Spaces:
Running
on
L40S
Running
on
L40S
| # coding=utf-8 | |
| # Copyright 2024 The Emu team, BAAI and 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 Emu3VisionVQ.""" | |
| import math | |
| from typing import Dict, List, Optional, Union | |
| import numpy as np | |
| from transformers.image_processing_utils import BaseImageProcessor, BatchFeature | |
| from transformers.image_transforms import ( | |
| convert_to_rgb, | |
| resize, | |
| to_channel_dimension_format, | |
| ) | |
| from transformers.image_utils import ( | |
| IMAGENET_STANDARD_MEAN, | |
| IMAGENET_STANDARD_STD, | |
| ChannelDimension, | |
| ImageInput, | |
| PILImageResampling, | |
| get_image_size, | |
| infer_channel_dimension_format, | |
| is_scaled_image, | |
| make_list_of_images, | |
| to_numpy_array, | |
| valid_images, | |
| validate_preprocess_arguments, | |
| ) | |
| from transformers.utils import TensorType, is_vision_available, logging | |
| logger = logging.get_logger(__name__) | |
| if is_vision_available(): | |
| from PIL import Image | |
| def smart_resize( | |
| height: int, width: int, factor: int = 8, min_pixels: int = 512 * 512, max_pixels: int = 1024 * 1024 | |
| ): | |
| """Rescales the image so that the following conditions are met: | |
| 1. Both dimensions (height and width) are divisible by 'factor'. | |
| 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. | |
| 3. The aspect ratio of the image is maintained as closely as possible. | |
| """ | |
| if height < factor or width < factor: | |
| raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}") | |
| elif max(height, width) / min(height, width) > 5: | |
| raise ValueError( | |
| f"absolute aspect ratio must be smaller than 5, got {max(height, width) / min(height, width)}" | |
| ) | |
| h_bar = round(height / factor) * factor | |
| w_bar = round(width / factor) * factor | |
| if h_bar * w_bar > max_pixels: | |
| beta = math.sqrt((height * width) / max_pixels) | |
| h_bar = math.floor(height / beta / factor) * factor | |
| w_bar = math.floor(width / beta / factor) * factor | |
| elif h_bar * w_bar < min_pixels: | |
| beta = math.sqrt(min_pixels / (height * width)) | |
| h_bar = math.ceil(height * beta / factor) * factor | |
| w_bar = math.ceil(width * beta / factor) * factor | |
| return h_bar, w_bar | |
| class Emu3VisionVQImageProcessor(BaseImageProcessor): | |
| r""" | |
| Constructs a Emu3VisionVQ image processor that dynamically resizes images based on the original images. | |
| Args: | |
| do_resize (`bool`, *optional*, defaults to `True`): | |
| Whether to resize the image's (height, width) dimensions. | |
| resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): | |
| Resampling filter to use when resizing the image. | |
| do_rescale (`bool`, *optional*, defaults to `True`): | |
| Whether to rescale the image by the specified scale `rescale_factor`. | |
| rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): | |
| Scale factor to use if rescaling the image. | |
| do_normalize (`bool`, *optional*, defaults to `True`): | |
| Whether to normalize the image. | |
| 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 for each channel in the image. | |
| 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 for each channel in the image. | |
| do_convert_rgb (`bool`, *optional*, defaults to `True`): | |
| Whether to convert the image to RGB. | |
| min_pixels (`int`, *optional*, defaults to `512 * 512`): | |
| The min pixels of the image to resize the image. | |
| max_pixels (`int`, *optional*, defaults to `1024 * 1024`): | |
| The max pixels of the image to resize the image. | |
| spatial_factor (`int`, *optional*, defautls to 8): | |
| The spatial downsample factor the image will be downsampled in feature extracting phase | |
| """ | |
| model_input_names = ["pixel_values"] | |
| def __init__( | |
| self, | |
| do_resize: bool = True, | |
| resample: PILImageResampling = PILImageResampling.BICUBIC, | |
| 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, | |
| min_pixels: int = 512 * 512, | |
| max_pixels: int = 1024 * 1024, | |
| spatial_factor: int = 8, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__(**kwargs) | |
| self.do_resize = do_resize | |
| 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 | |
| self.min_pixels = min_pixels | |
| self.max_pixels = max_pixels | |
| self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels} | |
| self.do_convert_rgb = do_convert_rgb | |
