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Update old existing feature extractor references (#24552)

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* Update old existing feature extractor references

* Typo

* Apply suggestions from code review

* Apply suggestions from code review

* Apply suggestions from code review

* Address comments from review - update 'feature extractor'
Co-authored by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
This commit is contained in:
amyeroberts
2023-06-29 10:17:36 +01:00
committed by GitHub
parent 10c2ac7bc6
commit ae454f41d4
138 changed files with 762 additions and 743 deletions
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6
tests/models/mask2former/test_image_processing_mask2former.py
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@@ -545,9 +545,9 @@ class Mask2FormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
self.assertEqual(segmentation[0].shape, target_sizes[0])
def test_post_process_instance_segmentation(self):
feature_extractor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
image_processor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
outputs = self.image_processor_tester.get_fake_mask2former_outputs()
segmentation = feature_extractor.post_process_instance_segmentation(outputs, threshold=0)
segmentation = image_processor.post_process_instance_segmentation(outputs, threshold=0)
self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
for el in segmentation:
@@ -556,7 +556,7 @@ class Mask2FormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
self.assertEqual(type(el["segments_info"]), list)
self.assertEqual(el["segmentation"].shape, (384, 384))
segmentation = feature_extractor.post_process_instance_segmentation(
segmentation = image_processor.post_process_instance_segmentation(
outputs, threshold=0, return_binary_maps=True
)

14
tests/models/mask2former/test_modeling_mask2former.py
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@@ -325,14 +325,14 @@ class Mask2FormerModelIntegrationTest(unittest.TestCase):
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return Mask2FormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None
def test_inference_no_head(self):
model = Mask2FormerModel.from_pretrained(self.model_checkpoints).to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(image, return_tensors="pt").to(torch_device)
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
@@ -371,9 +371,9 @@ class Mask2FormerModelIntegrationTest(unittest.TestCase):
def test_inference_universal_segmentation_head(self):
model = Mask2FormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval()
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(image, return_tensors="pt").to(torch_device)
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
@@ -408,9 +408,9 @@ class Mask2FormerModelIntegrationTest(unittest.TestCase):
def test_with_segmentation_maps_and_loss(self):
model = Mask2FormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval()
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
inputs = feature_extractor(
inputs = image_processor(
[np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))],
segmentation_maps=[np.zeros((384, 384)).astype(np.float32), np.zeros((384, 384)).astype(np.float32)],
return_tensors="pt",