File size: 11,256 Bytes
5fee096 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 | import numpy as np
from torchvision import transforms
class CIFARTransform:
MEAN = [0.5071, 0.4866, 0.4409]
STD = [0.2675, 0.2565, 0.2761]
common_trfs = [transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD)]
resnet_train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
*common_trfs
])
resnet_test_transform = transforms.Compose([*common_trfs])
# To Reproduce ERAML, ERACE
#resnet_train_transform = transforms.Compose([*common_trfs])
# from
dset_mean = (0., 0., 0.)
dset_std = (1., 1., 1.)
vit_train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std)])
vit_test_transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std)])
# from trust region gradient projection
mean=[x/255 for x in [125.3,123.0,113.9]]
std=[x/255 for x in [63.0,62.1,66.7]]
alexnet_train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean,std)])
alexnet_test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean,std)])
@staticmethod
def get_transform(model_type, mode):
if model_type == 'resnet':
if mode == 'train':
return CIFARTransform.resnet_train_transform
elif mode == 'test':
return CIFARTransform.resnet_test_transform
elif model_type == 'vit':
if mode == 'train':
return CIFARTransform.vit_train_transform
elif mode == 'test':
return CIFARTransform.vit_test_transform
elif model_type == 'alexnet':
if mode == 'train':
return CIFARTransform.alexnet_train_transform
elif mode == 'test':
return CIFARTransform.alexnet_test_transform
else:
raise ValueError("Unsupported model type")
class ImageNetTransform:
MEAN=[0.4914, 0.4822, 0.4465]
STD=[0.2023, 0.1994, 0.2010]
common_trfs = [transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD)]
resnet_train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
*common_trfs
])
resnet_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
*common_trfs
])
dset_mean = (0., 0., 0.)
dset_std = (1., 1., 1.)
vit_train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std),
])
vit_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std),
])
@staticmethod
def get_transform(model_type, mode):
if model_type == 'resnet':
if mode == 'train':
return ImageNetTransform.resnet_train_transform
elif mode == 'test':
return ImageNetTransform.resnet_test_transform
elif model_type == 'vit':
if mode == 'train':
return ImageNetTransform.vit_train_transform
elif mode == 'test':
return ImageNetTransform.vit_test_transform
else:
raise ValueError("Unsupported model type")
class ImageNetRTransform:
mean = [0.4914, 0.4822, 0.4465]
std = [0.2023, 0.1994, 0.2010]
common_trfs = [transforms.ToTensor(),
transforms.Normalize(mean, std)]
resnet_train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
*common_trfs])
resnet_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
*common_trfs])
mean = [0., 0., 0.]
std = [1., 1., 1.]
common_trfs = [transforms.ToTensor(),
transforms.Normalize(mean, std)]
vit_train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
*common_trfs])
vit_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
*common_trfs])
# from trust region gradient projection
mean=[x/255 for x in [125.3,123.0,113.9]]
std=[x/255 for x in [63.0,62.1,66.7]]
alexnet_train_transform = transforms.Compose([
transforms.RandomResizedCrop(32),
transforms.ToTensor(),
transforms.Normalize(mean,std)])
alexnet_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(32),
transforms.ToTensor(),
transforms.Normalize(mean,std)])
@staticmethod
def get_transform(model_type, mode):
if model_type == 'resnet':
if mode == 'train':
return ImageNetRTransform.resnet_train_transform
elif mode == 'test':
return ImageNetRTransform.resnet_test_transform
elif model_type == 'vit':
if mode == 'train':
return ImageNetRTransform.vit_train_transform
elif mode == 'test':
return ImageNetRTransform.vit_test_transform
elif model_type == 'alexnet':
if mode == 'train':
return ImageNetRTransform.alexnet_train_transform
elif mode == 'test':
return ImageNetRTransform.alexnet_test_transform
else:
raise ValueError("Unsupported model type")
class TinyImageNetTransform:
# Standard normalization values for Tiny-ImageNet
MEAN = [0.485, 0.456, 0.406]
STD = [0.229, 0.224, 0.225]
common_trfs = [transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD)]
# ResNet Transforms
resnet_train_transform = transforms.Compose([
transforms.RandomResizedCrop(64),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
*common_trfs
])
resnet_test_transform = transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
*common_trfs
])
# ViT Transforms (Using dataset mean/std as [0,0,0] and [1,1,1] for compatibility)
dset_mean = (0., 0., 0.)
dset_std = (1., 1., 1.)
vit_train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std)
])
vit_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std)
])
# from trust region gradient projection
mean=[x/255 for x in [125.3,123.0,113.9]]
std=[x/255 for x in [63.0,62.1,66.7]]
alexnet_train_transform = transforms.Compose([
transforms.RandomResizedCrop(32),
transforms.ToTensor(),
transforms.Normalize(mean,std)])
alexnet_test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(32),
transforms.ToTensor(),
transforms.Normalize(mean,std)])
@staticmethod
def get_transform(model_type, mode):
if model_type == 'resnet':
if mode == 'train':
return TinyImageNetTransform.resnet_train_transform
elif mode == 'test':
return TinyImageNetTransform.resnet_test_transform
elif model_type == 'vit':
if mode == 'train':
return TinyImageNetTransform.vit_train_transform
elif mode == 'test':
return TinyImageNetTransform.vit_test_transform
elif model_type == 'alexnet':
if mode == 'train':
return TinyImageNetTransform.alexnet_train_transform
elif mode == 'test':
return TinyImageNetTransform.alexnet_test_transform
else:
raise ValueError("Unsupported model type")
class FiveDatasetsTransform:
MEAN = [0.5071, 0.4866, 0.4409]
STD = [0.2675, 0.2565, 0.2761]
common_trfs = [transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD)]
resnet_train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
*common_trfs
])
resnet_test_transform = transforms.Compose([
transforms.Resize(32),
*common_trfs
])
# from
dset_mean = (0., 0., 0.)
dset_std = (1., 1., 1.)
vit_train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std)])
vit_test_transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(dset_mean, dset_std)])
# from trust region gradient projection
mean=[x/255 for x in [125.3,123.0,113.9]]
std=[x/255 for x in [63.0,62.1,66.7]]
alexnet_train_transform = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(mean,std)])
alexnet_test_transform = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(mean,std)])
@staticmethod
def get_transform(model_type, mode):
if model_type == 'resnet':
if mode == 'train':
return FiveDatasetsTransform.resnet_train_transform
elif mode == 'test':
return FiveDatasetsTransform.resnet_test_transform
elif model_type == 'vit':
if mode == 'train':
return FiveDatasetsTransform.vit_train_transform
elif mode == 'test':
return FiveDatasetsTransform.vit_test_transform
elif model_type == 'alexnet':
if mode == 'train':
return FiveDatasetsTransform.alexnet_train_transform
elif mode == 'test':
return FiveDatasetsTransform.alexnet_test_transform
else:
raise ValueError("Unsupported model type")
transform_classes = {
'cifar': CIFARTransform,
'imagenet': ImageNetTransform,
'imagenet-r': ImageNetRTransform,
'tiny-imagenet': TinyImageNetTransform,
'5-datasets': FiveDatasetsTransform
} |