def __init__(self, patch_size=8): # flipping image along vertical axis self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) # image augmentation functions normalize = transforms.Normalize( mean=[x / 255.0 for x in [125.3, 123.0, 113.9]], std=[x / 255.0 for x in [63.0, 62.1, 66.7]]) col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8) img_jitter = transforms.RandomApply([RandomTranslateWithReflect(4)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) # main transform for self-supervised training self.train_transform = transforms.Compose([ img_jitter, col_jitter, rnd_gray, transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=patch_size // 2), ]) # transform for testing self.test_transform = transforms.Compose([ transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=patch_size // 2), ])
def __init__(self, patch_size, overlap): # flipping image along vertical axis self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize(mean=(0.43, 0.42, 0.39), std=(0.27, 0.26, 0.27)) # image augmentation functions col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) rand_crop = \ transforms.RandomResizedCrop(64, scale=(0.3, 1.0), ratio=(0.7, 1.4), interpolation=3) self.test_transform = transforms.Compose([ transforms.Resize(70, interpolation=3), transforms.CenterCrop(64), transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap) ]) self.train_transform = transforms.Compose([ rand_crop, col_jitter, rnd_gray, transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap) ])
def __init__(self, patch_size=8, overlap=4): """ Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches """ if not _TORCHVISION_AVAILABLE: raise ModuleNotFoundError( # pragma: no-cover 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # flipping image along vertical axis self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize( mean=[x / 255.0 for x in [125.3, 123.0, 113.9]], std=[x / 255.0 for x in [63.0, 62.1, 66.7]]) self.transforms = transforms.Compose([ transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap), ])
def __init__(self, patch_size=8, overlap=4): """ Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches """ if not _TORCHVISION_AVAILABLE: raise ModuleNotFoundError( # pragma: no-cover 'You want to use `transforms` from `torchvision` which is not installed yet.' ) self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize( mean=[x / 255.0 for x in [125.3, 123.0, 113.9]], std=[x / 255.0 for x in [63.0, 62.1, 66.7]]) col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8) img_jitter = transforms.RandomApply([RandomTranslateWithReflect(4)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) self.transforms = transforms.Compose([ img_jitter, col_jitter, rnd_gray, transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap), ])
def __init__(self, patch_size=32, overlap=16): """ Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches """ if not _TORCHVISION_AVAILABLE: raise ModuleNotFoundError( # pragma: no-cover 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # image augmentation functions self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) rand_crop = transforms.RandomResizedCrop(128, scale=(0.3, 1.0), ratio=(0.7, 1.4), interpolation=3) col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) post_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), Patchify(patch_size=patch_size, overlap_size=overlap), ]) self.transforms = transforms.Compose( [rand_crop, col_jitter, rnd_gray, post_transform])
def __init__(self, patch_size=32, overlap=16): """ Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches """ if not _TORCHVISION_AVAILABLE: raise ModuleNotFoundError( # pragma: no-cover 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # image augmentation functions self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) post_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), Patchify(patch_size=patch_size, overlap_size=overlap), ]) self.transforms = transforms.Compose([ transforms.Resize(146, interpolation=3), transforms.CenterCrop(128), post_transform ])
def __init__(self, patch_size: int = 16, overlap: int = 8): """ Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches """ if not _TORCHVISION_AVAILABLE: # pragma: no cover raise ModuleNotFoundError( "You want to use `transforms` from `torchvision` which is not installed yet." ) # flipping image along vertical axis self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize(mean=(0.43, 0.42, 0.39), std=(0.27, 0.26, 0.27)) self.transforms = transforms.Compose([ transforms.Resize(70, interpolation=3), transforms.CenterCrop(64), transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap), ])
def __init__(self, patch_size=16, overlap=8): """ Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches """ if not _TORCHVISION_AVAILABLE: raise ModuleNotFoundError( # pragma: no-cover 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # flipping image along vertical axis self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize(mean=(0.43, 0.42, 0.39), std=(0.27, 0.26, 0.27)) # image augmentation functions col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) rand_crop = transforms.RandomResizedCrop(64, scale=(0.3, 1.0), ratio=(0.7, 1.4), interpolation=3) self.transforms = transforms.Compose([ rand_crop, col_jitter, rnd_gray, transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap) ])
def __init__(self, patch_size=16, overlap=8): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip img_jitter col_jitter rnd_gray transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2) Example:: # in a regular dataset STL10(..., transforms=CPCTrainTransformsSTL10()) # in a DataModule module = STL10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCTrainTransformsSTL10()) """ if not _TORCHVISION_AVAILABLE: raise ImportError( 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # flipping image along vertical axis self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize(mean=(0.43, 0.42, 0.39), std=(0.27, 0.26, 0.27)) # image augmentation functions col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) rand_crop = transforms.RandomResizedCrop(64, scale=(0.3, 1.0), ratio=(0.7, 1.4), interpolation=3) self.transforms = transforms.Compose([ rand_crop, col_jitter, rnd_gray, transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap) ])
