Function get_transform

data/base_dataset.py:47–78

(opt, params, method=Image.BICUBIC, normalize=True, toTensor=True)

def get_transform(opt, params, method=Image.BICUBIC, normalize=True, toTensor=True):
    transform_list = []
    if 'resize' in opt.preprocess_mode:
        osize = [opt.load_size, opt.load_size]
        transform_list.append(transforms.Resize(osize, interpolation=method))
    elif 'scale_width' in opt.preprocess_mode:
        transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method)))
    elif 'scale_shortside' in opt.preprocess_mode:
        transform_list.append(transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, method)))

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

    if opt.preprocess_mode == 'none':
        base = 32
        transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))

    if opt.preprocess_mode == 'fixed':
        w = opt.crop_size
        h = round(opt.crop_size / opt.aspect_ratio)
        transform_list.append(transforms.Lambda(lambda img: __resize(img, w, h, method)))

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

    if toTensor:
        transform_list += [transforms.ToTensor()]

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


def normalize():