[Vision] .to function for ImageProcessors

src/transformers/feature_extraction_utils.py

@@ -40,14 +40,15 @@
     is_tf_available,
     is_torch_available,
     is_torch_device,
+    is_torch_dtype,
     logging,
     torch_required,
 )
 
 
 if TYPE_CHECKING:
     if is_torch_available():
-        import torch
+        import torch  # noqa
 
 
 logger = logging.get_logger(__name__)
@@ -138,7 +139,7 @@ def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = Non
         elif tensor_type == TensorType.PYTORCH:
             if not is_torch_available():
                 raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
-            import torch
+            import torch  # noqa
 
             def as_tensor(value):
                 if isinstance(value, (list, tuple)) and len(value) > 0 and isinstance(value[0], np.ndarray):
@@ -175,25 +176,47 @@ def as_tensor(value):
         return self
 
     @torch_required
-    # Copied from transformers.tokenization_utils_base.BatchEncoding.to with BatchEncoding->BatchFeature
-    def to(self, device: Union[str, "torch.device"]) -> "BatchFeature":
+    def to(self, *args, **kwargs) -> "BatchFeature":
         """
-        Send all values to device by calling `v.to(device)` (PyTorch only).
+        Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
+        different `dtypes` and sending the `BatchFeature` to a different `device`.
 
         Args:
-            device (`str` or `torch.device`): The device to put the tensors on.
+            args (`Tuple`):
+                Will be passed to the `to(...)` function of the tensors.
+            kwargs (`Dict`, *optional*):
+                Will be passed to the `to(...)` function of the tensors.
 
         Returns:
             [`BatchFeature`]: The same instance after modification.
         """
-
-        # This check catches things like APEX blindly calling "to" on all inputs to a module
-        # Otherwise it passes the casts down and casts the LongTensor containing the token idxs
-        # into a HalfTensor
-        if isinstance(device, str) or is_torch_device(device) or isinstance(device, int):
-            self.data = {k: v.to(device=device) for k, v in self.data.items()}
-        else:
-            logger.warning(f"Attempting to cast a BatchFeature to type {str(device)}. This is not supported.")
+        import torch  # noqa
+
+        new_data = {}
+        device = kwargs.get("device")
+        # Check if the args are a device or a dtype
+        if device is None and len(args) > 0:
+            # device should be always the first argument
+            arg = args[0]
+            if is_torch_dtype(arg):
+                # The first argument is a dtype
+                pass
+            elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
+                device = arg
+            else:
+                # it's something else
+                raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")
+        # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
+        for k, v in self.items():
+            # check if v is a floating point
+            if torch.is_floating_point(v):
+                # cast and send to device
+                new_data[k] = v.to(*args, **kwargs)
+            elif device is not None:
+                new_data[k] = v.to(device=device)
+            else:
+                new_data[k] = v
+        self.data = new_data
         return self
 
 

src/transformers/utils/__init__.py

@@ -47,6 +47,7 @@
     is_tensor,
     is_tf_tensor,
     is_torch_device,
+    is_torch_dtype,
     is_torch_tensor,
     reshape,
     squeeze,

src/transformers/utils/generic.py

@@ -123,6 +123,24 @@ def is_torch_device(x):
     return False if not is_torch_available() else _is_torch_device(x)
 
 
+def _is_torch_dtype(x):
+    import torch
+
+    if isinstance(x, str):
+        if hasattr(torch, x):
+            x = getattr(torch, x)
+        else:
+            return False
+    return isinstance(x, torch.dtype)
+
+
+def is_torch_dtype(x):
+    """
+    Tests if `x` is a torch dtype or not. Safe to call even if torch is not installed.
+    """
+    return False if not is_torch_available() else _is_torch_dtype(x)
+
+
 def _is_tensorflow(x):
     import tensorflow as tf
 

tests/models/deit/test_feature_extraction_deit.py

@@ -84,6 +84,7 @@ def prepare_feat_extract_dict(self):
 class DeiTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
 
     feature_extraction_class = DeiTFeatureExtractor if is_vision_available() else None
+    test_cast_dtype = True
 
     def setUp(self):
         self.feature_extract_tester = DeiTFeatureExtractionTester(self)

tests/test_feature_extraction_common.py

@@ -25,7 +25,15 @@
 from huggingface_hub import HfFolder, delete_repo, set_access_token
 from requests.exceptions import HTTPError
 from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
-from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
+from transformers.testing_utils import (
+    TOKEN,
+    USER,
+    check_json_file_has_correct_format,
+    get_tests_dir,
+    is_staging_test,
+    require_torch,
+    require_vision,
+)
 from transformers.utils import is_torch_available, is_vision_available
 
 
@@ -134,6 +142,8 @@ def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify
 
 
 class FeatureExtractionSavingTestMixin:
+    test_cast_dtype = None
+
     def test_feat_extract_to_json_string(self):
         feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
         obj = json.loads(feat_extract.to_json_string())
@@ -164,6 +174,41 @@ def test_init_without_params(self):
         feat_extract = self.feature_extraction_class()
         self.assertIsNotNone(feat_extract)
 
+    @require_torch
+    @require_vision
+    def test_cast_dtype_device(self):
+        if self.test_cast_dtype is not None:
+            # Initialize feature_extractor
+            feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+
+            # create random PyTorch tensors
+            image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
+
+            encoding = feature_extractor(image_inputs, return_tensors="pt")
+            # for layoutLM compatiblity
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.float32)
+
+            encoding = feature_extractor(image_inputs, return_tensors="pt").to(torch.float16)
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.float16)
+
+            encoding = feature_extractor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
+
+            with self.assertRaises(TypeError):
+                _ = feature_extractor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
+
+            # Try with text + image feature
+            encoding = feature_extractor(image_inputs, return_tensors="pt")
+            encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
+            encoding = encoding.to(torch.float16)
+
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.float16)
+            self.assertEqual(encoding.input_ids.dtype, torch.long)
+
 
 class FeatureExtractorUtilTester(unittest.TestCase):
     def test_cached_files_are_used_when_internet_is_down(self):