# coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # 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. """Fast Image processor class for PoolFormer.""" from typing import Optional, Union from ...image_processing_utils_fast import ( BaseImageProcessorFast, BatchFeature, DefaultFastImageProcessorKwargs, ) from ...image_transforms import ( ChannelDimension, get_resize_output_image_size, get_size_with_aspect_ratio, group_images_by_shape, reorder_images, ) from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ImageInput, PILImageResampling, SizeDict, get_image_size_for_max_height_width, ) from ...processing_utils import Unpack from ...utils import ( TensorType, auto_docstring, is_torch_available, is_torchvision_available, is_torchvision_v2_available, ) if is_torch_available(): import torch if is_torchvision_available(): if is_torchvision_v2_available(): from torchvision.transforms.v2 import functional as F else: from torchvision.transforms import functional as F class PoolFormerFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): """ Args: crop_pct (`float`, *optional*, defaults to `self.crop_pct`): Percentage of the image to crop. Only has an effect if `do_resize` is set to `True`. """ crop_pct: Optional[float] @auto_docstring class PoolFormerImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.BICUBIC image_mean = IMAGENET_DEFAULT_MEAN image_std = IMAGENET_DEFAULT_STD size = {"shortest_edge": 224} default_to_square = False crop_size = {"height": 224, "width": 224} crop_pct = 0.9 do_resize = True do_center_crop = True do_rescale = True do_normalize = True valid_kwargs = PoolFormerFastImageProcessorKwargs def __init__(self, **kwargs: Unpack[PoolFormerFastImageProcessorKwargs]): super().__init__(**kwargs) @auto_docstring def preprocess(self, images: ImageInput, **kwargs: Unpack[PoolFormerFastImageProcessorKwargs]) -> BatchFeature: return super().preprocess(images, **kwargs) def resize( self, image: "torch.Tensor", size: SizeDict, crop_pct: Optional[float] = None, interpolation: "F.InterpolationMode" = None, antialias: bool = True, **kwargs, ) -> "torch.Tensor": """ Resize an image. If crop_pct is unset: - size is `{"height": h, "width": w}`: the image is resized to `(h, w)`. - size is `{"shortest_edge": s}`: the shortest edge of the image is resized to s whilst maintaining the aspect ratio. if crop_pct is set: - size is `{"height": h, "width": w}`: the image is resized to `(int(floor(h/crop_pct)), int(floor(w/crop_pct)))` - size is `{"height": c, "width": c}`: the shortest edge of the image is resized to `int(floor(c/crop_pct)` whilst maintaining the aspect ratio. - size is `{"shortest_edge": c}`: the shortest edge of the image is resized to `int(floor(c/crop_pct)` whilst maintaining the aspect ratio. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. crop_pct (`float`, *optional*): Percentage of the image that will be cropped from the center. If set, the image is resized resample (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`. Returns: `torch.Tensor`: The resized image. """ interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR if crop_pct is not None: if size.shortest_edge: scale_size = int(size.shortest_edge / crop_pct) elif size.height and size.width: if size.height == size.width: scale_size = int(size.height / crop_pct) else: scale_size = (int(size.height / crop_pct), int(size.width / crop_pct)) else: raise ValueError("Invalid size for resize: {}".format(size)) new_size = get_resize_output_image_size( image, size=scale_size, default_to_square=False, input_data_format=ChannelDimension.FIRST, ) else: if size.shortest_edge and size.longest_edge: # Resize the image so that the shortest edge or the longest edge is of the given size # while maintaining the aspect ratio of the original image. new_size = get_size_with_aspect_ratio( image.size()[-2:], size.shortest_edge, size.longest_edge, ) elif size.shortest_edge: new_size = get_resize_output_image_size( image, size=size.shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST, ) elif size.max_height and size.max_width: new_size = get_image_size_for_max_height_width(image.size()[-2:], size.max_height, size.max_width) elif size.height and size.width: new_size = (size.height, size.width) else: raise ValueError( "Size must contain 'height' and 'width' keys, or 'max_height' and 'max_width', or 'shortest_edge' key. Got" f" {size}." ) return F.resize(image, new_size, interpolation=interpolation, antialias=antialias) def center_crop( self, image: "torch.Tensor", size: SizeDict, **kwargs, ) -> "torch.Tensor": """ Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Args: image (`"torch.Tensor"`): Image to center crop. size (`Dict[str, int]`): Size of the output image. Returns: `torch.Tensor`: The center cropped image. """ if size.height is None or size.width is None: raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}") image_height, image_width = image.shape[-2:] crop_height, crop_width = size.height, size.width if crop_width > image_width or crop_height > image_height: padding_ltrb = [ (crop_width - image_width) // 2 if crop_width > image_width else 0, (crop_height - image_height) // 2 if crop_height > image_height else 0, (crop_width - image_width + 1) // 2 if crop_width > image_width else 0, (crop_height - image_height + 1) // 2 if crop_height > image_height else 0, ] image = F.pad(image, padding_ltrb, fill=0) # PIL uses fill value 0 image_height, image_width = image.shape[-2:] if crop_width == image_width and crop_height == image_height: return image crop_top = int((image_height - crop_height) / 2.0) crop_left = int((image_width - crop_width) / 2.0) return F.crop(image, crop_top, crop_left, crop_height, crop_width) def _preprocess( self, images: list["torch.Tensor"], do_resize: bool, size: SizeDict, crop_pct: float, interpolation: Optional["F.InterpolationMode"], do_center_crop: bool, crop_size: SizeDict, do_rescale: bool, rescale_factor: float, do_normalize: bool, image_mean: Optional[Union[float, list[float]]], image_std: Optional[Union[float, list[float]]], return_tensors: Optional[Union[str, TensorType]], **kwargs, ) -> BatchFeature: # Group images by size for batched resizing grouped_images, grouped_images_index = group_images_by_shape(images) resized_images_grouped = {} for shape, stacked_images in grouped_images.items(): if do_resize: stacked_images = self.resize( image=stacked_images, size=size, crop_pct=crop_pct, interpolation=interpolation ) resized_images_grouped[shape] = stacked_images resized_images = reorder_images(resized_images_grouped, grouped_images_index) # Group images by size for further processing # Needed in case do_resize is False, or resize returns images with different sizes grouped_images, grouped_images_index = group_images_by_shape(resized_images) processed_images_grouped = {} for shape, stacked_images in grouped_images.items(): if do_center_crop: stacked_images = self.center_crop(stacked_images, crop_size) # Fused rescale and normalize stacked_images = self.rescale_and_normalize( stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std ) processed_images_grouped[shape] = stacked_images processed_images = reorder_images(processed_images_grouped, grouped_images_index) processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) __all__ = ["PoolFormerImageProcessorFast"]