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from torchvision import datasets
import albumentations as A
from albumentations.pytorch import ToTensorV2
NORM_DATA_MEAN = (0.49139968, 0.48215841, 0.44653091)
NORM_DATA_STD = (0.24703223, 0.24348513, 0.26158784)
CIFAR_CLASS_LABELS = [
'airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck'
]
TRAIN_TRANSFORM = A.Compose([
A.Normalize(
mean=NORM_DATA_MEAN,
std=NORM_DATA_STD,
),
A.HorizontalFlip(),
A.Compose([
A.PadIfNeeded(min_height=40, min_width=40, p=1.0),
A.CoarseDropout(max_holes=1, max_height=16, max_width=16,
min_holes=1, min_height=16, min_width=16,
fill_value=NORM_DATA_MEAN, mask_fill_value=None, p=1.0),
A.RandomCrop(p=1.0, height=32, width=32)
]),
ToTensorV2(),
])
TEST_TRANSFORM = A.Compose([
A.Normalize(
mean=NORM_DATA_MEAN,
std=NORM_DATA_STD,
),
ToTensorV2(),
])
class CifarAlbumentationsDataset(datasets.CIFAR10):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __getitem__(self, idx):
img, target = self.data[idx], self.targets[idx]
if self.transform:
augmented = self.transform(image=img)
image = augmented['image']
return image, target
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