Add semantic segmentation post-processing method to MobileViT (#19105)

* add post-processing method for semantic segmentation

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

tests/models/mobilevit/test_modeling_mobilevit.py

@@ -340,3 +340,27 @@ class MobileViTModelIntegrationTest(unittest.TestCase):
         )
 
         self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
+
+    @slow
+    def test_post_processing_semantic_segmentation(self):
+        model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
+        model = model.to(torch_device)
+
+        feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
+
+        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=[(50, 60)])
+        expected_shape = torch.Size((50, 60))
+        self.assertEqual(segmentation[0].shape, expected_shape)
+
+        segmentation = feature_extractor.post_process_semantic_segmentation(outputs=outputs)
+        expected_shape = torch.Size((32, 32))
+        self.assertEqual(segmentation[0].shape, expected_shape)