wehub-resource-sync
285 lines
14 KiB
Python
285 lines
14 KiB
Python
# coding=utf-8
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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Image processor class for VisualGLM."""
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from typing import Dict, List, Optional, Union
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import numpy as np
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import PIL
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from ..image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
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from ..image_transforms import (
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convert_to_rgb,
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normalize,
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rescale,
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resize,
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to_channel_dimension_format,
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)
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from ..image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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is_batched,
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to_numpy_array,
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valid_images,
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)
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from ..tokenizer_utils_base import TensorType
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__all__ = [
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"VisualGLMImageProcessor",
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]
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class VisualGLMImageProcessor(BaseImageProcessor):
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r"""
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Constructs a VisualGLM image processor.
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Args:
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do_resize (`bool`, *optional*, defaults to `True`):
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Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
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`do_resize` parameter in the `preprocess` method.
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size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
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Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
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method.
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resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
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Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
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overridden by the `resample` parameter in the `preprocess` method.
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do_rescale (`bool`, *optional*, defaults to `True`):
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Wwhether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
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`do_rescale` parameter in the `preprocess` method.
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rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
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Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
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overridden by the `rescale_factor` parameter in the `preprocess` method.
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do_normalize (`bool`, *optional*, defaults to `True`):
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Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
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method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
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image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
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Mean to use if normalizing the image. This is a float or list of floats the length of the number of
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channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
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overridden by the `image_mean` parameter in the `preprocess` method.
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image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
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Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
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number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
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Can be overridden by the `image_std` parameter in the `preprocess` method.
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do_convert_rgb (`bool`, *optional*, defaults to `True`):
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Whether to convert the image to RGB.
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"""
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model_input_names = ["pixel_values"]
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def __init__(
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self,
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do_resize: bool = True,
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size: Dict[str, int] = None,
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resample: PILImageResampling = PILImageResampling.BICUBIC,
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do_rescale: bool = True,
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rescale_factor: Union[int, float] = 1 / 255,
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do_normalize: bool = True,
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image_mean: Optional[Union[float, List[float]]] = None,
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image_std: Optional[Union[float, List[float]]] = None,
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do_convert_rgb: bool = True,
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**kwargs
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) -> None:
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super().__init__(**kwargs)
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default_image_mean = [0.48145466, 0.4578275, 0.40821073]
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default_image_std = [0.26862954, 0.26130258, 0.27577711]
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size = size if size is not None else {"height": 224, "width": 224}
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size = get_size_dict(size, default_to_square=True)
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self.do_resize = do_resize
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self.size = size
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self.resample = resample
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self.do_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_normalize = do_normalize
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self.image_mean = image_mean if image_mean is not None else default_image_mean
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self.image_std = image_std if image_std is not None else default_image_std
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self.do_convert_rgb = do_convert_rgb
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def resize(
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self,
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image: np.ndarray,
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size: Dict[str, int],
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resample: PILImageResampling = PILImageResampling.BICUBIC,
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data_format: Optional[Union[str, ChannelDimension]] = None,
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**kwargs
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) -> np.ndarray:
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"""
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Resize an image.
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Resizes the shorter side of the image to `size["shortest_edge"]` while preserving the aspect ratio. If the
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longer side is larger than the max size `(int(`size["shortest_edge"]` * 1333 / 800))`, the longer side is then
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resized to the max size while preserving the aspect ratio.
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Args:
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image (`np.ndarray`):
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Image to resize.
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size (`Dict[str, int]`):
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Controls the size of the output image. Should be of the form `{"shortest_edge": int}`.
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resample (`PILImageResampling` filter, *optional*, defaults to `PILImageResampling.BICUBIC`):
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Resampling filter to use when resiizing the image.
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data_format (`str` or `ChannelDimension`, *optional*):
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The channel dimension format of the image. If not provided, it will be the same as the input image.
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"""
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size = get_size_dict(size, default_to_square=True)
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output_size = (size["width"], size["height"])
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return resize(image, size=output_size, resample=resample, data_format=data_format, **kwargs)
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def rescale(
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self,
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image: np.ndarray,
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scale: Union[int, float],
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data_format: Optional[Union[str, ChannelDimension]] = None,
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**kwargs
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):
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"""
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Rescale an image by a scale factor. image = image * scale.
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Args:
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image (`np.ndarray`):
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Image to rescale.
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scale (`int` or `float`):
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Scale to apply to the image.
