Fix MaskFormerImageProcessor.post_process_instance_segmentation (#21256)
* fix instance segmentation post processing * add Mask2FormerImageProcessor
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@@ -576,6 +576,34 @@ class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
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self.assertEqual(segmentation[0].shape, target_sizes[0])
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def test_post_process_instance_segmentation(self):
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feature_extractor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
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outputs = self.image_processor_tester.get_fake_maskformer_outputs()
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segmentation = feature_extractor.post_process_instance_segmentation(outputs, threshold=0)
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self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
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for el in segmentation:
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self.assertTrue("segmentation" in el)
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self.assertTrue("segments_info" in el)
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self.assertEqual(type(el["segments_info"]), list)
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self.assertEqual(
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el["segmentation"].shape, (self.image_processor_tester.height, self.image_processor_tester.width)
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)
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segmentation = feature_extractor.post_process_instance_segmentation(
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outputs, threshold=0, return_binary_maps=True
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)
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self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
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for el in segmentation:
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self.assertTrue("segmentation" in el)
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self.assertTrue("segments_info" in el)
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self.assertEqual(type(el["segments_info"]), list)
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self.assertEqual(len(el["segmentation"].shape), 3)
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self.assertEqual(
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el["segmentation"].shape[1:], (self.image_processor_tester.height, self.image_processor_tester.width)
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)
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def test_post_process_panoptic_segmentation(self):
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image_processing = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
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outputs = self.image_processor_tester.get_fake_maskformer_outputs()
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