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123 lines
5.0 KiB
123 lines
5.0 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/margin_rank_loss_op.h"
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namespace paddle {
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namespace operators {
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class MarginRankLossOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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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("X1"), "Input(X1) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
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auto label_dims = ctx->GetInputDim("Label");
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auto x1_dims = ctx->GetInputDim("X1");
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auto x2_dims = ctx->GetInputDim("X2");
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PADDLE_ENFORCE(
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(label_dims == x1_dims) && (x1_dims == x2_dims) &&
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(label_dims.size() == 2) && (label_dims[1] == 1),
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"All inputs must be 2-D tensor with shape [batch_size x 1].");
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ctx->SetOutputDim("Activated", label_dims);
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ctx->SetOutputDim("Out", label_dims);
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}
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};
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template <typename T>
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class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X1",
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"(2-D tensor with shape [batch_size x 1]) The score for "
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"one item X1 to be ranked, from pairwise ranking model.");
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AddInput("X2",
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"(2-D tensor with shape [batch_size x 1]) The score for "
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"another item X2 to be ranked, from pairwise ranking model.");
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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 X1 ranked higher than X2 or not, "
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"can only be +1 or -1.");
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AddOutput("Activated",
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"(2-D tensor with shape [batch_size x 1]) Intermediate tensor "
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"to indicate whether each element of Output(Out) is activated.")
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.AsIntermediate();
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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 MarginRankLoss operator.");
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AddAttr<T>("margin", "(scalar, default 0) Margin for MarginRankLossOp.")
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.SetDefault(static_cast<T>(0));
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AddComment(R"DOC(
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MarginRankLoss Operator.
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This operator measures the loss given a pair of training sample
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{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1`
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indicating X1 is ranked higher than `X2` and `Label = -1` otherwise. The loss
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is calculated as:
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$loss(X1, X2, Label) = \max(0, -Label * (X1 - X2) + margin)$
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The attribute `margin` here helps make the predictions more robust.
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Denote the item ranked higher as the positive sample, otherwise the negative
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sample. If the score of the two samples satisfies
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$positive sample - negative sample < margin$
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the pair of samples will contribute to the final loss, which will backpropagate
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and train the ranking model to enlarge the difference between the two scores.
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For batch input with size `batch_size`, `X1`, `X2` and `Label`
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all have the same shape [batch_size x 1].
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)DOC");
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}
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};
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class MarginRankLossGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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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("X1"), "Input(X1) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) 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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PADDLE_ENFORCE(ctx->HasInput("Activated"),
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"Intermediate(Activated) shouldn't be null.");
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auto dims = ctx->GetInputDim("Label");
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ctx->SetOutputDim(framework::GradVarName("X1"), dims);
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ctx->SetOutputDim(framework::GradVarName("X2"), dims);
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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(margin_rank_loss, ops::MarginRankLossOp,
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ops::MarginRankLossOpMaker<float>,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(margin_rank_loss_grad, ops::MarginRankLossGradOp);
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REGISTER_OP_CPU_KERNEL(
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margin_rank_loss,
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ops::MarginRankLossKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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margin_rank_loss_grad,
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ops::MarginRankLossGradKernel<paddle::platform::CPUDeviceContext, float>);
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