wehub-resource-sync
264 lines
13 KiB
Python
264 lines
13 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2022 The OpenAI Team Authors and The HuggingFace 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 ViT."""
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from typing import Dict, List, Optional, Union
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import numpy as np
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from ..image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
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from ..image_transforms import normalize, rescale, resize, to_channel_dimension_format
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from ..image_utils import (
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IMAGENET_STANDARD_MEAN,
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IMAGENET_STANDARD_STD,
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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make_list_of_images,
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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__ = ["ViTImageProcessor"]
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class ViTImageProcessor(BaseImageProcessor):
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r"""
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Constructs a ViT 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["height"],
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size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
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size (`dict`, *optional*, defaults to `{"height": 224, "width": 224}`):
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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.BILINEAR`):
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Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
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`preprocess` method.
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do_rescale (`bool`, *optional*, defaults to `True`):
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Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
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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. Can be overridden by the `rescale_factor` parameter in the
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`preprocess` method.
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do_normalize:
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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.
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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.
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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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"""
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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: Optional[Dict[str, int]] = None,
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resample: PILImageResampling = PILImageResampling.BILINEAR,
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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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**kwargs,
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) -> None:
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super().__init__(**kwargs)
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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)
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self.do_resize = do_resize
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self.do_rescale = do_rescale
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self.do_normalize = do_normalize
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self.size = size
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self.resample = resample
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self.rescale_factor = rescale_factor
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self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
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self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
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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.BILINEAR,
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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 to `(size["height"], size["width"])`.
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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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Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
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resample:
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`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
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data_format (`ChannelDimension` or `str`, *optional*):
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The channel dimension format for the output image. If unset, the channel dimension format of the input
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image is used. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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Returns:
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`np.ndarray`: The resized image.
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"""
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size = get_size_dict(size)
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if "height" not in size or "width" not in size:
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raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
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return resize(
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image, size=(size["height"], size["width"]), resample=resample, data_format=data_format, **kwargs
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)
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def rescale(
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self, image: np.ndarray, scale: float, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs
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) -> np.ndarray:
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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 (`float`):
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The scaling factor to rescale pixel values by.
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data_format (`str` or `ChannelDimension`, *optional*):
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The channel dimension format for the output image. If unset, the channel dimension format of the input
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image is used. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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Returns:
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`np.ndarray`: The rescaled 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 to use for normalization.
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std (`float` or `List[float]`):
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Image standard deviation to use for normalization.
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data_format (`str` or `ChannelDimension`, *optional*):
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The channel dimension format for the output image. If unset, the channel dimension format of the input
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image is used. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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Returns:
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`np.ndarray`: The normalized 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: 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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data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
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**kwargs,
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):
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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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Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after
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resizing.
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resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
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`PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
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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 use 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 use if `do_normalize` is set to `True`.
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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 `'pd'`: 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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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- Unset: Use 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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do_rescale = do_rescale if do_rescale is not None else self.do_rescale
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do_normalize = do_normalize if do_normalize is not None else self.do_normalize
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resample = resample if resample is not None else self.resample
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rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
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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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size = size if size is not None else self.size
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size_dict = get_size_dict(size)
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
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"paddle.Tensor, tf.Tensor or jax.ndarray."
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)
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if do_resize and size is None:
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raise ValueError("Size 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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# 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_dict, 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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