# Copyright (c) 2025 Baidu, Inc. 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 Ernie_45T_VL.""" import math from typing import List, Optional, Union from PIL import Image import numpy as np from transformers.image_processing_utils import BaseImageProcessor, BatchFeature from transformers.image_transforms import ( convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format, ) from transformers.image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_valid_image, make_list_of_images, to_numpy_array, valid_images, ) from transformers.utils import TensorType, logging from transformers.video_utils import VideoInput logger = logging.get_logger(__name__) def round_by_factor(number: int, factor: int) -> int: """Returns the closest integer to 'number' that is divisible by 'factor'.""" return round(number / factor) * factor def ceil_by_factor(number: int, factor: int) -> int: """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.""" return math.ceil(number / factor) * factor def floor_by_factor(number: int, factor: int) -> int: """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.""" return math.floor(number / factor) * factor def smart_resize( height: int, width: int, factor: int = 28, min_pixels: int = 4 * 28 * 28, max_pixels: int = 16384 * 28 * 28, ): """ 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. """ MAX_RATIO = 200 if max(height, width) / min(height, width) > MAX_RATIO: if height > width: new_width = max(factor, round_by_factor(width, factor)) new_height = floor_by_factor(new_width * MAX_RATIO, factor) else: new_height = max(factor, round_by_factor(height, factor)) new_width = floor_by_factor(new_height * MAX_RATIO, factor) logger.info( f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)},\ resize to {max(new_height, new_width) / min(new_height, new_width)}" ) height = new_height width = new_width h_bar = max(factor, round_by_factor(height, factor)) w_bar = max(factor, round_by_factor(width, factor)) if h_bar * w_bar > max_pixels: beta = math.sqrt((height * width) / max_pixels) h_bar = floor_by_factor(height / beta, factor) w_bar = floor_by_factor(width / beta, factor) elif h_bar * w_bar < min_pixels: beta = math.sqrt(min_pixels / (height * width)) h_bar = ceil_by_factor(height * beta, factor) w_bar = ceil_by_factor(width * beta, factor) if min_pixels > h_bar * w_bar or h_bar * w_bar > max_pixels: raise ValueError(f"encounter invalid h_bar: {h_bar}, w_bar: {w_bar}") return h_bar, w_bar def is_scaled_image(image: np.ndarray) -> bool: """ Checks to see whether the pixel values have already been rescaled to [0, 1]. """ if image.dtype == np.uint8: return False # It's possible the image has pixel values in [0, 255] but is of floating type return np.min(image) >= 0 and np.max(image) <= 1 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}") # Copied from transformers.models.llava_next_video.image_processing_llava_next_video.make_batched_videos def make_batched_videos(videos) -> List[VideoInput]: """dummy""" 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}") class Ernie_45T_VLImageProcessor(BaseImageProcessor): r""" Constructs a adaptive 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 `56 * 56`): The min pixels of the image to resize the image. max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`): 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_conv_size (`int`, *optional*, defaults to 2): The temporal conv size in resampler. 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[float, List[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 = 56 * 56, max_pixels: int = 28 * 28 * 1280, patch_size: int = 14, temporal_conv_size: int = 2, merge_size: int = 2, **kwargs, ) -> None: """init""" 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_conv_size = temporal_conv_size self.merge_size = merge_size self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels} self.do_convert_rgb = do_convert_rgb def set_pixels(self, min_pixels=None, max_pixels=None, msg=""): """set_pixels""" if min_pixels is not None: assert ( isinstance(min_pixels, int) and min_pixels >= 0 ), "min_pixels must be positive int" logger.info( f"{msg} Ernie_45T_VLImageProcessor set min_pixels = {min_pixels}" ) self.min_pixels = min_pixels self.size["min_pixels"] = int(min_pixels) if max_pixels is not None: assert ( isinstance(max_pixels, int) and max_pixels > 0 ), "max_pixels must be positive int" logger.info( f"{msg} Ernie_45T_VLImageProcessor set max_pixels = {max_pixels}" ) self.max_pixels = max_pixels self.size["max_pixels"] = int(max_pixels) def get_smarted_resize(self, height, width, min_pixels=None, max_pixels=None): """dummy""" actual_min_pixels = min_pixels if min_pixels is not None else self.min_pixels actual_max_pixels = max_pixels if max_pixels is not None else self.max_pixels resized_height, resized_width = smart_resize( height, width, factor=self.patch_size * self.merge_size, min_pixels=actual_min_pixels, max_pixels=actual_max_pixels, ) return (resized_height, resized_width), ( resized_height // self.patch_size, resized_width // self.patch_size, ) def _preprocess( self, images: Union[ImageInput, VideoInput], do_resize: bool = True, resample: PILImageResampling = None, do_rescale: bool = True, rescale_factor: 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 = False, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, predetermined_grid_thw=None, ): """ Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`. Args: images (`ImageInput` or `VideoInput`): 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. 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 = [] if predetermined_grid_thw is not None: assert len(predetermined_grid_thw) == len( images ), f"len(predetermined_grid_thw) {len(predetermined_grid_thw)} == len(images) {len(images)}" for img_idx, image in enumerate(images): if do_resize: if predetermined_grid_thw is not None: (resized_height, resized_width) = predetermined_grid_thw[img_idx] resized_height *= self.patch_size resized_width *= self.patch_size else: 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, data_format=input_data_format, ) if do_rescale: image = rescale( image, scale=rescale_factor, data_format=input_data_format ) if do_normalize: image = normalize( image=image, mean=image_mean, std=image_std, data_format=input_data_format, ) image = to_channel_dimension_format( image, data_format, input_channel_dim=input_data_format ) # [C, H, W] processed_images.append(image) patches = np.array(processed_images) if data_format == ChannelDimension.LAST: patches = patches.transpose([0, 3, 1, 2]) channel = patches.shape[1] # [time, C, H, W] grid_t = patches.shape[0] grid_h, grid_w = ( resized_height // self.patch_size, resized_width // self.patch_size, ) patches = patches.reshape( [ grid_t, channel, grid_h // self.merge_size, self.merge_size, self.patch_size, grid_w // self.merge_size, self.merge_size, self.patch_size, ] ) # [grid_t, grid_h/merge_size, grid_w/merge_size, merge_size, merge_size, C, psz, psz] patches = patches.transpose([0, 2, 5, 3, 6, 1, 4, 7]) flatten_patches = patches.reshape( [grid_t * grid_h * grid_w, channel * self.patch_size * self.patch_size] ) # [grid_t * grid_h * grid_w, C * psz * psz] return flatten_patches, (grid_t, grid_h, grid_w) def preprocess( self, images: ImageInput, videos: VideoInput = None, do_resize: bool = True, size: Optional[Union[int, List[int]]] = None, resample: PILImageResampling = None, do_rescale: bool = True, rescale_factor: 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 = False, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, predetermined_grid_thw=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.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.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 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." ) data = {} if images is not None: pixel_values, vision_grid_thws = [], [] for img_idx, image in enumerate(images): if predetermined_grid_thw is not None: predetermined_grid_thw_one = [predetermined_grid_thw[img_idx]] else: predetermined_grid_thw_one = None 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, predetermined_grid_thw=predetermined_grid_thw_one, ) 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.update( {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws} ) if videos is not None: videos = make_batched_videos(videos) 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, predetermined_grid_thw=predetermined_grid_thw, ) 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.update( { "pixel_values_videos": pixel_values, "video_grid_thw": vision_grid_thws, } ) return BatchFeature(data=data, tensor_type=return_tensors) __all__ = ["Ernie_45T_VLImageProcessor"]