 0.0288] 59.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.219 (Best: 37.307 @epoch 19) +Forward: 42.38s + +Saving... +Total: 42.84s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0277] 59.6+1.0s +[3200/16000] [L1: 0.0289] 59.3+0.1s +[4800/16000] [L1: 0.0290] 59.7+0.1s +[6400/16000] [L1: 0.0289] 58.9+0.1s +[8000/16000] [L1: 0.0285] 59.7+0.1s +[9600/16000] [L1: 0.0284] 59.1+0.1s +[11200/16000] [L1: 0.0282] 59.0+0.0s +[12800/16000] [L1: 0.0283] 60.2+0.1s +[14400/16000] [L1: 0.0282] 59.0+0.0s +[16000/16000] [L1: 0.0281] 58.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.946 (Best: 37.946 @epoch 21) +Forward: 42.42s + +Saving... +Total: 42.92s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0270] 59.9+0.8s +[3200/16000] [L1: 0.0272] 58.7+0.1s +[4800/16000] [L1: 0.0272] 58.7+0.1s +[6400/16000] [L1: 0.0270] 59.5+0.1s +[8000/16000] [L1: 0.0274] 59.2+0.1s +[9600/16000] [L1: 0.0276] 59.2+0.1s +[11200/16000] [L1: 0.0274] 58.6+0.0s +[12800/16000] [L1: 0.0272] 59.6+0.0s +[14400/16000] [L1: 0.0271] 58.6+0.0s +[16000/16000] [L1: 0.0271] 58.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.382 (Best: 37.946 @epoch 21) +Forward: 42.48s + +Saving... +Total: 43.00s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0256] 59.3+1.1s +[3200/16000] [L1: 0.0266] 58.9+0.0s +[4800/16000] [L1: 0.0267] 58.2+0.0s +[6400/16000] [L1: 0.0264] 57.9+0.0s +[8000/16000] [L1: 0.0263] 58.8+0.1s +[9600/16000] [L1: 0.0266] 58.7+0.0s +[11200/16000] [L1: 0.0269] 59.3+0.1s +[12800/16000] [L1: 0.0267] 59.8+0.1s +[14400/16000] [L1: 0.0265] 59.4+0.1s +[16000/16000] [L1: 0.0263] 59.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.610 (Best: 37.946 @epoch 21) +Forward: 42.63s + +Saving... +Total: 43.11s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0265] 59.6+0.9s +[3200/16000] [L1: 0.0256] 59.9+0.1s +[4800/16000] [L1: 0.0252] 59.0+0.0s +[6400/16000] [L1: 0.0254] 59.0+0.0s +[8000/16000] [L1: 0.0257] 58.3+0.0s +[9600/16000] [L1: 0.0256] 58.6+0.0s +[11200/16000] [L1: 0.0257] 59.6+0.1s +[12800/16000] [L1: 0.0258] 58.6+0.0s +[14400/16000] [L1: 0.0259] 58.9+0.0s +[16000/16000] [L1: 0.0260] 59.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 37.993 (Best: 37.993 @epoch 24) +Forward: 42.43s + +Saving... +Total: 43.00s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0256] 59.2+0.9s +[3200/16000] [L1: 0.0253] 58.5+0.0s +[4800/16000] [L1: 0.0256] 59.3+0.1s +[6400/16000] [L1: 0.0253] 60.1+0.1s +[8000/16000] [L1: 0.0252] 60.6+0.1s +[9600/16000] [L1: 0.0252] 61.1+0.1s +[11200/16000] [L1: 0.0252] 60.5+0.1s +[12800/16000] [L1: 0.0252] 60.4+0.1s +[14400/16000] [L1: 0.0251] 59.3+0.0s +[16000/16000] [L1: 0.0252] 58.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.948 (Best: 38.948 @epoch 25) +Forward: 42.62s + +Saving... +Total: 43.56s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0259] 60.7+0.9s +[3200/16000] [L1: 0.0256] 61.0+0.1s +[4800/16000] [L1: 0.0254] 58.9+0.0s +[6400/16000] [L1: 0.0255] 59.8+0.1s +[8000/16000] [L1: 0.0253] 58.9+0.0s +[9600/16000] [L1: 0.0253] 58.9+0.0s +[11200/16000] [L1: 0.0252] 60.2+0.1s +[12800/16000] [L1: 0.0252] 60.7+0.1s +[14400/16000] [L1: 0.0251] 60.4+0.1s +[16000/16000] [L1: 0.0250] 59.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.894 (Best: 38.948 @epoch 25) +Forward: 42.26s + +Saving... +Total: 42.78s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0236] 60.2+0.9s +[3200/16000] [L1: 0.0239] 59.1+0.1s +[4800/16000] [L1: 0.0242] 60.8+0.1s +[6400/16000] [L1: 0.0247] 59.5+0.0s +[8000/16000] [L1: 0.0248] 59.7+0.1s +[9600/16000] [L1: 0.0247] 59.1+0.1s +[11200/16000] [L1: 0.0248] 58.4+0.0s +[12800/16000] [L1: 0.0247] 60.1+0.1s +[14400/16000] [L1: 0.0245] 60.3+0.1s +[16000/16000] [L1: 0.0245] 60.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.943 (Best: 38.948 @epoch 25) +Forward: 42.41s + +Saving... +Total: 42.96s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0227] 59.9+1.0s +[3200/16000] [L1: 0.0231] 59.8+0.1s +[4800/16000] [L1: 0.0235] 58.7+0.0s +[6400/16000] [L1: 0.0239] 60.1+0.1s +[8000/16000] [L1: 0.0239] 60.1+0.1s +[9600/16000] [L1: 0.0239] 60.6+0.1s +[11200/16000] [L1: 0.0242] 59.9+0.1s +[12800/16000] [L1: 0.0242] 61.0+0.1s +[14400/16000] [L1: 0.0240] 61.1+0.1s +[16000/16000] [L1: 0.0240] 60.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.224 (Best: 39.224 @epoch 28) +Forward: 42.41s + +Saving... +Total: 42.86s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0247] 60.0+1.0s +[3200/16000] [L1: 0.0243] 60.3+0.1s +[4800/16000] [L1: 0.0242] 60.0+0.1s +[6400/16000] [L1: 0.0241] 60.8+0.1s +[8000/16000] [L1: 0.0241] 60.0+0.1s +[9600/16000] [L1: 0.0240] 60.7+0.1s +[11200/16000] [L1: 0.0238] 60.7+0.1s +[12800/16000] [L1: 0.0240] 61.1+0.1s +[14400/16000] [L1: 0.0239] 60.1+0.1s +[16000/16000] [L1: 0.0239] 60.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.842 (Best: 39.224 @epoch 28) +Forward: 42.40s + +Saving... +Total: 42.84s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0237] 60.1+0.9s +[3200/16000] [L1: 0.0236] 59.2+0.0s +[4800/16000] [L1: 0.0239] 61.2+0.1s +[6400/16000] [L1: 0.0239] 60.6+0.1s +[8000/16000] [L1: 0.0242] 60.6+0.1s +[9600/16000] [L1: 0.0242] 60.8+0.1s +[11200/16000] [L1: 0.0241] 59.8+0.1s +[12800/16000] [L1: 0.0240] 59.5+0.1s +[14400/16000] [L1: 0.0239] 59.7+0.1s +[16000/16000] [L1: 0.0239] 60.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 38.921 (Best: 39.224 @epoch 28) +Forward: 42.33s + +Saving... +Total: 42.87s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0237] 60.8+0.9s +[3200/16000] [L1: 0.0229] 58.2+0.0s +[4800/16000] [L1: 0.0231] 59.6+0.1s +[6400/16000] [L1: 0.0240] 60.3+0.1s +[8000/16000] [L1: 0.0240] 59.3+0.0s +[9600/16000] [L1: 0.0238] 59.1+0.0s +[11200/16000] [L1: 0.0237] 60.1+0.1s +[12800/16000] [L1: 0.0236] 60.3+0.1s +[14400/16000] [L1: 0.0235] 60.3+0.1s +[16000/16000] [L1: 0.0234] 60.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.305 (Best: 39.305 @epoch 31) +Forward: 42.67s + +Saving... +Total: 43.20s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0232] 60.6+0.9s +[3200/16000] [L1: 0.0232] 60.7+0.1s +[4800/16000] [L1: 0.0233] 59.7+0.1s +[6400/16000] [L1: 0.0230] 59.0+0.1s +[8000/16000] [L1: 0.0229] 61.3+0.1s +[9600/16000] [L1: 0.0230] 60.4+0.1s +[11200/16000] [L1: 0.0231] 60.6+0.1s +[12800/16000] [L1: 0.0231] 59.2+0.1s +[14400/16000] [L1: 0.0230] 59.4+0.1s +[16000/16000] [L1: 0.0229] 58.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.096 (Best: 39.305 @epoch 31) +Forward: 42.56s + +Saving... +Total: 43.02s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0222] 60.0+1.0s +[3200/16000] [L1: 0.0229] 59.5+0.1s +[4800/16000] [L1: 0.0225] 59.2+0.1s +[6400/16000] [L1: 0.0221] 59.5+0.1s +[8000/16000] [L1: 0.0222] 59.8+0.1s +[9600/16000] [L1: 0.0222] 59.8+0.1s +[11200/16000] [L1: 0.0223] 59.7+0.1s +[12800/16000] [L1: 0.0222] 59.8+0.1s +[14400/16000] [L1: 0.0222] 59.5+0.1s +[16000/16000] [L1: 0.0223] 59.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.259 (Best: 39.305 @epoch 31) +Forward: 42.33s + +Saving... +Total: 42.87s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0227] 60.4+0.9s +[3200/16000] [L1: 0.0224] 59.0+0.1s +[4800/16000] [L1: 0.0222] 60.0+0.1s +[6400/16000] [L1: 0.0224] 60.1+0.1s +[8000/16000] [L1: 0.0224] 59.5+0.1s +[9600/16000] [L1: 0.0223] 59.5+0.1s +[11200/16000] [L1: 0.0224] 59.3+0.1s +[12800/16000] [L1: 0.0223] 59.2+0.1s +[14400/16000] [L1: 0.0224] 58.6+0.0s +[16000/16000] [L1: 0.0223] 59.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.405 (Best: 39.405 @epoch 34) +Forward: 42.72s + +Saving... +Total: 43.32s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0235] 59.9+1.0s +[3200/16000] [L1: 0.0235] 58.3+0.0s +[4800/16000] [L1: 0.0231] 60.1+0.1s +[6400/16000] [L1: 0.0230] 59.7+0.1s +[8000/16000] [L1: 0.0228] 59.6+0.1s +[9600/16000] [L1: 0.0226] 59.7+0.1s +[11200/16000] [L1: 0.0226] 59.4+0.1s +[12800/16000] [L1: 0.0224] 59.0+0.1s +[14400/16000] [L1: 0.0224] 59.5+0.1s +[16000/16000] [L1: 0.0224] 59.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.652 (Best: 39.652 @epoch 35) +Forward: 42.19s + +Saving... +Total: 42.73s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0213] 60.3+0.9s +[3200/16000] [L1: 0.0216] 60.2+0.1s +[4800/16000] [L1: 0.0215] 59.4+0.1s +[6400/16000] [L1: 0.0218] 58.7+0.0s +[8000/16000] [L1: 0.0218] 59.2+0.1s +[9600/16000] [L1: 0.0218] 59.5+0.1s +[11200/16000] [L1: 0.0218] 59.7+0.1s +[12800/16000] [L1: 0.0218] 59.2+0.1s +[14400/16000] [L1: 0.0220] 59.6+0.1s +[16000/16000] [L1: 0.0220] 59.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.759 (Best: 39.759 @epoch 36) +Forward: 42.61s + +Saving... +Total: 43.12s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0218] 59.6+1.1s +[3200/16000] [L1: 0.0217] 59.4+0.1s +[4800/16000] [L1: 0.0217] 59.3+0.1s +[6400/16000] [L1: 0.0215] 59.7+0.1s +[8000/16000] [L1: 0.0216] 59.9+0.1s +[9600/16000] [L1: 0.0216] 59.9+0.1s +[11200/16000] [L1: 0.0215] 59.0+0.1s +[12800/16000] [L1: 0.0215] 59.1+0.1s +[14400/16000] [L1: 0.0215] 59.8+0.1s +[16000/16000] [L1: 0.0215] 59.7+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.352 (Best: 39.759 @epoch 36) +Forward: 42.49s + +Saving... +Total: 42.98s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0221] 59.8+1.0s +[3200/16000] [L1: 0.0219] 58.6+0.1s +[4800/16000] [L1: 0.0218] 58.9+0.1s +[6400/16000] [L1: 0.0216] 59.4+0.1s +[8000/16000] [L1: 0.0215] 60.0+0.1s +[9600/16000] [L1: 0.0215] 60.0+0.1s +[11200/16000] [L1: 0.0215] 59.3+0.1s +[12800/16000] [L1: 0.0215] 59.2+0.0s +[14400/16000] [L1: 0.0216] 59.4+0.1s +[16000/16000] [L1: 0.0217] 59.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.362 (Best: 39.759 @epoch 36) +Forward: 42.39s + +Saving... +Total: 42.88s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0227] 59.7+0.9s +[3200/16000] [L1: 0.0226] 58.8+0.1s +[4800/16000] [L1: 0.0220] 59.9+0.1s +[6400/16000] [L1: 0.0217] 59.1+0.1s +[8000/16000] [L1: 0.0219] 60.0+0.1s +[9600/16000] [L1: 0.0218] 60.2+0.1s +[11200/16000] [L1: 0.0216] 59.2+0.1s +[12800/16000] [L1: 0.0217] 59.3+0.0s +[14400/16000] [L1: 0.0216] 59.2+0.1s +[16000/16000] [L1: 0.0216] 58.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.648 (Best: 39.759 @epoch 36) +Forward: 42.46s + +Saving... +Total: 42.99s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0210] 60.2+1.1s +[3200/16000] [L1: 0.0209] 59.6+0.1s +[4800/16000] [L1: 0.0209] 59.9+0.1s +[6400/16000] [L1: 0.0211] 59.8+0.1s +[8000/16000] [L1: 0.0211] 59.1+0.1s +[9600/16000] [L1: 0.0210] 59.7+0.1s +[11200/16000] [L1: 0.0211] 59.4+0.1s +[12800/16000] [L1: 0.0210] 59.4+0.1s +[14400/16000] [L1: 0.0212] 58.8+0.1s +[16000/16000] [L1: 0.0211] 59.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.883 (Best: 39.883 @epoch 40) +Forward: 42.62s + +Saving... +Total: 43.19s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0212] 60.1+0.9s +[3200/16000] [L1: 0.0207] 60.0+0.1s +[4800/16000] [L1: 0.0209] 59.6+0.1s +[6400/16000] [L1: 0.0210] 59.0+0.1s +[8000/16000] [L1: 0.0209] 60.0+0.1s +[9600/16000] [L1: 0.0209] 58.8+0.0s +[11200/16000] [L1: 0.0210] 60.1+0.1s +[12800/16000] [L1: 0.0211] 59.0+0.1s +[14400/16000] [L1: 0.0210] 59.7+0.1s +[16000/16000] [L1: 0.0211] 59.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.814 (Best: 39.883 @epoch 40) +Forward: 42.38s + +Saving... +Total: 42.91s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0214] 59.8+0.9s +[3200/16000] [L1: 0.0207] 60.2+0.1s +[4800/16000] [L1: 0.0208] 58.5+0.0s +[6400/16000] [L1: 0.0211] 60.0+0.1s +[8000/16000] [L1: 0.0210] 59.1+0.0s +[9600/16000] [L1: 0.0210] 59.5+0.0s +[11200/16000] [L1: 0.0208] 59.8+0.0s +[12800/16000] [L1: 0.0208] 59.5+0.1s +[14400/16000] [L1: 0.0208] 60.6+0.1s +[16000/16000] [L1: 0.0208] 60.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.889 (Best: 39.889 @epoch 42) +Forward: 42.40s + +Saving... +Total: 42.94s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0205] 59.2+0.9s +[3200/16000] [L1: 0.0203] 58.9+0.1s +[4800/16000] [L1: 0.0206] 59.4+0.1s +[6400/16000] [L1: 0.0205] 58.5+0.0s +[8000/16000] [L1: 0.0209] 59.8+0.1s +[9600/16000] [L1: 0.0209] 59.2+0.0s +[11200/16000] [L1: 0.0209] 59.8+0.1s +[12800/16000] [L1: 0.0208] 59.2+0.1s +[14400/16000] [L1: 0.0208] 59.0+0.1s +[16000/16000] [L1: 0.0209] 60.1+0.1s + +Evaluation: +[DIV2K x1] PSNR: 40.017 (Best: 40.017 @epoch 43) +Forward: 42.45s + +Saving... +Total: 42.98s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0205] 59.9+0.9s +[3200/16000] [L1: 0.0206] 60.9+0.1s +[4800/16000] [L1: 0.0208] 60.3+0.1s +[6400/16000] [L1: 0.0208] 59.9+0.1s +[8000/16000] [L1: 0.0208] 59.8+0.1s +[9600/16000] [L1: 0.0207] 60.0+0.1s +[11200/16000] [L1: 0.0207] 61.4+0.1s +[12800/16000] [L1: 0.0207] 60.6+0.1s +[14400/16000] [L1: 0.0207] 60.6+0.1s +[16000/16000] [L1: 0.0206] 61.2+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.874 (Best: 40.017 @epoch 43) +Forward: 42.34s + +Saving... +Total: 42.81s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0213] 60.3+0.9s +[3200/16000] [L1: 0.0211] 60.7+0.1s +[4800/16000] [L1: 0.0207] 60.4+0.1s +[6400/16000] [L1: 0.0206] 60.7+0.1s +[8000/16000] [L1: 0.0208] 60.6+0.1s +[9600/16000] [L1: 0.0207] 58.8+0.0s +[11200/16000] [L1: 0.0210] 58.9+0.0s +[12800/16000] [L1: 0.0211] 59.9+0.0s +[14400/16000] [L1: 0.0212] 58.8+0.0s +[16000/16000] [L1: 0.0211] 60.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.849 (Best: 40.017 @epoch 43) +Forward: 42.37s + +Saving... +Total: 42.89s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0198] 60.5+0.8s +[3200/16000] [L1: 0.0201] 58.3+0.1s +[4800/16000] [L1: 0.0203] 59.4+0.1s +[6400/16000] [L1: 0.0203] 60.0+0.1s +[8000/16000] [L1: 0.0203] 60.7+0.1s +[9600/16000] [L1: 0.0201] 59.8+0.1s +[11200/16000] [L1: 0.0201] 60.6+0.1s +[12800/16000] [L1: 0.0201] 59.9+0.1s +[14400/16000] [L1: 0.0201] 59.9+0.1s +[16000/16000] [L1: 0.0201] 58.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.039 (Best: 40.039 @epoch 46) +Forward: 42.29s + +Saving... +Total: 42.75s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0205] 60.0+0.9s +[3200/16000] [L1: 0.0207] 59.3+0.1s +[4800/16000] [L1: 0.0204] 59.5+0.0s +[6400/16000] [L1: 0.0203] 60.3+0.1s +[8000/16000] [L1: 0.0203] 61.1+0.1s +[9600/16000] [L1: 0.0203] 59.9+0.1s +[11200/16000] [L1: 0.0204] 60.4+0.1s +[12800/16000] [L1: 0.0204] 60.9+0.1s +[14400/16000] [L1: 0.0203] 60.9+0.1s +[16000/16000] [L1: 0.0202] 60.6+0.1s + +Evaluation: +[DIV2K x1] PSNR: 40.115 (Best: 40.115 @epoch 47) +Forward: 42.35s + +Saving... +Total: 42.78s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0212] 60.9+1.0s +[3200/16000] [L1: 0.0210] 60.5+0.1s +[4800/16000] [L1: 0.0207] 59.8+0.1s +[6400/16000] [L1: 0.0204] 60.1+0.0s +[8000/16000] [L1: 0.0203] 59.5+0.0s +[9600/16000] [L1: 0.0205] 60.7+0.1s +[11200/16000] [L1: 0.0205] 60.6+0.1s +[12800/16000] [L1: 0.0203] 61.1+0.1s +[14400/16000] [L1: 0.0204] 61.3+0.1s +[16000/16000] [L1: 0.0204] 60.9+0.1s + +Evaluation: +[DIV2K x1] PSNR: 40.066 (Best: 40.115 @epoch 47) +Forward: 42.63s + +Saving... +Total: 43.12s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0201] 60.1+1.0s +[3200/16000] [L1: 0.0202] 60.1+0.1s +[4800/16000] [L1: 0.0202] 59.4+0.1s +[6400/16000] [L1: 0.0200] 60.7+0.1s +[8000/16000] [L1: 0.0200] 60.9+0.1s +[9600/16000] [L1: 0.0199] 60.1+0.1s +[11200/16000] [L1: 0.0200] 59.3+0.1s +[12800/16000] [L1: 0.0200] 61.1+0.1s +[14400/16000] [L1: 0.0200] 58.8+0.0s +[16000/16000] [L1: 0.0200] 60.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.149 (Best: 40.149 @epoch 49) +Forward: 42.55s + +Saving... +Total: 43.08s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0194] 60.7+0.9s +[3200/16000] [L1: 0.0196] 60.9+0.1s +[4800/16000] [L1: 0.0197] 59.7+0.1s +[6400/16000] [L1: 0.0200] 60.0+0.1s +[8000/16000] [L1: 0.0199] 60.6+0.1s +[9600/16000] [L1: 0.0197] 60.6+0.1s +[11200/16000] [L1: 0.0198] 61.0+0.1s +[12800/16000] [L1: 0.0199] 60.2+0.1s +[14400/16000] [L1: 0.0199] 59.6+0.1s +[16000/16000] [L1: 0.0199] 59.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.206 (Best: 40.206 @epoch 50) +Forward: 42.33s + +Saving... +Total: 42.74s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0195] 60.2+1.0s +[3200/16000] [L1: 0.0191] 60.5+0.1s +[4800/16000] [L1: 0.0194] 60.5+0.1s +[6400/16000] [L1: 0.0194] 60.8+0.1s +[8000/16000] [L1: 0.0197] 60.7+0.1s +[9600/16000] [L1: 0.0198] 60.3+0.1s +[11200/16000] [L1: 0.0198] 60.4+0.1s +[12800/16000] [L1: 0.0198] 59.8+0.1s +[14400/16000] [L1: 0.0198] 59.2+0.1s +[16000/16000] [L1: 0.0198] 60.4+0.1s + +Evaluation: +[DIV2K x1] PSNR: 39.968 (Best: 40.206 @epoch 50) +Forward: 42.46s + +Saving... +Total: 42.89s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0190] 60.3+1.0s +[3200/16000] [L1: 0.0194] 59.0+0.1s +[4800/16000] [L1: 0.0192] 59.8+0.0s +[6400/16000] [L1: 0.0194] 60.7+0.1s +[8000/16000] [L1: 0.0194] 59.0+0.1s +[9600/16000] [L1: 0.0193] 58.1+0.0s +[11200/16000] [L1: 0.0203] 60.1+0.1s +[12800/16000] [L1: 0.0277] 61.2+0.1s +[14400/16000] [L1: 0.0279] 60.9+0.1s +[16000/16000] [L1: 0.0277] 58.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.438 (Best: 40.206 @epoch 50) +Forward: 42.31s + +Saving... +Total: 42.71s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0248] 60.7+1.0s +[3200/16000] [L1: 0.0236] 59.9+0.1s +[4800/16000] [L1: 0.0231] 60.8+0.1s +[6400/16000] [L1: 0.0230] 61.2+0.1s +[8000/16000] [L1: 0.0229] 60.2+0.1s +[9600/16000] [L1: 0.0228] 60.0+0.1s +[11200/16000] [L1: 0.0226] 60.1+0.0s +[12800/16000] [L1: 0.0225] 59.3+0.0s +[14400/16000] [L1: 0.0225] 59.9+0.1s +[16000/16000] [L1: 0.0223] 60.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.467 (Best: 40.206 @epoch 50) +Forward: 42.45s + +Saving... +Total: 42.94s + diff --git 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False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:42:52 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:46:50 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:50:44 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:53:08 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:53:43 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:55:30 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:57:35 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-15:58:31 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-16:00:05 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-16:22:29 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-16:23:59 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-17:27:42 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: -1 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-17:28:34 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: -1 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-09-17:29:12 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: -1 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-10-08:36:11 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: -1 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-10-08:48:31 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.2 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: 90 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-10-08:50:44 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.2 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: 90 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-10-08:53:35 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: ../experiment/RAFT_DEMOSAIC20_R4/model/model_best.pt +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.2 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-10-09:23:11 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: ../experiment/RAFT_DEMOSAIC20_R4/model/model_89.pt +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.2 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4 +load: RAFT_DEMOSAIC20_R4 +resume: 89 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/RAFT_DEMOSAIC20_R4/log.txt b/Demosaic/experiment/RAFT_DEMOSAIC20_R4/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..adfb67424981f716d5e9eadabd7acbac1e42d1a9 --- /dev/null +++ b/Demosaic/experiment/RAFT_DEMOSAIC20_R4/log.txt @@ -0,0 +1,7021 @@ +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 6.1452] 108.6+0.7s +[3200/16000] [L1: 4.2487] 103.6+0.0s +[4800/16000] [L1: 3.2401] 104.2+0.1s +[6400/16000] [L1: 2.6553] 104.6+0.0s +[8000/16000] [L1: 2.2765] 104.8+0.0s +[9600/16000] [L1: 2.0097] 103.8+0.0s +[11200/16000] [L1: 1.8037] 103.7+0.0s +[12800/16000] [L1: 1.6427] 104.5+0.0s +[14400/16000] [L1: 1.5158] 104.6+0.0s +[16000/16000] [L1: 1.4132] 103.5+0.0s + +Evaluation: +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.3301] 108.8+0.6s +[3200/16000] [L1: 2.5343] 106.3+0.0s +[4800/16000] [L1: 2.0254] 107.2+0.0s +[6400/16000] [L1: 1.6892] 108.6+0.0s +[8000/16000] [L1: 1.4616] 106.7+0.0s +[9600/16000] [L1: 1.2977] 107.3+0.0s +[11200/16000] [L1: 1.1730] 105.2+0.0s +[12800/16000] [L1: 1.0738] 105.8+0.0s +[14400/16000] [L1: 0.9959] 106.0+0.0s +[16000/16000] [L1: 0.9309] 104.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.932 (Best: 10.932 @epoch 1) +Forward: 48.82s + +Saving... +Total: 51.35s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3253] 110.4+0.8s +[3200/16000] [L1: 0.3080] 106.4+0.0s +[4800/16000] [L1: 0.3003] 106.2+0.0s +[6400/16000] [L1: 0.2916] 106.6+0.0s +[8000/16000] [L1: 0.2865] 107.6+0.0s +[9600/16000] [L1: 0.2784] 108.1+0.0s +[11200/16000] [L1: 0.2714] 108.5+0.0s +[12800/16000] [L1: 0.2657] 108.2+0.0s +[14400/16000] [L1: 0.2612] 107.7+0.0s +[16000/16000] [L1: 0.2567] 104.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 11.276 (Best: 11.276 @epoch 2) +Forward: 48.76s + +Saving... +Total: 49.35s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1978] 106.9+0.8s +[3200/16000] [L1: 0.2007] 108.9+0.1s +[4800/16000] [L1: 0.1961] 106.5+0.0s +[6400/16000] [L1: 0.1911] 106.3+0.0s +[8000/16000] [L1: 0.1881] 107.6+0.0s +[9600/16000] [L1: 0.1846] 106.8+0.0s +[11200/16000] [L1: 0.1817] 106.9+0.0s +[12800/16000] [L1: 0.1789] 105.4+0.0s +[14400/16000] [L1: 0.1756] 105.1+0.0s +[16000/16000] [L1: 0.1729] 104.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.194 (Best: 12.194 @epoch 3) +Forward: 48.65s + +Saving... +Total: 49.34s + +[Epoch 4] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1480] 34.6+0.8s +[3200/16000] [L1: 0.1461] 31.7+0.1s +[4800/16000] [L1: 0.1436] 32.0+0.0s +[6400/16000] [L1: 0.1418] 32.2+0.0s +[8000/16000] [L1: 0.1385] 32.1+0.0s +[9600/16000] [L1: 0.1365] 32.0+0.0s +[11200/16000] [L1: 0.1343] 32.1+0.0s +[12800/16000] [L1: 0.1324] 32.0+0.0s +[14400/16000] [L1: 0.1311] 32.1+0.0s +[16000/16000] [L1: 0.1293] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.629 (Best: 13.629 @epoch 4) +Forward: 49.05s + +Saving... +Total: 49.71s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1184] 32.4+0.7s +[3200/16000] [L1: 0.1159] 32.0+0.0s +[4800/16000] [L1: 0.1125] 31.9+0.0s +[6400/16000] [L1: 0.1095] 31.8+0.0s +[8000/16000] [L1: 0.1078] 31.9+0.0s +[9600/16000] [L1: 0.1060] 31.7+0.0s +[11200/16000] [L1: 0.1045] 31.7+0.0s +[12800/16000] [L1: 0.1037] 31.4+0.0s +[14400/16000] [L1: 0.1026] 31.6+0.0s +[16000/16000] [L1: 0.1013] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.076 (Best: 15.076 @epoch 5) +Forward: 48.93s + +Saving... +Total: 49.50s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0873] 32.1+0.8s +[3200/16000] [L1: 0.0903] 32.2+0.0s +[4800/16000] [L1: 0.0889] 31.9+0.0s +[6400/16000] [L1: 0.0864] 32.3+0.0s +[8000/16000] [L1: 0.0848] 31.9+0.0s +[9600/16000] [L1: 0.0841] 32.2+0.0s +[11200/16000] [L1: 0.0828] 32.0+0.0s +[12800/16000] [L1: 0.0821] 32.0+0.0s +[14400/16000] [L1: 0.0812] 32.1+0.0s +[16000/16000] [L1: 0.0803] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 16.372 (Best: 16.372 @epoch 6) +Forward: 49.04s + +Saving... +Total: 49.61s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0679] 32.1+0.8s +[3200/16000] [L1: 0.0687] 32.0+0.0s +[4800/16000] [L1: 0.0695] 31.8+0.0s +[6400/16000] [L1: 0.0710] 31.9+0.0s +[8000/16000] [L1: 0.0712] 31.7+0.0s +[9600/16000] [L1: 0.0699] 32.1+0.0s +[11200/16000] [L1: 0.0695] 31.8+0.0s +[12800/16000] [L1: 0.0686] 31.6+0.0s +[14400/16000] [L1: 0.0679] 31.7+0.0s +[16000/16000] [L1: 0.0674] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.349 (Best: 17.349 @epoch 7) +Forward: 48.94s + +Saving... +Total: 49.51s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0574] 31.9+0.8s +[3200/16000] [L1: 0.0584] 32.0+0.0s +[4800/16000] [L1: 0.0589] 32.1+0.0s +[6400/16000] [L1: 0.0603] 32.1+0.0s +[8000/16000] [L1: 0.0592] 32.0+0.0s +[9600/16000] [L1: 0.0586] 32.2+0.0s +[11200/16000] [L1: 0.0575] 32.3+0.0s +[12800/16000] [L1: 0.0569] 32.0+0.0s +[14400/16000] [L1: 0.0568] 31.8+0.0s +[16000/16000] [L1: 0.0564] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 18.917 (Best: 18.917 @epoch 8) +Forward: 48.96s + +Saving... +Total: 49.56s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0544] 32.2+0.8s +[3200/16000] [L1: 0.0517] 32.1+0.0s +[4800/16000] [L1: 0.0510] 31.9+0.0s +[6400/16000] [L1: 0.0503] 31.7+0.0s +[8000/16000] [L1: 0.0511] 31.8+0.0s +[9600/16000] [L1: 0.0506] 31.8+0.0s +[11200/16000] [L1: 0.0505] 31.7+0.0s +[12800/16000] [L1: 0.0499] 31.7+0.0s +[14400/16000] [L1: 0.0497] 31.6+0.0s +[16000/16000] [L1: 0.0491] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.594 (Best: 20.594 @epoch 9) +Forward: 48.97s + +Saving... +Total: 49.55s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0484] 32.1+0.9s +[3200/16000] [L1: 0.0482] 32.2+0.0s +[4800/16000] [L1: 0.0474] 32.1+0.0s +[6400/16000] [L1: 0.0465] 32.0+0.0s +[8000/16000] [L1: 0.0462] 31.9+0.0s +[9600/16000] [L1: 0.0457] 32.1+0.0s +[11200/16000] [L1: 0.0449] 31.7+0.0s +[12800/16000] [L1: 0.0444] 31.7+0.0s +[14400/16000] [L1: 0.0440] 31.9+0.0s +[16000/16000] [L1: 0.0438] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.356 (Best: 22.356 @epoch 10) +Forward: 49.08s + +Saving... +Total: 49.70s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0412] 32.1+0.8s +[3200/16000] [L1: 0.0430] 31.7+0.0s +[4800/16000] [L1: 0.0417] 32.0+0.0s +[6400/16000] [L1: 0.0411] 31.8+0.0s +[8000/16000] [L1: 0.0405] 31.6+0.0s +[9600/16000] [L1: 0.0402] 31.8+0.0s +[11200/16000] [L1: 0.0400] 31.9+0.0s +[12800/16000] [L1: 0.0397] 32.0+0.0s +[14400/16000] [L1: 0.0398] 31.8+0.0s +[16000/16000] [L1: 0.0395] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 23.454 (Best: 23.454 @epoch 11) +Forward: 49.01s + +Saving... +Total: 49.61s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0365] 32.2+0.8s +[3200/16000] [L1: 0.0374] 32.2+0.0s +[4800/16000] [L1: 0.0380] 32.3+0.0s +[6400/16000] [L1: 0.0374] 32.3+0.0s +[8000/16000] [L1: 0.0370] 32.3+0.0s +[9600/16000] [L1: 0.0367] 32.0+0.0s +[11200/16000] [L1: 0.0367] 31.8+0.0s +[12800/16000] [L1: 0.0366] 32.2+0.0s +[14400/16000] [L1: 0.0366] 31.8+0.0s +[16000/16000] [L1: 0.0365] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.010 (Best: 25.010 @epoch 12) +Forward: 48.93s + +Saving... +Total: 49.51s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0352] 32.1+0.8s +[3200/16000] [L1: 0.0345] 32.1+0.0s +[4800/16000] [L1: 0.0348] 31.9+0.0s +[6400/16000] [L1: 0.0351] 31.9+0.0s +[8000/16000] [L1: 0.0349] 31.8+0.0s +[9600/16000] [L1: 0.0347] 31.9+0.0s +[11200/16000] [L1: 0.0346] 32.1+0.0s +[12800/16000] [L1: 0.0345] 31.9+0.0s +[14400/16000] [L1: 0.0344] 31.6+0.0s +[16000/16000] [L1: 0.0341] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.954 (Best: 25.954 @epoch 13) +Forward: 48.94s + +Saving... +Total: 49.58s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0328] 32.4+0.8s +[3200/16000] [L1: 0.0335] 32.2+0.0s +[4800/16000] [L1: 0.0344] 32.1+0.0s +[6400/16000] [L1: 0.0342] 32.1+0.0s +[8000/16000] [L1: 0.0341] 31.9+0.0s +[9600/16000] [L1: 0.0344] 31.9+0.0s +[11200/16000] [L1: 0.0340] 31.9+0.0s +[12800/16000] [L1: 0.0337] 31.8+0.0s +[14400/16000] [L1: 0.0334] 32.0+0.0s +[16000/16000] [L1: 0.0335] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.072 (Best: 27.072 @epoch 14) +Forward: 49.10s + +Saving... +Total: 49.67s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0347] 32.0+0.8s +[3200/16000] [L1: 0.0329] 32.1+0.0s +[4800/16000] [L1: 0.0327] 32.0+0.0s +[6400/16000] [L1: 0.0323] 32.0+0.0s +[8000/16000] [L1: 0.0321] 32.0+0.0s +[9600/16000] [L1: 0.0322] 31.8+0.0s +[11200/16000] [L1: 0.0322] 31.9+0.0s +[12800/16000] [L1: 0.0319] 31.8+0.0s +[14400/16000] [L1: 0.0319] 31.7+0.0s +[16000/16000] [L1: 0.0316] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.557 (Best: 27.557 @epoch 15) +Forward: 48.85s + +Saving... +Total: 49.43s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0317] 32.3+0.8s +[3200/16000] [L1: 0.0311] 31.8+0.0s +[4800/16000] [L1: 0.0305] 31.9+0.0s +[6400/16000] [L1: 0.0308] 32.2+0.0s +[8000/16000] [L1: 0.0305] 32.2+0.0s +[9600/16000] [L1: 0.0302] 31.7+0.0s +[11200/16000] [L1: 0.0304] 31.6+0.0s +[12800/16000] [L1: 0.0302] 31.9+0.0s +[14400/16000] [L1: 0.0302] 31.7+0.0s +[16000/16000] [L1: 0.0300] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.859 (Best: 27.859 @epoch 16) +Forward: 48.99s + +Saving... +Total: 49.59s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0333] 32.2+0.8s +[3200/16000] [L1: 0.0314] 32.0+0.0s +[4800/16000] [L1: 0.0303] 32.2+0.0s +[6400/16000] [L1: 0.0301] 32.2+0.0s +[8000/16000] [L1: 0.0297] 31.8+0.0s +[9600/16000] [L1: 0.0295] 31.8+0.0s +[11200/16000] [L1: 0.0293] 31.7+0.0s +[12800/16000] [L1: 0.0294] 31.6+0.0s +[14400/16000] [L1: 0.0292] 31.7+0.0s +[16000/16000] [L1: 0.0293] 31.