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/
transformers_4_35_0
/models
/bridgetower
/image_processing_bridgetower.py
| # coding=utf-8 | |
| # Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and 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 BridgeTower.""" | |
| from typing import Any, Dict, Iterable, List, Optional, Tuple, Union | |
| import numpy as np | |
| from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict | |
| from ...image_transforms import PaddingMode, center_crop, pad, resize, to_channel_dimension_format | |
| from ...image_utils import ( | |
| OPENAI_CLIP_MEAN, | |
| OPENAI_CLIP_STD, | |
| ChannelDimension, | |
| ImageInput, | |
| PILImageResampling, | |
| get_image_size, | |
| infer_channel_dimension_format, | |
| is_batched, | |
| is_scaled_image, | |
| to_numpy_array, | |
| valid_images, | |
| ) | |
| from ...utils import TensorType, is_vision_available, logging | |
| if is_vision_available(): | |
| import PIL | |
| logger = logging.get_logger(__name__) | |
| # Copied from transformers.models.vilt.image_processing_vilt.max_across_indices | |
| def max_across_indices(values: Iterable[Any]) -> List[Any]: | |
| """ | |
| Return the maximum value across all indices of an iterable of values. | |
| """ | |
| return [max(values_i) for values_i in zip(*values)] | |
| # Copied from transformers.models.vilt.image_processing_vilt.make_pixel_mask | |
| def make_pixel_mask( | |
| image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None | |
| ) -> np.ndarray: | |
| """ | |
| Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. | |
| Args: | |
| image (`np.ndarray`): | |
| Image to make the pixel mask for. | |
| output_size (`Tuple[int, int]`): | |
| Output size of the mask. | |
| """ | |
| input_height, input_width = get_image_size(image, channel_dim=input_data_format) | |
| mask = np.zeros(output_size, dtype=np.int64) | |
| mask[:input_height, :input_width] = 1 | |
| return mask | |
| # Copied from transformers.models.vilt.image_processing_vilt.get_max_height_width | |
| def get_max_height_width( | |
| images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None | |
| ) -> List[int]: | |
| """ | |
| Get the maximum height and width across all images in a batch. | |
| """ | |
| if input_data_format is None: | |
| input_data_format = infer_channel_dimension_format(images[0]) | |
| if input_data_format == ChannelDimension.FIRST: | |
| _, max_height, max_width = max_across_indices([img.shape for img in images]) | |
| elif input_data_format == ChannelDimension.LAST: | |
| max_height, max_width, _ = max_across_indices([img.shape for img in images]) | |
| else: | |
| raise ValueError(f"Invalid channel dimension format: {input_data_format}") | |
| return (max_height, max_width) | |
| # Copied from transformers.models.vilt.image_processing_vilt.get_resize_output_image_size | |
| def get_resize_output_image_size( | |
| input_image: np.ndarray, | |
| shorter: int = 800, | |
| longer: int = 1333, | |
| size_divisor: int = 32, | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| ) -> Tuple[int, int]: | |
| input_height, input_width = get_image_size(input_image, input_data_format) | |
| min_size, max_size = shorter, longer | |
| scale = min_size / min(input_height, input_width) | |
| if input_height < input_width: | |
| new_height = min_size | |
| new_width = scale * input_width | |
| else: | |
| new_height = scale * input_height | |
| new_width = min_size | |
| if max(new_height, new_width) > max_size: | |
| scale = max_size / max(new_height, new_width) | |
| new_height = scale * new_height | |
| new_width = scale * new_width | |
| new_height, new_width = int(new_height + 0.5), int(new_width + 0.5) | |
| new_height = new_height // size_divisor * size_divisor | |
| new_width = new_width // size_divisor * size_divisor | |
| return new_height, new_width | |
| class BridgeTowerImageProcessor(BaseImageProcessor): | |
| r""" | |
| Constructs a BridgeTower 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 the | |
| `do_resize` parameter in the `preprocess` method. | |
| size (`Dict[str, int]` *optional*, defaults to 288): | |
| Resize the shorter side of the input to `size["shortest_edge"]`. The longer side will be limited to under | |
| `int((1333 / 800) * size["shortest_edge"])` while preserving the aspect ratio. Only has an effect if | |
| `do_resize` is set to `True`. Can be overridden by the `size` parameter in the `preprocess` method. | |
| size_divisor (`int`, *optional*, defaults to 32): | |
| The size by which to make sure both the height and width can be divided. Only has an effect if `do_resize` | |
| is set to `True`. Can be overridden by the `size_divisor` parameter in the `preprocess` method. | |
| resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): | |
| Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be | |
| overridden by the `resample` 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. | |
| rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): | |
| Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be | |
| overridden by the `rescale_factor` 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. 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. 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. | |
| Can be overridden by the `image_std` parameter in the `preprocess` method. | |
| do_center_crop (`bool`, *optional*, defaults to `True`): | |
| Whether to center crop the image. Can be overridden by the `do_center_crop` parameter in the `preprocess` | |
| method. | |
| do_pad (`bool`, *optional*, defaults to `True`): | |
| Whether to pad the image to the `(max_height, max_width)` of the images in the batch. Can be overridden by | |
| the `do_pad` parameter in the `preprocess` method. | |
| """ | |
| model_input_names = ["pixel_values"] | |
| def __init__( | |
| self, | |
| do_resize: bool = True, | |
| size: Dict[str, int] = 288, | |
| size_divisor: int = 32, | |
| resample: PILImageResampling = 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_center_crop: bool = True, | |
| do_pad: bool = True, | |
| **kwargs, | |
| ) -> None: | |
| if "pad_and_return_pixel_mask" in kwargs: | |
| do_pad = kwargs.pop("pad_and_return_pixel_mask") | |
| super().__init__(**kwargs) | |
| size = size if size is not None else {"shortest_edge": 288} | |
| size = get_size_dict(size, default_to_square=False) | |
| self.do_resize = do_resize | |
| self.size = size | |
| self.size_divisor = size_divisor | |
| 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 OPENAI_CLIP_MEAN | |
| self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD | |
| self.do_pad = do_pad | |
| self.do_center_crop = do_center_crop | |
| # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.resize | |
| def resize( | |
| self, | |
| image: np.ndarray, | |
| size: Dict[str, int], | |
| size_divisor: int = 32, | |
| 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. | |
| Resizes the shorter side of the image to `size["shortest_edge"]` while preserving the aspect ratio. If the | |
| longer side is larger than the max size `(int(`size["shortest_edge"]` * 1333 / 800))`, the longer side is then | |
| resized to the max size while preserving the aspect ratio. | |
| Args: | |
| image (`np.ndarray`): | |
| Image to resize. | |
| size (`Dict[str, int]`): | |
| Controls the size of the output image. Should be of the form `{"shortest_edge": int}`. | |
| size_divisor (`int`, defaults to 32): | |
| The image is resized to a size that is a multiple of this value. | |
| resample (`PILImageResampling` filter, *optional*, defaults to `PILImageResampling.BICUBIC`): | |
| Resampling filter to use when resiizing the image. | |
| 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 (`str` or `ChannelDimension`, *optional*): | |
| The channel dimension format of the input image. If not provided, it will be inferred. | |
| """ | |
| size = get_size_dict(size, default_to_square=False) | |
| if "shortest_edge" not in size: | |
| raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}") | |
| shorter = size["shortest_edge"] | |
| longer = int(1333 / 800 * shorter) | |
| output_size = get_resize_output_image_size( | |
| image, shorter=shorter, longer=longer, size_divisor=size_divisor, input_data_format=input_data_format | |
| ) | |
| return resize( | |
| image, | |
| size=output_size, | |
| resample=resample, | |
| data_format=data_format, | |
| input_data_format=input_data_format, | |
| **kwargs, | |
| ) | |
| def center_crop( | |
| self, | |
| image: np.ndarray, | |
| size: Dict[str, int], | |
| data_format: Optional[Union[str, ChannelDimension]] = None, | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| **kwargs, | |
| ) -> np.ndarray: | |
| """ | |
| Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along | |
| any edge, the image is padded with 0's and then center cropped. | |
| Args: | |
| image (`np.ndarray`): | |
| Image to center crop. | |
| size (`Dict[str, int]`): | |
| Size of the output image in the form `{"height": h, "width": w}`. | |
| 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 from the input | |
| image. | |
| """ | |
| output_size = size["shortest_edge"] | |
| return center_crop( | |
| image, | |
| size=(output_size, output_size), | |
| data_format=data_format, | |
| input_data_format=input_data_format, | |
| **kwargs, | |
| ) | |
| # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image | |
| def _pad_image( | |
| self, | |
| image: np.ndarray, | |
| output_size: Tuple[int, int], | |
| constant_values: Union[float, Iterable[float]] = 0, | |
| data_format: Optional[ChannelDimension] = None, | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| ) -> np.ndarray: | |
| """ | |
| Pad an image with zeros to the given size. | |
| """ | |
| input_height, input_width = get_image_size(image, channel_dim=input_data_format) | |
| output_height, output_width = output_size | |
| pad_bottom = output_height - input_height | |
| pad_right = output_width - input_width | |
