Add guideline to use BiRefNet. Remove codes of model.

BiRefNet_config.py

@@ -1,9 +0,0 @@
-from transformers import PretrainedConfig
-
-class BiRefNetConfig(PretrainedConfig):
- model_type = "SegformerForSemanticSegmentation"
- def __init__(
- self,
- **kwargs
- ):
- super().__init__(**kwargs)

BiRefNet_pipe.py

@@ -1,9 +0,0 @@
-import torch
-from transformers import Pipeline
-
-
-class BiRefNetPipe(Pipeline):
- def __init__(self, **kwargs):
- Pipeline.__init__(self, **kwargs)
- self.model.to(['cpu', 0][torch.cuda.is_available()])
- self.model.eval()

README.md

@@ -14,4 +14,77 @@ pipeline_tag: image-segmentation
 
 This model has been pushed to the Hub using **birefnet**:
 - Repo: https://github.com/ZhengPeng7/BiRefNet
-- Docs: [More Information Needed]
+- Docs: https://www.birefnet.top
+
+## How to use
+
+```shell
+# Download Codes
+git clone https://github.com/ZhengPeng7/BiRefNet.git
+cd BiRefNet
+```
+
+```python
+# Imports
+from PIL import Image
+import matplotlib.pyplot as plt
+import torch
+from torchvision import transforms
+
+# Input Data
+transform_image = transforms.Compose([
+ transforms.Resize((256, 256)),
+ transforms.ToTensor(),
+ transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+imagepath = 'PATH-TO-YOUR_IMAGE.jpg'
+image = Image.open(imagepath)
+input_images = transform_image(image).unsqueeze(0).to('cuda')
+
+# Load Model
+device = '0'
+torch.set_float32_matmul_precision(['high', 'highest'][0])
+model = BiRefNet.from_pretrained('zhengpeng7/birefnet')
+model.to(device)
+model.eval()
+print('BiRefNet is ready to use.')
+
+# Prediction
+with torch.no_grad():
+ preds = model(input_images)[-1].sigmoid().cpu()
+pred = preds[0].squeeze()
+
+# Show Results
+plt.imshow(pred, cmap='gray')
+plt.show()
+
+```
+
+
+> This BiRefNet for standard dichotomous image segmentation (DIS) is trained on **DIS-TR** and validated on **DIS-TEs and DIS-VD**.
+
+## This repo holds the official model weights of "[<ins>Bilateral Reference for High-Resolution Dichotomous Image Segmentation</ins>](https://arxiv.org/pdf/2401.03407)" (_arXiv 2024_).
+
+This repo contains the weights of BiRefNet proposed in our paper, which has achieved the SOTA performance on three tasks (DIS, HRSOD, and COD).
+
+Go to my GitHub page for BiRefNet codes and the latest updates: https://github.com/ZhengPeng7/BiRefNet :)
+
+
+#### Try our online demos for inference:
+
++ Online **Single Image Inference** on Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba?usp=drive_link)
+<img src="https://drive.google.com/thumbnail?id=12XmDhKtO1o2fEvBu4OE4ULVB2BK0ecWi&sz=w1620" />
++ **Inference and evaluation** of your given weights: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl#scrollTo=DJ4meUYjia6S)
++ **Online Inference with GUI on Hugging Face** with adjustable resolutions: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo) 
+<img src="https://drive.google.com/thumbnail?id=12XmDhKtO1o2fEvBu4OE4ULVB2BK0ecWi&sz=w1080" />
+
+## Citation
+
+```
+@article{zheng2024birefnet,
+ title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
+ author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
+ journal={arXiv},
+ year={2024}
+}
+```

birefnet.py

@@ -1,287 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from kornia.filters import laplacian
-from huggingface_hub import PyTorchModelHubMixin
-
-from config import Config
-from dataset import class_labels_TR_sorted
-from models.build_backbone import build_backbone
-from models.decoder_blocks import BasicDecBlk, ResBlk, HierarAttDecBlk
-from models.lateral_blocks import BasicLatBlk
-from models.aspp import ASPP, ASPPDeformable
-from models.ing import *
-from models.refiner import Refiner, RefinerPVTInChannels4, RefUNet
-from models.stem_layer import StemLayer
-
-
-class BiRefNet(
- nn.Module,
- PyTorchModelHubMixin,
- library_name="birefnet",
- repo_url="https://github.com/ZhengPeng7/BiRefNet",
- tags=['Image Segmentation', 'Background Removal', 'Mask Generation', 'Dichotomous Image Segmentation', 'Camouflaged Object Detection', 'Salient Object Detection']
-):
- def __init__(self, bb_pretrained=True):
- super(BiRefNet, self).__init__()
- self.config = Config()
- self.epoch = 1
- self.bb = build_backbone(self.config.bb, pretrained=bb_pretrained)
-
- channels = self.config.lateral_channels_in_collection
-
- if self.config.auxiliary_classification:
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.cls_head = nn.Sequential(
- nn.Linear(channels[0], len(class_labels_TR_sorted))
- )
-
- if self.config.squeeze_block:
- self.squeeze_module = nn.Sequential(*[
- eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0])
- for _ in range(eval(self.config.squeeze_block.split('_x')[1]))
- ])
-
- self.decoder = Decoder(channels)
-
- if self.config.ender:
- self.dec_end = nn.Sequential(
- nn.Conv2d(1, 16, 3, 1, 1),
- nn.Conv2d(16, 1, 3, 1, 1),
- nn.ReLU(inplace=True),
- )
-
- # refine patch-level segmentation
- if self.config.refine:
- if self.config.refine == 'itself':
- self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
- else:
- self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1'))
-
- if self.config.freeze_bb:
- # Freeze the backbone...
- print(self.named_parameters())
- for key, value in self.named_parameters():
- if 'bb.' in key and 'refiner.' not in key:
- value.requires_grad = False
-
- def forward_enc(self, x):
- if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
- x1 = self.bb.conv1(x); x2 = self.bb.conv2(x1); x3 = self.bb.conv3(x2); x4 = self.bb.conv4(x3)
- else:
- x1, x2, x3, x4 = self.bb(x)
- if self.config.mul_scl_ipt == 'cat':
- B, C, H, W = x.shape
- x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
- x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
- x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
- x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
- x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
- elif self.config.mul_scl_ipt == 'add':
- B, C, H, W = x.shape
- x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
- x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)
- x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)
- x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)
- x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)
- class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None
- if self.config.cxt:
- x4 = torch.cat(
- (
- *[
- F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
- F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
- F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
- ][-len(self.config.cxt):],
- x4
- ),
- dim=1
- )
- return (x1, x2, x3, x4), class_preds
-
- def forward_ori(self, x):
- ########## Encoder ##########
- (x1, x2, x3, x4), class_preds = self.forward_enc(x)
- if self.config.squeeze_block:
- x4 = self.squeeze_module(x4)
- ########## Decoder ##########
- features = [x, x1, x2, x3, x4]
- if self.training and self.config.out_ref:
- features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5))
- scaled_preds = self.decoder(features)
- return scaled_preds, class_preds
-
- def forward(self, x):
- scaled_preds, class_preds = self.forward_ori(x)
- class_preds_lst = [class_preds]
- return [scaled_preds, class_preds_lst] if self.training else scaled_preds
-
-
-class Decoder(nn.Module):
- def __init__(self, channels):
- super(Decoder, self).__init__()
- self.config = Config()
- DecoderBlock = eval(self.config.dec_blk)
- LateralBlock = eval(self.config.lat_blk)
-
- if self.config.dec_ipt:
- self.split = self.config.dec_ipt_split
- N_dec_ipt = 64
- DBlock = SimpleConvs
- ic = 64
- ipt_cha_opt = 1
- self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
- self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
- self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
- self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
- self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
- else:
- self.split = None
-
- self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[1])
- self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2])
- self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3])
- self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2)
- self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0))
-
- self.lateral_block4 = LateralBlock(channels[1], channels[1])
- self.lateral_block3 = LateralBlock(channels[2], channels[2])
- self.lateral_block2 = LateralBlock(channels[3], channels[3])
-
- if self.config.ms_supervision:
- self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
- self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
- self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
-
- if self.config.out_ref:
- _N = 16
- self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
- self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
- self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
-
- self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
- self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
- self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
- 
- self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
- self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
- self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
-
- def get_patches_batch(self, x, p):
- _size_h, _size_w = p.shape[2:]
- patches_batch = []
- for idx in range(x.shape[0]):
- columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
- patches_x = []
- for column_x in columns_x:
- patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
- patch_sample = torch.cat(patches_x, dim=1)
- patches_batch.append(patch_sample)
- return torch.cat(patches_batch, dim=0)
-
- def forward(self, features):
- if self.training and self.config.out_ref:
- outs_gdt_pred = []
- outs_gdt_label = []
- x, x1, x2, x3, x4, gdt_gt = features
- else:
- x, x1, x2, x3, x4 = features
- outs = []
-
- if self.config.dec_ipt:
- patches_batch = self.get_patches_batch(x, x4) if self.split else x
- x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
- p4 = self.decoder_block4(x4)
- m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision else None
- if self.config.out_ref:
- p4_gdt = self.gdt_convs_4(p4)
- if self.training:
- # >> GT:
- m4_dia = m4
- gdt_label_main_4 = gdt_gt * F.interpolate(m4_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
- outs_gdt_label.append(gdt_label_main_4)
- # >> Pred:
- gdt_pred_4 = self.gdt_convs_pred_4(p4_gdt)
- outs_gdt_pred.append(gdt_pred_4)
- gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
- # >> Finally:
- p4 = p4 * gdt_attn_4
- _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
- _p3 = _p4 + self.lateral_block4(x3)
-
- if self.config.dec_ipt:
- patches_batch = self.get_patches_batch(x, _p3) if self.split else x
- _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
- p3 = self.decoder_block3(_p3)
- m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision else None
- if self.config.out_ref:
- p3_gdt = self.gdt_convs_3(p3)
- if self.training:
- # >> GT:
- # m3 --dilation--> m3_dia
- # G_3^gt * m3_dia --> G_3^m, which is the label of gradient
- m3_dia = m3
- gdt_label_main_3 = gdt_gt * F.interpolate(m3_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
- outs_gdt_label.append(gdt_label_main_3)
- # >> Pred:
- # p3 --conv--BN--> F_3^G, where F_3^G predicts the \hat{G_3} with xx
- # F_3^G --sigmoid--> A_3^G
- gdt_pred_3 = self.gdt_convs_pred_3(p3_gdt)
- outs_gdt_pred.append(gdt_pred_3)
- gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
- # >> Finally:
- # p3 = p3 * A_3^G
- p3 = p3 * gdt_attn_3
- _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
- _p2 = _p3 + self.lateral_block3(x2)
-
- if self.config.dec_ipt:
- patches_batch = self.get_patches_batch(x, _p2) if self.split else x
- _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
- p2 = self.decoder_block2(_p2)
- m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision else None
- if self.config.out_ref:
- p2_gdt = self.gdt_convs_2(p2)
- if self.training:
- # >> GT:
- m2_dia = m2
- gdt_label_main_2 = gdt_gt * F.interpolate(m2_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
- outs_gdt_label.append(gdt_label_main_2)
- # >> Pred:
- gdt_pred_2 = self.gdt_convs_pred_2(p2_gdt)
- outs_gdt_pred.append(gdt_pred_2)
- gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
- # >> Finally:
- p2 = p2 * gdt_attn_2
- _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
- _p1 = _p2 + self.lateral_block2(x1)
-
- if self.config.dec_ipt:
- patches_batch = self.get_patches_batch(x, _p1) if self.split else x
- _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
- _p1 = self.decoder_block1(_p1)
- _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
-
- if self.config.dec_ipt:
- patches_batch = self.get_patches_batch(x, _p1) if self.split else x
- _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
- p1_out = self.conv_out1(_p1)
-
- if self.config.ms_supervision:
- outs.append(m4)
- outs.append(m3)
- outs.append(m2)
- outs.append(p1_out)
- return outs if not (self.config.out_ref and self.training) else ([outs_gdt_pred, outs_gdt_label], outs)
-
-
-class SimpleConvs(nn.Module):
- def __init__(
- self, in_channels: int, out_channels: int, inter_channels=64
- ) -> None:
- super().__init__()
- self.conv1 = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
- self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
-
- def forward(self, x):
- return self.conv_out(self.conv1(x))