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.806 (Best: 28.806 @epoch 17) +Forward: 49.05s + +Saving... +Total: 49.64s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0294] 32.0+0.8s +[3200/16000] [L1: 0.0286] 31.9+0.0s +[4800/16000] [L1: 0.0283] 32.1+0.0s +[6400/16000] [L1: 0.0287] 32.0+0.0s +[8000/16000] [L1: 0.0289] 31.8+0.0s +[9600/16000] [L1: 0.0288] 32.1+0.0s +[11200/16000] [L1: 0.0287] 31.7+0.0s +[12800/16000] [L1: 0.0288] 31.5+0.0s +[14400/16000] [L1: 0.0287] 31.8+0.0s +[16000/16000] [L1: 0.0287] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.937 (Best: 29.937 @epoch 18) +Forward: 48.95s + +Saving... +Total: 49.54s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0269] 32.2+0.8s +[3200/16000] [L1: 0.0272] 32.1+0.0s +[4800/16000] [L1: 0.0270] 31.8+0.0s +[6400/16000] [L1: 0.0270] 32.0+0.0s +[8000/16000] [L1: 0.0269] 31.8+0.0s +[9600/16000] [L1: 0.0267] 31.6+0.0s +[11200/16000] [L1: 0.0266] 31.9+0.0s +[12800/16000] [L1: 0.0266] 31.7+0.0s +[14400/16000] [L1: 0.0268] 31.6+0.0s +[16000/16000] [L1: 0.0269] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.099 (Best: 30.099 @epoch 19) +Forward: 48.94s + +Saving... +Total: 49.59s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0268] 32.0+0.8s +[3200/16000] [L1: 0.0266] 32.1+0.0s +[4800/16000] [L1: 0.0270] 31.8+0.0s +[6400/16000] [L1: 0.0269] 31.9+0.0s +[8000/16000] [L1: 0.0266] 32.2+0.0s +[9600/16000] [L1: 0.0267] 31.9+0.0s +[11200/16000] [L1: 0.0265] 31.9+0.0s +[12800/16000] [L1: 0.0264] 32.0+0.0s +[14400/16000] [L1: 0.0263] 31.8+0.0s +[16000/16000] [L1: 0.0264] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.329 (Best: 30.329 @epoch 20) +Forward: 48.90s + +Saving... +Total: 49.56s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0252] 32.0+0.8s +[3200/16000] [L1: 0.0254] 32.0+0.0s +[4800/16000] [L1: 0.0254] 32.2+0.0s +[6400/16000] [L1: 0.0251] 32.2+0.0s +[8000/16000] [L1: 0.0253] 32.0+0.0s +[9600/16000] [L1: 0.0252] 31.8+0.0s +[11200/16000] [L1: 0.0254] 31.8+0.0s +[12800/16000] [L1: 0.0255] 31.7+0.0s +[14400/16000] [L1: 0.0255] 31.6+0.0s +[16000/16000] [L1: 0.0255] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.167 (Best: 30.329 @epoch 20) +Forward: 49.04s + +Saving... +Total: 49.56s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0241] 32.1+0.8s +[3200/16000] [L1: 0.0245] 31.8+0.0s +[4800/16000] [L1: 0.0247] 31.9+0.0s +[6400/16000] [L1: 0.0243] 32.0+0.0s +[8000/16000] [L1: 0.0242] 31.9+0.0s +[9600/16000] [L1: 0.0243] 31.8+0.0s +[11200/16000] [L1: 0.0242] 31.7+0.0s +[12800/16000] [L1: 0.0242] 31.5+0.0s +[14400/16000] [L1: 0.0242] 31.7+0.0s +[16000/16000] [L1: 0.0243] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.462 (Best: 31.462 @epoch 22) +Forward: 48.95s + +Saving... +Total: 49.51s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0237] 32.2+0.9s +[3200/16000] [L1: 0.0233] 32.2+0.0s +[4800/16000] [L1: 0.0235] 31.9+0.0s +[6400/16000] [L1: 0.0234] 31.8+0.0s +[8000/16000] [L1: 0.0232] 32.0+0.0s +[9600/16000] [L1: 0.0339] 31.5+0.0s +[11200/16000] [L1: 0.0368] 31.8+0.0s +[12800/16000] [L1: 0.0366] 31.9+0.0s +[14400/16000] [L1: 0.0361] 31.8+0.0s +[16000/16000] [L1: 0.0357] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.163 (Best: 35.163 @epoch 23) +Forward: 48.94s + +Saving... +Total: 49.49s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0315] 32.1+0.9s +[3200/16000] [L1: 0.0306] 32.1+0.0s +[4800/16000] [L1: 0.0302] 32.0+0.0s +[6400/16000] [L1: 0.0295] 31.8+0.0s +[8000/16000] [L1: 0.0293] 31.7+0.0s +[9600/16000] [L1: 0.0292] 31.9+0.0s +[11200/16000] [L1: 0.0291] 32.0+0.0s +[12800/16000] [L1: 0.0290] 31.9+0.0s +[14400/16000] [L1: 0.0290] 31.7+0.0s +[16000/16000] [L1: 0.0289] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.983 (Best: 35.983 @epoch 24) +Forward: 49.05s + +Saving... +Total: 49.60s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0279] 32.2+0.9s +[3200/16000] [L1: 0.0276] 32.0+0.0s +[4800/16000] [L1: 0.0270] 32.2+0.0s +[6400/16000] [L1: 0.0271] 31.8+0.0s +[8000/16000] [L1: 0.0270] 31.8+0.0s +[9600/16000] [L1: 0.0269] 32.1+0.0s +[11200/16000] [L1: 0.0267] 31.9+0.0s +[12800/16000] [L1: 0.0267] 32.1+0.0s +[14400/16000] [L1: 0.0266] 31.9+0.0s +[16000/16000] [L1: 0.0266] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.077 (Best: 36.077 @epoch 25) +Forward: 49.04s + +Saving... +Total: 49.61s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0259] 32.6+0.8s +[3200/16000] [L1: 0.0262] 32.3+0.0s +[4800/16000] [L1: 0.0265] 32.1+0.0s +[6400/16000] [L1: 0.0266] 31.7+0.0s +[8000/16000] [L1: 0.0262] 31.6+0.0s +[9600/16000] [L1: 0.0263] 31.9+0.0s +[11200/16000] [L1: 0.0263] 31.9+0.0s +[12800/16000] [L1: 0.0263] 31.8+0.0s +[14400/16000] [L1: 0.0262] 31.8+0.0s +[16000/16000] [L1: 0.0262] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.582 (Best: 36.582 @epoch 26) +Forward: 49.12s + +Saving... +Total: 49.71s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0266] 32.2+0.8s +[3200/16000] [L1: 0.0266] 32.1+0.0s +[4800/16000] [L1: 0.0262] 32.1+0.0s +[6400/16000] [L1: 0.0258] 32.0+0.0s +[8000/16000] [L1: 0.0258] 32.2+0.0s +[9600/16000] [L1: 0.0257] 32.1+0.0s +[11200/16000] [L1: 0.0256] 32.0+0.0s +[12800/16000] [L1: 0.0256] 32.2+0.0s +[14400/16000] [L1: 0.0256] 32.0+0.0s +[16000/16000] [L1: 0.0253] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.920 (Best: 36.920 @epoch 27) +Forward: 49.20s + +Saving... +Total: 49.77s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0250] 32.3+0.9s +[3200/16000] [L1: 0.0236] 32.1+0.1s +[4800/16000] [L1: 0.0238] 32.2+0.0s +[6400/16000] [L1: 0.0241] 31.9+0.0s +[8000/16000] [L1: 0.0245] 32.1+0.0s +[9600/16000] [L1: 0.0247] 32.1+0.0s +[11200/16000] [L1: 0.0244] 31.9+0.0s +[12800/16000] [L1: 0.0245] 31.7+0.0s +[14400/16000] [L1: 0.0243] 31.7+0.0s +[16000/16000] [L1: 0.0243] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.395 (Best: 37.395 @epoch 28) +Forward: 49.01s + +Saving... +Total: 49.64s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0231] 32.0+0.8s +[3200/16000] [L1: 0.0228] 31.8+0.0s +[4800/16000] [L1: 0.0231] 31.9+0.0s +[6400/16000] [L1: 0.0232] 32.0+0.0s +[8000/16000] [L1: 0.0231] 32.1+0.0s +[9600/16000] [L1: 0.0230] 32.0+0.0s +[11200/16000] [L1: 0.0230] 32.1+0.0s +[12800/16000] [L1: 0.0230] 31.9+0.0s +[14400/16000] [L1: 0.0230] 31.8+0.0s +[16000/16000] [L1: 0.0229] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.932 (Best: 37.932 @epoch 29) +Forward: 48.93s + +Saving... +Total: 49.48s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0217] 32.2+0.8s +[3200/16000] [L1: 0.0218] 32.5+0.0s +[4800/16000] [L1: 0.0216] 32.2+0.0s +[6400/16000] [L1: 0.0216] 32.4+0.0s +[8000/16000] [L1: 0.0216] 32.1+0.0s +[9600/16000] [L1: 0.0216] 32.1+0.0s +[11200/16000] [L1: 0.0217] 31.9+0.0s +[12800/16000] [L1: 0.0217] 32.0+0.0s +[14400/16000] [L1: 0.0218] 32.4+0.0s +[16000/16000] [L1: 0.0217] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.226 (Best: 38.226 @epoch 30) +Forward: 49.11s + +Saving... +Total: 49.69s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0219] 32.3+1.0s +[3200/16000] [L1: 0.0220] 32.3+0.0s +[4800/16000] [L1: 0.0220] 31.9+0.0s +[6400/16000] [L1: 0.0219] 31.9+0.0s +[8000/16000] [L1: 0.0219] 31.7+0.0s +[9600/16000] [L1: 0.0214] 31.9+0.0s +[11200/16000] [L1: 0.0214] 31.8+0.0s +[12800/16000] [L1: 0.0215] 32.1+0.0s +[14400/16000] [L1: 0.0215] 31.8+0.0s +[16000/16000] [L1: 0.0214] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.361 (Best: 38.361 @epoch 31) +Forward: 48.89s + +Saving... +Total: 49.49s + +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0210] 32.0+1.0s +[3200/16000] [L1: 0.0214] 32.1+0.0s +[4800/16000] [L1: 0.0213] 31.8+0.0s +[6400/16000] [L1: 0.0209] 31.9+0.0s +[8000/16000] [L1: 0.0209] 31.7+0.0s +[9600/16000] [L1: 0.0211] 32.0+0.0s +[11200/16000] [L1: 0.0210] 31.7+0.0s +[12800/16000] [L1: 0.0210] 31.6+0.0s +[14400/16000] [L1: 0.0210] 31.6+0.0s +[16000/16000] [L1: 0.0209] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.871 (Best: 38.871 @epoch 32) +Forward: 48.88s + +Saving... +Total: 49.51s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0209] 32.1+0.8s +[3200/16000] [L1: 0.0205] 32.1+0.0s +[4800/16000] [L1: 0.0207] 32.2+0.0s +[6400/16000] [L1: 0.0205] 32.2+0.0s +[8000/16000] [L1: 0.0205] 32.3+0.0s +[9600/16000] [L1: 0.0206] 31.8+0.0s +[11200/16000] [L1: 0.0205] 31.8+0.0s +[12800/16000] [L1: 0.0204] 31.9+0.0s +[14400/16000] [L1: 0.0203] 31.9+0.0s +[16000/16000] [L1: 0.0204] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.803 (Best: 38.871 @epoch 32) +Forward: 48.94s + +Saving... +Total: 49.50s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0198] 32.4+0.7s +[3200/16000] [L1: 0.0196] 32.0+0.0s +[4800/16000] [L1: 0.0197] 32.0+0.0s +[6400/16000] [L1: 0.0200] 32.1+0.0s +[8000/16000] [L1: 0.0199] 32.3+0.0s +[9600/16000] [L1: 0.0198] 32.1+0.0s +[11200/16000] [L1: 0.0200] 32.0+0.0s +[12800/16000] [L1: 0.0269] 31.9+0.0s +[14400/16000] [L1: 0.0268] 32.0+0.0s +[16000/16000] [L1: 0.0267] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.525 (Best: 38.871 @epoch 32) +Forward: 49.10s + +Saving... +Total: 49.65s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0243] 32.1+0.8s +[3200/16000] [L1: 0.0238] 32.3+0.0s +[4800/16000] [L1: 0.0233] 32.3+0.0s +[6400/16000] [L1: 0.0228] 31.9+0.0s +[8000/16000] [L1: 0.0223] 32.0+0.0s +[9600/16000] [L1: 0.0220] 31.8+0.0s +[11200/16000] [L1: 0.0220] 31.7+0.0s +[12800/16000] [L1: 0.0218] 32.2+0.0s +[14400/16000] [L1: 0.0218] 31.9+0.0s +[16000/16000] [L1: 0.0217] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.986 (Best: 38.986 @epoch 35) +Forward: 48.93s + +Saving... +Total: 49.50s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0194] 32.4+0.9s +[3200/16000] [L1: 0.0202] 31.9+0.0s +[4800/16000] [L1: 0.0200] 31.9+0.0s +[6400/16000] [L1: 0.0196] 31.8+0.0s +[8000/16000] [L1: 0.0200] 31.9+0.0s +[9600/16000] [L1: 0.0201] 32.0+0.0s +[11200/16000] [L1: 0.0201] 31.9+0.0s +[12800/16000] [L1: 0.0200] 31.8+0.0s +[14400/16000] [L1: 0.0200] 31.8+0.0s +[16000/16000] [L1: 0.0200] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.052 (Best: 39.052 @epoch 36) +Forward: 48.95s + +Saving... +Total: 49.52s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0199] 32.1+0.8s +[3200/16000] [L1: 0.0198] 32.0+0.0s +[4800/16000] [L1: 0.0198] 32.3+0.0s +[6400/16000] [L1: 0.0197] 32.0+0.0s +[8000/16000] [L1: 0.0197] 32.0+0.0s +[9600/16000] [L1: 0.0196] 31.9+0.0s +[11200/16000] [L1: 0.0196] 31.8+0.0s +[12800/16000] [L1: 0.0194] 31.9+0.0s +[14400/16000] [L1: 0.0194] 31.9+0.0s +[16000/16000] [L1: 0.0195] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.281 (Best: 39.281 @epoch 37) +Forward: 49.05s + +Saving... +Total: 49.62s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0196] 32.5+0.9s +[3200/16000] [L1: 0.0194] 31.9+0.0s +[4800/16000] [L1: 0.0195] 32.3+0.0s +[6400/16000] [L1: 0.0195] 31.8+0.0s +[8000/16000] [L1: 0.0194] 31.8+0.0s +[9600/16000] [L1: 0.0194] 31.6+0.0s +[11200/16000] [L1: 0.0194] 31.8+0.0s +[12800/16000] [L1: 0.0193] 31.7+0.0s +[14400/16000] [L1: 0.0193] 31.9+0.0s +[16000/16000] [L1: 0.0193] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.520 (Best: 39.520 @epoch 38) +Forward: 49.00s + +Saving... +Total: 49.60s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0191] 32.4+0.8s +[3200/16000] [L1: 0.0192] 32.1+0.0s +[4800/16000] [L1: 0.0191] 32.1+0.0s +[6400/16000] [L1: 0.0195] 32.1+0.0s +[8000/16000] [L1: 0.0194] 32.1+0.0s +[9600/16000] [L1: 0.0193] 31.8+0.0s +[11200/16000] [L1: 0.0192] 31.7+0.0s +[12800/16000] [L1: 0.0193] 32.0+0.0s +[14400/16000] [L1: 0.0194] 31.9+0.0s +[16000/16000] [L1: 0.0194] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.405 (Best: 39.520 @epoch 38) +Forward: 48.95s + +Saving... +Total: 49.50s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0193] 32.5+0.8s +[3200/16000] [L1: 0.0193] 32.3+0.0s +[4800/16000] [L1: 0.0189] 31.9+0.0s +[6400/16000] [L1: 0.0187] 32.0+0.0s +[8000/16000] [L1: 0.0188] 31.7+0.0s +[9600/16000] [L1: 0.0188] 32.2+0.0s +[11200/16000] [L1: 0.0187] 31.7+0.0s +[12800/16000] [L1: 0.0188] 31.7+0.0s +[14400/16000] [L1: 0.0189] 31.6+0.0s +[16000/16000] [L1: 0.0190] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.216 (Best: 39.520 @epoch 38) +Forward: 49.02s + +Saving... +Total: 49.57s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0198] 32.2+0.8s +[3200/16000] [L1: 0.0197] 32.1+0.0s +[4800/16000] [L1: 0.0195] 31.6+0.0s +[6400/16000] [L1: 0.0192] 32.1+0.0s +[8000/16000] [L1: 0.0189] 31.9+0.0s +[9600/16000] [L1: 0.0188] 31.7+0.0s +[11200/16000] [L1: 0.0190] 31.8+0.0s +[12800/16000] [L1: 0.0190] 31.8+0.0s +[14400/16000] [L1: 0.0188] 31.6+0.0s +[16000/16000] [L1: 0.0188] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.711 (Best: 39.711 @epoch 41) +Forward: 49.04s + +Saving... +Total: 49.64s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0179] 32.1+0.8s +[3200/16000] [L1: 0.0189] 32.6+0.0s +[4800/16000] [L1: 0.0191] 32.3+0.0s +[6400/16000] [L1: 0.0188] 32.0+0.0s +[8000/16000] [L1: 0.0188] 32.0+0.0s +[9600/16000] [L1: 0.0189] 31.8+0.0s +[11200/16000] [L1: 0.0188] 31.8+0.0s +[12800/16000] [L1: 0.0187] 31.6+0.0s +[14400/16000] [L1: 0.0186] 31.8+0.0s +[16000/16000] [L1: 0.0186] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.635 (Best: 39.711 @epoch 41) +Forward: 49.09s + +Saving... +Total: 49.69s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0188] 32.1+0.9s +[3200/16000] [L1: 0.0188] 32.4+0.0s +[4800/16000] [L1: 0.0185] 32.0+0.0s +[6400/16000] [L1: 0.0185] 31.7+0.0s +[8000/16000] [L1: 0.0185] 31.6+0.0s +[9600/16000] [L1: 0.0183] 32.0+0.0s +[11200/16000] [L1: 0.0183] 32.0+0.0s +[12800/16000] [L1: 0.0183] 31.9+0.0s +[14400/16000] [L1: 0.0183] 31.9+0.0s +[16000/16000] [L1: 0.0184] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.935 (Best: 39.935 @epoch 43) +Forward: 48.92s + +Saving... +Total: 50.30s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0170] 32.3+0.9s +[3200/16000] [L1: 0.0173] 31.9+0.0s +[4800/16000] [L1: 0.0174] 32.0+0.0s +[6400/16000] [L1: 0.0175] 32.1+0.0s +[8000/16000] [L1: 0.0175] 32.0+0.0s +[9600/16000] [L1: 0.0177] 32.1+0.0s +[11200/16000] [L1: 0.0177] 32.0+0.0s +[12800/16000] [L1: 0.0177] 32.1+0.0s +[14400/16000] [L1: 0.0177] 32.2+0.0s +[16000/16000] [L1: 0.0177] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.020 (Best: 40.020 @epoch 44) +Forward: 49.10s + +Saving... +Total: 49.61s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 32.3+0.9s +[3200/16000] [L1: 0.0175] 32.1+0.0s +[4800/16000] [L1: 0.0174] 32.4+0.0s +[6400/16000] [L1: 0.0177] 32.0+0.0s +[8000/16000] [L1: 0.0179] 32.0+0.0s +[9600/16000] [L1: 0.0179] 31.9+0.0s +[11200/16000] [L1: 0.0179] 31.8+0.0s +[12800/16000] [L1: 0.0180] 31.8+0.0s +[14400/16000] [L1: 0.0179] 31.5+0.0s +[16000/16000] [L1: 0.0179] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.094 (Best: 40.094 @epoch 45) +Forward: 48.95s + +Saving... +Total: 49.49s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0182] 32.3+0.8s +[3200/16000] [L1: 0.0177] 32.4+0.0s +[4800/16000] [L1: 0.0175] 32.4+0.0s +[6400/16000] [L1: 0.0175] 32.2+0.0s +[8000/16000] [L1: 0.0173] 32.2+0.0s +[9600/16000] [L1: 0.0175] 32.1+0.0s +[11200/16000] [L1: 0.0175] 31.9+0.0s +[12800/16000] [L1: 0.0176] 32.0+0.0s +[14400/16000] [L1: 0.0175] 31.7+0.0s +[16000/16000] [L1: 0.0174] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.999 (Best: 40.094 @epoch 45) +Forward: 48.93s + +Saving... +Total: 49.44s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0175] 32.3+0.8s +[3200/16000] [L1: 0.0367] 32.1+0.0s +[4800/16000] [L1: 0.0333] 32.3+0.0s +[6400/16000] [L1: 0.0311] 32.3+0.0s +[8000/16000] [L1: 0.0295] 32.3+0.0s +[9600/16000] [L1: 0.0286] 32.2+0.0s +[11200/16000] [L1: 0.0279] 32.1+0.0s +[12800/16000] [L1: 0.0273] 31.9+0.0s +[14400/16000] [L1: 0.0267] 31.9+0.0s +[16000/16000] [L1: 0.0260] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.487 (Best: 40.094 @epoch 45) +Forward: 49.14s + +Saving... +Total: 49.64s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0187] 32.2+0.8s +[3200/16000] [L1: 0.0182] 32.2+0.0s +[4800/16000] [L1: 0.0182] 31.9+0.0s +[6400/16000] [L1: 0.0179] 32.2+0.0s +[8000/16000] [L1: 0.0180] 32.1+0.0s +[9600/16000] [L1: 0.0179] 32.0+0.0s +[11200/16000] [L1: 0.0178] 31.7+0.0s +[12800/16000] [L1: 0.0178] 31.8+0.0s +[14400/16000] [L1: 0.0179] 31.9+0.0s +[16000/16000] [L1: 0.0180] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.927 (Best: 40.094 @epoch 45) +Forward: 49.00s + +Saving... +Total: 49.52s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 32.3+0.9s +[3200/16000] [L1: 0.0172] 32.1+0.0s +[4800/16000] [L1: 0.0174] 32.4+0.0s +[6400/16000] [L1: 0.0176] 31.6+0.0s +[8000/16000] [L1: 0.0176] 31.6+0.0s +[9600/16000] [L1: 0.0176] 31.7+0.0s +[11200/16000] [L1: 0.0177] 31.7+0.0s +[12800/16000] [L1: 0.0178] 32.0+0.0s +[14400/16000] [L1: 0.0177] 31.9+0.0s +[16000/16000] [L1: 0.0177] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.959 (Best: 40.094 @epoch 45) +Forward: 48.97s + +Saving... +Total: 49.53s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0178] 32.1+0.8s +[3200/16000] [L1: 0.0172] 31.9+0.0s +[4800/16000] [L1: 0.0176] 31.9+0.0s +[6400/16000] [L1: 0.0173] 32.0+0.0s +[8000/16000] [L1: 0.0173] 32.1+0.0s +[9600/16000] [L1: 0.0173] 31.9+0.0s +[11200/16000] [L1: 0.0173] 32.0+0.0s +[12800/16000] [L1: 0.0173] 31.8+0.0s +[14400/16000] [L1: 0.0174] 31.8+0.0s +[16000/16000] [L1: 0.0174] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.266 (Best: 40.266 @epoch 50) +Forward: 49.02s + +Saving... +Total: 49.63s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0178] 32.4+0.8s +[3200/16000] [L1: 0.0178] 32.1+0.0s +[4800/16000] [L1: 0.0174] 32.1+0.0s +[6400/16000] [L1: 0.0174] 31.6+0.0s +[8000/16000] [L1: 0.0173] 32.1+0.0s +[9600/16000] [L1: 0.0173] 31.8+0.0s +[11200/16000] [L1: 0.0173] 32.1+0.0s +[12800/16000] [L1: 0.0173] 31.8+0.0s +[14400/16000] [L1: 0.0173] 32.2+0.0s +[16000/16000] [L1: 0.0173] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.217 (Best: 40.266 @epoch 50) +Forward: 49.06s + +Saving... +Total: 49.56s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0172] 32.6+0.9s +[3200/16000] [L1: 0.0171] 32.0+0.0s +[4800/16000] [L1: 0.0172] 32.1+0.0s +[6400/16000] [L1: 0.0173] 32.1+0.0s +[8000/16000] [L1: 0.0173] 32.3+0.0s +[9600/16000] [L1: 0.0173] 32.1+0.0s +[11200/16000] [L1: 0.0173] 32.0+0.0s +[12800/16000] [L1: 0.0173] 31.7+0.0s +[14400/16000] [L1: 0.0173] 31.8+0.0s +[16000/16000] [L1: 0.0173] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.101 (Best: 40.266 @epoch 50) +Forward: 48.95s + +Saving... +Total: 49.48s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0167] 32.2+0.9s +[3200/16000] [L1: 0.0166] 31.8+0.0s +[4800/16000] [L1: 0.0168] 31.7+0.0s +[6400/16000] [L1: 0.0168] 31.9+0.0s +[8000/16000] [L1: 0.0169] 31.9+0.0s +[9600/16000] [L1: 0.0169] 31.9+0.0s +[11200/16000] [L1: 0.0169] 31.8+0.0s +[12800/16000] [L1: 0.0170] 31.8+0.0s +[14400/16000] [L1: 0.0170] 31.9+0.0s +[16000/16000] [L1: 0.0170] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.304 (Best: 40.304 @epoch 53) +Forward: 48.95s + +Saving... +Total: 49.53s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0164] 32.4+0.8s +[3200/16000] [L1: 0.0169] 32.0+0.0s +[4800/16000] [L1: 0.0170] 32.0+0.0s +[6400/16000] [L1: 0.0169] 32.1+0.0s +[8000/16000] [L1: 0.0170] 32.0+0.0s +[9600/16000] [L1: 0.0171] 32.0+0.0s +[11200/16000] [L1: 0.0171] 32.0+0.0s +[12800/16000] [L1: 0.0171] 32.4+0.0s +[14400/16000] [L1: 0.0171] 32.0+0.0s +[16000/16000] [L1: 0.0171] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.394 (Best: 40.394 @epoch 54) +Forward: 48.96s + +Saving... +Total: 49.55s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0163] 31.9+0.9s +[3200/16000] [L1: 0.0168] 31.9+0.0s +[4800/16000] [L1: 0.0171] 31.9+0.0s +[6400/16000] [L1: 0.0224] 31.9+0.0s +[8000/16000] [L1: 0.0251] 31.9+0.0s +[9600/16000] [L1: 0.0249] 31.9+0.0s +[11200/16000] [L1: 0.0246] 31.9+0.0s +[12800/16000] [L1: 0.0244] 31.9+0.0s +[14400/16000] [L1: 0.0243] 31.9+0.0s +[16000/16000] [L1: 0.0241] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.909 (Best: 40.394 @epoch 54) +Forward: 49.04s + +Saving... +Total: 49.56s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0225] 32.1+0.8s +[3200/16000] [L1: 0.0224] 32.3+0.0s +[4800/16000] [L1: 0.0224] 32.1+0.0s +[6400/16000] [L1: 0.0224] 31.9+0.0s +[8000/16000] [L1: 0.0224] 32.0+0.0s +[9600/16000] [L1: 0.0224] 31.9+0.0s +[11200/16000] [L1: 0.0223] 31.9+0.0s +[12800/16000] [L1: 0.0223] 31.8+0.0s +[14400/16000] [L1: 0.0224] 31.6+0.0s +[16000/16000] [L1: 0.0223] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.589 (Best: 40.394 @epoch 54) +Forward: 49.05s + +Saving... +Total: 49.57s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0221] 32.4+0.8s +[3200/16000] [L1: 0.0224] 32.2+0.0s +[4800/16000] [L1: 0.0222] 31.9+0.0s +[6400/16000] [L1: 0.0221] 32.0+0.0s +[8000/16000] [L1: 0.0220] 31.7+0.0s +[9600/16000] [L1: 0.0220] 31.5+0.0s +[11200/16000] [L1: 0.0219] 31.9+0.0s +[12800/16000] [L1: 0.0218] 31.7+0.0s +[14400/16000] [L1: 0.0217] 31.8+0.0s +[16000/16000] [L1: 0.0215] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.244 (Best: 40.394 @epoch 54) +Forward: 48.86s + +Saving... +Total: 49.52s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0212] 32.0+0.9s +[3200/16000] [L1: 0.0212] 32.0+0.0s +[4800/16000] [L1: 0.0212] 31.9+0.0s +[6400/16000] [L1: 0.0212] 31.8+0.0s +[8000/16000] [L1: 0.0210] 31.8+0.0s +[9600/16000] [L1: 0.0209] 31.9+0.0s +[11200/16000] [L1: 0.0209] 31.7+0.0s +[12800/16000] [L1: 0.0208] 31.7+0.0s +[14400/16000] [L1: 0.0208] 31.7+0.0s +[16000/16000] [L1: 0.0206] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.277 (Best: 40.394 @epoch 54) +Forward: 48.94s + +Saving... +Total: 49.52s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0186] 32.4+0.7s +[3200/16000] [L1: 0.0182] 32.2+0.0s +[4800/16000] [L1: 0.0179] 32.2+0.0s +[6400/16000] [L1: 0.0178] 32.0+0.0s +[8000/16000] [L1: 0.0177] 32.1+0.0s +[9600/16000] [L1: 0.0176] 32.2+0.0s +[11200/16000] [L1: 0.0176] 32.1+0.0s +[12800/16000] [L1: 0.0175] 31.9+0.0s +[14400/16000] [L1: 0.0175] 31.9+0.0s +[16000/16000] [L1: 0.0174] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.347 (Best: 40.394 @epoch 54) +Forward: 48.97s + +Saving... +Total: 49.49s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0175] 32.4+0.8s +[3200/16000] [L1: 0.0171] 32.3+0.0s +[4800/16000] [L1: 0.0169] 32.3+0.0s +[6400/16000] [L1: 0.0167] 32.0+0.0s +[8000/16000] [L1: 0.0167] 32.2+0.0s +[9600/16000] [L1: 0.0167] 32.0+0.0s +[11200/16000] [L1: 0.0168] 32.1+0.0s +[12800/16000] [L1: 0.0168] 31.8+0.0s +[14400/16000] [L1: 0.0168] 31.8+0.0s +[16000/16000] [L1: 0.0168] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.906 (Best: 40.394 @epoch 54) +Forward: 49.08s + +Saving... +Total: 49.62s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0174] 32.3+0.8s +[3200/16000] [L1: 0.0171] 31.9+0.0s +[4800/16000] [L1: 0.0173] 32.1+0.0s +[6400/16000] [L1: 0.0297] 32.0+0.0s +[8000/16000] [L1: 0.0301] 32.0+0.0s +[9600/16000] [L1: 0.0296] 31.7+0.0s +[11200/16000] [L1: 0.0288] 32.0+0.0s +[12800/16000] [L1: 0.0283] 31.9+0.0s +[14400/16000] [L1: 0.0279] 32.0+0.0s +[16000/16000] [L1: 0.0274] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.339 (Best: 40.394 @epoch 54) +Forward: 49.07s + +Saving... +Total: 49.65s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0232] 32.3+0.8s +[3200/16000] [L1: 0.0231] 32.1+0.0s +[4800/16000] [L1: 0.0228] 32.0+0.0s +[6400/16000] [L1: 0.0225] 32.0+0.0s +[8000/16000] [L1: 0.0224] 32.2+0.0s +[9600/16000] [L1: 0.0223] 32.4+0.0s +[11200/16000] [L1: 0.0221] 32.2+0.0s +[12800/16000] [L1: 0.0219] 32.2+0.0s +[14400/16000] [L1: 0.0218] 32.3+0.0s +[16000/16000] [L1: 0.0217] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.211 (Best: 40.394 @epoch 54) +Forward: 49.03s + +Saving... +Total: 49.54s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0218] 32.3+0.8s +[3200/16000] [L1: 0.0215] 32.2+0.0s +[4800/16000] [L1: 0.0214] 32.1+0.0s +[6400/16000] [L1: 0.0211] 32.2+0.0s +[8000/16000] [L1: 0.0211] 32.1+0.0s +[9600/16000] [L1: 0.0209] 32.0+0.0s +[11200/16000] [L1: 0.0210] 32.0+0.0s +[12800/16000] [L1: 0.0208] 32.1+0.0s +[14400/16000] [L1: 0.0208] 32.0+0.0s +[16000/16000] [L1: 0.0207] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.668 (Best: 40.394 @epoch 54) +Forward: 49.02s + +Saving... +Total: 49.55s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0198] 32.2+0.8s +[3200/16000] [L1: 0.0197] 32.1+0.0s +[4800/16000] [L1: 0.0193] 31.9+0.0s +[6400/16000] [L1: 0.0194] 32.0+0.0s +[8000/16000] [L1: 0.0194] 31.7+0.0s +[9600/16000] [L1: 0.0193] 31.7+0.0s +[11200/16000] [L1: 0.0192] 31.6+0.0s +[12800/16000] [L1: 0.0191] 32.1+0.0s +[14400/16000] [L1: 0.0190] 31.8+0.0s +[16000/16000] [L1: 0.0189] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.799 (Best: 40.394 @epoch 54) +Forward: 49.22s + +Saving... +Total: 49.76s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0180] 32.3+0.9s +[3200/16000] [L1: 0.0176] 32.3+0.0s +[4800/16000] [L1: 0.0179] 31.8+0.0s +[6400/16000] [L1: 0.0176] 32.0+0.0s +[8000/16000] [L1: 0.0175] 32.2+0.0s +[9600/16000] [L1: 0.0175] 32.2+0.0s +[11200/16000] [L1: 0.0176] 31.9+0.0s +[12800/16000] [L1: 0.0175] 31.9+0.0s +[14400/16000] [L1: 0.0175] 31.8+0.0s +[16000/16000] [L1: 0.0175] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.146 (Best: 40.394 @epoch 54) +Forward: 49.17s + +Saving... +Total: 49.73s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0172] 32.1+0.9s +[3200/16000] [L1: 0.0169] 32.2+0.0s +[4800/16000] [L1: 0.0170] 32.3+0.0s +[6400/16000] [L1: 0.0172] 32.3+0.0s +[8000/16000] [L1: 0.0171] 32.2+0.0s +[9600/16000] [L1: 0.0171] 32.0+0.0s +[11200/16000] [L1: 0.0171] 32.1+0.0s +[12800/16000] [L1: 0.0170] 31.8+0.0s +[14400/16000] [L1: 0.0171] 32.1+0.0s +[16000/16000] [L1: 0.0171] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.381 (Best: 40.394 @epoch 54) +Forward: 49.15s + +Saving... +Total: 49.78s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0164] 32.4+1.0s +[3200/16000] [L1: 0.0171] 32.2+0.0s +[4800/16000] [L1: 0.0169] 32.1+0.0s +[6400/16000] [L1: 0.0169] 32.2+0.0s +[8000/16000] [L1: 0.0170] 32.4+0.0s +[9600/16000] [L1: 0.0169] 32.2+0.0s +[11200/16000] [L1: 0.0168] 32.2+0.0s +[12800/16000] [L1: 0.0169] 32.2+0.0s +[14400/16000] [L1: 0.0168] 31.9+0.0s +[16000/16000] [L1: 0.0168] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.342 (Best: 40.394 @epoch 54) +Forward: 49.14s + +Saving... +Total: 49.81s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0161] 32.2+0.8s +[3200/16000] [L1: 0.0164] 31.9+0.0s +[4800/16000] [L1: 0.0164] 32.0+0.0s +[6400/16000] [L1: 0.0166] 32.0+0.0s +[8000/16000] [L1: 0.0165] 31.9+0.0s +[9600/16000] [L1: 0.0165] 31.9+0.0s +[11200/16000] [L1: 0.0165] 31.9+0.0s +[12800/16000] [L1: 0.0165] 32.0+0.0s +[14400/16000] [L1: 0.0166] 31.7+0.0s +[16000/16000] [L1: 0.0165] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.391 (Best: 40.394 @epoch 54) +Forward: 48.93s + +Saving... +Total: 49.48s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0169] 32.3+0.8s +[3200/16000] [L1: 0.0169] 32.1+0.0s +[4800/16000] [L1: 0.0168] 32.0+0.0s +[6400/16000] [L1: 0.0167] 32.0+0.0s +[8000/16000] [L1: 0.0166] 31.9+0.0s +[9600/16000] [L1: 0.0166] 32.0+0.0s +[11200/16000] [L1: 0.0167] 31.8+0.0s +[12800/16000] [L1: 0.0166] 32.1+0.0s +[14400/16000] [L1: 0.0167] 31.9+0.0s +[16000/16000] [L1: 0.0166] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.429 (Best: 40.429 @epoch 69) +Forward: 49.10s + +Saving... +Total: 49.74s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0168] 32.4+0.9s +[3200/16000] [L1: 0.0166] 32.2+0.0s +[4800/16000] [L1: 0.0168] 32.5+0.0s +[6400/16000] [L1: 0.0165] 32.2+0.0s +[8000/16000] [L1: 0.0165] 32.2+0.0s +[9600/16000] [L1: 0.0165] 31.8+0.0s +[11200/16000] [L1: 0.0165] 32.0+0.0s +[12800/16000] [L1: 0.0165] 32.0+0.0s +[14400/16000] [L1: 0.0165] 31.9+0.0s +[16000/16000] [L1: 0.0165] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.573 (Best: 40.573 @epoch 70) +Forward: 49.05s + +Saving... +Total: 49.64s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 32.4+0.8s +[3200/16000] [L1: 0.0172] 32.0+0.0s +[4800/16000] [L1: 0.0170] 32.1+0.0s +[6400/16000] [L1: 0.0169] 31.9+0.0s +[8000/16000] [L1: 0.0167] 32.1+0.0s +[9600/16000] [L1: 0.0167] 31.9+0.0s +[11200/16000] [L1: 0.0166] 31.7+0.0s +[12800/16000] [L1: 0.0166] 32.1+0.0s +[14400/16000] [L1: 0.0166] 31.9+0.0s +[16000/16000] [L1: 0.0165] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.406 (Best: 40.573 @epoch 70) +Forward: 49.02s + +Saving... +Total: 49.55s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0165] 32.4+0.9s +[3200/16000] [L1: 0.0163] 32.1+0.0s +[4800/16000] [L1: 0.0160] 31.9+0.0s +[6400/16000] [L1: 0.0159] 31.8+0.0s +[8000/16000] [L1: 0.0159] 31.8+0.0s +[9600/16000] [L1: 0.0160] 31.7+0.0s +[11200/16000] [L1: 0.0161] 31.7+0.0s +[12800/16000] [L1: 0.0162] 31.9+0.0s +[14400/16000] [L1: 0.0161] 31.8+0.0s +[16000/16000] [L1: 0.0162] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.642 (Best: 40.642 @epoch 72) +Forward: 49.03s + +Saving... +Total: 49.62s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0156] 32.0+0.9s +[3200/16000] [L1: 0.0161] 31.8+0.0s +[4800/16000] [L1: 0.0161] 31.8+0.0s +[6400/16000] [L1: 0.0163] 31.8+0.0s +[8000/16000] [L1: 0.0163] 32.0+0.0s +[9600/16000] [L1: 0.0164] 32.0+0.0s +[11200/16000] [L1: 0.0164] 31.9+0.0s +[12800/16000] [L1: 0.0165] 32.1+0.0s +[14400/16000] [L1: 0.0165] 32.0+0.0s +[16000/16000] [L1: 0.0164] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.595 (Best: 40.642 @epoch 72) +Forward: 48.98s + +Saving... +Total: 49.52s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0160] 32.4+0.9s +[3200/16000] [L1: 0.0159] 32.2+0.0s +[4800/16000] [L1: 0.0159] 32.2+0.0s +[6400/16000] [L1: 0.0160] 32.5+0.0s +[8000/16000] [L1: 0.0160] 32.1+0.0s +[9600/16000] [L1: 0.0160] 32.0+0.0s +[11200/16000] [L1: 0.0160] 31.7+0.0s +[12800/16000] [L1: 0.0160] 31.8+0.0s +[14400/16000] [L1: 0.0161] 31.9+0.0s +[16000/16000] [L1: 0.0161] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.635 (Best: 40.642 @epoch 72) +Forward: 49.07s + +Saving... +Total: 49.64s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0162] 32.1+0.8s +[3200/16000] [L1: 0.0160] 32.5+0.0s +[4800/16000] [L1: 0.0161] 32.2+0.0s +[6400/16000] [L1: 0.0160] 32.2+0.0s +[8000/16000] [L1: 0.0161] 31.9+0.0s +[9600/16000] [L1: 0.0231] 31.9+0.0s +[11200/16000] [L1: 0.0245] 32.0+0.0s +[12800/16000] [L1: 0.0245] 32.0+0.0s +[14400/16000] [L1: 0.0243] 32.1+0.0s +[16000/16000] [L1: 0.0242] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.486 (Best: 40.642 @epoch 72) +Forward: 49.21s + +Saving... +Total: 49.77s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0215] 32.3+1.0s +[3200/16000] [L1: 0.0216] 32.2+0.0s +[4800/16000] [L1: 0.0216] 32.3+0.0s +[6400/16000] [L1: 0.0215] 31.9+0.0s +[8000/16000] [L1: 0.0213] 32.0+0.0s +[9600/16000] [L1: 0.0212] 32.2+0.0s +[11200/16000] [L1: 0.0210] 31.7+0.0s +[12800/16000] [L1: 0.0209] 31.8+0.0s +[14400/16000] [L1: 0.0208] 32.1+0.0s +[16000/16000] [L1: 0.0208] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.219 (Best: 40.642 @epoch 72) +Forward: 48.99s + +Saving... +Total: 49.58s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0196] 32.3+0.8s +[3200/16000] [L1: 0.0196] 32.2+0.0s +[4800/16000] [L1: 0.0195] 31.9+0.0s +[6400/16000] [L1: 0.0191] 32.2+0.0s +[8000/16000] [L1: 0.0187] 32.1+0.0s +[9600/16000] [L1: 0.0185] 32.1+0.0s +[11200/16000] [L1: 0.0182] 31.9+0.0s +[12800/16000] [L1: 0.0179] 32.1+0.0s +[14400/16000] [L1: 0.0178] 31.5+0.0s +[16000/16000] [L1: 0.0177] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.488 (Best: 40.642 @epoch 72) +Forward: 49.02s + +Saving... +Total: 49.64s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0167] 32.3+0.9s +[3200/16000] [L1: 0.0166] 31.9+0.0s +[4800/16000] [L1: 0.0164] 32.4+0.0s +[6400/16000] [L1: 0.0163] 32.2+0.0s +[8000/16000] [L1: 0.0163] 32.3+0.0s +[9600/16000] [L1: 0.0162] 32.1+0.0s +[11200/16000] [L1: 0.0161] 32.1+0.0s +[12800/16000] [L1: 0.0161] 32.3+0.0s +[14400/16000] [L1: 0.0160] 31.8+0.0s +[16000/16000] [L1: 0.0160] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.587 (Best: 40.642 @epoch 72) +Forward: 49.02s + +Saving... +Total: 49.60s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0158] 32.4+0.9s +[3200/16000] [L1: 0.0160] 32.3+0.0s +[4800/16000] [L1: 0.0159] 32.1+0.0s +[6400/16000] [L1: 0.0159] 31.9+0.0s +[8000/16000] [L1: 0.0158] 31.9+0.0s +[9600/16000] [L1: 0.0160] 31.6+0.0s +[11200/16000] [L1: 0.0160] 31.5+0.0s +[12800/16000] [L1: 0.0161] 31.8+0.0s +[14400/16000] [L1: 0.0160] 31.5+0.0s +[16000/16000] [L1: 0.0160] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.726 (Best: 40.726 @epoch 79) +Forward: 49.10s + +Saving... +Total: 49.70s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0162] 32.1+0.8s +[3200/16000] [L1: 0.0161] 32.0+0.0s +[4800/16000] [L1: 0.0158] 31.7+0.0s +[6400/16000] [L1: 0.0159] 32.0+0.0s +[8000/16000] [L1: 0.0158] 32.0+0.0s +[9600/16000] [L1: 0.0158] 32.0+0.0s +[11200/16000] [L1: 0.0158] 32.1+0.0s +[12800/16000] [L1: 0.0158] 31.6+0.0s +[14400/16000] [L1: 0.0158] 31.7+0.0s +[16000/16000] [L1: 0.0158] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.642 (Best: 40.726 @epoch 79) +Forward: 48.99s + +Saving... +Total: 49.54s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0160] 32.6+0.8s +[3200/16000] [L1: 0.0158] 32.2+0.0s +[4800/16000] [L1: 0.0159] 32.1+0.0s +[6400/16000] [L1: 0.0158] 32.1+0.0s +[8000/16000] [L1: 0.0157] 32.1+0.0s +[9600/16000] [L1: 0.0158] 32.1+0.0s +[11200/16000] [L1: 0.0159] 32.4+0.0s +[12800/16000] [L1: 0.0159] 32.3+0.0s +[14400/16000] [L1: 0.0160] 32.1+0.0s +[16000/16000] [L1: 0.0205] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.128 (Best: 40.726 @epoch 79) +Forward: 49.00s + +Saving... +Total: 49.56s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0388] 32.2+0.8s +[3200/16000] [L1: 0.0311] 31.9+0.0s +[4800/16000] [L1: 0.0284] 31.6+0.0s +[6400/16000] [L1: 0.0269] 31.9+0.0s +[8000/16000] [L1: 0.0256] 31.9+0.0s +[9600/16000] [L1: 0.0249] 31.5+0.0s +[11200/16000] [L1: 0.0244] 31.7+0.0s +[12800/16000] [L1: 0.0240] 31.7+0.0s +[14400/16000] [L1: 0.0236] 31.6+0.0s +[16000/16000] [L1: 0.0233] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.396 (Best: 40.726 @epoch 79) +Forward: 49.16s + +Saving... +Total: 49.72s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0199] 32.1+0.8s +[3200/16000] [L1: 0.0195] 32.0+0.0s +[4800/16000] [L1: 0.0195] 32.0+0.0s +[6400/16000] [L1: 0.0196] 31.6+0.0s +[8000/16000] [L1: 0.0196] 31.7+0.0s +[9600/16000] [L1: 0.0196] 32.0+0.0s +[11200/16000] [L1: 0.0196] 31.5+0.0s +[12800/16000] [L1: 0.0196] 31.8+0.0s +[14400/16000] [L1: 0.0195] 31.4+0.0s +[16000/16000] [L1: 0.0194] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.785 (Best: 40.726 @epoch 79) +Forward: 48.95s + +Saving... +Total: 49.46s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0194] 32.3+0.8s +[3200/16000] [L1: 0.0197] 32.1+0.0s +[4800/16000] [L1: 0.0196] 32.1+0.0s +[6400/16000] [L1: 0.0195] 32.2+0.0s +[8000/16000] [L1: 0.0194] 