change the dims of input of rank_loss_op

paddle/operators/rank_loss_op.cc

-
 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
    Licensed under the Apache License, Version 2.0 (the "License");
@@ -37,11 +36,10 @@ class RankLossOp : public framework::OperatorWithKernel {
     auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
     auto left_dims = ctx.Input<framework::Tensor>("Left")->dims();
     auto right_dims = ctx.Input<framework::Tensor>("Right")->dims();
-    PADDLE_ENFORCE((label_dims.size() == 1) && (left_dims.size() == 1) &&
-                       (right_dims.size() == 1),
-                   "The rank of all inputs must be 1.");
     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_sizex1.");
     ctx.Output<framework::LoDTensor>("Out")->Resize(label_dims);
   }
 };
@@ -52,10 +50,10 @@ 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, 1-D tensor.");
-    AddInput("Left", "The output of RankNet for doc A, 1-D tensor.");
-    AddInput("Right", "The output of RankNet for doc B, 1-D tensor");
-    AddOutput("Out", "The output loss of RankLoss operator, 1-D tensor.");
+             "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.");
     AddComment(R"DOC(RankLoss operator
 
 Rank loss operator for RankNet[1]. RankNet is a pairwise ranking model with

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

@@ -8,9 +8,9 @@ class TestRankLossOp(OpTest):
         self.op_type = "rank_loss"
         batch_size = 5
         # labels_{i} = {0, 1.0} or {0, 0.5, 1.0}
-        label = np.random.randint(0, 2, size=(batch_size, )).astype("float32")
-        left = np.random.random((batch_size, )).astype("float32")
-        right = np.random.random((batch_size, )).astype("float32")
+        label = np.random.randint(0, 2, size=(batch_size, 1)).astype("float32")
+        left = np.random.random((batch_size, 1)).astype("float32")
+        right = np.random.random((batch_size, 1)).astype("float32")
         loss = np.log(1.0 + np.exp(left - right)) - label * (left - right)
         self.inputs = {'Label': label, 'Left': left, 'Right': right}
         self.outputs = {'Out': loss}