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🚨🚨🚨 Fix rescale ViVit Efficientnet (#25174)

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* Fix rescaling bug

* Add tests

* Update integration tests

* Fix up

* Update src/transformers/image_transforms.py

* Update test - new possible order in list
This commit is contained in:
amyeroberts
2023-07-28 19:52:51 +01:00
committed by GitHub
parent 03f98f9683
commit 05cda5df34
7 changed files with 57 additions and 15 deletions
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14
tests/models/efficientnet/test_image_processing_efficientnet.py
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@@ -193,3 +193,17 @@ class EfficientNetImageProcessorTest(ImageProcessingSavingTestMixin, unittest.Te
self.image_processor_tester.size["width"],
),
)
def test_rescale(self):
# EfficientNet optionally rescales between -1 and 1 instead of the usual 0 and 1
image = np.arange(0, 256, 1, dtype=np.uint8).reshape(1, 8, 32)
image_processor = self.image_processing_class(**self.image_processor_dict)
rescaled_image = image_processor.rescale(image, scale=1 / 255)
expected_image = image.astype(np.float32) * (2 / 255.0) - 1
self.assertTrue(np.allclose(rescaled_image, expected_image))
rescaled_image = image_processor.rescale(image, scale=1 / 255, offset=False)
expected_image = image.astype(np.float32) / 255.0
self.assertTrue(np.allclose(rescaled_image, expected_image))

14
tests/models/vivit/test_image_processing_vivit.py
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@@ -212,3 +212,17 @@ class VivitImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
self.image_processor_tester.crop_size["width"],
),
)
def test_rescale(self):
# ViVit optionally rescales between -1 and 1 instead of the usual 0 and 1
image = np.arange(0, 256, 1, dtype=np.uint8).reshape(1, 8, 32)
image_processor = self.image_processing_class(**self.image_processor_dict)
rescaled_image = image_processor.rescale(image, scale=1 / 255)
expected_image = image.astype(np.float32) * (2 / 255.0) - 1
self.assertTrue(np.allclose(rescaled_image, expected_image))
rescaled_image = image_processor.rescale(image, scale=1 / 255, offset=False)
expected_image = image.astype(np.float32) / 255.0
self.assertTrue(np.allclose(rescaled_image, expected_image))

2
tests/models/vivit/test_modeling_vivit.py
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@@ -345,6 +345,6 @@ class VivitModelIntegrationTest(unittest.TestCase):
self.assertEqual(outputs.logits.shape, expected_shape)
# taken from original model
expected_slice = torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]).to(torch_device)
expected_slice = torch.tensor([-0.9498, 2.7971, -1.4049, 0.1024, -1.8353]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :5], expected_slice, atol=1e-4))