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120 lines
4.8 KiB
120 lines
4.8 KiB
/* 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/smooth_l1_loss_op.h"
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namespace paddle {
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namespace operators {
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class SmoothL1LossOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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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("X"),
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"Input of SmoothL1LossOp must be initialized.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
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"Target of SmoothL1LossOp must be initialized.");
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auto* x = ctx.Input<framework::Tensor>("X");
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auto* y = ctx.Input<framework::Tensor>("Y");
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PADDLE_ENFORCE_EQ(x->dims(), y->dims(),
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"Dimensions of SmoothL1LossOp's input and target "
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"must be same.");
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PADDLE_ENFORCE_GE(framework::arity(x->dims()), 2,
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"Tensor rank of SmoothL1LossOp's input must be "
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"at least 2.");
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auto* inside_weight = ctx.Input<framework::Tensor>("InsideWeight");
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if (inside_weight) {
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auto* outside_weight = ctx.Input<framework::Tensor>("OutsideWeight");
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PADDLE_ENFORCE_NOT_NULL(outside_weight,
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"If weights are provided, must specify both "
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"inside and outside weights.");
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PADDLE_ENFORCE_EQ(inside_weight->dims(), x->dims(),
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"Dimensions of inside weight must be same with input.");
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PADDLE_ENFORCE_EQ(
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outside_weight->dims(), x->dims(),
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"Dimensions of outside weight must be same with input.");
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}
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auto* diff = ctx.Output<framework::Tensor>("diff");
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auto* out = ctx.Output<framework::Tensor>("Out");
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diff->Resize(x->dims());
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// loss is a two-rank tensor
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out->Resize({x->dims()[0], 1});
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}
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};
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template <typename AttrType>
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class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SmoothL1LossOpMaker(framework::OpProto* proto,
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framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "Input of SmoothL1LossOp.");
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AddInput("Y", "Target of SmoothL1LossOp.");
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AddInput("InsideWeight", "Optional input to scale (X-Y).");
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AddInput("OutsideWeight", "Optinal input to scale smooth l1 loss.");
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AddOutput("diff", "Intermediate variable to cache Win*(X-Y).")
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.AsIntermediate();
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AddOutput("Out", "Final smooth l1 loss of inputs.");
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AddComment(R"DOC(
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Compute SmoothL1Loss for input and target.
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The equation is: Out = 0.5 * (sigma * (X - Y)) ^ 2 if abs(X - Y) < 1 / sigma^2
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abs(X - Y) - 0.5 / sigma^2 otherwise
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)DOC");
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AddAttr<AttrType>("sigma", "Hyper parameter, default value is 3.0 .")
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.SetDefault(3.0);
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}
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};
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class SmoothL1LossGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(const framework::InferShapeContext& ctx) const override {
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auto in_dims = ctx.Input<framework::Tensor>("X")->dims();
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auto out_dims =
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ctx.Input<framework::Tensor>(framework::GradVarName("Out"))->dims();
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auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
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PADDLE_ENFORCE_GE(framework::arity(out_dims), 2,
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"Tensor rank of output gradient should be 2.");
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PADDLE_ENFORCE_EQ(out_dims[0], in_dims[0],
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"First dimension of ouptut gradient must be "
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"same with input.");
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PADDLE_ENFORCE_EQ(out_dims[1], 1,
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"Second dimension of output gradient must be 1.");
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if (x_grad) x_grad->Resize(in_dims);
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if (y_grad) y_grad->Resize(in_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_OP(smooth_l1_loss, ops::SmoothL1LossOp,
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ops::SmoothL1LossOpMaker<float>, ops::SmoothL1LossGradOp);
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REGISTER_OP_CPU_KERNEL(
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smooth_l1_loss, ops::SmoothL1LossKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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smooth_l1_loss_grad,
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ops::SmoothL1LossGradKernel<paddle::platform::CPUPlace, float>);
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