# coding=utf-8 # Copyright 2024 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 ConvNeXT.""" from typing import Dict, List, Optional, Union from ...image_processing_utils import BatchFeature from ...image_processing_utils_fast import ( BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, BaseImageProcessorFast, DefaultFastImageProcessorKwargs, group_images_by_shape, reorder_images, ) from ...image_transforms import get_resize_output_image_size from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, ) from ...processing_utils import Unpack from ...utils import ( TensorType, add_start_docstrings, 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 ConvNextFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): crop_pct: Optional[float] @add_start_docstrings( r"Constructs a fast ConvNeXT image processor.", BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, """ crop_pct (`float`, *optional*): Percentage of the image to crop. Only has an effect if size < 384. Can be overridden by `crop_pct` in the`preprocess` method. """, ) class ConvNextImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.BILINEAR image_mean = IMAGENET_STANDARD_MEAN image_std = IMAGENET_STANDARD_STD size = {"shortest_edge": 384} default_to_square = False do_resize = True do_rescale = True do_normalize = True crop_pct = 224 / 256 valid_kwargs = ConvNextFastImageProcessorKwargs def __init__(self, **kwargs: Unpack[ConvNextFastImageProcessorKwargs]): super().__init__(**kwargs) @add_start_docstrings( BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, """ crop_pct (`float`, *optional*): Percentage of the image to crop. Only has an effect if size < 384. Can be overridden by `crop_pct` in the`preprocess` method. """, ) def preprocess(self, images: ImageInput, **kwargs: Unpack[ConvNextFastImageProcessorKwargs]) -> BatchFeature: return super().preprocess(images, **kwargs) def resize( self, image: "torch.Tensor", size: Dict[str, int], crop_pct: float, interpolation: PILImageResampling = PILImageResampling.BICUBIC, **kwargs, ) -> "torch.Tensor": """ Resize an image. Args: image (`torch.Tensor`): Image to resize. size (`Dict[str, int]`): Dictionary of the form `{"shortest_edge": int}`, specifying the size of the output image. If `size["shortest_edge"]` >= 384 image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"] / crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. crop_pct (`float`): Percentage of the image to crop. Only has an effect if size < 384. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resizing the image. Returns: `torch.Tensor`: Resized image. """ if not size.shortest_edge: raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}") shortest_edge = size["shortest_edge"] if shortest_edge < 384: # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct resize_shortest_edge = int(shortest_edge / crop_pct) resize_size = get_resize_output_image_size( image, size=resize_shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST ) image = F.resize( image, resize_size, interpolation=interpolation, **kwargs, ) # then crop to (shortest_edge, shortest_edge) return F.center_crop( image, (shortest_edge, shortest_edge), **kwargs, ) else: # warping (no cropping) when evaluated at 384 or larger return F.resize( image, (shortest_edge, shortest_edge), interpolation=interpolation, **kwargs, ) def _preprocess( self, images: List["torch.Tensor"], do_resize: bool, size: Dict[str, int], crop_pct: float, interpolation: Optional["F.InterpolationMode"], do_center_crop: bool, crop_size: int, 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]], ) -> 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__ = ["ConvNextImageProcessorFast"]