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131 lines
4.1 KiB
131 lines
4.1 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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#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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#include "paddle/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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template <typename place, typename T>
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struct LRNFunctor {
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void operator()(const framework::ExecutionContext& ctx,
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const framework::Tensor& input, framework::Tensor* out,
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framework::Tensor* mid, int N, int C, int H, int W, int n,
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T k, T alpha, T beta);
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};
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template <typename DeviceContext, typename T>
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class LRNKernel : public framework::OpKernel<T> {
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public:
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using Tensor = framework::Tensor;
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// f(x) = x * ( k + alpha * SUM((x)^2) )^(-beta)
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// x represents inputs
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// f(x) represents outputs
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void Compute(const framework::ExecutionContext& ctx) const override {
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// input
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const Tensor& x = *ctx.Input<Tensor>("X");
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auto x_dims = x.dims();
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// NCHW
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int N = x_dims[0];
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int C = x_dims[1];
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int H = x_dims[2];
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int W = x_dims[3];
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Tensor* out = ctx.Output<Tensor>("Out");
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out->mutable_data<T>(ctx.GetPlace());
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// MidOut save the intermediate result for backward
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Tensor* mid = ctx.Output<Tensor>("MidOut");
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mid->mutable_data<T>(ctx.GetPlace());
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int n = ctx.Attr<int>("n");
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T alpha = ctx.Attr<float>("alpha");
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T beta = ctx.Attr<float>("beta");
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T k = ctx.Attr<float>("k");
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PADDLE_ENFORCE(n > 0, "n should >= 0");
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PADDLE_ENFORCE(alpha >= 0.0, "alpha should >= 0.0");
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PADDLE_ENFORCE(beta >= 0.0, "beta should >= 0.0");
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PADDLE_ENFORCE(k >= 0.0, "k should >= 0.0");
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LRNFunctor<DeviceContext, T> f;
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f(ctx, x, out, mid, N, C, H, W, n, k, alpha, beta);
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}
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};
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template <typename DeviceContext, typename T>
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struct LRNGradFunctor {
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void operator()(const framework::ExecutionContext& ctx,
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const framework::Tensor& x, const framework::Tensor& out,
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const framework::Tensor& mid, framework::Tensor* x_g,
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const framework::Tensor& out_g, int N, int C, int H, int W,
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int n, T alpha, T beta);
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};
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/**
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* \brief Backward calculation for normalization with across maps.
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*
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* Function implementation:
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*
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* The implementation of this Function is derived from the
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* CrossMapNormalFunc implementation.
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*
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* InputGrad = OutputGrad * MidOut ^ (-beta)
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* -- upper
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* + > (OutputGrad * OutputValue * (-2 * alpha * beta) / MidOut) * InputValue
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* -- lower
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*
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* The data of inputs/outputs format is the same as the forward interface
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* and is NCHW.
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*
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* The upper and lower is the same as forward. The logic of the sum
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* is also the same as forward.
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*/
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template <typename DeviceContext, typename T>
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class LRNGradKernel : public framework::OpKernel<T> {
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public:
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using Tensor = framework::Tensor;
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void Compute(const framework::ExecutionContext& ctx) const override {
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const Tensor& x = *ctx.Input<Tensor>("X");
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const Tensor& out = *ctx.Input<Tensor>("Out");
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const Tensor& out_g = *ctx.Input<Tensor>(framework::GradVarName("Out"));
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const Tensor& mid = *ctx.Input<Tensor>("MidOut");
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auto x_g = ctx.Output<Tensor>(framework::GradVarName("X"));
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x_g->mutable_data<T>(ctx.GetPlace());
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auto x_dims = x.dims();
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int N = x_dims[0];
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int C = x_dims[1];
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int H = x_dims[2];
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int W = x_dims[3];
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int n = ctx.Attr<int>("n");
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T alpha = ctx.Attr<T>("alpha");
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T beta = ctx.Attr<T>("beta");
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LRNGradFunctor<DeviceContext, T> f;
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f(ctx, x, out, mid, x_g, out_g, N, C, H, W, n, alpha, beta);
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}
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};
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} // namespace operators
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} // namespace paddle
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