Update old existing feature extractor references (#24552)

* Update old existing feature extractor references

* Typo

* Apply suggestions from code review

* Apply suggestions from code review

* Apply suggestions from code review

* Address comments from review - update 'feature extractor'
Co-authored by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

tests/models/beit/test_modeling_beit.py

@@ -49,7 +49,7 @@ if is_vision_available():
     import PIL
     from PIL import Image
 
-    from transformers import BeitFeatureExtractor
+    from transformers import BeitImageProcessor
 
 
 class BeitModelTester:
@@ -342,18 +342,16 @@ def prepare_img():
 @require_vision
 class BeitModelIntegrationTest(unittest.TestCase):
     @cached_property
-    def default_feature_extractor(self):
-        return (
-            BeitFeatureExtractor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
-        )
+    def default_image_processor(self):
+        return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
 
     @slow
     def test_inference_masked_image_modeling_head(self):
         model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k").to(torch_device)
 
-        feature_extractor = self.default_feature_extractor
+        image_processor = self.default_image_processor
         image = prepare_img()
-        pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(torch_device)
+        pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device)
 
         # prepare bool_masked_pos
         bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(torch_device)
@@ -377,9 +375,9 @@ class BeitModelIntegrationTest(unittest.TestCase):
     def test_inference_image_classification_head_imagenet_1k(self):
         model = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224").to(torch_device)
 
-        feature_extractor = self.default_feature_extractor
+        image_processor = self.default_image_processor
         image = prepare_img()
-        inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
 
         # forward pass
         with torch.no_grad():
@@ -403,9 +401,9 @@ class BeitModelIntegrationTest(unittest.TestCase):
             torch_device
         )
 
-        feature_extractor = self.default_feature_extractor
+        image_processor = self.default_image_processor
         image = prepare_img()
-        inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
 
         # forward pass
         with torch.no_grad():
@@ -428,11 +426,11 @@ class BeitModelIntegrationTest(unittest.TestCase):
         model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
         model = model.to(torch_device)
 
-        feature_extractor = BeitFeatureExtractor(do_resize=True, size=640, do_center_crop=False)
+        image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
 
         ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
         image = Image.open(ds[0]["file"])
-        inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
 
         # forward pass
         with torch.no_grad():
@@ -471,11 +469,11 @@ class BeitModelIntegrationTest(unittest.TestCase):
         model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
         model = model.to(torch_device)
 
-        feature_extractor = BeitFeatureExtractor(do_resize=True, size=640, do_center_crop=False)
+        image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
 
         ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
         image = Image.open(ds[0]["file"])
-        inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
 
         # forward pass
         with torch.no_grad():
@@ -483,10 +481,10 @@ class BeitModelIntegrationTest(unittest.TestCase):
 
         outputs.logits = outputs.logits.detach().cpu()
 
-        segmentation = feature_extractor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
+        segmentation = image_processor.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)
+        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
         expected_shape = torch.Size((160, 160))
         self.assertEqual(segmentation[0].shape, expected_shape)

tests/models/beit/test_modeling_flax_beit.py

@@ -33,7 +33,7 @@ if is_flax_available():
 if is_vision_available():
     from PIL import Image
 
-    from transformers import BeitFeatureExtractor
+    from transformers import BeitImageProcessor
 
 
 class FlaxBeitModelTester(unittest.TestCase):
@@ -219,18 +219,16 @@ def prepare_img():
 @require_flax
 class FlaxBeitModelIntegrationTest(unittest.TestCase):
     @cached_property
-    def default_feature_extractor(self):
-        return (
-            BeitFeatureExtractor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
-        )
+    def default_image_processor(self):
+        return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
 
     @slow
     def test_inference_masked_image_modeling_head(self):
         model = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
 
-        feature_extractor = self.default_feature_extractor
+        image_processor = self.default_image_processor
         image = prepare_img()
-        pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values
+        pixel_values = image_processor(images=image, return_tensors="np").pixel_values
 
         # prepare bool_masked_pos
         bool_masked_pos = np.ones((1, 196), dtype=bool)
@@ -253,9 +251,9 @@ class FlaxBeitModelIntegrationTest(unittest.TestCase):
     def test_inference_image_classification_head_imagenet_1k(self):
         model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224")
 
-        feature_extractor = self.default_feature_extractor
+        image_processor = self.default_image_processor
         image = prepare_img()
-        inputs = feature_extractor(images=image, return_tensors="np")
+        inputs = image_processor(images=image, return_tensors="np")
 
         # forward pass
         outputs = model(**inputs)
@@ -276,9 +274,9 @@ class FlaxBeitModelIntegrationTest(unittest.TestCase):
     def test_inference_image_classification_head_imagenet_22k(self):
         model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k")
 
-        feature_extractor = self.default_feature_extractor
+        image_processor = self.default_image_processor
         image = prepare_img()
-        inputs = feature_extractor(images=image, return_tensors="np")
+        inputs = image_processor(images=image, return_tensors="np")
 
         # forward pass
         outputs = model(**inputs)