376 lines
17 KiB
Python
376 lines
17 KiB
Python
# NOTE: This code is currently under review for inclusion in the main
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# huggingface/transformers repository:
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# https://github.com/huggingface/transformers/pull/18414
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from typing import List, Optional, Tuple, Union
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import numpy as np
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from transformers.utils import add_end_docstrings, is_torch_available, logging
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from transformers.pipelines.base import PIPELINE_INIT_ARGS, Pipeline
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from .qa_helpers import select_starts_ends, Image, load_image, VISION_LOADED, pytesseract, TESSERACT_LOADED
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if is_torch_available():
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import torch
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# We do not perform the check in this version of the pipeline code
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# from transformers.models.auto.modeling_auto import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
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logger = logging.get_logger(__name__)
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# normalize_bbox() and apply_tesseract() are derived from apply_tesseract in models/layoutlmv3/feature_extraction_layoutlmv3.py.
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# However, because the pipeline may evolve from what layoutlmv3 currently does, it's copied (vs. imported) to avoid creating an
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# unecessary dependency.
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def normalize_box(box, width, height):
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return [
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int(1000 * (box[0] / width)),
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int(1000 * (box[1] / height)),
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int(1000 * (box[2] / width)),
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int(1000 * (box[3] / height)),
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]
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def apply_tesseract(image: "Image.Image", lang: Optional[str], tesseract_config: Optional[str]):
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"""Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes."""
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# apply OCR
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data = pytesseract.image_to_data(image, lang=lang, output_type="dict", config=tesseract_config)
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words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"]
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# filter empty words and corresponding coordinates
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irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()]
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words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices]
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left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices]
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top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices]
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width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices]
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height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices]
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# turn coordinates into (left, top, left+width, top+height) format
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actual_boxes = []
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for x, y, w, h in zip(left, top, width, height):
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actual_box = [x, y, x + w, y + h]
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actual_boxes.append(actual_box)
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image_width, image_height = image.size
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# finally, normalize the bounding boxes
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normalized_boxes = []
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for box in actual_boxes:
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normalized_boxes.append(normalize_box(box, image_width, image_height))
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assert len(words) == len(normalized_boxes), "Not as many words as there are bounding boxes"
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return words, normalized_boxes
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@add_end_docstrings(PIPELINE_INIT_ARGS)
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class DocumentQuestionAnsweringPipeline(Pipeline):
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# TODO: Update task_summary docs to include an example with document QA and then update the first sentence
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"""
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Document Question Answering pipeline using any `AutoModelForDocumentQuestionAnswering`. See the [question answering
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examples](../task_summary#question-answering) for more information.
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This document question answering pipeline can currently be loaded from [`pipeline`] using the following task
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identifier: `"document-question-answering"`.
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The models that this pipeline can use are models that have been fine-tuned on a document question answering task.
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See the up-to-date list of available models on
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[huggingface.co/models](https://huggingface.co/models?filter=document-question-answering).
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# self.check_model_type(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING)
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def _sanitize_parameters(
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self,
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padding=None,
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doc_stride=None,
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max_question_len=None,
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lang: Optional[str] = None,
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tesseract_config: Optional[str] = None,
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max_answer_len=None,
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max_seq_len=None,
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top_k=None,
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handle_impossible_answer=None,
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**kwargs,
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):
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preprocess_params, postprocess_params = {}, {}
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if padding is not None:
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preprocess_params["padding"] = padding
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if doc_stride is not None:
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preprocess_params["doc_stride"] = doc_stride
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if max_question_len is not None:
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preprocess_params["max_question_len"] = max_question_len
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if max_seq_len is not None:
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preprocess_params["max_seq_len"] = max_seq_len
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if lang is not None:
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preprocess_params["lang"] = lang
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if tesseract_config is not None:
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preprocess_params["tesseract_config"] = tesseract_config
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if top_k is not None:
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if top_k < 1:
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raise ValueError(f"top_k parameter should be >= 1 (got {top_k})")
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postprocess_params["top_k"] = top_k
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if max_answer_len is not None:
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if max_answer_len < 1:
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raise ValueError(f"max_answer_len parameter should be >= 1 (got {max_answer_len}")
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postprocess_params["max_answer_len"] = max_answer_len
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if handle_impossible_answer is not None:
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postprocess_params["handle_impossible_answer"] = handle_impossible_answer
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return preprocess_params, {}, postprocess_params
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def __call__(
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self,
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image: Union["Image.Image", str],
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question: Optional[str] = None,
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word_boxes: Tuple[str, List[float]] = None,
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**kwargs,
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):
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"""
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Answer the question(s) given as inputs by using the document(s). A document is defined as an image and an
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optional list of (word, box) tuples which represent the text in the document. If the `word_boxes` are not
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provided, it will use the Tesseract OCR engine (if available) to extract the words and boxes automatically.
