refine comment of interp_op

paddle/operators/interp_op.cc

@@ -30,27 +30,26 @@ class InterpOp : public NetOp {
                       "Input(Y) of InterpOp should not be null.");
     PADDLE_ENFORCE_NE(Input("W"), framework::kEmptyVarName,
                       "Input(W) of InterpOp should not be null.");
-    PADDLE_ENFORCE_NE(Output("MinusOut"), framework::kEmptyVarName,
-                      "Output(MinusOut) of InterpOp should not be null.");
+    PADDLE_ENFORCE_NE(Output("SubOut"), framework::kEmptyVarName,
+                      "Output(SubOut) of InterpOp should not be null.");
     PADDLE_ENFORCE_NE(Output("MulOut"), framework::kEmptyVarName,
                       "Output(MulOut) of InterpOp should not be null.");
     PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName,
                       "Output(Out) of InterpOp should not be null.");
 
-    // MinusOut = X - Y
+    // SubOut = X - Y
     auto x = Input("X");
     auto y = Input("Y");
-    auto minus_out = Output("MinusOut");
-    AppendOp(framework::OpRegistry::CreateOp("elementwise_sub",
-                                             {{"X", {x}}, {"Y", {y}}},
-                                             {{"Out", {minus_out}}}, {}));
+    auto sub_out = Output("SubOut");
+    AppendOp(framework::OpRegistry::CreateOp(
+        "elementwise_sub", {{"X", {x}}, {"Y", {y}}}, {{"Out", {sub_out}}}, {}));
 
-    // MulOut = MinusOut * W = (X - Y) * W
+    // MulOut = SubOut * W = (X - Y) * W
     auto w = Input("W");
     auto mul_out = Output("MulOut");
     AppendOp(framework::OpRegistry::CreateOp(
-        "elementwise_mul", {{"X", {minus_out}}, {"Y", {w}}},
-        {{"Out", {mul_out}}}, {{"axis", 0}}));
+        "elementwise_mul", {{"X", {sub_out}}, {"Y", {w}}}, {{"Out", {mul_out}}},
+        {{"axis", 0}}));
 
     // Out = MulOut + Y = (X - Y) * W + Y = X * W + Y * (1 - W)
     AppendOp(framework::OpRegistry::CreateOp("elementwise_add",
@@ -65,18 +64,26 @@ class InterpOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   InterpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "A 2-D Tensor, the first input of interp_op");
-    AddInput("Y", "A 2-D Tensor, the second input of interp_op");
-    AddInput("W", "A 1-D Tensor, the interpolated values");
-    AddOutput("MinusOut",
-              "A 2-D Tensor, the intermediate outputs, saving X - Y.")
+    AddInput("X",
+             "(Tensor), 2-D Matrix of shape [batch_size, data_dim]"
+             "containing data samples, the first input of interp_op");
+    AddInput("Y",
+             "(Tensor), 2-D Matrix of shape `[batch_size, data_dim]`"
+             "containing data samples, the second input of interp_op");
+    AddInput("W",
+             "(Tensor), 1-D Vector of shape [batch_size],"
+             "the interpolated values in the half-open interval [0.0, 1.0)");
+    AddOutput("SubOut",
+              "(Tensor), the intermediate subtraction outputs, saving X - Y.")
         .AsIntermediate();
     AddOutput("MulOut",
-              "A 2-D Tensor, the intermediate outputs,"
-              "saving the mul mul of (X - Y) and W")
+              "(Tensor), the intermediate multiplication outputs,"
+              "saving the elementwise multiplication of (X - Y) and W.")
         .AsIntermediate();
     AddOutput("Out",
-              "A 2-D Tensor, the output of interp_op, same shape with X");
+              "(Tensor), the output of interp_op, same shape with X,"
+              "returns the first-dimensional piecewise linear interpolant "
+              "between X and Y");
     AddComment(R"DOC(
     Linear Interpolation with two inputs, used in NEURAL TURING MACHINE.
 

python/paddle/v2/framework/tests/test_interp_op.py

@@ -10,12 +10,12 @@ class TestInterpOp(OpTest):
         y = np.random.random((2, 3)).astype("float32")
         w = np.random.random(2).astype("float32")
 
-        minus_out = x - y
-        mul_out = minus_out * w.reshape(2, 1)
+        sub_out = x - y
+        mul_out = sub_out * w.reshape(2, 1)
         out = mul_out + y
 
         self.inputs = {'X': x, 'Y': y, 'W': w}
-        self.outputs = {'Out': out, 'MinusOut': minus_out, 'MulOut': mul_out}
+        self.outputs = {'Out': out, 'SubOut': sub_out, 'MulOut': mul_out}
 
     def test_check_output(self):
         self.check_output()