Yibing Liu
8 years ago
parent
4400284685
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79c2d90a7f
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/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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MarginRankLossOp(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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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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// input check
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
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"Input(Label) shouldn't be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X1"), "Input(X1) shouldn't be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X2"), "Input(X2) shouldn't be null");
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auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
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auto x1_dims = ctx.Input<framework::Tensor>("X1")->dims();
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auto x2_dims = ctx.Input<framework::Tensor>("X2")->dims();
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PADDLE_ENFORCE((label_dims.size() == 1) && (x1_dims.size() == 1) &&
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(x2_dims.size() == 1),
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"The rank of all inputs must be 1.");
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PADDLE_ENFORCE((label_dims == x1_dims) && (x1_dims == x2_dims),
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"All inputs must have the same size");
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ctx.Output<framework::LoDTensor>("Out")->Resize(label_dims);
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ctx.Output<framework::LoDTensor>("Activated")->Resize(label_dims);
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}
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};
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template <typename AttrType>
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class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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MarginRankLossOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Label", "The label indicating X1 ranked higher than X2 or not.");
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AddInput("X1", "The first input of MarginRankLossOp.");
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AddInput("X2", "The second input of MarginRankLossOp");
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AddAttr<AttrType>("margin", "Margin for MarginRankLossOp").SetDefault(0);
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AddOutput("Out", "The output loss of MarginRankLoss operator");
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AddOutput("Activated",
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"Intermediate tensor to indicate "
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"whether Output(Out) is activated")
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.AsIntermediate();
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AddComment(R"DOC(MarginRankLoss operator
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loss(x1, x2, y) = max(0, -label * (x1-x2) + margin)
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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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MarginRankLossGradOp(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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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
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"Input(Label) shouldn't be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X1"), "Input(X1) shouldn't be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X2"), "Input(X2) shouldn't be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Activated"),
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"Intermediate(Activated) shouldn't be null.");
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auto dims = ctx.Input<framework::Tensor>("X1")->dims();
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auto *x1_grad =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X1"));
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auto *x2_grad =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X2"));
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if (x1_grad) {
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x1_grad->Resize(dims);
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}
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if (x2_grad) {
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x2_grad->Resize(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_OP(margin_rank_loss, ops::MarginRankLossOp,
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ops::MarginRankLossOpMaker<float>, margin_rank_loss_grad,
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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::CPUPlace, 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::CPUPlace, float>);
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@ -0,0 +1,22 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/margin_rank_loss_op.h"
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REGISTER_OP_GPU_KERNEL(
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margin_rank_loss,
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paddle::operators::MarginRankLossKernel<paddle::platform::GPUPlace, float>);
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REGISTER_OP_GPU_KERNEL(margin_rank_loss_grad,
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paddle::operators::MarginRankLossGradKernel<
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paddle::platform::GPUPlace, float>);
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@ -0,0 +1,106 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct ReLU {
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HOSTDEVICE T operator()(const T& val) const {
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if (val < 0) {
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return static_cast<T>(0);
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} else {
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return val;
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}
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}
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};
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template <typename T>
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struct Heaviside {
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HOSTDEVICE T operator()(const T& val) const {
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if (val > 0) {
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return static_cast<T>(1);
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} else {
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return static_cast<T>(0);
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}
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}
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};
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template <typename Place, typename T, typename AttrType = T>
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class MarginRankLossKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* out_t = ctx.Output<framework::LoDTensor>("Out");
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auto* act_t = ctx.Output<framework::LoDTensor>("Activated");
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auto* label_t = ctx.Input<framework::Tensor>("Label");
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auto* x1_t = ctx.Input<framework::Tensor>("X1");
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auto* x2_t = ctx.Input<framework::Tensor>("X2");
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out_t->mutable_data<T>(ctx.GetPlace());
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act_t->mutable_data<T>(ctx.GetPlace());
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auto margin = static_cast<T>(ctx.Attr<AttrType>("margin"));
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auto out = framework::EigenVector<T>::Flatten(*out_t);
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auto act = framework::EigenVector<T>::Flatten(*act_t);
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auto label = framework::EigenVector<T>::Flatten(*label_t);
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auto x1 = framework::EigenVector<T>::Flatten(*x1_t);
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auto x2 = framework::EigenVector<T>::Flatten(*x2_t);
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auto& dev = ctx.GetEigenDevice<Place>();
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act.device(dev) = (-label * (x1 - x2) + margin).unaryExpr(Heaviside<T>());
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out.device(dev) = (-label * (x1 - x2) + margin).unaryExpr(ReLU<T>());
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}
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};
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template <typename Place, typename T>
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class MarginRankLossGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* d_x1_t =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X1"));
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auto* d_x2_t =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X2"));
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auto* act_t = ctx.Output<framework::LoDTensor>("Activated");
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auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* label_t = ctx.Input<framework::Tensor>("Label");
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auto& dev = ctx.GetEigenDevice<Place>();
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auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
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auto act = framework::EigenVector<T>::Flatten(*act_t);
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auto label = framework::EigenVector<T>::Flatten(*label_t);
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// compute d_x1
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if (d_x1_t) {
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d_x1_t->mutable_data<T>(ctx.GetPlace());
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auto d_x1 = framework::EigenVector<T>::Flatten(*d_x1_t);
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d_x1.device(dev) = -d_out * act * label;
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}
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// compute d_x2
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if (d_x2_t) {
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d_x2_t->mutable_data<T>(ctx.GetPlace());
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auto d_x2 = framework::EigenVector<T>::Flatten(*d_x2_t);
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d_x2.device(dev) = d_out * act * label;
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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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@ -0,0 +1,40 @@
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestMarginRankLossOp(OpTest):
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def setUp(self):
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self.op_type = "margin_rank_loss"
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batch_size = 5
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margin = 0.1
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# labels_{i} = {0, 1.0} or {0, 0.5, 1.0}
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label = np.random.randint(0, 2, size=(batch_size, )).astype("float32")
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x1 = np.random.random((batch_size, )).astype("float32")
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x2 = np.random.random((batch_size, )).astype("float32")
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# loss = max(0, -label * (x1 - x2) + margin)
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loss = [
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max(0, -label[i] * (x1[i] - x2[i]) + margin)
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for i in range(batch_size)
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]
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self.attrs = {'margin': margin}
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self.inputs = {'Label': label, 'X1': x1, 'X2': x2}
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self.outputs = {'Out': loss}
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def test_check_output(self):
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self.check_output()
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"""
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def test_check_grad(self):
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self.check_grad(["X1", "X2"], "Out")
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def test_check_grad_ignore_x1(self):
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self.check_grad(["X2"], "Out", no_grad_set=set('X1'))
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def test_check_grad_ignore_x2(self):
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self.check_grad(["X1"], "Out", no_grad_set=set('X2'))
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"""
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if __name__ == '__main__':
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unittest.main()
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