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99e7905422
* Fixing the pipeline with image processor. * Update the slow test. * Using only the first image processor. * Include exclusion mecanism for Image processor. * Do not handle Gitconfig, deemed as a bug. * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Remove `conversational` changes. They are not supposed to be here. * Address first row of comments. * Remove OneFormer modifications. Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
108 lines
4.0 KiB
Python
108 lines
4.0 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_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
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from transformers.pipelines import pipeline
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from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
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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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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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@require_torch
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@require_vision
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class VisualQuestionAnsweringPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
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def get_test_pipeline(self, model, tokenizer, feature_extractor, image_processor):
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vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
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examples = [
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{
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"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"question": "How many cats are there?",
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},
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{
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"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
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"question": "How many cats are there?",
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},
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]
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return vqa_pipeline, examples
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def run_pipeline_test(self, vqa_pipeline, examples):
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outputs = vqa_pipeline(examples, top_k=1)
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self.assertEqual(
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outputs,
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[
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[{"score": ANY(float), "answer": ANY(str)}],
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[{"score": ANY(float), "answer": ANY(str)}],
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],
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)
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@require_torch
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def test_small_model_pt(self):
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vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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question = "How many cats are there?"
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outputs = vqa_pipeline(image=image, question="How many cats are there?", top_k=2)
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self.assertEqual(
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outputs, [{"score": ANY(float), "answer": ANY(str)}, {"score": ANY(float), "answer": ANY(str)}]
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)
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outputs = vqa_pipeline({"image": image, "question": question}, top_k=2)
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self.assertEqual(
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outputs, [{"score": ANY(float), "answer": ANY(str)}, {"score": ANY(float), "answer": ANY(str)}]
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)
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@slow
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@require_torch
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def test_large_model_pt(self):
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vqa_pipeline = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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question = "How many cats are there?"
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outputs = vqa_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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outputs = vqa_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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outputs = vqa_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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[[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2,
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)
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@require_tf
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@unittest.skip("Visual 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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