# Copyright 2022 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 ImageGPT.""" from typing import Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import rescale, resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...processing_utils import ImagesKwargs from ...utils import TensorType, filter_out_non_signature_kwargs, is_torch_available, is_vision_available, logging from ...utils.import_utils import requires if is_vision_available(): import PIL if is_torch_available(): import torch logger = logging.get_logger(__name__) class ImageGPTImageProcessorKwargs(ImagesKwargs, total=False): """ clusters (`np.ndarray` or `list[list[int]]` or `torch.Tensor`, *optional*): The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overridden by `clusters` in `preprocess`. do_color_quantize (`bool`, *optional*, defaults to `True`): Controls whether to apply color quantization to convert continuous pixel values to discrete cluster indices. When True, each pixel is assigned to its nearest color cluster, enabling ImageGPT's discrete token modeling. """ clusters: Union[np.ndarray, list[list[int]], "torch.Tensor"] | None do_color_quantize: bool def squared_euclidean_distance(a, b): b = b.T a2 = np.sum(np.square(a), axis=1) b2 = np.sum(np.square(b), axis=0) ab = np.matmul(a, b) d = a2[:, None] - 2 * ab + b2[None, :] return d def color_quantize(x, clusters): x = x.reshape(-1, 3) d = squared_euclidean_distance(x, clusters) return np.argmin(d, axis=1) @requires(backends=("vision",)) class ImageGPTImageProcessor(BaseImageProcessor): r""" Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution (such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values" (color clusters). Args: clusters (`np.ndarray` or `list[list[int]]`, *optional*): The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overridden by `clusters` in `preprocess`. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by `do_resize` in `preprocess`. size (`dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`): Size of the image after resizing. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in `preprocess`. do_color_quantize (`bool`, *optional*, defaults to `True`): Whether to color quantize the image. Can be overridden by `do_color_quantize` in `preprocess`. """ model_input_names = ["pixel_values"] valid_kwargs = ImageGPTImageProcessorKwargs def __init__( self, # clusters is a first argument to maintain backwards compatibility with the old ImageGPTImageProcessor clusters: list[list[int]] | np.ndarray | None = None, do_resize: bool = True, size: dict[str, int] | None = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_normalize: bool = True, do_color_quantize: bool = True, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 256, "width": 256} size = get_size_dict(size) self.clusters = np.array(clusters) if clusters is not None else None self.do_resize = do_resize self.size = size self.resample = resample self.do_normalize = do_normalize self.do_color_quantize = do_color_quantize # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize def resize( self, image: np.ndarray, size: dict[str, int], resample: PILImageResampling = PILImageResampling.BILINEAR, data_format: str | ChannelDimension | None = None, input_data_format: str | ChannelDimension | None = None, **kwargs, ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. 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. 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. Returns: `np.ndarray`: The resized image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}") output_size = (size["height"], size["width"]) return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) def normalize( self, image: np.ndarray, data_format: str | ChannelDimension | None = None, input_data_format: str | ChannelDimension | None = None, ) -> np.ndarray: """ Normalizes an images' pixel values to between [-1, 1]. Args: image (`np.ndarray`): Image to normalize. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ image = rescale(image=image, scale=1 / 127.5, data_format=data_format, input_data_format=input_data_format) image = image - 1 return image @filter_out_non_signature_kwargs() def preprocess( self, images: ImageInput, do_resize: bool | None = None, size: dict[str, int] | None = None, resample: PILImageResampling | None = None, do_normalize: bool | None = None, do_color_quantize: bool | None = None, clusters: list[list[int]] | np.ndarray | None = None, return_tensors: str | TensorType | None = None, data_format: str | ChannelDimension | None = ChannelDimension.FIRST, input_data_format: str | ChannelDimension | None = None, ) -> PIL.Image.Image: """ 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_normalize=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. resample (`int`, *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_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`): Whether to color quantize the image. clusters (`np.ndarray` or `list[list[int]]`, *optional*, defaults to `self.clusters`): Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if `do_color_quantize` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. Only has an effect if `do_color_quantize` is set to `False`. 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. """ do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size) resample = resample if resample is not None else self.resample do_normalize = do_normalize if do_normalize is not None else self.do_normalize do_color_quantize = do_color_quantize if do_color_quantize is not None else self.do_color_quantize clusters = clusters if clusters is not None else self.clusters clusters = np.array(clusters) images = make_list_of_images(images) if not valid_images(images): raise ValueError("Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor") # Here, normalize() is using a constant factor to divide pixel values. # hence, the method does not need image_mean and image_std. validate_preprocess_arguments( do_resize=do_resize, size=size, resample=resample, ) if do_color_quantize and clusters is None: raise ValueError("Clusters must be specified if do_color_quantize is True.") # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_normalize and is_scaled_image(images[0]): logger.warning_once( "It looks like you are trying to rescale already rescaled images. If you wish to do this, " "make sure to set `do_normalize` to `False` and that pixel values are between [-1, 1].", ) if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) if do_resize: images = [ self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) for image in images ] if do_normalize: images = [self.normalize(image=image, input_data_format=input_data_format) for image in images] if do_color_quantize: images = [to_channel_dimension_format(image, ChannelDimension.LAST, input_data_format) for image in images] # color quantize from (batch_size, height, width, 3) to (batch_size, height, width) images = np.array(images) images = color_quantize(images, clusters).reshape(images.shape[:-1]) # flatten to (batch_size, height*width) batch_size = images.shape[0] images = images.reshape(batch_size, -1) # We need to convert back to a list of images to keep consistent behaviour across processors. images = list(images) data = {"input_ids": images} else: images = [to_channel_dimension_format(image, data_format, input_data_format) for image in images] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors) def to_dict(self): output = super().to_dict() # Ensure clusters are JSON/equality friendly if output.get("clusters") is not None and isinstance(output["clusters"], np.ndarray): output["clusters"] = output["clusters"].tolist() # Need to set missing keys from slow processor to match the expected behavior in save/load tests compared to fast processor missing_keys = ["image_mean", "image_std", "rescale_factor", "do_rescale"] for key in missing_keys: if key in output: output[key] = None return output __all__ = ["ImageGPTImageProcessor"]