31.9+0.0s +[9600/16000] [L1: 0.0194] 31.7+0.0s +[11200/16000] [L1: 0.0194] 31.7+0.0s +[12800/16000] [L1: 0.0193] 31.6+0.0s +[14400/16000] [L1: 0.0193] 31.6+0.0s +[16000/16000] [L1: 0.0193] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.838 (Best: 40.726 @epoch 79) +Forward: 49.04s + +Saving... +Total: 49.54s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0182] 32.5+0.8s +[3200/16000] [L1: 0.0187] 32.4+0.0s +[4800/16000] [L1: 0.0191] 32.0+0.0s +[6400/16000] [L1: 0.0189] 32.1+0.0s +[8000/16000] [L1: 0.0190] 32.2+0.0s +[9600/16000] [L1: 0.0189] 32.0+0.0s +[11200/16000] [L1: 0.0189] 32.1+0.0s +[12800/16000] [L1: 0.0191] 32.1+0.0s +[14400/16000] [L1: 0.0190] 31.8+0.0s +[16000/16000] [L1: 0.0190] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.824 (Best: 40.726 @epoch 79) +Forward: 49.16s + +Saving... +Total: 49.71s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0184] 32.2+0.8s +[3200/16000] [L1: 0.0184] 31.9+0.0s +[4800/16000] [L1: 0.0184] 32.1+0.0s +[6400/16000] [L1: 0.0187] 32.0+0.0s +[8000/16000] [L1: 0.0188] 32.3+0.0s +[9600/16000] [L1: 0.0188] 31.9+0.0s +[11200/16000] [L1: 0.0187] 32.1+0.0s +[12800/16000] [L1: 0.0187] 31.8+0.0s +[14400/16000] [L1: 0.0187] 31.9+0.0s +[16000/16000] [L1: 0.0187] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.971 (Best: 40.726 @epoch 79) +Forward: 49.20s + +Saving... +Total: 49.70s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0191] 32.4+0.9s +[3200/16000] [L1: 0.0191] 32.4+0.0s +[4800/16000] [L1: 0.0188] 32.4+0.0s +[6400/16000] [L1: 0.0188] 32.3+0.0s +[8000/16000] [L1: 0.0186] 32.4+0.0s +[9600/16000] [L1: 0.0187] 31.8+0.0s +[11200/16000] [L1: 0.0188] 32.1+0.0s +[12800/16000] [L1: 0.0187] 31.9+0.0s +[14400/16000] [L1: 0.0187] 31.8+0.0s +[16000/16000] [L1: 0.0186] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.026 (Best: 40.726 @epoch 79) +Forward: 49.11s + +Saving... +Total: 49.59s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0177] 32.5+0.8s +[3200/16000] [L1: 0.0180] 31.9+0.0s +[4800/16000] [L1: 0.0182] 31.8+0.0s +[6400/16000] [L1: 0.0182] 31.8+0.0s +[8000/16000] [L1: 0.0182] 32.1+0.0s +[9600/16000] [L1: 0.0181] 31.9+0.0s +[11200/16000] [L1: 0.0179] 32.1+0.0s +[12800/16000] [L1: 0.0177] 31.8+0.0s +[14400/16000] [L1: 0.0176] 31.8+0.0s +[16000/16000] [L1: 0.0175] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.578 (Best: 40.726 @epoch 79) +Forward: 49.00s + +Saving... +Total: 49.50s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0161] 32.4+0.9s +[3200/16000] [L1: 0.0165] 32.1+0.0s +[4800/16000] [L1: 0.0164] 32.3+0.0s +[6400/16000] [L1: 0.0163] 32.3+0.0s +[8000/16000] [L1: 0.0163] 31.9+0.0s +[9600/16000] [L1: 0.0163] 31.8+0.0s +[11200/16000] [L1: 0.0163] 32.0+0.0s +[12800/16000] [L1: 0.0162] 32.5+0.0s +[14400/16000] [L1: 0.0162] 31.7+0.0s +[16000/16000] [L1: 0.0161] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.640 (Best: 40.726 @epoch 79) +Forward: 48.96s + +Saving... +Total: 49.44s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0167] 32.0+0.8s +[3200/16000] [L1: 0.0163] 32.0+0.0s +[4800/16000] [L1: 0.0163] 31.8+0.0s +[6400/16000] [L1: 0.0162] 31.9+0.0s +[8000/16000] [L1: 0.0161] 32.1+0.0s +[9600/16000] [L1: 0.0160] 31.9+0.0s +[11200/16000] [L1: 0.0160] 31.6+0.0s +[12800/16000] [L1: 0.0160] 31.7+0.0s +[14400/16000] [L1: 0.0160] 31.7+0.0s +[16000/16000] [L1: 0.0160] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.756 (Best: 40.756 @epoch 90) +Forward: 49.15s + +Saving... +Total: 49.77s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 32.0+0.9s +[3200/16000] [L1: 0.0156] 32.1+0.0s +[4800/16000] [L1: 0.0156] 32.0+0.0s +[6400/16000] [L1: 0.0156] 31.9+0.0s +[8000/16000] [L1: 0.0156] 32.0+0.0s +[9600/16000] [L1: 0.0157] 31.9+0.0s +[11200/16000] [L1: 0.0157] 31.9+0.0s +[12800/16000] [L1: 0.0158] 32.0+0.0s +[14400/16000] [L1: 0.0157] 31.6+0.0s +[16000/16000] [L1: 0.0157] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.730 (Best: 40.756 @epoch 90) +Forward: 49.06s + +Saving... +Total: 49.55s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0156] 32.5+0.8s +[3200/16000] [L1: 0.0156] 32.0+0.0s +[4800/16000] [L1: 0.0157] 32.1+0.0s +[6400/16000] [L1: 0.0155] 32.3+0.0s +[8000/16000] [L1: 0.0154] 31.7+0.0s +[9600/16000] [L1: 0.0155] 31.8+0.0s +[11200/16000] [L1: 0.0155] 31.9+0.0s +[12800/16000] [L1: 0.0156] 31.7+0.0s +[14400/16000] [L1: 0.0157] 31.8+0.0s +[16000/16000] [L1: 0.0232] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.367 (Best: 40.756 @epoch 90) +Forward: 49.03s + +Saving... +Total: 49.54s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0271] 32.0+0.8s +[3200/16000] [L1: 0.0253] 32.0+0.0s +[4800/16000] [L1: 0.0241] 31.7+0.0s +[6400/16000] [L1: 0.0233] 31.9+0.0s +[8000/16000] [L1: 0.0227] 31.8+0.0s +[9600/16000] [L1: 0.0223] 32.0+0.0s +[11200/16000] [L1: 0.0219] 31.8+0.0s +[12800/16000] [L1: 0.0217] 32.3+0.0s +[14400/16000] [L1: 0.0215] 32.0+0.0s +[16000/16000] [L1: 0.0213] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.772 (Best: 40.756 @epoch 90) +Forward: 49.01s + +Saving... +Total: 49.55s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0190] 32.2+0.9s +[3200/16000] [L1: 0.0190] 32.1+0.0s +[4800/16000] [L1: 0.0191] 31.8+0.0s +[6400/16000] [L1: 0.0189] 31.8+0.0s +[8000/16000] [L1: 0.0189] 32.2+0.0s +[9600/16000] [L1: 0.0189] 31.8+0.0s +[11200/16000] [L1: 0.0189] 31.9+0.0s +[12800/16000] [L1: 0.0189] 31.8+0.0s +[14400/16000] [L1: 0.0189] 31.8+0.0s +[16000/16000] [L1: 0.0189] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.038 (Best: 40.756 @epoch 90) +Forward: 48.99s + +Saving... +Total: 49.51s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0186] 32.3+0.8s +[3200/16000] [L1: 0.0186] 32.1+0.0s +[4800/16000] [L1: 0.0187] 32.2+0.0s +[6400/16000] [L1: 0.0186] 32.0+0.0s +[8000/16000] [L1: 0.0185] 31.7+0.0s +[9600/16000] [L1: 0.0184] 31.7+0.0s +[11200/16000] [L1: 0.0184] 32.1+0.0s +[12800/16000] [L1: 0.0184] 31.8+0.0s +[14400/16000] [L1: 0.0184] 31.8+0.0s +[16000/16000] [L1: 0.0183] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.313 (Best: 40.756 @epoch 90) +Forward: 49.17s + +Saving... +Total: 49.67s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0184] 32.4+0.8s +[3200/16000] [L1: 0.0182] 32.3+0.0s +[4800/16000] [L1: 0.0184] 32.2+0.0s +[6400/16000] [L1: 0.0181] 31.9+0.0s +[8000/16000] [L1: 0.0183] 32.1+0.0s +[9600/16000] [L1: 0.0183] 32.0+0.0s +[11200/16000] [L1: 0.0184] 31.9+0.0s +[12800/16000] [L1: 0.0184] 32.1+0.0s +[14400/16000] [L1: 0.0184] 31.6+0.0s +[16000/16000] [L1: 0.0184] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.309 (Best: 40.756 @epoch 90) +Forward: 48.94s + +Saving... +Total: 49.46s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0180] 32.3+0.8s +[3200/16000] [L1: 0.0182] 32.1+0.0s +[4800/16000] [L1: 0.0183] 32.0+0.0s +[6400/16000] [L1: 0.0182] 32.1+0.0s +[8000/16000] [L1: 0.0182] 32.2+0.0s +[9600/16000] [L1: 0.0182] 31.8+0.0s +[11200/16000] [L1: 0.0182] 31.8+0.0s +[12800/16000] [L1: 0.0182] 31.9+0.0s +[14400/16000] [L1: 0.0182] 32.2+0.0s +[16000/16000] [L1: 0.0182] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.404 (Best: 40.756 @epoch 90) +Forward: 48.84s + +Saving... +Total: 49.35s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0176] 32.4+0.8s +[3200/16000] [L1: 0.0182] 32.2+0.0s +[4800/16000] [L1: 0.0181] 32.2+0.0s +[6400/16000] [L1: 0.0181] 31.7+0.0s +[8000/16000] [L1: 0.0180] 32.0+0.0s +[9600/16000] [L1: 0.0180] 32.1+0.0s +[11200/16000] [L1: 0.0181] 32.2+0.0s +[12800/16000] [L1: 0.0182] 31.9+0.0s +[14400/16000] [L1: 0.0183] 31.9+0.0s +[16000/16000] [L1: 0.0183] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.172 (Best: 40.756 @epoch 90) +Forward: 48.90s + +Saving... +Total: 49.42s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0180] 32.6+0.9s +[3200/16000] [L1: 0.0178] 32.2+0.0s +[4800/16000] [L1: 0.0179] 32.3+0.0s +[6400/16000] [L1: 0.0178] 31.8+0.0s +[8000/16000] [L1: 0.0179] 31.6+0.0s +[9600/16000] [L1: 0.0180] 31.7+0.0s +[11200/16000] [L1: 0.0180] 32.0+0.0s +[12800/16000] [L1: 0.0181] 32.1+0.0s +[14400/16000] [L1: 0.0181] 31.9+0.0s +[16000/16000] [L1: 0.0181] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.401 (Best: 40.756 @epoch 90) +Forward: 49.01s + +Saving... +Total: 49.52s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0179] 32.3+0.8s +[3200/16000] [L1: 0.0181] 32.5+0.0s +[4800/16000] [L1: 0.0180] 31.9+0.0s +[6400/16000] [L1: 0.0180] 32.0+0.0s +[8000/16000] [L1: 0.0180] 31.9+0.0s +[9600/16000] [L1: 0.0180] 32.0+0.0s +[11200/16000] [L1: 0.0180] 31.8+0.0s +[12800/16000] [L1: 0.0179] 32.1+0.0s +[14400/16000] [L1: 0.0179] 31.9+0.0s +[16000/16000] [L1: 0.0180] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.502 (Best: 40.756 @epoch 90) +Forward: 49.10s + +Saving... +Total: 49.70s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0178] 32.2+0.9s +[3200/16000] [L1: 0.0181] 32.0+0.0s +[4800/16000] [L1: 0.0180] 32.0+0.0s +[6400/16000] [L1: 0.0180] 31.9+0.0s +[8000/16000] [L1: 0.0180] 32.0+0.0s +[9600/16000] [L1: 0.0179] 32.0+0.0s +[11200/16000] [L1: 0.0178] 31.9+0.0s +[12800/16000] [L1: 0.0178] 31.8+0.0s +[14400/16000] [L1: 0.0178] 32.4+0.0s +[16000/16000] [L1: 0.0244] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.738 (Best: 40.756 @epoch 90) +Forward: 49.01s + +Saving... +Total: 49.60s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0235] 32.4+0.9s +[3200/16000] [L1: 0.0234] 31.8+0.0s +[4800/16000] [L1: 0.0229] 32.0+0.0s +[6400/16000] [L1: 0.0221] 31.7+0.0s +[8000/16000] [L1: 0.0214] 31.9+0.0s +[9600/16000] [L1: 0.0211] 31.7+0.0s +[11200/16000] [L1: 0.0207] 31.9+0.0s +[12800/16000] [L1: 0.0205] 31.6+0.0s +[14400/16000] [L1: 0.0203] 31.8+0.0s +[16000/16000] [L1: 0.0203] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.452 (Best: 40.756 @epoch 90) +Forward: 48.93s + +Saving... +Total: 49.44s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0184] 32.1+0.8s +[3200/16000] [L1: 0.0183] 31.6+0.0s +[4800/16000] [L1: 0.0181] 32.0+0.0s +[6400/16000] [L1: 0.0184] 31.8+0.0s +[8000/16000] [L1: 0.0182] 31.8+0.0s +[9600/16000] [L1: 0.0183] 31.7+0.0s +[11200/16000] [L1: 0.0183] 31.9+0.0s +[12800/16000] [L1: 0.0183] 31.6+0.0s +[14400/16000] [L1: 0.0183] 31.6+0.0s +[16000/16000] [L1: 0.0183] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.584 (Best: 40.756 @epoch 90) +Forward: 48.91s + +Saving... +Total: 49.43s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0180] 32.2+0.7s +[3200/16000] [L1: 0.0181] 32.2+0.0s +[4800/16000] [L1: 0.0181] 32.1+0.0s +[6400/16000] [L1: 0.0181] 32.2+0.0s +[8000/16000] [L1: 0.0180] 32.1+0.0s +[9600/16000] [L1: 0.0179] 31.9+0.0s +[11200/16000] [L1: 0.0180] 31.9+0.0s +[12800/16000] [L1: 0.0180] 31.8+0.0s +[14400/16000] [L1: 0.0179] 32.0+0.0s +[16000/16000] [L1: 0.0179] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.590 (Best: 40.756 @epoch 90) +Forward: 48.90s + +Saving... +Total: 49.43s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0177] 32.0+0.9s +[3200/16000] [L1: 0.0178] 31.7+0.0s +[4800/16000] [L1: 0.0178] 31.7+0.0s +[6400/16000] [L1: 0.0178] 31.8+0.0s +[8000/16000] [L1: 0.0177] 32.1+0.0s +[9600/16000] [L1: 0.0177] 31.9+0.0s +[11200/16000] [L1: 0.0176] 31.5+0.0s +[12800/16000] [L1: 0.0177] 31.9+0.0s +[14400/16000] [L1: 0.0177] 32.1+0.0s +[16000/16000] [L1: 0.0177] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.479 (Best: 40.756 @epoch 90) +Forward: 48.90s + +Saving... +Total: 49.41s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0177] 32.4+0.9s +[3200/16000] [L1: 0.0179] 31.9+0.0s +[4800/16000] [L1: 0.0176] 31.8+0.0s +[6400/16000] [L1: 0.0177] 32.0+0.0s +[8000/16000] [L1: 0.0177] 32.0+0.0s +[9600/16000] [L1: 0.0179] 32.0+0.0s +[11200/16000] [L1: 0.0178] 32.0+0.0s +[12800/16000] [L1: 0.0178] 32.2+0.0s +[14400/16000] [L1: 0.0178] 31.8+0.0s +[16000/16000] [L1: 0.0178] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.493 (Best: 40.756 @epoch 90) +Forward: 48.89s + +Saving... +Total: 49.41s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0175] 32.4+0.8s +[3200/16000] [L1: 0.0174] 32.2+0.0s +[4800/16000] [L1: 0.0175] 32.2+0.0s +[6400/16000] [L1: 0.0174] 32.0+0.0s +[8000/16000] [L1: 0.0175] 32.3+0.0s +[9600/16000] [L1: 0.0176] 32.4+0.0s +[11200/16000] [L1: 0.0176] 31.9+0.0s +[12800/16000] [L1: 0.0175] 32.0+0.0s +[14400/16000] [L1: 0.0175] 31.9+0.0s +[16000/16000] [L1: 0.0175] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.613 (Best: 40.756 @epoch 90) +Forward: 48.89s + +Saving... +Total: 49.40s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0177] 32.3+0.8s +[3200/16000] [L1: 0.0180] 32.4+0.0s +[4800/16000] [L1: 0.0411] 32.0+0.0s +[6400/16000] [L1: 0.0379] 32.1+0.0s +[8000/16000] [L1: 0.0347] 31.8+0.0s +[9600/16000] [L1: 0.0322] 31.8+0.0s +[11200/16000] [L1: 0.0303] 32.1+0.0s +[12800/16000] [L1: 0.0288] 31.7+0.0s +[14400/16000] [L1: 0.0277] 31.9+0.0s +[16000/16000] [L1: 0.0267] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.573 (Best: 40.756 @epoch 90) +Forward: 48.91s + +Saving... +Total: 49.45s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0175] 32.2+0.9s +[3200/16000] [L1: 0.0174] 32.3+0.0s +[4800/16000] [L1: 0.0177] 31.9+0.0s +[6400/16000] [L1: 0.0178] 32.0+0.0s +[8000/16000] [L1: 0.0179] 31.9+0.0s +[9600/16000] [L1: 0.0180] 32.0+0.0s +[11200/16000] [L1: 0.0184] 31.9+0.0s +[12800/16000] [L1: 0.0183] 31.5+0.0s +[14400/16000] [L1: 0.0185] 31.9+0.0s +[16000/16000] [L1: 0.0184] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.542 (Best: 40.756 @epoch 90) +Forward: 48.91s + +Saving... +Total: 49.47s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0178] 32.2+0.8s +[3200/16000] [L1: 0.0176] 31.9+0.0s +[4800/16000] [L1: 0.0183] 32.0+0.0s +[6400/16000] [L1: 0.0184] 31.8+0.0s +[8000/16000] [L1: 0.0214] 31.6+0.0s +[9600/16000] [L1: 0.0219] 32.1+0.0s +[11200/16000] [L1: 0.0217] 31.9+0.0s +[12800/16000] [L1: 0.0216] 32.0+0.0s +[14400/16000] [L1: 0.0233] 31.9+0.0s +[16000/16000] [L1: 0.0232] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.928 (Best: 40.756 @epoch 90) +Forward: 48.86s + +Saving... +Total: 49.45s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0401] 32.1+0.8s +[3200/16000] [L1: 0.0310] 32.0+0.0s +[4800/16000] [L1: 0.0372] 31.9+0.0s +[6400/16000] [L1: 0.0347] 32.0+0.0s +[8000/16000] [L1: 0.0377] 31.9+0.0s +[9600/16000] [L1: 0.0357] 32.0+0.0s +[11200/16000] [L1: 0.0338] 31.7+0.0s +[12800/16000] [L1: 0.0323] 31.6+0.0s +[14400/16000] [L1: 0.0310] 31.6+0.0s +[16000/16000] [L1: 0.0300] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.984 (Best: 40.756 @epoch 90) +Forward: 49.00s + +Saving... +Total: 49.51s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0218] 31.9+0.8s +[3200/16000] [L1: 0.0648] 31.9+0.0s +[4800/16000] [L1: 0.0546] 32.2+0.0s +[6400/16000] [L1: 0.0477] 31.7+0.0s +[8000/16000] [L1: 0.0426] 31.6+0.0s +[9600/16000] [L1: 0.0396] 31.6+0.0s +[11200/16000] [L1: 0.0392] 31.6+0.0s +[12800/16000] [L1: 0.0377] 31.7+0.0s +[14400/16000] [L1: 0.0379] 31.6+0.0s +[16000/16000] [L1: 0.0377] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.933 (Best: 40.756 @epoch 90) +Forward: 49.08s + +Saving... +Total: 49.59s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0423] 32.4+0.8s +[3200/16000] [L1: 8.9637] 31.8+0.0s +[4800/16000] [L1: 657.8040] 31.6+0.0s +[6400/16000] [L1: 718.6570] 31.5+0.0s +[8000/16000] [L1: 1078.8483] 31.8+0.0s +[9600/16000] [L1: 1437.9784] 32.0+0.0s +[11200/16000] [L1: 12455.1045] 31.7+0.0s +[12800/16000] [L1: 12599.0615] 31.8+0.0s +[14400/16000] [L1: 12576.2197] 32.0+0.0s +[16000/16000] [L1: 17171.6270] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.936 (Best: 40.756 @epoch 90) +Forward: 48.76s + +Saving... +Total: 49.29s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [L1: 1896333.5000] 32.3+0.7s +[3200/16000] [L1: 1676170.3750] 31.9+0.0s +[4800/16000] [L1: 1311880.6250] 31.7+0.0s +[6400/16000] [L1: 1099853.5000] 31.7+0.0s +[8000/16000] [L1: 959097.7500] 31.5+0.0s +[9600/16000] [L1: 856243.2500] 31.5+0.0s +[11200/16000] [L1: 786079.2500] 31.6+0.0s +[12800/16000] [L1: 725553.1875] 32.0+0.0s +[14400/16000] [L1: 672614.6250] 31.6+0.0s +[16000/16000] [L1: 628213.9375] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.089 (Best: 40.756 @epoch 90) +Forward: 48.45s + +Saving... +Total: 48.98s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [L1: 223063.4219] 32.1+0.9s +[3200/16000] [L1: 206588.5625] 31.8+0.0s +[4800/16000] [L1: 196700.4688] 31.5+0.0s +[6400/16000] [L1: 190545.9531] 31.8+0.0s +[8000/16000] [L1: 181978.1875] 31.6+0.0s +[9600/16000] [L1: 175535.2344] 32.0+0.0s +[11200/16000] [L1: 169666.8750] 31.5+0.0s +[12800/16000] [L1: 164704.4219] 31.4+0.0s +[14400/16000] [L1: 159585.3438] 31.3+0.0s +[16000/16000] [L1: 155134.5469] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.112 (Best: 40.756 @epoch 90) +Forward: 48.59s + +Saving... +Total: 49.16s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [L1: 106852.6875] 32.0+0.8s +[3200/16000] [L1: 103993.5000] 32.0+0.0s +[4800/16000] [L1: 100955.0625] 31.9+0.0s +[6400/16000] [L1: 98214.9688] 32.0+0.0s +[8000/16000] [L1: 95764.6875] 32.0+0.0s +[9600/16000] [L1: 93611.2734] 31.9+0.0s +[11200/16000] [L1: 91871.9375] 31.7+0.0s +[12800/16000] [L1: 89917.6562] 32.1+0.0s +[14400/16000] [L1: 88406.8828] 31.8+0.0s +[16000/16000] [L1: 87148.4297] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.260 (Best: 40.756 @epoch 90) +Forward: 48.56s + +Saving... +Total: 49.11s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [L1: 78431.1016] 32.4+0.8s +[3200/16000] [L1: 74357.0312] 32.0+0.0s +[4800/16000] [L1: 73860.3438] 31.9+0.0s +[6400/16000] [L1: 73044.3672] 31.9+0.0s +[8000/16000] [L1: 85566.2109] 31.8+0.0s +[9600/16000] [L1: 85890.7266] 31.7+0.0s +[11200/16000] [L1: 83092.0312] 31.6+0.0s +[12800/16000] [L1: 81737.2344] 31.6+0.0s +[14400/16000] [L1: 80593.6953] 31.5+0.0s +[16000/16000] [L1: 79949.7969] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.478 (Best: 40.756 @epoch 90) +Forward: 48.57s + +Saving... +Total: 49.15s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [L1: 64993.4453] 31.9+0.8s +[3200/16000] [L1: 70210.6406] 31.6+0.0s +[4800/16000] [L1: 71134.0391] 31.6+0.0s +[6400/16000] [L1: 71479.7031] 31.5+0.0s +[8000/16000] [L1: 70865.1094] 31.7+0.0s +[9600/16000] [L1: 69744.0625] 31.8+0.0s +[11200/16000] [L1: 68879.7969] 31.5+0.0s +[12800/16000] [L1: 67699.8828] 31.8+0.0s +[14400/16000] [L1: 66140.7500] 31.7+0.0s +[16000/16000] [L1: 65343.5000] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.781 (Best: 40.756 @epoch 90) +Forward: 48.49s + +Saving... +Total: 49.10s + +[Epoch 119] Learning rate: 1.00e-4 +[1600/16000] [L1: 55246.3359] 32.1+0.7s +[3200/16000] [L1: 53378.4141] 31.5+0.0s +[4800/16000] [L1: 53495.5039] 31.5+0.0s +[6400/16000] [L1: 52949.3789] 31.5+0.0s +[8000/16000] [L1: 53306.1523] 31.6+0.0s +[9600/16000] [L1: 54436.1211] 31.6+0.0s +[11200/16000] [L1: 57373.6914] 31.7+0.0s +[12800/16000] [L1: 59109.8516] 31.6+0.0s +[14400/16000] [L1: 58640.7539] 31.5+0.0s +[16000/16000] [L1: 58709.3711] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.418 (Best: 40.756 @epoch 90) +Forward: 48.46s + +Saving... +Total: 49.07s + +[Epoch 120] Learning rate: 1.00e-4 +[1600/16000] [L1: 52841.0312] 32.1+0.8s +[3200/16000] [L1: 54290.2305] 31.8+0.0s +[4800/16000] [L1: 52324.9961] 32.1+0.0s +[6400/16000] [L1: 50129.6953] 32.0+0.0s +[8000/16000] [L1: 47998.3281] 31.8+0.0s +[9600/16000] [L1: 50693.4414] 31.8+0.0s +[11200/16000] [L1: 54959.2227] 31.6+0.0s +[12800/16000] [L1: 53582.3008] 31.5+0.0s +[14400/16000] [L1: 52357.5703] 31.5+0.0s +[16000/16000] [L1: 51262.3906] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.727 (Best: 40.756 @epoch 90) +Forward: 48.54s + +Saving... +Total: 49.09s + +[Epoch 121] Learning rate: 1.00e-4 +[1600/16000] [L1: 37639.0195] 32.3+0.9s +[3200/16000] [L1: 38238.4219] 32.2+0.0s +[4800/16000] [L1: 46947.6406] 32.0+0.0s +[6400/16000] [L1: 47088.8359] 31.8+0.0s +[8000/16000] [L1: 45752.1055] 31.9+0.0s +[9600/16000] [L1: 45280.9648] 31.7+0.0s +[11200/16000] [L1: 44266.4883] 31.7+0.0s +[12800/16000] [L1: 43341.2031] 31.6+0.0s +[14400/16000] [L1: 42410.5430] 31.6+0.0s +[16000/16000] [L1: 41671.2734] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.717 (Best: 40.756 @epoch 90) +Forward: 48.47s + +Saving... +Total: 49.02s + +[Epoch 122] Learning rate: 1.00e-4 +[1600/16000] [L1: 106609.8672] 32.3+1.0s +[3200/16000] [L1: 76432.9688] 32.1+0.0s +[4800/16000] [L1: 65433.1406] 31.9+0.0s +[6400/16000] [L1: 124974.7188] 31.7+0.0s +[8000/16000] [L1: 113053.8594] 31.8+0.0s +[9600/16000] [L1: 100249.0859] 31.9+0.0s +[11200/16000] [L1: 92140.4688] 32.0+0.0s +[12800/16000] [L1: 85607.1484] 31.8+0.0s +[14400/16000] [L1: 80093.0781] 32.1+0.0s +[16000/16000] [L1: 75392.1406] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.417 (Best: 40.756 @epoch 90) +Forward: 48.45s + +Saving... +Total: 49.12s + +[Epoch 123] Learning rate: 1.00e-4 +[1600/16000] [L1: 37086.4961] 32.1+0.8s +[3200/16000] [L1: 35213.6758] 32.0+0.0s +[4800/16000] [L1: 33242.2617] 32.0+0.0s +[6400/16000] [L1: 33594.4336] 31.8+0.0s +[8000/16000] [L1: 33351.5508] 31.6+0.0s +[9600/16000] [L1: 31996.5957] 31.5+0.0s +[11200/16000] [L1: 31411.1738] 32.0+0.0s +[12800/16000] [L1: 31332.6230] 31.7+0.0s +[14400/16000] [L1: 31564.3262] 31.5+0.0s +[16000/16000] [L1: 31597.9570] 31.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.466 (Best: 40.756 @epoch 90) +Forward: 48.72s + +Saving... +Total: 49.27s + +[Epoch 124] Learning rate: 1.00e-4 +[1600/16000] [L1: 37507.3086] 32.1+0.8s +[3200/16000] [L1: 42159.5156] 32.1+0.0s +[4800/16000] [L1: 38197.5039] 31.9+0.0s +[6400/16000] [L1: 36710.4180] 31.9+0.0s +[8000/16000] [L1: 35312.0195] 32.0+0.0s +[9600/16000] [L1: 35094.1523] 31.6+0.0s +[11200/16000] [L1: 33919.2500] 32.0+0.0s +[12800/16000] [L1: 33410.3242] 31.7+0.0s +[14400/16000] [L1: 33463.8711] 31.6+0.0s +[16000/16000] [L1: 33334.3320] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.470 (Best: 40.756 @epoch 90) +Forward: 48.71s + +Saving... +Total: 49.27s + +[Epoch 125] Learning rate: 1.00e-4 +[1600/16000] [L1: 27002.1523] 32.3+0.8s +[3200/16000] [L1: 27182.3496] 32.5+0.0s +[4800/16000] [L1: 178641.0469] 31.8+0.0s +[6400/16000] [L1: 171932.8438] 31.8+0.0s +[8000/16000] [L1: 147194.1562] 31.8+0.0s +[9600/16000] [L1: 128801.6562] 31.4+0.0s +[11200/16000] [L1: 115022.5625] 31.4+0.0s +[12800/16000] [L1: 104910.0469] 31.7+0.0s +[14400/16000] [L1: 96667.9531] 31.5+0.0s +[16000/16000] [L1: 89481.1797] 31.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.799 (Best: 40.756 @epoch 90) +Forward: 48.56s + +Saving... +Total: 49.14s + +[Epoch 126] Learning rate: 1.00e-4 +[1600/16000] [L1: 21663.8594] 32.1+0.8s +[3200/16000] [L1: 21620.3047] 32.4+0.0s +[4800/16000] [L1: 21651.9395] 32.0+0.0s +[6400/16000] [L1: 21540.1875] 31.9+0.0s +[8000/16000] [L1: 21463.7852] 31.9+0.0s +[9600/16000] [L1: 21453.9746] 31.7+0.0s +[11200/16000] [L1: 22210.5078] 31.6+0.0s +[12800/16000] [L1: 22263.5078] 31.6+0.0s +[14400/16000] [L1: 22372.4355] 31.9+0.0s +[16000/16000] [L1: 21801.4336] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.455 (Best: 40.756 @epoch 90) +Forward: 48.54s + +Saving... +Total: 49.26s + +[Epoch 127] Learning rate: 1.00e-4 +[1600/16000] [L1: 15313.1641] 32.0+0.8s +[3200/16000] [L1: 15043.8516] 32.1+0.0s +[4800/16000] [L1: 14542.7666] 31.8+0.0s +[6400/16000] [L1: 14663.3711] 32.1+0.0s +[8000/16000] [L1: 14966.4131] 31.7+0.0s +[9600/16000] [L1: 14898.0371] 31.8+0.0s +[11200/16000] [L1: 14639.1201] 31.7+0.0s +[12800/16000] [L1: 14691.6748] 32.1+0.0s +[14400/16000] [L1: 15347.2832] 31.8+0.0s +[16000/16000] [L1: 15761.5029] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 5.331 (Best: 40.756 @epoch 90) +Forward: 48.55s + +Saving... +Total: 49.16s + +[Epoch 128] Learning rate: 1.00e-4 +[1600/16000] [L1: 18398.8281] 32.1+0.8s +[3200/16000] [L1: 18491.3652] 31.9+0.0s +[4800/16000] [L1: 20303.4805] 31.9+0.0s +[6400/16000] [L1: 21899.0352] 31.7+0.0s +[8000/16000] [L1: 23045.7637] 31.9+0.0s +[9600/16000] [L1: 23297.2324] 31.8+0.0s +[11200/16000] [L1: 23533.2480] 31.5+0.0s +[12800/16000] [L1: 54415.9297] 31.5+0.0s +[14400/16000] [L1: 77651.2734] 31.7+0.0s +[16000/16000] [L1: 72418.1797] 31.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 4.804 (Best: 40.756 @epoch 90) +Forward: 48.40s + +Saving... +Total: 48.94s + +[Epoch 129] Learning rate: 1.00e-4 +[1600/16000] [L1: 32776.7031] 31.9+0.9s +[3200/16000] [L1: 20782.2070] 32.0+0.0s +[4800/16000] [L1: inf] 32.2+0.0s +[6400/16000] [L1: inf] 31.7+0.0s +[8000/16000] [L1: inf] 31.6+0.0s +[9600/16000] [L1: inf] 31.6+0.0s +[11200/16000] [L1: inf] 31.6+0.0s +[12800/16000] [L1: inf] 31.6+0.0s +[14400/16000] [L1: inf] 31.6+0.0s +[16000/16000] [L1: inf] 31.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.40s + +Saving... +Total: 47.98s + +[Epoch 130] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.7+0.8s +[3200/16000] [L1: inf] 31.5+0.0s +[4800/16000] [L1: inf] 31.2+0.0s +[6400/16000] [L1: inf] 31.4+0.0s +[8000/16000] [L1: inf] 31.1+0.0s +[9600/16000] [L1: inf] 31.1+0.0s +[11200/16000] [L1: inf] 31.2+0.0s +[12800/16000] [L1: inf] 31.4+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 31.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.26s + +Saving... +Total: 47.81s + +[Epoch 131] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.5+0.9s +[3200/16000] [L1: inf] 31.3+0.0s +[4800/16000] [L1: inf] 31.3+0.0s +[6400/16000] [L1: inf] 31.4+0.0s +[8000/16000] [L1: inf] 31.3+0.0s +[9600/16000] [L1: inf] 31.4+0.0s +[11200/16000] [L1: inf] 31.1+0.0s +[12800/16000] [L1: inf] 31.5+0.0s +[14400/16000] [L1: inf] 31.6+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.33s + +Saving... +Total: 47.88s + +[Epoch 132] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.7+0.8s +[3200/16000] [L1: inf] 31.3+0.0s +[4800/16000] [L1: inf] 31.2+0.0s +[6400/16000] [L1: inf] 31.1+0.0s +[8000/16000] [L1: inf] 31.1+0.0s +[9600/16000] [L1: inf] 31.4+0.0s +[11200/16000] [L1: inf] 31.3+0.0s +[12800/16000] [L1: inf] 31.2+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.37s + +Saving... +Total: 47.90s + +[Epoch 133] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.7+0.9s +[3200/16000] [L1: inf] 31.4+0.0s +[4800/16000] [L1: inf] 31.4+0.0s +[6400/16000] [L1: inf] 31.7+0.0s +[8000/16000] [L1: inf] 31.6+0.0s +[9600/16000] [L1: inf] 31.4+0.0s +[11200/16000] [L1: inf] 31.7+0.0s +[12800/16000] [L1: inf] 31.4+0.0s +[14400/16000] [L1: inf] 31.1+0.0s +[16000/16000] [L1: inf] 30.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.43s + +Saving... +Total: 48.00s + +[Epoch 134] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.8+0.8s +[3200/16000] [L1: inf] 32.2+0.0s +[4800/16000] [L1: inf] 31.6+0.0s +[6400/16000] [L1: inf] 31.8+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.5+0.0s +[11200/16000] [L1: inf] 31.5+0.0s +[12800/16000] [L1: inf] 31.6+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 31.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.53s + +Saving... +Total: 48.06s + +[Epoch 135] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.6+0.9s +[3200/16000] [L1: inf] 31.5+0.0s +[4800/16000] [L1: inf] 31.4+0.0s +[6400/16000] [L1: inf] 31.3+0.0s +[8000/16000] [L1: inf] 31.1+0.0s +[9600/16000] [L1: inf] 31.1+0.0s +[11200/16000] [L1: inf] 31.3+0.0s +[12800/16000] [L1: inf] 31.5+0.0s +[14400/16000] [L1: inf] 31.5+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.35s + +Saving... +Total: 47.90s + +[Epoch 136] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.6+0.8s +[3200/16000] [L1: inf] 31.4+0.0s +[4800/16000] [L1: inf] 31.3+0.0s +[6400/16000] [L1: inf] 31.3+0.0s +[8000/16000] [L1: inf] 31.3+0.0s +[9600/16000] [L1: inf] 31.3+0.0s +[11200/16000] [L1: inf] 31.3+0.0s +[12800/16000] [L1: inf] 31.5+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.26s + +Saving... +Total: 47.90s + +[Epoch 137] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.9+0.8s +[3200/16000] [L1: inf] 31.5+0.0s +[4800/16000] [L1: inf] 31.3+0.0s +[6400/16000] [L1: inf] 31.6+0.0s +[8000/16000] [L1: inf] 31.2+0.0s +[9600/16000] [L1: inf] 31.3+0.0s +[11200/16000] [L1: inf] 31.3+0.0s +[12800/16000] [L1: inf] 31.1+0.0s +[14400/16000] [L1: inf] 31.2+0.0s +[16000/16000] [L1: inf] 31.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.34s + +Saving... +Total: 47.99s + +[Epoch 138] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.6+0.8s +[3200/16000] [L1: inf] 31.5+0.0s +[4800/16000] [L1: inf] 31.6+0.0s +[6400/16000] [L1: inf] 31.5+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.2+0.0s +[11200/16000] [L1: inf] 31.2+0.0s +[12800/16000] [L1: inf] 31.1+0.0s +[14400/16000] [L1: inf] 31.1+0.0s +[16000/16000] [L1: inf] 30.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.36s + +Saving... +Total: 47.92s + +[Epoch 139] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 32.2+0.8s +[3200/16000] [L1: inf] 31.7+0.0s +[4800/16000] [L1: inf] 31.6+0.0s +[6400/16000] [L1: inf] 31.7+0.0s +[8000/16000] [L1: inf] 31.8+0.0s +[9600/16000] [L1: inf] 31.5+0.0s +[11200/16000] [L1: inf] 31.6+0.0s +[12800/16000] [L1: inf] 31.4+0.0s +[14400/16000] [L1: inf] 31.6+0.0s +[16000/16000] [L1: inf] 31.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.45s + +Saving... +Total: 48.01s + +[Epoch 140] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.8+0.8s +[3200/16000] [L1: inf] 31.4+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +[6400/16000] [L1: inf] 31.4+0.0s +[8000/16000] [L1: inf] 31.1+0.0s +[9600/16000] [L1: inf] 31.3+0.0s +[11200/16000] [L1: inf] 31.6+0.0s +[12800/16000] [L1: inf] 31.8+0.0s +[14400/16000] [L1: inf] 31.2+0.0s +[16000/16000] [L1: inf] 31.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.28s + +Saving... +Total: 47.84s + +[Epoch 141] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.6+1.0s +[3200/16000] [L1: inf] 31.2+0.0s +[4800/16000] [L1: inf] 31.6+0.0s +[6400/16000] [L1: inf] 31.5+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.7+0.0s +[11200/16000] [L1: inf] 31.2+0.0s +[12800/16000] [L1: inf] 31.2+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.22s + +Saving... +Total: 47.76s + +[Epoch 142] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.5+1.1s +[3200/16000] [L1: inf] 31.6+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +[6400/16000] [L1: inf] 31.7+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.5+0.0s +[11200/16000] [L1: inf] 31.7+0.0s +[12800/16000] [L1: inf] 31.7+0.0s +[14400/16000] [L1: inf] 31.4+0.0s +[16000/16000] [L1: inf] 31.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.53s + +Saving... +Total: 48.07s + +[Epoch 143] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.9+0.8s +[3200/16000] [L1: inf] 31.8+0.0s +[4800/16000] [L1: inf] 31.5+0.0s +[6400/16000] [L1: inf] 31.4+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.3+0.0s +[11200/16000] [L1: inf] 31.5+0.0s +[12800/16000] [L1: inf] 31.3+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.39s + +Saving... +Total: 47.93s + +[Epoch 144] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.4+0.8s +[3200/16000] [L1: inf] 31.7+0.0s +[4800/16000] [L1: inf] 31.6+0.0s +[6400/16000] [L1: inf] 31.4+0.0s +[8000/16000] [L1: inf] 31.4+0.0s +[9600/16000] [L1: inf] 31.7+0.0s +[11200/16000] [L1: inf] 31.5+0.0s +[12800/16000] [L1: inf] 31.3+0.0s +[14400/16000] [L1: inf] 31.7+0.0s +[16000/16000] [L1: inf] 31.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.41s + +Saving... +Total: 48.04s + +[Epoch 145] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.8+0.8s +[3200/16000] [L1: inf] 31.8+0.0s +[4800/16000] [L1: inf] 31.4+0.0s +[6400/16000] [L1: inf] 31.2+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.4+0.0s +[11200/16000] [L1: inf] 31.8+0.0s +[12800/16000] [L1: inf] 31.2+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 31.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.32s + +Saving... +Total: 48.72s + +[Epoch 146] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.8+0.8s +[3200/16000] [L1: inf] 31.4+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +[6400/16000] [L1: inf] 31.5+0.0s +[8000/16000] [L1: inf] 32.0+0.0s +[9600/16000] [L1: inf] 31.8+0.0s +[11200/16000] [L1: inf] 31.7+0.0s +[12800/16000] [L1: inf] 31.5+0.0s +[14400/16000] [L1: inf] 31.7+0.0s +[16000/16000] [L1: inf] 31.