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| # 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 DeiT.""" | |
| 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 resize, to_channel_dimension_format | |
| from ...image_utils import ( | |
| IMAGENET_STANDARD_MEAN, | |
| IMAGENET_STANDARD_STD, | |
| ChannelDimension, | |
| ImageInput, | |
| PILImageResampling, | |
| infer_channel_dimension_format, | |
| is_scaled_image, | |
| make_list_of_images, | |
| to_numpy_array, | |
| valid_images, | |
| ) | |
| from ...utils import TensorType, is_vision_available, logging | |
| if is_vision_available(): | |
| import PIL | |
| logger = logging.get_logger(__name__) | |
| class DeiTImageProcessor(BaseImageProcessor): | |
| r""" | |
| Constructs a DeiT image processor. | |
| 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 `preprocess`. | |
| size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`): | |
| Size of the image after `resize`. Can be overridden by `size` in `preprocess`. | |
| resample (`PILImageResampling` filter, *optional*, defaults to `Resampling.BICUBIC`): | |
| Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. | |
| do_center_crop (`bool`, *optional*, defaults to `True`): | |
| Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image | |
| is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in `preprocess`. | |
| crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): | |
| Desired output size when applying center-cropping. Can be overridden by `crop_size` in `preprocess`. | |
| rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): | |
| Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter 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 the `do_rescale` | |
| parameter in the `preprocess` method. | |
| do_normalize (`bool`, *optional*, defaults to `True`): | |
| Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` | |
| method. | |
| image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): | |
| 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 `IMAGENET_STANDARD_STD`): | |
| 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. | |
| """ | |
| model_input_names = ["pixel_values"] | |
| def __init__( | |
| self, | |
| do_resize: bool = True, | |
| size: Dict[str, int] = None, | |
| resample: PILImageResampling = PIL.Image.BICUBIC, | |
| do_center_crop: bool = True, | |
| crop_size: Dict[str, int] = None, | |
| rescale_factor: Union[int, float] = 1 / 255, | |
| do_rescale: bool = True, | |
| do_normalize: bool = True, | |
| image_mean: Optional[Union[float, List[float]]] = None, | |
| image_std: Optional[Union[float, List[float]]] = None, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__(**kwargs) | |
| size = size if size is not None else {"height": 256, "width": 256} | |
| size = get_size_dict(size) | |
| crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} | |
| crop_size = get_size_dict(crop_size, param_name="crop_size") | |
| self.do_resize = do_resize | |
| self.size = size | |
| self.resample = resample | |
| self.do_center_crop = do_center_crop | |
| self.crop_size = crop_size | |
| 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 IMAGENET_STANDARD_MEAN | |
| self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD | |
| # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC | |
| def resize( | |
| self, | |
| image: np.ndarray, | |
| size: Dict[str, int], | |
| resample: PILImageResampling = PILImageResampling.BICUBIC, | |
| 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.BICUBIC`): | |
| `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`. | |
| 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 preprocess( | |
| self, | |
| images: ImageInput, | |
| do_resize: bool = None, | |
| size: Dict[str, int] = None, | |
| resample=None, | |
| do_center_crop: bool = None, | |
| crop_size: Dict[str, int] = 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, | |
| return_tensors: Optional[Union[str, TensorType]] = None, | |
| data_format: ChannelDimension = ChannelDimension.FIRST, | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| **kwargs, | |
| ) -> 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_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`): | |
| Size of the image after `resize`. | |
| resample (`PILImageResampling`, *optional*, defaults to `self.resample`): | |
| PILImageResampling filter to use if resizing the image Only has an effect if `do_resize` is set to | |
| `True`. | |
| do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): | |
| Whether to center crop the image. | |
| crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): | |
| Size of the image after center crop. If one edge the image is smaller than `crop_size`, it will be | |
| padded with zeros and then cropped | |
| do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): | |
| Whether to rescale the image values between [0 - 1]. | |
| 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. | |
| image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): | |
| Image standard deviation. | |
| return_tensors (`str` or `TensorType`, *optional*): | |
| The type of tensors to return. Can be one of: | |
| - `None`: 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. | |
| 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 | |
| resample = resample if resample is not None else self.resample | |
| do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop | |
| 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 | |
| size = size if size is not None else self.size | |
| size = get_size_dict(size) | |
| crop_size = crop_size if crop_size is not None else self.crop_size | |
| crop_size = get_size_dict(crop_size, param_name="crop_size") | |
| 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." | |
| ) | |
| if do_resize and size is None or resample is None: | |
| raise ValueError("Size and resample must be specified if do_resize is True.") | |
| if do_center_crop and crop_size is None: | |
| raise ValueError("Crop size must be specified if do_center_crop is True.") | |
| if do_rescale and rescale_factor is None: | |
| raise ValueError("Rescale factor must be specified if do_rescale is True.") | |
| if do_normalize and (image_mean is None or image_std is None): | |
| raise ValueError("Image mean and std must be specified if do_normalize is True.") | |
| # All transformations expect numpy arrays. | |
| images = [to_numpy_array(image) for image in images] | |
| if is_scaled_image(images[0]) and do_rescale: | |
| logger.warning_once( | |
| "It looks like you are trying to rescale already rescaled images. If the input" | |
| " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." | |
| ) | |
| 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_center_crop: | |
| images = [ | |
| self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images | |
| ] | |
| if do_rescale: | |
| images = [ | |
| self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) | |
| for image in images | |
| ] | |
| if do_normalize: | |
| images = [ | |
| self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) | |
| for image in images | |
| ] | |
| images = [ | |
| to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images | |
| ] | |
| data = {"pixel_values": images} | |
| return BatchFeature(data=data, tensor_type=return_tensors) | |