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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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8
tests/models/bridgetower/test_image_processing_bridgetower.py
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@@ -145,7 +145,7 @@ class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
pass
def test_call_pil(self):
# Initialize feature_extractor
# Initialize image processor
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
@@ -176,7 +176,7 @@ class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
)
def test_call_numpy(self):
# Initialize feature_extractor
# Initialize image processor
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
@@ -207,7 +207,7 @@ class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
)
def test_call_pytorch(self):
# Initialize feature_extractor
# Initialize image processor
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
@@ -238,7 +238,7 @@ class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
)
def test_equivalence_pad_and_create_pixel_mask(self):
# Initialize feature_extractors
# Initialize image processors
image_processing_1 = self.image_processing_class(**self.image_processor_dict)
image_processing_2 = self.image_processing_class(do_resize=False, do_normalize=False, do_rescale=False)
# create random PyTorch tensors