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.29s + +Saving... +Total: 47.85s + +[Epoch 147] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.9+0.9s +[3200/16000] [L1: inf] 31.9+0.0s +[4800/16000] [L1: inf] 32.0+0.0s +[6400/16000] [L1: inf] 31.7+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.5+0.0s +[11200/16000] [L1: inf] 31.5+0.0s +[12800/16000] [L1: inf] 31.3+0.0s +[14400/16000] [L1: inf] 31.2+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.26s + +Saving... +Total: 47.81s + +[Epoch 148] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.9+0.8s +[3200/16000] [L1: inf] 31.6+0.0s +[4800/16000] [L1: inf] 31.5+0.0s +[6400/16000] [L1: inf] 31.2+0.0s +[8000/16000] [L1: inf] 31.2+0.0s +[9600/16000] [L1: inf] 31.1+0.0s +[11200/16000] [L1: inf] 31.2+0.0s +[12800/16000] [L1: inf] 31.2+0.0s +[14400/16000] [L1: inf] 31.1+0.0s +[16000/16000] [L1: inf] 31.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.44s + +Saving... +Total: 48.00s + +[Epoch 149] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.9+0.8s +[3200/16000] [L1: inf] 31.7+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +[6400/16000] [L1: inf] 31.5+0.0s +[8000/16000] [L1: inf] 31.2+0.0s +[9600/16000] [L1: inf] 31.4+0.0s +[11200/16000] [L1: inf] 31.5+0.0s +[12800/16000] [L1: inf] 31.6+0.0s +[14400/16000] [L1: inf] 31.1+0.0s +[16000/16000] [L1: inf] 30.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.39s + +Saving... +Total: 47.95s + +[Epoch 150] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.7+1.0s +[3200/16000] [L1: inf] 31.6+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +[6400/16000] [L1: inf] 31.9+0.0s +[8000/16000] [L1: inf] 31.7+0.0s +[9600/16000] [L1: inf] 31.9+0.0s +[11200/16000] [L1: inf] 31.6+0.0s +[12800/16000] [L1: inf] 31.6+0.0s +[14400/16000] [L1: inf] 31.7+0.0s +[16000/16000] [L1: inf] 31.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.55s + +Saving... +Total: 48.12s + +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.7+0.9s +[3200/16000] [L1: inf] 31.8+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 151] Learning rate: 1.00e-4 +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 34.9+1.0s +[3200/16000] [L1: inf] 31.5+0.0s +[4800/16000] [L1: inf] 31.6+0.0s +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 35.0+0.8s +[3200/16000] [L1: inf] 31.7+0.0s +[4800/16000] [L1: inf] 32.0+0.0s +[6400/16000] [L1: inf] 32.0+0.0s +[8000/16000] [L1: inf] 32.1+0.0s +[9600/16000] [L1: inf] 32.0+0.1s +[11200/16000] [L1: inf] 32.2+0.0s +[12800/16000] [L1: inf] 31.8+0.0s +[14400/16000] [L1: inf] 31.9+0.0s +[16000/16000] [L1: inf] 30.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.80s + +Saving... +Total: 48.57s + +[Epoch 152] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 32.0+1.4s +[3200/16000] [L1: inf] 31.8+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +[6400/16000] [L1: inf] 31.1+0.0s +[8000/16000] [L1: inf] 31.4+0.0s +[9600/16000] [L1: inf] 31.3+0.0s +[11200/16000] [L1: inf] 31.3+0.0s +[12800/16000] [L1: inf] 31.6+0.0s +[14400/16000] [L1: inf] 31.6+0.0s +[16000/16000] [L1: inf] 31.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.71s + +Saving... +Total: 48.28s + +[Epoch 153] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.8+1.2s +[3200/16000] [L1: inf] 31.8+0.0s +[4800/16000] [L1: inf] 31.7+0.0s +[6400/16000] [L1: inf] 31.3+0.0s +[8000/16000] [L1: inf] 31.4+0.0s +[9600/16000] [L1: inf] 31.5+0.0s +[11200/16000] [L1: inf] 31.5+0.0s +[12800/16000] [L1: inf] 31.7+0.0s +[14400/16000] [L1: inf] 31.6+0.0s +[16000/16000] [L1: inf] 31.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.70s + +Saving... +Total: 48.25s + +[Epoch 154] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.8+1.1s +[3200/16000] [L1: inf] 31.8+0.0s +[4800/16000] [L1: inf] 31.5+0.0s +[6400/16000] [L1: inf] 31.5+0.0s +[8000/16000] [L1: inf] 31.1+0.0s +[9600/16000] [L1: inf] 31.4+0.0s +[11200/16000] [L1: inf] 31.4+0.0s +[12800/16000] [L1: inf] 31.2+0.0s +[14400/16000] [L1: inf] 31.3+0.0s +[16000/16000] [L1: inf] 30.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 40.756 @epoch 90) +Forward: 47.68s + +Saving... +Total: 48.25s + +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 31.5+1.2s +[3200/16000] [L1: inf] 31.8+0.0s +[4800/16000] [L1: inf] 31.4+0.0s +[6400/16000] [L1: inf] 31.5+0.0s +[8000/16000] [L1: inf] 31.2+0.0s +[9600/16000] [L1: inf] 31.4+0.0s +[11200/16000] [L1: inf] 31.2+0.0s +[12800/16000] [L1: inf] 31.3+0.0s +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [L1: inf] 34.4+1.0s +[3200/16000] [L1: inf] 31.7+0.0s +[4800/16000] [L1: inf] 31.8+0.0s +[6400/16000] [L1: inf] 31.6+0.0s +[8000/16000] [L1: inf] 31.5+0.0s +[9600/16000] [L1: inf] 31.4+0.0s diff --git a/Demosaic/experiment/RAFT_DEMOSAIC20_R4/loss.pt b/Demosaic/experiment/RAFT_DEMOSAIC20_R4/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..2cb8aeac7a3784a1b17d156d419747cef2ed44bf --- /dev/null +++ b/Demosaic/experiment/RAFT_DEMOSAIC20_R4/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48da99bdddb436cdb4a093ba3a3efbe02ec42ba0bd17f6415b4f6645eb17b79f +size 559 diff --git a/Demosaic/experiment/RAFT_DEMOSAIC20_R4/loss_L1.pdf b/Demosaic/experiment/RAFT_DEMOSAIC20_R4/loss_L1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..04461ceac0e9a70ab30600e69191cdfa0daa71ee Binary files /dev/null and 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b/Demosaic/experiment/RAFT_DEMOSAIC20_R4_re/config.txt @@ -0,0 +1,132 @@ +2020-11-10-09:38:08 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: ../experiment/RAFT_DEMOSAIC20_R4/model/model_89.pt +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: True +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.2 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4_re +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-10-13:10:56 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 1.2 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RAFT_DEMOSAIC20_R4_re +load: RAFT_DEMOSAIC20_R4_re +resume: -1 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/RAFT_DEMOSAIC20_R4_re/log.txt b/Demosaic/experiment/RAFT_DEMOSAIC20_R4_re/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..3b5dca81c4a6559a3e5293713bd2797d041a7d32 --- /dev/null +++ b/Demosaic/experiment/RAFT_DEMOSAIC20_R4_re/log.txt @@ -0,0 +1,4760 @@ +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0174] 35.8+0.8s +[3200/16000] [L1: 0.0168] 32.7+0.1s +[4800/16000] [L1: 0.0165] 32.6+0.0s +[6400/16000] [L1: 0.0163] 32.9+0.0s +[8000/16000] [L1: 0.0161] 32.9+0.0s +[9600/16000] [L1: 0.0161] 32.5+0.0s +[11200/16000] [L1: 0.0161] 32.7+0.0s +[12800/16000] [L1: 0.0161] 33.0+0.0s +[14400/16000] [L1: 0.0161] 32.7+0.0s +[16000/16000] [L1: 0.0162] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.472 (Best: 40.472 @epoch 1) +Forward: 49.41s + +Saving... +Total: 51.94s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0167] 32.7+1.3s +[3200/16000] [L1: 0.0161] 32.8+0.1s +[4800/16000] [L1: 0.0160] 33.0+0.0s +[6400/16000] [L1: 0.0160] 33.1+0.0s +[8000/16000] [L1: 0.0158] 32.9+0.0s +[9600/16000] [L1: 0.0158] 32.5+0.0s +[11200/16000] [L1: 0.0158] 32.9+0.0s +[12800/16000] [L1: 0.0158] 32.9+0.0s +[14400/16000] [L1: 0.0158] 32.3+0.0s +[16000/16000] [L1: 0.0158] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.655 (Best: 40.655 @epoch 2) +Forward: 49.39s + +Saving... +Total: 50.28s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0160] 33.0+1.3s +[3200/16000] [L1: 0.0160] 32.8+0.0s +[4800/16000] [L1: 0.0161] 32.8+0.0s +[6400/16000] [L1: 0.0159] 32.7+0.0s +[8000/16000] [L1: 0.0158] 33.0+0.0s +[9600/16000] [L1: 0.0157] 32.8+0.0s +[11200/16000] [L1: 0.0157] 32.8+0.0s +[12800/16000] [L1: 0.0158] 33.1+0.0s +[14400/16000] [L1: 0.0157] 32.4+0.0s +[16000/16000] [L1: 0.0157] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.789 (Best: 40.789 @epoch 3) +Forward: 49.27s + +Saving... +Total: 49.89s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 32.5+1.2s +[3200/16000] [L1: 0.0155] 32.4+0.0s +[4800/16000] [L1: 0.0156] 32.1+0.0s +[6400/16000] [L1: 0.0157] 32.0+0.0s +[8000/16000] [L1: 0.0157] 31.6+0.0s +[9600/16000] [L1: 0.0156] 32.3+0.0s +[11200/16000] [L1: 0.0157] 32.1+0.0s +[12800/16000] [L1: 0.0157] 31.9+0.0s +[14400/16000] [L1: 0.0157] 32.2+0.0s +[16000/16000] [L1: 0.0157] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.631 (Best: 40.789 @epoch 3) +Forward: 49.35s + +Saving... +Total: 49.99s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0161] 32.7+1.1s +[3200/16000] [L1: 0.0157] 33.1+0.0s +[4800/16000] [L1: 0.0158] 32.9+0.0s +[6400/16000] [L1: 0.0159] 32.9+0.0s +[8000/16000] [L1: 0.0158] 33.0+0.0s +[9600/16000] [L1: 0.0158] 32.9+0.0s +[11200/16000] [L1: 0.0158] 33.1+0.0s +[12800/16000] [L1: 0.0157] 32.7+0.0s +[14400/16000] [L1: 0.0158] 32.6+0.0s +[16000/16000] [L1: 0.0157] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.816 (Best: 40.816 @epoch 5) +Forward: 49.60s + +Saving... +Total: 50.20s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0152] 32.7+1.1s +[3200/16000] [L1: 0.0154] 32.8+0.0s +[4800/16000] [L1: 0.0154] 32.9+0.0s +[6400/16000] [L1: 0.0154] 32.8+0.0s +[8000/16000] [L1: 0.0154] 32.4+0.0s +[9600/16000] [L1: 0.0155] 32.4+0.0s +[11200/16000] [L1: 0.0156] 32.0+0.0s +[12800/16000] [L1: 0.0156] 32.8+0.0s +[14400/16000] [L1: 0.0157] 32.3+0.0s +[16000/16000] [L1: 0.0188] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.234 (Best: 40.816 @epoch 5) +Forward: 49.34s + +Saving... +Total: 49.87s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0214] 32.5+1.4s +[3200/16000] [L1: 0.0204] 32.7+0.0s +[4800/16000] [L1: 0.0200] 32.7+0.0s +[6400/16000] [L1: 0.0200] 32.1+0.0s +[8000/16000] [L1: 0.0197] 32.1+0.0s +[9600/16000] [L1: 0.0195] 32.7+0.0s +[11200/16000] [L1: 0.0194] 32.9+0.0s +[12800/16000] [L1: 0.0194] 32.5+0.0s +[14400/16000] [L1: 0.0193] 32.6+0.0s +[16000/16000] [L1: 0.0192] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.036 (Best: 40.816 @epoch 5) +Forward: 49.23s + +Saving... +Total: 49.89s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0179] 32.5+1.1s +[3200/16000] [L1: 0.0185] 32.8+0.0s +[4800/16000] [L1: 0.0182] 32.7+0.0s +[6400/16000] [L1: 0.0183] 32.6+0.0s +[8000/16000] [L1: 0.0182] 32.9+0.0s +[9600/16000] [L1: 0.0181] 32.2+0.0s +[11200/16000] [L1: 0.0180] 32.5+0.0s +[12800/16000] [L1: 0.0181] 32.6+0.0s +[14400/16000] [L1: 0.0182] 32.4+0.0s +[16000/16000] [L1: 0.0182] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.242 (Best: 40.816 @epoch 5) +Forward: 49.22s + +Saving... +Total: 49.78s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0181] 33.1+1.1s +[3200/16000] [L1: 0.0181] 32.7+0.0s +[4800/16000] [L1: 0.0183] 32.9+0.0s +[6400/16000] [L1: 0.0182] 32.8+0.0s +[8000/16000] [L1: 0.0179] 32.8+0.0s +[9600/16000] [L1: 0.0176] 32.4+0.0s +[11200/16000] [L1: 0.0175] 32.7+0.0s +[12800/16000] [L1: 0.0173] 32.6+0.0s +[14400/16000] [L1: 0.0171] 32.5+0.0s +[16000/16000] [L1: 0.0170] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.671 (Best: 40.816 @epoch 5) +Forward: 49.34s + +Saving... +Total: 49.88s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0156] 32.5+1.3s +[3200/16000] [L1: 0.0157] 32.7+0.0s +[4800/16000] [L1: 0.0156] 32.7+0.0s +[6400/16000] [L1: 0.0156] 32.9+0.0s +[8000/16000] [L1: 0.0157] 32.7+0.0s +[9600/16000] [L1: 0.0157] 32.8+0.0s +[11200/16000] [L1: 0.0156] 32.8+0.0s +[12800/16000] [L1: 0.0156] 32.8+0.0s +[14400/16000] [L1: 0.0155] 33.0+0.0s +[16000/16000] [L1: 0.0155] 32.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.812 (Best: 40.816 @epoch 5) +Forward: 49.34s + +Saving... +Total: 49.86s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0152] 32.9+1.1s +[3200/16000] [L1: 0.0154] 32.8+0.0s +[4800/16000] [L1: 0.0153] 32.8+0.0s +[6400/16000] [L1: 0.0154] 32.9+0.0s +[8000/16000] [L1: 0.0156] 32.6+0.0s +[9600/16000] [L1: 0.0156] 32.5+0.0s +[11200/16000] [L1: 0.0156] 32.3+0.0s +[12800/16000] [L1: 0.0156] 32.6+0.0s +[14400/16000] [L1: 0.0156] 32.2+0.0s +[16000/16000] [L1: 0.0156] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.863 (Best: 40.863 @epoch 11) +Forward: 49.27s + +Saving... +Total: 49.90s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0156] 32.9+1.1s +[3200/16000] [L1: 0.0155] 32.9+0.1s +[4800/16000] [L1: 0.0153] 33.1+0.0s +[6400/16000] [L1: 0.0154] 32.9+0.0s +[8000/16000] [L1: 0.0154] 32.7+0.0s +[9600/16000] [L1: 0.0155] 32.8+0.0s +[11200/16000] [L1: 0.0155] 32.9+0.0s +[12800/16000] [L1: 0.0155] 32.8+0.0s +[14400/16000] [L1: 0.0155] 32.9+0.0s +[16000/16000] [L1: 0.0155] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.670 (Best: 40.863 @epoch 11) +Forward: 49.46s + +Saving... +Total: 50.02s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0165] 32.8+1.2s +[3200/16000] [L1: 0.0159] 33.0+0.0s +[4800/16000] [L1: 0.0157] 32.8+0.0s +[6400/16000] [L1: 0.0156] 32.8+0.0s +[8000/16000] [L1: 0.0156] 32.4+0.0s +[9600/16000] [L1: 0.0154] 32.5+0.0s +[11200/16000] [L1: 0.0155] 32.3+0.0s +[12800/16000] [L1: 0.0155] 32.3+0.0s +[14400/16000] [L1: 0.0155] 32.4+0.0s +[16000/16000] [L1: 0.0154] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.737 (Best: 40.863 @epoch 11) +Forward: 49.53s + +Saving... +Total: 50.14s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0152] 32.6+1.1s +[3200/16000] [L1: 0.0157] 32.8+0.0s +[4800/16000] [L1: 0.0157] 32.9+0.0s +[6400/16000] [L1: 0.0156] 32.9+0.0s +[8000/16000] [L1: 0.0156] 33.1+0.0s +[9600/16000] [L1: 0.0155] 32.8+0.0s +[11200/16000] [L1: 0.0154] 32.8+0.0s +[12800/16000] [L1: 0.0154] 32.4+0.0s +[14400/16000] [L1: 0.0155] 32.8+0.0s +[16000/16000] [L1: 0.0155] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.764 (Best: 40.863 @epoch 11) +Forward: 49.62s + +Saving... +Total: 50.19s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 33.0+1.2s +[3200/16000] [L1: 0.0151] 32.9+0.0s +[4800/16000] [L1: 0.0152] 32.6+0.0s +[6400/16000] [L1: 0.0151] 32.8+0.0s +[8000/16000] [L1: 0.0152] 32.7+0.0s +[9600/16000] [L1: 0.0152] 32.6+0.0s +[11200/16000] [L1: 0.0153] 32.0+0.0s +[12800/16000] [L1: 0.0153] 32.5+0.0s +[14400/16000] [L1: 0.0153] 32.4+0.0s +[16000/16000] [L1: 0.0153] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.875 (Best: 40.875 @epoch 15) +Forward: 49.44s + +Saving... +Total: 50.12s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0156] 33.0+1.1s +[3200/16000] [L1: 0.0155] 32.5+0.0s +[4800/16000] [L1: 0.0153] 32.8+0.0s +[6400/16000] [L1: 0.0153] 33.1+0.0s +[8000/16000] [L1: 0.0154] 32.7+0.0s +[9600/16000] [L1: 0.0153] 32.6+0.0s +[11200/16000] [L1: 0.0153] 32.4+0.0s +[12800/16000] [L1: 0.0152] 32.2+0.0s +[14400/16000] [L1: 0.0153] 32.6+0.0s +[16000/16000] [L1: 0.0153] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.930 (Best: 40.930 @epoch 16) +Forward: 49.38s + +Saving... +Total: 50.01s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0151] 33.0+1.1s +[3200/16000] [L1: 0.0149] 32.8+0.0s +[4800/16000] [L1: 0.0151] 33.0+0.0s +[6400/16000] [L1: 0.0152] 32.9+0.0s +[8000/16000] [L1: 0.0152] 32.8+0.0s +[9600/16000] [L1: 0.0153] 32.8+0.0s +[11200/16000] [L1: 0.0152] 32.4+0.0s +[12800/16000] [L1: 0.0153] 32.3+0.0s +[14400/16000] [L1: 0.0152] 32.4+0.0s +[16000/16000] [L1: 0.0152] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.827 (Best: 40.930 @epoch 16) +Forward: 49.44s + +Saving... +Total: 50.03s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0153] 33.1+1.1s +[3200/16000] [L1: 0.0152] 32.7+0.0s +[4800/16000] [L1: 0.0150] 32.6+0.0s +[6400/16000] [L1: 0.0150] 32.1+0.0s +[8000/16000] [L1: 0.0151] 32.3+0.0s +[9600/16000] [L1: 0.0150] 32.4+0.0s +[11200/16000] [L1: 0.0152] 32.4+0.0s +[12800/16000] [L1: 0.0152] 32.4+0.0s +[14400/16000] [L1: 0.0152] 32.6+0.0s +[16000/16000] [L1: 0.0152] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.751 (Best: 40.930 @epoch 16) +Forward: 49.28s + +Saving... +Total: 49.84s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 32.9+1.3s +[3200/16000] [L1: 0.0150] 33.1+0.1s +[4800/16000] [L1: 0.0150] 32.9+0.0s +[6400/16000] [L1: 0.0151] 32.9+0.0s +[8000/16000] [L1: 0.0150] 33.0+0.0s +[9600/16000] [L1: 0.0151] 32.7+0.0s +[11200/16000] [L1: 0.0150] 32.8+0.0s +[12800/16000] [L1: 0.0151] 32.4+0.0s +[14400/16000] [L1: 0.0151] 32.4+0.0s +[16000/16000] [L1: 0.0152] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.844 (Best: 40.930 @epoch 16) +Forward: 49.25s + +Saving... +Total: 49.86s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 32.6+1.1s +[3200/16000] [L1: 0.0147] 33.0+0.0s +[4800/16000] [L1: 0.0152] 32.7+0.0s +[6400/16000] [L1: 0.0151] 32.9+0.0s +[8000/16000] [L1: 0.0151] 32.6+0.0s +[9600/16000] [L1: 0.0152] 32.6+0.0s +[11200/16000] [L1: 0.0153] 32.4+0.0s +[12800/16000] [L1: 0.0153] 32.3+0.0s +[14400/16000] [L1: 0.0153] 32.2+0.0s +[16000/16000] [L1: 0.0153] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.835 (Best: 40.930 @epoch 16) +Forward: 49.54s + +Saving... +Total: 50.11s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0150] 32.7+1.1s +[3200/16000] [L1: 0.0151] 33.0+0.0s +[4800/16000] [L1: 0.0152] 33.0+0.0s +[6400/16000] [L1: 0.0151] 32.9+0.0s +[8000/16000] [L1: 0.0151] 32.8+0.0s +[9600/16000] [L1: 0.0151] 32.9+0.0s +[11200/16000] [L1: 0.0150] 32.6+0.0s +[12800/16000] [L1: 0.0150] 33.0+0.0s +[14400/16000] [L1: 0.0151] 32.7+0.0s +[16000/16000] [L1: 0.0151] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.862 (Best: 40.930 @epoch 16) +Forward: 49.36s + +Saving... +Total: 49.91s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0155] 32.7+1.2s +[3200/16000] [L1: 0.0153] 32.9+0.0s +[4800/16000] [L1: 0.0152] 32.6+0.0s +[6400/16000] [L1: 0.0152] 32.8+0.0s +[8000/16000] [L1: 0.0152] 32.5+0.0s +[9600/16000] [L1: 0.0151] 32.5+0.0s +[11200/16000] [L1: 0.0151] 32.4+0.0s +[12800/16000] [L1: 0.0151] 32.1+0.0s +[14400/16000] [L1: 0.0151] 32.6+0.0s +[16000/16000] [L1: 0.0151] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.032 (Best: 41.032 @epoch 22) +Forward: 49.42s + +Saving... +Total: 50.07s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 32.8+1.2s +[3200/16000] [L1: 0.0147] 32.8+0.0s +[4800/16000] [L1: 0.0151] 32.6+0.0s +[6400/16000] [L1: 0.0152] 32.3+0.0s +[8000/16000] [L1: 0.0153] 32.1+0.0s +[9600/16000] [L1: 0.0152] 32.1+0.0s +[11200/16000] [L1: 0.0152] 32.0+0.0s +[12800/16000] [L1: 0.0151] 32.0+0.0s +[14400/16000] [L1: 0.0151] 31.7+0.0s +[16000/16000] [L1: 0.0151] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.934 (Best: 41.032 @epoch 22) +Forward: 49.38s + +Saving... +Total: 49.94s + +[Epoch 24] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0154] 32.2+1.1s +[3200/16000] [L1: 0.0150] 32.0+0.0s +[4800/16000] [L1: 0.0150] 32.2+0.0s +[6400/16000] [L1: 0.0150] 31.9+0.0s +[8000/16000] [L1: 0.0150] 32.1+0.0s +[9600/16000] [L1: 0.0151] 32.1+0.0s +[11200/16000] [L1: 0.0151] 31.8+0.0s +[12800/16000] [L1: 0.0151] 32.1+0.0s +[14400/16000] [L1: 0.0151] 32.2+0.0s +[16000/16000] [L1: 0.0151] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.940 (Best: 41.032 @epoch 22) +Forward: 49.28s + +Saving... +Total: 49.81s + +[Epoch 25] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0147] 32.4+1.2s +[3200/16000] [L1: 0.0145] 32.1+0.0s +[4800/16000] [L1: 0.0147] 32.2+0.0s +[6400/16000] [L1: 0.0148] 32.1+0.0s +[8000/16000] [L1: 0.0150] 32.4+0.0s +[9600/16000] [L1: 0.0150] 32.4+0.0s +[11200/16000] [L1: 0.0150] 32.2+0.0s +[12800/16000] [L1: 0.0150] 32.2+0.0s +[14400/16000] [L1: 0.0150] 32.3+0.0s +[16000/16000] [L1: 0.0151] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.037 (Best: 41.037 @epoch 25) +Forward: 49.55s + +Saving... +Total: 50.13s + +[Epoch 26] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 32.6+1.1s +[3200/16000] [L1: 0.0149] 32.4+0.0s +[4800/16000] [L1: 0.0150] 32.4+0.0s +[6400/16000] [L1: 0.0152] 32.6+0.0s +[8000/16000] [L1: 0.0151] 32.3+0.0s +[9600/16000] [L1: 0.0150] 32.1+0.0s +[11200/16000] [L1: 0.0150] 32.0+0.0s +[12800/16000] [L1: 0.0151] 31.9+0.0s +[14400/16000] [L1: 0.0151] 32.2+0.0s +[16000/16000] [L1: 0.0150] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.038 (Best: 41.038 @epoch 26) +Forward: 49.37s + +Saving... +Total: 49.93s + +[Epoch 27] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0147] 32.4+1.1s +[3200/16000] [L1: 0.0149] 32.7+0.0s +[4800/16000] [L1: 0.0148] 32.3+0.0s +[6400/16000] [L1: 0.0148] 32.4+0.0s +[8000/16000] [L1: 0.0148] 32.3+0.0s +[9600/16000] [L1: 0.0148] 32.3+0.0s +[11200/16000] [L1: 0.0148] 32.1+0.0s +[12800/16000] [L1: 0.0148] 32.2+0.0s +[14400/16000] [L1: 0.0148] 32.3+0.0s +[16000/16000] [L1: 0.0148] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.992 (Best: 41.038 @epoch 26) +Forward: 49.25s + +Saving... +Total: 49.78s + +[Epoch 28] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0150] 32.2+1.3s +[3200/16000] [L1: 0.0150] 32.4+0.0s +[4800/16000] [L1: 0.0151] 32.5+0.0s +[6400/16000] [L1: 0.0151] 32.3+0.0s +[8000/16000] [L1: 0.0151] 32.2+0.0s +[9600/16000] [L1: 0.0149] 31.9+0.0s +[11200/16000] [L1: 0.0148] 31.9+0.0s +[12800/16000] [L1: 0.0149] 32.0+0.0s +[14400/16000] [L1: 0.0149] 32.2+0.0s +[16000/16000] [L1: 0.0150] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.888 (Best: 41.038 @epoch 26) +Forward: 49.33s + +Saving... +Total: 49.85s + +[Epoch 29] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0147] 32.5+1.3s +[3200/16000] [L1: 0.0146] 32.5+0.0s +[4800/16000] [L1: 0.0149] 32.4+0.0s +[6400/16000] [L1: 0.0148] 32.1+0.0s +[8000/16000] [L1: 0.0150] 32.2+0.0s +[9600/16000] [L1: 0.0151] 32.1+0.0s +[11200/16000] [L1: 0.0150] 32.1+0.0s +[12800/16000] [L1: 0.0150] 32.2+0.0s +[14400/16000] [L1: 0.0150] 32.0+0.0s +[16000/16000] [L1: 0.0150] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.086 (Best: 41.086 @epoch 29) +Forward: 49.36s + +Saving... +Total: 49.94s + +[Epoch 30] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 32.3+1.2s +[3200/16000] [L1: 0.0147] 32.3+0.0s +[4800/16000] [L1: 0.0147] 32.6+0.0s +[6400/16000] [L1: 0.0146] 32.5+0.0s +[8000/16000] [L1: 0.0146] 31.9+0.0s +[9600/16000] [L1: 0.0147] 32.0+0.0s +[11200/16000] [L1: 0.0147] 31.8+0.0s +[12800/16000] [L1: 0.0147] 32.3+0.0s +[14400/16000] [L1: 0.0147] 32.3+0.0s +[16000/16000] [L1: 0.0148] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.863 (Best: 41.086 @epoch 29) +Forward: 49.48s + +Saving... +Total: 50.08s + +[Epoch 31] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 32.4+1.2s +[3200/16000] [L1: 0.0148] 32.4+0.0s +[4800/16000] [L1: 0.0147] 32.6+0.0s +[6400/16000] [L1: 0.0148] 32.3+0.0s +[8000/16000] [L1: 0.0148] 32.3+0.0s +[9600/16000] [L1: 0.0148] 32.0+0.0s +[11200/16000] [L1: 0.0148] 32.1+0.0s +[12800/16000] [L1: 0.0148] 32.1+0.0s +[14400/16000] [L1: 0.0148] 32.0+0.0s +[16000/16000] [L1: 0.0148] 31.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.956 (Best: 41.086 @epoch 29) +Forward: 49.31s + +Saving... +Total: 49.82s + +DataParallel( + (module): RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 32] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 35.8+0.8s +[3200/16000] [L1: 0.0152] 32.5+0.0s +[4800/16000] [L1: 0.0151] 32.4+0.0s +[6400/16000] [L1: 0.0149] 32.4+0.0s +[8000/16000] [L1: 0.0149] 32.3+0.0s +[9600/16000] [L1: 0.0148] 32.6+0.0s +[11200/16000] [L1: 0.0149] 32.2+0.0s +[12800/16000] [L1: 0.0149] 32.4+0.0s +[14400/16000] [L1: 0.0148] 32.6+0.0s +[16000/16000] [L1: 0.0149] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.909 (Best: 41.086 @epoch 29) +Forward: 49.16s + +Saving... +Total: 49.94s + +[Epoch 33] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0159] 33.0+0.9s +[3200/16000] [L1: 0.0156] 32.5+0.0s +[4800/16000] [L1: 0.0153] 32.8+0.0s +[6400/16000] [L1: 0.0151] 32.2+0.0s +[8000/16000] [L1: 0.0150] 32.6+0.0s +[9600/16000] [L1: 0.0151] 32.5+0.0s +[11200/16000] [L1: 0.0150] 32.4+0.0s +[12800/16000] [L1: 0.0150] 32.3+0.0s +[14400/16000] [L1: 0.0150] 32.4+0.0s +[16000/16000] [L1: 0.0189] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.676 (Best: 41.086 @epoch 29) +Forward: 49.10s + +Saving... +Total: 49.63s + +[Epoch 34] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0321] 32.7+0.7s +[3200/16000] [L1: 0.0267] 32.7+0.0s +[4800/16000] [L1: 0.0245] 32.9+0.0s +[6400/16000] [L1: 0.0231] 32.8+0.0s +[8000/16000] [L1: 0.0221] 33.1+0.0s +[9600/16000] [L1: 0.0211] 32.6+0.0s +[11200/16000] [L1: 0.0204] 32.7+0.0s +[12800/16000] [L1: 0.0199] 32.3+0.0s +[14400/16000] [L1: 0.0193] 32.2+0.0s +[16000/16000] [L1: 0.0190] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.606 (Best: 41.086 @epoch 29) +Forward: 49.31s + +Saving... +Total: 49.86s + +[Epoch 35] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 32.7+0.7s +[3200/16000] [L1: 0.0150] 32.5+0.0s +[4800/16000] [L1: 0.0152] 32.7+0.0s +[6400/16000] [L1: 0.0151] 32.6+0.0s +[8000/16000] [L1: 0.0152] 32.5+0.0s +[9600/16000] [L1: 0.0152] 32.5+0.0s +[11200/16000] [L1: 0.0152] 32.2+0.0s +[12800/16000] [L1: 0.0152] 32.3+0.0s +[14400/16000] [L1: 0.0151] 32.3+0.0s +[16000/16000] [L1: 0.0151] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.803 (Best: 41.086 @epoch 29) +Forward: 49.24s + +Saving... +Total: 49.86s + +[Epoch 36] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 32.7+0.8s +[3200/16000] [L1: 0.0147] 32.6+0.0s +[4800/16000] [L1: 0.0149] 32.7+0.0s +[6400/16000] [L1: 0.0149] 32.9+0.0s +[8000/16000] [L1: 0.0148] 32.5+0.0s +[9600/16000] [L1: 0.0150] 32.5+0.0s +[11200/16000] [L1: 0.0149] 32.5+0.0s +[12800/16000] [L1: 0.0149] 32.3+0.0s +[14400/16000] [L1: 0.0149] 32.5+0.0s +[16000/16000] [L1: 0.0149] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.983 (Best: 41.086 @epoch 29) +Forward: 49.05s + +Saving... +Total: 49.64s + +[Epoch 37] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 32.9+0.9s +[3200/16000] [L1: 0.0146] 33.1+0.0s +[4800/16000] [L1: 0.0147] 32.6+0.0s +[6400/16000] [L1: 0.0147] 32.9+0.0s +[8000/16000] [L1: 0.0148] 32.4+0.0s +[9600/16000] [L1: 0.0148] 32.5+0.0s +[11200/16000] [L1: 0.0149] 32.0+0.0s +[12800/16000] [L1: 0.0148] 32.7+0.0s +[14400/16000] [L1: 0.0149] 32.5+0.0s +[16000/16000] [L1: 0.0148] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.011 (Best: 41.086 @epoch 29) +Forward: 49.00s + +Saving... +Total: 49.55s + +[Epoch 38] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 32.7+1.0s +[3200/16000] [L1: 0.0149] 32.9+0.0s +[4800/16000] [L1: 0.0149] 32.9+0.0s +[6400/16000] [L1: 0.0149] 33.0+0.0s +[8000/16000] [L1: 0.0149] 33.1+0.0s +[9600/16000] [L1: 0.0149] 32.9+0.0s +[11200/16000] [L1: 0.0149] 32.9+0.0s +[12800/16000] [L1: 0.0149] 32.7+0.0s +[14400/16000] [L1: 0.0149] 32.8+0.0s +[16000/16000] [L1: 0.0149] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.710 (Best: 41.086 @epoch 29) +Forward: 48.97s + +Saving... +Total: 49.50s + +[Epoch 39] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 33.1+0.8s +[3200/16000] [L1: 0.0146] 32.5+0.1s +[4800/16000] [L1: 0.0145] 32.5+0.0s +[6400/16000] [L1: 0.0145] 33.0+0.0s +[8000/16000] [L1: 0.0145] 32.4+0.0s +[9600/16000] [L1: 0.0144] 32.4+0.0s +[11200/16000] [L1: 0.0145] 32.5+0.0s +[12800/16000] [L1: 0.0144] 32.6+0.0s +[14400/16000] [L1: 0.0145] 32.8+0.0s +[16000/16000] [L1: 0.0146] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.035 (Best: 41.086 @epoch 29) +Forward: 48.98s + +Saving... +Total: 49.61s + +[Epoch 40] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 32.6+0.9s +[3200/16000] [L1: 0.0146] 32.6+0.0s +[4800/16000] [L1: 0.0148] 32.3+0.0s +[6400/16000] [L1: 0.0149] 32.3+0.0s +[8000/16000] [L1: 0.0148] 32.3+0.0s +[9600/16000] [L1: 0.0147] 32.5+0.0s +[11200/16000] [L1: 0.0148] 32.1+0.0s +[12800/16000] [L1: 0.0148] 32.6+0.0s +[14400/16000] [L1: 0.0148] 32.5+0.0s +[16000/16000] [L1: 0.0147] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.035 (Best: 41.086 @epoch 29) +Forward: 49.02s + +Saving... +Total: 49.59s + +[Epoch 41] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 32.3+0.8s +[3200/16000] [L1: 0.0148] 32.5+0.0s +[4800/16000] [L1: 0.0148] 33.1+0.0s +[6400/16000] [L1: 0.0147] 32.4+0.0s +[8000/16000] [L1: 0.0147] 32.5+0.0s +[9600/16000] [L1: 0.0147] 32.5+0.0s +[11200/16000] [L1: 0.0147] 32.7+0.0s +[12800/16000] [L1: 0.0147] 32.4+0.0s +[14400/16000] [L1: 0.0147] 32.7+0.0s +[16000/16000] [L1: 0.0147] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.198 (Best: 41.198 @epoch 41) +Forward: 49.20s + +Saving... +Total: 49.84s + +[Epoch 42] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 32.9+0.9s +[3200/16000] [L1: 0.0143] 32.4+0.0s +[4800/16000] [L1: 0.0144] 32.4+0.0s +[6400/16000] [L1: 0.0145] 31.9+0.0s +[8000/16000] [L1: 0.0147] 32.2+0.0s +[9600/16000] [L1: 0.0147] 32.8+0.0s +[11200/16000] [L1: 0.0147] 32.0+0.0s +[12800/16000] [L1: 0.0147] 32.0+0.0s +[14400/16000] [L1: 0.0147] 32.5+0.0s +[16000/16000] [L1: 0.0147] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.242 (Best: 41.242 @epoch 42) +Forward: 48.97s + +Saving... +Total: 49.51s + +[Epoch 43] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 32.7+0.8s +[3200/16000] [L1: 0.0145] 32.9+0.0s +[4800/16000] [L1: 0.0144] 33.0+0.0s +[6400/16000] [L1: 0.0146] 32.8+0.0s +[8000/16000] [L1: 0.0146] 32.3+0.0s +[9600/16000] [L1: 0.0147] 32.6+0.0s +[11200/16000] [L1: 0.0147] 32.5+0.0s +[12800/16000] [L1: 0.0147] 32.7+0.0s +[14400/16000] [L1: 0.0147] 32.9+0.0s +[16000/16000] [L1: 0.0147] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.742 (Best: 41.242 @epoch 42) +Forward: 48.91s + +Saving... +Total: 49.47s + +[Epoch 44] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 32.8+0.8s +[3200/16000] [L1: 0.0147] 32.7+0.1s +[4800/16000] [L1: 0.0146] 32.9+0.0s +[6400/16000] [L1: 0.0146] 32.4+0.0s +[8000/16000] [L1: 0.0147] 32.7+0.0s +[9600/16000] [L1: 0.0147] 32.2+0.0s +[11200/16000] [L1: 0.0147] 32.2+0.0s +[12800/16000] [L1: 0.0147] 32.3+0.0s +[14400/16000] [L1: 0.0147] 32.6+0.0s +[16000/16000] [L1: 0.0147] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.995 (Best: 41.242 @epoch 42) +Forward: 48.96s + +Saving... +Total: 49.47s + +[Epoch 45] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0149] 33.0+0.8s +[3200/16000] [L1: 0.0149] 32.8+0.0s +[4800/16000] [L1: 0.0148] 32.3+0.0s +[6400/16000] [L1: 0.0148] 32.3+0.0s +[8000/16000] [L1: 0.0147] 32.7+0.0s +[9600/16000] [L1: 0.0147] 32.6+0.0s +[11200/16000] [L1: 0.0147] 32.2+0.0s +[12800/16000] [L1: 0.0147] 32.4+0.0s +[14400/16000] [L1: 0.0147] 32.3+0.0s +[16000/16000] [L1: 0.0147] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.926 (Best: 41.242 @epoch 42) +Forward: 48.83s + +Saving... +Total: 49.34s + +[Epoch 46] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 33.0+0.8s +[3200/16000] [L1: 0.0144] 32.9+0.0s +[4800/16000] [L1: 0.0144] 32.7+0.0s +[6400/16000] [L1: 0.0144] 33.1+0.0s +[8000/16000] [L1: 0.0144] 32.5+0.0s +[9600/16000] [L1: 0.0144] 32.6+0.0s +[11200/16000] [L1: 0.0146] 32.7+0.0s +[12800/16000] [L1: 0.0146] 32.6+0.0s +[14400/16000] [L1: 0.0145] 32.6+0.0s +[16000/16000] [L1: 0.0145] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.199 (Best: 41.242 @epoch 42) +Forward: 48.91s + +Saving... +Total: 49.41s + +[Epoch 47] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0150] 32.6+0.8s +[3200/16000] [L1: 0.0147] 32.2+0.0s +[4800/16000] [L1: 0.0145] 32.5+0.0s +[6400/16000] [L1: 0.0145] 32.6+0.0s +[8000/16000] [L1: 0.0146] 32.5+0.0s +[9600/16000] [L1: 0.0145] 32.5+0.0s +[11200/16000] [L1: 0.0145] 32.6+0.0s +[12800/16000] [L1: 0.0144] 32.3+0.0s +[14400/16000] [L1: 0.0145] 32.2+0.0s +[16000/16000] [L1: 0.0145] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.083 (Best: 41.242 @epoch 42) +Forward: 48.84s + +Saving... +Total: 49.35s + +[Epoch 48] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 32.7+0.7s +[3200/16000] [L1: 0.0143] 32.9+0.0s +[4800/16000] [L1: 0.0144] 32.5+0.0s +[6400/16000] [L1: 0.0145] 32.6+0.0s +[8000/16000] [L1: 0.0145] 32.3+0.0s +[9600/16000] [L1: 0.0146] 32.5+0.0s +[11200/16000] [L1: 0.0146] 32.1+0.0s +[12800/16000] [L1: 0.0146] 32.3+0.0s +[14400/16000] [L1: 0.0146] 32.4+0.0s +[16000/16000] [L1: 0.0146] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.207 (Best: 41.242 @epoch 42) +Forward: 48.93s + +Saving... +Total: 49.46s + +[Epoch 49] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 32.6+0.9s +[3200/16000] [L1: 0.0145] 32.4+0.0s +[4800/16000] [L1: 0.0145] 32.4+0.0s +[6400/16000] [L1: 0.0145] 32.7+0.0s +[8000/16000] [L1: 0.0145] 32.3+0.0s +[9600/16000] [L1: 0.0144] 32.4+0.0s +[11200/16000] [L1: 0.0145] 32.6+0.0s +[12800/16000] [L1: 0.0145] 32.3+0.0s +[14400/16000] [L1: 0.0145] 32.8+0.0s +[16000/16000] [L1: 0.0145] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.070 (Best: 41.242 @epoch 42) +Forward: 48.98s + +Saving... +Total: 49.48s + +[Epoch 50] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 32.2+0.9s +[3200/16000] [L1: 0.0141] 32.4+0.0s +[4800/16000] [L1: 0.0142] 32.3+0.0s +[6400/16000] [L1: 0.0143] 32.6+0.0s +[8000/16000] [L1: 0.0143] 32.7+0.0s +[9600/16000] [L1: 0.0143] 32.7+0.0s +[11200/16000] [L1: 0.0143] 32.8+0.0s +[12800/16000] [L1: 0.0143] 32.6+0.0s +[14400/16000] [L1: 0.0143] 32.4+0.0s +[16000/16000] [L1: 0.0144] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.160 (Best: 41.242 @epoch 42) +Forward: 48.89s + +Saving... +Total: 49.47s + +[Epoch 51] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 32.5+1.0s +[3200/16000] [L1: 0.0142] 32.6+0.0s +[4800/16000] [L1: 0.0144] 32.5+0.0s +[6400/16000] [L1: 0.0144] 33.0+0.0s +[8000/16000] [L1: 0.0144] 32.8+0.0s +[9600/16000] [L1: 0.0145] 32.6+0.0s +[11200/16000] [L1: 0.0146] 32.7+0.0s +[12800/16000] [L1: 0.0147] 32.3+0.0s +[14400/16000] [L1: 0.0147] 32.2+0.0s +[16000/16000] [L1: 0.0147] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.102 (Best: 41.242 @epoch 42) +Forward: 48.93s + +Saving... +Total: 49.85s + +[Epoch 52] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 33.0+0.8s +[3200/16000] [L1: 0.0144] 33.1+0.0s +[4800/16000] [L1: 0.0146] 33.0+0.0s +[6400/16000] [L1: 0.0146] 32.5+0.0s +[8000/16000] [L1: 0.0145] 32.6+0.0s +[9600/16000] [L1: 0.0146] 32.6+0.0s +[11200/16000] [L1: 0.0144] 32.3+0.0s +[12800/16000] [L1: 0.0145] 32.5+0.0s +[14400/16000] [L1: 0.0145] 32.3+0.0s +[16000/16000] [L1: 0.0145] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.996 (Best: 41.242 @epoch 42) +Forward: 48.98s + +Saving... +Total: 49.48s + +[Epoch 53] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0148] 33.0+0.9s +[3200/16000] [L1: 0.0146] 32.7+0.0s +[4800/16000] [L1: 0.0144] 32.8+0.0s +[6400/16000] [L1: 0.0144] 32.7+0.0s +[8000/16000] [L1: 0.0144] 32.4+0.0s +[9600/16000] [L1: 0.0144] 32.6+0.0s +[11200/16000] [L1: 0.0144] 32.4+0.0s +[12800/16000] [L1: 0.0144] 32.5+0.0s +[14400/16000] [L1: 0.0144] 32.4+0.0s +[16000/16000] [L1: 0.0144] 32.