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You can invoke the pipeline several ways:
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- `pipeline(image=image, question=question)`
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- `pipeline(image=image, question=question, word_boxes=word_boxes)`
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- `pipeline([{"image": image, "question": question}])`
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- `pipeline([{"image": image, "question": question, "word_boxes": word_boxes}])`
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Args:
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image (`str` or `PIL.Image`):
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The pipeline handles three types of images:
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- A string containing a http link pointing to an image
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- A string containing a local path to an image
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- An image loaded in PIL directly
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The pipeline accepts either a single image or a batch of images. If given a single image, it can be
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broadcasted to multiple questions.
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question (`str`):
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A question to ask of the document.
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word_boxes (`List[str, Tuple[float, float, float, float]]`, *optional*):
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A list of words and bounding boxes (normalized 0->1000). If you provide this optional input, then the
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pipeline will use these words and boxes instead of running OCR on the image to derive them. This allows
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you to reuse OCR'd results across many invocations of the pipeline without having to re-run it each
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time.
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top_k (`int`, *optional*, defaults to 1):
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The number of answers to return (will be chosen by order of likelihood). Note that we return less than
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top_k answers if there are not enough options available within the context.
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doc_stride (`int`, *optional*, defaults to 128):
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If the words in the document are too long to fit with the question for the model, it will be split in
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several chunks with some overlap. This argument controls the size of that overlap.
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max_answer_len (`int`, *optional*, defaults to 15):
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The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
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max_seq_len (`int`, *optional*, defaults to 384):
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The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
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model. The context will be split in several chunks (using `doc_stride` as overlap) if needed.
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max_question_len (`int`, *optional*, defaults to 64):
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The maximum length of the question after tokenization. It will be truncated if needed.
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handle_impossible_answer (`bool`, *optional*, defaults to `False`):
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Whether or not we accept impossible as an answer.
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lang (`str`, *optional*):
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Language to use while running OCR. Defaults to english.
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tesseract_config (`str`, *optional*):
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Additional flags to pass to tesseract while running OCR.
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Return:
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A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:
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- **score** (`float`) -- The probability associated to the answer.
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- **start** (`int`) -- The start word index of the answer (in the OCR'd version of the input or provided
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`word_boxes`).
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- **end** (`int`) -- The end word index of the answer (in the OCR'd version of the input or provided
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`word_boxes`).
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- **answer** (`str`) -- The answer to the question.
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"""
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if isinstance(question, str):
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inputs = {"question": question, "image": image, "word_boxes": word_boxes}
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else:
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inputs = image
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return super().__call__(inputs, **kwargs)
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def preprocess(
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self,
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input,
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padding="do_not_pad",
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doc_stride=None,
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max_question_len=64,
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max_seq_len=None,
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word_boxes: Tuple[str, List[float]] = None,
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lang=None,
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tesseract_config="",
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):
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# NOTE: This code mirrors the code in question answering and will be implemented in a follow up PR
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# to support documents with enough tokens that overflow the model's window
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# if max_seq_len is None:
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# # TODO: LayoutLM's stride is 512 by default. Is it ok to use that as the min
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# # instead of 384 (which the QA model uses)?
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# max_seq_len = min(self.tokenizer.model_max_length, 512)
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if doc_stride is not None:
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# TODO implement
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# doc_stride = min(max_seq_len // 2, 128)
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raise ValueError("Unsupported: striding inputs")
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image = None
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image_features = {}
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if "image" in input:
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if not VISION_LOADED:
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raise ValueError(
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"If you provide an image, then the pipeline will run process it with PIL (Pillow), but"
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" PIL is not available. Install it with pip install Pillow."
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)
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image = load_image(input["image"])
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if self.feature_extractor is not None:
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image_features.update(self.feature_extractor(images=image, return_tensors=self.framework))
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words, boxes = None, None
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if "word_boxes" in input:
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words = [x[0] for x in input["word_boxes"]]
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boxes = [x[1] for x in input["word_boxes"]]
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elif "words" in image_features and "boxes" in image_features:
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words = image_features.pop("words")
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boxes = image_features.pop("boxes")
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elif image is not None:
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if not TESSERACT_LOADED:
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raise ValueError(
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"If you provide an image without word_boxes, then the pipeline will run OCR using Tesseract, but"
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" pytesseract is not available. Install it with pip install pytesseract."
