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https://github.com/huggingface/transformers.git
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2ef7742117
* [WIP] Skeleton of VisualQuestionAnweringPipeline extended to support LayoutLM-like models * Fixup * Use the full encoding * Basic refactoring to DocumentQuestionAnsweringPipeline * Cleanup * Improve args, docs, and implement preprocessing * Integrate OCR * Refactor question_answering pipeline * Use refactored QA code in the document qa pipeline * Fix tests * Some small cleanups * Use a string type annotation for Image.Image * Update encoding with image features * Wire through the basic docs * Handle invalid response * Handle empty word_boxes properly * Docstring fix * Integrate Donut model * Fixup * Incorporate comments * Address comments * Initial incorporation of tests * Address Comments * Change assert to ValueError * Comments * Wrap `score` in float to make it JSON serializable * Incorporate AutoModeLForDocumentQuestionAnswering changes * Fixup * Rename postprocess function * Fix auto import * Applying comments * Improve docs * Remove extra assets and add copyright * Address comments Co-authored-by: Ankur Goyal <ankur@impira.com>
281 lines
10 KiB
Python
281 lines
10 KiB
Python
# Copyright 2022 The HuggingFace Team. 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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import unittest
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from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
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from transformers.pipelines import pipeline
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from transformers.pipelines.document_question_answering import apply_tesseract
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from transformers.testing_utils import (
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is_pipeline_test,
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nested_simplify,
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require_detectron2,
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require_pytesseract,
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require_tf,
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require_torch,
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require_vision,
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slow,
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)
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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if is_vision_available():
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from PIL import Image
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from transformers.image_utils import load_image
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else:
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class Image:
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@staticmethod
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def open(*args, **kwargs):
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pass
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def load_image(_):
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return None
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# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
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# so we can expect it to be available.
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INVOICE_URL = (
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"https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
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)
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@is_pipeline_test
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@require_torch
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@require_vision
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class DocumentQuestionAnsweringPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
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@require_pytesseract
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@require_vision
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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dqa_pipeline = pipeline(
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"document-question-answering", model=model, tokenizer=tokenizer, feature_extractor=feature_extractor
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)
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image = INVOICE_URL
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word_boxes = list(zip(*apply_tesseract(load_image(image), None, "")))
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question = "What is the placebo?"
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examples = [
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{
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"image": load_image(image),
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"question": question,
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},
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{
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"image": image,
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"question": question,
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},
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{
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"image": image,
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"question": question,
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"word_boxes": word_boxes,
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},
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{
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"image": None,
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"question": question,
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"word_boxes": word_boxes,
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},
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]
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return dqa_pipeline, examples
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def run_pipeline_test(self, dqa_pipeline, examples):
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outputs = dqa_pipeline(examples, top_k=2)
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self.assertEqual(
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outputs,
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[
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[
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{"score": ANY(float), "answer": ANY(str), "start": ANY(int), "end": ANY(int)},
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{"score": ANY(float), "answer": ANY(str), "start": ANY(int), "end": ANY(int)},
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]
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]
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* 4,
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)
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@require_torch
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@require_detectron2
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@require_pytesseract
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def test_small_model_pt(self):
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dqa_pipeline = pipeline("document-question-answering", model="hf-internal-testing/tiny-random-layoutlmv2")
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image = INVOICE_URL
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question = "How many cats are there?"