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.256 (Best: 41.256 @epoch 53) +Forward: 48.90s + +Saving... +Total: 49.47s + +[Epoch 54] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 32.7+0.8s +[3200/16000] [L1: 0.0143] 32.6+0.0s +[4800/16000] [L1: 0.0143] 32.4+0.0s +[6400/16000] [L1: 0.0144] 32.2+0.0s +[8000/16000] [L1: 0.0143] 32.5+0.0s +[9600/16000] [L1: 0.0143] 32.2+0.0s +[11200/16000] [L1: 0.0143] 32.3+0.0s +[12800/16000] [L1: 0.0142] 32.2+0.0s +[14400/16000] [L1: 0.0143] 32.4+0.0s +[16000/16000] [L1: 0.0144] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.120 (Best: 41.256 @epoch 53) +Forward: 48.95s + +Saving... +Total: 49.48s + +[Epoch 55] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0147] 32.8+0.8s +[3200/16000] [L1: 0.0143] 33.0+0.0s +[4800/16000] [L1: 0.0144] 33.1+0.0s +[6400/16000] [L1: 0.0145] 33.0+0.0s +[8000/16000] [L1: 0.0145] 32.8+0.0s +[9600/16000] [L1: 0.0145] 32.5+0.0s +[11200/16000] [L1: 0.0145] 32.3+0.0s +[12800/16000] [L1: 0.0146] 32.4+0.0s +[14400/16000] [L1: 0.0145] 32.6+0.0s +[16000/16000] [L1: 0.0145] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.123 (Best: 41.256 @epoch 53) +Forward: 48.94s + +Saving... +Total: 49.53s + +[Epoch 56] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0138] 32.8+0.9s +[3200/16000] [L1: 0.0137] 32.4+0.0s +[4800/16000] [L1: 0.0139] 32.7+0.0s +[6400/16000] [L1: 0.0142] 32.3+0.0s +[8000/16000] [L1: 0.0143] 32.3+0.0s +[9600/16000] [L1: 0.0143] 32.5+0.0s +[11200/16000] [L1: 0.0143] 32.2+0.0s +[12800/16000] [L1: 0.0144] 32.3+0.0s +[14400/16000] [L1: 0.0144] 32.4+0.0s +[16000/16000] [L1: 0.0144] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.993 (Best: 41.256 @epoch 53) +Forward: 49.08s + +Saving... +Total: 49.61s + +[Epoch 57] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 33.1+0.8s +[3200/16000] [L1: 0.0142] 32.7+0.0s +[4800/16000] [L1: 0.0143] 32.7+0.0s +[6400/16000] [L1: 0.0144] 32.6+0.0s +[8000/16000] [L1: 0.0143] 32.2+0.0s +[9600/16000] [L1: 0.0143] 32.3+0.0s +[11200/16000] [L1: 0.0143] 32.7+0.0s +[12800/16000] [L1: 0.0144] 32.3+0.0s +[14400/16000] [L1: 0.0144] 32.7+0.0s +[16000/16000] [L1: 0.0144] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.156 (Best: 41.256 @epoch 53) +Forward: 49.06s + +Saving... +Total: 49.58s + +[Epoch 58] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 32.7+1.0s +[3200/16000] [L1: 0.0141] 32.6+0.0s +[4800/16000] [L1: 0.0140] 32.7+0.0s +[6400/16000] [L1: 0.0141] 32.4+0.0s +[8000/16000] [L1: 0.0142] 32.4+0.0s +[9600/16000] [L1: 0.0142] 31.8+0.0s +[11200/16000] [L1: 0.0142] 32.4+0.0s +[12800/16000] [L1: 0.0143] 32.4+0.0s +[14400/16000] [L1: 0.0142] 32.2+0.0s +[16000/16000] [L1: 0.0142] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.271 (Best: 41.271 @epoch 58) +Forward: 49.01s + +Saving... +Total: 49.60s + +[Epoch 59] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 33.1+0.9s +[3200/16000] [L1: 0.0144] 33.0+0.0s +[4800/16000] [L1: 0.0145] 32.9+0.0s +[6400/16000] [L1: 0.0145] 32.8+0.0s +[8000/16000] [L1: 0.0144] 32.7+0.0s +[9600/16000] [L1: 0.0143] 32.7+0.0s +[11200/16000] [L1: 0.0142] 32.5+0.0s +[12800/16000] [L1: 0.0143] 32.6+0.0s +[14400/16000] [L1: 0.0143] 32.3+0.0s +[16000/16000] [L1: 0.0144] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.210 (Best: 41.271 @epoch 58) +Forward: 48.96s + +Saving... +Total: 49.53s + +[Epoch 60] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 32.8+0.9s +[3200/16000] [L1: 0.0143] 32.7+0.0s +[4800/16000] [L1: 0.0145] 32.9+0.0s +[6400/16000] [L1: 0.0143] 32.8+0.0s +[8000/16000] [L1: 0.0144] 32.5+0.0s +[9600/16000] [L1: 0.0144] 32.7+0.0s +[11200/16000] [L1: 0.0144] 32.4+0.0s +[12800/16000] [L1: 0.0144] 32.5+0.0s +[14400/16000] [L1: 0.0144] 32.1+0.0s +[16000/16000] [L1: 0.0144] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.269 (Best: 41.271 @epoch 58) +Forward: 48.94s + +Saving... +Total: 49.53s + +[Epoch 61] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 32.8+0.8s +[3200/16000] [L1: 0.0142] 32.6+0.0s +[4800/16000] [L1: 0.0141] 32.7+0.0s +[6400/16000] [L1: 0.0140] 32.6+0.0s +[8000/16000] [L1: 0.0140] 32.8+0.0s +[9600/16000] [L1: 0.0141] 32.9+0.0s +[11200/16000] [L1: 0.0141] 32.3+0.0s +[12800/16000] [L1: 0.0141] 32.8+0.0s +[14400/16000] [L1: 0.0141] 32.4+0.0s +[16000/16000] [L1: 0.0141] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.658 (Best: 41.271 @epoch 58) +Forward: 49.20s + +Saving... +Total: 49.74s + +[Epoch 62] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 32.9+0.8s +[3200/16000] [L1: 0.0144] 32.7+0.0s +[4800/16000] [L1: 0.0143] 32.6+0.0s +[6400/16000] [L1: 0.0143] 32.3+0.0s +[8000/16000] [L1: 0.0142] 32.4+0.0s +[9600/16000] [L1: 0.0143] 32.6+0.0s +[11200/16000] [L1: 0.0143] 32.1+0.0s +[12800/16000] [L1: 0.0143] 32.6+0.0s +[14400/16000] [L1: 0.0143] 32.3+0.0s +[16000/16000] [L1: 0.0143] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.218 (Best: 41.271 @epoch 58) +Forward: 48.93s + +Saving... +Total: 49.55s + +[Epoch 63] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 33.3+0.8s +[3200/16000] [L1: 0.0141] 32.8+0.0s +[4800/16000] [L1: 0.0140] 32.7+0.0s +[6400/16000] [L1: 0.0141] 32.7+0.0s +[8000/16000] [L1: 0.0140] 32.7+0.0s +[9600/16000] [L1: 0.0141] 32.4+0.0s +[11200/16000] [L1: 0.0142] 32.7+0.0s +[12800/16000] [L1: 0.0142] 32.4+0.0s +[14400/16000] [L1: 0.0142] 32.4+0.0s +[16000/16000] [L1: 0.0142] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.249 (Best: 41.271 @epoch 58) +Forward: 48.94s + +Saving... +Total: 49.50s + +[Epoch 64] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 33.1+0.8s +[3200/16000] [L1: 0.0144] 32.5+0.0s +[4800/16000] [L1: 0.0143] 32.6+0.0s +[6400/16000] [L1: 0.0142] 32.5+0.0s +[8000/16000] [L1: 0.0143] 32.1+0.0s +[9600/16000] [L1: 0.0144] 32.3+0.0s +[11200/16000] [L1: 0.0144] 32.3+0.0s +[12800/16000] [L1: 0.0144] 32.1+0.0s +[14400/16000] [L1: 0.0144] 32.1+0.0s +[16000/16000] [L1: 0.0144] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.291 (Best: 41.291 @epoch 64) +Forward: 48.92s + +Saving... +Total: 49.52s + +[Epoch 65] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 32.6+1.0s +[3200/16000] [L1: 0.0141] 32.8+0.0s +[4800/16000] [L1: 0.0143] 32.5+0.0s +[6400/16000] [L1: 0.0143] 32.7+0.0s +[8000/16000] [L1: 0.0142] 32.4+0.0s +[9600/16000] [L1: 0.0143] 33.0+0.0s +[11200/16000] [L1: 0.0142] 32.3+0.0s +[12800/16000] [L1: 0.0142] 32.7+0.0s +[14400/16000] [L1: 0.0142] 32.3+0.0s +[16000/16000] [L1: 0.0143] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.263 (Best: 41.291 @epoch 64) +Forward: 48.88s + +Saving... +Total: 49.41s + +[Epoch 66] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 33.1+0.8s +[3200/16000] [L1: 0.0145] 32.9+0.1s +[4800/16000] [L1: 0.0144] 32.6+0.0s +[6400/16000] [L1: 0.0144] 32.5+0.0s +[8000/16000] [L1: 0.0143] 32.5+0.0s +[9600/16000] [L1: 0.0142] 33.0+0.0s +[11200/16000] [L1: 0.0142] 32.4+0.0s +[12800/16000] [L1: 0.0141] 32.7+0.0s +[14400/16000] [L1: 0.0142] 32.4+0.0s +[16000/16000] [L1: 0.0141] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.180 (Best: 41.291 @epoch 64) +Forward: 48.90s + +Saving... +Total: 49.49s + +[Epoch 67] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 32.9+0.8s +[3200/16000] [L1: 0.0144] 32.3+0.0s +[4800/16000] [L1: 0.0143] 32.5+0.0s +[6400/16000] [L1: 0.0143] 32.5+0.0s +[8000/16000] [L1: 0.0143] 32.2+0.0s +[9600/16000] [L1: 0.0144] 32.5+0.0s +[11200/16000] [L1: 0.0144] 32.1+0.0s +[12800/16000] [L1: 0.0144] 32.7+0.0s +[14400/16000] [L1: 0.0144] 32.7+0.0s +[16000/16000] [L1: 0.0144] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.224 (Best: 41.291 @epoch 64) +Forward: 48.97s + +Saving... +Total: 49.52s + +[Epoch 68] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0144] 32.6+0.8s +[3200/16000] [L1: 0.0143] 32.5+0.0s +[4800/16000] [L1: 0.0142] 32.3+0.0s +[6400/16000] [L1: 0.0142] 32.7+0.0s +[8000/16000] [L1: 0.0142] 32.5+0.0s +[9600/16000] [L1: 0.0141] 32.8+0.0s +[11200/16000] [L1: 0.0141] 32.3+0.0s +[12800/16000] [L1: 0.0142] 32.4+0.0s +[14400/16000] [L1: 0.0142] 32.3+0.0s +[16000/16000] [L1: 0.0142] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.295 (Best: 41.295 @epoch 68) +Forward: 48.92s + +Saving... +Total: 49.50s + +[Epoch 69] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 32.7+0.8s +[3200/16000] [L1: 0.0144] 32.8+0.0s +[4800/16000] [L1: 0.0144] 32.8+0.0s +[6400/16000] [L1: 0.0144] 32.9+0.0s +[8000/16000] [L1: 0.0143] 32.7+0.0s +[9600/16000] [L1: 0.0143] 32.7+0.0s +[11200/16000] [L1: 0.0144] 32.5+0.0s +[12800/16000] [L1: 0.0144] 32.5+0.0s +[14400/16000] [L1: 0.0143] 32.8+0.0s +[16000/16000] [L1: 0.0144] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.188 (Best: 41.295 @epoch 68) +Forward: 49.07s + +Saving... +Total: 49.61s + +[Epoch 70] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 33.2+0.8s +[3200/16000] [L1: 0.0142] 33.1+0.0s +[4800/16000] [L1: 0.0143] 32.8+0.0s +[6400/16000] [L1: 0.0141] 32.6+0.0s +[8000/16000] [L1: 0.0141] 32.7+0.0s +[9600/16000] [L1: 0.0142] 32.3+0.0s +[11200/16000] [L1: 0.0141] 32.3+0.0s +[12800/16000] [L1: 0.0141] 32.3+0.0s +[14400/16000] [L1: 0.0141] 32.7+0.0s +[16000/16000] [L1: 0.0141] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.285 (Best: 41.295 @epoch 68) +Forward: 49.06s + +Saving... +Total: 49.58s + +[Epoch 71] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 32.7+0.8s +[3200/16000] [L1: 0.0140] 32.5+0.0s +[4800/16000] [L1: 0.0140] 32.2+0.0s +[6400/16000] [L1: 0.0141] 32.7+0.0s +[8000/16000] [L1: 0.0142] 32.4+0.0s +[9600/16000] [L1: 0.0141] 32.5+0.0s +[11200/16000] [L1: 0.0141] 32.6+0.0s +[12800/16000] [L1: 0.0142] 32.4+0.0s +[14400/16000] [L1: 0.0141] 32.7+0.0s +[16000/16000] [L1: 0.0142] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.311 (Best: 41.311 @epoch 71) +Forward: 48.90s + +Saving... +Total: 49.59s + +[Epoch 72] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0137] 32.9+0.8s +[3200/16000] [L1: 0.0137] 32.7+0.0s +[4800/16000] [L1: 0.0338] 32.9+0.0s +[6400/16000] [L1: 0.0317] 32.6+0.0s +[8000/16000] [L1: 0.0295] 32.9+0.0s +[9600/16000] [L1: 0.0279] 32.5+0.0s +[11200/16000] [L1: 0.0266] 32.5+0.0s +[12800/16000] [L1: 0.0257] 32.4+0.0s +[14400/16000] [L1: 0.0250] 32.3+0.0s +[16000/16000] [L1: 0.0243] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.692 (Best: 41.311 @epoch 71) +Forward: 48.86s + +Saving... +Total: 49.41s + +[Epoch 73] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0185] 32.9+0.8s +[3200/16000] [L1: 0.0183] 33.1+0.0s +[4800/16000] [L1: 0.0181] 32.6+0.0s +[6400/16000] [L1: 0.0182] 32.4+0.0s +[8000/16000] [L1: 0.0184] 32.3+0.0s +[9600/16000] [L1: 0.0184] 32.5+0.0s +[11200/16000] [L1: 0.0183] 32.0+0.0s +[12800/16000] [L1: 0.0183] 32.3+0.0s +[14400/16000] [L1: 0.0181] 32.0+0.0s +[16000/16000] [L1: 0.0180] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.249 (Best: 41.311 @epoch 71) +Forward: 48.88s + +Saving... +Total: 49.47s + +[Epoch 74] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0157] 33.1+0.8s +[3200/16000] [L1: 0.0154] 32.8+0.0s +[4800/16000] [L1: 0.0152] 32.9+0.0s +[6400/16000] [L1: 0.0151] 32.7+0.0s +[8000/16000] [L1: 0.0149] 32.3+0.0s +[9600/16000] [L1: 0.0148] 32.5+0.0s +[11200/16000] [L1: 0.0148] 32.1+0.0s +[12800/16000] [L1: 0.0147] 32.2+0.0s +[14400/16000] [L1: 0.0146] 32.5+0.0s +[16000/16000] [L1: 0.0146] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.073 (Best: 41.311 @epoch 71) +Forward: 48.86s + +Saving... +Total: 49.43s + +[Epoch 75] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0140] 32.4+1.1s +[3200/16000] [L1: 0.0142] 32.4+0.0s +[4800/16000] [L1: 0.0142] 32.4+0.0s +[6400/16000] [L1: 0.0141] 32.5+0.0s +[8000/16000] [L1: 0.0141] 32.6+0.0s +[9600/16000] [L1: 0.0141] 32.7+0.0s +[11200/16000] [L1: 0.0141] 32.4+0.0s +[12800/16000] [L1: 0.0141] 32.4+0.0s +[14400/16000] [L1: 0.0141] 32.4+0.0s +[16000/16000] [L1: 0.0141] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.184 (Best: 41.311 @epoch 71) +Forward: 49.02s + +Saving... +Total: 49.60s + +[Epoch 76] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0143] 32.9+0.8s +[3200/16000] [L1: 0.0141] 32.9+0.0s +[4800/16000] [L1: 0.0142] 33.1+0.0s +[6400/16000] [L1: 0.0141] 33.0+0.0s +[8000/16000] [L1: 0.0141] 32.6+0.0s +[9600/16000] [L1: 0.0141] 32.9+0.0s +[11200/16000] [L1: 0.0140] 32.3+0.0s +[12800/16000] [L1: 0.0140] 32.4+0.0s +[14400/16000] [L1: 0.0141] 32.2+0.0s +[16000/16000] [L1: 0.0141] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.175 (Best: 41.311 @epoch 71) +Forward: 48.92s + +Saving... +Total: 49.46s + +[Epoch 77] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0142] 33.0+0.8s +[3200/16000] [L1: 0.0143] 32.7+0.0s +[4800/16000] [L1: 0.0143] 32.7+0.0s +[6400/16000] [L1: 0.0143] 32.4+0.0s +[8000/16000] [L1: 0.0143] 32.3+0.0s +[9600/16000] [L1: 0.0142] 32.2+0.0s +[11200/16000] [L1: 0.0142] 32.5+0.0s +[12800/16000] [L1: 0.0142] 32.3+0.0s +[14400/16000] [L1: 0.0142] 32.3+0.0s +[16000/16000] [L1: 0.0142] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.190 (Best: 41.311 @epoch 71) +Forward: 49.13s + +Saving... +Total: 49.70s + +[Epoch 78] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0145] 32.9+0.9s +[3200/16000] [L1: 0.0141] 32.7+0.0s +[4800/16000] [L1: 0.0140] 32.5+0.0s +[6400/16000] [L1: 0.0139] 32.8+0.0s +[8000/16000] [L1: 0.0140] 32.8+0.0s +[9600/16000] [L1: 0.0140] 32.5+0.0s +[11200/16000] [L1: 0.0140] 32.9+0.0s +[12800/16000] [L1: 0.0141] 33.0+0.0s +[14400/16000] [L1: 0.0141] 32.8+0.0s +[16000/16000] [L1: 0.0141] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.278 (Best: 41.311 @epoch 71) +Forward: 49.09s + +Saving... +Total: 49.63s + +[Epoch 79] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 32.5+0.9s +[3200/16000] [L1: 0.0144] 32.7+0.0s +[4800/16000] [L1: 0.0143] 32.6+0.0s +[6400/16000] [L1: 0.0144] 32.8+0.0s +[8000/16000] [L1: 0.0143] 32.7+0.0s +[9600/16000] [L1: 0.0142] 32.9+0.0s +[11200/16000] [L1: 0.0142] 32.6+0.0s +[12800/16000] [L1: 0.0143] 32.7+0.0s +[14400/16000] [L1: 0.0143] 32.6+0.0s +[16000/16000] [L1: 0.0142] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.183 (Best: 41.311 @epoch 71) +Forward: 49.09s + +Saving... +Total: 49.73s + +[Epoch 80] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0141] 32.8+0.8s +[3200/16000] [L1: 0.0141] 32.9+0.0s +[4800/16000] [L1: 0.0142] 32.6+0.0s +[6400/16000] [L1: 0.0142] 32.7+0.0s +[8000/16000] [L1: 0.0142] 32.3+0.0s +[9600/16000] [L1: 0.0142] 32.3+0.0s +[11200/16000] [L1: 0.0141] 32.4+0.0s +[12800/16000] [L1: 0.0140] 32.3+0.0s +[14400/16000] [L1: 0.0140] 32.4+0.0s +[16000/16000] [L1: 0.0140] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.197 (Best: 41.311 @epoch 71) +Forward: 48.91s + +Saving... +Total: 49.57s + +[Epoch 81] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0137] 32.8+0.9s +[3200/16000] [L1: 0.0138] 32.5+0.0s +[4800/16000] [L1: 0.0139] 32.3+0.0s +[6400/16000] [L1: 0.0139] 32.6+0.0s +[8000/16000] [L1: 0.0139] 32.2+0.0s +[9600/16000] [L1: 0.0140] 32.6+0.0s +[11200/16000] [L1: 0.0140] 32.0+0.0s +[12800/16000] [L1: 0.0140] 32.4+0.0s +[14400/16000] [L1: 0.0140] 32.4+0.0s +[16000/16000] [L1: 0.0140] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 41.319 (Best: 41.319 @epoch 81) +Forward: 49.17s + +Saving... +Total: 49.76s + +[Epoch 82] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0138] 33.1+0.8s +[3200/16000] [L1: 0.0139] 32.6+0.0s +[4800/16000] [L1: 0.0140] 32.7+0.0s +[6400/16000] [L1: 0.0140] 32.5+0.0s +[8000/16000] [L1: 0.0141] 32.6+0.0s +[9600/16000] [L1: 0.0141] 32.5+0.0s +[11200/16000] [L1: 0.0140] 32.3+0.0s +[12800/16000] [L1: 0.0140] 32.7+0.0s +[14400/16000] [L1: 0.0141] 32.4+0.0s +[16000/16000] [L1: 0.0142] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 40.968 (Best: 41.319 @epoch 81) +Forward: 49.08s + +Saving... +Total: 49.65s + +[Epoch 83] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1486] 32.7+0.8s +[3200/16000] [L1: 0.2550] 32.6+0.0s +[4800/16000] [L1: 0.1935] 32.6+0.0s +[6400/16000] [L1: 0.1571] 32.4+0.0s +[8000/16000] [L1: 0.1341] 32.6+0.0s +[9600/16000] [L1: 0.1179] 32.9+0.0s +[11200/16000] [L1: 0.1061] 32.5+0.0s +[12800/16000] [L1: 0.0971] 32.6+0.0s +[14400/16000] [L1: 0.0899] 32.2+0.0s +[16000/16000] [L1: 0.0839] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 35.396 (Best: 41.319 @epoch 81) +Forward: 48.90s + +Saving... +Total: 49.45s + +[Epoch 84] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0286] 32.8+0.8s +[3200/16000] [L1: 0.0285] 32.8+0.0s +[4800/16000] [L1: 0.0286] 32.7+0.0s +[6400/16000] [L1: 0.0282] 32.2+0.0s +[8000/16000] [L1: 0.0277] 32.6+0.0s +[9600/16000] [L1: 0.0276] 32.3+0.0s +[11200/16000] [L1: 0.0274] 32.6+0.0s +[12800/16000] [L1: 0.0272] 32.5+0.0s +[14400/16000] [L1: 0.0269] 32.4+0.0s +[16000/16000] [L1: 0.0267] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.449 (Best: 41.319 @epoch 81) +Forward: 48.97s + +Saving... +Total: 49.51s + +[Epoch 85] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0251] 32.9+0.8s +[3200/16000] [L1: 0.0254] 32.7+0.0s +[4800/16000] [L1: 0.0252] 32.5+0.0s +[6400/16000] [L1: 0.0248] 32.5+0.0s +[8000/16000] [L1: 0.0245] 32.4+0.0s +[9600/16000] [L1: 0.0243] 32.4+0.0s +[11200/16000] [L1: 0.0241] 32.8+0.0s +[12800/16000] [L1: 0.0240] 32.4+0.0s +[14400/16000] [L1: 0.0239] 32.5+0.0s +[16000/16000] [L1: 0.0236] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.721 (Best: 41.319 @epoch 81) +Forward: 49.02s + +Saving... +Total: 49.55s + +[Epoch 86] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0214] 32.4+1.1s +[3200/16000] [L1: 0.0219] 32.6+0.0s +[4800/16000] [L1: 0.0216] 32.5+0.0s +[6400/16000] [L1: 0.0218] 32.7+0.0s +[8000/16000] [L1: 0.0218] 32.5+0.0s +[9600/16000] [L1: 0.0217] 32.8+0.0s +[11200/16000] [L1: 0.0216] 32.4+0.0s +[12800/16000] [L1: 0.0215] 32.5+0.0s +[14400/16000] [L1: 0.0216] 32.3+0.0s +[16000/16000] [L1: 0.0215] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.268 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.65s + +[Epoch 87] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0207] 33.0+0.9s +[3200/16000] [L1: 0.0205] 33.0+0.0s +[4800/16000] [L1: 0.0203] 32.7+0.0s +[6400/16000] [L1: 0.0203] 33.1+0.0s +[8000/16000] [L1: 0.0203] 32.6+0.0s +[9600/16000] [L1: 0.0203] 32.9+0.0s +[11200/16000] [L1: 0.0203] 32.3+0.0s +[12800/16000] [L1: 0.0203] 32.4+0.0s +[14400/16000] [L1: 0.0203] 32.2+0.0s +[16000/16000] [L1: 0.0202] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.741 (Best: 41.319 @epoch 81) +Forward: 49.07s + +Saving... +Total: 49.58s + +[Epoch 88] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0196] 33.1+0.8s +[3200/16000] [L1: 0.0195] 32.9+0.0s +[4800/16000] [L1: 0.0193] 32.7+0.0s +[6400/16000] [L1: 0.0193] 32.7+0.0s +[8000/16000] [L1: 0.0193] 32.7+0.0s +[9600/16000] [L1: 0.0192] 32.4+0.0s +[11200/16000] [L1: 0.0192] 32.1+0.0s +[12800/16000] [L1: 0.0192] 32.4+0.0s +[14400/16000] [L1: 0.0191] 32.6+0.0s +[16000/16000] [L1: 0.0190] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 38.824 (Best: 41.319 @epoch 81) +Forward: 49.16s + +Saving... +Total: 49.72s + +[Epoch 89] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0191] 33.0+0.8s +[3200/16000] [L1: 0.0191] 32.5+0.0s +[4800/16000] [L1: 0.0191] 32.8+0.0s +[6400/16000] [L1: 0.0190] 32.9+0.0s +[8000/16000] [L1: 0.0187] 32.7+0.0s +[9600/16000] [L1: 0.0188] 32.3+0.0s +[11200/16000] [L1: 0.0187] 32.3+0.0s +[12800/16000] [L1: 0.0187] 32.9+0.0s +[14400/16000] [L1: 0.0187] 32.6+0.0s +[16000/16000] [L1: 0.0187] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.175 (Best: 41.319 @epoch 81) +Forward: 48.92s + +Saving... +Total: 49.53s + +[Epoch 90] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0186] 33.0+0.8s +[3200/16000] [L1: 0.0187] 32.9+0.0s +[4800/16000] [L1: 0.0187] 32.8+0.0s +[6400/16000] [L1: 0.0186] 32.8+0.0s +[8000/16000] [L1: 0.0186] 32.9+0.0s +[9600/16000] [L1: 0.0186] 32.5+0.0s +[11200/16000] [L1: 0.0185] 32.4+0.0s +[12800/16000] [L1: 0.0183] 32.8+0.0s +[14400/16000] [L1: 0.0183] 32.4+0.0s +[16000/16000] [L1: 0.0183] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.351 (Best: 41.319 @epoch 81) +Forward: 48.94s + +Saving... +Total: 49.46s + +[Epoch 91] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0190] 33.0+0.8s +[3200/16000] [L1: 0.0184] 33.1+0.0s +[4800/16000] [L1: 0.0184] 33.3+0.0s +[6400/16000] [L1: 0.0183] 32.5+0.0s +[8000/16000] [L1: 0.0183] 32.9+0.0s +[9600/16000] [L1: 0.0181] 32.3+0.0s +[11200/16000] [L1: 0.0182] 32.5+0.0s +[12800/16000] [L1: 0.0181] 32.5+0.0s +[14400/16000] [L1: 0.0181] 32.2+0.0s +[16000/16000] [L1: 0.0180] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.289 (Best: 41.319 @epoch 81) +Forward: 48.99s + +Saving... +Total: 49.50s + +[Epoch 92] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0180] 32.6+1.0s +[3200/16000] [L1: 0.0181] 33.0+0.0s +[4800/16000] [L1: 0.0177] 32.8+0.0s +[6400/16000] [L1: 0.0178] 32.3+0.0s +[8000/16000] [L1: 0.0179] 32.1+0.0s +[9600/16000] [L1: 0.0179] 32.3+0.0s +[11200/16000] [L1: 0.0178] 32.3+0.0s +[12800/16000] [L1: 0.0177] 32.1+0.0s +[14400/16000] [L1: 0.0177] 32.6+0.0s +[16000/16000] [L1: 0.0177] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.543 (Best: 41.319 @epoch 81) +Forward: 48.95s + +Saving... +Total: 49.50s + +[Epoch 93] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0176] 32.7+1.0s +[3200/16000] [L1: 0.0173] 32.8+0.0s +[4800/16000] [L1: 0.0176] 32.5+0.0s +[6400/16000] [L1: 0.0173] 32.7+0.0s +[8000/16000] [L1: 0.0173] 32.7+0.0s +[9600/16000] [L1: 0.0174] 33.0+0.0s +[11200/16000] [L1: 0.0173] 32.9+0.0s +[12800/16000] [L1: 0.0173] 32.8+0.0s +[14400/16000] [L1: 0.0173] 32.5+0.0s +[16000/16000] [L1: 0.0174] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.563 (Best: 41.319 @epoch 81) +Forward: 49.08s + +Saving... +Total: 49.68s + +[Epoch 94] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0173] 33.2+0.9s +[3200/16000] [L1: 0.0173] 32.4+0.0s +[4800/16000] [L1: 0.0173] 32.9+0.0s +[6400/16000] [L1: 0.0172] 32.6+0.0s +[8000/16000] [L1: 0.0171] 32.3+0.0s +[9600/16000] [L1: 0.0172] 32.5+0.0s +[11200/16000] [L1: 0.0172] 32.2+0.0s +[12800/16000] [L1: 0.0171] 32.4+0.0s +[14400/16000] [L1: 0.0172] 33.0+0.0s +[16000/16000] [L1: 0.0172] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.613 (Best: 41.319 @epoch 81) +Forward: 48.96s + +Saving... +Total: 50.01s + +[Epoch 95] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0170] 32.8+0.8s +[3200/16000] [L1: 0.0173] 32.8+0.0s +[4800/16000] [L1: 0.0173] 33.0+0.0s +[6400/16000] [L1: 0.0172] 32.6+0.0s +[8000/16000] [L1: 0.0172] 32.4+0.0s +[9600/16000] [L1: 0.0172] 32.4+0.0s +[11200/16000] [L1: 0.0172] 32.4+0.0s +[12800/16000] [L1: 0.0172] 32.6+0.0s +[14400/16000] [L1: 0.0172] 32.5+0.0s +[16000/16000] [L1: 0.0172] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.621 (Best: 41.319 @epoch 81) +Forward: 48.81s + +Saving... +Total: 49.49s + +[Epoch 96] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0166] 33.1+0.9s +[3200/16000] [L1: 0.0166] 33.0+0.0s +[4800/16000] [L1: 0.0166] 32.5+0.0s +[6400/16000] [L1: 0.0168] 33.0+0.0s +[8000/16000] [L1: 0.0168] 32.4+0.0s +[9600/16000] [L1: 0.0168] 32.7+0.0s +[11200/16000] [L1: 0.0168] 32.9+0.0s +[12800/16000] [L1: 0.0169] 33.1+0.0s +[14400/16000] [L1: 0.0169] 32.9+0.0s +[16000/16000] [L1: 0.0169] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 39.556 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.43s + +[Epoch 97] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0170] 32.7+0.8s +[3200/16000] [L1: 0.0171] 32.8+0.0s +[4800/16000] [L1: 0.0448] 32.9+0.0s +[6400/16000] [L1: 0.0705] 32.7+0.0s +[8000/16000] [L1: 0.0678] 32.5+0.0s +[9600/16000] [L1: 0.0633] 32.9+0.0s +[11200/16000] [L1: 0.0589] 32.8+0.0s +[12800/16000] [L1: 0.0552] 32.3+0.0s +[14400/16000] [L1: 0.0525] 32.3+0.0s +[16000/16000] [L1: 0.0532] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 36.110 (Best: 41.319 @epoch 81) +Forward: 49.17s + +Saving... +Total: 49.73s + +[Epoch 98] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0680] 33.0+0.9s +[3200/16000] [L1: 0.0614] 33.0+0.0s +[4800/16000] [L1: 0.0635] 33.5+0.0s +[6400/16000] [L1: 0.0566] 32.7+0.0s +[8000/16000] [L1: 0.0521] 32.6+0.0s +[9600/16000] [L1: 0.0486] 32.7+0.0s +[11200/16000] [L1: 0.0455] 32.6+0.0s +[12800/16000] [L1: 0.0431] 32.6+0.0s +[14400/16000] [L1: 0.0411] 32.1+0.0s +[16000/16000] [L1: 0.0425] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 34.709 (Best: 41.319 @epoch 81) +Forward: 49.02s + +Saving... +Total: 50.04s + +[Epoch 99] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0325] 33.2+0.8s +[3200/16000] [L1: 0.0302] 32.9+0.0s +[4800/16000] [L1: 0.0287] 32.7+0.0s +[6400/16000] [L1: 0.0284] 32.6+0.0s +[8000/16000] [L1: 0.0279] 32.5+0.0s +[9600/16000] [L1: 0.0274] 32.7+0.0s +[11200/16000] [L1: 0.0268] 32.5+0.0s +[12800/16000] [L1: 0.0263] 32.5+0.0s +[14400/16000] [L1: 0.0270] 32.7+0.0s +[16000/16000] [L1: 0.0268] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 37.365 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.45s + +[Epoch 100] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0240] 32.9+0.8s +[3200/16000] [L1: 0.0235] 32.8+0.0s +[4800/16000] [L1: 0.0230] 32.7+0.0s +[6400/16000] [L1: 0.0227] 32.4+0.0s +[8000/16000] [L1: 0.0241] 32.5+0.0s +[9600/16000] [L1: 0.0242] 32.6+0.0s +[11200/16000] [L1: 0.0238] 32.8+0.0s +[12800/16000] [L1: 0.0402] 32.4+0.0s +[14400/16000] [L1: 0.0879] 32.5+0.0s +[16000/16000] [L1: 0.1111] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.401 (Best: 41.319 @epoch 81) +Forward: 48.92s + +Saving... +Total: 49.47s + +[Epoch 101] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.4521] 32.7+0.8s +[3200/16000] [L1: 0.3647] 32.7+0.0s +[4800/16000] [L1: 0.3430] 32.4+0.0s +[6400/16000] [L1: 0.2924] 32.2+0.0s +[8000/16000] [L1: 0.2688] 32.3+0.0s +[9600/16000] [L1: 0.2459] 32.4+0.0s +[11200/16000] [L1: 0.2275] 32.2+0.0s +[12800/16000] [L1: 0.2113] 32.5+0.0s +[14400/16000] [L1: 0.1959] 32.4+0.0s +[16000/16000] [L1: 0.2163] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.705 (Best: 41.319 @epoch 81) +Forward: 49.01s + +Saving... +Total: 49.58s + +[Epoch 102] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1620] 32.8+1.0s +[3200/16000] [L1: 0.1359] 33.2+0.0s +[4800/16000] [L1: 0.1330] 32.8+0.0s +[6400/16000] [L1: 0.1410] 32.3+0.0s +[8000/16000] [L1: 0.1379] 32.6+0.0s +[9600/16000] [L1: 3.2737] 33.1+0.0s +[11200/16000] [L1: 38.3094] 32.3+0.0s +[12800/16000] [L1: 33.5611] 32.4+0.0s +[14400/16000] [L1: 29.8640] 32.6+0.0s +[16000/16000] [L1: 26.9031] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.011 (Best: 41.319 @epoch 81) +Forward: 49.08s + +Saving... +Total: 49.68s + +[Epoch 103] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3408] 32.7+0.9s +[3200/16000] [L1: 0.3294] 32.5+0.0s +[4800/16000] [L1: 0.3471] 32.4+0.0s +[6400/16000] [L1: 0.3207] 32.2+0.0s +[8000/16000] [L1: 0.3103] 32.4+0.0s +[9600/16000] [L1: 0.3066] 32.5+0.0s +[11200/16000] [L1: 0.2962] 32.6+0.0s +[12800/16000] [L1: 0.2820] 32.4+0.0s +[14400/16000] [L1: 0.2681] 32.4+0.0s +[16000/16000] [L1: 0.2561] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.904 (Best: 41.319 @epoch 81) +Forward: 48.95s + +Saving... +Total: 49.47s + +[Epoch 104] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1437] 32.8+0.9s +[3200/16000] [L1: 0.2063] 32.8+0.0s +[4800/16000] [L1: 0.2106] 32.8+0.0s +[6400/16000] [L1: 0.1912] 32.7+0.0s +[8000/16000] [L1: 0.1774] 32.8+0.0s +[9600/16000] [L1: 0.1668] 32.7+0.0s +[11200/16000] [L1: 0.1574] 33.1+0.0s +[12800/16000] [L1: 0.1495] 32.9+0.0s +[14400/16000] [L1: 0.1463] 32.5+0.0s +[16000/16000] [L1: 0.1487] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 23.012 (Best: 41.319 @epoch 81) +Forward: 48.90s + +Saving... +Total: 49.41s + +[Epoch 105] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1322] 33.1+0.8s +[3200/16000] [L1: 0.2381] 32.7+0.0s +[4800/16000] [L1: 0.2047] 33.0+0.0s +[6400/16000] [L1: 0.1796] 33.0+0.0s +[8000/16000] [L1: 0.1928] 32.5+0.0s +[9600/16000] [L1: 0.2181] 32.8+0.0s +[11200/16000] [L1: 0.2339] 32.5+0.0s +[12800/16000] [L1: 0.2311] 32.5+0.0s +[14400/16000] [L1: 0.2186] 32.5+0.0s +[16000/16000] [L1: 0.2075] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.302 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.48s + +[Epoch 106] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0955] 33.3+0.8s +[3200/16000] [L1: 0.0922] 32.9+0.0s +[4800/16000] [L1: 0.0895] 32.9+0.0s +[6400/16000] [L1: 0.1091] 32.7+0.0s +[8000/16000] [L1: 0.1092] 32.5+0.0s +[9600/16000] [L1: 0.1070] 32.1+0.0s +[11200/16000] [L1: 0.1165] 32.7+0.0s +[12800/16000] [L1: 0.1413] 32.8+0.0s +[14400/16000] [L1: 0.1445] 32.7+0.0s +[16000/16000] [L1: 0.1402] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.952 (Best: 41.319 @epoch 81) +Forward: 48.82s + +Saving... +Total: 49.39s + +[Epoch 107] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0939] 33.1+0.8s +[3200/16000] [L1: 0.0919] 33.0+0.1s +[4800/16000] [L1: 0.1012] 32.9+0.0s +[6400/16000] [L1: 0.0981] 32.9+0.0s +[8000/16000] [L1: 0.0985] 32.7+0.0s +[9600/16000] [L1: 0.1272] 32.6+0.0s +[11200/16000] [L1: 0.1372] 32.4+0.0s +[12800/16000] [L1: 0.1355] 32.7+0.0s +[14400/16000] [L1: 0.1309] 32.3+0.0s +[16000/16000] [L1: 0.1262] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.734 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.50s + +[Epoch 108] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2332] 32.8+1.0s +[3200/16000] [L1: 0.2700] 32.8+0.0s +[4800/16000] [L1: 0.2864] 32.4+0.0s +[6400/16000] [L1: 0.2571] 32.8+0.0s +[8000/16000] [L1: 0.2333] 32.2+0.0s +[9600/16000] [L1: 0.2152] 32.6+0.0s +[11200/16000] [L1: 0.2009] 32.1+0.0s +[12800/16000] [L1: 0.1889] 32.2+0.0s +[14400/16000] [L1: 0.1786] 32.5+0.0s +[16000/16000] [L1: 0.2511] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.090 (Best: 41.319 @epoch 81) +Forward: 48.79s + +Saving... +Total: 49.39s + +[Epoch 109] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2323] 32.9+0.9s +[3200/16000] [L1: 0.3344] 32.7+0.0s +[4800/16000] [L1: 0.2770] 33.2+0.0s +[6400/16000] [L1: 0.2365] 32.8+0.0s +[8000/16000] [L1: 0.2418] 32.1+0.0s +[9600/16000] [L1: 0.2565] 