margin_rank_loss_op.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License. */

#include "paddle/operators/margin_rank_loss_op.h"

namespace paddle {
namespace operators {

class MarginRankLossOp : public framework::OperatorWithKernel {
 public:
  MarginRankLossOp(const std::string &type,
                   const framework::VariableNameMap &inputs,
                   const framework::VariableNameMap &outputs,
                   const framework::AttributeMap &attrs)
      : OperatorWithKernel(type, inputs, outputs, attrs) {}

 protected:
  void InferShape(const framework::InferShapeContext &ctx) const override {
    // input check
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
                            "Input(Label) shouldn't be null");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X1"), "Input(X1) shouldn't be null");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X2"), "Input(X2) shouldn't be null");
    auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
    auto x1_dims = ctx.Input<framework::Tensor>("X1")->dims();
    auto x2_dims = ctx.Input<framework::Tensor>("X2")->dims();
    PADDLE_ENFORCE((label_dims.size() == 1) && (x1_dims.size() == 1) &&
                       (x2_dims.size() == 1),
                   "The rank of all inputs must be 1.");
    PADDLE_ENFORCE((label_dims == x1_dims) && (x1_dims == x2_dims),
                   "All inputs must have the same size");
    ctx.Output<framework::LoDTensor>("Out")->Resize(label_dims);
    ctx.Output<framework::LoDTensor>("Activated")->Resize(label_dims);
  }
};

template <typename AttrType>
class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  MarginRankLossOpMaker(framework::OpProto *proto,
                        framework::OpAttrChecker *op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("Label", "The label indicating X1 ranked higher than X2 or not.");
    AddInput("X1", "The first input of MarginRankLossOp.");
    AddInput("X2", "The second input of MarginRankLossOp");
    AddAttr<AttrType>("margin", "Margin for MarginRankLossOp").SetDefault(0);
    AddOutput("Out", "The output loss of MarginRankLoss operator");
    AddOutput("Activated",
              "Intermediate tensor to indicate "
              "whether Output(Out) is activated")
        .AsIntermediate();
    AddComment(R"DOC(MarginRankLoss operator

loss(x1, x2, y) = max(0, -label * (x1-x2) + margin)

)DOC");
  }
};

class MarginRankLossGradOp : public framework::OperatorWithKernel {
 public:
  MarginRankLossGradOp(const std::string &type,
                       const framework::VariableNameMap &inputs,
                       const framework::VariableNameMap &outputs,
                       const framework::AttributeMap &attrs)
      : OperatorWithKernel(type, inputs, outputs, attrs) {}

 protected:
  void InferShape(const framework::InferShapeContext &ctx) const override {
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
                            "Input(Label) shouldn't be null.");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X1"), "Input(X1) shouldn't be null.");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X2"), "Input(X2) shouldn't be null.");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
                            "Input(Out@GRAD) shouldn't be null.");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Activated"),
                            "Intermediate(Activated) shouldn't be null.");
    auto dims = ctx.Input<framework::Tensor>("X1")->dims();
    auto *x1_grad =
        ctx.Output<framework::LoDTensor>(framework::GradVarName("X1"));
    auto *x2_grad =
        ctx.Output<framework::LoDTensor>(framework::GradVarName("X2"));
    if (x1_grad) {
      x1_grad->Resize(dims);
    }
    if (x2_grad) {
      x2_grad->Resize(dims);
    }
  }
};

}  // namespace operators
}  // namespace paddle
namespace ops = paddle::operators;

REGISTER_OP(margin_rank_loss, ops::MarginRankLossOp,
            ops::MarginRankLossOpMaker<float>, margin_rank_loss_grad,
            ops::MarginRankLossGradOp);
REGISTER_OP_CPU_KERNEL(
    margin_rank_loss,
    ops::MarginRankLossKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
    margin_rank_loss_grad,
    ops::MarginRankLossGradKernel<paddle::platform::CPUPlace, float>);