Promote prototype transforms to beta status

test/prototype_common_utils.py

@@ -15,8 +15,9 @@
 from datasets_utils import combinations_grid
 from torch.nn.functional import one_hot
 from torch.testing._comparison import BooleanPair, NonePair, not_close_error_metas, NumberPair, TensorLikePair
-from torchvision.prototype import datapoints
-from torchvision.prototype.transforms.functional import convert_dtype_image_tensor, to_image_tensor
+import torchvision.prototype.datapoints as proto_datapoints
+from torchvision import datapoints
+from torchvision.transforms.v2.functional import convert_dtype_image_tensor, to_image_tensor
 from torchvision.transforms.functional_tensor import _max_value as get_max_value
 
 __all__ = [
@@ -457,7 +458,7 @@ def fn(shape, dtype, device):
         # The idiom `make_tensor(..., dtype=torch.int64).to(dtype)` is intentional to only get integer values,
         # regardless of the requested dtype, e.g. 0 or 0.0 rather than 0 or 0.123
         data = torch.testing.make_tensor(shape, low=0, high=num_categories, dtype=torch.int64, device=device).to(dtype)
-        return datapoints.Label(data, categories=categories)
+        return proto_datapoints.Label(data, categories=categories)
 
     return LabelLoader(fn, shape=extra_dims, dtype=dtype, categories=categories)
 
@@ -481,7 +482,7 @@ def fn(shape, dtype, device):
             # since `one_hot` only supports int64
             label = make_label_loader(extra_dims=extra_dims, categories=num_categories, dtype=torch.int64).load(device)
             data = one_hot(label, num_classes=num_categories).to(dtype)
-        return datapoints.OneHotLabel(data, categories=categories)
+        return proto_datapoints.OneHotLabel(data, categories=categories)
 
     return OneHotLabelLoader(fn, shape=(*extra_dims, num_categories), dtype=dtype, categories=categories)
 

test/prototype_transforms_dispatcher_infos.py

@@ -1,10 +1,10 @@
 import collections.abc
 
 import pytest
-import torchvision.prototype.transforms.functional as F
+import torchvision.transforms.v2.functional as F
 from prototype_common_utils import InfoBase, TestMark
 from prototype_transforms_kernel_infos import KERNEL_INFOS, pad_xfail_jit_fill_condition
-from torchvision.prototype import datapoints
+from torchvision import datapoints
 
 __all__ = ["DispatcherInfo", "DISPATCHER_INFOS"]
 

test/prototype_transforms_kernel_infos.py

@@ -8,7 +8,7 @@
 import pytest
 import torch.testing
 import torchvision.ops
-import torchvision.prototype.transforms.functional as F
+import torchvision.transforms.v2.functional as F
 from datasets_utils import combinations_grid
 from prototype_common_utils import (
     ArgsKwargs,
@@ -28,7 +28,7 @@
     TestMark,
 )
 from torch.utils._pytree import tree_map
-from torchvision.prototype import datapoints
+from torchvision import datapoints
 from torchvision.transforms.functional_tensor import _max_value as get_max_value, _parse_pad_padding
 
 __all__ = ["KernelInfo", "KERNEL_INFOS"]
@@ -2383,19 +2383,18 @@ def sample_inputs_convert_dtype_video():
 
 def sample_inputs_uniform_temporal_subsample_video():
     for video_loader in make_video_loaders(sizes=["random"], num_frames=[4]):
-        for temporal_dim in [-4, len(video_loader.shape) - 4]:
-            yield ArgsKwargs(video_loader, num_samples=2, temporal_dim=temporal_dim)
+        yield ArgsKwargs(video_loader, num_samples=2)
 
 
-def reference_uniform_temporal_subsample_video(x, num_samples, temporal_dim=-4):
+def reference_uniform_temporal_subsample_video(x, num_samples):
     # Copy-pasted from
     # https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/transforms/functional.py#L19
-    t = x.shape[temporal_dim]
+    t = x.shape[-4]
     assert num_samples > 0 and t > 0
     # Sample by nearest neighbor interpolation if num_samples > t.
     indices = torch.linspace(0, t - 1, num_samples)
     indices = torch.clamp(indices, 0, t - 1).long()
-    return torch.index_select(x, temporal_dim, indices)
+    return torch.index_select(x, -4, indices)
 
 
 def reference_inputs_uniform_temporal_subsample_video():
@@ -2410,12 +2409,5 @@ def reference_inputs_uniform_temporal_subsample_video():
         sample_inputs_fn=sample_inputs_uniform_temporal_subsample_video,
         reference_fn=reference_uniform_temporal_subsample_video,
         reference_inputs_fn=reference_inputs_uniform_temporal_subsample_video,
-        test_marks=[
-            TestMark(
-                ("TestKernels", "test_batched_vs_single"),
-                pytest.mark.skip("Positive `temporal_dim` arguments are not equivalent for batched and single inputs"),
-                condition=lambda args_kwargs: args_kwargs.kwargs.get("temporal_dim") >= 0,
-            ),
-        ],
     )
 )