Datapoint -> TVTensor; datapoint[s] -> tv_tensor[s] (#7894)

torchvision/datapoints/_image.py → torchvision/tv_tensors/_image.py

@@ -5,10 +5,10 @@ from typing import Any, Optional, Union
 import PIL.Image
 import torch
 
-from ._datapoint import Datapoint
+from ._tv_tensor import TVTensor
 
 
-class Image(Datapoint):
+class Image(TVTensor):
     """[BETA] :class:`torch.Tensor` subclass for images.
 
     .. note::

torchvision/datapoints/_mask.py → torchvision/tv_tensors/_mask.py

@@ -5,10 +5,10 @@ from typing import Any, Optional, Union
 import PIL.Image
 import torch
 
-from ._datapoint import Datapoint
+from ._tv_tensor import TVTensor
 
 
-class Mask(Datapoint):
+class Mask(TVTensor):
     """[BETA] :class:`torch.Tensor` subclass for segmentation and detection masks.
 
     Args:

torchvision/datapoints/_torch_function_helpers.py → torchvision/tv_tensors/_torch_function_helpers.py

@@ -16,7 +16,7 @@ class _ReturnTypeCM:
 
 
 def set_return_type(return_type: str):
-    """[BETA] Set the return type of torch operations on datapoints.
+    """[BETA] Set the return type of torch operations on tv_tensors.
 
     This only affects the behaviour of torch operations. It has no effect on
     ``torchvision`` transforms or functionals, which will always return as
@@ -33,28 +33,28 @@ def set_return_type(return_type: str):
 
     .. code:: python
 
-        img = datapoints.Image(torch.rand(3, 5, 5))
+        img = tv_tensors.Image(torch.rand(3, 5, 5))
         img + 2  # This is a pure Tensor (default behaviour)
 
-        set_return_type("datapoints")
+        set_return_type("tv_tensors")
         img + 2  # This is an Image
 
     or as a context manager to restrict the scope:
 
     .. code:: python
 
-        img = datapoints.Image(torch.rand(3, 5, 5))
+        img = tv_tensors.Image(torch.rand(3, 5, 5))
         img + 2  # This is a pure Tensor
-        with set_return_type("datapoints"):
+        with set_return_type("tv_tensors"):
             img + 2  # This is an Image
         img + 2  # This is a pure Tensor
 
     Args:
-        return_type (str): Can be "datapoint" or "tensor". Default is "tensor".
+        return_type (str): Can be "tv_tensor" or "tensor". Default is "tensor".
     """
     global _TORCHFUNCTION_SUBCLASS
     to_restore = _TORCHFUNCTION_SUBCLASS
-    _TORCHFUNCTION_SUBCLASS = {"tensor": False, "datapoint": True}[return_type.lower()]
+    _TORCHFUNCTION_SUBCLASS = {"tensor": False, "tv_tensor": True}[return_type.lower()]
 
     return _ReturnTypeCM(to_restore)
 

torchvision/datapoints/_datapoint.py → torchvision/tv_tensors/_tv_tensor.py

@@ -6,18 +6,18 @@ import torch
 from torch._C import DisableTorchFunctionSubclass
 from torch.types import _device, _dtype, _size
 
-from torchvision.datapoints._torch_function_helpers import _FORCE_TORCHFUNCTION_SUBCLASS, _must_return_subclass
+from torchvision.tv_tensors._torch_function_helpers import _FORCE_TORCHFUNCTION_SUBCLASS, _must_return_subclass
 
 
-D = TypeVar("D", bound="Datapoint")
+D = TypeVar("D", bound="TVTensor")
 
 
-class Datapoint(torch.Tensor):
-    """[Beta] Base class for all datapoints.
+class TVTensor(torch.Tensor):
+    """[Beta] Base class for all tv_tensors.
 
     You probably don't want to use this class unless you're defining your own
-    custom Datapoints. See
-    :ref:`sphx_glr_auto_examples_transforms_plot_custom_datapoints.py` for details.
+    custom TVTensors. See
+    :ref:`sphx_glr_auto_examples_transforms_plot_custom_tv_tensors.py` for details.
     """
 
     @staticmethod
@@ -62,9 +62,9 @@ class Datapoint(torch.Tensor):
         ``__torch_function__`` method. If one is found, it is invoked with the operator as ``func`` as well as the
         ``args`` and ``kwargs`` of the original call.
 
-        Why do we override this? Because the base implementation in torch.Tensor would preserve the Datapoint type
+        Why do we override this? Because the base implementation in torch.Tensor would preserve the TVTensor type
         of the output. In our case, we want to return pure tensors instead (with a few exceptions). Refer to the
-        "Datapoints FAQ" gallery example for a rationale of this behaviour (TL;DR: perf + no silver bullet).
+        "TVTensors FAQ" gallery example for a rationale of this behaviour (TL;DR: perf + no silver bullet).
 
         Our implementation below is very similar to the base implementation in ``torch.Tensor`` - go check it out.
         """
@@ -79,7 +79,7 @@ class Datapoint(torch.Tensor):
         must_return_subclass = _must_return_subclass()
         if must_return_subclass or (func in _FORCE_TORCHFUNCTION_SUBCLASS and isinstance(args[0], cls)):
             # If you're wondering why we need the `isinstance(args[0], cls)` check, remove it and see what fails
-            # in test_to_datapoint_reference().
+            # in test_to_tv_tensor_reference().
             # The __torch_function__ protocol will invoke the __torch_function__ method on *all* types involved in
             # the computation by walking the MRO upwards. For example,
             # `out = a_pure_tensor.to(an_image)` will invoke `Image.__torch_function__` with
@@ -89,7 +89,7 @@ class Datapoint(torch.Tensor):
 
         if not must_return_subclass and isinstance(output, cls):
             # DisableTorchFunctionSubclass is ignored by inplace ops like `.add_(...)`,
-            # so for those, the output is still a Datapoint. Thus, we need to manually unwrap.
+            # so for those, the output is still a TVTensor. Thus, we need to manually unwrap.
             return output.as_subclass(torch.Tensor)
 
         return output

torchvision/datapoints/_video.py → torchvision/tv_tensors/_video.py

@@ -4,10 +4,10 @@ from typing import Any, Optional, Union
 
 import torch
 
-from ._datapoint import Datapoint
+from ._tv_tensor import TVTensor
 
 
-class Video(Datapoint):
+class Video(TVTensor):
     """[BETA] :class:`torch.Tensor` subclass for videos.
 
     Args: