init

2025-10-09 16:47:16 +08:00
parent c8feb4deb5
commit e27e3f16bb
5248 changed files with 1778505 additions and 0 deletions
--- a/transformers/tests/pipelines/test_pipelines_document_question_answering.py
+++ b/transformers/tests/pipelines/test_pipelines_document_question_answering.py
@@ -0,0 +1,424 @@
+# Copyright 2022 The HuggingFace Team. 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.
+
+import unittest
+
+from transformers import (
+    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
+    AutoTokenizer,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.pipelines import DocumentQuestionAnsweringPipeline, pipeline
+from transformers.pipelines.document_question_answering import apply_tesseract
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_detectron2,
+    require_pytesseract,
+    require_torch,
+    require_torch_bf16,
+    require_vision,
+    slow,
+)
+
+from .test_pipelines_common import ANY
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers.image_utils import load_image
+else:
+
+    class Image:
+        @staticmethod
+        def open(*args, **kwargs):
+            pass
+
+    def load_image(_):
+        return None
+
+
+# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
+# so we can expect it to be available.
+INVOICE_URL = (
+    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
+)
+
+
+@is_pipeline_test
+@require_torch
+@require_vision
+class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
+
+    @require_pytesseract
+    @require_vision
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        dqa_pipeline = DocumentQuestionAnsweringPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            dtype=dtype,
+            max_new_tokens=20,
+        )
+
+        image = INVOICE_URL
+        word_boxes = list(zip(*apply_tesseract(load_image(image), None, "")))
+        question = "What is the placebo?"
+        examples = [
+            {
+                "image": load_image(image),
+                "question": question,
+            },
+            {
+                "image": image,
+                "question": question,
+            },
+            {
+                "image": image,
+                "question": question,
+                "word_boxes": word_boxes,
+            },
+        ]
+        return dqa_pipeline, examples
+
+    def run_pipeline_test(self, dqa_pipeline, examples):
+        outputs = dqa_pipeline(examples, top_k=2)
+        self.assertEqual(
+            outputs,
+            [
+                [
+                    {"score": ANY(float), "answer": ANY(str), "start": ANY(int), "end": ANY(int)},
+                    {"score": ANY(float), "answer": ANY(str), "start": ANY(int), "end": ANY(int)},
+                ]
+            ]
+            * 3,
+        )
+
+    @require_torch
+    @require_detectron2
+    @require_pytesseract
+    def test_small_model_pt(self):
+        dqa_pipeline = pipeline(
+            "document-question-answering", model="hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test"
+        )
+        image = INVOICE_URL
+        question = "How many cats are there?"
+
+        expected_output = [
+            {"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
+            {"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
+        ]
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(nested_simplify(outputs, decimals=4), expected_output)
+
+        outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
+        self.assertEqual(nested_simplify(outputs, decimals=4), expected_output)
+
+        # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
+        # Empty answer probably
+        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(outputs, [])
+
+        # We can optionally pass directly the words and bounding boxes
+        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
+        words = []
+        boxes = []
+        outputs = dqa_pipeline(image=image, question=question, words=words, boxes=boxes, top_k=2)
+        self.assertEqual(outputs, [])
+
+    @require_torch
+    @require_torch_bf16
+    @require_detectron2
+    @require_pytesseract
+    def test_small_model_pt_bf16(self):
+        dqa_pipeline = pipeline(
+            "document-question-answering",
+            model="hf-internal-testing/tiny-random-layoutlmv2-for-dqa-test",
+            dtype=torch.bfloat16,
+        )
+        image = INVOICE_URL
+        question = "How many cats are there?"
+
+        expected_output = [
+            {"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
+            {"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
+        ]
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(nested_simplify(outputs, decimals=4), expected_output)
+
+        outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
+        self.assertEqual(nested_simplify(outputs, decimals=4), expected_output)
+
+        # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
+        # Empty answer probably
+        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(outputs, [])
+
+        # We can optionally pass directly the words and bounding boxes
+        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
+        words = []
+        boxes = []
+        outputs = dqa_pipeline(image=image, question=question, words=words, boxes=boxes, top_k=2)
+        self.assertEqual(outputs, [])
+
+    # 	 TODO: Enable this once hf-internal-testing/tiny-random-donut is implemented
+    #    @require_torch
+    #    def test_small_model_pt_donut(self):
+    #        dqa_pipeline = pipeline("document-question-answering", model="hf-internal-testing/tiny-random-donut")
+    #        # dqa_pipeline = pipeline("document-question-answering", model="../tiny-random-donut")
+    #        image = "https://templates.invoicehome.com/invoice-template-us-neat-750px.png"
+    #        question = "How many cats are there?"
