update document of NMSWithMask

mindspore/ops/operations/math_ops.py

@@ -3747,8 +3747,14 @@ class Asin(PrimitiveWithInfer):
 
 
 class NMSWithMask(PrimitiveWithInfer):
-    """
-    Selects some bounding boxes in descending order of score.
+    r"""
+    When object detection problem is performed in the computer vision field, object detection algorithm generates
+    a plurality of bounding boxes. Selects some bounding boxes in descending order of score. Use the box with the
+    highest score calculate the overlap between other boxes and the current box, and delete the box based on a
+    certain threshold(IOU). The IOU is as follows,
+
+    .. math::
+        \text{IOU} = \frac{\text{Area of Overlap}}{\text{Area of Union}}
 
     Args:
         iou_threshold (float): Specifies the threshold of overlap boxes with respect to
@@ -3781,7 +3787,7 @@ class NMSWithMask(PrimitiveWithInfer):
 
     Examples:
         >>> bbox = np.array([[100.0, 100.0, 50.0, 68.0, 0.63], [150.0, 75.0, 165.0, 115.0, 0.55],
-        [12.0, 190.0, 288.0, 200.0, 0.9], [28.0, 130.0, 106.0, 172.0, 0.3]])
+        ...                  [12.0, 190.0, 288.0, 200.0, 0.9], [28.0, 130.0, 106.0, 172.0, 0.3]])
         >>> bbox[:, 2] += bbox[:, 0]
         >>> bbox[:, 3] += bbox[:, 1]
         >>> inputs = Tensor(bbox, mindspore.float32)

mindspore/ops/operations/nn_ops.py

@@ -4439,7 +4439,7 @@ class FusedSparseAdam(PrimitiveWithInfer):
         >>> epsilon = Tensor(1e-8, mstype.float32)
         >>> gradient = Tensor(np.random.rand(2, 1, 2), mstype.float32)
         >>> indices = Tensor([0, 1], mstype.int32)
-        >>> net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, gradient, indices)
+        >>> output = net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, gradient, indices)
         >>> print(net.var.asnumpy())
         [[[0.9996963  0.9996977 ]]
          [[0.99970144 0.9996992 ]]
@@ -4587,7 +4587,7 @@ class FusedSparseLazyAdam(PrimitiveWithInfer):
         >>> epsilon = Tensor(1e-8, mstype.float32)
         >>> gradient = Tensor(np.random.rand(2, 1, 2), mstype.float32)
         >>> indices = Tensor([0, 1], mstype.int32)
-        >>> net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, gradient, indices)
+        >>> output = net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, gradient, indices)
         >>> print(net.var.asnumpy())
         [[[0.9996866 0.9997078]]
          [[0.9997037 0.9996869]]

mindspore/ops/primitive.py

@@ -270,10 +270,10 @@ class PrimitiveWithCheck(Primitive):
         ...         pass
         ...     def check_shape(self, input_x):
         ...         validator.check_int(len(input_x), 1, Rel.GE, 'input_x rank', self.name)
-        >>>
+        ...
         ...     def check_dtype(self, input_x):
         ...         validator.check_subclass("input_x", input_x, mstype.tensor, self.name)
-        >>>
+        ...
         >>> # init a Primitive obj
         >>> add = Flatten()
     """
@@ -348,13 +348,13 @@ class PrimitiveWithInfer(Primitive):
         ...     @prim_attr_register
         ...     def __init__(self):
         ...         pass
-        >>>
+        ...
         ...     def infer_shape(self, x, y):
         ...         return x # output shape same as first input 'x'
-        >>>
+        ...
         ...     def infer_dtype(self, x, y):
         ...         return x # output type same as first input 'x'
-        >>>
+        ...
         >>> # init a Primitive obj
         >>> add = Add()
     """