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TingquanGao
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processing_paddleocr_vl.py
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293
processing_paddleocr_vl.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Union
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import numpy as np
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import torch
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.processing_utils import (
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ProcessingKwargs,
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ProcessorMixin,
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Unpack,
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VideosKwargs,
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)
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from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
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ImageInput = Union[
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"PIL.Image.Image",
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np.ndarray,
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"torch.Tensor",
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List["PIL.Image.Image"],
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List[np.ndarray],
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List["torch.Tensor"],
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] # noqa
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VideoInput = Union[
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List["PIL.Image.Image"],
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"np.ndarray",
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"torch.Tensor",
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List["np.ndarray"],
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List["torch.Tensor"],
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List[List["PIL.Image.Image"]],
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List[List["np.ndarrray"]],
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List[List["torch.Tensor"]],
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] # noqa
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class PaddleOCRVLVideosProcessorKwargs(VideosKwargs, total=False):
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fps: Union[List[float], float]
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class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False):
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videos_kwargs: PaddleOCRVLVideosProcessorKwargs
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_defaults = {
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"text_kwargs": {
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"padding": False,
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},
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"videos_kwargs": {"fps": 2.0},
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}
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class PaddleOCRVLProcessor(ProcessorMixin):
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r"""
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[`PaddleOCRVLProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`Qwen2TokenizerFast`]. See the
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[`~PaddleOCRVLProcessor.__call__`] and [`~PaddleOCRVLProcessor.decode`] for more information.
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Args:
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image_processor ([`SiglipImageProcessor`], *optional*):
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The image processor is a required input.
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tokenizer ([`Qwen2TokenizerFast`], *optional*):
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The tokenizer is a required input.
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chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
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in a chat into a tokenizable string.
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"""
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attributes = ["image_processor", "tokenizer"]
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valid_kwargs = [
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"chat_template",
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"image_std",
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"min_pixels",
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"image_mean",
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"merge_size",
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"image_processor_type",
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"temporal_patch_size",
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"patch_size",
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"max_pixels",
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]
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image_processor_class = "AutoImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(
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self, image_processor=None, tokenizer=None, chat_template=None, **kwargs
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):
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self.image_token = (
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"<|IMAGE_PLACEHOLDER|>"
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if not hasattr(tokenizer, "image_token")
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else tokenizer.image_token
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)
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self.video_token = (
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"<|video_pad|>"
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if not hasattr(tokenizer, "video_token")
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else tokenizer.video_token
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)
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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def __call__(
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self,
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images: ImageInput = None,
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text: Union[
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TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
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] = None,
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videos: VideoInput = None,
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**kwargs: Unpack[PaddleOCRVLProcessorKwargs],
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) -> BatchFeature:
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"""
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Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
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and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
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the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
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SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `vision_infos` is not `None`.
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Args:
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images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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tensor. Both channels-first and channels-last formats are supported.
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text (`str`, `List[str]`, `List[List[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
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The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
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tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
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return_tensors (`str` or [`~utils.TensorType`], *optional*):
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If set, will return tensors of a particular framework. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return NumPy `np.ndarray` objects.
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- `'jax'`: Return JAX `jnp.ndarray` objects.
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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
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- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
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- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
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- **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
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"""
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output_kwargs = self._merge_kwargs(
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PaddleOCRVLProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if images is not None:
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image_inputs = self.image_processor(images=images, return_tensors="pt")
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image_inputs["pixel_values"] = image_inputs["pixel_values"]
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image_grid_thw = image_inputs["image_grid_thw"]
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else:
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image_inputs = {}
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image_grid_thw = None
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if videos is not None:
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# TODO: add video processing
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videos_inputs = self.image_processor(
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images=None, videos=videos, **output_kwargs["images_kwargs"]
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)
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video_grid_thw = videos_inputs["video_grid_thw"]
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fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
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if isinstance(fps, (int, float)):
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second_per_grid_ts = [
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self.image_processor.temporal_patch_size / fps
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] * len(video_grid_thw)
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elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
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second_per_grid_ts = [
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self.image_processor.temporal_patch_size / tmp for tmp in fps
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]
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else:
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raise ValueError(
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f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
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)
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videos_inputs.update(
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{"second_per_grid_ts": torch.tensor(second_per_grid_ts)}
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)
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else:
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videos_inputs = {}
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video_grid_thw = None
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if not isinstance(text, list):
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text = [text]
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if image_grid_thw is not None:
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index = 0
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for i in range(len(text)):
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while self.image_token in text[i]:
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text[i] = text[i].replace(
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self.image_token,
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"<|placeholder|>"
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* (
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image_grid_thw[index].prod()
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// self.image_processor.merge_size
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// self.image_processor.merge_size
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),
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1,
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)
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index += 1
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text[i] = text[i].replace("<|placeholder|>", self.image_token)
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if video_grid_thw is not None:
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index = 0
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for i in range(len(text)):
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while self.video_token in text[i]:
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text[i] = text[i].replace(
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self.video_token,
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"<|placeholder|>"
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* (
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video_grid_thw[index].prod()
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// self.image_processor.merge_size
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// self.image_processor.merge_size
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),
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1,
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)
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index += 1
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text[i] = text[i].replace("<|placeholder|>", self.video_token)
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text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
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return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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def post_process_image_text_to_text(
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self,
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generated_outputs,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False,
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**kwargs,
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):
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"""
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Post-process the output of the model to decode the text.
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Args:
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generated_outputs (`torch.Tensor` or `np.ndarray`):
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The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
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or `(sequence_length,)`.
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skip_special_tokens (`bool`, *optional*, defaults to `True`):
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Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
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Clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
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Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
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**kwargs:
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Additional arguments to be passed to the tokenizer's `batch_decode method`.
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Returns:
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`List[str]`: The decoded text.
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"""
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return self.tokenizer.batch_decode(
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generated_outputs,
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skip_special_tokens=skip_special_tokens,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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names_from_processor = list(
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dict.fromkeys(tokenizer_input_names + image_processor_input_names)
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)
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return names_from_processor + ["second_per_grid_ts"]
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__all__ = ["PaddleOCRVLProcessor", "PaddleOCRVLProcessor"]
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