# 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. # coding: utf-8 import math from typing import List, Tuple, Union import numpy as np import torch from PIL import Image from torchvision.transforms import RandomResizedCrop from torchvision.transforms.functional import InterpolationMode, to_tensor class BucketResize: def __init__( self, max_area: float, interpolation: InterpolationMode = InterpolationMode.LANCZOS, aspect_ratios: List[str] = None, stride: Union[int, Tuple[int]] = None, ): self.max_area = max_area self.interpolation = interpolation assert aspect_ratios and stride, "`aspect_ratios` or `stride` not given!" self.buckets, self.bucket_ratios = self.init_buckets(aspect_ratios, max_area, stride) self.bucket_resize = { # NOTICE: 虽然名字叫 random, 但在这个 setting 下是 center crop, 无随机性 # bucket: (h,w) bucket: RandomResizedCrop( size=(bucket[0], bucket[1]), scale=(1, 1), ratio=(bucket_ratio, bucket_ratio), interpolation=self.interpolation, ) for bucket, bucket_ratio in zip(self.buckets, self.bucket_ratios) } def __call__(self, image: Union[torch.Tensor, Image.Image, List[Image.Image]]): if isinstance(image, torch.Tensor): height, width = image.shape[-2:] elif isinstance(image, Image.Image): width, height = image.size elif isinstance(image, list) and isinstance(image[0], Image.Image): width, height = image[0].size else: raise NotImplementedError bucket = self.find_nearest_bucket(width, height) resizer = self.bucket_resize[bucket] if isinstance(image, list) and isinstance(image[0], Image.Image): return torch.stack([to_tensor(resizer(_image)) for _image in image]) else: image = resizer(image) if isinstance(image, Image.Image): image = to_tensor(image) return image def find_nearest_bucket(self, width, height): """ 找到与给定图片最近的bucket尺寸 """ image_ratio = width / height diff = np.abs(image_ratio - self.bucket_ratios) index = diff.argmin() return self.buckets[index] @staticmethod def init_buckets(aspect_ratio_names, max_area, stride): """ 指定一些列最接近给定宽高比和面积的,同时整除vae降采样和patch_size倍数的宽高 """ if not isinstance(stride, (tuple, list)): stride = (stride, stride) height_factor, width_factor = stride buckets, bucket_ratios = [], [] for name in aspect_ratio_names: w, h = (int(v) for v in name.split(":")) aspect_ratio = w / h resize_width1 = math.sqrt(max_area * aspect_ratio) bucket_width1 = round(resize_width1 / width_factor) * width_factor resize_height1 = bucket_width1 / aspect_ratio bucket_height1 = round(resize_height1 / height_factor) * height_factor bucket_ratio1 = bucket_width1 / bucket_height1 bucket_area1 = bucket_width1 * bucket_height1 resize_height2 = math.sqrt(max_area / aspect_ratio) bucket_height2 = round(resize_height2 / height_factor) * height_factor resize_width2 = bucket_height2 * aspect_ratio bucket_width2 = round(resize_width2 / width_factor) * width_factor bucket_ratio2 = bucket_width2 / bucket_height2 bucket_area2 = bucket_width2 * bucket_height2 if abs(bucket_ratio1 - aspect_ratio) < abs(bucket_ratio2 - aspect_ratio): bucket_width, bucket_height = bucket_width1, bucket_height1 elif abs(bucket_ratio1 - aspect_ratio) > abs(bucket_ratio2 - aspect_ratio): bucket_width, bucket_height = bucket_width2, bucket_height2 else: if abs(bucket_area1 - max_area) <= abs(bucket_area2 - max_area): bucket_width, bucket_height = bucket_width1, bucket_height1 else: bucket_width, bucket_height = bucket_width2, bucket_height2 bucket_ratio = bucket_width / bucket_height buckets.append((bucket_height, bucket_width)) bucket_ratios.append(bucket_ratio) bucket_ratios = np.array(bucket_ratios) return buckets, bucket_ratios # ================================================================= # # <<< 这里是为您编写的 check 函数 >>> # ================================================================= # def check_buckets(max_area: int, aspect_ratios: List[str], stride: int): """ 一个检查并打印 BucketResize.init_buckets 输出的辅助函数。 Args: max_area (int): 目标总像素面积。 aspect_ratios (List[str]): 目标宽高比列表 (例如: ["1:1", "4:3"])。 stride (int): 步幅，高度和宽度必须是它的整数倍。 """ print(f"--- Checking Configuration ---") print(f"Max Area: {max_area} | Aspect Ratios: {aspect_ratios} | Stride: {stride}") print("-" * 35) buckets, bucket_ratios = BucketResize.init_buckets(aspect_ratios, max_area, stride) print("Generated Buckets (Height, Width) and Ratios:") for (h, w), ratio in zip(buckets, bucket_ratios): # 打印每个桶的尺寸、宽高比和总面积 print(f" - Bucket: ({h:4d}, {w:4d}) | Ratio: {ratio:.4f} | Area: {h*w}") print("\n") if __name__ == '__main__': # 示例1: 您提到的 256x256 的情况 # 注意: max_area 是总像素，所以是 256*256 check_buckets( # max_area=256*256, max_area=224*224, aspect_ratios=["21:9", '1:1', '4:3', '3:4', '9:16', '16:9'], stride=28 #16 ) # check_buckets( # max_area=640*640, # aspect_ratios=['1:1', '4:3', '3:4', '9:16', '16:9'], # stride=16 # ) # check_buckets( # max_area=512*512, # aspect_ratios=['1:1', '4:3', '3:4', '9:16', '16:9'], # stride=16 # ) # check_buckets( # max_area=1024*1024, # aspect_ratios=['1:1', '4:3', '3:4', '16:9', '9:16'], # stride=16 # )