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expected_output = [
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{
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"score": 0.0001,
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"answer": "2312/2019 DUE DATE 26102/2019 ay DESCRIPTION UNIT PRICE",
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"start": 38,
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"end": 45,
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},
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{"score": 0.0001, "answer": "2312/2019 DUE", "start": 38, "end": 39},
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]
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outputs = dqa_pipeline(image=image, question=question, top_k=2)
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self.assertEqual(nested_simplify(outputs, decimals=4), expected_output)
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outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
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self.assertEqual(nested_simplify(outputs, decimals=4), expected_output)
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# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
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# Empty answer probably
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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outputs = dqa_pipeline(image=image, question=question, top_k=2)
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self.assertEqual(outputs, [])
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# We can optionnally pass directly the words and bounding boxes
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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words = []
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boxes = []
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outputs = dqa_pipeline(image=image, question=question, words=words, boxes=boxes, top_k=2)
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self.assertEqual(outputs, [])
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# TODO: Enable this once hf-internal-testing/tiny-random-donut is implemented
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# @require_torch
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# def test_small_model_pt_donut(self):
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# dqa_pipeline = pipeline("document-question-answering", model="hf-internal-testing/tiny-random-donut")
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# # dqa_pipeline = pipeline("document-question-answering", model="../tiny-random-donut")
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# image = "https://templates.invoicehome.com/invoice-template-us-neat-750px.png"
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# question = "How many cats are there?"
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#
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# outputs = dqa_pipeline(image=image, question=question, top_k=2)
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# self.assertEqual(
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# nested_simplify(outputs, decimals=4), [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]
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# )
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@slow
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@require_torch
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@require_detectron2
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@require_pytesseract
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def test_large_model_pt(self):
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dqa_pipeline = pipeline(
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"document-question-answering",
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model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa",
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revision="9977165",
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)
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image = INVOICE_URL
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question = "What is the invoice number?"
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outputs = dqa_pipeline(image=image, question=question, top_k=2)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9966, "answer": "us-001", "start": 15, "end": 15},
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{"score": 0.0009, "answer": "us-001", "start": 15, "end": 15},
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],
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)
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outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9966, "answer": "us-001", "start": 15, "end": 15},
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{"score": 0.0009, "answer": "us-001", "start": 15, "end": 15},
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],
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)
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outputs = dqa_pipeline(
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[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.9966, "answer": "us-001", "start": 15, "end": 15},
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{"score": 0.0009, "answer": "us-001", "start": 15, "end": 15},
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],
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]
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* 2,
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)
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@slow
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@require_torch
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@require_pytesseract
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@require_vision
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def test_large_model_pt_layoutlm(self):
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tokenizer = AutoTokenizer.from_pretrained(
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"impira/layoutlm-document-qa", revision="3dc6de3", add_prefix_space=True
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)
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dqa_pipeline = pipeline(
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"document-question-answering",
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model="impira/layoutlm-document-qa",
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tokenizer=tokenizer,
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revision="3dc6de3",
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)
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image = INVOICE_URL
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question = "What is the invoice number?"
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outputs = dqa_pipeline(image=image, question=question, top_k=2)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9998, "answer": "us-001", "start": 15, "end": 15},
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{"score": 0.0, "answer": "INVOICE # us-001", "start": 13, "end": 15},
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],
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)
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outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9998, "answer": "us-001", "start": 15, "end": 15},
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{"score": 0.0, "answer": "INVOICE # us-001", "start": 13, "end": 15},
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],
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)
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outputs = dqa_pipeline(
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[{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.9998, "answer": "us-001", "start": 15, "end": 15},
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{"score": 0.0, "answer": "INVOICE # us-001", "start": 13, "end": 15},
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]
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]
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* 2,
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)
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word_boxes = list(zip(*apply_tesseract(load_image(image), None, "")))
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# This model should also work if `image` is set to None
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outputs = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}, top_k=2)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9998, "answer": "us-001", "start": 15, "end": 15},
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{"score": 0.0, "answer": "INVOICE # us-001", "start": 13, "end": 15},
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],
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)
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@slow
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@require_torch
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def test_large_model_pt_donut(self):
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dqa_pipeline = pipeline(
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"document-question-answering",
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model="naver-clova-ix/donut-base-finetuned-docvqa",
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tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"),
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feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa",
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)
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image = INVOICE_URL
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question = "What is the invoice number?"
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outputs = dqa_pipeline(image=image, question=question, top_k=2)
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self.assertEqual(nested_simplify(outputs, decimals=4), {"answer": "us-001"})
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@require_tf
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@unittest.skip("Document question answering not implemented in TF")
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def test_small_model_tf(self):
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pass
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