+    #
+    #        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+    #        self.assertEqual(
+    #            nested_simplify(outputs, decimals=4), [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]
+    #        )
+
+    @slow
+    @require_torch
+    @require_detectron2
+    @require_pytesseract
+    def test_large_model_pt(self):
+        dqa_pipeline = pipeline(
+            "document-question-answering",
+            model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa",
+            revision="9977165",
+        )
+        image = INVOICE_URL
+        question = "What is the invoice number?"
+
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
+                {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
+            ],
+        )
+
+        outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
+                {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
+            ],
+        )
+
+        outputs = dqa_pipeline(
+            [{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [
+                    {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
+                    {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
+                ],
+            ]
+            * 2,
+        )
+
+    @slow
+    @require_torch
+    @require_detectron2
+    @require_pytesseract
+    def test_large_model_pt_chunk(self):
+        dqa_pipeline = pipeline(
+            "document-question-answering",
+            model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa",
+            revision="9977165",
+            max_seq_len=50,
+        )
+        image = INVOICE_URL
+        question = "What is the invoice number?"
+
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
+                {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
+            ],
+        )
+
+        outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
+                {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
+            ],
+        )
+
+        outputs = dqa_pipeline(
+            [{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [
+                    {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
+                    {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
+                ]
+            ]
+            * 2,
+        )
+
+    @slow
+    @require_torch
+    @require_pytesseract
+    @require_vision
+    def test_large_model_pt_layoutlm(self):
+        tokenizer = AutoTokenizer.from_pretrained(
+            "impira/layoutlm-document-qa", revision="3dc6de3", add_prefix_space=True
+        )
+        dqa_pipeline = pipeline(
+            "document-question-answering",
+            model="impira/layoutlm-document-qa",
+            tokenizer=tokenizer,
+            revision="3dc6de3",
+        )
+        image = INVOICE_URL
+        question = "What is the invoice number?"
+
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=3),
+            [
+                {"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
+                {"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
+            ],
+        )
+
+        outputs = dqa_pipeline({"image": image, "question": question}, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=3),
+            [
+                {"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
+                {"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
+            ],
+        )
+
+        outputs = dqa_pipeline(
+            [{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=3),
+            [
+                [
+                    {"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
+                    {"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
+                ]
+            ]
+            * 2,
+        )
+
+        word_boxes = list(zip(*apply_tesseract(load_image(image), None, "")))
+
+        # This model should also work if `image` is set to None
+        outputs = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=3),
+            [
+                {"score": 0.425, "answer": "us-001", "start": 16, "end": 16},
+                {"score": 0.082, "answer": "1110212019", "start": 23, "end": 23},
+            ],
+        )
+
+    @slow
+    @require_torch
+    @require_pytesseract
+    @require_vision
+    def test_large_model_pt_layoutlm_chunk(self):
+        tokenizer = AutoTokenizer.from_pretrained(
+            "impira/layoutlm-document-qa", revision="3dc6de3", add_prefix_space=True
+        )
+        dqa_pipeline = pipeline(
+            "document-question-answering",
+            model="impira/layoutlm-document-qa",
+            tokenizer=tokenizer,
+            revision="3dc6de3",
+            max_seq_len=50,
+        )
+        image = INVOICE_URL
+        question = "What is the invoice number?"
+
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
+                {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
+            ],
+        )
+
+        outputs = dqa_pipeline(
+            [{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [
+                    {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
+                    {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
+                ]
+            ]
+            * 2,
+        )
+
+        word_boxes = list(zip(*apply_tesseract(load_image(image), None, "")))
+
+        # This model should also work if `image` is set to None
+        outputs = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}, top_k=2)
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
+                {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
+            ],
+        )
+
+    @slow
+    @require_torch
+    def test_large_model_pt_donut(self):
+        dqa_pipeline = pipeline(
+            "document-question-answering",
+            model="naver-clova-ix/donut-base-finetuned-docvqa",
+            tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"),
+            image_processor="naver-clova-ix/donut-base-finetuned-docvqa",
+        )
+
+        image = INVOICE_URL
+        question = "What is the invoice number?"
+        outputs = dqa_pipeline(image=image, question=question, top_k=2)
+        self.assertEqual(nested_simplify(outputs, decimals=4), [{"answer": "us-001"}])