add tensor dim(), numpy(), param copy

docs/api/api_python/mindspore/Tensor/mindspore.Tensor.numpy.rst

@@ -0,0 +1,6 @@
+mindspore.Tensor.numpy
+======================
+
+.. py:method:: mindspore.Tensor.numpy()
+
+    参考 `Tensor.asnumpy() <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.asnumpy.html>`_。

docs/api/api_python/mindspore/mindspore.Parameter.rst

@@ -37,6 +37,13 @@
         返回：
             Parameter，返回克隆的新参数。
 
+    .. py:method:: copy
+
+        拷贝参数。
+
+        返回：
+            Parameter，返回拷贝的新参数。
+
     .. py:method:: comm_fusion
         :property:
 

docs/api/api_python/nn/mindspore.nn.Rprop.rst

@@ -5,7 +5,7 @@ mindspore.nn.Rprop
 
     弹性反向传播（Rprop）算法的实现。
 
-    请参阅论文 `A Direct Adaptive Method for Faster Backpropagation Learning <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>`_ 。
+    请参阅论文 `A Direct Adaptive Method for Faster Backpropagation Learning <https://ieeexplore.ieee.org/document/298623>`_ 。
 
     更新公式如下：
 

mindspore/ccsrc/pipeline/jit/resource.cc

@@ -337,6 +337,7 @@ BuiltInTypeMap &GetMethodMap() {
        {"div", std::string("div")},                                        // div()
        {"equal", std::string("equal")},                                    // equal()
        {"expm1", std::string("expm1")},                                    // expm1()
+       {"dim", prim::kPrimRank},                                           // P.Rank()
      }},
     {kObjectTypeRowTensorType,
      {

mindspore/python/mindspore/common/jit_config.py

@@ -34,7 +34,7 @@ class JitConfig:
         **kwargs (dict): A dictionary of keyword arguments that the class needs.
 
     Examples:
-        >>> from mindspore.common.jit_config import JitConfig
+        >>> from mindspore import JitConfig
         >>>
         >>> jitconfig = JitConfig(jit_level="O1")
         >>> net = LeNet5()

mindspore/python/mindspore/common/parameter.py

@@ -331,6 +331,15 @@ class Parameter(Tensor_):
         self.init_in_server = init_in_server
         self.param_info.init_in_server = init_in_server
 
+    def copy(self):
+        """
+        Copy the parameter.
+
+        Returns:
+            Parameter, a new parameter.
+        """
+        return self.clone(init='same')
+
     def set_param_fl(self, push_to_server=False, pull_from_server=False, requires_aggr=True):
         """
         Set the way of parameter and server interaction.

mindspore/python/mindspore/common/tensor.py

@@ -616,6 +616,13 @@ class Tensor(Tensor_):
         self._init_check()
         return Tensor_.asnumpy(self)
 
+    def numpy(self):
+        """
+        Refer to `Tensor.asnumpy() \
+        <https://www.mindspore.cn/docs/en/master/api_python/mindspore/Tensor/mindspore.Tensor.asnumpy.html>`_.
+        """
+        return self.asnumpy()
+
     def is_persistent_data(self):
         """
         Check if size of tensor is huge, and need save data to persistent storage.

mindspore/python/mindspore/nn/optim/rprop.py

@@ -30,7 +30,7 @@ class Rprop(Optimizer):
     Implements Resilient backpropagation.
 
     Further information about this implementation can be found at  `A Direct Adaptive Method for Faster Backpropagation
-    Learning: The RPROP Algorithm <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>`_.
+    Learning: The RPROP Algorithm <https://ieeexplore.ieee.org/document/298623>`_.
 
     The updating formulas are as follows:
 

tests/st/nn/test_margin_ranking_loss.py

@@ -96,3 +96,33 @@ def test_margin_ranking_loss_mean(mode):
     output = loss(input1, input2, target)
     expect_output = np.array(1.2293333)
     assert np.allclose(output.asnumpy(), expect_output)
+
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [ms.GRAPH_MODE])
+def test_tensor_dim(mode):
+    """
+    Feature: test tensor dim
+    Description: Verify the result of dim.
+    Expectation: expect correct forward result.
+    """
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.tensor = Tensor([[1, 2, 3], [4, 5, 6]])
+
+        def construct(self, x):
+            return x.dim(), self.tensor.dim()
+
+    net = Net()
+    input11 = Tensor([[1, 2, 3], [4, 5, 6]])
+    input22 = Tensor([[[1, 2, 3], [4, 5, 6]]])
+    net(input11)
+    net(input22)

tests/ut/python/nn/test_parameter.py

@@ -277,3 +277,15 @@ def test_parameter_init_from_tensor():
     assert np.allclose(param.asnumpy(), np.array([1]))
     tensor.asnumpy()[0] = 2
     assert np.allclose(param.asnumpy(), np.array([2]))
+
+
+def test_parameter_copy():
+    """
+    Feature: Parameter copy.
+    Description: Parameter copy.
+    Expectation: The two Parameter's data are the same.
+    """
+    tensor = Tensor(np.array([[1, 2, 3], [2, 3, 4]]))
+    param1 = Parameter(tensor, name="testParameter")
+    param2 = param1.copy()
+    np.all(param1.data.asnumpy() == param2.data.asnumpy())