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107 lines
4.1 KiB
107 lines
4.1 KiB
7 years ago
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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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Indicesou 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/norm_op.h"
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namespace paddle {
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namespace operators {
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class NormOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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NormOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"X",
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"(Tensor) The input tensor of norm operator. "
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"The format of input tensor is NCHW. Where N is batch size, C is the "
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"number of channels, H and W is the height and width of feature.");
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AddInput("Scale",
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"(Tensor) The input tensor of norm operator. "
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"The format of input tensor is C * 1.");
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AddAttr<float>("epsilon",
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"(float, default 1e-10) Constant "
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"for numerical stability.")
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.SetDefault(1.0e-10f);
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AddOutput("Out",
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"(Tensor) The output tensor of norm operator."
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"N * M."
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"M = C * H * W");
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AddComment(R"DOC(
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"Input shape: $(N, C, H, W)$
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Sclae shape: $(C, 1)$
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Output shape: $(N, C, H, W)$
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Where
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forward
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$$
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[\frac {x_{1}}{\sqrt{\sum{x_{i}^{2}}}} \frac {x_{2}}{\sqrt{\sum{x_{i}^{2}}}} \frac {x_{3}}{\sqrt{\sum{x_{i}^{2}}}} \cdot \cdot \cdot \frac {x_{n}}{\sqrt{\sum{x_{i}^{2}}}}]
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$$
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backward
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$$
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\frac{\frac{\mathrm{d}L }{\mathrm{d}y_{1}} - \frac {x_{1}\sum {\frac{\mathrm{d} L}{\mathrm{d} y_{j}}}x_{j}}{\sum x_{j}^{2}} }{\sqrt{\sum{x_{j}^{2}}}}
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$$
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)DOC");
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}
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};
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class NormOp : public framework::OperatorWithKernel {
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protected:
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framework::OpKernelType GetKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
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ctx.device_context());
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}
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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("X"),
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"Input(X) of NormOp"
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"should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of NormOp should not be null.");
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auto in_x_dims = ctx->GetInputDim("X");
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ctx->SetOutputDim("Out", in_x_dims);
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}
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};
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class NormOpGrad : public framework::OperatorWithKernel {
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protected:
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framework::OpKernelType GetKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
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ctx.device_context());
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}
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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("X"), "Input(X) must not be null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Input(X@GRAD) should not be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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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(norm, ops::NormOp, ops::NormOpMaker, norm_grad, ops::NormOpGrad);
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REGISTER_OP_CPU_KERNEL(
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norm, ops::NormKernel<paddle::platform::CPUDeviceContext, float>,
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ops::NormKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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norm_grad, ops::NormGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::NormGradKernel<paddle::platform::CPUDeviceContext, double>);
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