32.2+0.0s +[11200/16000] [L1: 0.2617] 32.9+0.0s +[12800/16000] [L1: 0.2602] 32.5+0.0s +[14400/16000] [L1: 0.2475] 32.2+0.0s +[16000/16000] [L1: 0.2598] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.172 (Best: 41.319 @epoch 81) +Forward: 48.88s + +Saving... +Total: 49.50s + +[Epoch 110] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2092] 33.2+0.8s +[3200/16000] [L1: 0.1806] 32.8+0.0s +[4800/16000] [L1: 0.1616] 32.4+0.0s +[6400/16000] [L1: 0.1490] 32.5+0.0s +[8000/16000] [L1: 0.1382] 32.3+0.0s +[9600/16000] [L1: 0.1315] 32.6+0.0s +[11200/16000] [L1: 0.1263] 32.3+0.0s +[12800/16000] [L1: 0.1218] 32.6+0.0s +[14400/16000] [L1: 0.1176] 32.4+0.0s +[16000/16000] [L1: 0.1140] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.906 (Best: 41.319 @epoch 81) +Forward: 48.85s + +Saving... +Total: 49.36s + +[Epoch 111] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0775] 33.1+0.9s +[3200/16000] [L1: 0.0773] 33.1+0.1s +[4800/16000] [L1: 0.0777] 32.6+0.0s +[6400/16000] [L1: 0.0769] 32.8+0.0s +[8000/16000] [L1: 0.0818] 32.8+0.0s +[9600/16000] [L1: 0.1009] 32.7+0.0s +[11200/16000] [L1: 0.1151] 32.6+0.0s +[12800/16000] [L1: 0.1138] 32.8+0.0s +[14400/16000] [L1: 0.1113] 32.3+0.0s +[16000/16000] [L1: 0.1097] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.379 (Best: 41.319 @epoch 81) +Forward: 48.87s + +Saving... +Total: 49.38s + +[Epoch 112] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0980] 32.8+0.7s +[3200/16000] [L1: 0.2990] 32.4+0.0s +[4800/16000] [L1: 0.2793] 32.6+0.0s +[6400/16000] [L1: 0.2984] 32.6+0.0s +[8000/16000] [L1: 0.2768] 32.7+0.0s +[9600/16000] [L1: 0.2649] 32.9+0.0s +[11200/16000] [L1: 0.2445] 32.4+0.0s +[12800/16000] [L1: 0.2269] 32.5+0.0s +[14400/16000] [L1: 0.2122] 32.5+0.0s +[16000/16000] [L1: 0.2212] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.093 (Best: 41.319 @epoch 81) +Forward: 48.93s + +Saving... +Total: 49.47s + +[Epoch 113] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2973] 32.8+1.0s +[3200/16000] [L1: 0.2448] 32.8+0.0s +[4800/16000] [L1: 0.2406] 33.0+0.0s +[6400/16000] [L1: 0.2338] 32.7+0.0s +[8000/16000] [L1: 0.2156] 32.4+0.0s +[9600/16000] [L1: 0.2277] 32.6+0.0s +[11200/16000] [L1: 0.2170] 32.8+0.0s +[12800/16000] [L1: 0.2196] 32.3+0.0s +[14400/16000] [L1: 0.2093] 32.4+0.0s +[16000/16000] [L1: 0.2052] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.256 (Best: 41.319 @epoch 81) +Forward: 49.01s + +Saving... +Total: 49.53s + +[Epoch 114] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1364] 33.0+0.9s +[3200/16000] [L1: 0.1196] 32.7+0.0s +[4800/16000] [L1: 0.1101] 32.5+0.0s +[6400/16000] [L1: 0.1042] 32.6+0.0s +[8000/16000] [L1: 0.1056] 32.7+0.0s +[9600/16000] [L1: 0.1032] 32.6+0.0s +[11200/16000] [L1: 0.1214] 32.7+0.0s +[12800/16000] [L1: 0.1217] 32.7+0.0s +[14400/16000] [L1: 0.1193] 32.9+0.0s +[16000/16000] [L1: 0.1256] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.084 (Best: 41.319 @epoch 81) +Forward: 48.78s + +Saving... +Total: 49.34s + +[Epoch 115] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3939] 32.8+0.8s +[3200/16000] [L1: 0.2944] 32.7+0.0s +[4800/16000] [L1: 0.2558] 32.8+0.0s +[6400/16000] [L1: 0.2353] 32.3+0.0s +[8000/16000] [L1: 0.2253] 32.8+0.0s +[9600/16000] [L1: 0.2072] 32.6+0.0s +[11200/16000] [L1: 0.1915] 32.4+0.0s +[12800/16000] [L1: 0.1787] 32.3+0.0s +[14400/16000] [L1: 0.1692] 32.4+0.0s +[16000/16000] [L1: 0.1610] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.109 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.42s + +[Epoch 116] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1465] 32.6+0.8s +[3200/16000] [L1: 0.1285] 32.7+0.0s +[4800/16000] [L1: 0.1251] 32.8+0.0s +[6400/16000] [L1: 0.1340] 32.9+0.0s +[8000/16000] [L1: 0.1267] 32.8+0.0s +[9600/16000] [L1: 0.1229] 32.7+0.0s +[11200/16000] [L1: 0.1224] 32.7+0.0s +[12800/16000] [L1: 0.1344] 32.4+0.0s +[14400/16000] [L1: 0.1315] 32.2+0.0s +[16000/16000] [L1: 0.1442] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.661 (Best: 41.319 @epoch 81) +Forward: 48.89s + +Saving... +Total: 49.42s + +[Epoch 117] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2047] 33.2+0.9s +[3200/16000] [L1: 0.2232] 33.0+0.0s +[4800/16000] [L1: 0.1913] 32.7+0.0s +[6400/16000] [L1: 0.2441] 32.6+0.0s +[8000/16000] [L1: 37.8005] 32.3+0.0s +[9600/16000] [L1: 31.7136] 32.7+0.0s +[11200/16000] [L1: 27.2603] 32.8+0.0s +[12800/16000] [L1: 23.9064] 32.4+0.0s +[14400/16000] [L1: 21.2852] 32.7+0.0s +[16000/16000] [L1: 19.1863] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.428 (Best: 41.319 @epoch 81) +Forward: 48.95s + +Saving... +Total: 49.49s + +[Epoch 118] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2404] 32.9+0.8s +[3200/16000] [L1: 0.2309] 32.6+0.0s +[4800/16000] [L1: 0.2206] 33.0+0.0s +[6400/16000] [L1: 0.2136] 32.4+0.0s +[8000/16000] [L1: 0.2109] 32.7+0.0s +[9600/16000] [L1: 0.2042] 32.6+0.0s +[11200/16000] [L1: 0.1994] 32.7+0.0s +[12800/16000] [L1: 0.1974] 32.8+0.0s +[14400/16000] [L1: 0.1971] 32.8+0.0s +[16000/16000] [L1: 0.1951] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.758 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.38s + +[Epoch 119] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1429] 32.6+0.8s +[3200/16000] [L1: 0.1723] 32.7+0.0s +[4800/16000] [L1: 0.1866] 32.6+0.0s +[6400/16000] [L1: 0.2263] 32.4+0.0s +[8000/16000] [L1: 0.2665] 33.0+0.0s +[9600/16000] [L1: 0.2588] 32.7+0.0s +[11200/16000] [L1: 0.2504] 33.0+0.0s +[12800/16000] [L1: 0.2437] 32.6+0.0s +[14400/16000] [L1: 0.2386] 32.5+0.0s +[16000/16000] [L1: 0.2368] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 23.525 (Best: 41.319 @epoch 81) +Forward: 48.77s + +Saving... +Total: 52.73s + +[Epoch 120] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2201] 32.8+0.8s +[3200/16000] [L1: 0.2030] 32.7+0.0s +[4800/16000] [L1: 0.6157] 33.1+0.0s +[6400/16000] [L1: 0.6187] 32.9+0.0s +[8000/16000] [L1: 0.8679] 32.4+0.0s +[9600/16000] [L1: 0.8048] 32.7+0.0s +[11200/16000] [L1: 0.7596] 32.1+0.0s +[12800/16000] [L1: 0.7096] 32.4+0.0s +[14400/16000] [L1: 0.6728] 32.2+0.0s +[16000/16000] [L1: 0.6316] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.087 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.48s + +[Epoch 121] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1993] 32.8+0.8s +[3200/16000] [L1: 0.2175] 32.8+0.0s +[4800/16000] [L1: 0.2320] 32.6+0.0s +[6400/16000] [L1: 0.2222] 32.4+0.0s +[8000/16000] [L1: 0.2220] 32.8+0.0s +[9600/16000] [L1: 0.2216] 32.7+0.0s +[11200/16000] [L1: 0.2212] 32.5+0.0s +[12800/16000] [L1: 0.2281] 32.6+0.0s +[14400/16000] [L1: 0.2227] 32.4+0.0s +[16000/16000] [L1: 0.2144] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.027 (Best: 41.319 @epoch 81) +Forward: 48.92s + +Saving... +Total: 49.47s + +[Epoch 122] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1469] 32.5+0.8s +[3200/16000] [L1: 0.2294] 32.8+0.0s +[4800/16000] [L1: 0.2241] 32.7+0.0s +[6400/16000] [L1: 0.2043] 32.8+0.0s +[8000/16000] [L1: 0.1911] 32.5+0.0s +[9600/16000] [L1: 0.1810] 32.5+0.0s +[11200/16000] [L1: 0.1718] 32.4+0.0s +[12800/16000] [L1: 0.1671] 32.5+0.0s +[14400/16000] [L1: 0.1607] 32.6+0.0s +[16000/16000] [L1: 0.1544] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.892 (Best: 41.319 @epoch 81) +Forward: 49.05s + +Saving... +Total: 49.61s + +[Epoch 123] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2889] 32.3+0.8s +[3200/16000] [L1: 0.2373] 32.6+0.0s +[4800/16000] [L1: 0.2840] 32.5+0.0s +[6400/16000] [L1: 0.2656] 32.6+0.0s +[8000/16000] [L1: 0.2442] 32.6+0.0s +[9600/16000] [L1: 0.2300] 32.6+0.0s +[11200/16000] [L1: 0.2160] 32.5+0.0s +[12800/16000] [L1: 0.2042] 32.3+0.0s +[14400/16000] [L1: 0.1938] 32.4+0.0s +[16000/16000] [L1: 0.1847] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.530 (Best: 41.319 @epoch 81) +Forward: 48.94s + +Saving... +Total: 49.55s + +[Epoch 124] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1009] 32.5+0.8s +[3200/16000] [L1: 0.0968] 33.1+0.0s +[4800/16000] [L1: 0.0957] 32.6+0.0s +[6400/16000] [L1: 0.0942] 32.6+0.0s +[8000/16000] [L1: 0.0932] 32.7+0.0s +[9600/16000] [L1: 0.0927] 32.3+0.0s +[11200/16000] [L1: 0.1148] 32.6+0.0s +[12800/16000] [L1: 0.1350] 32.7+0.0s +[14400/16000] [L1: 0.1578] 32.6+0.0s +[16000/16000] [L1: 0.1623] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.577 (Best: 41.319 @epoch 81) +Forward: 48.88s + +Saving... +Total: 49.55s + +[Epoch 125] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1413] 32.7+0.8s +[3200/16000] [L1: 0.1325] 32.7+0.0s +[4800/16000] [L1: 0.1255] 32.9+0.0s +[6400/16000] [L1: 0.1215] 32.4+0.0s +[8000/16000] [L1: 0.1180] 32.8+0.0s +[9600/16000] [L1: 0.1141] 32.4+0.0s +[11200/16000] [L1: 0.1112] 32.1+0.0s +[12800/16000] [L1: 0.1100] 32.3+0.0s +[14400/16000] [L1: 0.1101] 32.6+0.0s +[16000/16000] [L1: 0.1089] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.659 (Best: 41.319 @epoch 81) +Forward: 48.88s + +Saving... +Total: 49.42s + +[Epoch 126] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0946] 32.9+0.8s +[3200/16000] [L1: 0.1761] 33.0+0.0s +[4800/16000] [L1: 0.1772] 32.7+0.0s +[6400/16000] [L1: 0.2235] 32.7+0.0s +[8000/16000] [L1: 0.2051] 32.5+0.0s +[9600/16000] [L1: 0.1900] 32.6+0.0s +[11200/16000] [L1: 0.1776] 32.3+0.0s +[12800/16000] [L1: 0.1678] 32.6+0.0s +[14400/16000] [L1: 0.1593] 32.2+0.0s +[16000/16000] [L1: 0.1519] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.251 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.36s + +[Epoch 127] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0821] 32.8+0.9s +[3200/16000] [L1: 0.0808] 32.7+0.0s +[4800/16000] [L1: 0.0792] 32.2+0.0s +[6400/16000] [L1: 0.0785] 32.7+0.0s +[8000/16000] [L1: 0.0781] 32.1+0.0s +[9600/16000] [L1: 0.0875] 32.6+0.0s +[11200/16000] [L1: 0.1027] 32.1+0.0s +[12800/16000] [L1: 0.1068] 32.3+0.0s +[14400/16000] [L1: 0.1136] 32.2+0.0s +[16000/16000] [L1: 0.1209] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.773 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.43s + +[Epoch 128] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1048] 33.3+0.9s +[3200/16000] [L1: 0.1002] 32.7+0.0s +[4800/16000] [L1: 0.1280] 32.5+0.0s +[6400/16000] [L1: 0.1986] 32.7+0.0s +[8000/16000] [L1: 0.2039] 32.3+0.0s +[9600/16000] [L1: 0.1976] 32.8+0.0s +[11200/16000] [L1: 0.2113] 32.3+0.0s +[12800/16000] [L1: 0.2147] 32.5+0.0s +[14400/16000] [L1: 0.2146] 32.3+0.0s +[16000/16000] [L1: 0.2072] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.938 (Best: 41.319 @epoch 81) +Forward: 48.93s + +Saving... +Total: 49.48s + +[Epoch 129] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1515] 33.1+0.9s +[3200/16000] [L1: 0.1366] 33.1+0.0s +[4800/16000] [L1: 0.1342] 32.7+0.0s +[6400/16000] [L1: 0.1750] 32.4+0.0s +[8000/16000] [L1: 0.1727] 32.3+0.0s +[9600/16000] [L1: 0.1793] 32.3+0.0s +[11200/16000] [L1: 0.1700] 32.8+0.0s +[12800/16000] [L1: 0.1612] 32.7+0.0s +[14400/16000] [L1: 0.1538] 32.7+0.0s +[16000/16000] [L1: 0.1476] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.171 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.44s + +[Epoch 130] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0864] 32.9+0.8s +[3200/16000] [L1: 0.1166] 32.9+0.0s +[4800/16000] [L1: 0.1233] 32.5+0.0s +[6400/16000] [L1: 0.1168] 32.5+0.0s +[8000/16000] [L1: 0.1413] 32.8+0.0s +[9600/16000] [L1: 0.1421] 32.5+0.0s +[11200/16000] [L1: 0.1594] 32.2+0.0s +[12800/16000] [L1: 0.1658] 32.2+0.0s +[14400/16000] [L1: 0.1613] 32.5+0.0s +[16000/16000] [L1: 0.1553] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.825 (Best: 41.319 @epoch 81) +Forward: 49.06s + +Saving... +Total: 49.61s + +[Epoch 131] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0917] 32.6+0.9s +[3200/16000] [L1: 0.1372] 32.8+0.0s +[4800/16000] [L1: 0.1516] 32.8+0.0s +[6400/16000] [L1: 0.1584] 32.5+0.0s +[8000/16000] [L1: 0.1754] 32.5+0.0s +[9600/16000] [L1: 0.1787] 32.5+0.0s +[11200/16000] [L1: 0.1801] 32.4+0.0s +[12800/16000] [L1: 0.1827] 32.6+0.0s +[14400/16000] [L1: 0.1855] 32.5+0.0s +[16000/16000] [L1: 0.1885] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.024 (Best: 41.319 @epoch 81) +Forward: 49.03s + +Saving... +Total: 49.55s + +[Epoch 132] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2235] 33.1+0.8s +[3200/16000] [L1: 0.1813] 33.1+0.0s +[4800/16000] [L1: 0.1594] 32.6+0.0s +[6400/16000] [L1: 0.1472] 32.5+0.0s +[8000/16000] [L1: 0.1473] 32.8+0.0s +[9600/16000] [L1: 0.1499] 32.8+0.0s +[11200/16000] [L1: 0.1435] 32.3+0.0s +[12800/16000] [L1: 0.1377] 32.0+0.0s +[14400/16000] [L1: 0.1322] 32.6+0.0s +[16000/16000] [L1: 0.1274] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.045 (Best: 41.319 @epoch 81) +Forward: 49.00s + +Saving... +Total: 49.60s + +[Epoch 133] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1035] 32.9+0.8s +[3200/16000] [L1: 0.1040] 32.8+0.0s +[4800/16000] [L1: 0.1248] 32.3+0.0s +[6400/16000] [L1: 0.1206] 32.7+0.0s +[8000/16000] [L1: 0.1328] 32.6+0.0s +[9600/16000] [L1: 0.1551] 32.8+0.0s +[11200/16000] [L1: 0.1497] 32.8+0.0s +[12800/16000] [L1: 0.1452] 32.2+0.0s +[14400/16000] [L1: 0.1439] 32.6+0.0s +[16000/16000] [L1: 0.1391] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.139 (Best: 41.319 @epoch 81) +Forward: 48.90s + +Saving... +Total: 49.50s + +[Epoch 134] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0886] 32.7+0.8s +[3200/16000] [L1: 0.1414] 32.8+0.0s +[4800/16000] [L1: 0.1294] 32.5+0.0s +[6400/16000] [L1: 0.1204] 32.4+0.0s +[8000/16000] [L1: 0.1283] 32.7+0.0s +[9600/16000] [L1: 0.1312] 32.4+0.0s +[11200/16000] [L1: 0.1260] 32.2+0.0s +[12800/16000] [L1: 0.1216] 32.7+0.0s +[14400/16000] [L1: 0.1177] 32.1+0.0s +[16000/16000] [L1: 0.1139] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.175 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.62s + +[Epoch 135] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0768] 32.8+0.8s +[3200/16000] [L1: 0.0882] 32.9+0.0s +[4800/16000] [L1: 0.0890] 32.6+0.0s +[6400/16000] [L1: 0.0874] 32.5+0.0s +[8000/16000] [L1: 0.0852] 32.6+0.0s +[9600/16000] [L1: 0.0832] 32.7+0.0s +[11200/16000] [L1: 0.0821] 32.4+0.0s +[12800/16000] [L1: 0.0803] 32.8+0.0s +[14400/16000] [L1: 0.0788] 32.3+0.0s +[16000/16000] [L1: 0.0780] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.671 (Best: 41.319 @epoch 81) +Forward: 48.83s + +Saving... +Total: 49.45s + +[Epoch 136] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1095] 32.9+0.9s +[3200/16000] [L1: 0.0955] 32.9+0.0s +[4800/16000] [L1: 0.0872] 32.7+0.0s +[6400/16000] [L1: 0.0862] 32.4+0.0s +[8000/16000] [L1: 0.1045] 32.3+0.0s +[9600/16000] [L1: 0.1029] 32.2+0.0s +[11200/16000] [L1: 0.0997] 32.7+0.0s +[12800/16000] [L1: 0.1138] 32.2+0.0s +[14400/16000] [L1: 0.1150] 32.4+0.0s +[16000/16000] [L1: 0.1195] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.228 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.51s + +[Epoch 137] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1071] 33.1+1.0s +[3200/16000] [L1: 0.0987] 33.2+0.0s +[4800/16000] [L1: 0.1100] 32.7+0.0s +[6400/16000] [L1: 0.1081] 32.6+0.0s +[8000/16000] [L1: 0.1050] 32.6+0.0s +[9600/16000] [L1: 0.1015] 32.9+0.0s +[11200/16000] [L1: 0.0982] 33.0+0.0s +[12800/16000] [L1: 0.0950] 32.7+0.0s +[14400/16000] [L1: 0.0987] 32.4+0.0s +[16000/16000] [L1: 0.0996] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.231 (Best: 41.319 @epoch 81) +Forward: 48.85s + +Saving... +Total: 49.41s + +[Epoch 138] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0852] 32.6+0.8s +[3200/16000] [L1: 0.0783] 32.6+0.0s +[4800/16000] [L1: 0.0745] 32.4+0.0s +[6400/16000] [L1: 0.0730] 32.5+0.0s +[8000/16000] [L1: 0.0710] 32.7+0.0s +[9600/16000] [L1: 0.0690] 32.3+0.0s +[11200/16000] [L1: 0.0678] 32.4+0.0s +[12800/16000] [L1: 0.0772] 32.4+0.0s +[14400/16000] [L1: 0.0976] 32.2+0.0s +[16000/16000] [L1: 0.1024] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.596 (Best: 41.319 @epoch 81) +Forward: 48.99s + +Saving... +Total: 49.54s + +[Epoch 139] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.3562] 33.0+1.0s +[3200/16000] [L1: 0.2427] 32.9+0.0s +[4800/16000] [L1: 0.1989] 32.8+0.0s +[6400/16000] [L1: 0.1737] 32.4+0.0s +[8000/16000] [L1: 0.1575] 32.2+0.0s +[9600/16000] [L1: 0.1461] 32.3+0.0s +[11200/16000] [L1: 0.1373] 32.1+0.0s +[12800/16000] [L1: 0.1304] 32.5+0.0s +[14400/16000] [L1: 0.1240] 32.4+0.0s +[16000/16000] [L1: 0.1189] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.806 (Best: 41.319 @epoch 81) +Forward: 48.77s + +Saving... +Total: 49.31s + +[Epoch 140] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0837] 32.7+0.8s +[3200/16000] [L1: 0.0842] 32.6+0.0s +[4800/16000] [L1: 0.0816] 32.5+0.0s +[6400/16000] [L1: 0.0791] 32.4+0.0s +[8000/16000] [L1: 0.0777] 32.5+0.0s +[9600/16000] [L1: 0.0764] 32.5+0.0s +[11200/16000] [L1: 0.0808] 32.4+0.0s +[12800/16000] [L1: 0.0814] 32.6+0.0s +[14400/16000] [L1: 0.0809] 32.8+0.0s +[16000/16000] [L1: 0.0800] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.680 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.46s + +[Epoch 141] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0659] 33.0+0.8s +[3200/16000] [L1: 0.0634] 32.8+0.0s +[4800/16000] [L1: 0.0632] 32.5+0.0s +[6400/16000] [L1: 0.0626] 32.3+0.0s +[8000/16000] [L1: 0.0625] 32.8+0.0s +[9600/16000] [L1: 0.0632] 32.6+0.0s +[11200/16000] [L1: 0.0630] 32.7+0.0s +[12800/16000] [L1: 0.0627] 32.5+0.0s +[14400/16000] [L1: 0.0621] 32.5+0.0s +[16000/16000] [L1: 0.0617] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.090 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.48s + +[Epoch 142] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0580] 32.4+0.8s +[3200/16000] [L1: 0.0558] 32.9+0.0s +[4800/16000] [L1: 0.0544] 32.8+0.0s +[6400/16000] [L1: 0.0538] 33.0+0.0s +[8000/16000] [L1: 0.0542] 32.2+0.0s +[9600/16000] [L1: 0.0539] 32.0+0.0s +[11200/16000] [L1: 0.0536] 32.1+0.0s +[12800/16000] [L1: 0.0530] 32.1+0.0s +[14400/16000] [L1: 0.0527] 32.5+0.0s +[16000/16000] [L1: 0.0613] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.517 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.43s + +[Epoch 143] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1814] 32.9+0.8s +[3200/16000] [L1: 0.1984] 33.0+0.0s +[4800/16000] [L1: 0.1737] 32.8+0.0s +[6400/16000] [L1: 0.1961] 32.4+0.0s +[8000/16000] [L1: 0.1799] 32.3+0.0s +[9600/16000] [L1: 0.1648] 32.2+0.0s +[11200/16000] [L1: 0.1962] 32.5+0.0s +[12800/16000] [L1: 0.1900] 32.3+0.0s +[14400/16000] [L1: 0.1817] 32.3+0.0s +[16000/16000] [L1: 0.1783] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.804 (Best: 41.319 @epoch 81) +Forward: 48.88s + +Saving... +Total: 49.47s + +[Epoch 144] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1035] 32.9+1.0s +[3200/16000] [L1: 0.0961] 32.9+0.0s +[4800/16000] [L1: 0.0920] 32.6+0.0s +[6400/16000] [L1: 0.1020] 32.6+0.0s +[8000/16000] [L1: 0.1074] 32.6+0.0s +[9600/16000] [L1: 0.1077] 32.4+0.0s +[11200/16000] [L1: 0.1110] 32.7+0.0s +[12800/16000] [L1: 0.1111] 32.9+0.0s +[14400/16000] [L1: 0.1090] 32.2+0.0s +[16000/16000] [L1: 0.1067] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.005 (Best: 41.319 @epoch 81) +Forward: 48.77s + +Saving... +Total: 49.39s + +[Epoch 145] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0911] 33.0+0.8s +[3200/16000] [L1: 0.0872] 32.5+0.0s +[4800/16000] [L1: 0.0835] 32.8+0.0s +[6400/16000] [L1: 0.0810] 32.7+0.0s +[8000/16000] [L1: 0.0792] 32.5+0.0s +[9600/16000] [L1: 0.0778] 32.5+0.0s +[11200/16000] [L1: 0.0829] 32.8+0.0s +[12800/16000] [L1: 0.0835] 32.6+0.0s +[14400/16000] [L1: 0.0837] 32.5+0.0s +[16000/16000] [L1: 0.0831] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.440 (Best: 41.319 @epoch 81) +Forward: 48.76s + +Saving... +Total: 49.54s + +[Epoch 146] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0720] 32.7+0.9s +[3200/16000] [L1: 0.0726] 32.6+0.0s +[4800/16000] [L1: 0.0702] 32.9+0.0s +[6400/16000] [L1: 0.0693] 32.6+0.0s +[8000/16000] [L1: 0.1319] 32.7+0.0s +[9600/16000] [L1: 0.1308] 32.8+0.0s +[11200/16000] [L1: 0.1234] 32.6+0.0s +[12800/16000] [L1: 0.1164] 32.4+0.0s +[14400/16000] [L1: 0.1134] 32.6+0.0s +[16000/16000] [L1: 0.1143] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.212 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.43s + +[Epoch 147] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1963] 32.7+0.8s +[3200/16000] [L1: 0.1512] 32.9+0.0s +[4800/16000] [L1: 0.1290] 32.8+0.0s +[6400/16000] [L1: 0.1145] 32.7+0.0s +[8000/16000] [L1: 0.1048] 32.8+0.0s +[9600/16000] [L1: 0.1000] 32.5+0.0s +[11200/16000] [L1: 0.1017] 32.4+0.0s +[12800/16000] [L1: 0.1002] 32.4+0.0s +[14400/16000] [L1: 0.0976] 32.3+0.0s +[16000/16000] [L1: 0.0952] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.217 (Best: 41.319 @epoch 81) +Forward: 48.89s + +Saving... +Total: 49.44s + +[Epoch 148] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1129] 33.0+0.9s +[3200/16000] [L1: 0.1020] 33.2+0.0s +[4800/16000] [L1: 0.0928] 32.6+0.0s +[6400/16000] [L1: 0.0869] 32.4+0.0s +[8000/16000] [L1: 0.0855] 32.3+0.0s +[9600/16000] [L1: 0.0878] 32.8+0.0s +[11200/16000] [L1: 0.1000] 32.2+0.0s +[12800/16000] [L1: 0.1056] 32.4+0.0s +[14400/16000] [L1: 0.1309] 32.5+0.0s +[16000/16000] [L1: 0.1398] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.688 (Best: 41.319 @epoch 81) +Forward: 49.00s + +Saving... +Total: 49.56s + +[Epoch 149] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1065] 32.7+0.9s +[3200/16000] [L1: 0.1235] 33.0+0.0s +[4800/16000] [L1: 0.1326] 32.6+0.0s +[6400/16000] [L1: 0.1644] 32.7+0.0s +[8000/16000] [L1: 0.1543] 32.9+0.0s +[9600/16000] [L1: 0.1455] 32.8+0.0s +[11200/16000] [L1: 0.1376] 32.5+0.0s +[12800/16000] [L1: 0.1344] 32.4+0.0s +[14400/16000] [L1: 0.1298] 32.4+0.0s +[16000/16000] [L1: 0.1250] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.392 (Best: 41.319 @epoch 81) +Forward: 49.06s + +Saving... +Total: 49.61s + +[Epoch 150] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0764] 33.0+0.9s +[3200/16000] [L1: 0.0738] 32.9+0.0s +[4800/16000] [L1: 0.0728] 32.8+0.0s +[6400/16000] [L1: 0.0749] 32.1+0.0s +[8000/16000] [L1: 0.0749] 33.1+0.0s +[9600/16000] [L1: 0.0843] 32.4+0.0s +[11200/16000] [L1: 0.0914] 32.3+0.0s +[12800/16000] [L1: 0.1039] 32.0+0.0s +[14400/16000] [L1: 0.1046] 32.6+0.0s +[16000/16000] [L1: 0.1146] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.637 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.52s + +[Epoch 151] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1337] 32.7+0.8s +[3200/16000] [L1: 0.1237] 33.3+0.0s +[4800/16000] [L1: 0.1152] 32.5+0.0s +[6400/16000] [L1: 0.1076] 32.8+0.0s +[8000/16000] [L1: 0.1020] 32.9+0.0s +[9600/16000] [L1: 0.0977] 32.3+0.0s +[11200/16000] [L1: 0.0940] 32.5+0.0s +[12800/16000] [L1: 0.0914] 32.6+0.0s +[14400/16000] [L1: 0.0888] 33.1+0.0s +[16000/16000] [L1: 0.0892] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.770 (Best: 41.319 @epoch 81) +Forward: 48.73s + +Saving... +Total: 49.36s + +[Epoch 152] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0891] 32.7+0.8s +[3200/16000] [L1: 0.0897] 32.9+0.0s +[4800/16000] [L1: 0.0852] 32.5+0.0s +[6400/16000] [L1: 0.0819] 32.5+0.0s +[8000/16000] [L1: 0.0798] 32.1+0.0s +[9600/16000] [L1: 0.0783] 32.5+0.0s +[11200/16000] [L1: 0.0760] 32.2+0.0s +[12800/16000] [L1: 0.0744] 32.5+0.0s +[14400/16000] [L1: 0.0729] 32.4+0.0s +[16000/16000] [L1: 0.0720] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.141 (Best: 41.319 @epoch 81) +Forward: 49.05s + +Saving... +Total: 49.63s + +[Epoch 153] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0596] 33.2+0.9s +[3200/16000] [L1: 0.1142] 32.7+0.0s +[4800/16000] [L1: 0.2211] 32.8+0.0s +[6400/16000] [L1: 0.2484] 32.9+0.0s +[8000/16000] [L1: 0.2172] 32.3+0.0s +[9600/16000] [L1: 0.1941] 32.4+0.0s +[11200/16000] [L1: 0.1766] 32.8+0.0s +[12800/16000] [L1: 0.1635] 32.6+0.0s +[14400/16000] [L1: 0.1532] 32.2+0.0s +[16000/16000] [L1: 0.1446] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.773 (Best: 41.319 @epoch 81) +Forward: 48.83s + +Saving... +Total: 49.41s + +[Epoch 154] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1000] 33.1+0.9s +[3200/16000] [L1: 0.1082] 32.6+0.0s +[4800/16000] [L1: 0.1030] 32.5+0.0s +[6400/16000] [L1: 0.0967] 32.6+0.1s +[8000/16000] [L1: 0.0928] 32.5+0.0s +[9600/16000] [L1: 0.0886] 32.7+0.0s +[11200/16000] [L1: 0.0856] 32.6+0.0s +[12800/16000] [L1: 0.0891] 32.6+0.0s +[14400/16000] [L1: 0.0892] 32.4+0.0s +[16000/16000] [L1: 0.0882] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.428 (Best: 41.319 @epoch 81) +Forward: 48.94s + +Saving... +Total: 49.51s + +[Epoch 155] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0871] 33.0+0.9s +[3200/16000] [L1: 0.0783] 32.6+0.0s +[4800/16000] [L1: 0.0731] 32.7+0.0s +[6400/16000] [L1: 0.0735] 32.7+0.0s +[8000/16000] [L1: 0.0898] 32.2+0.0s +[9600/16000] [L1: 0.0916] 32.4+0.0s +[11200/16000] [L1: 0.0880] 32.3+0.0s +[12800/16000] [L1: 0.0847] 32.5+0.0s +[14400/16000] [L1: 0.0819] 32.6+0.0s +[16000/16000] [L1: 0.0799] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.118 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.45s + +[Epoch 156] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0594] 32.7+0.9s +[3200/16000] [L1: 0.0572] 32.7+0.0s +[4800/16000] [L1: 0.0565] 32.9+0.0s +[6400/16000] [L1: 0.0555] 32.4+0.0s +[8000/16000] [L1: 0.0554] 32.4+0.0s +[9600/16000] [L1: 0.0549] 32.3+0.0s +[11200/16000] [L1: 0.0548] 32.1+0.0s +[12800/16000] [L1: 0.0543] 32.3+0.0s +[14400/16000] [L1: 0.0539] 32.1+0.0s +[16000/16000] [L1: 0.0636] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.364 (Best: 41.319 @epoch 81) +Forward: 48.77s + +Saving... +Total: 49.33s + +[Epoch 157] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1028] 33.0+0.8s +[3200/16000] [L1: 0.0936] 33.1+0.0s +[4800/16000] [L1: 0.0870] 33.4+0.0s +[6400/16000] [L1: 0.0819] 32.4+0.0s +[8000/16000] [L1: 0.0790] 32.9+0.0s +[9600/16000] [L1: 0.0760] 32.5+0.0s +[11200/16000] [L1: 0.0740] 32.2+0.0s +[12800/16000] [L1: 0.0721] 32.3+0.0s +[14400/16000] [L1: 0.0705] 32.3+0.0s +[16000/16000] [L1: 0.0691] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.895 (Best: 41.319 @epoch 81) +Forward: 49.02s + +Saving... +Total: 49.60s + +[Epoch 158] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0563] 33.0+0.8s +[3200/16000] [L1: 0.0562] 33.0+0.0s +[4800/16000] [L1: 0.0563] 32.8+0.0s +[6400/16000] [L1: 0.0559] 32.7+0.0s +[8000/16000] [L1: 0.0554] 32.4+0.0s +[9600/16000] [L1: 0.0542] 32.3+0.0s +[11200/16000] [L1: 0.0661] 32.3+0.0s +[12800/16000] [L1: 0.0675] 32.4+0.0s +[14400/16000] [L1: 0.0676] 32.3+0.0s +[16000/16000] [L1: 0.0669] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.458 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.47s + +[Epoch 159] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0568] 32.9+0.9s +[3200/16000] [L1: 0.0608] 32.9+0.0s +[4800/16000] [L1: 0.0590] 32.6+0.0s +[6400/16000] [L1: 0.0571] 32.6+0.0s +[8000/16000] [L1: 0.0563] 32.3+0.0s +[9600/16000] [L1: 0.0559] 32.5+0.0s +[11200/16000] [L1: 0.0548] 32.9+0.0s +[12800/16000] [L1: 0.0547] 32.7+0.0s +[14400/16000] [L1: 0.0541] 32.4+0.0s +[16000/16000] [L1: 0.0541] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.582 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.51s + +[Epoch 160] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0478] 32.8+0.8s +[3200/16000] [L1: 0.0511] 33.1+0.0s +[4800/16000] [L1: 0.0988] 32.8+0.0s +[6400/16000] [L1: 0.1124] 32.9+0.0s +[8000/16000] [L1: 0.1048] 32.8+0.0s +[9600/16000] [L1: 0.1005] 32.5+0.0s +[11200/16000] [L1: 0.0957] 32.6+0.0s +[12800/16000] [L1: 0.0912] 32.6+0.0s +[14400/16000] [L1: 0.0928] 32.5+0.0s +[16000/16000] [L1: 0.0911] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.771 (Best: 41.319 @epoch 81) +Forward: 49.04s + +Saving... +Total: 49.67s + +[Epoch 161] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0637] 32.7+0.9s +[3200/16000] [L1: 0.0604] 32.8+0.0s +[4800/16000] [L1: 0.0667] 32.9+0.0s +[6400/16000] [L1: 0.0677] 32.9+0.0s +[8000/16000] [L1: 0.0665] 32.6+0.0s +[9600/16000] [L1: 0.0650] 33.0+0.0s +[11200/16000] [L1: 0.0734] 32.8+0.0s +[12800/16000] [L1: 0.0747] 32.4+0.0s +[14400/16000] [L1: 0.0745] 32.5+0.0s +[16000/16000] [L1: 0.0735] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.390 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.60s + +[Epoch 162] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0632] 32.8+1.0s +[3200/16000] [L1: 0.0624] 32.7+0.0s +[4800/16000] [L1: 0.0601] 32.7+0.0s +[6400/16000] [L1: 0.0585] 32.6+0.0s +[8000/16000] [L1: 0.0733] 32.3+0.0s +[9600/16000] [L1: 0.0748] 32.7+0.0s +[11200/16000] [L1: 0.0732] 32.4+0.0s +[12800/16000] [L1: 0.0720] 32.0+0.0s +[14400/16000] [L1: 0.0734] 32.8+0.0s +[16000/16000] [L1: 0.0732] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.397 (Best: 41.319 @epoch 81) +Forward: 48.87s + +Saving... +Total: 49.43s + +[Epoch 163] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0779] 33.1+0.9s +[3200/16000] [L1: 0.0749] 32.6+0.0s +[4800/16000] [L1: 0.0868] 32.7+0.0s +[6400/16000] [L1: 0.1075] 32.4+0.0s +[8000/16000] [L1: 0.1019] 32.2+0.0s +[9600/16000] [L1: 0.0964] 32.4+0.0s +[11200/16000] [L1: 0.0919] 32.5+0.0s +[12800/16000] [L1: 0.0939] 32.6+0.0s +[14400/16000] [L1: 0.0926] 32.6+0.0s +[16000/16000] [L1: 0.0901] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.198 (Best: 41.319 @epoch 81) +Forward: 48.98s + +Saving... +Total: 49.54s + +[Epoch 164] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1150] 32.7+0.8s +[3200/16000] [L1: 0.1030] 32.7+0.0s +[4800/16000] [L1: 0.0882] 32.7+0.0s +[6400/16000] [L1: 0.0794] 32.7+0.0s +[8000/16000] [L1: 0.0778] 32.7+0.0s +[9600/16000] [L1: 0.0740] 32.7+0.0s +[11200/16000] [L1: 0.0712] 32.7+0.0s +[12800/16000] [L1: 0.0814] 32.8+0.0s +[14400/16000] [L1: 0.0794] 32.7+0.0s +[16000/16000] [L1: 0.0770] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.260 (Best: 41.319 @epoch 81) +Forward: 49.01s + +Saving... +Total: 49.54s + +[Epoch 165] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0703] 32.9+0.8s +[3200/16000] [L1: 0.1146] 32.7+0.0s +[4800/16000] [L1: 0.1164] 32.5+0.0s +[6400/16000] [L1: 0.1165] 32.4+0.0s +[8000/16000] [L1: 0.1139] 32.6+0.0s +[9600/16000] [L1: 0.1119] 32.5+0.0s +[11200/16000] [L1: 0.1072] 32.3+0.0s +[12800/16000] [L1: 0.1038] 32.0+0.0s +[14400/16000] [L1: 0.1221] 32.1+0.0s +[16000/16000] [L1: 0.1236] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.008 (Best: 41.319 @epoch 81) +Forward: 48.90s + +Saving... +Total: 49.56s + +[Epoch 166] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0925] 32.8+0.8s +[3200/16000] [L1: 0.1090] 32.8+0.0s +[4800/16000] [L1: 0.1061] 32.8+0.0s +[6400/16000] [L1: 0.1474] 32.8+0.0s +[8000/16000] [L1: 0.1406] 32.4+0.0s +[9600/16000] [L1: 0.1311] 32.5+0.0s +[11200/16000] [L1: 0.1227] 32.2+0.0s +[12800/16000] [L1: 0.1153] 32.9+0.0s +[14400/16000] [L1: 0.1093] 32.6+0.0s +[16000/16000] [L1: 0.1086] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.188 (Best: 41.319 @epoch 81) +Forward: 49.00s + +Saving... +Total: 49.53s + +[Epoch 167] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0689] 33.0+1.1s +[3200/16000] [L1: 0.0661] 32.9+0.0s +[4800/16000] [L1: 0.0684] 32.6+0.0s +[6400/16000] [L1: 0.1113] 32.5+0.0s +[8000/16000] [L1: 0.1320] 32.7+0.0s +[9600/16000] [L1: 0.1237] 32.6+0.0s +[11200/16000] [L1: 0.1155] 32.7+0.0s +[12800/16000] [L1: 0.1264] 32.7+0.0s +[14400/16000] [L1: 0.1236] 32.6+0.0s +[16000/16000] [L1: 0.1212] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.469 (Best: 41.319 @epoch 81) +Forward: 49.08s + +Saving... +Total: 49.62s + +[Epoch 168] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1187] 33.0+0.8s +[3200/16000] [L1: 0.1202] 33.3+0.0s +[4800/16000] [L1: 0.1089] 33.2+0.0s +[6400/16000] [L1: 0.1688] 32.7+0.0s +[8000/16000] [L1: 0.1505] 32.6+0.0s +[9600/16000] [L1: 0.1375] 32.3+0.0s +[11200/16000] [L1: 0.1373] 32.3+0.0s +[12800/16000] [L1: 0.1431] 32.8+0.0s +[14400/16000] [L1: 0.1485] 32.6+0.0s +[16000/16000] [L1: 0.1417] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.589 (Best: 41.319 @epoch 81) +Forward: 48.97s + +Saving... +Total: 49.64s + +[Epoch 169] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0711] 32.9+0.9s +[3200/16000] [L1: 0.0667] 33.0+0.1s +[4800/16000] [L1: 0.0636] 33.1+0.1s +[6400/16000] [L1: 0.0726] 32.8+0.0s +[8000/16000] [L1: 0.0714] 33.1+0.0s +[9600/16000] [L1: 0.0695] 32.9+0.0s +[11200/16000] [L1: 0.0741] 32.8+0.0s +[12800/16000] [L1: 0.0733] 32.8+0.0s +[14400/16000] [L1: 0.0721] 32.8+0.0s +[16000/16000] [L1: 0.0803] 32.