siglip-so400m-14-980-flash-attn2-navit / image_processing_siglip.py

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	# coding=utf-8
	# Copyright 2024 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 SigLIP."""

	from typing import Dict, List, Optional, Union

	from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
	from transformers.image_transforms import (
	resize,
	to_channel_dimension_format,
	)
	from transformers.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 transformers.utils import TensorType, is_vision_available, logging


	logger = logging.get_logger(__name__)


	if is_vision_available():
	import PIL


	class SiglipImageProcessor(BaseImageProcessor):
	r"""
	Constructs a SigLIP 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 `{"height": 224, "width": 224}`):
	Size of the image after resizing. Can be overridden by `size` in the `preprocess` method.
	resample (`PILImageResampling`, optional, defaults to `Resampling.BILINEAR`):
	Resampling filter to use if resizing the image. Can be overridden 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 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 (`bool`, optional, defaults to `True`):
	Whether to normalize the image by the specified mean and standard deviation. Can be overridden by
	`do_normalize` in the `preprocess` method.
	image_mean (`float` or `List[float]`, optional, defaults to `[0.5, 0.5, 0.5]`):
	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 `[0.5, 0.5, 0.5]`):
	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.
	"""

	model_input_names = ["pixel_values"]

	def __init__(
	self,
	do_resize: bool = True,
	size: Dict[str, int] = 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 {"height": 224, "width": 224}
	image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
	image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD

	self.do_resize = do_resize
	self.size = size
	self.resample = resample
	self.do_rescale = do_rescale
	self.rescale_factor = rescale_factor
	self.do_normalize = do_normalize
	self.image_mean = image_mean
	self.image_std = image_std

	def preprocess(
	self,
	images: ImageInput,
	do_resize: bool = None,
	size: Dict[str, int] = 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: 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.
	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_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`.
	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_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

	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_rescale and rescale_factor is None:
	raise ValueError("Rescale factor must be specified if do_rescale 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:
	height, width = size["height"], size["width"]
	images = [
	resize(image=image, size=(height, width), 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)