train and valid transforms for cancer classification

train_transform.py

train_transforms = transforms.Compose([
    transforms.Resize((196, 196)),
    transforms.RandomChoice([
        transforms.ColorJitter(brightness=0.5),
        transforms.ColorJitter(contrast=0.5),
        transforms.ColorJitter(saturation=0.5),
        transforms.ColorJitter(hue=0.5),
        transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
        transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3),
        transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
    ]),
    transforms.RandomChoice([
        transforms.RandomRotation((0, 0)),
        transforms.RandomHorizontalFlip(p=1),
        transforms.RandomVerticalFlip(p=1),
        transforms.RandomRotation((90, 90)),
        transforms.RandomRotation((180, 180)),
        transforms.RandomRotation((270, 270)),
        transforms.Compose([
            transforms.RandomHorizontalFlip(p=1),
            transforms.RandomRotation((90, 90)),
        ]),
        transforms.Compose([
            transforms.RandomHorizontalFlip(p=1),
            transforms.RandomRotation((270, 270)),
        ])
    ]),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )])
# however, 4-TTA (D4) is later applied to validation
valid_transforms = transforms.Compose([
    transforms.Resize((196, 196)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )])