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* remove uses of require_tf * remove redundant import guards * this class has no tests * nits * del tf rng comment
248 lines
9.2 KiB
Python
248 lines
9.2 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 datasets import load_dataset
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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 (
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is_pipeline_test,
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is_torch_available,
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nested_simplify,
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require_torch,
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require_torch_accelerator,
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require_vision,
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slow,
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torch_device,
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)
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from .test_pipelines_common import ANY
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if is_torch_available():
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import torch
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from transformers.pipelines.pt_utils import KeyDataset
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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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@is_pipeline_test
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@require_torch
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@require_vision
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class VisualQuestionAnsweringPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
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def get_test_pipeline(
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self,
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model,
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tokenizer=None,
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image_processor=None,
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feature_extractor=None,
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processor=None,
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torch_dtype="float32",
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):
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vqa_pipeline = pipeline(
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"visual-question-answering",
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model="hf-internal-testing/tiny-vilt-random-vqa",
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torch_dtype=torch_dtype,
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)
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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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@require_torch
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@require_torch_accelerator
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def test_small_model_pt_blip2(self):
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vqa_pipeline = pipeline(
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"visual-question-answering", model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration"
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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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outputs = vqa_pipeline(image=image, question=question)
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self.assertEqual(outputs, [{"answer": ANY(str)}])
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outputs = vqa_pipeline({"image": image, "question": question})
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self.assertEqual(outputs, [{"answer": ANY(str)}])
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outputs = vqa_pipeline([{"image": image, "question": question}, {"image": image, "question": question}])
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self.assertEqual(outputs, [[{"answer": ANY(str)}]] * 2)
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vqa_pipeline = pipeline(
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"visual-question-answering",
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model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration",
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model_kwargs={"torch_dtype": torch.float16},
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device=torch_device,
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)
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self.assertEqual(vqa_pipeline.model.device, torch.device(f"{torch_device}:0"))
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self.assertEqual(vqa_pipeline.model.language_model.dtype, torch.float16)
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self.assertEqual(vqa_pipeline.model.vision_model.dtype, torch.float16)
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outputs = vqa_pipeline(image=image, question=question)
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self.assertEqual(outputs, [{"answer": ANY(str)}])
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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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@slow
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@require_torch
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@require_torch_accelerator
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def test_large_model_pt_blip2(self):
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vqa_pipeline = pipeline(
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"visual-question-answering",
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model="Salesforce/blip2-opt-2.7b",
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model_kwargs={"torch_dtype": torch.float16},
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device=torch_device,
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)
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self.assertEqual(vqa_pipeline.model.device, torch.device(f"{torch_device}:0"))
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self.assertEqual(vqa_pipeline.model.language_model.dtype, torch.float16)
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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question = "Question: how many cats are there? Answer:"
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outputs = vqa_pipeline(image=image, question=question)
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self.assertEqual(outputs, [{"answer": "two"}])
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outputs = vqa_pipeline({"image": image, "question": question})
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self.assertEqual(outputs, [{"answer": "two"}])
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outputs = vqa_pipeline([{"image": image, "question": question}, {"image": image, "question": question}])
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self.assertEqual(outputs, [[{"answer": "two"}]] * 2)
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@require_torch
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def test_small_model_pt_image_list(self):
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vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
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images = [
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000004016.png",
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]
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outputs = vqa_pipeline(image=images, question="How many cats are there?", top_k=1)
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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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@require_torch
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def test_small_model_pt_question_list(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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questions = ["How many cats are there?", "Are there any dogs?"]
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outputs = vqa_pipeline(image=image, question=questions, top_k=1)
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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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@require_torch
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def test_small_model_pt_both_list(self):
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vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
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images = [
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000004016.png",
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]
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questions = ["How many cats are there?", "Are there any dogs?"]
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outputs = vqa_pipeline(image=images, question=questions, 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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[{"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_dataset(self):
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vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
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dataset = load_dataset("hf-internal-testing/dummy_image_text_data", split="train[:2]")
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question = "What's in the image?"
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outputs = vqa_pipeline(image=KeyDataset(dataset, "image"), question=question, 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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