def get_transform()

in separate_vae/data/base_dataset.py [0:0]

• 26 lines of code
• 11 McCabe index (conditional complexity)

def get_transform(opt, params, method=Image.BICUBIC, normalize=True, labimg=False):
    transform_list = []
    if 'pad' in opt.resize_or_crop:
        if labimg:
            transform_list.append(PadToSquare((0, 128, 128))) # Black in Lab space is (0, 128, 128)
        else:
            transform_list.append(PadToSquare(0))

    if 'resize' in opt.resize_or_crop:
        osize = [opt.loadSize, opt.loadSize]
        transform_list.append(transforms.Scale(osize, method))
    elif 'scale_width' in opt.resize_or_crop:
        transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.loadSize, method)))

    if 'crop' in opt.resize_or_crop:
        transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.fineSize)))

    if opt.resize_or_crop == 'none':
        base = float(2 ** opt.n_downsample_global)
        if opt.netG == 'local':
            base *= (2 ** opt.n_local_enhancers)
        transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))

    if opt.isTrain and not opt.no_flip:
        transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))

    transform_list += [transforms.ToTensor()]
    # transform_list.append(transforms.Lambda(lambda tensor: __one_hot_tensor(tensor, opt.output_nc)))

    if normalize:
        transform_list += [transforms.Normalize((0.5, 0.5, 0.5),
                                                (0.5, 0.5, 0.5))]
    return transforms.Compose(transform_list)