Add ViTImageProcessorFast to tests (#31424)

* Add ViTImageProcessor to tests

* Correct data format

* Review comments

src/transformers/image_processing_utils.py

@@ -151,6 +151,11 @@ def center_crop(
             **kwargs,
         )
 
+    def to_dict(self):
+        encoder_dict = super().to_dict()
+        encoder_dict.pop("_valid_processor_keys", None)
+        return encoder_dict
+
 
 VALID_SIZE_DICT_KEYS = (
     {"height", "width"},

src/transformers/image_processing_utils_fast.py

@@ -61,3 +61,8 @@ def _validate_params(self, **kwargs) -> None:
     def get_transforms(self, **kwargs) -> "Compose":
         self._validate_params(**kwargs)
         return self._build_transforms(**kwargs)
+
+    def to_dict(self):
+        encoder_dict = super().to_dict()
+        encoder_dict.pop("_transform_params", None)
+        return encoder_dict

src/transformers/models/auto/image_processing_auto.py

@@ -399,7 +399,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
             kwargs["token"] = use_auth_token
 
         config = kwargs.pop("config", None)
-        use_fast = kwargs.pop("use_fast", False)
+        use_fast = kwargs.pop("use_fast", None)
         trust_remote_code = kwargs.pop("trust_remote_code", None)
         kwargs["_from_auto"] = True
 
@@ -430,10 +430,11 @@ def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
 
         if image_processor_class is not None:
             # Update class name to reflect the use_fast option. If class is not found, None is returned.
-            if use_fast and not image_processor_class.endswith("Fast"):
-                image_processor_class += "Fast"
-            elif not use_fast and image_processor_class.endswith("Fast"):
-                image_processor_class = image_processor_class[:-4]
+            if use_fast is not None:
+                if use_fast and not image_processor_class.endswith("Fast"):
+                    image_processor_class += "Fast"
+                elif not use_fast and image_processor_class.endswith("Fast"):
+                    image_processor_class = image_processor_class[:-4]
             image_processor_class = image_processor_class_from_name(image_processor_class)
 
         has_remote_code = image_processor_auto_map is not None

src/transformers/models/mask2former/image_processing_mask2former.py

@@ -772,7 +772,7 @@ def preprocess(
             ignore_index,
             do_reduce_labels,
             return_tensors,
-            input_data_format=input_data_format,
+            input_data_format=data_format,
         )
         return encoded_inputs
 

src/transformers/models/maskformer/image_processing_maskformer.py

@@ -772,7 +772,7 @@ def preprocess(
             ignore_index,
             do_reduce_labels,
             return_tensors,
-            input_data_format=input_data_format,
+            input_data_format=data_format,
         )
         return encoded_inputs
 

src/transformers/models/oneformer/image_processing_oneformer.py

@@ -772,7 +772,7 @@ def preprocess(
             ignore_index,
             do_reduce_labels,
             return_tensors,
-            input_data_format=input_data_format,
+            input_data_format=data_format,
         )
         return encoded_inputs
 

src/transformers/models/vit/image_processing_vit_fast.py

@@ -114,7 +114,6 @@ def __init__(
         self.rescale_factor = rescale_factor
         self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
-        self._transform_settings = {}
 
     def _build_transforms(
         self,
@@ -285,5 +284,5 @@ def preprocess(
         )
         transformed_images = [transforms(image) for image in images]
 
-        data = {"pixel_values": torch.vstack(transformed_images)}
+        data = {"pixel_values": torch.stack(transformed_images, dim=0)}
         return BatchFeature(data, tensor_type=return_tensors)

tests/models/bridgetower/test_image_processing_bridgetower.py

@@ -17,6 +17,8 @@
 import unittest
 from typing import Dict, List, Optional, Union
 
+import numpy as np
+
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_vision_available
 
