change sigma to beta and update doc

fix ci

fix ci

mindspore/nn/loss/loss.py

@@ -158,16 +158,16 @@ class SmoothL1Loss(_Loss):
     .. math::
         L_{i} =
         \begin{cases}
-        0.5 (x_i - y_i)^2, & \text{if } |x_i - y_i| < \text{sigma}; \\
-        |x_i - y_i| - 0.5, & \text{otherwise. }
+        \frac{0.5 (x_i - y_i)^{2}}{\text{beta}}, & \text{if } |x_i - y_i| < \text{beta} \\
+        |x_i - y_i| - 0.5 \text{beta}, & \text{otherwise. }
         \end{cases}
 
-    Here :math:`\text{sigma}` controls the point where the loss function changes from quadratic to linear.
+    Here :math:`\text{beta}` controls the point where the loss function changes from quadratic to linear.
     Its default value is 1.0. :math:`N` is the batch size. This function returns an
     unreduced loss Tensor.
 
     Args:
-        sigma (float): A parameter used to control the point where the function will change from
+        beta (float): A parameter used to control the point where the function will change from
             quadratic to linear. Default: 1.0.
 
     Inputs:
@@ -183,10 +183,10 @@ class SmoothL1Loss(_Loss):
         >>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
         >>> loss(input_data, target_data)
     """
-    def __init__(self, sigma=1.0):
+    def __init__(self, beta=1.0):
         super(SmoothL1Loss, self).__init__()
-        self.sigma = sigma
-        self.smooth_l1_loss = P.SmoothL1Loss(self.sigma)
+        self.beta = beta
+        self.smooth_l1_loss = P.SmoothL1Loss(self.beta)
 
     def construct(self, base, target):
         return self.smooth_l1_loss(base, target)