update to standards from flip PR

Author: justincdavis

test/test_transforms_v2.py

@@ -25,7 +25,6 @@
     assert_equal,
     cache,
     cpu_and_cuda,
-    cvcuda_to_pil_compatible_tensor,
     freeze_rng_state,
     ignore_jit_no_profile_information_warning,
     make_bounding_boxes,

torchvision/transforms/v2/_misc.py

@@ -9,6 +9,7 @@
 
 from torchvision import transforms as _transforms, tv_tensors
 from torchvision.transforms.v2 import functional as F, Transform
+from torchvision.transforms.v2.functional._utils import _is_cvcuda_available, _is_cvcuda_tensor
 
 from ._utils import (
     _parse_labels_getter,
@@ -17,11 +18,13 @@
     get_bounding_boxes,
     get_keypoints,
     has_any,
-    is_cvcuda_tensor,
     is_pure_tensor,
 )
 
 
+CVCUDA_AVAILABLE = _is_cvcuda_available()
+
+
 # TODO: do we want/need to expose this?
 class Identity(Transform):
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
@@ -268,7 +271,8 @@ class ToDtype(Transform):
             Default: ``False``.
     """
 
-    _transformed_types = (torch.Tensor, is_cvcuda_tensor)
+    if CVCUDA_AVAILABLE:
+        _transformed_types = Transform._transformed_types + (_is_cvcuda_tensor,)
 
     def __init__(
         self, dtype: Union[torch.dtype, dict[Union[type, str], Optional[torch.dtype]]], scale: bool = False

torchvision/transforms/v2/_utils.py

@@ -15,8 +15,8 @@
 from torchvision._utils import sequence_to_str
 
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
-from torchvision.transforms.v2.functional import get_dimensions, get_size, is_cvcuda_tensor, is_pure_tensor
-from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
+from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
+from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT, _is_cvcuda_tensor
 
 
 def _setup_number_or_seq(arg: int | float | Sequence[int | float], name: str) -> Sequence[float]:
@@ -182,7 +182,7 @@ def query_chw(flat_inputs: list[Any]) -> tuple[int, int, int]:
     chws = {
         tuple(get_dimensions(inpt))
         for inpt in flat_inputs
-        if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video, is_cvcuda_tensor))
+        if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video, _is_cvcuda_tensor))
     }
     if not chws:
         raise TypeError("No image or video was found in the sample")
@@ -207,7 +207,7 @@ def query_size(flat_inputs: list[Any]) -> tuple[int, int]:
                 tv_tensors.Mask,
                 tv_tensors.BoundingBoxes,
                 tv_tensors.KeyPoints,
-                is_cvcuda_tensor,
+                _is_cvcuda_tensor,
             ),
         )
     }