!3253 update docstring for compose/randomAppply/randomChoice to stay consistent with py_transforms

Merge pull request !3253 from ZiruiWu/master

mindspore/dataset/transforms/c_transforms.py

@@ -240,42 +240,47 @@ class Compose(cde.ComposeOp):
 
     Args:
         transforms (list): List of transformations to be applied.
-    Example:
+
+    Examples:
         >>> compose = Compose([vision.Decode(), vision.RandomCrop()])
         >>> dataset = ds.map(operations=compose)
     """
 
     @check_random_transform_ops
-    def __init__(self, op_list):
-        super().__init__(op_list)
+    def __init__(self, transforms):
+        super().__init__(transforms)
 
 
 class RandomApply(cde.RandomApplyOp):
     """
     Randomly performs a series of transforms with a given probability.
+
     Args:
         transforms (list): List of transformations to be applied.
         prob (float, optional): The probability to apply the transformation list (default=0.5)
-    Example:
+
+    Examples:
         >>> rand_apply = RandomApply([vision.RandomCrop()])
         >>> dataset = ds.map(operations=rand_apply)
     """
 
     @check_random_transform_ops
-    def __init__(self, op_list, prob=0.5):
-        super().__init__(prob, op_list)
+    def __init__(self, transforms, prob=0.5):
+        super().__init__(prob, transforms)
 
 
 class RandomChoice(cde.RandomChoiceOp):
     """
     Randomly selects one transform from a list of transforms to perform operation.
+
     Args:
         transforms (list): List of transformations to be chosen from to apply.
-    Example:
+
+    Examples:
         >>> rand_choice = RandomChoice([vision.CenterCrop(), vision.RandomCrop()])
         >>> dataset = ds.map(operations=rand_choice)
     """
 
     @check_random_transform_ops
-    def __init__(self, op_list):
-        super().__init__(op_list)
+    def __init__(self, transforms):
+        super().__init__(transforms)

mindspore/dataset/transforms/vision/c_transforms.py

@@ -738,6 +738,21 @@ class UniformAugment(cde.UniformAugOp):
 
 
 class RandomSelectSubpolicy(cde.RandomSelectSubpolicyOp):
+    """
+    Choose a random sub-policy from a list to be applied on the input image. A sub-policy is a list of tuples
+    (op, prob), where op is a TensorOp operation and prob is the probability that this op will be applied. Once
+    a sub-policy is selected, each op within the subpolicy with be applied in sequence according to its probability
+
+    Args:
+        policy (list(list(tuple(TensorOp,float))): List of sub-policies to choose from.
+
+    Examples:
+        >>> policy = [[(c_vision.RandomRotation((45, 45))), (c_transforms.RandomVerticalFlip()),
+        >>>            (c_transforms.RandomColorAdjust())],
+        >>>           [(c_vision.RandomRotation((90, 90))), (c_transforms.RandomColorAdjust())]]
+        >>> ds_policy = ds.map(input_columns=["image"], operations=visions.RandomSelectSubpolicy(policy))
+    """
+
     @check_random_select_subpolicy_op
     def __init__(self, policy):
         super().__init__(policy)