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https://github.com/huggingface/transformers.git
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871c31a6f1
* Add PipelineTesterMixin * remove class PipelineTestCaseMeta * move validate_test_components * Add for ViT * Add to SPECIAL_MODULE_TO_TEST_MAP * style and quality * Add feature-extraction * update * raise instead of skip * add tiny_model_summary.json * more explicit * skip tasks not in mapping * add availability check * Add Copyright * A way to diable irrelevant tests * update with main * remove disable_irrelevant_tests * skip tests * better skip message * better skip message * Add all pipeline task tests * revert * Import PipelineTesterMixin * subclass test classes with PipelineTesterMixin * Add pipieline_model_mapping * Fix import after adding pipieline_model_mapping * Fix style and quality after adding pipieline_model_mapping * Fix one more import after adding pipieline_model_mapping * Fix style and quality after adding pipieline_model_mapping * Fix test issues * Fix import requirements * Fix mapping for MobileViTModelTest * Update * Better skip message * pipieline_model_mapping could not be None * Remove some PipelineTesterMixin * Fix typo * revert tests_fetcher.py * update * rename * revert * Remove PipelineTestCaseMeta from ZeroShotAudioClassificationPipelineTests * style and quality * test fetcher for all pipeline/model tests --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
# Copyright 2021 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 huggingface_hub import hf_hub_download
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from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
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from transformers.pipelines import VideoClassificationPipeline, pipeline
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from transformers.testing_utils import (
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nested_simplify,
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require_decord,
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require_tf,
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require_torch,
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require_torch_or_tf,
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require_vision,
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)
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from .test_pipelines_common import ANY
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@require_torch_or_tf
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@require_vision
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@require_decord
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class VideoClassificationPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
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def get_test_pipeline(self, model, tokenizer, processor):
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example_video_filepath = hf_hub_download(
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repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset"
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)
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video_classifier = VideoClassificationPipeline(model=model, image_processor=processor, top_k=2)
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examples = [
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example_video_filepath,
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"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
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]
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return video_classifier, examples
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def run_pipeline_test(self, video_classifier, examples):
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for example in examples:
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outputs = video_classifier(example)
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self.assertEqual(
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outputs,
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[
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{"score": ANY(float), "label": ANY(str)},
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{"score": ANY(float), "label": 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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small_model = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
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small_feature_extractor = VideoMAEFeatureExtractor(
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size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
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)
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video_classifier = pipeline(
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"video-classification", model=small_model, feature_extractor=small_feature_extractor, frame_sampling_rate=4
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)
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video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
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outputs = video_classifier(video_file_path, top_k=2)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
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)
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outputs = video_classifier(
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[
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video_file_path,
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video_file_path,
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],
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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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[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
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[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
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],
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)
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@require_tf
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def test_small_model_tf(self):
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pass
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