config.json

@@ -1,21 +1,3 @@
 {
- "_name_or_path": "ZhengPeng7/BiRefNet",
- "architectures": [
- "BiRefNet"
- ],
- "auto_map": {
- "AutoConfig": "BiRefNet_config.BiRefNetConfig",
- "AutoModelForImageSegmentation": "birefnet.BiRefNet"
- },
- "custom_pipelines": {
- "image-segmentation": {
- "impl": "BiRefNet_pipe.BiRefNetPipe",
- "pt": [
- "AutoModelForImageSegmentation"
- ],
- "tf": [],
- "type": "image"
- }
- },
  "bb_pretrained": false
 }

config.py

@@ -1,156 +0,0 @@
-import os
-import math
-
-
-class Config():
- def __init__(self) -> None:
- # PATH settings
- self.sys_home_dir = os.environ['HOME'] # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
-
- # TASK settings
- self.task = ['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'][0]
- self.training_set = {
- 'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
- 'COD': 'TR-COD10K+TR-CAMO',
- 'HRSOD': ['TR-DUTS', 'TR-HRSOD', 'TR-UHRSD', 'TR-DUTS+TR-HRSOD', 'TR-DUTS+TR-UHRSD', 'TR-HRSOD+TR-UHRSD', 'TR-DUTS+TR-HRSOD+TR-UHRSD'][5],
- 'DIS5K+HRSOD+HRS10K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TE-HRS10K+TE-HRSOD+TE-UHRSD+TR-HRS10K+TR-HRSOD+TR-UHRSD', # leave DIS-VD for evaluation.
- 'P3M-10k': 'TR-P3M-10k',
- }[self.task]
- self.prompt4loc = ['dense', 'sparse'][0]
-
- # Faster-Training settings
- self.load_all = True
- self.compile = True # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
- # Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
- # 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
- # 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
- self.precisionHigh = True
-
- # MODEL settings
- self.ms_supervision = True
- self.out_ref = self.ms_supervision and True
- self.dec_ipt = True
- self.dec_ipt_split = True
- self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder
- self.mul_scl_ipt = ['', 'add', 'cat'][2]
- self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
- self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
- self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0]
-
- # TRAINING settings
- self.batch_size = 4
- self.IoU_finetune_last_epochs = [
- 0,
- {
- 'DIS5K': -50,
- 'COD': -20,
- 'HRSOD': -20,
- 'DIS5K+HRSOD+HRS10K': -20,
- 'P3M-10k': -20,
- }[self.task]
- ][1] # choose 0 to skip
- self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4) # DIS needs high lr to converge faster. Adapt the lr linearly
- self.size = 1024
- self.num_workers = max(4, self.batch_size) # will be decrease to min(it, batch_size) at the initialization of the data_loader
-
- # Backbone settings
- self.bb = [
- 'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2
- 'swin_v1_t', 'swin_v1_s', # 3, 4
- 'swin_v1_b', 'swin_v1_l', # 5-bs9, 6-bs4
- 'pvt_v2_b0', 'pvt_v2_b1', # 7, 8
- 'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5
- ][6]
- self.lateral_channels_in_collection = {
- 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
- 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
- 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
- 'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96],
- 'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64],
- }[self.bb]
- if self.mul_scl_ipt == 'cat':
- self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
- self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
-
- # MODEL settings - inactive
- self.lat_blk = ['BasicLatBlk'][0]
- self.dec_channels_inter = ['fixed', 'adap'][0]
- self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
- self.progressive_ref = self.refine and True
- self.ender = self.progressive_ref and False
- self.scale = self.progressive_ref and 2
- self.auxiliary_classification = False # Only for DIS5K, where class labels are saved in `dataset.py`.
- self.refine_iteration = 1
- self.freeze_bb = False
- self.model = [
- 'BiRefNet',
- ][0]
- if self.dec_blk == 'HierarAttDecBlk':
- self.batch_size = 2 ** [0, 1, 2, 3, 4][2]
-
- # TRAINING settings - inactive
- self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
- self.optimizer = ['Adam', 'AdamW'][1]
- self.lr_decay_epochs = [1e5] # Set to negative N to decay the lr in the last N-th epoch.
- self.lr_decay_rate = 0.5
- # Loss
- self.lambdas_pix_last = {
- # not 0 means opening this loss
- # original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
- 'bce': 30 * 1, # high performance
- 'iou': 0.5 * 1, # 0 / 255
- 'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
- 'mse': 150 * 0, # can smooth the saliency map
- 'triplet': 3 * 0,
- 'reg': 100 * 0,
- 'ssim': 10 * 1, # help contours,
- 'cnt': 5 * 0, # help contours
- 'structure': 5 * 0, # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
- }
- self.lambdas_cls = {
- 'ce': 5.0
- }
- # Adv
- self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training
- self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
-
- # PATH settings - inactive
- self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
- self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights')
- self.weights = {
- 'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
- 'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
- 'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]),
- 'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]),
- 'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]),
- 'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]),
- 'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]),
- 'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]),
- }
-
- # Callbacks - inactive
- self.verbose_eval = True
- self.only_S_MAE = False
- self.use_fp16 = False # Bugs. It may cause nan in training.
- self.SDPA_enabled = False # Bugs. Slower and errors occur in multi-GPUs
-
- # others
- self.device = [0, 'cpu'][0] # .to(0) == .to('cuda:0')
-
- self.batch_size_valid = 1
- self.rand_seed = 7
- run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f]
- with open(run_sh_file[0], 'r') as f:
- lines = f.readlines()
- self.save_last = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'val_last=' in l][0].split('val_last=')[-1].split()[0])
- self.save_step = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'step=' in l][0].split('step=')[-1].split()[0])
- self.val_step = [0, self.save_step][0]
-
- def print_task(self) -> None:
- # Return task for choosing settings in shell scripts.
- print(self.task)
-
-if __name__ == '__main__':
- config = Config()
- config.print_task()
- 

dataset.py

@@ -1,112 +0,0 @@
-import os
-import cv2
-from tqdm import tqdm
-from PIL import Image
-from torch.utils import data
-from torchvision import transforms
-
-from preproc import preproc
-from config import Config
-from utils import path_to_image
-
-
-Image.MAX_IMAGE_PIXELS = None # remove DecompressionBombWarning
-config = Config()
-_class_labels_TR_sorted = (
- 'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, '
- 'BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, '
- 'CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, '
- 'Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, '
- 'Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, '
- 'Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, '
- 'KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, '
- 'Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, '
- 'OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, '
- 'RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, '
- 'ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, '
- 'Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, '
- 'TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, '
- 'UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht'
-)
-class_labels_TR_sorted = _class_labels_TR_sorted.split(', ')
-
-
-class MyData(data.Dataset):
- def __init__(self, datasets, image_size, is_train=True):
- self.size_train = image_size
- self.size_test = image_size
- self.keep_size = not config.size
- self.data_size = (config.size, config.size)
- self.is_train = is_train
- self.load_all = config.load_all
- self.device = config.device
- if self.is_train and config.auxiliary_classification:
- self.cls_name2id = {_name: _id for _id, _name in enumerate(class_labels_TR_sorted)}
- self.transform_image = transforms.Compose([
- transforms.Resize(self.data_size),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
- ][self.load_all or self.keep_size:])
- self.transform_label = transforms.Compose([
- transforms.Resize(self.data_size),
- transforms.ToTensor(),
- ][self.load_all or self.keep_size:])
- dataset_root = os.path.join(config.data_root_dir, config.task)
- # datasets can be a list of different datasets for training on combined sets.
- self.image_paths = []
- for dataset in datasets.split('+'):
- image_root = os.path.join(dataset_root, dataset, 'im')
- self.image_paths += [os.path.join(image_root, p) for p in os.listdir(image_root)]
- self.label_paths = []
- for p in self.image_paths:
- for ext in ['.png', '.jpg', '.PNG', '.JPG', '.JPEG']:
- ## 'im' and 'gt' may need modifying
- p_gt = p.replace('/im/', '/gt/')[:-(len(p.split('.')[-1])+1)] + ext
- file_exists = False
- if os.path.exists(p_gt):
- self.label_paths.append(p_gt)
- file_exists = True
- break
- if not file_exists:
- print('Not exists:', p_gt)
- if self.load_all:
- self.images_loaded, self.labels_loaded = [], []
- self.class_labels_loaded = []
- # for image_path, label_path in zip(self.image_paths, self.label_paths):
- for image_path, label_path in tqdm(zip(self.image_paths, self.label_paths), total=len(self.image_paths)):
- _image = path_to_image(image_path, size=(config.size, config.size), color_type='rgb')
- _label = path_to_image(label_path, size=(config.size, config.size), color_type='gray')
- self.images_loaded.append(_image)
- self.labels_loaded.append(_label)
- self.class_labels_loaded.append(
- self.cls_name2id[label_path.split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
- )
-
- def __getitem__(self, index):
-
- if self.load_all:
- image = self.images_loaded[index]
- label = self.labels_loaded[index]
- class_label = self.class_labels_loaded[index] if self.is_train and config.auxiliary_classification else -1
- else:
- image = path_to_image(self.image_paths[index], size=(config.size, config.size), color_type='rgb')
- label = path_to_image(self.label_paths[index], size=(config.size, config.size), color_type='gray')
- class_label = self.cls_name2id[self.label_paths[index].split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
-
- # loading image and label
- if self.is_train:
- image, label = preproc(image, label, preproc_methods=config.preproc_methods)
- # else:
- # if _label.shape[0] > 2048 or _label.shape[1] > 2048:
- # _image = cv2.resize(_image, (2048, 2048), interpolation=cv2.INTER_LINEAR)
- # _label = cv2.resize(_label, (2048, 2048), interpolation=cv2.INTER_LINEAR)
-
- image, label = self.transform_image(image), self.transform_label(label)
-
- if self.is_train:
- return image, label, class_label
- else:
- return image, label, self.label_paths[index]
-
- def __len__(self):
- return len(self.image_paths)

