generation/data/base_dataset.py [117:169]:
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    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()]

    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():
    return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

def __make_power_2(img, base, method=Image.BICUBIC):
    ow, oh = img.size
    h = int(round(oh / base) * base)
    w = int(round(ow / base) * base)
    if (h == oh) and (w == ow):
        return img
    return img.resize((w, h), method)

def __scale_width(img, target_width, method=Image.BICUBIC):
    ow, oh = img.size
    if (ow == target_width):
        return img
    w = target_width
    h = int(target_width * oh / ow)
    return img.resize((w, h), method)

def __crop(img, pos, size):
    ow, oh = img.size
    x1, y1 = pos
    tw = th = size
    if (ow > tw or oh > th):
        return img.crop((x1, y1, x1 + tw, y1 + th))
    return img

def __flip(img, flip):
    if flip:
        return img.transpose(Image.FLIP_LEFT_RIGHT)
    return img
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separate_vae/data/base_dataset.py [117:170]:
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    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)

def normalize():
    return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

def __make_power_2(img, base, method=Image.BICUBIC):
    ow, oh = img.size
    h = int(round(oh / base) * base)
    w = int(round(ow / base) * base)
    if (h == oh) and (w == ow):
        return img
    return img.resize((w, h), method)

def __scale_width(img, target_width, method=Image.BICUBIC):
    ow, oh = img.size
    if (ow == target_width):
        return img
    w = target_width
    h = int(target_width * oh / ow)
    return img.resize((w, h), method)

def __crop(img, pos, size):
    ow, oh = img.size
    x1, y1 = pos
    tw = th = size
    if (ow > tw or oh > th):
        return img.crop((x1, y1, x1 + tw, y1 + th))
    return img

def __flip(img, flip):
    if flip:
        return img.transpose(Image.FLIP_LEFT_RIGHT)
    return img
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