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data_format (`str` or `ChannelDimension`, *optional*):
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The channel dimension format of the image. If not provided, it will be the same as the input image.
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"""
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return rescale(image, scale=scale, data_format=data_format, **kwargs)
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def normalize(
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self,
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image: np.ndarray,
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mean: Union[float, List[float]],
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std: Union[float, List[float]],
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data_format: Optional[Union[str, ChannelDimension]] = None,
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**kwargs
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) -> np.ndarray:
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"""
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Normalize an image. image = (image - image_mean) / image_std.
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Args:
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image (`np.ndarray`):
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Image to normalize.
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mean (`float` or `List[float]`):
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Image mean.
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std (`float` or `List[float]`):
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Image standard deviation.
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data_format (`str` or `ChannelDimension`, *optional*):
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The channel dimension format of the image. If not provided, it will be the same as the input image.
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"""
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return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs)
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def preprocess(
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self,
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images: ImageInput,
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do_resize: Optional[bool] = None,
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size: Optional[Dict[str, int]] = None,
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resample: PILImageResampling = None,
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do_rescale: Optional[bool] = None,
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rescale_factor: Optional[float] = None,
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do_normalize: Optional[bool] = None,
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image_mean: Optional[Union[float, List[float]]] = None,
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image_std: Optional[Union[float, List[float]]] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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do_convert_rgb: bool = None,
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data_format: ChannelDimension = ChannelDimension.FIRST,
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**kwargs,
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) -> PIL.Image.Image:
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"""
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Preprocess an image or batch of images.
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Args:
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images (`ImageInput`):
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Image to preprocess.
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do_resize (`bool`, *optional*, defaults to `self.do_resize`):
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Whether to resize the image.
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size (`Dict[str, int]`, *optional*, defaults to `self.size`):
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Controls the size of the image after `resize`. The shortest edge of the image is resized to
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`size["shortest_edge"]` while preserving the aspect ratio. If the longest edge of this resized image
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is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
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edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
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resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
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Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
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do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
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Whether to rescale the image values between [0 - 1].
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rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
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Rescale factor to rescale the image by if `do_rescale` is set to `True`.
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do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
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Whether to normalize the image.
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image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
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Image mean to normalize the image by if `do_normalize` is set to `True`.
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image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
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Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
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do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
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Whether to convert the image to RGB.
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return_tensors (`str` or `TensorType`, *optional*):
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The type of tensors to return. Can be one of:
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- Unset: Return a list of `np.ndarray`.
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- `TensorType.PADDLE` or `'pt'`: Return a batch of type `paddle.Tensor`.
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- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
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data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
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The channel dimension format for the output image. Can be one of:
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- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- Unset: defaults to the channel dimension format of the input image.
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"""
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do_resize = do_resize if do_resize is not None else self.do_resize
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resample = resample if resample is not None else self.resample
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do_rescale = do_rescale if do_rescale is not None else self.do_rescale
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rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
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do_normalize = do_normalize if do_normalize is not None else self.do_normalize
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image_mean = image_mean if image_mean is not None else self.image_mean
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image_std = image_std if image_std is not None else self.image_std
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do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
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size = size if size is not None else self.size
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size = get_size_dict(size, default_to_square=False)
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if not is_batched(images):
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images = [images]
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if not valid_images(images):
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raise ValueError("Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "paddle.Tensor.")
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if do_resize and size is None or resample is None:
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raise ValueError("Size and resample must be specified if do_resize is True.")
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if do_rescale and rescale_factor is None:
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raise ValueError("Rescale factor must be specified if do_rescale is True.")
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if do_normalize and (image_mean is None or image_std is None):
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raise ValueError("Image mean and std must be specified if do_normalize is True.")
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# PIL RGBA images are converted to RGB
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if do_convert_rgb:
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images = [convert_to_rgb(image) for image in images]
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# All transformations expect numpy arrays.
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images = [to_numpy_array(image) for image in images]
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if do_resize:
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images = [self.resize(image=image, size=size, resample=resample) for image in images]
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if do_rescale:
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images = [self.rescale(image=image, scale=rescale_factor) for image in images]
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if do_normalize:
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images = [self.normalize(image=image, mean=image_mean, std=image_std) for image in images]
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images = [to_channel_dimension_format(image, data_format) for image in images]
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data = {"pixel_values": images}
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return BatchFeature(data=data, tensor_type=return_tensors)
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