models/__init__.py

@@ -1,6 +0,0 @@
-from os.path import dirname, basename, isfile, join
-import glob
-
-
-modules = glob.glob(join(dirname(__file__), "*.py"))
-__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]

models/aspp.py

@@ -1,119 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from models.deform_conv import DeformableConv2d
-from config import Config
-
-
-config = Config()
-
-
-class _ASPPModule(nn.Module):
- def __init__(self, in_channels, planes, kernel_size, padding, dilation):
- super(_ASPPModule, self).__init__()
- self.atrous_conv = nn.Conv2d(in_channels, planes, kernel_size=kernel_size,
- stride=1, padding=padding, dilation=dilation, bias=False)
- self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
- self.relu = nn.ReLU(inplace=True)
-
- def forward(self, x):
- x = self.atrous_conv(x)
- x = self.bn(x)
-
- return self.relu(x)
-
-
-class ASPP(nn.Module):
- def __init__(self, in_channels=64, out_channels=None, output_stride=16):
- super(ASPP, self).__init__()
- self.down_scale = 1
- if out_channels is None:
- out_channels = in_channels
- self.in_channelster = 256 // self.down_scale
- if output_stride == 16:
- dilations = [1, 6, 12, 18]
- elif output_stride == 8:
- dilations = [1, 12, 24, 36]
- else:
- raise NotImplementedError
-
- self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0])
- self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1])
- self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2])
- self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3])
-
- self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
- nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
- nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
- nn.ReLU(inplace=True))
- self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False)
- self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
- self.relu = nn.ReLU(inplace=True)
- self.dropout = nn.Dropout(0.5)
-
- def forward(self, x):
- x1 = self.aspp1(x)
- x2 = self.aspp2(x)
- x3 = self.aspp3(x)
- x4 = self.aspp4(x)
- x5 = self.global_avg_pool(x)
- x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
- x = torch.cat((x1, x2, x3, x4, x5), dim=1)
-
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
-
- return self.dropout(x)
-
-
-##################### Deformable
-class _ASPPModuleDeformable(nn.Module):
- def __init__(self, in_channels, planes, kernel_size, padding):
- super(_ASPPModuleDeformable, self).__init__()
- self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
- stride=1, padding=padding, bias=False)
- self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
- self.relu = nn.ReLU(inplace=True)
-
- def forward(self, x):
- x = self.atrous_conv(x)
- x = self.bn(x)
-
- return self.relu(x)
-
-
-class ASPPDeformable(nn.Module):
- def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7]):
- super(ASPPDeformable, self).__init__()
- self.down_scale = 1
- if out_channels is None:
- out_channels = in_channels
- self.in_channelster = 256 // self.down_scale
-
- self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0)
- self.aspp_deforms = nn.ModuleList([
- _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2)) for conv_size in parallel_block_sizes
- ])
-
- self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
- nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
- nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
- nn.ReLU(inplace=True))
- self.conv1 = nn.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False)
- self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
- self.relu = nn.ReLU(inplace=True)
- self.dropout = nn.Dropout(0.5)
-
- def forward(self, x):
- x1 = self.aspp1(x)
- x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
- x5 = self.global_avg_pool(x)
- x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
- x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
-
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
-
- return self.dropout(x)

models/attentions.py

@@ -1,93 +0,0 @@
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import init
-
-
-class SEWeightModule(nn.Module):
- def __init__(self, channels, reduction=16):
- super(SEWeightModule, self).__init__()
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
- self.fc1 = nn.Conv2d(channels, channels//reduction, kernel_size=1, padding=0)
- self.relu = nn.ReLU(inplace=True)
- self.fc2 = nn.Conv2d(channels//reduction, channels, kernel_size=1, padding=0)
- self.sigmoid = nn.Sigmoid()
-
- def forward(self, x):
- out = self.avg_pool(x)
- out = self.fc1(out)
- out = self.relu(out)
- out = self.fc2(out)
- weight = self.sigmoid(out)
- return weight
-
-
-class PSA(nn.Module):
-
- def __init__(self, in_channels, S=4, reduction=4):
- super().__init__()
- self.S = S
-
- _convs = []
- for i in range(S):
- _convs.append(nn.Conv2d(in_channels//S, in_channels//S, kernel_size=2*(i+1)+1, padding=i+1))
- self.convs = nn.ModuleList(_convs)
-
- self.se_block = SEWeightModule(in_channels//S, reduction=S*reduction)
-
- self.softmax = nn.Softmax(dim=1)
-
- def forward(self, x):
- b, c, h, w = x.size()
-
- # Step1: SPC module
- SPC_out = x.view(b, self.S, c//self.S, h, w) #bs,s,ci,h,w
- for idx, conv in enumerate(self.convs):
- SPC_out[:,idx,:,:,:] = conv(SPC_out[:,idx,:,:,:].clone())
-
- # Step2: SE weight
- se_out=[]
- for idx in range(self.S):
- se_out.append(self.se_block(SPC_out[:, idx, :, :, :]))
- SE_out = torch.stack(se_out, dim=1)
- SE_out = SE_out.expand_as(SPC_out)
-
- # Step3: Softmax
- softmax_out = self.softmax(SE_out)
-
- # Step4: SPA
- PSA_out = SPC_out * softmax_out
- PSA_out = PSA_out.view(b, -1, h, w)
-
- return PSA_out
-
-
-class SGE(nn.Module):
-
- def __init__(self, groups):
- super().__init__()
- self.groups=groups
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
- self.weight=nn.Parameter(torch.zeros(1,groups,1,1))
- self.bias=nn.Parameter(torch.zeros(1,groups,1,1))
- self.sig=nn.Sigmoid()
-
- def forward(self, x):
- b, c, h,w=x.shape
- x=x.view(b*self.groups,-1,h,w) #bs*g,dim//g,h,w
- xn=x*self.avg_pool(x) #bs*g,dim//g,h,w
- xn=xn.sum(dim=1,keepdim=True) #bs*g,1,h,w
- t=xn.view(b*self.groups,-1) #bs*g,h*w
-
- t=t-t.mean(dim=1,keepdim=True) #bs*g,h*w
- std=t.std(dim=1,keepdim=True)+1e-5
- t=t/std #bs*g,h*w
- t=t.view(b,self.groups,h,w) #bs,g,h*w
- 
- t=t*self.weight+self.bias #bs,g,h*w
- t=t.view(b*self.groups,1,h,w) #bs*g,1,h*w
- x=x*self.sig(t)
- x=x.view(b,c,h,w)
-
- return x 
-

models/backbones/__init__.py

@@ -1,6 +0,0 @@
-from os.path import dirname, basename, isfile, join
-import glob
-
-
-modules = glob.glob(join(dirname(__file__), "*.py"))
-__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]

models/build_backbone.py

@@ -1,44 +0,0 @@
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights
-from models.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5
-from models.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l
-from config import Config
-
-
-config = Config()
-
-def build_backbone(bb_name, pretrained=True, params_settings=''):
- if bb_name == 'vgg16':
- bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0]
- bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]}))
- elif bb_name == 'vgg16bn':
- bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0]
- bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]}))
- elif bb_name == 'resnet50':
- bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children())
- bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]}))
- else:
- bb = eval('{}({})'.format(bb_name, params_settings))
- if pretrained:
- bb = load_weights(bb, bb_name)
- return bb
-
-def load_weights(model, model_name):
- save_model = torch.load(config.weights[model_name], map_location='cpu')
- model_dict = model.state_dict()
- state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model.items() if k in model_dict.keys()}
- # to ignore the weights with mismatched size when I modify the backbone itself.
- if not state_dict:
- save_model_keys = list(save_model.keys())
- sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None
- state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model[sub_item].items() if k in model_dict.keys()}
- if not state_dict or not sub_item:
- print('Weights are not successully loaded. Check the state dict of weights file.')
- return None
- else:
- print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item))
- model_dict.update(state_dict)
- model.load_state_dict(model_dict)
- return model

