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image-segmentation pipeline: re-enable small_model_pt test. (#19716)

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* Re-enable `small_model_pt`.

Re-enable `small_model_pt`.

Enabling the current test with the current values.

Debugging the values on the CI.

More logs ? Printing doesn't work ?

Using the CI values instead. Seems to be a Pillow sensitivity.

* Update src/transformers/pipelines/image_segmentation.py

Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>

Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
This commit is contained in:
Nicolas Patry
2022-10-20 11:57:11 +02:00
committed by GitHub
parent eb98da9880
commit a40386669f
1 changed files with 79 additions and 24 deletions
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103
tests/pipelines/test_pipelines_image_segmentation.py
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@@ -147,39 +147,60 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
pass pass
@require_torch @require_torch
@unittest.skip("No weights found for hf-internal-testing/tiny-detr-mobilenetsv3-panoptic")
def test_small_model_pt(self): def test_small_model_pt(self):
model_id = "hf-internal-testing/tiny-detr-mobilenetsv3-panoptic" model_id = "hf-internal-testing/tiny-detr-mobilenetsv3-panoptic"
model = AutoModelForImageSegmentation.from_pretrained(model_id) model = AutoModelForImageSegmentation.from_pretrained(model_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id) feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor) image_segmenter = ImageSegmentationPipeline(
model=model,
feature_extractor=feature_extractor,
task="semantic",
threshold=0.0,
overlap_mask_area_threshold=0.0,
)
outputs = image_segmenter( outputs = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg", "http://images.cocodataset.org/val2017/000000039769.jpg",
task="panoptic",
threshold=0.0,
overlap_mask_area_threshold=0.0,
) )
# Shortening by hashing # Shortening by hashing
for o in outputs: for o in outputs:
o["mask"] = mask_to_test_readable(o["mask"]) o["mask"] = mask_to_test_readable(o["mask"])
# This is extremely brittle, and those values are made specific for the CI.
self.assertEqual( self.assertEqual(
nested_simplify(outputs, decimals=4), nested_simplify(outputs, decimals=4),
[ [
{ {
"score": 0.004, "label": "LABEL_88",
"label": "LABEL_215", "mask": {"hash": "7f0bf661a4", "shape": (480, 640), "white_pixels": 3},
"mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0}, "score": None,
}, },
{ {
"score": 0.004, "label": "LABEL_101",
"label": "LABEL_215", "mask": {"hash": "10ab738dc9", "shape": (480, 640), "white_pixels": 8948},
"mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0}, "score": None,
}, },
], {
"label": "LABEL_215",
"mask": {"hash": "b431e0946c", "shape": (480, 640), "white_pixels": 298249},
"score": None,
},
]
# Temporary: Keeping around the old values as they might provide useful later
# [
# {
# "score": 0.004,
# "label": "LABEL_215",
# "mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0},
# },
# {
# "score": 0.004,
# "label": "LABEL_215",
# "mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0},
# },
# ],
) )
outputs = image_segmenter( outputs = image_segmenter(
@@ -198,28 +219,62 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
[ [
[ [
{ {
"score": 0.004, "label": "LABEL_88",
"label": "LABEL_215", "mask": {"hash": "7f0bf661a4", "shape": (480, 640), "white_pixels": 3},
"mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0}, "score": None,
},
{
"label": "LABEL_101",
"mask": {"hash": "10ab738dc9", "shape": (480, 640), "white_pixels": 8948},
"score": None,
}, },
{ {
"score": 0.004,
"label": "LABEL_215", "label": "LABEL_215",
"mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0}, "mask": {"hash": "b431e0946c", "shape": (480, 640), "white_pixels": 298249},
"score": None,
}, },
], ],
[ [
{ {
"score": 0.004, "label": "LABEL_88",
"label": "LABEL_215", "mask": {"hash": "7f0bf661a4", "shape": (480, 640), "white_pixels": 3},
"mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0}, "score": None,
}, },
{ {
"score": 0.004, "label": "LABEL_101",
"label": "LABEL_215", "mask": {"hash": "10ab738dc9", "shape": (480, 640), "white_pixels": 8948},
"mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0}, "score": None,
}, },
], {
"label": "LABEL_215",
"mask": {"hash": "b431e0946c", "shape": (480, 640), "white_pixels": 298249},
"score": None,
},
]
# [
# {
# "score": 0.004,
# "label": "LABEL_215",
# "mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0},
# },
# {
# "score": 0.004,
# "label": "LABEL_215",
# "mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0},
# },
# ],
# [
# {
# "score": 0.004,
# "label": "LABEL_215",
# "mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0},
# },
# {
# "score": 0.004,
# "label": "LABEL_215",
# "mask": {"hash": "34eecd16bb", "shape": (480, 640), "white_pixels": 0},
# },
# ],
], ],
) )