hinge_loss_op.cc

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

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/fluid/operators/hinge_loss_op.h"

namespace paddle {
namespace operators {

class HingeLossOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Logits"),
                   "Input(Logits) must be initialized.");
    PADDLE_ENFORCE(ctx->HasInput("Labels"),
                   "Input(Labels) must be initialized.");

    auto pred_dims = ctx->GetInputDim("Logits");
    auto label_dims = ctx->GetInputDim("Labels");

    PADDLE_ENFORCE_EQ(pred_dims, label_dims);
    PADDLE_ENFORCE_EQ(pred_dims.size(), 2,
                      "The rank of Input(Logits) must be 2 and the shape is "
                      "[batch_size, 1].");
    PADDLE_ENFORCE_EQ(pred_dims[1], 1,
                      "Each row of Input(Logits) contains a real value, "
                      "so the 2nd dimension of Input(Logits) must be 1.");

    ctx->SetOutputDim("Loss", {pred_dims[0], 1});
    ctx->ShareLoD("Logits", "Loss");
  }
};

template <typename AttrType>
class HingeLossOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  HingeLossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("Logits",
             "The input value (Logits) of Hinge loss op."
             "Logits is a 2-D tensor with shape [batch_size, 1].");
    AddInput("Labels",
             "The target value (Labels) of Hinge loss op."
             "Labels is a 2-D tensor with shape [batch_size, 1].");
    AddOutput("Loss",
              "The output tensor with shape [batch_size, 1] "
              "which represents the hinge loss.");
    AddComment(R"DOC(
HingeLoss Operator.

Let x be a logit (prediction) and y be the actual label. The logit can
take any values from (-inf, inf), but the labels should be either -1 or 1.
Then, the hinge loss is computed as follows:

$$
L_(x, y) = max(1 - y.x, 0) 
$$

Note that the labels passed as input will have values as either 0 or 1.

)DOC");
  }
};

class HingeLossGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Logits"),
                   "Input(Logits) should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Labels"),
                   "Input(Labels) should not be null.");
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
                   "Input(Loss@GRAD) should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
                   "Input(Logits@GRAD) should not be null.");

    auto pred_dims = ctx->GetInputDim("Logits");
    auto lab_dims = ctx->GetInputDim("Labels");
    auto loss_grad_dims = ctx->GetInputDim(framework::GradVarName("Loss"));

    PADDLE_ENFORCE_EQ(loss_grad_dims, pred_dims);

    auto pred_grad_name = framework::GradVarName("Logits");
    ctx->SetOutputDim(pred_grad_name, pred_dims);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(hinge_loss, ops::HingeLossOp, ops::HingeLossOpMaker<float>,
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(hinge_loss_grad, ops::HingeLossGradOp);
REGISTER_OP_CPU_KERNEL(
    hinge_loss,
    ops::HingeLossKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
    hinge_loss_grad,
    ops::HingeLossGradKernel<paddle::platform::CPUDeviceContext, float>);