def build_transform()

in datasets.py [0:0]

• 41 lines of code
• 9 McCabe index (conditional complexity)

def build_transform(is_train, args):
    resize_im = args.input_size > 32
    imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
    mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
    std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD

    if is_train:
        # this should always dispatch to transforms_imagenet_train
        transform = create_transform(
            input_size=args.input_size,
            is_training=True,
            color_jitter=args.color_jitter,
            auto_augment=args.aa,
            interpolation=args.train_interpolation,
            re_prob=args.reprob,
            re_mode=args.remode,
            re_count=args.recount,
            mean=mean,
            std=std,
        )
        if not resize_im:
            transform.transforms[0] = transforms.RandomCrop(
                args.input_size, padding=4)
        return transform

    t = []
    if resize_im:
        # warping (no cropping) when evaluated at 384 or larger
        if args.input_size >= 384:  
            t.append(
            transforms.Resize((args.input_size, args.input_size), 
                            interpolation=transforms.InterpolationMode.BICUBIC), 
        )
            print(f"Warping {args.input_size} size input images...")
        else:
            if args.crop_pct is None:
                args.crop_pct = 224 / 256
            size = int(args.input_size / args.crop_pct)
            t.append(
                # to maintain same ratio w.r.t. 224 images
                transforms.Resize(size, interpolation=transforms.InterpolationMode.BICUBIC),  
            )
            t.append(transforms.CenterCrop(args.input_size))

    t.append(transforms.ToTensor())
    t.append(transforms.Normalize(mean, std))
    return transforms.Compose(t)