@@ -84,6 +86,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
             scale = size / min(w, h)

tests/models/conditional_detr/test_image_processing_conditional_detr.py

@@ -18,6 +18,8 @@
 import pathlib
 import unittest
 
+import numpy as np
+
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
@@ -87,6 +89,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
             if w < h:

tests/models/deformable_detr/test_image_processing_deformable_detr.py

@@ -18,6 +18,8 @@
 import pathlib
 import unittest
 
+import numpy as np
+
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
@@ -87,6 +89,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
             if w < h:

tests/models/detr/test_image_processing_detr.py

@@ -17,6 +17,8 @@
 import pathlib
 import unittest
 
+import numpy as np
+
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
@@ -86,6 +88,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
             if w < h:

tests/models/glpn/test_image_processing_glpn.py

@@ -66,6 +66,8 @@ def prepare_image_processor_dict(self):
     def expected_output_image_shape(self, images):
         if isinstance(images[0], Image.Image):
             width, height = images[0].size
+        elif isinstance(images[0], np.ndarray):
+            height, width = images[0].shape[0], images[0].shape[1]
         else:
             height, width = images[0].shape[1], images[0].shape[2]
 

tests/models/grounding_dino/test_image_processing_grounding_dino.py

@@ -18,6 +18,8 @@
 import pathlib
 import unittest
 
+import numpy as np
+
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
@@ -93,6 +95,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
             if w < h:

tests/models/idefics/test_image_processing_idefics.py

@@ -16,6 +16,8 @@
 
 import unittest
 
+import numpy as np
+
 from transformers.testing_utils import require_torch, require_torchvision, require_vision
 from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
 
@@ -75,6 +77,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
             scale = size / min(w, h)

tests/models/idefics2/test_image_processing_idefics2.py

@@ -99,6 +99,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
 
@@ -176,6 +178,10 @@ def prepare_image_inputs(
         if torchify:
             images_list = [[torch.from_numpy(image) for image in images] for images in images_list]
 
+        if numpify:
+            # Numpy images are typically in channels last format
+            images_list = [[image.transpose(1, 2, 0) for image in images] for images in images_list]
+
         return images_list
 
 
@@ -206,66 +212,100 @@ def test_image_processor_properties(self):
         self.assertTrue(hasattr(image_processing, "do_image_splitting"))
 
     def test_call_numpy(self):
-        # Initialize image_processing
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        # create random numpy tensors
-        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
-        for sample_images in image_inputs:
-            for image in sample_images:
-                self.assertIsInstance(image, np.ndarray)
-
-        # Test not batched input
-        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
-        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
-        self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
-
-        # Test batched
-        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
-        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
-        self.assertEqual(
-            tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
-        )
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = self.image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for sample_images in image_inputs:
+                for image in sample_images:
+                    self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    def test_call_numpy_4_channels(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processor_dict = self.image_processor_dict
+            image_processor_dict["image_mean"] = [0.5, 0.5, 0.5, 0.5]
+            image_processor_dict["image_std"] = [0.5, 0.5, 0.5, 0.5]
+            image_processing = self.image_processing_class(**image_processor_dict)
+            # create random numpy tensors
+            self.image_processor_tester.num_channels = 4
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+
+            for sample_images in image_inputs:
+                for image in sample_images:
+                    self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(
+                image_inputs[0], input_data_format="channels_last", return_tensors="pt"
+            ).pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(
+                image_inputs, input_data_format="channels_last", return_tensors="pt"
+            ).pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
 
     def test_call_pil(self):
-        # Initialize image_processing
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        # create random PIL images
-        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
-        for images in image_inputs:
-            for image in images:
-                self.assertIsInstance(image, Image.Image)
-
-        # Test not batched input
-        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
-        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
-        self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
-
-        # Test batched
-        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
-        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
-        self.assertEqual(
-            tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
-        )
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = self.image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for images in image_inputs:
+                for image in images:
+                    self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
 
     def test_call_pytorch(self):
-        # Initialize image_processing
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        # create random PyTorch tensors
-        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
-
-        for images in image_inputs:
-            for image in images:
-                self.assertIsInstance(image, torch.Tensor)
-
-        # Test not batched input
-        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
-        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
-        self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
-
-        # Test batched
-        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
-        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
-        self.assertEqual(
-            tuple(encoded_images.shape),
-            (self.image_processor_tester.batch_size, *expected_output_image_shape),
-        )
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = self.image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+            for images in image_inputs:
+                for image in images:
+                    self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(
+                tuple(encoded_images.shape),
+                (self.image_processor_tester.batch_size, *expected_output_image_shape),
+            )

tests/models/mask2former/test_image_processing_mask2former.py

@@ -98,6 +98,8 @@ def get_expected_values(self, image_inputs, batched=False):
             image = image_inputs[0]
             if isinstance(image, Image.Image):
                 w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
             else:
                 h, w = image.shape[1], image.shape[2]
             if w < h: