Add docs for containers and undeprecate p for RandomChoice (#7311)

Co-authored-by: vfdev <vfdev.5@gmail.com>

test/test_transforms_v2.py

@@ -1359,11 +1359,8 @@ class TestContainers:
 
 class TestRandomChoice:
     def test_assertions(self):
-        with pytest.warns(UserWarning, match="Argument p is deprecated and will be removed"):
-            transforms.RandomChoice([transforms.Pad(2), transforms.RandomCrop(28)], p=[1, 2])
-
         with pytest.raises(ValueError, match="The number of probabilities doesn't match the number of transforms"):
-            transforms.RandomChoice([transforms.Pad(2), transforms.RandomCrop(28)], probabilities=[1])
+            transforms.RandomChoice([transforms.Pad(2), transforms.RandomCrop(28)], p=[1])
 
 
 class TestRandomIoUCrop:

torchvision/transforms/v2/_container.py

-import warnings
 from typing import Any, Callable, Dict, List, Optional, Sequence, Union
 
 import torch
@@ -78,7 +77,7 @@ class RandomApply(Transform):
 
     Args:
         transforms (sequence or torch.nn.Module): list of transformations
-        p (float): probability
+        p (float): probability of applying the list of transforms
     """
 
     _v1_transform_cls = _transforms.RandomApply
@@ -119,39 +118,38 @@ class RandomChoice(Transform):
 
     .. betastatus:: RandomChoice transform
 
-    This transform does not support torchscript."""
+    This transform does not support torchscript.
+
+    Args:
+        transforms (sequence or torch.nn.Module): list of transformations
+        p (list of floats or None, optional): probability of each transform being picked.
+            If ``p`` doesn't sum to 1, it is automatically normalized. If ``None``
+            (default), all transforms have the same probability.
+    """
 
     def __init__(
         self,
         transforms: Sequence[Callable],
         p: Optional[List[float]] = None,
-        probabilities: Optional[List[float]] = None,
     ) -> None:
         if not isinstance(transforms, Sequence):
             raise TypeError("Argument transforms should be a sequence of callables")
-        if p is not None:
-            warnings.warn(
-                "Argument p is deprecated and will be removed in a future release. "
-                "Please use probabilities argument instead."
-            )
-            probabilities = p
 
-        if probabilities is None:
-            probabilities = [1] * len(transforms)
-        elif len(probabilities) != len(transforms):
+        if p is None:
+            p = [1] * len(transforms)
+        elif len(p) != len(transforms):
             raise ValueError(
-                f"The number of probabilities doesn't match the number of transforms: "
-                f"{len(probabilities)} != {len(transforms)}"
+                f"The number of p doesn't match the number of transforms: " f"{len(p)} != {len(transforms)}"
             )
 
         super().__init__()
 
         self.transforms = transforms
-        total = sum(probabilities)
-        self.probabilities = [prob / total for prob in probabilities]
+        total = sum(p)
+        self.p = [prob / total for prob in p]
 
     def forward(self, *inputs: Any) -> Any:
-        idx = int(torch.multinomial(torch.tensor(self.probabilities), 1))
+        idx = int(torch.multinomial(torch.tensor(self.p), 1))
         transform = self.transforms[idx]
         return transform(*inputs)
 
@@ -162,6 +160,9 @@ class RandomOrder(Transform):
     .. betastatus:: RandomOrder transform
 
     This transform does not support torchscript.
+
+    Args:
+        transforms (sequence or torch.nn.Module): list of transformations
     """
 
     def __init__(self, transforms: Sequence[Callable]) -> None: