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Supporting ImageProcessor in place of FeatureExtractor for pipelines (#20851)

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* 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>
This commit is contained in:
Nicolas Patry
2023-01-25 10:16:31 +01:00
committed by GitHub
parent efdbad56ab
commit 99e7905422
28 changed files with 138 additions and 47 deletions
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17
tests/pipelines/test_pipelines_image_segmentation.py
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@@ -26,6 +26,7 @@ from transformers import (
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
AutoFeatureExtractor,
AutoImageProcessor,
AutoModelForImageSegmentation,
AutoModelForInstanceSegmentation,
DetrForSegmentation,
@@ -80,8 +81,10 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
+ (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else [])
}
def get_test_pipeline(self, model, tokenizer, feature_extractor):
image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)
def get_test_pipeline(self, model, tokenizer, feature_extractor, image_processor):
image_segmenter = ImageSegmentationPipeline(
model=model, feature_extractor=feature_extractor, image_processor=image_processor
)
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
@@ -139,7 +142,11 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
outputs = image_segmenter(
batch, threshold=0.0, mask_threshold=0, overlap_mask_area_threshold=0, batch_size=batch_size
batch,
threshold=0.0,
mask_threshold=0,
overlap_mask_area_threshold=0,
batch_size=batch_size,
)
self.assertEqual(len(batch), len(outputs))
self.assertEqual(len(outputs[0]), n)
@@ -188,10 +195,10 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
model_id = "hf-internal-testing/tiny-detr-mobilenetsv3-panoptic"
model = AutoModelForImageSegmentation.from_pretrained(model_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
image_processor = AutoImageProcessor.from_pretrained(model_id)
image_segmenter = ImageSegmentationPipeline(
model=model,
feature_extractor=feature_extractor,
image_processor=image_processor,
subtask="panoptic",
threshold=0.0,
mask_threshold=0.0,