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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 BiT.""" | |
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 ( | |
convert_to_rgb, | |
get_resize_output_image_size, | |
resize, | |
to_channel_dimension_format, | |
) | |
from ...image_utils import ( | |
OPENAI_CLIP_MEAN, | |
OPENAI_CLIP_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 | |
logger = logging.get_logger(__name__) | |
if is_vision_available(): | |
import PIL | |
class BitImageProcessor(BaseImageProcessor): | |
r""" | |
Constructs a BiT 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 the `preprocess` method. | |
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): | |
Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with | |
the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` | |
method. | |
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): | |
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. | |
do_center_crop (`bool`, *optional*, defaults to `True`): | |
Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the | |
`preprocess` method. | |
crop_size (`Dict[str, int]` *optional*, defaults to 224): | |
Size of the output image after applying `center_crop`. Can be overridden by `crop_size` 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 `do_rescale` in | |
the `preprocess` method. | |
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): | |
Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` | |
method. | |
do_normalize: | |
Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method. | |
image_mean (`float` or `List[float]`, *optional*, defaults to `OPENAI_CLIP_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 `OPENAI_CLIP_MEAN`): | |
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_convert_rgb (`bool`, *optional*, defaults to `True`): | |
Whether to convert the image to RGB. | |
""" | |
model_input_names = ["pixel_values"] | |
def __init__( | |
self, | |
do_resize: bool = True, | |
size: Dict[str, int] = None, | |
resample: PILImageResampling = PILImageResampling.BICUBIC, | |
do_center_crop: bool = True, | |
crop_size: Dict[str, int] = None, | |
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_convert_rgb: bool = True, | |
**kwargs, | |
) -> None: | |
super().__init__(**kwargs) | |
size = size if size is not None else {"shortest_edge": 224} | |
size = get_size_dict(size, default_to_square=False) | |
crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} | |
crop_size = get_size_dict(crop_size, default_to_square=True, 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 OPENAI_CLIP_MEAN | |
self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD | |
self.do_convert_rgb = do_convert_rgb | |
# Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize | |
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. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge | |
resized to keep the input aspect ratio. | |
Args: | |
image (`np.ndarray`): | |
Image to resize. | |
size (`Dict[str, int]`): | |
Size of the output image. | |
resample (`PILImageResampling`, *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 (`ChannelDimension` or `str`, *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` parameter must contain the key `shortest_edge`. Got {size.keys()}") | |
output_size = get_resize_output_image_size( | |
image, size=size["shortest_edge"], default_to_square=False, 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 preprocess( | |
self, | |
images: ImageInput, | |
do_resize: bool = None, | |
size: Dict[str, int] = None, | |
resample: PILImageResampling = None, | |
do_center_crop: bool = None, | |
crop_size: 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, | |
do_convert_rgb: bool = None, | |
return_tensors: Optional[Union[str, TensorType]] = None, | |
data_format: Optional[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 resizing. Shortest edge of the image is resized to size["shortest_edge"], with | |
the longest edge resized to keep the input aspect ratio. | |
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_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 center crop. Only has an effect if `do_center_crop` is set to `True`. | |
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): | |
Whether to rescale the image. | |
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 use for normalization. Only has an effect if `do_normalize` is set to `True`. | |
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): | |
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to | |
`True`. | |
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): | |
Whether to convert the image to RGB. | |
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 = size if size is not None else self.size | |
size = get_size_dict(size, param_name="size", default_to_square=False) | |
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 | |
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", default_to_square=True) | |
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_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb | |
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: | |
raise ValueError("Size 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.") | |
# PIL RGBA images are converted to RGB | |
if do_convert_rgb: | |
images = [convert_to_rgb(image) for image in images] | |
# 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) | |