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.994 (Best: 41.319 @epoch 81) +Forward: 48.79s + +Saving... +Total: 49.36s + +[Epoch 170] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0964] 32.7+0.8s +[3200/16000] [L1: 0.0835] 33.0+0.0s +[4800/16000] [L1: 0.0758] 32.8+0.0s +[6400/16000] [L1: 0.0713] 32.6+0.0s +[8000/16000] [L1: 0.0709] 32.2+0.0s +[9600/16000] [L1: 0.0777] 32.9+0.0s +[11200/16000] [L1: 0.0769] 32.5+0.0s +[12800/16000] [L1: 0.0755] 32.2+0.0s +[14400/16000] [L1: 0.0757] 32.3+0.0s +[16000/16000] [L1: 0.0748] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.810 (Best: 41.319 @epoch 81) +Forward: 49.02s + +Saving... +Total: 49.64s + +[Epoch 171] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0660] 32.8+0.8s +[3200/16000] [L1: 0.0629] 32.6+0.0s +[4800/16000] [L1: 0.0610] 32.4+0.0s +[6400/16000] [L1: 0.0602] 32.8+0.0s +[8000/16000] [L1: 0.0592] 32.7+0.0s +[9600/16000] [L1: 0.0579] 32.5+0.0s +[11200/16000] [L1: 0.0698] 32.4+0.0s +[12800/16000] [L1: 0.0734] 32.8+0.0s +[14400/16000] [L1: 0.0858] 32.4+0.0s +[16000/16000] [L1: 0.0856] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.976 (Best: 41.319 @epoch 81) +Forward: 48.82s + +Saving... +Total: 49.42s + +[Epoch 172] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0729] 32.8+0.8s +[3200/16000] [L1: 0.0682] 32.9+0.0s +[4800/16000] [L1: 0.0659] 33.0+0.0s +[6400/16000] [L1: 0.0643] 32.5+0.0s +[8000/16000] [L1: 0.0628] 32.6+0.0s +[9600/16000] [L1: 0.0617] 32.7+0.0s +[11200/16000] [L1: 0.0798] 32.5+0.0s +[12800/16000] [L1: 0.1028] 32.0+0.0s +[14400/16000] [L1: 0.1058] 32.1+0.0s +[16000/16000] [L1: 0.1115] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.343 (Best: 41.319 @epoch 81) +Forward: 48.87s + +Saving... +Total: 49.41s + +[Epoch 173] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1528] 33.1+0.9s +[3200/16000] [L1: 0.2469] 33.0+0.0s +[4800/16000] [L1: 0.2740] 32.5+0.0s +[6400/16000] [L1: 0.2420] 32.4+0.0s +[8000/16000] [L1: 0.2123] 32.0+0.0s +[9600/16000] [L1: 0.1949] 32.1+0.0s +[11200/16000] [L1: 0.1839] 32.0+0.0s +[12800/16000] [L1: 0.1799] 32.2+0.0s +[14400/16000] [L1: 0.1783] 32.4+0.0s +[16000/16000] [L1: 0.1856] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.303 (Best: 41.319 @epoch 81) +Forward: 48.85s + +Saving... +Total: 49.41s + +[Epoch 174] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1483] 32.8+0.8s +[3200/16000] [L1: 0.1945] 32.7+0.0s +[4800/16000] [L1: 0.1813] 32.1+0.0s +[6400/16000] [L1: 0.1593] 32.2+0.0s +[8000/16000] [L1: 0.1429] 32.5+0.0s +[9600/16000] [L1: 0.1317] 32.2+0.0s +[11200/16000] [L1: 0.1230] 32.4+0.0s +[12800/16000] [L1: 0.1337] 32.3+0.0s +[14400/16000] [L1: 0.1324] 32.2+0.0s +[16000/16000] [L1: 0.1274] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.163 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.41s + +[Epoch 175] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0988] 32.8+0.8s +[3200/16000] [L1: 0.0952] 33.1+0.0s +[4800/16000] [L1: 0.1242] 32.4+0.0s +[6400/16000] [L1: 0.1146] 32.9+0.0s +[8000/16000] [L1: 0.1071] 32.5+0.0s +[9600/16000] [L1: 0.1034] 32.8+0.0s +[11200/16000] [L1: 0.1005] 32.4+0.0s +[12800/16000] [L1: 0.0970] 32.5+0.0s +[14400/16000] [L1: 0.0944] 32.5+0.0s +[16000/16000] [L1: 0.0916] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.467 (Best: 41.319 @epoch 81) +Forward: 48.78s + +Saving... +Total: 49.36s + +[Epoch 176] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0652] 33.3+0.8s +[3200/16000] [L1: 0.0654] 33.0+0.0s +[4800/16000] [L1: 0.0664] 32.7+0.0s +[6400/16000] [L1: 0.0659] 32.8+0.0s +[8000/16000] [L1: 0.0656] 32.7+0.0s +[9600/16000] [L1: 0.0646] 32.8+0.0s +[11200/16000] [L1: 0.0635] 32.5+0.0s +[12800/16000] [L1: 0.0633] 32.7+0.0s +[14400/16000] [L1: 0.0627] 32.1+0.0s +[16000/16000] [L1: 0.0622] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.091 (Best: 41.319 @epoch 81) +Forward: 48.80s + +Saving... +Total: 49.36s + +[Epoch 177] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0583] 32.7+0.8s +[3200/16000] [L1: 0.0575] 32.5+0.0s +[4800/16000] [L1: 0.0562] 32.4+0.0s +[6400/16000] [L1: 0.0557] 32.5+0.0s +[8000/16000] [L1: 0.0548] 32.7+0.0s +[9600/16000] [L1: 0.0549] 32.6+0.0s +[11200/16000] [L1: 0.0547] 32.5+0.0s +[12800/16000] [L1: 0.0541] 33.2+0.0s +[14400/16000] [L1: 0.0543] 32.6+0.0s +[16000/16000] [L1: 0.0540] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.106 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.38s + +[Epoch 178] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0515] 33.0+0.9s +[3200/16000] [L1: 0.0515] 32.9+0.0s +[4800/16000] [L1: 0.0515] 32.9+0.0s +[6400/16000] [L1: 0.0514] 33.0+0.0s +[8000/16000] [L1: 0.0507] 32.8+0.0s +[9600/16000] [L1: 0.0503] 33.0+0.0s +[11200/16000] [L1: 0.0503] 32.7+0.0s +[12800/16000] [L1: 0.0669] 32.5+0.0s +[14400/16000] [L1: 0.0718] 32.3+0.0s +[16000/16000] [L1: 0.0717] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.263 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.46s + +[Epoch 179] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0676] 32.9+0.8s +[3200/16000] [L1: 0.0937] 32.8+0.0s +[4800/16000] [L1: 0.0875] 32.7+0.0s +[6400/16000] [L1: 0.1298] 32.6+0.0s +[8000/16000] [L1: 0.1276] 32.2+0.0s +[9600/16000] [L1: 0.1305] 32.2+0.0s +[11200/16000] [L1: 0.1347] 31.8+0.0s +[12800/16000] [L1: 0.1333] 32.7+0.0s +[14400/16000] [L1: 0.1283] 32.3+0.0s +[16000/16000] [L1: 0.1233] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.425 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.42s + +[Epoch 180] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1109] 33.0+0.9s +[3200/16000] [L1: 0.1061] 33.1+0.0s +[4800/16000] [L1: 0.1656] 32.7+0.0s +[6400/16000] [L1: 0.1991] 32.4+0.0s +[8000/16000] [L1: 0.1976] 32.4+0.0s +[9600/16000] [L1: 0.1820] 32.9+0.0s +[11200/16000] [L1: 0.1731] 32.7+0.0s +[12800/16000] [L1: 0.1630] 32.8+0.0s +[14400/16000] [L1: 0.1695] 32.6+0.0s +[16000/16000] [L1: 0.2289] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.651 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.53s + +[Epoch 181] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1567] 32.7+0.9s +[3200/16000] [L1: 0.1721] 32.7+0.0s +[4800/16000] [L1: 0.3955] 32.8+0.0s +[6400/16000] [L1: 0.4205] 32.6+0.0s +[8000/16000] [L1: 0.3666] 32.6+0.0s +[9600/16000] [L1: 0.3267] 32.5+0.0s +[11200/16000] [L1: 0.3001] 33.3+0.0s +[12800/16000] [L1: 0.2877] 32.6+0.0s +[14400/16000] [L1: 0.2695] 32.4+0.0s +[16000/16000] [L1: 0.2525] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.361 (Best: 41.319 @epoch 81) +Forward: 48.88s + +Saving... +Total: 49.51s + +[Epoch 182] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0867] 32.8+0.9s +[3200/16000] [L1: 0.0850] 32.7+0.0s +[4800/16000] [L1: 0.0880] 32.9+0.0s +[6400/16000] [L1: 0.0888] 32.7+0.0s +[8000/16000] [L1: 0.0869] 32.9+0.0s +[9600/16000] [L1: 0.0858] 32.6+0.0s +[11200/16000] [L1: 0.0860] 32.2+0.0s +[12800/16000] [L1: 0.0881] 32.3+0.0s +[14400/16000] [L1: 0.0881] 32.0+0.0s +[16000/16000] [L1: 0.0994] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.778 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.45s + +[Epoch 183] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1021] 32.9+0.9s +[3200/16000] [L1: 0.0969] 33.0+0.0s +[4800/16000] [L1: 0.0943] 33.1+0.0s +[6400/16000] [L1: 0.0902] 32.9+0.0s +[8000/16000] [L1: 0.0867] 33.0+0.0s +[9600/16000] [L1: 0.0840] 32.3+0.0s +[11200/16000] [L1: 0.0825] 32.5+0.0s +[12800/16000] [L1: 0.0804] 32.8+0.0s +[14400/16000] [L1: 0.0869] 32.4+0.0s +[16000/16000] [L1: 0.0873] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.680 (Best: 41.319 @epoch 81) +Forward: 48.86s + +Saving... +Total: 49.40s + +[Epoch 184] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0715] 32.7+0.8s +[3200/16000] [L1: 0.0703] 32.7+0.0s +[4800/16000] [L1: 0.1271] 32.4+0.0s +[6400/16000] [L1: 0.1230] 32.3+0.0s +[8000/16000] [L1: 0.1174] 32.4+0.0s +[9600/16000] [L1: 0.1119] 32.8+0.0s +[11200/16000] [L1: 0.1094] 32.5+0.0s +[12800/16000] [L1: 0.1055] 32.9+0.0s +[14400/16000] [L1: 0.1019] 32.2+0.0s +[16000/16000] [L1: 0.1028] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.880 (Best: 41.319 @epoch 81) +Forward: 48.80s + +Saving... +Total: 49.36s + +[Epoch 185] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0835] 33.0+0.7s +[3200/16000] [L1: 0.0893] 32.5+0.0s +[4800/16000] [L1: 0.0864] 32.5+0.0s +[6400/16000] [L1: 0.0837] 32.9+0.0s +[8000/16000] [L1: 0.0896] 32.4+0.0s +[9600/16000] [L1: 0.0901] 32.1+0.0s +[11200/16000] [L1: 0.0884] 32.5+0.0s +[12800/16000] [L1: 0.0887] 32.5+0.0s +[14400/16000] [L1: 0.0868] 32.7+0.0s +[16000/16000] [L1: 0.0852] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.428 (Best: 41.319 @epoch 81) +Forward: 48.96s + +Saving... +Total: 49.52s + +[Epoch 186] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0649] 33.1+0.9s +[3200/16000] [L1: 0.0660] 32.6+0.0s +[4800/16000] [L1: 0.0646] 32.7+0.0s +[6400/16000] [L1: 0.0642] 32.7+0.0s +[8000/16000] [L1: 0.0637] 32.6+0.0s +[9600/16000] [L1: 0.0633] 32.6+0.0s +[11200/16000] [L1: 0.0628] 32.4+0.0s +[12800/16000] [L1: 0.0621] 32.9+0.0s +[14400/16000] [L1: 0.0617] 32.1+0.0s +[16000/16000] [L1: 0.0611] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 33.331 (Best: 41.319 @epoch 81) +Forward: 48.90s + +Saving... +Total: 49.47s + +[Epoch 187] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0530] 32.8+0.9s +[3200/16000] [L1: 0.0534] 33.1+0.0s +[4800/16000] [L1: 0.1630] 32.8+0.0s +[6400/16000] [L1: 0.1417] 32.6+0.0s +[8000/16000] [L1: 0.1255] 32.6+0.0s +[9600/16000] [L1: 0.1139] 32.2+0.0s +[11200/16000] [L1: 0.1059] 32.2+0.0s +[12800/16000] [L1: 0.0997] 32.1+0.0s +[14400/16000] [L1: 0.0946] 32.0+0.0s +[16000/16000] [L1: 0.0901] 32.6+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.250 (Best: 41.319 @epoch 81) +Forward: 49.06s + +Saving... +Total: 49.62s + +[Epoch 188] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0510] 32.9+0.8s +[3200/16000] [L1: 0.0508] 32.6+0.0s +[4800/16000] [L1: 0.0503] 33.0+0.0s +[6400/16000] [L1: 0.0497] 32.5+0.0s +[8000/16000] [L1: 0.0491] 32.3+0.0s +[9600/16000] [L1: 0.0536] 32.6+0.0s +[11200/16000] [L1: 0.0581] 32.7+0.0s +[12800/16000] [L1: 0.0598] 32.6+0.0s +[14400/16000] [L1: 0.0656] 32.1+0.0s +[16000/16000] [L1: 0.0712] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.095 (Best: 41.319 @epoch 81) +Forward: 49.07s + +Saving... +Total: 49.62s + +[Epoch 189] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0936] 32.7+0.7s +[3200/16000] [L1: 0.0936] 33.1+0.0s +[4800/16000] [L1: 0.0869] 32.6+0.0s +[6400/16000] [L1: 0.0813] 32.6+0.0s +[8000/16000] [L1: 0.0773] 32.3+0.0s +[9600/16000] [L1: 0.0741] 32.5+0.0s +[11200/16000] [L1: 0.0718] 32.6+0.0s +[12800/16000] [L1: 0.0735] 32.5+0.0s +[14400/16000] [L1: 0.0750] 32.5+0.0s +[16000/16000] [L1: 0.0787] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.697 (Best: 41.319 @epoch 81) +Forward: 49.00s + +Saving... +Total: 49.54s + +[Epoch 190] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0848] 32.7+0.9s +[3200/16000] [L1: 0.0757] 32.8+0.0s +[4800/16000] [L1: 0.0712] 32.8+0.0s +[6400/16000] [L1: 0.0680] 32.9+0.0s +[8000/16000] [L1: 0.0762] 32.2+0.0s +[9600/16000] [L1: 0.0766] 32.2+0.0s +[11200/16000] [L1: 0.0777] 32.3+0.0s +[12800/16000] [L1: 0.1001] 32.2+0.0s +[14400/16000] [L1: 0.1018] 32.5+0.0s +[16000/16000] [L1: 0.1001] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.262 (Best: 41.319 @epoch 81) +Forward: 48.90s + +Saving... +Total: 49.52s + +[Epoch 191] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1087] 33.2+0.9s +[3200/16000] [L1: 0.0970] 33.0+0.0s +[4800/16000] [L1: 0.0889] 32.8+0.0s +[6400/16000] [L1: 0.0827] 32.7+0.0s +[8000/16000] [L1: 0.0778] 32.7+0.0s +[9600/16000] [L1: 0.0743] 32.5+0.0s +[11200/16000] [L1: 0.0718] 32.4+0.0s +[12800/16000] [L1: 0.0697] 32.3+0.0s +[14400/16000] [L1: 0.0730] 32.3+0.0s +[16000/16000] [L1: 0.0772] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.527 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.42s + +[Epoch 192] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0672] 33.2+0.8s +[3200/16000] [L1: 0.0643] 32.8+0.0s +[4800/16000] [L1: 0.1086] 33.1+0.0s +[6400/16000] [L1: 0.0970] 33.1+0.0s +[8000/16000] [L1: 0.0973] 32.3+0.0s +[9600/16000] [L1: 0.0973] 32.4+0.0s +[11200/16000] [L1: 0.0957] 32.5+0.0s +[12800/16000] [L1: 0.0946] 32.4+0.0s +[14400/16000] [L1: 0.0937] 32.5+0.0s +[16000/16000] [L1: 0.0934] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.840 (Best: 41.319 @epoch 81) +Forward: 48.88s + +Saving... +Total: 49.43s + +[Epoch 193] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0739] 33.2+0.9s +[3200/16000] [L1: 0.0706] 32.9+0.0s +[4800/16000] [L1: 0.0723] 32.4+0.0s +[6400/16000] [L1: 0.0907] 32.4+0.0s +[8000/16000] [L1: 0.1156] 32.7+0.0s +[9600/16000] [L1: 0.1104] 32.5+0.0s +[11200/16000] [L1: 0.1043] 32.6+0.0s +[12800/16000] [L1: 0.1027] 32.1+0.0s +[14400/16000] [L1: 0.1001] 32.4+0.0s +[16000/16000] [L1: 0.0968] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 32.517 (Best: 41.319 @epoch 81) +Forward: 48.91s + +Saving... +Total: 49.47s + +[Epoch 194] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0614] 32.9+0.8s +[3200/16000] [L1: 0.0699] 32.7+0.0s +[4800/16000] [L1: 0.1173] 32.9+0.0s +[6400/16000] [L1: 0.1127] 32.9+0.0s +[8000/16000] [L1: 0.1059] 32.6+0.0s +[9600/16000] [L1: 0.1064] 32.4+0.0s +[11200/16000] [L1: 0.1058] 32.9+0.0s +[12800/16000] [L1: 0.1017] 32.4+0.0s +[14400/16000] [L1: 0.1065] 32.3+0.0s +[16000/16000] [L1: 0.1047] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.174 (Best: 41.319 @epoch 81) +Forward: 48.90s + +Saving... +Total: 49.45s + +[Epoch 195] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0903] 32.9+0.8s +[3200/16000] [L1: 0.0842] 32.7+0.0s +[4800/16000] [L1: 0.0817] 32.6+0.0s +[6400/16000] [L1: 0.0822] 32.7+0.0s +[8000/16000] [L1: 0.0792] 32.8+0.0s +[9600/16000] [L1: 0.0772] 32.9+0.0s +[11200/16000] [L1: 0.0758] 32.6+0.0s +[12800/16000] [L1: 0.0769] 32.4+0.0s +[14400/16000] [L1: 0.1569] 32.4+0.0s +[16000/16000] [L1: 0.1526] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 23.654 (Best: 41.319 @epoch 81) +Forward: 49.12s + +Saving... +Total: 49.69s + +[Epoch 196] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0716] 33.0+0.8s +[3200/16000] [L1: 0.0668] 32.9+0.0s +[4800/16000] [L1: 0.0649] 32.4+0.0s +[6400/16000] [L1: 0.0629] 32.4+0.0s +[8000/16000] [L1: 0.0639] 32.3+0.0s +[9600/16000] [L1: 0.0633] 32.4+0.0s +[11200/16000] [L1: 0.0618] 32.4+0.0s +[12800/16000] [L1: 0.0614] 32.1+0.0s +[14400/16000] [L1: 0.0615] 32.8+0.0s +[16000/16000] [L1: 0.0610] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 31.263 (Best: 41.319 @epoch 81) +Forward: 49.14s + +Saving... +Total: 49.70s + +[Epoch 197] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0521] 32.9+0.8s +[3200/16000] [L1: 0.0622] 32.9+0.0s +[4800/16000] [L1: 0.0627] 32.7+0.0s +[6400/16000] [L1: 0.0643] 33.2+0.0s +[8000/16000] [L1: 0.0952] 32.5+0.0s +[9600/16000] [L1: 0.1047] 32.5+0.0s +[11200/16000] [L1: 0.1086] 32.6+0.0s +[12800/16000] [L1: 0.1122] 32.3+0.0s +[14400/16000] [L1: 0.1126] 32.9+0.0s +[16000/16000] [L1: 0.2799] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 6.955 (Best: 41.319 @epoch 81) +Forward: 49.06s + +Saving... +Total: 49.60s + +[Epoch 198] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.6656] 32.9+0.9s +[3200/16000] [L1: 0.4017] 33.3+0.0s +[4800/16000] [L1: 0.3044] 32.8+0.0s +[6400/16000] [L1: 0.2550] 33.0+0.0s +[8000/16000] [L1: 0.2373] 32.7+0.0s +[9600/16000] [L1: 0.2395] 32.7+0.0s +[11200/16000] [L1: 0.2222] 32.4+0.0s +[12800/16000] [L1: 0.2051] 32.4+0.0s +[14400/16000] [L1: 0.1930] 32.4+0.0s +[16000/16000] [L1: 0.1815] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.858 (Best: 41.319 @epoch 81) +Forward: 48.99s + +Saving... +Total: 49.57s + +[Epoch 199] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0707] 32.9+0.9s +[3200/16000] [L1: 0.0702] 32.9+0.0s +[4800/16000] [L1: 0.0694] 32.7+0.0s +[6400/16000] [L1: 0.0709] 32.3+0.0s +[8000/16000] [L1: 0.0791] 32.1+0.0s +[9600/16000] [L1: 0.0873] 32.7+0.0s +[11200/16000] [L1: 0.1147] 32.3+0.0s +[12800/16000] [L1: 0.1193] 32.4+0.0s +[14400/16000] [L1: 0.1287] 32.7+0.0s +[16000/16000] [L1: 0.1264] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.948 (Best: 41.319 @epoch 81) +Forward: 48.96s + +Saving... +Total: 49.61s + +[Epoch 200] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1810] 33.0+0.9s +[3200/16000] [L1: 1.4589] 32.6+0.0s +[4800/16000] [L1: 1.2163] 32.7+0.0s +[6400/16000] [L1: 0.9654] 32.3+0.0s +[8000/16000] [L1: 0.8063] 32.4+0.0s +[9600/16000] [L1: 0.7019] 32.6+0.0s +[11200/16000] [L1: 0.6320] 32.2+0.0s +[12800/16000] [L1: 0.5831] 32.5+0.0s +[14400/16000] [L1: 0.5357] 32.4+0.0s +[16000/16000] [L1: 0.5012] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.989 (Best: 41.319 @epoch 81) +Forward: 48.80s + +Saving... +Total: 49.43s + +[Epoch 201] Learning rate: 2.50e-5 +[1600/16000] [L1: 0.1353] 32.7+0.9s +[3200/16000] [L1: 0.1337] 32.8+0.0s +[4800/16000] [L1: 0.1329] 32.5+0.0s +[6400/16000] [L1: 0.1294] 32.8+0.0s +[8000/16000] [L1: 0.1263] 32.5+0.0s +[9600/16000] [L1: 0.1248] 32.9+0.0s +[11200/16000] [L1: 0.1231] 32.4+0.0s +[12800/16000] [L1: 0.1225] 32.4+0.0s +[14400/16000] [L1: 0.1210] 32.4+0.0s +[16000/16000] [L1: 0.1266] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.058 (Best: 41.319 @epoch 81) +Forward: 48.77s + +Saving... +Total: 49.38s + +[Epoch 202] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1422] 33.0+0.8s +[3200/16000] [L1: 0.1358] 32.7+0.0s +[4800/16000] [L1: 0.1304] 33.0+0.0s +[6400/16000] [L1: 0.1262] 32.6+0.0s +[8000/16000] [L1: 0.1233] 32.8+0.0s +[9600/16000] [L1: 0.1217] 32.7+0.0s +[11200/16000] [L1: 0.1225] 32.6+0.0s +[12800/16000] [L1: 0.1216] 32.8+0.0s +[14400/16000] [L1: 0.1207] 32.7+0.0s +[16000/16000] [L1: 0.1211] 32.5+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.573 (Best: 41.319 @epoch 81) +Forward: 48.77s + +Saving... +Total: 49.36s + +[Epoch 203] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1075] 32.7+0.8s +[3200/16000] [L1: 0.1128] 33.1+0.0s +[4800/16000] [L1: 0.1242] 32.9+0.0s +[6400/16000] [L1: 0.1259] 32.7+0.0s +[8000/16000] [L1: 0.1227] 32.6+0.0s +[9600/16000] [L1: 0.1195] 32.6+0.0s +[11200/16000] [L1: 0.1187] 32.6+0.0s +[12800/16000] [L1: 0.1179] 32.5+0.0s +[14400/16000] [L1: 0.1170] 32.2+0.0s +[16000/16000] [L1: 0.1168] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 24.818 (Best: 41.319 @epoch 81) +Forward: 48.74s + +Saving... +Total: 49.32s + +[Epoch 204] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1082] 33.1+0.9s +[3200/16000] [L1: 0.1033] 32.9+0.1s +[4800/16000] [L1: 0.1010] 33.0+0.0s +[6400/16000] [L1: 0.1052] 33.0+0.0s +[8000/16000] [L1: 0.1082] 32.9+0.0s +[9600/16000] [L1: 0.1083] 32.5+0.0s +[11200/16000] [L1: 0.1072] 32.5+0.0s +[12800/16000] [L1: 0.1078] 32.6+0.0s +[14400/16000] [L1: 0.1074] 32.5+0.0s +[16000/16000] [L1: 0.1078] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.015 (Best: 41.319 @epoch 81) +Forward: 48.84s + +Saving... +Total: 49.45s + +[Epoch 205] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1048] 32.8+0.9s +[3200/16000] [L1: 0.1078] 33.2+0.0s +[4800/16000] [L1: 0.1044] 32.8+0.0s +[6400/16000] [L1: 0.1026] 32.7+0.0s +[8000/16000] [L1: 0.1024] 32.3+0.0s +[9600/16000] [L1: 0.1027] 32.4+0.0s +[11200/16000] [L1: 0.1040] 32.5+0.0s +[12800/16000] [L1: 0.1036] 32.4+0.0s +[14400/16000] [L1: 0.1048] 32.4+0.0s +[16000/16000] [L1: 0.1048] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 26.157 (Best: 41.319 @epoch 81) +Forward: 48.58s + +Saving... +Total: 49.52s + +[Epoch 206] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1079] 32.8+0.9s +[3200/16000] [L1: 0.1024] 32.7+0.0s +[4800/16000] [L1: 0.1022] 32.6+0.0s +[6400/16000] [L1: 0.1002] 32.4+0.0s +[8000/16000] [L1: 0.0986] 32.5+0.0s +[9600/16000] [L1: 0.0991] 32.6+0.0s +[11200/16000] [L1: 0.1000] 32.4+0.0s +[12800/16000] [L1: 0.1025] 32.8+0.0s +[14400/16000] [L1: 0.1090] 32.6+0.0s +[16000/16000] [L1: 0.1220] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 23.469 (Best: 41.319 @epoch 81) +Forward: 48.57s + +Saving... +Total: 49.16s + +[Epoch 207] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1695] 32.7+0.8s +[3200/16000] [L1: 0.1363] 32.8+0.0s +[4800/16000] [L1: 0.1224] 32.7+0.0s +[6400/16000] [L1: 0.1162] 32.4+0.0s +[8000/16000] [L1: 0.1120] 32.3+0.0s +[9600/16000] [L1: 0.1085] 32.4+0.0s +[11200/16000] [L1: 0.1054] 32.8+0.0s +[12800/16000] [L1: 0.1027] 32.8+0.0s +[14400/16000] [L1: 0.1010] 32.6+0.0s +[16000/16000] [L1: 0.0992] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.235 (Best: 41.319 @epoch 81) +Forward: 48.68s + +Saving... +Total: 49.21s + +[Epoch 208] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0844] 32.9+0.9s +[3200/16000] [L1: 0.0828] 32.7+0.0s +[4800/16000] [L1: 0.0855] 33.2+0.0s +[6400/16000] [L1: 0.0858] 32.7+0.0s +[8000/16000] [L1: 0.0900] 32.5+0.0s +[9600/16000] [L1: 0.0928] 32.5+0.0s +[11200/16000] [L1: 0.0944] 32.7+0.0s +[12800/16000] [L1: 0.0942] 32.6+0.0s +[14400/16000] [L1: 0.0941] 32.5+0.0s +[16000/16000] [L1: 0.0938] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.140 (Best: 41.319 @epoch 81) +Forward: 48.47s + +Saving... +Total: 48.99s + +[Epoch 209] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0911] 33.1+0.9s +[3200/16000] [L1: 0.0888] 33.0+0.0s +[4800/16000] [L1: 0.0853] 32.7+0.0s +[6400/16000] [L1: 0.0850] 32.4+0.0s +[8000/16000] [L1: 0.0844] 32.3+0.0s +[9600/16000] [L1: 0.0844] 32.6+0.0s +[11200/16000] [L1: 0.0835] 32.5+0.0s +[12800/16000] [L1: 0.0844] 32.6+0.0s +[14400/16000] [L1: 0.0840] 32.2+0.0s +[16000/16000] [L1: 0.0838] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.011 (Best: 41.319 @epoch 81) +Forward: 48.55s + +Saving... +Total: 49.05s + +[Epoch 210] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0865] 32.8+1.0s +[3200/16000] [L1: 0.0884] 32.8+0.0s +[4800/16000] [L1: 0.0838] 32.6+0.0s +[6400/16000] [L1: 0.0815] 33.0+0.0s +[8000/16000] [L1: 0.0831] 32.7+0.0s +[9600/16000] [L1: 0.0861] 32.4+0.0s +[11200/16000] [L1: 0.0858] 32.6+0.0s +[12800/16000] [L1: 0.0849] 32.3+0.0s +[14400/16000] [L1: 0.0840] 32.7+0.0s +[16000/16000] [L1: 0.0834] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.351 (Best: 41.319 @epoch 81) +Forward: 48.74s + +Saving... +Total: 49.25s + +[Epoch 211] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0900] 32.8+1.0s +[3200/16000] [L1: 0.0853] 32.8+0.0s +[4800/16000] [L1: 0.0802] 32.9+0.0s +[6400/16000] [L1: 0.0813] 32.5+0.0s +[8000/16000] [L1: 0.0814] 32.7+0.0s +[9600/16000] [L1: 0.0829] 33.0+0.0s +[11200/16000] [L1: 0.0978] 32.7+0.0s +[12800/16000] [L1: 0.0965] 32.5+0.0s +[14400/16000] [L1: 0.0976] 32.4+0.0s +[16000/16000] [L1: 0.0983] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.338 (Best: 41.319 @epoch 81) +Forward: 48.48s + +Saving... +Total: 49.05s + +[Epoch 212] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0856] 32.7+0.7s +[3200/16000] [L1: 0.0953] 32.6+0.0s +[4800/16000] [L1: 0.0937] 32.1+0.0s +[6400/16000] [L1: 0.0967] 32.8+0.0s +[8000/16000] [L1: 0.0953] 32.3+0.0s +[9600/16000] [L1: 0.0954] 32.2+0.0s +[11200/16000] [L1: 0.0948] 32.7+0.0s +[12800/16000] [L1: 0.0929] 32.5+0.0s +[14400/16000] [L1: 0.0920] 32.6+0.0s +[16000/16000] [L1: 0.0923] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.791 (Best: 41.319 @epoch 81) +Forward: 48.51s + +Saving... +Total: 49.09s + +[Epoch 213] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0930] 33.1+0.8s +[3200/16000] [L1: 0.0893] 32.7+0.0s +[4800/16000] [L1: 0.0944] 32.7+0.0s +[6400/16000] [L1: 0.0935] 32.5+0.0s +[8000/16000] [L1: 0.0906] 32.5+0.0s +[9600/16000] [L1: 0.0881] 32.7+0.0s +[11200/16000] [L1: 0.0868] 32.7+0.0s +[12800/16000] [L1: 0.0876] 32.3+0.0s +[14400/16000] [L1: 0.0871] 32.6+0.0s +[16000/16000] [L1: 0.0859] 32.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.497 (Best: 41.319 @epoch 81) +Forward: 48.61s + +Saving... +Total: 49.13s + +[Epoch 214] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0763] 32.6+0.8s +[3200/16000] [L1: 0.0759] 32.8+0.0s +[4800/16000] [L1: 0.0743] 32.7+0.0s +[6400/16000] [L1: 0.0719] 32.8+0.0s +[8000/16000] [L1: 0.0708] 32.8+0.0s +[9600/16000] [L1: 0.0696] 32.6+0.0s +[11200/16000] [L1: 0.0691] 32.4+0.0s +[12800/16000] [L1: 0.0695] 32.7+0.0s +[14400/16000] [L1: 0.0705] 32.5+0.0s +[16000/16000] [L1: 0.0719] 32.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 30.344 (Best: 41.319 @epoch 81) +Forward: 48.58s + +Saving... +Total: 49.15s + +[Epoch 215] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0715] 33.1+0.9s +[3200/16000] [L1: 0.0738] 32.9+0.0s +[4800/16000] [L1: 0.0777] 32.6+0.0s +[6400/16000] [L1: 0.0784] 32.8+0.0s +[8000/16000] [L1: 0.0776] 33.0+0.0s +[9600/16000] [L1: 0.0824] 32.5+0.0s +[11200/16000] [L1: 0.0889] 32.5+0.0s +[12800/16000] [L1: 0.0900] 32.4+0.0s +[14400/16000] [L1: 0.0945] 32.4+0.0s +[16000/16000] [L1: 0.1031] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 25.039 (Best: 41.319 @epoch 81) +Forward: 48.56s + +Saving... +Total: 49.07s + +[Epoch 216] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1194] 32.8+0.9s +[3200/16000] [L1: 0.1055] 33.2+0.0s +[4800/16000] [L1: 0.0981] 33.0+0.0s +[6400/16000] [L1: 0.0938] 32.4+0.0s +[8000/16000] [L1: 0.0905] 32.6+0.0s +[9600/16000] [L1: 0.0887] 32.1+0.0s +[11200/16000] [L1: 0.0871] 32.6+0.0s +[12800/16000] [L1: 0.0870] 32.6+0.0s +[14400/16000] [L1: 0.0867] 32.4+0.0s +[16000/16000] [L1: 0.0862] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.915 (Best: 41.319 @epoch 81) +Forward: 48.45s + +Saving... +Total: 48.97s + +[Epoch 217] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0739] 33.1+0.9s +[3200/16000] [L1: 0.0753] 32.9+0.0s +[4800/16000] [L1: 0.0809] 32.6+0.0s +[6400/16000] [L1: 0.0931] 32.5+0.0s +[8000/16000] [L1: 0.0997] 32.3+0.0s +[9600/16000] [L1: 0.1051] 32.5+0.0s +[11200/16000] [L1: 0.1046] 32.3+0.0s +[12800/16000] [L1: 0.1052] 32.7+0.0s +[14400/16000] [L1: 0.1055] 32.8+0.0s +[16000/16000] [L1: 0.1052] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.202 (Best: 41.319 @epoch 81) +Forward: 48.33s + +Saving... +Total: 48.95s + +[Epoch 218] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0920] 32.7+0.8s +[3200/16000] [L1: 0.0919] 33.0+0.0s +[4800/16000] [L1: 0.0900] 32.5+0.0s +[6400/16000] [L1: 0.0892] 32.6+0.0s +[8000/16000] [L1: 0.0886] 32.3+0.0s +[9600/16000] [L1: 0.0877] 32.1+0.0s +[11200/16000] [L1: 0.0915] 32.8+0.0s +[12800/16000] [L1: 0.0925] 32.3+0.0s +[14400/16000] [L1: 0.0919] 32.5+0.0s +[16000/16000] [L1: 0.0934] 32.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 27.550 (Best: 41.319 @epoch 81) +Forward: 48.33s + +Saving... +Total: 48.92s + +[Epoch 219] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1210] 32.8+0.8s +[3200/16000] [L1: 0.2618] 33.0+0.0s +[4800/16000] [L1: 0.2374] 32.7+0.0s +[6400/16000] [L1: 0.2087] 32.9+0.0s +[8000/16000] [L1: 0.1986] 32.8+0.0s +[9600/16000] [L1: 0.2108] 32.6+0.0s +[11200/16000] [L1: 0.2640] 32.8+0.0s +[12800/16000] [L1: 0.2463] 32.5+0.0s +[14400/16000] [L1: 0.2320] 32.6+0.0s +[16000/16000] [L1: 0.2184] 32.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 28.318 (Best: 41.319 @epoch 81) +Forward: 48.58s + +Saving... +Total: 49.12s + +[Epoch 220] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0915] 33.2+0.8s +[3200/16000] [L1: 0.0887] 32.7+0.0s +[4800/16000] [L1: 0.0898] 32.6+0.0s +[6400/16000] [L1: 0.0917] 32.6+0.0s +[8000/16000] [L1: 0.0915] 32.6+0.0s +[9600/16000] [L1: 0.0918] 32.2+0.0s +[11200/16000] [L1: 0.0916] 32.3+0.0s +[12800/16000] [L1: 0.1055] 32.3+0.0s +[14400/16000] [L1: 0.1034] 32.4+0.0s +[16000/16000] [L1: 0.1010] 32.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.723 (Best: 41.319 @epoch 81) +Forward: 48.57s + +Saving... +Total: 49.08s + +[Epoch 221] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.0837] 32.4+0.8s +[3200/16000] [L1: 0.0848] 32.7+0.0s +[4800/16000] [L1: 0.0879] 32.2+0.0s +[6400/16000] [L1: 0.0895] 32.5+0.0s +[8000/16000] [L1: 0.1794] 32.5+0.0s +[9600/16000] [L1: 0.2426] 32.6+0.0s +[11200/16000] [L1: 0.2258] 32.8+0.0s +[12800/16000] [L1: 0.2113] 33.0+0.0s +[14400/16000] [L1: 0.1999] 32.2+0.0s +[16000/16000] [L1: 0.1891] 32.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 29.089 (Best: 41.319 @epoch 81) +Forward: 48.38s + +Saving... +Total: 48.87s + +[Epoch 222] Learning rate: 5.00e-5 +[1600/16000] [L1: 0.1120] 32.8+0.8s +[3200/16000] [L1: 0.1107] 32.5+0.0s +[4800/16000] [L1: inf] 32.5+0.0s +[6400/16000] [L1: inf] 32.1+0.0s +[8000/16000] [L1: inf] 32.0+0.0s +[9600/16000] [L1: inf] 32.2+0.0s +[11200/16000] [L1: inf] 31.8+0.0s +[12800/16000] [L1: inf] 31.8+0.0s +[14400/16000] [L1: inf] 31.9+0.0s +[16000/16000] [L1: inf] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 41.319 @epoch 81) +Forward: 47.23s + +Saving... +Total: 47.74s + +[Epoch 223] Learning rate: 5.00e-5 +[1600/16000] [L1: inf] 32.6+0.8s +[3200/16000] [L1: inf] 32.5+0.0s +[4800/16000] [L1: inf] 32.2+0.0s +[6400/16000] [L1: inf] 32.1+0.0s +[8000/16000] [L1: inf] 31.8+0.0s +[9600/16000] [L1: inf] 32.2+0.0s +[11200/16000] [L1: inf] 31.9+0.0s +[12800/16000] [L1: inf] 31.8+0.0s +[14400/16000] [L1: inf] 31.6+0.0s +[16000/16000] [L1: inf] 31.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 41.319 @epoch 81) +Forward: 47.18s + +Saving... +Total: 47.73s + +[Epoch 224] Learning rate: 5.00e-5 +[1600/16000] [L1: inf] 32.6+0.9s +[3200/16000] [L1: inf] 32.2+0.0s +[4800/16000] [L1: inf] 32.0+0.0s +[6400/16000] [L1: inf] 32.1+0.0s +[8000/16000] [L1: inf] 31.9+0.0s +[9600/16000] [L1: inf] 31.8+0.0s +[11200/16000] [L1: inf] 32.1+0.0s +[12800/16000] [L1: inf] 31.7+0.0s +[14400/16000] [L1: inf] 31.9+0.0s +[16000/16000] [L1: inf] 31.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 41.319 @epoch 81) +Forward: 47.31s + +Saving... +Total: 47.91s + +[Epoch 225] Learning rate: 5.00e-5 +[1600/16000] [L1: inf] 32.9+0.8s +[3200/16000] [L1: inf] 32.4+0.0s +[4800/16000] [L1: inf] 31.9+0.0s +[6400/16000] [L1: inf] 32.2+0.0s +[8000/16000] [L1: inf] 32.2+0.0s +[9600/16000] [L1: inf] 32.1+0.0s +[11200/16000] [L1: inf] 32.4+0.0s +[12800/16000] [L1: inf] 31.9+0.0s +[14400/16000] [L1: inf] 32.3+0.0s +[16000/16000] [L1: inf] 31.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 7.056 (Best: 41.319 @epoch 81) +Forward: 47.39s + +Saving... +Total: 47.93s + +[Epoch 226] Learning rate: 5.00e-5 +[1600/16000] [L1: inf] 32.5+0.8s +[3200/16000] [L1: inf] 32.6+0.0s +[4800/16000] [L1: inf] 32.5+0.0s +[6400/16000] [L1: inf] 32.3+0.0s +[8000/16000] [L1: inf] 32.6+0.0s +[9600/16000] [L1: inf] 31.7+0.0s +[11200/16000] [L1: inf] 31.6+0.0s +[12800/16000] [L1: inf] 32.1+0.0s diff --git a/Demosaic/experiment/RAFT_DEMOSAIC20_R4_re/loss.pt b/Demosaic/experiment/RAFT_DEMOSAIC20_R4_re/loss.pt new file mode 100644 index 0000000000000000000000000000000000000000..2cb8aeac7a3784a1b17d156d419747cef2ed44bf --- /dev/null +++ b/Demosaic/experiment/RAFT_DEMOSAIC20_R4_re/loss.