# coding=utf-8 # 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 Dict, List, Optional, 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 ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging if is_vision_available(): import PIL logger = logging.get_logger(__name__) 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) 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 overriden 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"] def __init__( self, # clusters is a first argument to maintain backwards compatibility with the old ImageGPTImageProcessor clusters: Optional[Union[List[List[int]], np.ndarray]] = None, do_resize: bool = True, size: Dict[str, int] = 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: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = 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: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = 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: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_normalize: Optional[bool] = None, do_color_quantize: Optional[bool] = None, clusters: Optional[Union[List[List[int]], np.ndarray]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = 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.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: - `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, " "torch.Tensor, tf.Tensor or jax.ndarray." ) # Here, normalize() is using a constant factor to divide pixel values. # hence, the method does not need iamge_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) else: images = [ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images ] data = {"input_ids": images} return BatchFeature(data=data, tensor_type=return_tensors) __all__ = ["ImageGPTImageProcessor"]