From df382c63f5d1b1f93caf69d24e8b5547ca71fac5 Mon Sep 17 00:00:00 2001 From: TingquanGao Date: Tue, 21 Oct 2025 10:13:18 +0000 Subject: [PATCH] System delete file --- PaddleOCR-VL-0.9B/image_processing.py | 569 -------------------------- 1 file changed, 569 deletions(-) delete mode 100644 PaddleOCR-VL-0.9B/image_processing.py diff --git a/PaddleOCR-VL-0.9B/image_processing.py b/PaddleOCR-VL-0.9B/image_processing.py deleted file mode 100644 index 4bec897..0000000 --- a/PaddleOCR-VL-0.9B/image_processing.py +++ /dev/null @@ -1,569 +0,0 @@ -# Copyright (c) 2025 PaddlePaddle Authors. 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 PaddleOCR-VL.""" - -import math -from typing import Dict, List, Optional, Union - -import numpy as np -import torch -from transformers.image_processing_utils import BaseImageProcessor, BatchFeature -from torchvision.transforms import functional as TF -from transformers.image_transforms import ( - convert_to_rgb, - resize, - to_channel_dimension_format, -) -from transformers.image_utils import ( - OPENAI_CLIP_MEAN, - OPENAI_CLIP_STD, - ChannelDimension, - PILImageResampling, - get_image_size, - infer_channel_dimension_format, - is_scaled_image, - is_valid_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 - -ImageInput = Union[ - "PIL.Image.Image", - np.ndarray, - "torch.Tensor", - List["PIL.Image.Image"], - List[np.ndarray], - List["torch.Tensor"], -] # noqa - - -VideoInput = Union[ - List["PIL.Image.Image"], - "np.ndarray", - "torch.Tensor", - List["np.ndarray"], - List["torch.Tensor"], - List[List["PIL.Image.Image"]], - List[List["np.ndarrray"]], - List[List["torch.Tensor"]], -] # noqa - - -def make_batched_images(images) -> List[List[ImageInput]]: - """ - Accepts images in list or nested list format, and makes a list of images for preprocessing. - - Args: - images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`): - The input image. - - Returns: - list: A list of images. - """ - if ( - isinstance(images, (list, tuple)) - and isinstance(images[0], (list, tuple)) - and is_valid_image(images[0][0]) - ): - return [img for img_list in images for img in img_list] - - elif isinstance(images, (list, tuple)) and is_valid_image(images[0]): - return images - - elif is_valid_image(images): - return [images] - - raise ValueError(f"Could not make batched images from {images}") - - -def adjust_size(size, patch_size): - num_patches = size // patch_size - if num_patches % 2 != 0: # 如果是奇数，减1 - num_patches -= 1 - return num_patches * patch_size - - -def make_batched_videos(videos) -> List[VideoInput]: - if ( - isinstance(videos, (list, tuple)) - and isinstance(videos[0], (list, tuple)) - and is_valid_image(videos[0][0]) - ): - return videos - - elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]): - if isinstance(videos[0], Image.Image): - return [videos] - elif len(videos[0].shape) == 4: - return [list(video) for video in videos] - - elif is_valid_image(videos) and len(videos.shape) == 4: - return [list(videos)] - - raise ValueError(f"Could not make batched video from {videos}") - - -def smart_resize( - height: int, - width: int, - factor: int = 28, - min_pixels: int = 28 * 28 * 130, - max_pixels: int = 28 * 28 * 1280, -): - """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}") - # if int(height < factor//4) + int(width < factor//4): - # raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor//4}") - - if height < factor: - print(f"smart_resize: height={height} < factor={factor}, reset height=factor") - width = round((width * factor) / height) - height = factor - - if width < factor: - print(f"smart_resize: width={width} < factor={factor}, reset width=factor") - height = round((height * factor) / width) - width = factor - - if max(height, width) / min(height, width) > 200: - raise ValueError( - f"absolute aspect ratio must be smaller than 200, 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 SiglipImageProcessor(BaseImageProcessor): - r""" - Constructs a Siglip 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 `[0.48145466, 0.4578275, 0.40821073]`): - 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 `[0.26862954, 0.26130258, 0.27577711]`): - 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 `28 * 28 * 130`): - The min pixels of the image to resize the image. - max_pixels (`int`, *optional*, defaults to `28 * 28 * 1670`): - The max pixels of the image to resize the image. - patch_size (`int`, *optional*, defaults to 14): - The spacial patch size of the vision encoder. - temporal_patch_size (`int`, *optional*, defaults to 2): - The temporal patch size of the vision encoder. - merge_size (`int`, *optional*, defaults to 2): - The merge size of the vision encoder to llm encoder. - """ - - model_input_names = [ - "pixel_values", - "image_grid_thw", - "pixel_values_videos", - "video_grid_thw", - ] - - 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 = 28 * 28 * 130, - max_pixels: int = 28 * 28 * 1280, - patch_size: int = 14, - temporal_patch_size: int = 1, - merge_size: int = 2, - **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 OPENAI_CLIP_MEAN - self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD - self.min_pixels = min_pixels - self.max_pixels = max_pixels - self.patch_size = patch_size - self.temporal_patch_size = temporal_patch_size - self.merge_size = merge_size - self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels} # not used - self.do_convert_rgb = do_convert_rgb - - def mvit_rescale(self, image: Image.Image, merge_size: int = 2) -> Image.Image: - try: - w, h = image.size - except: - raise ValueError(str((type(image), image))) - patch_size = self.patch_size - - if (w // patch_size) * (h // patch_size) > self.in_token_limit: - scale = math.sqrt( - self.in_token_limit / ((w // patch_size) * (h // patch_size)) - ) - new_w, new_h = int(w * scale), int(h * scale) - - image = image.resize((new_w, new_h), Image.Resampling.BICUBIC) - if self.pad_input: - new_w, new_h = image.size - pad_size_h = merge_size * patch_size - pad_size_w = merge_size * patch_size - - pad_h = (pad_size_h - new_h % pad_size_h) % pad_size_h - pad_w = (pad_size_w - new_w % pad_size_w) % pad_size_w - - image = TF.pad(image, (0, 0, pad_w, pad_h)) - else: - new_w, new_h = image.size - new_w = new_w - new_w % patch_size - new_h = new_h - new_h % patch_size - - new_w = adjust_size(new_w, patch_size) - new_h = adjust_size(new_h, patch_size) - - image = TF.center_crop(image, (new_h, new_w)) - - w, h = image.size - if w // patch_size >= 512 or h // patch_size >= 512: - new_h = min(patch_size * 510, h) - new_w = min(patch_size * 510, w) - image = TF.center_crop(image, (new_h, new_w)) - # raise ValueError("Exceed pos emb") - return image - - def _preprocess( - self, - images: Union[ImageInput, VideoInput], - do_resize: bool = 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, - do_convert_rgb: bool = None, - data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, - input_data_format: Optional[Union[str, ChannelDimension]] = None, - ): - """ - 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`. - vision_info (`List[Dict]`, *optional*): - Optional list of dictionaries containing additional information about vision inputs. - 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. - 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. - 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. - """ - 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" - " 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=self.patch_size * self.merge_size, - 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, data_format, input_channel_dim=input_data_format - ) - processed_images.append(image) - - patches = np.array(processed_images) - if data_format == ChannelDimension.LAST: - patches = patches.transpose(0, 3, 1, 2) - if patches.shape[0] == 1: - patches = np.tile(patches, (self.temporal_patch_size, 1, 1, 1)) - init_patches = patches - channel = patches.shape[1] - grid_t = patches.shape[0] // self.temporal_patch_size - grid_h, grid_w = ( - resized_height // self.patch_size, - resized_width // self.patch_size, - ) - patches = patches.reshape( - grid_t, - self.temporal_patch_size, - channel, - grid_h, - self.patch_size, - grid_w, - self.patch_size, - ) - patches = patches.transpose(0, 3, 5, 2, 1, 4, 6) - assert self.temporal_patch_size == 1 - flatten_patches = patches.reshape( - grid_t * grid_h * grid_w, channel, self.patch_size, self.patch_size - ) - return flatten_patches, (grid_t, grid_h, grid_w) - - def preprocess( - self, - images: ImageInput, - videos: VideoInput = None, - 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, - 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, - ): - """ - 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`. - videos (`VideoInput`): - Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If - passing in videos 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_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 - 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 - ) - - if images is not None: - images = make_batched_images(images) - if videos is not None: - videos = make_batched_videos(videos) - - if images is not None and 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=size, - resample=resample, - ) - - if images is not None: - pixel_values, vision_grid_thws = [], [] - for image in images: - patches, image_grid_thw = 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, - data_format=data_format, - do_convert_rgb=do_convert_rgb, - input_data_format=input_data_format, - ) - pixel_values.extend(patches) - vision_grid_thws.append(image_grid_thw) - pixel_values = np.array(pixel_values) - vision_grid_thws = np.array(vision_grid_thws) - data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws} - - if videos is not None: - pixel_values, vision_grid_thws = [], [] - for images in videos: - patches, video_grid_thw = self._preprocess( - images, - 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, - data_format=data_format, - do_convert_rgb=do_convert_rgb, - input_data_format=input_data_format, - ) - pixel_values.extend(patches) - vision_grid_thws.append(video_grid_thw) - pixel_values = np.array(pixel_values) - vision_grid_thws = np.array(vision_grid_thws) - data = { - "pixel_values_videos": pixel_values, - "video_grid_thw": vision_grid_thws, - } - - return BatchFeature(data=data, tensor_type=return_tensors)