| padding = ((0, pad_bottom), (0, pad_right)) | |
| padded_image = pad( | |
| image, | |
| padding, | |
| mode=PaddingMode.CONSTANT, | |
| constant_values=constant_values, | |
| data_format=data_format, | |
| input_data_format=input_data_format, | |
| ) | |
| return padded_image | |
| # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad | |
| def pad( | |
| self, | |
| images: List[np.ndarray], | |
| constant_values: Union[float, Iterable[float]] = 0, | |
| return_pixel_mask: bool = True, | |
| return_tensors: Optional[Union[str, TensorType]] = None, | |
| data_format: Optional[ChannelDimension] = None, | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| ) -> BatchFeature: | |
| """ | |
| Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width | |
| in the batch and optionally returns their corresponding pixel mask. | |
| Args: | |
| image (`np.ndarray`): | |
| Image to pad. | |
| constant_values (`float` or `Iterable[float]`, *optional*): | |
| The value to use for the padding if `mode` is `"constant"`. | |
| return_pixel_mask (`bool`, *optional*, defaults to `True`): | |
| Whether to return a pixel mask. | |
| 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 (`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. | |
| """ | |
| pad_size = get_max_height_width(images, input_data_format=input_data_format) | |
| padded_images = [ | |
| self._pad_image( | |
| image, | |
| pad_size, | |
| constant_values=constant_values, | |
| data_format=data_format, | |
| input_data_format=input_data_format, | |
| ) | |
| for image in images | |
| ] | |
| data = {"pixel_values": padded_images} | |
| if return_pixel_mask: | |
| masks = [ | |
| make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format) | |
| for image in images | |
| ] | |
| data["pixel_mask"] = masks | |
| return BatchFeature(data=data, tensor_type=return_tensors) | |
| def preprocess( | |
| self, | |
| images: ImageInput, | |
| do_resize: Optional[bool] = None, | |
| size: Optional[Dict[str, int]] = None, | |
| size_divisor: Optional[int] = None, | |
| resample: PILImageResampling = None, | |
| do_rescale: Optional[bool] = None, | |
| rescale_factor: Optional[float] = None, | |
| do_normalize: Optional[bool] = None, | |
| image_mean: Optional[Union[float, List[float]]] = None, | |
| image_std: Optional[Union[float, List[float]]] = None, | |
| do_pad: Optional[bool] = None, | |
| do_center_crop: Optional[bool] = 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`): | |
| Controls the size of the image after `resize`. The shortest edge of the image is resized to | |
| `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image | |
| is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest | |
| edge equal to `int(size["shortest_edge"] * (1333 / 800))`. | |
| size_divisor (`int`, *optional*, defaults to `self.size_divisor`): | |
| The image is resized to a size that is a multiple of this value. | |
| resample (`PILImageResampling`, *optional*, defaults to `self.resample`): | |
| Resampling filter to use if resizing the image. 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 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 to normalize the image by if `do_normalize` is set to `True`. | |
| image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): | |
| Image standard deviation to normalize the image by if `do_normalize` is set to `True`. | |
| do_pad (`bool`, *optional*, defaults to `self.do_pad`): | |
| Whether to pad the image to the (max_height, max_width) in the batch. If `True`, a pixel mask is also | |
| created and returned. | |
| do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): | |
| 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. | |
| 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. | |
| """ | |
| do_resize = do_resize if do_resize is not None else self.do_resize | |
| size_divisor = size_divisor if size_divisor is not None else self.size_divisor | |
| 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_pad = do_pad if do_pad is not None else self.do_pad | |
| do_center_crop if do_center_crop is not None else self.do_center_crop | |
| size = size if size is not None else self.size | |
| size = get_size_dict(size, default_to_square=False) | |
| if not is_batched(images): | |
| 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_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 do_resize: | |
| images = [ | |
| self.resize( | |
| image=image, | |
| size=size, | |
| size_divisor=size_divisor, | |
| resample=resample, | |
| input_data_format=input_data_format, | |
| ) | |
| for image in images | |
| ] | |
| if do_center_crop: | |
| images = [ | |
| self.center_crop(image=image, size=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 | |
| ] | |
| if do_pad: | |
| encoded_outputs = self.pad( | |
| images, return_pixel_mask=True, return_tensors=return_tensors, input_data_format=data_format | |
| ) | |
| else: | |
| encoded_outputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors) | |
| return encoded_outputs | |