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Removal of deprecated vision methods and specify deprecation versions (#24570)

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* Removal of deprecated methods and specify versions

* Fix tests
This commit is contained in:
amyeroberts
2023-06-29 15:09:51 +01:00
committed by GitHub
parent 77db28dc52
commit b324557aac
32 changed files with 144 additions and 760 deletions
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23
tests/models/maskformer/test_image_processing_maskformer.py
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@@ -147,7 +147,6 @@ class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "max_size"))
self.assertTrue(hasattr(image_processing, "ignore_index"))
self.assertTrue(hasattr(image_processing, "num_labels"))
@@ -263,28 +262,6 @@ class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
),
)
def test_equivalence_pad_and_create_pixel_mask(self):
# Initialize image_processings
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, num_labels=self.image_processor_tester.num_classes
)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test whether the method "pad_and_return_pixel_mask" and calling the image processor return the same tensors
encoded_images_with_method = image_processing_1.encode_inputs(image_inputs, return_tensors="pt")
encoded_images = image_processing_2(image_inputs, return_tensors="pt")
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
)
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
)
def comm_get_image_processing_inputs(
self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"
):