def return_augmentations_types()

in data_utils/functions_bis.py [0:0]

• 42 lines of code
• 3 McCabe index (conditional complexity)

def return_augmentations_types(args):

    if args.tvalues is None:
        args.tvalues = [1,1]

    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
    )
    scale_magnitudes = np.linspace(1,6,15)
    interpolation = transforms.RandomResizedCrop(224).interpolation
    augmentations = {}
    print(args.scale_mag, 1./scale_magnitudes[args.scale_mag]**2)
    interpolation = transforms.RandomResizedCrop(224).interpolation
    augmentations["C"] = transforms.Compose(
                    [
                        transforms.RandomResizedCrop(
                            224
                        ),  # resize is not needed since the crop will be in function of the size and ratio of the image
                        transforms.ToTensor(),
                        normalize,
                    ]
                )
    augmentations["Cvary"] = transforms.Compose(
            [
                transforms.Resize(256),  # ensures that the minimal size is 256
                transforms.CenterCrop(224),
                RandomSizeResizedCenterCrop(
                    224, 
                    scale=(1./scale_magnitudes[args.scale_mag]**2,1.),
                    ratio=(1.,1.), interpolation=interpolation
                ),  
                transforms.ToTensor(),
                normalize,
            ]
        )
    augmentations["None"] = transforms.Compose(
            [
                transforms.Resize(256),  # ensures that the minimal size is 256
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                normalize,
            ]
        )
    
    return augmentations