def build_custom_pipeline():
    'Builds augmentation pipelines for custom data.\n    If you want to do exoteric augmentations, you can just re-write this function.\n    Needs to return a dict with the same structure.\n    '
    pipeline = {'T_train': transforms.Compose([transforms.RandomResizedCrop(size=224, scale=(0.08, 1.0)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.228, 0.224, 0.225))]), 'T_val': transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.228, 0.224, 0.225))])}
    return pipeline
Builds augmentation pipelines for custom data.
If you want to do exoteric augmentations, you can just re-write this function.
Needs to return a dict with the same structure.
def build_custom_pipeline():
    'Builds augmentation pipelines for custom data.\n    If you want to do exoteric augmentations, you can just re-write this function.\n    Needs to return a dict with the same structure.\n    '
    pipeline = {'T_train': transforms.Compose([transforms.RandomResizedCrop(size=224, scale=(0.08, 1.0)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.228, 0.224, 0.225))]), 'T_val': transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.228, 0.224, 0.225))])}
    return pipeline