# 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. """Image processor class for Pixtral.""" from typing import Dict, List, Optional, Union from ...image_processing_utils import get_size_dict from ...image_processing_utils_fast import BaseImageProcessorFast from ...image_utils import ( ChannelDimension, ImageInput, ImageType, PILImageResampling, get_image_size, get_image_type, infer_channel_dimension_format, validate_fast_preprocess_arguments, validate_kwargs, ) from ...utils import ( TensorType, is_torch_available, is_torchvision_available, is_torchvision_v2_available, is_vision_available, logging, ) from .image_processing_pixtral import ( BatchMixFeature, convert_to_rgb, get_resize_output_image_size, make_list_of_images, ) logger = logging.get_logger(__name__) if is_torch_available(): import torch if is_torchvision_available(): if is_vision_available(): from ...image_utils import pil_torch_interpolation_mapping if is_torchvision_v2_available(): from torchvision.transforms.v2 import functional as F else: from torchvision.transforms import functional as F class PixtralImageProcessorFast(BaseImageProcessorFast): r""" Constructs a fast Pixtral image processor that leverages torchvision. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"longest_edge": 1024}`): Size of the maximum dimension of either the height or width dimension of the image. Used to control how images are resized. If either the height or width are greater than `size["longest_edge"]` then both the height and width are rescaled by `height / ratio`, `width /ratio` where `ratio = max(height / longest_edge, width / longest_edge)` patch_size (`Dict[str, int]` *optional*, defaults to `{"height": 16, "width": 16}`): Size of the patches in the model, used to calculate the output image size. Can be overridden by `patch_size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, patch_size: Dict[str, int] = None, resample: Union[PILImageResampling, "F.InterpolationMode"] = PILImageResampling.BICUBIC, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_convert_rgb: bool = True, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"longest_edge": 1024} patch_size = patch_size if patch_size is not None else {"height": 16, "width": 16} patch_size = get_size_dict(patch_size, default_to_square=True) self.do_resize = do_resize self.size = size self.patch_size = patch_size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073] self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711] self.do_convert_rgb = do_convert_rgb self._valid_processor_keys = [ "images", "do_resize", "size", "patch_size", "resample", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "do_convert_rgb", "return_tensors", "data_format", "input_data_format", ] def resize( self, image: torch.Tensor, size: Dict[str, int], patch_size: Dict[str, int], interpolation: "F.InterpolationMode" = None, **kwargs, ) -> torch.Tensor: """ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Args: image (`torch.Tensor`): Image to resize. size (`Dict[str, int]`): Dict containing the longest possible edge of the image. patch_size (`Dict[str, int]`): Patch size used to calculate the size of the output image. interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): Resampling filter to use when resiizing the image. """ interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR if "longest_edge" in size: size = (size["longest_edge"], size["longest_edge"]) elif "height" in size and "width" in size: size = (size["height"], size["width"]) else: raise ValueError("size must contain either 'longest_edge' or 'height' and 'width'.") if "height" in patch_size and "width" in patch_size: patch_size = (patch_size["height"], patch_size["width"]) else: raise ValueError("patch_size must contain either 'shortest_edge' or 'height' and 'width'.") output_size = get_resize_output_image_size( image, size=size, patch_size=patch_size, ) return F.resize( image, size=output_size, interpolation=interpolation, **kwargs, ) def preprocess( self, images: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, patch_size: Dict[str, int] = None, resample: Optional[Union[PILImageResampling, "F.InterpolationMode"]] = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_convert_rgb: bool = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> BatchMixFeature: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Describes the maximum input dimensions to the model. patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`): Patch size in the model. Used to calculate the image after resizing. resample (`PILImageResampling` or `InterpolationMode`, *optional*, defaults to self.resample): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ patch_size = patch_size if patch_size is not None else self.patch_size patch_size = get_size_dict(patch_size, default_to_square=True) do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size resample = resample if resample is not None else self.resample do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb device = kwargs.pop("device", None) validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) images_list = make_list_of_images(images) image_type = get_image_type(images_list[0][0]) if image_type not in [ImageType.PIL, ImageType.TORCH, ImageType.NUMPY]: raise ValueError(f"Unsupported input image type {image_type}") validate_fast_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_resize=do_resize, size=size, resample=resample, return_tensors=return_tensors, data_format=data_format, ) if do_convert_rgb: images_list = [[convert_to_rgb(image) for image in images] for images in images_list] if image_type == ImageType.PIL: images_list = [[F.pil_to_tensor(image) for image in images] for images in images_list] elif image_type == ImageType.NUMPY: # not using F.to_tensor as it doesn't handle (C, H, W) numpy arrays images_list = [[torch.from_numpy(image).contiguous() for image in images] for images in images_list] if device is not None: images_list = [[image.to(device) for image in images] for images in images_list] # We assume that all images have the same channel dimension format. if input_data_format is None: input_data_format = infer_channel_dimension_format(images_list[0][0]) if input_data_format == ChannelDimension.LAST: images_list = [[image.permute(2, 0, 1).contiguous() for image in images] for images in images_list] input_data_format = ChannelDimension.FIRST if do_rescale and do_normalize: # fused rescale and normalize new_mean = torch.tensor(image_mean, device=images_list[0][0].device) * (1.0 / rescale_factor) new_std = torch.tensor(image_std, device=images_list[0][0].device) * (1.0 / rescale_factor) batch_images = [] batch_image_sizes = [] for sample_images in images_list: images = [] image_sizes = [] for image in sample_images: if do_resize: interpolation = ( pil_torch_interpolation_mapping[resample] if isinstance(resample, (PILImageResampling, int)) else resample ) image = self.resize( image=image, size=size, patch_size=patch_size, interpolation=interpolation, ) if do_rescale and do_normalize: # fused rescale and normalize image = F.normalize(image.to(dtype=torch.float32), new_mean, new_std) elif do_rescale: image = image * rescale_factor elif do_normalize: image = F.normalize(image, image_mean, image_std) images.append(image) image_sizes.append(get_image_size(image, input_data_format)) batch_images.append(images) batch_image_sizes.append(image_sizes) return BatchMixFeature(data={"pixel_values": batch_images, "image_sizes": batch_image_sizes}, tensor_type=None)