| self.spatial_factor = spatial_factor | |
| def _preprocess( | |
| self, | |
| images: ImageInput, | |
| do_resize: Optional[bool] = None, | |
| resample: PILImageResampling = None, | |
| do_rescale: Optional[bool] = None, | |
| rescale_factor: Optional[float] = None, | |
| do_normalize: Optional[bool] = None, | |
| image_mean: Optional[Union[float, List[float]]] = None, | |
| image_std: Optional[Union[float, List[float]]] = None, | |
| do_convert_rgb: Optional[bool] = None, | |
| spatial_factor: Optional[int] = None, | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| output_data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST, | |
| ): | |
| """ | |
| Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`. | |
| Args: | |
| images (`ImageInput`): | |
| Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`. | |
| do_resize (`bool`, *optional*, defaults to `self.do_resize`): | |
| Whether to resize the image. | |
| resample (`PILImageResampling`, *optional*, defaults to `self.resample`): | |
| Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums. | |
| do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): | |
| Whether to rescale the image. | |
| rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): | |
| Scale factor to use if rescaling the image. | |
| 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`): | |
| Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. | |
| image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): | |
| Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. | |
| do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): | |
| Whether to convert the image to RGB. | |
| spatial_factor (`int`, *optional*, defaults to `self.spatial_factor`): | |
| The spatial downsample factor the image will be downsampled in feature extracting phase | |
| input_data_format (`ChannelDimension` or `str`, *optional*): | |
| The channel dimension format for 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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. | |
| output_data_format (`ChannelDimension`, *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. | |
| """ | |
| spatial_factor = spatial_factor if spatial_factor is not None else self.spatial_factor | |
| images = make_list_of_images(images) | |
| 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" | |
| "pixel_values.append()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]) | |
| height, width = get_image_size(images[0], channel_dim=input_data_format) | |
| resized_height, resized_width = height, width | |
| processed_images = [] | |
| for image in images: | |
| if do_resize: | |
| resized_height, resized_width = smart_resize( | |
| height, | |
| width, | |
| factor=spatial_factor, | |
| min_pixels=self.min_pixels, | |
| max_pixels=self.max_pixels, | |
| ) | |
| image = resize( | |
| image, size=(resized_height, resized_width), resample=resample, input_data_format=input_data_format | |
| ) | |
| if do_rescale: | |
| image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format) | |
| if do_normalize: | |
| image = self.normalize( | |
| image=image, mean=image_mean, std=image_std, input_data_format=input_data_format | |
| ) | |
| image = to_channel_dimension_format(image, output_data_format, input_channel_dim=input_data_format) | |
| processed_images.append(image) | |
| image = np.array(processed_images) | |
| return image | |
| def preprocess( | |
| self, | |
| images: ImageInput, | |
| do_resize: Optional[bool] = None, | |
| resample: PILImageResampling = None, | |
| do_rescale: Optional[bool] = None, | |
| rescale_factor: Optional[float] = None, | |
| do_normalize: Optional[bool] = None, | |
| image_mean: Optional[Union[float, List[float]]] = None, | |
| image_std: Optional[Union[float, List[float]]] = None, | |
| do_convert_rgb: Optional[bool] = None, | |
| spatial_factor: Optional[int] = None, | |
| return_tensors: Optional[Union[str, TensorType]] = None, | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| output_data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST, | |
| ): | |
| """ | |
| 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. | |
| 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`. | |
| do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): | |
| Whether to convert the image to RGB. | |
| spatial_factor (`int`, *optional*, defaults to `self.spatial_factor`): | |
| The spatial downsample factor the image will be downsampled in feature extracting phase | |
