Merge pull request #6058 from kuke/refine_rank_loss_op

Revise comments in rank_loss_op

paddle/operators/rank_loss_op.cc

@@ -35,9 +35,10 @@ class RankLossOp : public framework::OperatorWithKernel {
     auto right_dims = ctx->GetInputDim("Right");
 
     PADDLE_ENFORCE((label_dims == left_dims) && (left_dims == right_dims),
-                   "All inputs must have the same size");
-    PADDLE_ENFORCE((label_dims.size() == 2) && (label_dims[1] == 1),
-                   "All inputs must be row vector with size batch_size x 1.");
+                   "All inputs must have the same size.");
+    PADDLE_ENFORCE(
+        (label_dims.size() == 2) && (label_dims[1] == 1),
+        "All inputs must be 2-D tensors with shape [batch_size x 1].");
     ctx->SetOutputDim("Out", label_dims);
   }
 };
@@ -48,10 +49,17 @@ class RankLossOpMaker : public framework::OpProtoAndCheckerMaker {
                   framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("Label",
-             "The label indicating A ranked higher than B or not, row vector.");
-    AddInput("Left", "The output of RankNet for doc A, vector.");
-    AddInput("Right", "The output of RankNet for doc B, vetor.");
-    AddOutput("Out", "The output loss of RankLoss operator, vector.");
+             "(2-D Tensor with shape [batch_size x 1]) "
+             "The label indicating A ranked higher than B or not.");
+    AddInput("Left",
+             "(2-D Tensor with shape [batch_size x 1]) "
+             "The output of RankNet for doc A.");
+    AddInput("Right",
+             "(2-D Tensor with shape [batch_size x 1]) "
+             "The output of RankNet for doc B.");
+    AddOutput("Out",
+              "(2-D Tensor with shape [batch_size x 1]) "
+              "The output loss of RankLoss operator.");
     AddComment(R"DOC(
 RankLoss Operator.
 
@@ -65,16 +73,17 @@ P = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of
 the input pair.
 
 The RankLoss operator takes three inputs: Left (o_i), Right (o_j) and Label
-(P_{i,j}), which represent the output of RankNet for the two docs and the label, 
-respectively, and yields the rank loss C_{i,j} using the following equation:
+(P_{i,j}), which represent the output score of RankNet for the two docs and 
+the label respectively, and yields the rank loss C_{i,j} using the following 
+equation:
 
-\f$$
-  C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}}) \\
+$$
+  C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\
   o_{i,j} =  o_i - o_j  \\
   \tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
-\f$$
+$$
 
-The operator can take inputs of one sample or in batch.
+The operator can take batch inputs with size batch_size (batch_size >= 1).
 
 )DOC");
   }