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131 lines
4.9 KiB
131 lines
4.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/rank_loss_op.h"
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#include <string>
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namespace paddle {
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namespace operators {
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class RankLossOp : public framework::OperatorWithKernel {
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public:
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RankLossOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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// input check
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null.");
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auto label_dims = ctx->GetInputDim("Label");
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auto left_dims = ctx->GetInputDim("Left");
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auto right_dims = ctx->GetInputDim("Right");
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PADDLE_ENFORCE((label_dims == left_dims) && (left_dims == right_dims),
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"All inputs must have the same size.");
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PADDLE_ENFORCE(
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(label_dims.size() == 2) && (label_dims[1] == 1),
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"All inputs must be 2-D tensors with shape [batch_size x 1].");
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ctx->SetOutputDim("Out", label_dims);
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}
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};
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class RankLossOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Label",
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"(2-D Tensor with shape [batch_size x 1]) "
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"The label indicating A ranked higher than B or not.");
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AddInput("Left",
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"(2-D Tensor with shape [batch_size x 1]) "
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"The output of RankNet for doc A.");
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AddInput("Right",
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"(2-D Tensor with shape [batch_size x 1]) "
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"The output of RankNet for doc B.");
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AddOutput("Out",
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"(2-D Tensor with shape [batch_size x 1]) "
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"The output loss of RankLoss operator.");
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AddComment(R"DOC(
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RankLoss Operator.
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RankLoss operator for RankNet
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(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf).
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RankNet is a pairwise ranking model with
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one training sample consisting of a pair of doc A and B, and the label P
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indicating that A is ranked higher than B or not:
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P = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of
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the input pair.
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The RankLoss operator takes three inputs: Left (o_i), Right (o_j) and Label
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(P_{i,j}), which represent the output score of RankNet for the two docs and
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the label respectively, and yields the rank loss C_{i,j} using the following
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equation:
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$$
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C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\
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o_{i,j} = o_i - o_j \\
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\tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
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$$
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The operator can take batch inputs with size batch_size (batch_size >= 1).
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)DOC");
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}
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};
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class RankLossGradOp : public framework::OperatorWithKernel {
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public:
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RankLossGradOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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auto dims = ctx->GetInputDim("Left");
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auto left_grad_name = framework::GradVarName("Left");
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auto right_grad_name = framework::GradVarName("Right");
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if (ctx->HasOutput(left_grad_name)) {
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ctx->SetOutputDim(left_grad_name, dims);
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}
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if (ctx->HasOutput(right_grad_name)) {
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ctx->SetOutputDim(right_grad_name, dims);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(rank_loss, ops::RankLossOp, ops::RankLossOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(rank_loss_grad, ops::RankLossGradOp);
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REGISTER_OP_CPU_KERNEL(
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rank_loss, ops::RankLossKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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rank_loss_grad,
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ops::RankLossGradKernel<paddle::platform::CPUDeviceContext, float>);
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