trainset = torchvision.datasets.CIFAR10(root=self._dataroot, train=True, download=True, transform=transform_train) if load_test: testset = torchvision.datasets.CIFAR10(root=self._dataroot, train=False, download=True, transform=transform_test) return trainset, testset @overrides def get_transforms(self) -> tuple: MEAN = [0.49139968, 0.48215827, 0.44653124] STD = [0.24703233, 0.24348505, 0.26158768] transf = [ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip() ] normalize = [transforms.ToTensor(), transforms.Normalize(MEAN, STD)] train_transform = transforms.Compose(transf + normalize) test_transform = transforms.Compose(normalize) return train_transform, test_transform register_dataset_provider('cifar10', Cifar10Provider)
testpath = os.path.join(self._dataroot, 'food-101', 'test') testset = torchvision.datasets.ImageFolder( testpath, transform=transform_train) return trainset, testset @overrides def get_transforms(self) -> tuple: # TODO: Need to rethink the food101 transforms MEAN = [0.5451, 0.4435, 0.3436] STD = [0.2171, 0.2251, 0.2260] # TODO: should be [0.2517, 0.2521, 0.2573] train_transf = [ transforms.Resize((32, 32)), transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip() ] # food101 has images of varying sizes and are ~512 each side test_transf = [transforms.Resize((32, 32))] normalize = [transforms.ToTensor(), transforms.Normalize(MEAN, STD)] train_transform = transforms.Compose(train_transf + normalize) test_transform = transforms.Compose(test_transf + normalize) return train_transform, test_transform register_dataset_provider('food101', Food101Provider)
if load_train: trainset = torchvision.datasets.FashionMNIST(root=self._dataroot, train=True, download=True, transform=transform_train) if load_test: testset = torchvision.datasets.FashionMNIST(root=self._dataroot, train=False, download=True, transform=transform_test) return trainset, testset @overrides def get_transforms(self)->tuple: MEAN = [0.28604063146254594] STD = [0.35302426207299326] transf = [ transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=0.1), transforms.RandomVerticalFlip() ] normalize = [ transforms.ToTensor(), transforms.Normalize(MEAN, STD) ] train_transform = transforms.Compose(transf + normalize) test_transform = transforms.Compose(normalize) return train_transform, test_transform register_dataset_provider('fashion_mnist', FashionMnistProvider)
download=True, transform=transform_train) if load_test: testset = torchvision.datasets.MNIST(root=self._dataroot, train=False, download=True, transform=transform_test) return trainset, testset @overrides def get_transforms(self) -> tuple: MEAN = [0.13066051707548254] STD = [0.30810780244715075] transf = [ transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=0.1) ] normalize = [transforms.ToTensor(), transforms.Normalize(MEAN, STD)] train_transform = transforms.Compose(transf + normalize) test_transform = transforms.Compose(normalize) return train_transform, test_transform register_dataset_provider('mnist', MnistProvider)
return trainset, testset @overrides def get_transforms(self) -> tuple: # MEAN, STD computed for mit67 MEAN = [0.4893, 0.4270, 0.3625] STD = [0.2631, 0.2565, 0.2582] # transformations match that in # https://github.com/antoyang/NAS-Benchmark/blob/master/DARTS/preproc.py train_transf = [ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2) ] test_transf = [transforms.Resize(256), transforms.CenterCrop(224)] normalize = [transforms.ToTensor(), transforms.Normalize(MEAN, STD)] train_transform = transforms.Compose(train_transf + normalize) test_transform = transforms.Compose(test_transf + normalize) return train_transform, test_transform register_dataset_provider('mit67', Mit67Provider)
def get_transforms(self)->tuple: # MEAN, STD computed for sport8 MEAN = [0.4734, 0.4856, 0.4526] STD = [0.2478, 0.2444, 0.2667] # transformations match that in # https://github.com/antoyang/NAS-Benchmark/blob/master/DARTS/preproc.py train_transf = [ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ColorJitter( brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2) ] test_transf = [transforms.Resize(256), transforms.CenterCrop(224)] normalize = [ transforms.ToTensor(), transforms.Normalize(MEAN, STD) ] train_transform = transforms.Compose(train_transf + normalize) test_transform = transforms.Compose(test_transf + normalize) return train_transform, test_transform register_dataset_provider('sport8', Sport8Provider)
split='extra', download=True, transform=transform_train) trainset = ConcatDataset([trainset, extraset]) if load_test: testset = torchvision.datasets.SVHN(root=self._dataroot, split='test', download=True, transform=transform_test) return trainset, testset @overrides def get_transforms(self) -> tuple: MEAN = [0.4914, 0.4822, 0.4465] STD = [0.2023, 0.1994, 0.20100] transf = [ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip() ] normalize = [transforms.ToTensor(), transforms.Normalize(MEAN, STD)] train_transform = transforms.Compose(transf + normalize) test_transform = transforms.Compose(normalize) return train_transform, test_transform register_dataset_provider('svhn', SvhnProvider)
return trainset, testset @overrides def get_transforms(self) -> tuple: # MEAN, STD computed for flower102 MEAN = [0.5190, 0.4101, 0.3274] STD = [0.2972, 0.2488, 0.2847] # transformations match that in # https://github.com/antoyang/NAS-Benchmark/blob/master/DARTS/preproc.py train_transf = [ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2) ] test_transf = [transforms.Resize(256), transforms.CenterCrop(224)] normalize = [transforms.ToTensor(), transforms.Normalize(MEAN, STD)] train_transform = transforms.Compose(train_transf + normalize) test_transform = transforms.Compose(test_transf + normalize) return train_transform, test_transform register_dataset_provider('flower102', Flower102Provider)
transform_train = transforms.Compose([ transforms.RandomResizedCrop( 224, scale=( 0.08, 1.0), # TODO: these two params are normally not specified interpolation=Image.BICUBIC), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2), transforms.ToTensor(), # TODO: Lighting is not used in original darts paper # Lighting(0.1, _IMAGENET_PCA['eigval'], _IMAGENET_PCA['eigvec']), transforms.Normalize(mean=MEAN, std=STD) ]) transform_test = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=MEAN, std=STD) ]) return transform_train, transform_test register_dataset_provider('imagenet', ImagenetProvider)