| return_tensors (`str` or `TensorType`, *optional*): | |
| The type of tensors to return. Can be one of: | |
| - Unset: Return a list of `np.ndarray`. | |
| - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. | |
| - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. | |
| 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. | |
| output_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. | |
| """ | |
| do_resize = do_resize if do_resize is not None else self.do_resize | |
| 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 | |
| do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb | |
| spatial_factor = spatial_factor if spatial_factor is not None else self.spatial_factor | |
| images = make_list_of_images(images) | |
| if images is None or 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." | |
| ) | |
| validate_preprocess_arguments( | |
| rescale_factor=rescale_factor, | |
| do_normalize=do_normalize, | |
| image_mean=image_mean, | |
| image_std=image_std, | |
| do_resize=do_resize, | |
| size=self.size, | |
| resample=resample, | |
| ) | |
| pixel_values = [] | |
| for image in images: | |
| norm_image = self._preprocess( | |
| image, | |
| do_resize=do_resize, | |
| resample=resample, | |
| do_rescale=do_rescale, | |
| rescale_factor=rescale_factor, | |
| do_normalize=do_normalize, | |
| image_mean=image_mean, | |
| image_std=image_std, | |
| do_convert_rgb=do_convert_rgb, | |
| spatial_factor=spatial_factor, | |
| input_data_format=input_data_format, | |
| output_data_format=output_data_format, | |
| ) | |
| pixel_values.extend(norm_image) | |
| pixel_values = np.array(pixel_values) | |
| data = {"pixel_values": pixel_values} | |
| return BatchFeature(data=data, tensor_type=return_tensors) | |
| def postprocess( | |
| self, | |
| images: ImageInput, | |
| do_rescale: Optional[bool] = None, | |
| rescale_factor: Optional[float] = None, | |
| do_normalize: Optional[bool] = None, | |
| image_mean: Optional[Union[float, List[float]]] = None, | |
| image_std: Optional[Union[float, List[float]]] = None, | |
| return_tensors: str | TensorType = "PIL.Image.Image", | |
| input_data_format: Optional[Union[str, ChannelDimension]] = None, | |
| ): | |
| """ | |
| Postprocess an image or batch of images tensor. Postprocess is the reverse process of preprocess. | |
| The parameters should be same as in preprocess. | |
| Args: | |
| images (`ImageInput`): | |
| Image to postprocess. Expects a single or batch of images with pixel values ranging from -1 to 1. | |
| 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.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. | |
| - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. | |
| 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_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 | |
| rescale_factor = 1 / 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 | |
| image_mean, image_std = self.inverse_meanstd(image_mean, image_std) | |
| images = make_list_of_images(images) | |
| if isinstance(images[0], Image.Image): | |
| return images if len(images) > 1 else images[0] | |
| 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]) | |
| pixel_values = [] | |
| for image in images: | |
| image = to_numpy_array(image) | |
| if do_normalize: | |
| image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) | |
| if do_rescale: | |
| image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format) | |
| image = image.clip(0, 255).astype(np.uint8) | |
| if do_normalize and do_rescale and return_tensors == "PIL.Image.Image": | |
| image = to_channel_dimension_format(image, ChannelDimension.LAST, input_channel_dim=input_data_format) | |
| pixel_values.append(Image.fromarray(image)) | |
| else: | |
| pixel_values.extend(image) | |
| data = {"pixel_values": pixel_values} | |
| return_tensors = return_tensors if return_tensors != "PIL.Image.Image" else None | |
| return BatchFeature(data=data, tensor_type=return_tensors) | |
| def inverse_meanstd(self, image_mean, image_std): | |
| image_mean = self.to_tuple(image_mean) | |
| image_std = self.to_tuple(image_std) | |
| rev_image_mean = tuple(-m / s for m, s in zip(image_mean, image_std)) | |
| rev_image_std = tuple(1 / s for s in image_std) | |
| return rev_image_mean, rev_image_std | |
| def to_tuple(self, value, dim=3): | |
| if isinstance(value, int | float): | |
| return (value,) * dim | |
| return tuple(value) | |