Spaces:
Runtime error
Runtime error
File size: 7,969 Bytes
3b49518 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
# Copyright (c) EPFL VILAB.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# Based on BEiT, timm, DINO, DeiT and MAE-priv code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# https://github.com/BUPT-PRIV/MAE-priv
# --------------------------------------------------------
import os
import random
import numpy as np
import torch
import torchvision.transforms.functional as TF
from torchvision import datasets, transforms
from utils import create_transform
from .data_constants import (IMAGE_TASKS, IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN,
IMAGENET_INCEPTION_STD)
from .dataset_folder import ImageFolder, MultiTaskImageFolder
def denormalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
return TF.normalize(
img.clone(),
mean= [-m/s for m, s in zip(mean, std)],
std= [1/s for s in std]
)
class DataAugmentationForMAE(object):
def __init__(self, args):
imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
trans = [transforms.RandomResizedCrop(args.input_size)]
if args.hflip > 0.0:
trans.append(transforms.RandomHorizontalFlip(args.hflip))
trans.extend([
transforms.ToTensor(),
transforms.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std))])
self.transform = transforms.Compose(trans)
def __call__(self, image):
return self.transform(image)
def __repr__(self):
repr = "(DataAugmentationForBEiT,\n"
repr += " transform = %s,\n" % str(self.transform)
repr += ")"
return repr
class DataAugmentationForMultiMAE(object):
def __init__(self, args):
imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
self.rgb_mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
self.rgb_std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
self.input_size = args.input_size
self.hflip = args.hflip
def __call__(self, task_dict):
flip = random.random() < self.hflip # Stores whether to flip all images or not
ijhw = None # Stores crop coordinates used for all tasks
# Crop and flip all tasks randomly, but consistently for all tasks
for task in task_dict:
if task not in IMAGE_TASKS:
continue
if ijhw is None:
# Official MAE code uses (0.2, 1.0) for scale and (0.75, 1.3333) for ratio
ijhw = transforms.RandomResizedCrop.get_params(
task_dict[task], scale=(0.2, 1.0), ratio=(0.75, 1.3333)
)
i, j, h, w = ijhw
task_dict[task] = TF.crop(task_dict[task], i, j, h, w)
task_dict[task] = task_dict[task].resize((self.input_size, self.input_size))
if flip:
task_dict[task] = TF.hflip(task_dict[task])
# Convert to Tensor
for task in task_dict:
if task in ['depth']:
img = torch.Tensor(np.array(task_dict[task]) / 2 ** 16)
img = img.unsqueeze(0) # 1 x H x W
elif task in ['rgb']:
img = TF.to_tensor(task_dict[task])
img = TF.normalize(img, mean=self.rgb_mean, std=self.rgb_std)
elif task in ['semseg', 'semseg_coco']:
# TODO: add this to a config instead
# Rescale to 0.25x size (stride 4)
scale_factor = 0.25
img = task_dict[task].resize((int(self.input_size * scale_factor), int(self.input_size * scale_factor)))
# Using pil_to_tensor keeps it in uint8, to_tensor converts it to float (rescaled to [0, 1])
img = TF.pil_to_tensor(img).to(torch.long).squeeze(0)
task_dict[task] = img
return task_dict
def __repr__(self):
repr = "(DataAugmentationForMultiMAE,\n"
#repr += " transform = %s,\n" % str(self.transform)
repr += ")"
return repr
def build_pretraining_dataset(args):
transform = DataAugmentationForMAE(args)
print("Data Aug = %s" % str(transform))
return ImageFolder(args.data_path, transform=transform)
def build_multimae_pretraining_dataset(args):
transform = DataAugmentationForMultiMAE(args)
return MultiTaskImageFolder(args.data_path, args.all_domains, transform=transform)
def build_dataset(is_train, args):
transform = build_transform(is_train, args)
print("Transform = ")
if isinstance(transform, tuple):
for trans in transform:
print(" - - - - - - - - - - ")
for t in trans.transforms:
print(t)
else:
for t in transform.transforms:
print(t)
print("---------------------------")
if args.data_set == 'CIFAR':
dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform)
nb_classes = 100
elif args.data_set == 'IMNET':
# root = os.path.join(args.data_path, 'train' if is_train else 'val')
root = args.data_path if is_train else args.eval_data_path
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = 1000
elif args.data_set == "image_folder":
root = args.data_path if is_train else args.eval_data_path
dataset = ImageFolder(root, transform=transform)
nb_classes = args.nb_classes
assert len(dataset.class_to_idx) == nb_classes
else:
raise NotImplementedError()
assert nb_classes == args.nb_classes
print("Number of the class = %d" % args.nb_classes)
return dataset, nb_classes
def build_transform(is_train, args):
resize_im = args.input_size > 32
imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
if is_train:
# this should always dispatch to transforms_imagenet_train
transform = create_transform(
input_size=args.input_size,
is_training=True,
color_jitter=args.color_jitter,
auto_augment=args.aa,
interpolation=args.train_interpolation,
re_prob=args.reprob,
re_mode=args.remode,
re_count=args.recount,
mean=mean,
std=std,
)
if not resize_im:
# replace RandomResizedCropAndInterpolation with
# RandomCrop
transform.transforms[0] = transforms.RandomCrop(
args.input_size, padding=4)
return transform
t = []
if resize_im:
if args.crop_pct is None:
if args.input_size < 384:
args.crop_pct = 224 / 256
else:
args.crop_pct = 1.0
size = int(args.input_size / args.crop_pct)
t.append(
transforms.Resize(size, interpolation=3), # to maintain same ratio w.r.t. 224 images
)
t.append(transforms.CenterCrop(args.input_size))
t.append(transforms.ToTensor())
t.append(transforms.Normalize(mean, std))
return transforms.Compose(t)
|