wehub-resource-sync
127 lines
5.0 KiB
Python
127 lines
5.0 KiB
Python
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# coding: utf-8
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import math
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from typing import List, Tuple, Union
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import numpy as np
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import torch
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from PIL import Image
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from torchvision.transforms import RandomResizedCrop
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from torchvision.transforms.functional import InterpolationMode, to_tensor
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class BucketResize:
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def __init__(
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self,
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max_area: float,
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interpolation: InterpolationMode = InterpolationMode.LANCZOS,
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aspect_ratios: List[str] = None,
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stride: Union[int, Tuple[int]] = None,
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):
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self.max_area = max_area
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self.interpolation = interpolation
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assert aspect_ratios and stride, "`aspect_ratios` or `stride` not given!"
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self.buckets, self.bucket_ratios = self.init_buckets(aspect_ratios, max_area, stride)
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self.bucket_resize = {
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# NOTICE: despite the name, this setting performs a deterministic center crop.
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# bucket: (h,w)
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bucket: RandomResizedCrop(
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size=(bucket[0], bucket[1]),
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scale=(1, 1),
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ratio=(bucket_ratio, bucket_ratio),
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interpolation=self.interpolation,
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)
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for bucket, bucket_ratio in zip(self.buckets, self.bucket_ratios)
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}
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def __call__(self, image: Union[torch.Tensor, Image.Image, List[Image.Image]]):
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if isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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elif isinstance(image, Image.Image):
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width, height = image.size
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elif isinstance(image, list) and isinstance(image[0], Image.Image):
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width, height = image[0].size
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else:
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raise NotImplementedError
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bucket = self.find_nearest_bucket(width, height)
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resizer = self.bucket_resize[bucket]
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if isinstance(image, list) and isinstance(image[0], Image.Image):
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return torch.stack([to_tensor(resizer(_image)) for _image in image])
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else:
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image = resizer(image)
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if isinstance(image, Image.Image):
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image = to_tensor(image)
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return image
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def find_nearest_bucket(self, width, height):
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"""
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找到与给定图片最近的bucket尺寸
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"""
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image_ratio = width / height
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diff = np.abs(image_ratio - self.bucket_ratios)
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index = diff.argmin()
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return self.buckets[index]
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@staticmethod
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def init_buckets(aspect_ratio_names, max_area, stride):
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"""
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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
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"""
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if not isinstance(stride, (tuple, list)):
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stride = (stride, stride)
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height_factor, width_factor = stride
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buckets, bucket_ratios = [], []
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for name in aspect_ratio_names:
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w, h = (int(v) for v in name.split(":"))
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aspect_ratio = w / h
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resize_width1 = math.sqrt(max_area * aspect_ratio)
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bucket_width1 = round(resize_width1 / width_factor) * width_factor
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resize_height1 = bucket_width1 / aspect_ratio
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bucket_height1 = round(resize_height1 / height_factor) * height_factor
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bucket_ratio1 = bucket_width1 / bucket_height1
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bucket_area1 = bucket_width1 * bucket_height1
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resize_height2 = math.sqrt(max_area / aspect_ratio)
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bucket_height2 = round(resize_height2 / height_factor) * height_factor
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resize_width2 = bucket_height2 * aspect_ratio
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bucket_width2 = round(resize_width2 / width_factor) * width_factor
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bucket_ratio2 = bucket_width2 / bucket_height2
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bucket_area2 = bucket_width2 * bucket_height2
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if abs(bucket_ratio1 - aspect_ratio) < abs(bucket_ratio2 - aspect_ratio):
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bucket_width, bucket_height = bucket_width1, bucket_height1
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elif abs(bucket_ratio1 - aspect_ratio) > abs(bucket_ratio2 - aspect_ratio):
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bucket_width, bucket_height = bucket_width2, bucket_height2
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else:
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if abs(bucket_area1 - max_area) <= abs(bucket_area2 - max_area):
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bucket_width, bucket_height = bucket_width1, bucket_height1
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else:
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bucket_width, bucket_height = bucket_width2, bucket_height2
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bucket_ratio = bucket_width / bucket_height
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buckets.append((bucket_height, bucket_width))
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bucket_ratios.append(bucket_ratio)
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bucket_ratios = np.array(bucket_ratios)
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return buckets, bucket_ratios
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