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# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // Licensed under the Apache License, Version 2.0 (the "License");
# // you may not use this file except in compliance with the License.
# // You may obtain a copy of the License at
# //
# // http://www.apache.org/licenses/LICENSE-2.0
# //
# // Unless required by applicable law or agreed to in writing, software
# // distributed under the License is distributed on an "AS IS" BASIS,
# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# // See the License for the specific language governing permissions and
# // limitations under the License.
from typing import Union
import torch
from PIL import Image
from torchvision.transforms import InterpolationMode
from torchvision.transforms import functional as TVF
class SideResize:
def __init__(
self,
size: int,
downsample_only: bool = False,
interpolation: InterpolationMode = InterpolationMode.BICUBIC,
):
self.size = size
self.downsample_only = downsample_only
self.interpolation = interpolation
def __call__(self, image: Union[torch.Tensor, Image.Image]):
"""
Args:
image (PIL Image or Tensor): Image to be scaled.
Returns:
PIL Image or Tensor: Rescaled image.
"""
if isinstance(image, torch.Tensor):
height, width = image.shape[-2:]
elif isinstance(image, Image.Image):
width, height = image.size
else:
raise NotImplementedError
if self.downsample_only and min(width, height) < self.size:
# keep original height and width for small pictures.
size = min(width, height)
else:
size = self.size
return TVF.resize(image, size, self.interpolation)
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