models/decoder_blocks.py

@@ -1,101 +0,0 @@
-import torch
-import torch.nn as nn
-from models.aspp import ASPP, ASPPDeformable
-from models.attentions import PSA, SGE
-from config import Config
-
-
-config = Config()
-
-
-class BasicDecBlk(nn.Module):
- def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
- super(BasicDecBlk, self).__init__()
- inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
- self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
- self.relu_in = nn.ReLU(inplace=True)
- if config.dec_att == 'ASPP':
- self.dec_att = ASPP(in_channels=inter_channels)
- elif config.dec_att == 'ASPPDeformable':
- self.dec_att = ASPPDeformable(in_channels=inter_channels)
- self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
- self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
- self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
-
- def forward(self, x):
- x = self.conv_in(x)
- x = self.bn_in(x)
- x = self.relu_in(x)
- if hasattr(self, 'dec_att'):
- x = self.dec_att(x)
- x = self.conv_out(x)
- x = self.bn_out(x)
- return x
-
-
-class ResBlk(nn.Module):
- def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
- super(ResBlk, self).__init__()
- if out_channels is None:
- out_channels = in_channels
- inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
-
- self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
- self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
- self.relu_in = nn.ReLU(inplace=True)
-
- if config.dec_att == 'ASPP':
- self.dec_att = ASPP(in_channels=inter_channels)
- elif config.dec_att == 'ASPPDeformable':
- self.dec_att = ASPPDeformable(in_channels=inter_channels)
-
- self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
- self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
- 
- self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
-
- def forward(self, x):
- _x = self.conv_resi(x)
- x = self.conv_in(x)
- x = self.bn_in(x)
- x = self.relu_in(x)
- if hasattr(self, 'dec_att'):
- x = self.dec_att(x)
- x = self.conv_out(x)
- x = self.bn_out(x)
- return x + _x
-
-
-class HierarAttDecBlk(nn.Module):
- def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
- super(HierarAttDecBlk, self).__init__()
- if out_channels is None:
- out_channels = in_channels
- inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
- self.split_y = 8 # must be divided by channels of all intermediate features
- self.split_x = 8
-
- self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
-
- self.psa = PSA(inter_channels*self.split_y*self.split_x, S=config.batch_size)
- self.sge = SGE(groups=config.batch_size)
-
- if config.dec_att == 'ASPP':
- self.dec_att = ASPP(in_channels=inter_channels)
- elif config.dec_att == 'ASPPDeformable':
- self.dec_att = ASPPDeformable(in_channels=inter_channels)
- self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
-
- def forward(self, x):
- x = self.conv_in(x)
- N, C, H, W = x.shape
- x_patchs = x.reshape(N, -1, H//self.split_y, W//self.split_x)
-
- # Hierarchical attention: group attention X patch spatial attention
- x_patchs = self.psa(x_patchs) # Group Channel Attention -- each group is a single image
- x_patchs = self.sge(x_patchs) # Patch Spatial Attention
- x = x.reshape(N, C, H, W)
- if hasattr(self, 'dec_att'):
- x = self.dec_att(x)
- x = self.conv_out(x)
- return x

models/deform_conv.py

@@ -1,66 +0,0 @@
-import torch
-import torch.nn as nn
-from torchvision.ops import deform_conv2d
-
-
-class DeformableConv2d(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- bias=False):
-
- super(DeformableConv2d, self).__init__()
- 
- assert type(kernel_size) == tuple or type(kernel_size) == int
-
- kernel_size = kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size)
- self.stride = stride if type(stride) == tuple else (stride, stride)
- self.padding = padding
- 
- self.offset_conv = nn.Conv2d(in_channels,
- 2 * kernel_size[0] * kernel_size[1],
- kernel_size=kernel_size,
- stride=stride,
- padding=self.padding,
- bias=True)
-
- nn.init.constant_(self.offset_conv.weight, 0.)
- nn.init.constant_(self.offset_conv.bias, 0.)
- 
- self.modulator_conv = nn.Conv2d(in_channels,
- 1 * kernel_size[0] * kernel_size[1],
- kernel_size=kernel_size,
- stride=stride,
- padding=self.padding,
- bias=True)
-
- nn.init.constant_(self.modulator_conv.weight, 0.)
- nn.init.constant_(self.modulator_conv.bias, 0.)
-
- self.regular_conv = nn.Conv2d(in_channels,
- out_channels=out_channels,
- kernel_size=kernel_size,
- stride=stride,
- padding=self.padding,
- bias=bias)
-
- def forward(self, x):
- #h, w = x.shape[2:]
- #max_offset = max(h, w)/4.
-
- offset = self.offset_conv(x)#.clamp(-max_offset, max_offset)
- modulator = 2. * torch.sigmoid(self.modulator_conv(x))
- 
- x = deform_conv2d(
- input=x,
- offset=offset,
- weight=self.regular_conv.weight,
- bias=self.regular_conv.bias,
- padding=self.padding,
- mask=modulator,
- stride=self.stride,
- )
- return x

models/ing.py

@@ -1,29 +0,0 @@
-import torch.nn as nn
-from models.mlp import MLPLayer
-
-
-class BlockA(nn.Module):
- def __init__(self, in_channels=64, out_channels=64, inter_channels=64, mlp_ratio=4.):
- super(BlockA, self).__init__()
- inter_channels = in_channels
- self.conv = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
- self.norm1 = nn.LayerNorm(inter_channels)
- self.ffn = MLPLayer(in_features=inter_channels,
- hidden_features=int(inter_channels * mlp_ratio),
- act_layer=nn.GELU,
- drop=0.)
- self.norm2 = nn.LayerNorm(inter_channels)
-
- def forward(self, x):
- B, C, H, W = x.shape
- _x = self.conv(x)
- _x = _x.flatten(2).transpose(1, 2)
- _x = self.norm1(_x)
- x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-
- x = x + _x
- _x1 = self.ffn(x)
- _x1 = self.norm2(_x1)
- _x1 = _x1.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
- x = x + _x1
- return x

models/lateral_blocks.py

@@ -1,21 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from functools import partial
-
-from config import Config
-
-
-config = Config()
-
-
-class BasicLatBlk(nn.Module):
- def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
- super(BasicLatBlk, self).__init__()
- inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
- self.conv = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
-
- def forward(self, x):
- x = self.conv(x)
- return x

models/mlp.py

@@ -1,118 +0,0 @@
-import torch
-import torch.nn as nn
-from functools import partial
-
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
-
-import math
-
-
-class MLPLayer(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-class Attention(nn.Module):
- def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
- super().__init__()
- assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
-
- self.dim = dim
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- self.q = nn.Linear(dim, dim, bias=qkv_bias)
- self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- self.sr_ratio = sr_ratio
- if sr_ratio > 1:
- self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
- self.norm = nn.LayerNorm(dim)
-
- def forward(self, x, H, W):
- B, N, C = x.shape
- q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
- if self.sr_ratio > 1:
- x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
- x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
- x_ = self.norm(x_)
- kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- else:
- kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- k, v = kv[0], kv[1]
-
- attn = (q @ k.transpose(-2, -1)) * self.scale
- attn = attn.softmax(dim=-1)
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
-class Block(nn.Module):
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
- attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
- # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = MLPLayer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- def forward(self, x, H, W):
- x = x + self.drop_path(self.attn(self.norm1(x), H, W))
- x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
- return x
-
-
-class OverlapPatchEmbed(nn.Module):
- """ Image to Patch Embedding
- """
-
- def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
-
- self.img_size = img_size
- self.patch_size = patch_size
- self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
- self.num_patches = self.H * self.W
- self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
- padding=(patch_size[0] // 2, patch_size[1] // 2))
- self.norm = nn.LayerNorm(embed_dim)
-
- def forward(self, x):
- x = self.proj(x)
- _, _, H, W = x.shape
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- return x, H, W
-

models/modules/__init__.py

@@ -1,6 +0,0 @@
-from os.path import dirname, basename, isfile, join
-import glob
-
-
-modules = glob.glob(join(dirname(__file__), "*.py"))
-__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]

models/modules/refinement/__init__.py

@@ -1,6 +0,0 @@
-from os.path import dirname, basename, isfile, join
-import glob
-
-
-modules = glob.glob(join(dirname(__file__), "*.py"))
-__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]

