Skip to content

GitLab

"...git@developer.sourcefind.cn:chenpangpang/transformers.git" did not exist on "b0d539ccad090c8949c1740a9758b4152fad5f72"
Unverified Commit a4038666 authored by Nicolas Patry's avatar Nicolas Patry Committed by GitHub
Browse files

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



* 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: default avatarAlara Dirik <8944735+alaradirik@users.noreply.github.com>
Co-authored-by: default avatarAlara Dirik <8944735+alaradirik@users.noreply.github.com>
parent eb98da98
Show whitespace changes
Inline Side-by-side
Showing with 77 additions and 22 deletions
tests/pipelines/test_pipelines_image_segmentation.py
View file @ a4038666
...@@ -147,39 +147,60 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa ...@@ -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,
},
{
"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,
}, },
], ]
# 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 ...@@ -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,
},
{
"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_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},
# },
# ],
], ],
) )
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment