!32164 [assistant][ops] Add new Rad2Deg

Merge pull request !32164 from wxy220/Rad2Deg

mindspore/python/mindspore/ops/function/__init__.py

@@ -184,6 +184,7 @@ from .math_func import (
     exp2,
     deg2rad,
     isreal,
+    rad2deg,
 )
 from .nn_func import (
     deformable_conv2d,

mindspore/python/mindspore/ops/function/math_func.py

@@ -2764,6 +2764,46 @@ def deg2rad(x):
     return out
 
 
+def rad2deg(x):
+    """
+    Returns a new tensor with each of the elements of `x` converted from angles in radians to degrees.
+
+    Args:
+        x (Tensor): The input tensor.
+
+    Outputs:
+        Tensor, has the same shape and dtype as the `x`.
+
+    Raises:
+        TypeError: If `x` is not a Tensor.
+        TypeError: If dtype of `x` isn't float16, float32 or float64.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
+    Examples:
+        >>> import mindspore
+        >>> from mindspore import Tensor
+        >>> import mindspore.ops as ops
+        >>> x = Tensor([[6.283, -3.142],[1.570, -6.283],[3.142, -1.570]], mindspore.float32)
+        >>> output = rad2deg(x)
+        >>> print(output)
+        [[ 359.98935 -180.02333]
+         [  89.95438 -359.98935]
+         [ 180.02333  -89.95438]]
+
+    """
+    if not isinstance(x, (Tensor, Tensor_)):
+        raise TypeError("The input x must be tensor")
+    dtype_op = P.DType()
+    x_dtype = dtype_op(x)
+    _check_input_dtype("x", x_dtype, [mstype.float16, mstype.float32, mstype.float64], "")
+    if x_dtype == mstype.float16:
+        out = x * (Tensor(180.0 / math.pi).astype(mstype.float16))
+    else:
+        out = x * 180.0 / math.pi
+    return out
+
 #####################################
 # Reduction Operation Functions.
 #####################################
@@ -2904,6 +2944,7 @@ __all__ = [
     'bessel_k1',
     'bessel_k1e',
     'exp2',
-    'deg2rad'
+    'deg2rad',
+    'rad2deg'
 ]
 __all__.sort()

tests/ut/python/ops/test_math_ops.py

@@ -445,6 +445,15 @@ class IsRealFunc(nn.Cell):
         return y
 
 
+class Rad2degNet(nn.Cell):
+    def __init__(self):
+        super(Rad2degNet, self).__init__()
+        self.rad2deg = ops.rad2deg
+
+    def construct(self, x):
+        return self.rad2deg(x)
+
+
 test_case_math_ops = [
     ('MatMulGrad', {
         'block': GradWrap(NetWithLoss(MatMulNet())),
@@ -530,6 +539,11 @@ test_case_math_ops = [
         'block': IsRealFunc(),
         'desc_inputs': [Tensor([1, 1+1j, 2+0j])],
     }),
+    ('Rad2deg', {
+        'block': Rad2degNet(),
+        'desc_inputs': [Tensor(np.array([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]], np.float32))],
+        'desc_bprop': [Tensor(np.array([[3.142, -3.142], [6.283, -6.283], [1.570, -1.570]], np.float32))],
+    }),
 ]
 
 test_case_lists = [test_case_math_ops]
@@ -594,6 +608,12 @@ raise_set = [
     ('Deg2rad_2_Error', {
         'block': (lambda x: Deg2radNet(), {'exception': TypeError}),
         'desc_inputs': [Tensor(np.array([[90, -90], [180, -180], [270, -270]], np.int32))]}),
+    ('Rad2deg_1_Error', {
+        'block': (lambda x: Rad2degNet(), {'exception': TypeError}),
+        'desc_inputs': [0]}),
+    ('Rad2deg_2_Error', {
+        'block': (lambda x: Rad2degNet(), {'exception': TypeError}),
+        'desc_inputs': [Tensor(np.array([[3, -3], [6, -6], [1, -1]], np.int32))]}),
 ]