!266 Unify the import of paramter and mstype in front example

Merge pull request !266 from ghzl/modify-some-example-in-me-front

mindspore/nn/layer/normalization.py

@@ -256,7 +256,7 @@ class LayerNorm(Cell):
         Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `input_x`.
 
     Examples:
-        >>> x = Tensor(np.ones([20, 5, 10, 10], np.float32))
+        >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32)
         >>> shape1 = x.shape()[1:]
         >>> m = nn.LayerNorm(shape1,  begin_norm_axis=1, begin_params_axis=1)
         >>> m(x)

mindspore/nn/layer/pooling.py

@@ -104,7 +104,7 @@ class MaxPool2d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Examples:
-        >>> pool = MaxPool2d(kernel_size=3, stride=1)
+        >>> pool = nn.MaxPool2d(kernel_size=3, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[1. 5. 5. 1.]
            [0. 3. 4. 8.]
@@ -186,7 +186,7 @@ class AvgPool2d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
     Examples:
-        >>> pool = AvgPool2d(kernel_size=3, strides=1)
+        >>> pool = nn.AvgPool2d(kernel_size=3, strides=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
         [[[[5. 5. 9. 9.]
             [8. 4. 3. 0.]

mindspore/nn/loss/loss.py

@@ -284,7 +284,7 @@ class SoftmaxCrossEntropyExpand(Cell):
         Tensor, a scalar tensor including the mean loss.
 
     Examples:
-        >>> loss = SoftmaxCrossEntropyExpand(sparse=True)
+        >>> loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
         >>> input_data = Tensor(np.ones([64, 512]), dtype=mindspore.float32)
         >>> label = Tensor(np.ones([64]), dtype=mindspore.int32)
         >>> loss(input_data, label)

mindspore/nn/metrics/__init__.py

@@ -83,7 +83,7 @@ def get_metric_fn(name, *args, **kwargs):
         Metric object, class instance of the metric method.
 
     Examples:
-        >>> metric = get_metric_fn('precision', eval_type='classification')
+        >>> metric = nn.get_metric_fn('precision', eval_type='classification')
     """
     if name not in __factory__:
         raise KeyError("Unknown Metric:", name)

mindspore/nn/metrics/error.py

@@ -97,7 +97,7 @@ class MSE(Metric):
     Examples:
         >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
         >>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
-        >>> error = MSE()
+        >>> error = nn.MSE()
         >>> error.clear()
         >>> error.update(x, y)
         >>> result = error.eval()

mindspore/ops/operations/control_ops.py

@@ -51,9 +51,9 @@ class ControlDepend(Primitive):
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>        super(Net, self).__init__()
-        >>>         self.global_step = Parameter(initializer(0, [1]), name="global_step")
+        >>>         self.global_step = mindspore.Parameter(initializer(0, [1]), name="global_step")
         >>>         self.rate = 0.2
-        >>>         self.control_depend = ControlDepend()
+        >>>         self.control_depend = P.ControlDepend()
         >>>
         >>>     def construct(self, x):
         >>>         data = self.rate * self.global_step + x
@@ -92,7 +92,7 @@ class GeSwitch(PrimitiveWithInfer):
         >>>	    super(Net, self).__init__()
         >>>         self.square = P.Square()
         >>>         self.add = P.TensorAdd()
-        >>>         self.value = Tensor(np.full((1), 3, dtype=np.float32))
+        >>>         self.value = Tensor(np.full((1), 3), mindspore.float32)
         >>>         self.switch = P.GeSwitch()
         >>>         self.merge = P.Merge()
         >>>         self.less = P.Less()

mindspore/ops/operations/math_ops.py

@@ -133,7 +133,7 @@ class AssignAdd(PrimitiveWithInfer):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.AssignAdd = P.AssignAdd()
-        >>>         self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
+        >>>         self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int64), name="global_step")
         >>>
         >>>     def construct(self, x):
         >>>         self.AssignAdd(self.variable, x)
@@ -176,7 +176,7 @@ class AssignSub(PrimitiveWithInfer):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.AssignSub = P.AssignSub()
-        >>>         self.variable = Parameter(initializer(1, [1], mindspore.int64), name="global_step")
+        >>>         self.variable = mindspore.Parameter(initializer(1, [1], mindspore.int64), name="global_step")
         >>>
         >>>     def construct(self, x):
         >>>         self.AssignSub(self.variable, x)

mindspore/ops/operations/nn_ops.py

@@ -156,7 +156,7 @@ class ReLU(PrimitiveWithInfer):
         Tensor, with the same type and shape as the `input_x`.
 
     Examples:
-        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> relu = P.ReLU()
         >>> result = relu(input_x)
         [[0, 4.0, 0.0], [2.0, 0.0, 9.0]]
@@ -189,7 +189,7 @@ class ReLU6(PrimitiveWithInfer):
         Tensor, with the same type and shape as the `input_x`.
 
     Examples:
-        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> relu6 = P.ReLU6()
         >>> result = relu6(input_x)
     """
@@ -223,7 +223,7 @@ class Elu(PrimitiveWithInfer):
         Tensor, has the same shape and data type as `input_x`.
 
     Examples:
-        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], np.float32))
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
         >>> elu = P.Elu()
         >>> result = elu(input_x)
         Tensor([[-0.632  4.0   -0.999]

mindspore/ops/operations/other_ops.py

@@ -76,7 +76,7 @@ class BoundingBoxEncode(PrimitiveWithInfer):
         Tensor, encoded bounding boxes.
 
     Examples:
-        >>> boundingbox_encode = BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
+        >>> boundingbox_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
         >>> delta_box = boundingbox_encode(anchor_box, groundtruth_box)
     """
 
@@ -119,7 +119,7 @@ class BoundingBoxDecode(PrimitiveWithInfer):
         Tensor, decoded boxes.
 
     Examples:
-        >>> boundingbox_decode = BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
+        >>> boundingbox_decode = P.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
                                                    max_shape=(768, 1280), wh_ratio_clip=0.016)
         >>> bbox = boundingbox_decode(anchor_box, deltas)
     """
@@ -208,7 +208,7 @@ class IOU(PrimitiveWithInfer):
         KeyError: When `mode` is not 'iou' or 'iof'.
 
     Examples:
-        >>> iou = IOU()
+        >>> iou = P.IOU()
         >>> anchor_boxes = Tensor(np.random.randint(1,5, [10, 4]))
         >>> gt_boxes = Tensor(np.random.randint(1,5, [3, 4]))
         >>> iou(anchor_boxes, gt_boxes)
@@ -255,15 +255,15 @@ class MakeRefKey(Primitive):
         >>> class Net(nn.Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
-        >>>         self.y = Parameter(Tensor(np.ones([6, 8, 10], np.int32)), name="y")
-        >>>         self.make_ref_key = MakeRefKey("y")
+        >>>         self.y = mindspore.Parameter(Tensor(np.ones([6, 8, 10]), mindspore.int32), name="y")
+        >>>         self.make_ref_key = P.MakeRefKey("y")
         >>>
         >>>     def construct(self, x):
         >>>         key = self.make_ref_key()
         >>>         ref = F.make_ref(key, x, self.y)
         >>>         return ref * x
         >>>
-        >>> x = Tensor(np.ones([3, 4, 5], np.int32))
+        >>> x = Tensor(np.ones([3, 4, 5]), mindspore.int32)
         >>> net = Net()
         >>> net(x)
     """

mindspore/ops/operations/random_ops.py

@@ -44,7 +44,7 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         - **mask** (Tensor) - The output has shape 1-D.
 
     Examples:
-        >>> rnd_choice_mask = RandomChoiceWithMask()
+        >>> rnd_choice_mask = P.RandomChoiceWithMask()
         >>> input_x = Tensor(np.ones(shape=[240000, 4]), mindspore.bool_)
         >>> output_y, output_mask = rnd_choice_mask(input_x)
     """