# 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 EfficientNet.""" from functools import lru_cache from typing import Optional, Union from ...image_processing_utils_fast import ( BaseImageProcessorFast, BatchFeature, DefaultFastImageProcessorKwargs, ) from ...image_transforms import group_images_by_shape, reorder_images from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ImageInput, PILImageResampling, SizeDict, ) 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 EfficientNetFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): """ Args: rescale_offset (`bool`, *optional*, defaults to `self.rescale_offset`): Whether to rescale the image between [-max_range/2, scale_range/2] instead of [0, scale_range]. include_top (`bool`, *optional*, defaults to `self.include_top`): Normalize the image again with the standard deviation only for image classification if set to True. """ rescale_offset: bool include_top: bool @auto_docstring class EfficientNetImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.NEAREST image_mean = IMAGENET_STANDARD_MEAN image_std = IMAGENET_STANDARD_STD size = {"height": 346, "width": 346} crop_size = {"height": 289, "width": 289} do_resize = True do_center_crop = False do_rescale = True rescale_factor = 1 / 255 rescale_offset = False do_normalize = True include_top = True valid_kwargs = EfficientNetFastImageProcessorKwargs def __init__(self, **kwargs: Unpack[EfficientNetFastImageProcessorKwargs]): super().__init__(**kwargs) def rescale( self, image: "torch.Tensor", scale: float, offset: Optional[bool] = True, **kwargs, ) -> "torch.Tensor": """ Rescale an image by a scale factor. If `offset` is `True`, the image has its values rescaled by `scale` and then offset by 1. If `scale` is 1/127.5, the image is rescaled between [-1, 1]. image = image * scale - 1 If `offset` is `False`, and `scale` is 1/255, the image is rescaled between [0, 1]. image = image * scale Args: image (`torch.Tensor`): Image to rescale. scale (`float`): The scaling factor to rescale pixel values by. offset (`bool`, *optional*): Whether to scale the image in both negative and positive directions. Returns: `torch.Tensor`: The rescaled image. """ rescaled_image = image * scale if offset: rescaled_image -= 1 return rescaled_image @lru_cache(maxsize=10) def _fuse_mean_std_and_rescale_factor( self, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, list[float]]] = None, image_std: Optional[Union[float, list[float]]] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, device: Optional["torch.device"] = None, rescale_offset: Optional[bool] = False, ) -> tuple: if do_rescale and do_normalize and not rescale_offset: # Fused rescale and normalize image_mean = torch.tensor(image_mean, device=device) * (1.0 / rescale_factor) image_std = torch.tensor(image_std, device=device) * (1.0 / rescale_factor) do_rescale = False return image_mean, image_std, do_rescale def rescale_and_normalize( self, images: "torch.Tensor", do_rescale: bool, rescale_factor: float, do_normalize: bool, image_mean: Union[float, list[float]], image_std: Union[float, list[float]], rescale_offset: bool = False, ) -> "torch.Tensor": """ Rescale and normalize images. """ image_mean, image_std, do_rescale = self._fuse_mean_std_and_rescale_factor( do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_rescale=do_rescale, rescale_factor=rescale_factor, device=images.device, rescale_offset=rescale_offset, ) # if/elif as we use fused rescale and normalize if both are set to True if do_rescale: images = self.rescale(images, rescale_factor, rescale_offset) if do_normalize: images = self.normalize(images.to(dtype=torch.float32), image_mean, image_std) return images def _preprocess( self, images: list["torch.Tensor"], do_resize: bool, size: SizeDict, interpolation: Optional["F.InterpolationMode"], do_center_crop: bool, crop_size: SizeDict, do_rescale: bool, rescale_factor: float, rescale_offset: bool, do_normalize: bool, include_top: 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, 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, rescale_offset ) if include_top: stacked_images = self.normalize(stacked_images, 0, 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) @auto_docstring def preprocess(self, images: ImageInput, **kwargs: Unpack[EfficientNetFastImageProcessorKwargs]) -> BatchFeature: return super().preprocess(images, **kwargs) __all__ = ["EfficientNetImageProcessorFast"]