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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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6
tests/pipelines/test_pipelines_feature_extraction.py
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@@ -175,7 +175,7 @@ class FeatureExtractionPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
raise ValueError("We expect lists of floats, nothing else")
return shape
def get_test_pipeline(self, model, tokenizer, feature_extractor, image_processor):
def get_test_pipeline(self, model, tokenizer, processor):
if tokenizer is None:
self.skipTest("No tokenizer")
return
@@ -196,9 +196,7 @@ class FeatureExtractionPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
)
return
feature_extractor = FeatureExtractionPipeline(
model=model, tokenizer=tokenizer, feature_extractor=feature_extractor
)
feature_extractor = FeatureExtractionPipeline(model=model, tokenizer=tokenizer, feature_extractor=processor)
return feature_extractor, ["This is a test", "This is another test"]
def run_pipeline_test(self, feature_extractor, examples):