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Pipeline testing - using tiny models on Hub (#20426)

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* rework pipeline tests

* run pipeline tests

* fix

* fix

* fix

* revert the changes in get_test_pipeline() parameter list

* fix expected error message

* skip a test

* clean up

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-01-30 10:39:43 +01:00
committed by GitHub
parent a582cfce3c
commit c749bd405e
26 changed files with 286 additions and 274 deletions
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4
tests/pipelines/test_pipelines_video_classification.py
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@@ -35,11 +35,11 @@ from .test_pipelines_common import ANY, PipelineTestCaseMeta
class VideoClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def get_test_pipeline(self, model, tokenizer, feature_extractor, image_processor):
def get_test_pipeline(self, model, tokenizer, processor):
example_video_filepath = hf_hub_download(
repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset"
)
video_classifier = VideoClassificationPipeline(model=model, feature_extractor=feature_extractor, top_k=2)
video_classifier = VideoClassificationPipeline(model=model, feature_extractor=processor, top_k=2)
examples = [
example_video_filepath,
"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",