models/modules/refinement/refiner.py

@@ -1,253 +0,0 @@
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.models import vgg16, vgg16_bn
-from torchvision.models import resnet50
-
-from config import Config
-from dataset import class_labels_TR_sorted
-from models.build_backbone import build_backbone
-from models.decoder_blocks import BasicDecBlk
-from models.lateral_blocks import BasicLatBlk
-from models.ing import *
-from models.stem_layer import StemLayer
-
-
-class RefinerPVTInChannels4(nn.Module):
- def __init__(self, in_channels=3+1):
- super(RefinerPVTInChannels4, self).__init__()
- self.config = Config()
- self.epoch = 1
- self.bb = build_backbone(self.config.bb, params_settings='in_channels=4')
-
- lateral_channels_in_collection = {
- 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
- 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
- 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
- }
- channels = lateral_channels_in_collection[self.config.bb]
- self.squeeze_module = BasicDecBlk(channels[0], channels[0])
-
- self.decoder = Decoder(channels)
-
- if 0:
- for key, value in self.named_parameters():
- if 'bb.' in key:
- value.requires_grad = False
-
- def forward(self, x):
- if isinstance(x, list):
- x = torch.cat(x, dim=1)
- ########## Encoder ##########
- if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
- x1 = self.bb.conv1(x)
- x2 = self.bb.conv2(x1)
- x3 = self.bb.conv3(x2)
- x4 = self.bb.conv4(x3)
- else:
- x1, x2, x3, x4 = self.bb(x)
-
- x4 = self.squeeze_module(x4)
-
- ########## Decoder ##########
-
- features = [x, x1, x2, x3, x4]
- scaled_preds = self.decoder(features)
-
- return scaled_preds
-
-
-class Refiner(nn.Module):
- def __init__(self, in_channels=3+1):
- super(Refiner, self).__init__()
- self.config = Config()
- self.epoch = 1
- self.stem_layer = StemLayer(in_channels=in_channels, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
- self.bb = build_backbone(self.config.bb)
-
- lateral_channels_in_collection = {
- 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
- 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
- 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
- }
- channels = lateral_channels_in_collection[self.config.bb]
- self.squeeze_module = BasicDecBlk(channels[0], channels[0])
-
- self.decoder = Decoder(channels)
-
- if 0:
- for key, value in self.named_parameters():
- if 'bb.' in key:
- value.requires_grad = False
-
- def forward(self, x):
- if isinstance(x, list):
- x = torch.cat(x, dim=1)
- x = self.stem_layer(x)
- ########## Encoder ##########
- if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
- x1 = self.bb.conv1(x)
- x2 = self.bb.conv2(x1)
- x3 = self.bb.conv3(x2)
- x4 = self.bb.conv4(x3)
- else:
- x1, x2, x3, x4 = self.bb(x)
-
- x4 = self.squeeze_module(x4)
-
- ########## Decoder ##########
-
- features = [x, x1, x2, x3, x4]
- scaled_preds = self.decoder(features)
-
- return scaled_preds
-
-
-class Decoder(nn.Module):
- def __init__(self, channels):
- super(Decoder, self).__init__()
- self.config = Config()
- DecoderBlock = eval('BasicDecBlk')
- LateralBlock = eval('BasicLatBlk')
-
- self.decoder_block4 = DecoderBlock(channels[0], channels[1])
- self.decoder_block3 = DecoderBlock(channels[1], channels[2])
- self.decoder_block2 = DecoderBlock(channels[2], channels[3])
- self.decoder_block1 = DecoderBlock(channels[3], channels[3]//2)
-
- self.lateral_block4 = LateralBlock(channels[1], channels[1])
- self.lateral_block3 = LateralBlock(channels[2], channels[2])
- self.lateral_block2 = LateralBlock(channels[3], channels[3])
-
- if self.config.ms_supervision:
- self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
- self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
- self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
- self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2, 1, 1, 1, 0))
-
- def forward(self, features):
- x, x1, x2, x3, x4 = features
- outs = []
- p4 = self.decoder_block4(x4)
- _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
- _p3 = _p4 + self.lateral_block4(x3)
-
- p3 = self.decoder_block3(_p3)
- _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
- _p2 = _p3 + self.lateral_block3(x2)
-
- p2 = self.decoder_block2(_p2)
- _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
- _p1 = _p2 + self.lateral_block2(x1)
-
- _p1 = self.decoder_block1(_p1)
- _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
- p1_out = self.conv_out1(_p1)
-
- if self.config.ms_supervision:
- outs.append(self.conv_ms_spvn_4(p4))
- outs.append(self.conv_ms_spvn_3(p3))
- outs.append(self.conv_ms_spvn_2(p2))
- outs.append(p1_out)
- return outs
-
-
-class RefUNet(nn.Module):
- # Refinement
- def __init__(self, in_channels=3+1):
- super(RefUNet, self).__init__()
- self.encoder_1 = nn.Sequential(
- nn.Conv2d(in_channels, 64, 3, 1, 1),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.encoder_2 = nn.Sequential(
- nn.MaxPool2d(2, 2, ceil_mode=True),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.encoder_3 = nn.Sequential(
- nn.MaxPool2d(2, 2, ceil_mode=True),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.encoder_4 = nn.Sequential(
- nn.MaxPool2d(2, 2, ceil_mode=True),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)
- #####
- self.decoder_5 = nn.Sequential(
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
- #####
- self.decoder_4 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.decoder_3 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.decoder_2 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.decoder_1 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1)
-
- self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
-
- def forward(self, x):
- outs = []
- if isinstance(x, list):
- x = torch.cat(x, dim=1)
- hx = x
-
- hx1 = self.encoder_1(hx)
- hx2 = self.encoder_2(hx1)
- hx3 = self.encoder_3(hx2)
- hx4 = self.encoder_4(hx3)
-
- hx = self.decoder_5(self.pool4(hx4))
- hx = torch.cat((self.upscore2(hx), hx4), 1)
-
- d4 = self.decoder_4(hx)
- hx = torch.cat((self.upscore2(d4), hx3), 1)
-
- d3 = self.decoder_3(hx)
- hx = torch.cat((self.upscore2(d3), hx2), 1)
-
- d2 = self.decoder_2(hx)
- hx = torch.cat((self.upscore2(d2), hx1), 1)
-
- d1 = self.decoder_1(hx)
-
- x = self.conv_d0(d1)
- outs.append(x)
- return outs

models/modules/refinement/stem_layer.py

@@ -1,45 +0,0 @@
-import torch.nn as nn
-from models.utils import build_act_layer, build_norm_layer
-
-
-class StemLayer(nn.Module):
- r""" Stem layer of InternImage
- Args:
- in_channels (int): number of input channels
- out_channels (int): number of output channels
- act_layer (str): activation layer
- norm_layer (str): normalization layer
- """
-
- def __init__(self,
- in_channels=3+1,
- inter_channels=48,
- out_channels=96,
- act_layer='GELU',
- norm_layer='BN'):
- super().__init__()
- self.conv1 = nn.Conv2d(in_channels,
- inter_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.norm1 = build_norm_layer(
- inter_channels, norm_layer, 'channels_first', 'channels_first'
- )
- self.act = build_act_layer(act_layer)
- self.conv2 = nn.Conv2d(inter_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.norm2 = build_norm_layer(
- out_channels, norm_layer, 'channels_first', 'channels_first'
- )
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.norm1(x)
- x = self.act(x)
- x = self.conv2(x)
- x = self.norm2(x)
- return x

models/prompt_encoder.py

@@ -1,222 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from typing import Any, Optional, Tuple, Type
-
-
-class PromptEncoder(nn.Module):
- def __init__(
- self,
- embed_dim=256,
- image_embedding_size=1024,
- input_image_size=(1024, 1024),
- mask_in_chans=16,
- activation=nn.GELU
- ) -> None:
- super().__init__()
- """
- Codes are partially from SAM: https://github.com/facebookresearch/segment-anything/blob/6fdee8f2727f4506cfbbe553e23b895e27956588/segment_anything/modeling/prompt_encoder.py.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- """
- super().__init__()
- self.embed_dim = embed_dim
- self.input_image_size = input_image_size
- self.image_embedding_size = image_embedding_size
- self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
-
- self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
- point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
- self.point_embeddings = nn.ModuleList(point_embeddings)
- self.not_a_point_embed = nn.Embedding(1, embed_dim)
-
- self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
- self.mask_downscaling = nn.Sequential(
- nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans // 4),
- activation(),
- nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans),
- activation(),
- nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
- )
- self.no_mask_embed = nn.Embedding(1, embed_dim)
-
- def get_dense_pe(self) -> torch.Tensor:
- """
- Returns the positional encoding used to encode point prompts,
- applied to a dense set of points the shape of the image encoding.
-
- Returns:
- torch.Tensor: Positional encoding with shape
- 1x(embed_dim)x(embedding_h)x(embedding_w)
- """
- return self.pe_layer(self.image_embedding_size).unsqueeze(0)
-
- def _embed_points(
- self,
- points: torch.Tensor,
- labels: torch.Tensor,
- pad: bool,
- ) -> torch.Tensor:
- """Embeds point prompts."""
- points = points + 0.5 # Shift to center of pixel
- if pad:
- padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
- padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
- points = torch.cat([points, padding_point], dim=1)
- labels = torch.cat([labels, padding_label], dim=1)
- point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
- point_embedding[labels == -1] = 0.0
- point_embedding[labels == -1] += self.not_a_point_embed.weight
- point_embedding[labels == 0] += self.point_embeddings[0].weight
- point_embedding[labels == 1] += self.point_embeddings[1].weight
- return point_embedding
-
- def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
- """Embeds box prompts."""
- boxes = boxes + 0.5 # Shift to center of pixel
- coords = boxes.reshape(-1, 2, 2)
- corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
- corner_embedding[:, 0, :] += self.point_embeddings[2].weight
- corner_embedding[:, 1, :] += self.point_embeddings[3].weight
- return corner_embedding
-
- def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
- """Embeds mask inputs."""
- mask_embedding = self.mask_downscaling(masks)
- return mask_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- else:
- return 1
-
- def _get_device(self) -> torch.device:
- return self.point_embeddings[0].weight.device
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks)
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
-
-
-class PositionEmbeddingRandom(nn.Module):
- """
- Positional encoding using random spatial frequencies.
- """
-
- def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
- super().__init__()
- if scale is None or scale <= 0.0:
- scale = 1.0
- self.register_buffer(
- "positional_encoding_gaussian_matrix",
- scale * torch.randn((2, num_pos_feats)),
- )
-
- def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
- """Positionally encode points that are normalized to [0,1]."""
- # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
- coords = 2 * coords - 1
- coords = coords @ self.positional_encoding_gaussian_matrix
- coords = 2 * np.pi * coords
- # outputs d_1 x ... x d_n x C shape
- return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
-
- def forward(self, size: Tuple[int, int]) -> torch.Tensor:
- """Generate positional encoding for a grid of the specified size."""
- h, w = size
- device: Any = self.positional_encoding_gaussian_matrix.device
- grid = torch.ones((h, w), device=device, dtype=torch.float32)
- y_embed = grid.cumsum(dim=0) - 0.5
- x_embed = grid.cumsum(dim=1) - 0.5
- y_embed = y_embed / h
- x_embed = x_embed / w
-
- pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
- return pe.permute(2, 0, 1) # C x H x W
-
- def forward_with_coords(
- self, coords_input: torch.Tensor, image_size: Tuple[int, int]
- ) -> torch.Tensor:
- """Positionally encode points that are not normalized to [0,1]."""
- coords = coords_input.clone()
- coords[:, :, 0] = coords[:, :, 0] / image_size[1]
- coords[:, :, 1] = coords[:, :, 1] / image_size[0]
- return self._pe_encoding(coords.to(torch.float)) # B x N x C
-
-
-class LayerNorm2d(nn.Module):
- def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
- super().__init__()
- self.weight = nn.Parameter(torch.ones(num_channels))
- self.bias = nn.Parameter(torch.zeros(num_channels))
- self.eps = eps
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- u = x.mean(1, keepdim=True)
- s = (x - u).pow(2).mean(1, keepdim=True)
- x = (x - u) / torch.sqrt(s + self.eps)
- x = self.weight[:, None, None] * x + self.bias[:, None, None]
- return x
- 

