Add post_process_semantic_segmentation method to DPTFeatureExtractor (#19107)

* add post-processing method for semantic segmentation

* add test for post-processing

tests/models/dpt/test_modeling_dpt.py

@@ -298,3 +298,24 @@ class DPTModelIntegrationTest(unittest.TestCase):
         ).to(torch_device)
 
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))
+
+    def test_post_processing_semantic_segmentation(self):
+        feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade")
+        model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device)
+
+        image = prepare_img()
+        inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        outputs.logits = outputs.logits.detach().cpu()
+
+        segmentation = feature_extractor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
+        expected_shape = torch.Size((500, 300))
+        self.assertEqual(segmentation[0].shape, expected_shape)
+
+        segmentation = feature_extractor.post_process_semantic_segmentation(outputs=outputs)
+        expected_shape = torch.Size((480, 480))
+        self.assertEqual(segmentation[0].shape, expected_shape)