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48da99bdddb436cdb4a093ba3a3efbe02ec42ba0bd17f6415b4f6645eb17b79f +size 559 diff --git 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b/Demosaic/experiment/RLAMBDA_DEMOSAIC20_R4_detach/.ipynb_checkpoints/test_DIV2K-checkpoint.pdf differ diff --git a/Demosaic/experiment/RLAMBDA_DEMOSAIC20_R4_detach/config.txt b/Demosaic/experiment/RLAMBDA_DEMOSAIC20_R4_detach/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..77c2cb988be1458a01333fd11f21a95b1c17ef5c --- /dev/null +++ b/Demosaic/experiment/RLAMBDA_DEMOSAIC20_R4_detach/config.txt @@ -0,0 +1,196 @@ +2020-11-06-19:07:39 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RLAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RLAMBDA_DEMOSAIC20_R4_detach +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-19:42:57 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RLAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RLAMBDA_DEMOSAIC20_R4_detach +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + +2020-11-06-23:57:24 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 2 +seed: 1 +dir_data: /data/ssd/public/czli/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['DIV2K'] +data_range: 1-800/901-942 +ext: sep +scale: [1] +patch_size: 48 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RLAMBDANET +act: relu +pre_train: . +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: False +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: RLAMBDA_DEMOSAIC20_R4_detach +load: +resume: 0 +save_models: True +print_every: 100 +save_results: False +save_gt: False + diff --git a/Demosaic/experiment/RLAMBDA_DEMOSAIC20_R4_detach/log.txt b/Demosaic/experiment/RLAMBDA_DEMOSAIC20_R4_detach/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..83fe9a50aed491c0529c2b53e9de0043fe42fea1 --- /dev/null +++ b/Demosaic/experiment/RLAMBDA_DEMOSAIC20_R4_detach/log.txt @@ -0,0 +1,765 @@ +DataParallel( + (module): RLAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 2.9578] 51.6+0.5s +[3200/16000] [L1: 2.0605] 47.0+0.1s +[4800/16000] [L1: 1.5746] 49.1+0.0s +[6400/16000] [L1: 1.3003] 49.3+0.0s +[8000/16000] [L1: 1.1122] 50.0+0.0s +[9600/16000] [L1: 0.9779] 49.1+0.0s +[11200/16000] [L1: 0.8768] 50.7+0.1s +[12800/16000] [L1: 0.7984] 51.7+0.1s +[14400/16000] [L1: 0.7339] 51.7+0.1s +[16000/16000] [L1: 0.6812] 51.6+0.0s + +Evaluation: +DataParallel( + (module): RLAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) +) +[Epoch 1] Learning rate: 1.00e-4 +[1600/16000] [L1: 3.1531] 51.4+0.7s +[3200/16000] [L1: 2.0836] 46.7+0.1s +[4800/16000] [L1: 1.6088] 49.5+0.1s +[6400/16000] [L1: 1.3442] 50.1+0.0s +[8000/16000] [L1: 1.1582] 49.9+0.0s +[9600/16000] [L1: 1.0251] 50.1+0.0s +[11200/16000] [L1: 0.9277] 51.5+0.1s +[12800/16000] [L1: 0.8480] 50.4+0.1s +[14400/16000] [L1: 0.7845] 48.9+0.0s +[16000/16000] [L1: 0.7314] 51.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 9.190 (Best: 9.190 @epoch 1) +Forward: 36.97s + +Saving... +Total: 37.94s + +[Epoch 2] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.2249] 51.2+0.9s +[3200/16000] [L1: 0.2154] 50.0+0.0s +[4800/16000] [L1: 0.2084] 49.8+0.0s +[6400/16000] [L1: 0.2035] 50.9+0.0s +[8000/16000] [L1: 0.1998] 50.0+0.0s +[9600/16000] [L1: 0.1948] 50.5+0.0s +[11200/16000] [L1: 0.1896] 49.9+0.0s +[12800/16000] [L1: 0.1847] 50.0+0.0s +[14400/16000] [L1: 0.1811] 49.3+0.0s +[16000/16000] [L1: 0.1769] 50.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.918 (Best: 9.190 @epoch 1) +Forward: 36.55s + +Saving... +Total: 37.02s + +[Epoch 3] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.1230] 51.5+0.8s +[3200/16000] [L1: 0.1251] 51.2+0.1s +[4800/16000] [L1: 0.1235] 49.6+0.0s +[6400/16000] [L1: 0.1227] 49.6+0.0s +[8000/16000] [L1: 0.1213] 50.1+0.0s +[9600/16000] [L1: 0.1195] 49.7+0.0s +[11200/16000] [L1: 0.1169] 49.7+0.0s +[12800/16000] [L1: 0.1151] 50.2+0.0s +[14400/16000] [L1: 0.1131] 50.1+0.0s +[16000/16000] [L1: 0.1112] 49.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.799 (Best: 9.190 @epoch 1) +Forward: 36.60s + +Saving... +Total: 37.17s + +[Epoch 4] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0919] 51.4+0.8s +[3200/16000] [L1: 0.0908] 50.5+0.1s +[4800/16000] [L1: 0.0883] 50.0+0.0s +[6400/16000] [L1: 0.0867] 50.1+0.0s +[8000/16000] [L1: 0.0855] 49.8+0.0s +[9600/16000] [L1: 0.0841] 49.0+0.0s +[11200/16000] [L1: 0.0830] 49.4+0.0s +[12800/16000] [L1: 0.0819] 51.5+0.1s +[14400/16000] [L1: 0.0811] 49.5+0.0s +[16000/16000] [L1: 0.0798] 49.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.825 (Best: 9.190 @epoch 1) +Forward: 36.71s + +Saving... +Total: 37.21s + +[Epoch 5] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0681] 50.4+0.7s +[3200/16000] [L1: 0.0675] 50.9+0.1s +[4800/16000] [L1: 0.0663] 50.1+0.0s +[6400/16000] [L1: 0.0652] 50.2+0.0s +[8000/16000] [L1: 0.0637] 48.8+0.0s +[9600/16000] [L1: 0.0631] 50.6+0.1s +[11200/16000] [L1: 0.0623] 51.2+0.1s +[12800/16000] [L1: 0.0618] 49.4+0.0s +[14400/16000] [L1: 0.0615] 49.4+0.0s +[16000/16000] [L1: 0.0606] 48.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.654 (Best: 9.190 @epoch 1) +Forward: 36.51s + +Saving... +Total: 37.05s + +[Epoch 6] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0569] 51.1+0.8s +[3200/16000] [L1: 0.0561] 51.2+0.1s +[4800/16000] [L1: 0.0533] 49.4+0.0s +[6400/16000] [L1: 0.0519] 50.4+0.0s +[8000/16000] [L1: 0.0514] 49.7+0.0s +[9600/16000] [L1: 0.0505] 50.5+0.0s +[11200/16000] [L1: 0.0496] 49.3+0.0s +[12800/16000] [L1: 0.0490] 49.5+0.0s +[14400/16000] [L1: 0.0484] 49.9+0.0s +[16000/16000] [L1: 0.0482] 49.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 8.628 (Best: 9.190 @epoch 1) +Forward: 36.45s + +Saving... +Total: 37.06s + +[Epoch 7] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0443] 49.7+0.9s +[3200/16000] [L1: 0.0433] 49.4+0.0s +[4800/16000] [L1: 0.0429] 50.7+0.1s +[6400/16000] [L1: 0.0425] 49.8+0.0s +[8000/16000] [L1: 0.0425] 48.9+0.0s +[9600/16000] [L1: 0.0416] 50.4+0.0s +[11200/16000] [L1: 0.0411] 49.2+0.0s +[12800/16000] [L1: 0.0403] 51.3+0.0s +[14400/16000] [L1: 0.0400] 49.7+0.0s +[16000/16000] [L1: 0.0396] 50.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 9.004 (Best: 9.190 @epoch 1) +Forward: 36.78s + +Saving... +Total: 37.24s + +[Epoch 8] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0332] 50.7+0.7s +[3200/16000] [L1: 0.0352] 49.9+0.0s +[4800/16000] [L1: 0.0351] 49.4+0.0s +[6400/16000] [L1: 0.0351] 49.9+0.0s +[8000/16000] [L1: 0.0348] 50.1+0.0s +[9600/16000] [L1: 0.0348] 50.0+0.0s +[11200/16000] [L1: 0.0345] 51.5+0.1s +[12800/16000] [L1: 0.0343] 49.8+0.0s +[14400/16000] [L1: 0.0341] 49.8+0.0s +[16000/16000] [L1: 0.0339] 49.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 9.533 (Best: 9.533 @epoch 8) +Forward: 36.63s + +Saving... +Total: 37.26s + +[Epoch 9] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0421] 51.3+0.8s +[3200/16000] [L1: 0.0368] 50.5+0.1s +[4800/16000] [L1: 0.0346] 50.5+0.0s +[6400/16000] [L1: 0.0341] 49.8+0.0s +[8000/16000] [L1: 0.0328] 50.5+0.1s +[9600/16000] [L1: 0.0322] 48.7+0.0s +[11200/16000] [L1: 0.0314] 51.1+0.0s +[12800/16000] [L1: 0.0307] 50.9+0.1s +[14400/16000] [L1: 0.0299] 50.8+0.1s +[16000/16000] [L1: 0.0296] 50.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 9.870 (Best: 9.870 @epoch 9) +Forward: 36.44s + +Saving... +Total: 37.06s + +[Epoch 10] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0260] 50.7+0.9s +[3200/16000] [L1: 0.0265] 50.2+0.1s +[4800/16000] [L1: 0.0264] 49.9+0.0s +[6400/16000] [L1: 0.0257] 49.5+0.0s +[8000/16000] [L1: 0.0252] 49.2+0.0s +[9600/16000] [L1: 0.0249] 50.0+0.0s +[11200/16000] [L1: 0.0250] 49.6+0.0s +[12800/16000] [L1: 0.0248] 50.4+0.0s +[14400/16000] [L1: 0.0246] 51.4+0.1s +[16000/16000] [L1: 0.0244] 49.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 10.933 (Best: 10.933 @epoch 10) +Forward: 36.68s + +Saving... +Total: 37.29s + +[Epoch 11] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0217] 51.0+0.9s +[3200/16000] [L1: 0.0229] 50.5+0.1s +[4800/16000] [L1: 0.0228] 49.0+0.1s +[6400/16000] [L1: 0.0230] 49.8+0.0s +[8000/16000] [L1: 0.0233] 49.5+0.0s +[9600/16000] [L1: 0.0231] 49.3+0.0s +[11200/16000] [L1: 0.0226] 49.7+0.0s +[12800/16000] [L1: 0.0223] 50.7+0.1s +[14400/16000] [L1: 0.0221] 51.0+0.0s +[16000/16000] [L1: 0.0219] 50.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 12.240 (Best: 12.240 @epoch 11) +Forward: 36.70s + +Saving... +Total: 37.18s + +[Epoch 12] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0201] 51.4+0.8s +[3200/16000] [L1: 0.0197] 49.1+0.0s +[4800/16000] [L1: 0.0191] 49.1+0.0s +[6400/16000] [L1: 0.0194] 50.3+0.1s +[8000/16000] [L1: 0.0189] 51.3+0.1s +[9600/16000] [L1: 0.0186] 51.1+0.1s +[11200/16000] [L1: 0.0185] 50.2+0.1s +[12800/16000] [L1: 0.0183] 50.3+0.0s +[14400/16000] [L1: 0.0181] 49.3+0.0s +[16000/16000] [L1: 0.0179] 50.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 13.591 (Best: 13.591 @epoch 12) +Forward: 36.60s + +Saving... +Total: 37.12s + +[Epoch 13] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0164] 50.8+0.7s +[3200/16000] [L1: 0.0163] 50.0+0.0s +[4800/16000] [L1: 0.0160] 50.6+0.1s +[6400/16000] [L1: 0.0163] 51.2+0.0s +[8000/16000] [L1: 0.0166] 50.4+0.0s +[9600/16000] [L1: 0.0167] 50.2+0.0s +[11200/16000] [L1: 0.0167] 50.0+0.0s +[12800/16000] [L1: 0.0165] 50.7+0.1s +[14400/16000] [L1: 0.0164] 49.6+0.0s +[16000/16000] [L1: 0.0164] 49.2+0.0s + +Evaluation: +[DIV2K x1] PSNR: 15.362 (Best: 15.362 @epoch 13) +Forward: 36.53s + +Saving... +Total: 37.11s + +[Epoch 14] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0146] 51.1+0.7s +[3200/16000] [L1: 0.0151] 50.5+0.1s +[4800/16000] [L1: 0.0158] 50.9+0.1s +[6400/16000] [L1: 0.0154] 50.0+0.0s +[8000/16000] [L1: 0.0152] 50.9+0.1s +[9600/16000] [L1: 0.0150] 50.4+0.1s +[11200/16000] [L1: 0.0149] 49.2+0.0s +[12800/16000] [L1: 0.0147] 50.5+0.1s +[14400/16000] [L1: 0.0146] 50.7+0.1s +[16000/16000] [L1: 0.0146] 50.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 17.182 (Best: 17.182 @epoch 14) +Forward: 36.60s + +Saving... +Total: 37.12s + +[Epoch 15] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0128] 51.5+0.7s +[3200/16000] [L1: 0.0132] 51.5+0.1s +[4800/16000] [L1: 0.0133] 51.5+0.1s +[6400/16000] [L1: 0.0133] 50.3+0.0s +[8000/16000] [L1: 0.0132] 51.2+0.1s +[9600/16000] [L1: 0.0131] 50.3+0.0s +[11200/16000] [L1: 0.0130] 50.8+0.0s +[12800/16000] [L1: 0.0130] 51.2+0.0s +[14400/16000] [L1: 0.0130] 51.3+0.1s +[16000/16000] [L1: 0.0129] 50.9+0.0s + +Evaluation: +[DIV2K x1] PSNR: 18.605 (Best: 18.605 @epoch 15) +Forward: 36.79s + +Saving... +Total: 37.34s + +[Epoch 16] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0129] 50.9+0.8s +[3200/16000] [L1: 0.0132] 49.6+0.1s +[4800/16000] [L1: 0.0129] 50.5+0.0s +[6400/16000] [L1: 0.0130] 50.4+0.0s +[8000/16000] [L1: 0.0128] 49.5+0.0s +[9600/16000] [L1: 0.0127] 50.5+0.0s +[11200/16000] [L1: 0.0126] 48.9+0.0s +[12800/16000] [L1: 0.0126] 49.1+0.0s +[14400/16000] [L1: 0.0124] 51.1+0.1s +[16000/16000] [L1: 0.0123] 49.8+0.0s + +Evaluation: +[DIV2K x1] PSNR: 19.953 (Best: 19.953 @epoch 16) +Forward: 36.52s + +Saving... +Total: 37.15s + +[Epoch 17] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0124] 50.7+0.8s +[3200/16000] [L1: 0.0120] 49.7+0.0s +[4800/16000] [L1: 0.0118] 49.9+0.0s +[6400/16000] [L1: 0.0117] 50.1+0.0s +[8000/16000] [L1: 0.0117] 51.0+0.1s +[9600/16000] [L1: 0.0117] 49.8+0.0s +[11200/16000] [L1: 0.0118] 49.6+0.0s +[12800/16000] [L1: 0.0119] 50.4+0.1s +[14400/16000] [L1: 0.0118] 51.3+0.1s +[16000/16000] [L1: 0.0117] 49.3+0.0s + +Evaluation: +[DIV2K x1] PSNR: 20.834 (Best: 20.834 @epoch 17) +Forward: 36.51s + +Saving... +Total: 37.07s + +[Epoch 18] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0106] 50.8+0.8s +[3200/16000] [L1: 0.0110] 48.7+0.0s +[4800/16000] [L1: 0.0108] 49.7+0.0s +[6400/16000] [L1: 0.0108] 48.5+0.0s +[8000/16000] [L1: 0.0108] 49.8+0.0s +[9600/16000] [L1: 0.0110] 51.0+0.1s +[11200/16000] [L1: 0.0110] 51.2+0.1s +[12800/16000] [L1: 0.0109] 51.1+0.0s +[14400/16000] [L1: 0.0109] 48.8+0.0s +[16000/16000] [L1: 0.0109] 50.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.466 (Best: 21.466 @epoch 18) +Forward: 36.63s + +Saving... +Total: 37.17s + +[Epoch 19] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0107] 50.9+0.9s +[3200/16000] [L1: 0.0107] 49.2+0.0s +[4800/16000] [L1: 0.0108] 49.0+0.0s +[6400/16000] [L1: 0.0109] 49.7+0.0s +[8000/16000] [L1: 0.0108] 51.0+0.1s +[9600/16000] [L1: 0.0107] 49.9+0.0s +[11200/16000] [L1: 0.0107] 49.6+0.0s +[12800/16000] [L1: 0.0106] 50.0+0.0s +[14400/16000] [L1: 0.0105] 49.5+0.0s +[16000/16000] [L1: 0.0105] 49.1+0.0s + +Evaluation: +[DIV2K x1] PSNR: 21.952 (Best: 21.952 @epoch 19) +Forward: 36.57s + +Saving... +Total: 37.13s + +[Epoch 20] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0104] 51.2+0.7s +[3200/16000] [L1: 0.0104] 50.8+0.1s +[4800/16000] [L1: 0.0104] 51.1+0.1s +[6400/16000] [L1: 0.0103] 50.6+0.0s +[8000/16000] [L1: 0.0103] 50.3+0.0s +[9600/16000] [L1: 0.0104] 50.5+0.0s +[11200/16000] [L1: 0.0104] 50.5+0.0s +[12800/16000] [L1: 0.0103] 51.5+0.1s +[14400/16000] [L1: 0.0103] 49.3+0.0s +[16000/16000] [L1: 0.0103] 51.4+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.624 (Best: 22.624 @epoch 20) +Forward: 36.49s + +Saving... +Total: 37.04s + +[Epoch 21] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0098] 50.9+0.7s +[3200/16000] [L1: 0.0097] 49.0+0.0s +[4800/16000] [L1: 0.0097] 51.0+0.1s +[6400/16000] [L1: 0.0097] 49.9+0.0s +[8000/16000] [L1: 0.0096] 49.5+0.0s +[9600/16000] [L1: 0.0096] 48.7+0.0s +[11200/16000] [L1: 0.0096] 48.8+0.0s +[12800/16000] [L1: 0.0096] 48.8+0.0s +[14400/16000] [L1: 0.0096] 50.4+0.0s +[16000/16000] [L1: 0.0096] 50.7+0.0s + +Evaluation: +[DIV2K x1] PSNR: 22.887 (Best: 22.887 @epoch 21) +Forward: 36.52s + +Saving... +Total: 37.15s + +[Epoch 22] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0096] 50.9+0.9s +[3200/16000] [L1: 0.0096] 50.8+0.1s +[4800/16000] [L1: 0.0095] 51.3+0.1s +[6400/16000] [L1: 0.0097] 51.1+0.1s +[8000/16000] [L1: 0.0096] 51.1+0.1s +[9600/16000] [L1: 0.0095] 49.8+0.0s +[11200/16000] [L1: 0.0095] 48.9+0.0s +[12800/16000] [L1: 0.0094] 49.0+0.0s +[14400/16000] [L1: 0.0094] 50.2+0.0s +[16000/16000] [L1: 0.0094] 49.0+0.0s + +Evaluation: +[DIV2K x1] PSNR: 23.476 (Best: 23.476 @epoch 22) +Forward: 36.75s + +Saving... +Total: 37.30s + +[Epoch 23] Learning rate: 1.00e-4 +[1600/16000] [L1: 0.0096] 50.2+0.7s +[3200/16000] [L1: 0.1840] 50.5+0.1s +[4800/16000] [L1: 0.1394] 50.3+0.0s +[6400/16000] [L1: 0.1132] 50.1+0.1s +[8000/16000] [L1: 0.0961] 50.7+0.1s +[9600/16000] [L1: 0.0840] 50.4+0.0s 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+cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: True +no_augment: False +model: LAMBDANET +act: relu +pre_train: ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: False +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + +2020-11-06-13:05:48 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + +2020-11-06-13:18:53 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + +2020-11-06-13:47:52 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: LAMBDANET +act: relu +pre_train: ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt +extend: . +n_resblocks: 20 +recurrence: 1 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True 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../experiment/RAFTS_DEMOSAIC20_R4/model/model_best.pt +extend: . +n_resblocks: 10 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + +2020-11-11-09:33:27 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: ../experiment/RAFTS_DEMOSAIC20_R4/model/model_best.pt +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +normalization: layer +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +decay_gamma: 0.8 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + +2020-11-11-11:56:15 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: ../experiment/RAFTS_DEMOSAIC20_R4/model/model_468.pt +extend: . +n_resblocks: 20 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +normalization: layer +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +decay_gamma: 0.8 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + +2020-11-11-11:58:49 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: ../experiment/RAFTS_DEMOSAIC20_R4/model/model_468.pt +extend: . +n_resblocks: 10 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +normalization: layer +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +decay_gamma: 0.8 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + +2020-11-11-12:47:27 + +debug: False +template: . +n_threads: 18 +cpu: False +n_GPUs: 1 +seed: 1 +dir_data: /data/ssd/public/liuhy/DNDM/dataset +data_train: ['DIV2K'] +data_test: ['McM', 'Kodak24', 'CBSD68', 'Urban100'] +data_range: 1-800/801-805 +ext: sep +scale: [1] +patch_size: 192 +rgb_range: 1 +n_colors: 3 +chop: False +no_augment: False +model: RAFTNETS +act: relu +pre_train: ../experiment/RAFTS_DEMOSAIC20_R4/model/model_best.pt +extend: . +n_resblocks: 10 +recurrence: 4 +n_feats: 64 +res_scale: 1 +shift_mean: True +amp: True +detach: False +spectral: False +dilation: False +precision: single +normalization: layer +G0: 64 +RDNkSize: 3 +RDNconfig: B +depth: 12 +n_resgroups: 10 +reduction: 16 +reset: False +test_every: 1000 +epochs: 1000 +batch_size: 16 +split_batch: 1 +self_ensemble: False +test_only: True +gan_k: 1 +lr: 0.0001 +decay: 200-400-600-800 +gamma: 0.5 +optimizer: ADAM +momentum: 0.9 +betas: (0.9, 0.999) +epsilon: 1e-08 +weight_decay: 0 +gclip: 0 +decay_gamma: 0.8 +loss: 1*L1 +skip_threshold: 100000000.0 +save: test +load: +resume: 0 +save_models: False +print_every: 100 +save_results: True +save_gt: False + diff --git a/Demosaic/experiment/test/log.txt b/Demosaic/experiment/test/log.txt new file mode 100644 index 0000000000000000000000000000000000000000..05e74db3ea806b3f3e8858628f2f096d8af41340 --- /dev/null +++ b/Demosaic/experiment/test/log.txt @@ -0,0 +1,8681 @@ +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 36.384 (Best: 36.384 @epoch 1) +[Kodak24 x1] PSNR: 39.478 (Best: 39.478 @epoch 1) +[CBSD68 x1] PSNR: 38.528 (Best: 38.528 @epoch 1) +[Urban100 x1] PSNR: 35.082 (Best: 35.082 @epoch 1) +Forward: 63.35s + +Saving... +Total: 63.77s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 36.998 (Best: 36.998 @epoch 1) +[Kodak24 x1] PSNR: 40.893 (Best: 40.893 @epoch 1) +[CBSD68 x1] PSNR: 40.013 (Best: 40.013 @epoch 1) +[Urban100 x1] PSNR: 36.205 (Best: 36.205 @epoch 1) +Forward: 63.11s + +Saving... +Total: 63.53s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 37.862 (Best: 37.862 @epoch 1) +[Kodak24 x1] PSNR: 41.271 (Best: 41.271 @epoch 1) +[CBSD68 x1] PSNR: 40.579 (Best: 40.579 @epoch 1) +[Urban100 x1] PSNR: 36.854 (Best: 36.854 @epoch 1) +Forward: 63.64s + +Saving... +Total: 64.07s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.176 (Best: 38.176 @epoch 1) +[Kodak24 x1] PSNR: 41.324 (Best: 41.324 @epoch 1) +[CBSD68 x1] PSNR: 40.564 (Best: 40.564 @epoch 1) +[Urban100 x1] PSNR: 37.178 (Best: 37.178 @epoch 1) +Forward: 63.71s + +Saving... +Total: 64.13s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.347 (Best: 38.347 @epoch 1) +[Kodak24 x1] PSNR: 41.752 (Best: 41.752 @epoch 1) +[CBSD68 x1] PSNR: 40.982 (Best: 40.982 @epoch 1) +[Urban100 x1] PSNR: 37.467 (Best: 37.467 @epoch 1) +Forward: 63.79s + +Saving... +Total: 64.24s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.484 (Best: 38.484 @epoch 1) +[Kodak24 x1] PSNR: 41.779 (Best: 41.779 @epoch 1) +[CBSD68 x1] PSNR: 40.861 (Best: 40.861 @epoch 1) +[Urban100 x1] PSNR: 37.639 (Best: 37.639 @epoch 1) +Forward: 63.81s + +Saving... +Total: 64.14s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.478 (Best: 38.478 @epoch 1) +[Kodak24 x1] PSNR: 41.955 (Best: 41.955 @epoch 1) +[CBSD68 x1] PSNR: 41.317 (Best: 41.317 @epoch 1) +[Urban100 x1] PSNR: 37.856 (Best: 37.856 @epoch 1) +Forward: 63.27s + +Saving... +Total: 63.57s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.478 (Best: 38.478 @epoch 1) +[Kodak24 x1] PSNR: 41.955 (Best: 41.955 @epoch 1) +[CBSD68 x1] PSNR: 41.317 (Best: 41.317 @epoch 1) +[Urban100 x1] PSNR: 37.856 (Best: 37.856 @epoch 1) +Forward: 63.36s + +Saving... +Total: 63.81s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.668 (Best: 38.668 @epoch 1) +[Kodak24 x1] PSNR: 41.999 (Best: 41.999 @epoch 1) +[CBSD68 x1] PSNR: 41.265 (Best: 41.265 @epoch 1) +[Urban100 x1] PSNR: 38.027 (Best: 38.027 @epoch 1) +Forward: 63.37s + +Saving... +Total: 63.70s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.648 (Best: 38.648 @epoch 1) +[Kodak24 x1] PSNR: 41.988 (Best: 41.988 @epoch 1) +[CBSD68 x1] PSNR: 41.201 (Best: 41.201 @epoch 1) +[Urban100 x1] PSNR: 37.840 (Best: 37.840 @epoch 1) +Forward: 63.60s + +Saving... +Total: 64.05s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.682 (Best: 38.682 @epoch 1) +[Kodak24 x1] PSNR: 42.035 (Best: 42.035 @epoch 1) +[CBSD68 x1] PSNR: 41.228 (Best: 41.228 @epoch 1) +[Urban100 x1] PSNR: 37.987 (Best: 37.987 @epoch 1) +Forward: 63.64s + +Saving... +Total: 63.94s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.093 (Best: 39.093 @epoch 1) +[Kodak24 x1] PSNR: 42.527 (Best: 42.527 @epoch 1) +[CBSD68 x1] PSNR: 41.822 (Best: 41.822 @epoch 1) +[Urban100 x1] PSNR: 38.625 (Best: 38.625 @epoch 1) +Forward: 64.02s + +Saving... +Total: 64.54s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.093 (Best: 39.093 @epoch 1) +[Kodak24 x1] PSNR: 42.527 (Best: 42.527 @epoch 1) +[CBSD68 x1] PSNR: 41.822 (Best: 41.822 @epoch 1) +[Urban100 x1] PSNR: 38.625 (Best: 38.625 @epoch 1) +Forward: 64.37s + +Saving... +Total: 64.82s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.626 (Best: 39.626 @epoch 1) +[Kodak24 x1] PSNR: 42.828 (Best: 42.828 @epoch 1) +[CBSD68 x1] PSNR: 42.265 (Best: 42.265 @epoch 1) +[Urban100 x1] PSNR: 39.402 (Best: 39.402 @epoch 1) +Forward: 63.44s + +Saving... +Total: 63.89s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 23.531 (Best: 23.531 @epoch 1) +[Kodak24 x1] PSNR: 24.366 (Best: 24.366 @epoch 1) +[CBSD68 x1] PSNR: 23.246 (Best: 23.246 @epoch 1) +[Urban100 x1] PSNR: 20.781 (Best: 20.781 @epoch 1) +Forward: 242.82s + +Saving... +Total: 243.03s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.626 (Best: 39.626 @epoch 1) +[Kodak24 x1] PSNR: 42.828 (Best: 42.828 @epoch 1) +[CBSD68 x1] PSNR: 42.265 (Best: 42.265 @epoch 1) +[Urban100 x1] PSNR: 39.402 (Best: 39.402 @epoch 1) +Forward: 96.11s + +Saving... +Total: 96.49s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 37.639 (Best: 37.639 @epoch 1) +[Kodak24 x1] PSNR: 40.934 (Best: 40.934 @epoch 1) +[CBSD68 x1] PSNR: 40.183 (Best: 40.183 @epoch 1) +[Urban100 x1] PSNR: 36.634 (Best: 36.634 @epoch 1) +Forward: 506.65s + +Saving... +Total: 507.00s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.183 (Best: 38.183 @epoch 1) +[Kodak24 x1] PSNR: 41.522 (Best: 41.522 @epoch 1) +[CBSD68 x1] PSNR: 40.737 (Best: 40.737 @epoch 1) +[Urban100 x1] PSNR: 37.155 (Best: 37.155 @epoch 1) +Forward: 496.52s + +Saving... +Total: 496.88s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.183 (Best: 38.183 @epoch 1) +[Kodak24 x1] PSNR: 41.522 (Best: 41.522 @epoch 1) +[CBSD68 x1] PSNR: 40.737 (Best: 40.737 @epoch 1) +[Urban100 x1] PSNR: 37.155 (Best: 37.155 @epoch 1) +Forward: 496.22s + +Saving... +Total: 496.59s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.380 (Best: 38.380 @epoch 1) +[Kodak24 x1] PSNR: 41.673 (Best: 41.673 @epoch 1) +[CBSD68 x1] PSNR: 41.059 (Best: 41.059 @epoch 1) +[Urban100 x1] PSNR: 37.525 (Best: 37.525 @epoch 1) +Forward: 494.23s + +Saving... +Total: 494.59s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.500 (Best: 38.500 @epoch 1) +[Kodak24 x1] PSNR: 41.814 (Best: 41.814 @epoch 1) +[CBSD68 x1] PSNR: 41.105 (Best: 41.105 @epoch 1) +[Urban100 x1] PSNR: 37.686 (Best: 37.686 @epoch 1) +Forward: 496.09s + +Saving... +Total: 496.44s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.500 (Best: 38.500 @epoch 1) +[Kodak24 x1] PSNR: 41.814 (Best: 41.814 @epoch 1) +[CBSD68 x1] PSNR: 41.105 (Best: 41.105 @epoch 1) +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 38.941 (Best: 38.941 @epoch 1) +[Kodak24 x1] PSNR: 42.103 (Best: 42.103 @epoch 1) +[CBSD68 x1] PSNR: 41.321 (Best: 41.321 @epoch 1) +[Urban100 x1] PSNR: 38.080 (Best: 38.080 @epoch 1) +Forward: 495.54s + +Saving... +Total: 495.89s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.371 (Best: 39.371 @epoch 1) +[Kodak24 x1] PSNR: 42.719 (Best: 42.719 @epoch 1) +[CBSD68 x1] PSNR: 42.346 (Best: 42.346 @epoch 1) +[Urban100 x1] PSNR: 39.248 (Best: 39.248 @epoch 1) +Forward: 425.36s + +Saving... +Total: 425.68s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.626 (Best: 39.626 @epoch 1) +[Kodak24 x1] PSNR: 42.828 (Best: 42.828 @epoch 1) +[CBSD68 x1] PSNR: 42.265 (Best: 42.265 @epoch 1) +[Urban100 x1] PSNR: 39.402 (Best: 39.402 @epoch 1) +Forward: 120.37s + +Saving... +Total: 120.72s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.358 (Best: 39.358 @epoch 1) +[Kodak24 x1] PSNR: 42.806 (Best: 42.806 @epoch 1) +[CBSD68 x1] PSNR: 42.471 (Best: 42.471 @epoch 1) +[Urban100 x1] PSNR: 39.338 (Best: 39.338 @epoch 1) +Forward: 415.11s + +Saving... +Total: 415.43s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.358 (Best: 39.358 @epoch 1) +[Kodak24 x1] PSNR: 42.806 (Best: 42.806 @epoch 1) +[CBSD68 x1] PSNR: 42.471 (Best: 42.471 @epoch 1) +[Urban100 x1] PSNR: 39.338 (Best: 39.338 @epoch 1) +Forward: 415.25s + +Saving... +Total: 415.59s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.561 (Best: 39.561 @epoch 1) +[Kodak24 x1] PSNR: 42.842 (Best: 42.842 @epoch 1) +[CBSD68 x1] PSNR: 42.508 (Best: 42.508 @epoch 1) +[Urban100 x1] PSNR: 39.566 (Best: 39.566 @epoch 1) +Forward: 441.26s + +Saving... +Total: 441.62s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.626 (Best: 39.626 @epoch 1) +[Kodak24 x1] PSNR: 42.828 (Best: 42.828 @epoch 1) +[CBSD68 x1] PSNR: 42.265 (Best: 42.265 @epoch 1) +[Urban100 x1] PSNR: 39.402 (Best: 39.402 @epoch 1) +Forward: 123.79s + +Saving... +Total: 124.14s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.519 (Best: 39.519 @epoch 1) +[Kodak24 x1] PSNR: 42.890 (Best: 42.890 @epoch 1) +[CBSD68 x1] PSNR: 42.534 (Best: 42.534 @epoch 1) +[Urban100 x1] PSNR: 39.581 (Best: 39.581 @epoch 1) +Forward: 419.02s + +Saving... +Total: 419.35s + +LAMBDANET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.626 (Best: 39.626 @epoch 1) +[Kodak24 x1] PSNR: 42.828 (Best: 42.828 @epoch 1) +[CBSD68 x1] PSNR: 42.265 (Best: 42.265 @epoch 1) +[Urban100 x1] PSNR: 39.402 (Best: 39.402 @epoch 1) +Forward: 125.28s + +Saving... +Total: 125.69s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.538 (Best: 39.538 @epoch 1) +[Kodak24 x1] PSNR: 42.901 (Best: 42.901 @epoch 1) +[CBSD68 x1] PSNR: 42.583 (Best: 42.583 @epoch 1) +[Urban100 x1] PSNR: 39.666 (Best: 39.666 @epoch 1) +Forward: 427.91s + +Saving... +Total: 428.26s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.185 (Best: 39.185 @epoch 1) +[Kodak24 x1] PSNR: 42.509 (Best: 42.509 @epoch 1) +[CBSD68 x1] PSNR: 42.009 (Best: 42.009 @epoch 1) +[Urban100 x1] PSNR: 38.835 (Best: 38.835 @epoch 1) +Forward: 507.10s + +Saving... +Total: 507.47s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.550 (Best: 39.550 @epoch 1) +[Kodak24 x1] PSNR: 42.930 (Best: 42.930 @epoch 1) +[CBSD68 x1] PSNR: 42.593 (Best: 42.593 @epoch 1) +[Urban100 x1] PSNR: 39.704 (Best: 39.704 @epoch 1) +Forward: 442.02s + +Saving... +Total: 442.38s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.550 (Best: 39.550 @epoch 1) +[Kodak24 x1] PSNR: 42.930 (Best: 42.930 @epoch 1) +[CBSD68 x1] PSNR: 42.593 (Best: 42.593 @epoch 1) +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.550 (Best: 39.550 @epoch 1) +[Kodak24 x1] PSNR: 42.930 (Best: 42.930 @epoch 1) +[CBSD68 x1] PSNR: 42.593 (Best: 42.593 @epoch 1) +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: nan (Best: nan @epoch 1) +[Kodak24 x1] PSNR: nan (Best: nan @epoch 1) +[CBSD68 x1] PSNR: nan (Best: nan @epoch 1) +[Urban100 x1] PSNR: nan (Best: nan @epoch 1) +Forward: 1.66s + +Saving... +Total: 2.66s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.590 (Best: 39.590 @epoch 1) +[Kodak24 x1] PSNR: 42.917 (Best: 42.917 @epoch 1) +[CBSD68 x1] PSNR: 42.546 (Best: 42.546 @epoch 1) +[Urban100 x1] PSNR: 39.617 (Best: 39.617 @epoch 1) +Forward: 234.30s + +Saving... +Total: 234.77s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.632 (Best: 39.632 @epoch 1) +[Kodak24 x1] PSNR: 42.916 (Best: 42.916 @epoch 1) +[CBSD68 x1] PSNR: 42.590 (Best: 42.590 @epoch 1) +[Urban100 x1] PSNR: 39.715 (Best: 39.715 @epoch 1) +Forward: 234.81s + +Saving... +Total: 235.27s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.632 (Best: 39.632 @epoch 1) +[Kodak24 x1] PSNR: 42.916 (Best: 42.916 @epoch 1) +[CBSD68 x1] PSNR: 42.590 (Best: 42.590 @epoch 1) +[Urban100 x1] PSNR: 39.715 (Best: 39.715 @epoch 1) +Forward: 234.32s + +Saving... +Total: 234.73s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.632 (Best: 39.632 @epoch 1) +[Kodak24 x1] PSNR: 42.916 (Best: 42.916 @epoch 1) +[CBSD68 x1] PSNR: 42.590 (Best: 42.590 @epoch 1) +[Urban100 x1] PSNR: 39.715 (Best: 39.715 @epoch 1) +Forward: 233.42s + +Saving... +Total: 233.86s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.634 (Best: 39.634 @epoch 1) +[Kodak24 x1] PSNR: 42.927 (Best: 42.927 @epoch 1) +[CBSD68 x1] PSNR: 42.537 (Best: 42.537 @epoch 1) +[Urban100 x1] PSNR: 39.695 (Best: 39.695 @epoch 1) +Forward: 236.52s + +Saving... +Total: 236.83s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.673 (Best: 39.673 @epoch 1) +[Kodak24 x1] PSNR: 42.964 (Best: 42.964 @epoch 1) +[CBSD68 x1] PSNR: 42.588 (Best: 42.588 @epoch 1) +[Urban100 x1] PSNR: 39.725 (Best: 39.725 @epoch 1) +Forward: 237.85s + +Saving... +Total: 238.16s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 7.427 (Best: 7.427 @epoch 1) +[Kodak24 x1] PSNR: 6.777 (Best: 6.777 @epoch 1) +[CBSD68 x1] PSNR: 6.631 (Best: 6.631 @epoch 1) +[Urban100 x1] PSNR: 5.740 (Best: 5.740 @epoch 1) +Forward: 474.32s + +Saving... +Total: 474.38s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (11): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (12): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (13): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (14): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (15): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (16): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (17): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (18): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (19): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (20): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (21): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 7.427 (Best: 7.427 @epoch 1) +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.707 (Best: 39.707 @epoch 1) +[Kodak24 x1] PSNR: 42.994 (Best: 42.994 @epoch 1) +[CBSD68 x1] PSNR: 42.569 (Best: 42.569 @epoch 1) +[Urban100 x1] PSNR: 39.797 (Best: 39.797 @epoch 1) +Forward: 414.97s + +Saving... +Total: 415.29s + +RAFTNET( + (sub_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (add_mean): MeanShift(3, 3, kernel_size=(1, 1), stride=(1, 1)) + (hidden_encoder): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (head): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (body): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (3): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (4): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (5): LambdaLayer( + (to_q): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_k): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (to_v): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (norm_q): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (norm_v): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pos_conv): Conv3d(4, 16, kernel_size=(1, 23, 23), stride=(1, 1, 1), padding=(0, 11, 11)) + ) + (6): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (7): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (8): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (9): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (10): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (tail): Sequential( + (0): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (1): ResBlock( + (body): Sequential( + (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (1): PReLU(num_parameters=1) + (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (gru): ConvGRU( + (convz): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convr): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (convq): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) +) + +Evaluation: +[McM x1] PSNR: 39.694 (Best: 39.694 @epoch 1) +[Kodak24 x1] PSNR: 43.010 (Best: 43.010 @epoch 1) +[CBSD68 x1] PSNR: 42.632 (Best: 42.632 @epoch 1) +[Urban100 x1] PSNR: 39.901 (Best: 39.901 @epoch 1) +Forward: 245.81s + +Saving... +Total: 246.19s + diff --git a/Demosaic/experiment/test/results-CBSD68/101085_x1_DM.png b/Demosaic/experiment/test/results-CBSD68/101085_x1_DM.png new file mode 100644 index 0000000000000000000000000000000000000000..9231a632c34717e068d9c44b576c18ab05d0165b Binary files /dev/null and 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