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)
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words, boxes = apply_tesseract(image, lang=lang, tesseract_config=tesseract_config)
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else:
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raise ValueError(
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"You must provide an image or word_boxes. If you provide an image, the pipeline will automatically run"
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" OCR to derive words and boxes"
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)
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if self.tokenizer.padding_side != "right":
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raise ValueError(
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"Document question answering only supports tokenizers whose padding side is 'right', not"
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f" {self.tokenizer.padding_side}"
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)
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encoding = self.tokenizer(
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text=input["question"].split(),
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text_pair=words,
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padding=padding,
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max_length=max_seq_len,
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stride=doc_stride,
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return_token_type_ids=True,
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is_split_into_words=True,
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return_tensors=self.framework,
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# TODO: In a future PR, use these feature to handle sequences whose length is longer than
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# the maximum allowed by the model. Currently, the tokenizer will produce a sequence that
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# may be too long for the model to handle.
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# truncation="only_second",
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# return_overflowing_tokens=True,
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)
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encoding.update(image_features)
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# TODO: For now, this should always be num_spans == 1 given the flags we've passed in above, but the
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# code is written to naturally handle multiple spans at the right time.
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num_spans = len(encoding["input_ids"])
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# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
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# We put 0 on the tokens from the context and 1 everywhere else (question and special tokens)
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# This logic mirrors the logic in the question_answering pipeline
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p_mask = [[tok != 1 for tok in encoding.sequence_ids(span_id)] for span_id in range(num_spans)]
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for span_idx in range(num_spans):
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input_ids_span_idx = encoding["input_ids"][span_idx]
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# keep the cls_token unmasked (some models use it to indicate unanswerable questions)
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if self.tokenizer.cls_token_id is not None:
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cls_indices = np.nonzero(np.array(input_ids_span_idx) == self.tokenizer.cls_token_id)[0]
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for cls_index in cls_indices:
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p_mask[span_idx][cls_index] = 0
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# For each span, place a bounding box [0,0,0,0] for question and CLS tokens, [1000,1000,1000,1000]
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# for SEP tokens, and the word's bounding box for words in the original document.
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bbox = []
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for batch_index in range(num_spans):
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for i, s, w in zip(
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encoding.input_ids[batch_index],
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encoding.sequence_ids(batch_index),
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encoding.word_ids(batch_index),
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):
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if s == 1:
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bbox.append(boxes[w])
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elif i == self.tokenizer.sep_token_id:
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bbox.append([1000] * 4)
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else:
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bbox.append([0] * 4)
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if self.framework == "tf":
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raise ValueError("Unsupported: Tensorflow preprocessing for DocumentQuestionAnsweringPipeline")
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elif self.framework == "pt":
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encoding["bbox"] = torch.tensor([bbox])
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word_ids = [encoding.word_ids(i) for i in range(num_spans)]
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# TODO This will be necessary when we implement overflow support
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# encoding.pop("overflow_to_sample_mapping", None)
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return {
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**encoding,
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"p_mask": p_mask,
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"word_ids": word_ids,
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"words": words,
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}
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def _forward(self, model_inputs):
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p_mask = model_inputs.pop("p_mask", None)
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word_ids = model_inputs.pop("word_ids", None)
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words = model_inputs.pop("words", None)
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model_outputs = self.model(**model_inputs)
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model_outputs["p_mask"] = p_mask
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model_outputs["word_ids"] = word_ids
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model_outputs["words"] = words
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model_outputs["attention_mask"] = model_inputs["attention_mask"]
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return model_outputs
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def postprocess(self, model_outputs, top_k=1, handle_impossible_answer=False, max_answer_len=15):
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min_null_score = 1000000 # large and positive
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answers = []
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words = model_outputs["words"]
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# TODO: Currently, we expect the length of model_outputs to be 1, because we do not stride
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# in the preprocessor code. When we implement that, we'll either need to handle tensors of size
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# > 1 or use the ChunkPipeline and handle multiple outputs (each of size = 1).
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starts, ends, scores, min_null_score = select_starts_ends(
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model_outputs["start_logits"],
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model_outputs["end_logits"],
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model_outputs["p_mask"],
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model_outputs["attention_mask"].numpy() if model_outputs.get("attention_mask", None) is not None else None,
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min_null_score,
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top_k,
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handle_impossible_answer,
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max_answer_len,
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)
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word_ids = model_outputs["word_ids"][0]
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for s, e, score in zip(starts, ends, scores):
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word_start, word_end = word_ids[s], word_ids[e]
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if word_start is not None and word_end is not None:
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answers.append(
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{
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"score": score,
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"answer": " ".join(words[word_start : word_end + 1]),
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"start": word_start,
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"end": word_end,
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}
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)
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if handle_impossible_answer:
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answers.append({"score": min_null_score, "answer": "", "start": 0, "end": 0})
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answers = sorted(answers, key=lambda x: x["score"], reverse=True)[:top_k]
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if len(answers) == 1:
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return answers[0]
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return answers
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