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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 ConvNeXT.""" | |
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 ( | |
center_crop, | |
get_resize_output_image_size, | |
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 ConvNextImageProcessor(BaseImageProcessor): | |
r""" | |
Constructs a ConvNeXT image processor. | |
Args: | |
do_resize (`bool`, *optional*, defaults to `True`): | |
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden | |
by `do_resize` in the `preprocess` method. | |
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 384}`): | |
Resolution of the output image after `resize` is applied. If `size["shortest_edge"]` >= 384, the image is | |
resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will | |
be matched to `int(size["shortest_edge"]/crop_pct)`, after which the image is cropped to | |
`(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. Can | |
be overriden by `size` in the `preprocess` method. | |
crop_pct (`float` *optional*, defaults to 224 / 256): | |
Percentage of the image to crop. Only has an effect if `do_resize` is `True` and size < 384. Can be | |
overriden by `crop_pct` in the `preprocess` method. | |
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): | |
Resampling filter to use if resizing the image. Can be overriden by `resample` in the `preprocess` method. | |
do_rescale (`bool`, *optional*, defaults to `True`): | |
Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden 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 overriden by `rescale_factor` 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, | |
crop_pct: float = None, | |
resample: PILImageResampling = PILImageResampling.BILINEAR, | |
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, | |
**kwargs, | |
) -> None: | |
super().__init__(**kwargs) | |
size = size if size is not None else {"shortest_edge": 384} | |
size = get_size_dict(size, default_to_square=False) | |
self.do_resize = do_resize | |
self.size = size | |
# Default value set here for backwards compatibility where the value in config is None | |
self.crop_pct = crop_pct if crop_pct is not None else 224 / 256 | |
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 IMAGENET_STANDARD_MEAN | |
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD | |
def resize( | |
self, | |
image: np.ndarray, | |
size: Dict[str, int], | |
crop_pct: float, | |
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. | |
Args: | |
image (`np.ndarray`): | |
Image to resize. | |
size (`Dict[str, int]`): | |
Dictionary of the form `{"shortest_edge": int}`, specifying the size of the output image. If | |
`size["shortest_edge"]` >= 384 image is resized to `(size["shortest_edge"], size["shortest_edge"])`. | |
Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"] / crop_pct)`, | |
after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. | |
crop_pct (`float`): | |
Percentage of the image to crop. Only has an effect if size < 384. | |
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): | |
Resampling filter to use when resizing 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 from the input | |
image. | |
""" | |
size = get_size_dict(size, default_to_square=False) | |
if "shortest_edge" not in size: | |
raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}") | |
shortest_edge = size["shortest_edge"] | |
if shortest_edge < 384: | |
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct | |
resize_shortest_edge = int(shortest_edge / crop_pct) | |
resize_size = get_resize_output_image_size( | |
image, size=resize_shortest_edge, default_to_square=False, input_data_format=input_data_format | |
) | |
image = resize( | |
image=image, | |
size=resize_size, | |
resample=resample, | |
data_format=data_format, | |
input_data_format=input_data_format, | |
**kwargs, | |
) | |
# then crop to (shortest_edge, shortest_edge) | |
return center_crop( | |
image=image, | |
size=(shortest_edge, shortest_edge), | |
data_format=data_format, | |
input_data_format=input_data_format, | |
**kwargs, | |
) | |
else: | |
# warping (no cropping) when evaluated at 384 or larger | |
return resize( | |
image, | |
size=(shortest_edge, shortest_edge), | |
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, | |
crop_pct: float = None, | |
resample: PILImageResampling = 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 output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image | |
is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the | |
image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to | |
`(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. | |
crop_pct (`float`, *optional*, defaults to `self.crop_pct`): | |
Percentage of the image to crop if size < 384. | |
resample (`int`, *optional*, defaults to `self.resample`): | |
Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. 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. | |
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: | |
- 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 | |
crop_pct = crop_pct if crop_pct is not None else self.crop_pct | |
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 | |
size = size if size is not None else self.size | |
size = get_size_dict(size, default_to_square=False) | |
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_resize and size["shortest_edge"] < 384 and crop_pct is None: | |
raise ValueError("crop_pct must be specified if size < 384.") | |
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, crop_pct=crop_pct, resample=resample, 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) | |