models/pvt_v2.py

@@ -1,435 +0,0 @@
-import torch
-import torch.nn as nn
-from functools import partial
-
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
-
-import math
-
-from config import Config
-
-config = Config()
-
-class Mlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.dwconv = DWConv(hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
- elif isinstance(m, nn.Conv2d):
- fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- fan_out //= m.groups
- m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
- if m.bias is not None:
- m.bias.data.zero_()
-
- def forward(self, x, H, W):
- x = self.fc1(x)
- x = self.dwconv(x, H, W)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-class Attention(nn.Module):
- def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
- super().__init__()
- assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
-
- self.dim = dim
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- self.q = nn.Linear(dim, dim, bias=qkv_bias)
- self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
- self.attn_drop_prob = attn_drop
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- self.sr_ratio = sr_ratio
- if sr_ratio > 1:
- self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
- self.norm = nn.LayerNorm(dim)
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
- elif isinstance(m, nn.Conv2d):
- fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- fan_out //= m.groups
- m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
- if m.bias is not None:
- m.bias.data.zero_()
-
- def forward(self, x, H, W):
- B, N, C = x.shape
- q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
- if self.sr_ratio > 1:
- x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
- x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
- x_ = self.norm(x_)
- kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- else:
- kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- k, v = kv[0], kv[1]
-
- if config.SDPA_enabled:
- x = torch.nn.functional.scaled_dot_product_attention(
- q, k, v,
- attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
- ).transpose(1, 2).reshape(B, N, C)
- else:
- attn = (q @ k.transpose(-2, -1)) * self.scale
- attn = attn.softmax(dim=-1)
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
-
- return x
-
-
-class Block(nn.Module):
-
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
- attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
- # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
- elif isinstance(m, nn.Conv2d):
- fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- fan_out //= m.groups
- m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
- if m.bias is not None:
- m.bias.data.zero_()
-
- def forward(self, x, H, W):
- x = x + self.drop_path(self.attn(self.norm1(x), H, W))
- x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
-
- return x
-
-
-class OverlapPatchEmbed(nn.Module):
- """ Image to Patch Embedding
- """
-
- def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
-
- self.img_size = img_size
- self.patch_size = patch_size
- self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
- self.num_patches = self.H * self.W
- self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
- padding=(patch_size[0] // 2, patch_size[1] // 2))
- self.norm = nn.LayerNorm(embed_dim)
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
- elif isinstance(m, nn.Conv2d):
- fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- fan_out //= m.groups
- m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
- if m.bias is not None:
- m.bias.data.zero_()
-
- def forward(self, x):
- x = self.proj(x)
- _, _, H, W = x.shape
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
-
- return x, H, W
-
-
-class PyramidVisionTransformerImpr(nn.Module):
- def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
- num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
- attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
- depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
- super().__init__()
- self.num_classes = num_classes
- self.depths = depths
-
- # patch_embed
- self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels,
- embed_dim=embed_dims[0])
- self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0],
- embed_dim=embed_dims[1])
- self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1],
- embed_dim=embed_dims[2])
- self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2],
- embed_dim=embed_dims[3])
-
- # transformer encoder
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
- cur = 0
- self.block1 = nn.ModuleList([Block(
- dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
- sr_ratio=sr_ratios[0])
- for i in range(depths[0])])
- self.norm1 = norm_layer(embed_dims[0])
-
- cur += depths[0]
- self.block2 = nn.ModuleList([Block(
- dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
- sr_ratio=sr_ratios[1])
- for i in range(depths[1])])
- self.norm2 = norm_layer(embed_dims[1])
-
- cur += depths[1]
- self.block3 = nn.ModuleList([Block(
- dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
- sr_ratio=sr_ratios[2])
- for i in range(depths[2])])
- self.norm3 = norm_layer(embed_dims[2])
-
- cur += depths[2]
- self.block4 = nn.ModuleList([Block(
- dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
- sr_ratio=sr_ratios[3])
- for i in range(depths[3])])
- self.norm4 = norm_layer(embed_dims[3])
-
- # classification head
- # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
- elif isinstance(m, nn.Conv2d):
- fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- fan_out //= m.groups
- m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
- if m.bias is not None:
- m.bias.data.zero_()
-
- def init_weights(self, pretrained=None):
- if isinstance(pretrained, str):
- logger = 1
- #load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
-
- def reset_drop_path(self, drop_path_rate):
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
- cur = 0
- for i in range(self.depths[0]):
- self.block1[i].drop_path.drop_prob = dpr[cur + i]
-
- cur += self.depths[0]
- for i in range(self.depths[1]):
- self.block2[i].drop_path.drop_prob = dpr[cur + i]
-
- cur += self.depths[1]
- for i in range(self.depths[2]):
- self.block3[i].drop_path.drop_prob = dpr[cur + i]
-
- cur += self.depths[2]
- for i in range(self.depths[3]):
- self.block4[i].drop_path.drop_prob = dpr[cur + i]
-
- def freeze_patch_emb(self):
- self.patch_embed1.requires_grad = False
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
-
- def get_classifier(self):
- return self.head
-
- def reset_classifier(self, num_classes, global_pool=''):
- self.num_classes = num_classes
- self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
- def forward_features(self, x):
- B = x.shape[0]
- outs = []
-
- # stage 1
- x, H, W = self.patch_embed1(x)
- for i, blk in enumerate(self.block1):
- x = blk(x, H, W)
- x = self.norm1(x)
- x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
- outs.append(x)
-
- # stage 2
- x, H, W = self.patch_embed2(x)
- for i, blk in enumerate(self.block2):
- x = blk(x, H, W)
- x = self.norm2(x)
- x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
- outs.append(x)
-
- # stage 3
- x, H, W = self.patch_embed3(x)
- for i, blk in enumerate(self.block3):
- x = blk(x, H, W)
- x = self.norm3(x)
- x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
- outs.append(x)
-
- # stage 4
- x, H, W = self.patch_embed4(x)
- for i, blk in enumerate(self.block4):
- x = blk(x, H, W)
- x = self.norm4(x)
- x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
- outs.append(x)
-
- return outs
-
- # return x.mean(dim=1)
-
- def forward(self, x):
- x = self.forward_features(x)
- # x = self.head(x)
-
- return x
-
-
-class DWConv(nn.Module):
- def __init__(self, dim=768):
- super(DWConv, self).__init__()
- self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
-
- def forward(self, x, H, W):
- B, N, C = x.shape
- x = x.transpose(1, 2).view(B, C, H, W).contiguous()
- x = self.dwconv(x)
- x = x.flatten(2).transpose(1, 2)
-
- return x
-
-
-def _conv_filter(state_dict, patch_size=16):
- """ convert patch embedding weight from manual patchify + linear proj to conv"""
- out_dict = {}
- for k, v in state_dict.items():
- if 'patch_embed.proj.weight' in k:
- v = v.reshape((v.shape[0], 3, patch_size, patch_size))
- out_dict[k] = v
-
- return out_dict
-
-
-## @register_model
-class pvt_v2_b0(PyramidVisionTransformerImpr):
- def __init__(self, **kwargs):
- super(pvt_v2_b0, self).__init__(
- patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
- drop_rate=0.0, drop_path_rate=0.1)
-
-
-
-## @register_model
-class pvt_v2_b1(PyramidVisionTransformerImpr):
- def __init__(self, **kwargs):
- super(pvt_v2_b1, self).__init__(
- patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
- drop_rate=0.0, drop_path_rate=0.1)
-
-## @register_model
-class pvt_v2_b2(PyramidVisionTransformerImpr):
- def __init__(self, in_channels=3, **kwargs):
- super(pvt_v2_b2, self).__init__(
- patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
- drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels)
-
-## @register_model
-class pvt_v2_b3(PyramidVisionTransformerImpr):
- def __init__(self, **kwargs):
- super(pvt_v2_b3, self).__init__(
- patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
- drop_rate=0.0, drop_path_rate=0.1)
-
-## @register_model
-class pvt_v2_b4(PyramidVisionTransformerImpr):
- def __init__(self, **kwargs):
- super(pvt_v2_b4, self).__init__(
- patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
- drop_rate=0.0, drop_path_rate=0.1)
-
-
-## @register_model
-class pvt_v2_b5(PyramidVisionTransformerImpr):
- def __init__(self, **kwargs):
- super(pvt_v2_b5, self).__init__(
- patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
- qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
- drop_rate=0.0, drop_path_rate=0.1)

models/refinement/__init__.py

@@ -1,6 +0,0 @@
-from os.path import dirname, basename, isfile, join
-import glob
-
-
-modules = glob.glob(join(dirname(__file__), "*.py"))
-__all__ = [ basename(f)[:-3] for f in modules if isfile(f) and not f.endswith('__init__.py')]

