Fix MatrixSetDiag, Select, Conv2D op annotation problem.

mindspore/ccsrc/transform/graph_ir/df_graph_manager.cc

@@ -66,7 +66,7 @@ Status DfGraphManager::AddGraph(const std::string &name, const DfGraphPtr &graph
   }
 
   if (graph_ptr == nullptr) {
-    MS_LOG(WARNING) << "The new graph {" << name << "}'s pointer is null, add graph failed";
+    MS_LOG(INFO) << "The new graph {" << name << "}'s pointer is null, add graph failed";
     return Status::INVALID_ARGUMENT;
   }
 

mindspore/nn/layer/basic.py

@@ -629,9 +629,9 @@ class MatrixSetDiag(Cell):
     Modify the batched diagonal part of a batched tensor.
 
     Inputs:
-        - **x** (Tensor) - The batched tensor. It can be one of the following data types:
+        - **x** (Tensor) - The batched tensor. Rank k+1, where k >= 1. It can be one of the following data types:
           float32, float16, int32, int8, and uint8.
-        - **diagonal** (Tensor) - The diagonal values.
+        - **diagonal** (Tensor) - The diagonal values. Must have the same type as input `x`. Rank k, where k >= 1.
 
     Outputs:
         Tensor, has the same type and shape as input `x`.

mindspore/ops/operations/_inner_ops.py

@@ -410,10 +410,10 @@ class MatrixSetDiag(PrimitiveWithInfer):
     Modifies the batched diagonal part of a batched tensor.
 
     Inputs:
-        - **x** (Tensor) - The batched tensor. It can be one of the following data types:
+        - **x** (Tensor) - The batched tensor. Rank k+1, where k >= 1. It can be one of the following data types:
           float32, float16, int32, int8, uint8.
+        - **diagonal** (Tensor) - The diagonal values. Must have the same type as input `x`. Rank k, where k >= 1.
         - **assist** (Tensor) - A eye tensor of the same type as `x`. With shape same as `x`.
-        - **diagonal** (Tensor) - The diagonal values.
 
     Outputs:
         Tensor, data type same as input `x`. The shape same as `x`.

mindspore/ops/operations/array_ops.py

@@ -2026,22 +2026,22 @@ class Select(PrimitiveWithInfer):
     and :math:`y`.
 
     Inputs:
-        - **input_x** (Tensor[bool]) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
+        - **input_cond** (Tensor[bool]) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
           The condition tensor, decides which element is chosen.
-        - **input_y** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
+        - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
           The first input tensor.
-        - **input_z** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
+        - **input_y** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
           The second input tensor.
 
     Outputs:
-        Tensor, has the same shape as `input_y`. The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
+        Tensor, has the same shape as `input_x`. The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
 
     Examples:
         >>> select = P.Select()
-        >>> input_x = Tensor([True, False])
-        >>> input_y = Tensor([2,3], mindspore.float32)
-        >>> input_z = Tensor([1,2], mindspore.float32)
-        >>> select(input_x, input_y, input_z)
+        >>> input_cond = Tensor([True, False])
+        >>> input_x = Tensor([2,3], mindspore.float32)
+        >>> input_y = Tensor([1,2], mindspore.float32)
+        >>> select(input_cond, input_x, input_y)
     """
 
     @prim_attr_register

mindspore/ops/operations/nn_ops.py

@@ -58,6 +58,23 @@ def _check_positive_int_or_tuple(arg_name, arg_value, prim_name, allow_four=Fals
     return ret_value
 
 
+def _check_shape(arg_name, arg_value, prim_name):
+    """
+    Checks whether an shape dims is a positive int elements.
+    """
+
+    def _raise_message():
+        raise ValueError(f"For '{prim_name}' attr '{arg_name}' dims elements should be positive int numbers, "
+                         f"but got {arg_value}")
+
+    validator.check_value_type(arg_name, arg_value, (list, tuple), prim_name)
+    for item in arg_value:
+        if isinstance(item, int) and item > 0:
+            continue
+        _raise_message()
+    return arg_value
+
+
 class Flatten(PrimitiveWithInfer):
     r"""
     Flattens a tensor without changing its batch size on the 0-th axis.
@@ -1052,6 +1069,7 @@ class Conv2D(PrimitiveWithInfer):
         self.add_prim_attr('pad_list', (pad_top, pad_bottom, pad_left, pad_right))
         out_channel = self.out_channel
         out_shape = [x_shape[0], out_channel, h_out, w_out]
+        _check_shape('output', out_shape, self.name)
         return out_shape
 
     def infer_dtype(self, x_dtype, w_dtype, b_dtype=None):