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Tests for MaskFormerFeatureExtractor's post_process*** methods (#15929)

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* proper tests for post_process*** methods in feature extractor

* mask th == 0

* Update tests/maskformer/test_feature_extraction_maskformer.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* make style

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Francesco Saverio Zuppichini
2022-03-04 18:04:19 +01:00
committed by GitHub
parent f0aacc140b
commit 040c11f6da
2 changed files with 72 additions and 20 deletions
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72
tests/maskformer/test_feature_extraction_maskformer.py
View File

@@ -29,6 +29,7 @@ if is_torch_available():
if is_vision_available():
from transformers import MaskFormerFeatureExtractor
from transformers.models.maskformer.modeling_maskformer import MaskFormerForInstanceSegmentationOutput
if is_vision_available():
from PIL import Image
@@ -61,6 +62,12 @@ class MaskFormerFeatureExtractionTester(unittest.TestCase):
self.image_mean = image_mean
self.image_std = image_std
self.size_divisibility = 0
# for the post_process_functions
self.batch_size = 2
self.num_queries = 3
self.num_classes = 2
self.height = 3
self.width = 4
def prepare_feat_extract_dict(self):
return {
@@ -104,6 +111,13 @@ class MaskFormerFeatureExtractionTester(unittest.TestCase):
return expected_height, expected_width
def get_fake_maskformer_outputs(self):
return MaskFormerForInstanceSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)),
masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)),
)
@require_torch
@require_vision
@@ -301,3 +315,61 @@ class MaskFormerFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest
self.assertEqual(pixel_values.shape[-1], mask_labels.shape[-1])
self.assertEqual(mask_labels.shape[1], class_labels.shape[1])
self.assertEqual(mask_labels.shape[1], num_classes)
def test_post_process_segmentation(self):
fature_extractor = self.feature_extraction_class()
outputs = self.feature_extract_tester.get_fake_maskformer_outputs()
segmentation = fature_extractor.post_process_segmentation(outputs)
self.assertEqual(
segmentation.shape,
(
self.feature_extract_tester.batch_size,
self.feature_extract_tester.num_classes,
self.feature_extract_tester.height,
self.feature_extract_tester.width,
),
)
target_size = (1, 4)
segmentation = fature_extractor.post_process_segmentation(outputs, target_size=target_size)
self.assertEqual(
segmentation.shape,
(self.feature_extract_tester.batch_size, self.feature_extract_tester.num_classes, *target_size),
)
def test_post_process_semantic_segmentation(self):
fature_extractor = self.feature_extraction_class()
outputs = self.feature_extract_tester.get_fake_maskformer_outputs()
segmentation = fature_extractor.post_process_semantic_segmentation(outputs)
self.assertEqual(
segmentation.shape,
(
self.feature_extract_tester.batch_size,
self.feature_extract_tester.height,
self.feature_extract_tester.width,
),
)
target_size = (1, 4)
segmentation = fature_extractor.post_process_semantic_segmentation(outputs, target_size=target_size)
self.assertEqual(segmentation.shape, (self.feature_extract_tester.batch_size, *target_size))
def test_post_process_panoptic_segmentation(self):
fature_extractor = self.feature_extraction_class()
outputs = self.feature_extract_tester.get_fake_maskformer_outputs()
segmentation = fature_extractor.post_process_panoptic_segmentation(outputs, object_mask_threshold=0)
self.assertTrue(len(segmentation) == self.feature_extract_tester.batch_size)
for el in segmentation:
self.assertTrue("segmentation" in el)
self.assertTrue("segments" in el)
self.assertEqual(type(el["segments"]), list)
self.assertEqual(
el["segmentation"].shape, (self.feature_extract_tester.height, self.feature_extract_tester.width)
)

20
tests/maskformer/test_modeling_maskformer.py
View File

@@ -404,23 +404,3 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
outputs = model(**inputs)
self.assertTrue(outputs.loss is not None)
def test_panoptic_segmentation(self):
model = MaskFormerForInstanceSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval()
feature_extractor = self.default_feature_extractor
inputs = feature_extractor(
[np.zeros((3, 384, 384)), np.zeros((3, 384, 384))],
annotations=[
{"masks": np.random.rand(10, 384, 384).astype(np.float32), "labels": np.zeros(10).astype(np.int64)},
{"masks": np.random.rand(10, 384, 384).astype(np.float32), "labels": np.zeros(10).astype(np.int64)},
],
return_tensors="pt",
)
with torch.no_grad():
outputs = model(**inputs)
panoptic_segmentation = feature_extractor.post_process_panoptic_segmentation(outputs)
self.assertTrue(len(panoptic_segmentation) == 2)