models/refiner.py

@@ -1,253 +0,0 @@
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.models import vgg16, vgg16_bn
-from torchvision.models import resnet50
-
-from config import Config
-from dataset import class_labels_TR_sorted
-from models.build_backbone import build_backbone
-from models.decoder_blocks import BasicDecBlk
-from models.lateral_blocks import BasicLatBlk
-from models.ing import *
-from models.stem_layer import StemLayer
-
-
-class RefinerPVTInChannels4(nn.Module):
- def __init__(self, in_channels=3+1):
- super(RefinerPVTInChannels4, self).__init__()
- self.config = Config()
- self.epoch = 1
- self.bb = build_backbone(self.config.bb, params_settings='in_channels=4')
-
- lateral_channels_in_collection = {
- 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
- 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
- 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
- }
- channels = lateral_channels_in_collection[self.config.bb]
- self.squeeze_module = BasicDecBlk(channels[0], channels[0])
-
- self.decoder = Decoder(channels)
-
- if 0:
- for key, value in self.named_parameters():
- if 'bb.' in key:
- value.requires_grad = False
-
- def forward(self, x):
- if isinstance(x, list):
- x = torch.cat(x, dim=1)
- ########## Encoder ##########
- if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
- x1 = self.bb.conv1(x)
- x2 = self.bb.conv2(x1)
- x3 = self.bb.conv3(x2)
- x4 = self.bb.conv4(x3)
- else:
- x1, x2, x3, x4 = self.bb(x)
-
- x4 = self.squeeze_module(x4)
-
- ########## Decoder ##########
-
- features = [x, x1, x2, x3, x4]
- scaled_preds = self.decoder(features)
-
- return scaled_preds
-
-
-class Refiner(nn.Module):
- def __init__(self, in_channels=3+1):
- super(Refiner, self).__init__()
- self.config = Config()
- self.epoch = 1
- self.stem_layer = StemLayer(in_channels=in_channels, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
- self.bb = build_backbone(self.config.bb)
-
- lateral_channels_in_collection = {
- 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
- 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
- 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
- }
- channels = lateral_channels_in_collection[self.config.bb]
- self.squeeze_module = BasicDecBlk(channels[0], channels[0])
-
- self.decoder = Decoder(channels)
-
- if 0:
- for key, value in self.named_parameters():
- if 'bb.' in key:
- value.requires_grad = False
-
- def forward(self, x):
- if isinstance(x, list):
- x = torch.cat(x, dim=1)
- x = self.stem_layer(x)
- ########## Encoder ##########
- if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
- x1 = self.bb.conv1(x)
- x2 = self.bb.conv2(x1)
- x3 = self.bb.conv3(x2)
- x4 = self.bb.conv4(x3)
- else:
- x1, x2, x3, x4 = self.bb(x)
-
- x4 = self.squeeze_module(x4)
-
- ########## Decoder ##########
-
- features = [x, x1, x2, x3, x4]
- scaled_preds = self.decoder(features)
-
- return scaled_preds
-
-
-class Decoder(nn.Module):
- def __init__(self, channels):
- super(Decoder, self).__init__()
- self.config = Config()
- DecoderBlock = eval('BasicDecBlk')
- LateralBlock = eval('BasicLatBlk')
-
- self.decoder_block4 = DecoderBlock(channels[0], channels[1])
- self.decoder_block3 = DecoderBlock(channels[1], channels[2])
- self.decoder_block2 = DecoderBlock(channels[2], channels[3])
- self.decoder_block1 = DecoderBlock(channels[3], channels[3]//2)
-
- self.lateral_block4 = LateralBlock(channels[1], channels[1])
- self.lateral_block3 = LateralBlock(channels[2], channels[2])
- self.lateral_block2 = LateralBlock(channels[3], channels[3])
-
- if self.config.ms_supervision:
- self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
- self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
- self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
- self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2, 1, 1, 1, 0))
-
- def forward(self, features):
- x, x1, x2, x3, x4 = features
- outs = []
- p4 = self.decoder_block4(x4)
- _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
- _p3 = _p4 + self.lateral_block4(x3)
-
- p3 = self.decoder_block3(_p3)
- _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
- _p2 = _p3 + self.lateral_block3(x2)
-
- p2 = self.decoder_block2(_p2)
- _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
- _p1 = _p2 + self.lateral_block2(x1)
-
- _p1 = self.decoder_block1(_p1)
- _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
- p1_out = self.conv_out1(_p1)
-
- if self.config.ms_supervision:
- outs.append(self.conv_ms_spvn_4(p4))
- outs.append(self.conv_ms_spvn_3(p3))
- outs.append(self.conv_ms_spvn_2(p2))
- outs.append(p1_out)
- return outs
-
-
-class RefUNet(nn.Module):
- # Refinement
- def __init__(self, in_channels=3+1):
- super(RefUNet, self).__init__()
- self.encoder_1 = nn.Sequential(
- nn.Conv2d(in_channels, 64, 3, 1, 1),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.encoder_2 = nn.Sequential(
- nn.MaxPool2d(2, 2, ceil_mode=True),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.encoder_3 = nn.Sequential(
- nn.MaxPool2d(2, 2, ceil_mode=True),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.encoder_4 = nn.Sequential(
- nn.MaxPool2d(2, 2, ceil_mode=True),
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)
- #####
- self.decoder_5 = nn.Sequential(
- nn.Conv2d(64, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
- #####
- self.decoder_4 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.decoder_3 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.decoder_2 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.decoder_1 = nn.Sequential(
- nn.Conv2d(128, 64, 3, 1, 1),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True)
- )
-
- self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1)
-
- self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
-
- def forward(self, x):
- outs = []
- if isinstance(x, list):
- x = torch.cat(x, dim=1)
- hx = x
-
- hx1 = self.encoder_1(hx)
- hx2 = self.encoder_2(hx1)
- hx3 = self.encoder_3(hx2)
- hx4 = self.encoder_4(hx3)
-
- hx = self.decoder_5(self.pool4(hx4))
- hx = torch.cat((self.upscore2(hx), hx4), 1)
-
- d4 = self.decoder_4(hx)
- hx = torch.cat((self.upscore2(d4), hx3), 1)
-
- d3 = self.decoder_3(hx)
- hx = torch.cat((self.upscore2(d3), hx2), 1)
-
- d2 = self.decoder_2(hx)
- hx = torch.cat((self.upscore2(d2), hx1), 1)
-
- d1 = self.decoder_1(hx)
-
- x = self.conv_d0(d1)
- outs.append(x)
- return outs

models/stem_layer.py

@@ -1,45 +0,0 @@
-import torch.nn as nn
-from models.utils import build_act_layer, build_norm_layer
-
-
-class StemLayer(nn.Module):
- r""" Stem layer of InternImage
- Args:
- in_channels (int): number of input channels
- out_channels (int): number of output channels
- act_layer (str): activation layer
- norm_layer (str): normalization layer
- """
-
- def __init__(self,
- in_channels=3+1,
- inter_channels=48,
- out_channels=96,
- act_layer='GELU',
- norm_layer='BN'):
- super().__init__()
- self.conv1 = nn.Conv2d(in_channels,
- inter_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.norm1 = build_norm_layer(
- inter_channels, norm_layer, 'channels_first', 'channels_first'
- )
- self.act = build_act_layer(act_layer)
- self.conv2 = nn.Conv2d(inter_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.norm2 = build_norm_layer(
- out_channels, norm_layer, 'channels_first', 'channels_first'
- )
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.norm1(x)
- x = self.act(x)
- x = self.conv2(x)
- x = self.norm2(x)
- return x

models/swin_v1.py

@@ -1,627 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu, Yutong Lin, Yixuan Wei
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from config import Config
-
-
-config = Config()
-
-class Mlp(nn.Module):
- """ Multilayer perceptron."""
-
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
-
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
-
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class WindowAttention(nn.Module):
- """ Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
-
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop_prob = attn_drop
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- trunc_normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """ Forward function.
-
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
-
- if config.SDPA_enabled:
- x = torch.nn.functional.scaled_dot_product_attention(
- q, k, v,
- attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
- ).transpose(1, 2).reshape(B_, N, C)
- else:
- attn = (q @ k.transpose(-2, -1))
-
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
-class SwinTransformerBlock(nn.Module):
- """ Swin Transformer Block.
-
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- self.H = None
- self.W = None
-
- def forward(self, x, mask_matrix):
- """ Forward function.
-
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- mask_matrix: Attention mask for cyclic shift.
- """
- B, L, C = x.shape
- H, W = self.H, self.W
- assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
-
- # pad feature maps to multiples of window size
- pad_l = pad_t = 0
- pad_r = (self.window_size - W % self.window_size) % self.window_size
- pad_b = (self.window_size - H % self.window_size) % self.window_size
- x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
- _, Hp, Wp, _ = x.shape
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- attn_mask = mask_matrix
- else:
- shifted_x = x
- attn_mask = None
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
-
- if pad_r > 0 or pad_b > 0:
- x = x[:, :H, :W, :].contiguous()
-
- x = x.view(B, H * W, C)
-
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
-
- return x
-
-
-class PatchMerging(nn.Module):
- """ Patch Merging Layer
-
- Args:
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
- def __init__(self, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
-
- def forward(self, x, H, W):
- """ Forward function.
-
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
-
- x = x.view(B, H, W, C)
-
- # padding
- pad_input = (H % 2 == 1) or (W % 2 == 1)
- if pad_input:
- x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.norm(x)
- x = self.reduction(x)
-
- return x
-
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
-
- Args:
- dim (int): Number of feature channels
- depth (int): Depths of this stage.
- num_heads (int): Number of attention head.
- window_size (int): Local window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- dim,
- depth,
- num_heads,
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop=0.,
- attn_drop=0.,
- drop_path=0.,
- norm_layer=nn.LayerNorm,
- downsample=None,
- use_checkpoint=False):
- super().__init__()
- self.window_size = window_size
- self.shift_size = window_size // 2
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(
- dim=dim,
- num_heads=num_heads,
- window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop,
- attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, H, W):
- """ Forward function.
-
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
-
- # calculate attention mask for SW-MSA
- Hp = int(np.ceil(H / self.window_size)) * self.window_size
- Wp = int(np.ceil(W / self.window_size)) * self.window_size
- img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- for blk in self.blocks:
- blk.H, blk.W = H, W
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, attn_mask)
- else:
- x = blk(x, attn_mask)
- if self.downsample is not None:
- x_down = self.downsample(x, H, W)
- Wh, Ww = (H + 1) // 2, (W + 1) // 2
- return x, H, W, x_down, Wh, Ww
- else:
- return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
- """ Image to Patch Embedding
-
- Args:
- patch_size (int): Patch token size. Default: 4.
- in_channels (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None):
- super().__init__()
- patch_size = to_2tuple(patch_size)
- self.patch_size = patch_size
-
- self.in_channels = in_channels
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- """Forward function."""
- # padding
- _, _, H, W = x.size()
- if W % self.patch_size[1] != 0:
- x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
- if H % self.patch_size[0] != 0:
- x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
- x = self.proj(x) # B C Wh Ww
- if self.norm is not None:
- Wh, Ww = x.size(2), x.size(3)
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
- return x
-
-
-class SwinTransformer(nn.Module):
- """ Swin Transformer backbone.
- A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
- https://arxiv.org/pdf/2103.14030
-
- Args:
- pretrain_img_size (int): Input image size for training the pretrained model,
- used in absolute postion embedding. Default 224.
- patch_size (int | tuple(int)): Patch size. Default: 4.
- in_channels (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- depths (tuple[int]): Depths of each Swin Transformer stage.
- num_heads (tuple[int]): Number of attention head of each stage.
- window_size (int): Window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
- drop_rate (float): Dropout rate.
- attn_drop_rate (float): Attention dropout rate. Default: 0.
- drop_path_rate (float): Stochastic depth rate. Default: 0.2.
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
- patch_norm (bool): If True, add normalization after patch embedding. Default: True.
- out_indices (Sequence[int]): Output from which stages.
- frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
- -1 means not freezing any parameters.
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- pretrain_img_size=224,
- patch_size=4,
- in_channels=3,
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.2,
- norm_layer=nn.LayerNorm,
- ape=False,
- patch_norm=True,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- use_checkpoint=False):
- super().__init__()
-
- self.pretrain_img_size = pretrain_img_size
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.out_indices = out_indices
- self.frozen_stages = frozen_stages
-
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
-
- # absolute position embedding
- if self.ape:
- pretrain_img_size = to_2tuple(pretrain_img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
-
- # build layers
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = BasicLayer(
- dim=int(embed_dim * 2 ** i_layer),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop_rate,
- attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
- norm_layer=norm_layer,
- downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
- use_checkpoint=use_checkpoint)
- self.layers.append(layer)
-
- num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
- self.num_features = num_features
-
- # add a norm layer for each output
- for i_layer in out_indices:
- layer = norm_layer(num_features[i_layer])
- layer_name = f'norm{i_layer}'
- self.add_module(layer_name, layer)
-
- self._freeze_stages()
-
- def _freeze_stages(self):
- if self.frozen_stages >= 0:
- self.patch_embed.eval()
- for param in self.patch_embed.parameters():
- param.requires_grad = False
-
- if self.frozen_stages >= 1 and self.ape:
- self.absolute_pos_embed.requires_grad = False
-
- if self.frozen_stages >= 2:
- self.pos_drop.eval()
- for i in range(0, self.frozen_stages - 1):
- m = self.layers[i]
- m.eval()
- for param in m.parameters():
- param.requires_grad = False
-
-
- def forward(self, x):
- """Forward function."""
- x = self.patch_embed(x)
-
- Wh, Ww = x.size(2), x.size(3)
- if self.ape:
- # interpolate the position embedding to the corresponding size
- absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
- x = (x + absolute_pos_embed) # B Wh*Ww C
- 
- outs = []#x.contiguous()]
- x = x.flatten(2).transpose(1, 2)
- x = self.pos_drop(x)
- for i in range(self.num_layers):
- layer = self.layers[i]
- x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
- if i in self.out_indices:
- norm_layer = getattr(self, f'norm{i}')
- x_out = norm_layer(x_out)
-
- out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
- outs.append(out)
-
- return tuple(outs)
-
- def train(self, mode=True):
- """Convert the model into training mode while keep layers freezed."""
- super(SwinTransformer, self).train(mode)
- self._freeze_stages()
-
-def swin_v1_t():
- model = SwinTransformer(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7)
- return model
-
-def swin_v1_s():
- model = SwinTransformer(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=7)
- return model
-
-def swin_v1_b():
- model = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12)
- return model
-
-def swin_v1_l():
- model = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12)
- return model

models/utils.py

@@ -1,54 +0,0 @@
-import torch.nn as nn
-
-
-def build_act_layer(act_layer):
- if act_layer == 'ReLU':
- return nn.ReLU(inplace=True)
- elif act_layer == 'SiLU':
- return nn.SiLU(inplace=True)
- elif act_layer == 'GELU':
- return nn.GELU()
-
- raise NotImplementedError(f'build_act_layer does not support {act_layer}')
-
-
-def build_norm_layer(dim,
- norm_layer,
- in_format='channels_last',
- out_format='channels_last',
- eps=1e-6):
- layers = []
- if norm_layer == 'BN':
- if in_format == 'channels_last':
- layers.append(to_channels_first())
- layers.append(nn.BatchNorm2d(dim))
- if out_format == 'channels_last':
- layers.append(to_channels_last())
- elif norm_layer == 'LN':
- if in_format == 'channels_first':
- layers.append(to_channels_last())
- layers.append(nn.LayerNorm(dim, eps=eps))
- if out_format == 'channels_first':
- layers.append(to_channels_first())
- else:
- raise NotImplementedError(
- f'build_norm_layer does not support {norm_layer}')
- return nn.Sequential(*layers)
-
-
-class to_channels_first(nn.Module):
-
- def __init__(self):
- super().__init__()
-
- def forward(self, x):
- return x.permute(0, 3, 1, 2)
-
-
-class to_channels_last(nn.Module):
-
- def __init__(self):
- super().__init__()
-
- def forward(self, x):
- return x.permute(0, 2, 3, 1)

preproc.py

@@ -1,85 +0,0 @@
-from PIL import Image, ImageEnhance
-import random
-import numpy as np
-import random
-
-
-def preproc(image, label, preproc_methods=['flip']):
- if 'flip' in preproc_methods:
- image, label = cv_random_flip(image, label)
- if 'crop' in preproc_methods:
- image, label = random_crop(image, label)
- if 'rotate' in preproc_methods:
- image, label = random_rotate(image, label)
- if 'enhance' in preproc_methods:
- image = color_enhance(image)
- if 'pepper' in preproc_methods:
- label = random_pepper(label)
- return image, label
-
-
-def cv_random_flip(img, label):
- if random.random() > 0.5:
- img = img.transpose(Image.FLIP_LEFT_RIGHT)
- label = label.transpose(Image.FLIP_LEFT_RIGHT)
- return img, label
-
-
-def random_crop(image, label):
- border = 30
- image_width = image.size[0]
- image_height = image.size[1]
- border = int(min(image_width, image_height) * 0.1)
- crop_win_width = np.random.randint(image_width - border, image_width)
- crop_win_height = np.random.randint(image_height - border, image_height)
- random_region = (
- (image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
- (image_height + crop_win_height) >> 1)
- return image.crop(random_region), label.crop(random_region)
-
-
-def random_rotate(image, label, angle=15):
- mode = Image.BICUBIC
- if random.random() > 0.8:
- random_angle = np.random.randint(-angle, angle)
- image = image.rotate(random_angle, mode)
- label = label.rotate(random_angle, mode)
- return image, label
-
-
-def color_enhance(image):
- bright_intensity = random.randint(5, 15) / 10.0
- image = ImageEnhance.Brightness(image).enhance(bright_intensity)
- contrast_intensity = random.randint(5, 15) / 10.0
- image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
- color_intensity = random.randint(0, 20) / 10.0
- image = ImageEnhance.Color(image).enhance(color_intensity)
- sharp_intensity = random.randint(0, 30) / 10.0
- image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
- return image
-
-
-def random_gaussian(image, mean=0.1, sigma=0.35):
- def gaussianNoisy(im, mean=mean, sigma=sigma):
- for _i in range(len(im)):
- im[_i] += random.gauss(mean, sigma)
- return im
-
- img = np.asarray(image)
- width, height = img.shape
- img = gaussianNoisy(img[:].flatten(), mean, sigma)
- img = img.reshape([width, height])
- return Image.fromarray(np.uint8(img))
-
-
-def random_pepper(img, N=0.0015):
- img = np.array(img)
- noiseNum = int(N * img.shape[0] * img.shape[1])
- for i in range(noiseNum):
- randX = random.randint(0, img.shape[0] - 1)
- randY = random.randint(0, img.shape[1] - 1)
- if random.randint(0, 1) == 0:
- img[randX, randY] = 0
- else:
- img[randX, randY] = 255
- return Image.fromarray(img)

train.sh

@@ -1,41 +0,0 @@
-#!/bin/bash
-# Run script
-# Settings of training & test for different tasks.
-method="$1"
-task=$(python3 config.py)
-case "${task}" in
- "DIS5K") epochs=600 && val_last=100 && step=5 ;;
- "COD") epochs=150 && val_last=50 && step=5 ;;
- "HRSOD") epochs=150 && val_last=50 && step=5 ;;
- "DIS5K+HRSOD+HRS10K") epochs=250 && val_last=50 && step=5 ;;
- "P3M-10k") epochs=150 && val_last=50 && step=5 ;;
-esac
-testsets=NO # Non-existing folder to skip.
-# testsets=TE-COD10K # for COD
-
-# Train
-devices=$2
-nproc_per_node=$(echo ${devices%%,} | grep -o "," | wc -l)
-
-to_be_distributed=`echo ${nproc_per_node} | awk '{if($e > 0) print "True"; else print "False";}'`
-
-echo Training started at $(date)
-if [ ${to_be_distributed} == "True" ]
-then
- # Adapt the nproc_per_node by the number of GPUs. Give 8989 as the default value of master_port.
- echo "Multi-GPU mode received..."
- CUDA_VISIBLE_DEVICES=${devices} \
- torchrun --nproc_per_node $((nproc_per_node+1)) --master_port=${3:-8989} \
- train.py --ckpt_dir ckpt/${method} --epochs ${epochs} \
- --testsets ${testsets} \
- --dist ${to_be_distributed}
-else
- echo "Single-GPU mode received..."
- CUDA_VISIBLE_DEVICES=${devices} \
- python train.py --ckpt_dir ckpt/${method} --epochs ${epochs} \
- --testsets ${testsets} \
- --dist ${to_be_distributed} \
- --resume ckpt/xx/ep100.pth
-fi
-
-echo Training finished at $(date)

utils.py

@@ -1,97 +0,0 @@
-import logging
-import os
-import torch
-from torchvision import transforms
-import numpy as np
-import random
-import cv2
-from PIL import Image
-
-
-def path_to_image(path, size=(1024, 1024), color_type=['rgb', 'gray'][0]):
- if color_type.lower() == 'rgb':
- image = cv2.imread(path)
- elif color_type.lower() == 'gray':
- image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
- else:
- print('Select the color_type to return, either to RGB or gray image.')
- return
- if size:
- image = cv2.resize(image, size, interpolation=cv2.INTER_LINEAR)
- if color_type.lower() == 'rgb':
- image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).convert('RGB')
- else:
- image = Image.fromarray(image).convert('L')
- return image
-
-
-
-def check_state_dict(state_dict, unwanted_prefix='_orig_mod.'):
- for k, v in list(state_dict.items()):
- if k.startswith(unwanted_prefix):
- state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
- return state_dict
-
-
-def generate_smoothed_gt(gts):
- epsilon = 0.001
- new_gts = (1-epsilon)*gts+epsilon/2
- return new_gts
-
-
-class Logger():
- def __init__(self, path="log.txt"):
- self.logger = logging.getLogger('BiRefNet')
- self.file_handler = logging.FileHandler(path, "w")
- self.stdout_handler = logging.StreamHandler()
- self.stdout_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %(message)s'))
- self.file_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %(message)s'))
- self.logger.addHandler(self.file_handler)
- self.logger.addHandler(self.stdout_handler)
- self.logger.setLevel(logging.INFO)
- self.logger.propagate = False
- 
- def info(self, txt):
- self.logger.info(txt)
- 
- def close(self):
- self.file_handler.close()
- self.stdout_handler.close()
-
-
-class AverageMeter(object):
- """Computes and stores the average and current value"""
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.val = 0.0
- self.avg = 0.0
- self.sum = 0.0
- self.count = 0.0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = self.sum / self.count
-
-
-def save_checkpoint(state, path, filename="latest.pth"):
- torch.save(state, os.path.join(path, filename))
-
-
-def save_tensor_img(tenor_im, path):
- im = tenor_im.cpu().clone()
- im = im.squeeze(0)
- tensor2pil = transforms.ToPILImage()
- im = tensor2pil(im)
- im.save(path)
-
-
-def set_seed(seed):
- torch.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
- np.random.seed(seed)
- random.seed(seed)
- torch.backends.cudnn.deterministic = True