def __init__(self, patch_size=8, overlap=4): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip img_jitter col_jitter rnd_gray transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2) Example:: # in a regular dataset CIFAR10(..., transforms=CPCTrainTransformsCIFAR10()) # in a DataModule module = CIFAR10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCTrainTransformsCIFAR10()) """ if not _TORCHVISION_AVAILABLE: raise ImportError( 'You want to use `transforms` from `torchvision` which is not installed yet.' ) self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize( mean=[x / 255.0 for x in [125.3, 123.0, 113.9]], std=[x / 255.0 for x in [63.0, 62.1, 66.7]]) col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8) img_jitter = transforms.RandomApply([RandomTranslateWithReflect(4)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) self.transforms = transforms.Compose([ img_jitter, col_jitter, rnd_gray, transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap), ])
def __init__(self, patch_size=32, overlap=16): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2) Example:: # in a regular dataset Imagenet(..., transforms=CPCTrainTransformsImageNet128()) # in a DataModule module = ImagenetDataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCTrainTransformsImageNet128()) """ if not _TORCHVISION_AVAILABLE: raise ImportError( 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # image augmentation functions self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) rand_crop = transforms.RandomResizedCrop(128, scale=(0.3, 1.0), ratio=(0.7, 1.4), interpolation=3) col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) post_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), Patchify(patch_size=patch_size, overlap_size=overlap), ]) self.transforms = transforms.Compose( [rand_crop, col_jitter, rnd_gray, post_transform])
def __init__(self, patch_size=8, overlap=4): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=overlap) Example:: # in a regular dataset CIFAR10(..., transforms=CPCEvalTransformsCIFAR10()) # in a DataModule module = CIFAR10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsCIFAR10()) """ if not _TORCHVISION_AVAILABLE: raise ImportError( 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # flipping image along vertical axis self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize( mean=[x / 255.0 for x in [125.3, 123.0, 113.9]], std=[x / 255.0 for x in [63.0, 62.1, 66.7]]) self.transforms = transforms.Compose([ transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap), ])
def __init__(self, patch_size=16, overlap=8): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2) Example:: # in a regular dataset STL10(..., transforms=CPCEvalTransformsSTL10()) # in a DataModule module = STL10DataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsSTL10()) """ if not _TORCHVISION_AVAILABLE: raise ImportError( 'You want to use `transforms` from `torchvision` which is not installed yet.' ) # flipping image along vertical axis self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize(mean=(0.43, 0.42, 0.39), std=(0.27, 0.26, 0.27)) self.transforms = transforms.Compose([ transforms.Resize(70, interpolation=3), transforms.CenterCrop(64), transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap) ])
def __init__(self, patch_size, overlap): # image augmentation functions self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) rand_crop = \ transforms.RandomResizedCrop(128, scale=(0.3, 1.0), ratio=(0.7, 1.4), interpolation=3) col_jitter = transforms.RandomApply( [transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8) rnd_gray = transforms.RandomGrayscale(p=0.25) post_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), Patchify(patch_size=patch_size, overlap_size=overlap), ]) self.test_transform = transforms.Compose([ transforms.Resize(146, interpolation=3), transforms.CenterCrop(128), post_transform ]) self.train_transform = transforms.Compose( [rand_crop, col_jitter, rnd_gray, post_transform])
def __init__(self, patch_size=32, overlap=16): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2) Example:: # in a regular dataset Imagenet(..., transforms=CPCEvalTransformsImageNet128()) # in a DataModule module = ImagenetDataModule(PATH) train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsImageNet128()) """ # image augmentation functions self.patch_size = patch_size self.overlap = overlap self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) post_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), Patchify(patch_size=patch_size, overlap_size=overlap), ]) self.transforms = transforms.Compose([ transforms.Resize(146, interpolation=3), transforms.CenterCrop(128), post_transform ])
def __init__(self, patch_size=8, overlap=4): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=overlap) Example:: # in a regular dataset CIFAR10(..., transforms=CPCEvalTransformsCIFAR10()) # in a DataModule module = CIFAR10DataModule() train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsCIFAR10()) """ # flipping image along vertical axis self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize( mean=[x / 255.0 for x in [125.3, 123.0, 113.9]], std=[x / 255.0 for x in [63.0, 62.1, 66.7]]) self.transforms = transforms.Compose([ transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap), ])
def __init__(self, patch_size=16, overlap=8): """ Transforms used for CPC: Args: patch_size: size of patches when cutting up the image into overlapping patches overlap: how much to overlap patches Transforms:: random_flip transforms.ToTensor() normalize Patchify(patch_size=patch_size, overlap_size=patch_size // 2) Example:: # in a regular dataset STL10(..., transforms=CPCEvalTransformsSTL10()) # in a DataModule module = STL10DataModule() train_loader = module.train_dataloader(batch_size=32, transforms=CPCEvalTransformsSTL10()) """ # flipping image along vertical axis self.flip_lr = transforms.RandomHorizontalFlip(p=0.5) normalize = transforms.Normalize(mean=(0.43, 0.42, 0.39), std=(0.27, 0.26, 0.27)) self.transforms = transforms.Compose([ transforms.Resize(70, interpolation=3), transforms.CenterCrop(64), transforms.ToTensor(), normalize, Patchify(patch_size=patch_size, overlap_size=overlap) ])
def __init__(self, path, patch_size=8): self.fileList = pd.read_csv(path) if patch_size: self.tfms = transforms.Compose([transforms.ToTensor(), Patchify(patch_size, patch_size // 2)]) else: self.tfms = transforms.ToTensor()