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237 lines
8.2 KiB
237 lines
8.2 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/lrn_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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template <typename T>
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struct LRNFunctor<platform::CPUDeviceContext, T> {
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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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auto x_v = framework::EigenVector<T>::Flatten(input);
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const int start = -(n - 1) / 2;
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const int end = start + n;
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auto e_mid = framework::EigenTensor<T, 4>::From(*mid);
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e_mid = e_mid.constant(k);
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auto e_x = framework::EigenTensor<T, 4>::From(input);
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for (int m = 0; m < N; m++) {
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for (int i = 0; i < C; i++) {
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for (int c = start; c <= end; c++) {
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int ch = i + c;
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if (ch >= 0 && ch < C) {
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auto s = e_mid.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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auto r = e_x.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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s += alpha * r.square();
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}
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}
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}
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}
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auto out_e = framework::EigenVector<T>::Flatten(*out);
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out_e = x_v * e_mid.reshape(Eigen::DSizes<int, 1>(e_mid.size())).pow(-beta);
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}
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};
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template struct LRNFunctor<platform::CPUDeviceContext, float>;
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template struct LRNFunctor<platform::CPUDeviceContext, double>;
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template <typename T>
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struct LRNGradFunctor<platform::CPUDeviceContext, T> {
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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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T ratio = -2 * alpha * beta;
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auto x_g_e = framework::EigenVector<T>::Flatten(*x_g);
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x_g_e = x_g_e.constant(0.0);
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auto e_x = framework::EigenTensor<T, 4>::From(x);
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auto e_x_g = framework::EigenTensor<T, 4>::From(*x_g);
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auto e_out = framework::EigenTensor<T, 4>::From(out);
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auto e_out_g = framework::EigenTensor<T, 4>::From(out_g);
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auto e_mid = framework::EigenTensor<T, 4>::From(mid);
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const int start = -(n - 1) / 2;
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const int end = start + n;
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for (int m = 0; m < N; m++) {
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for (int i = 0; i < C; i++) {
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auto i_x = e_x.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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auto i_x_g = e_x_g.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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auto i_out_g = e_out_g.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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auto i_mid = e_mid.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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i_x_g = i_mid.pow(-beta) * i_out_g;
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for (int c = start; c <= end; c++) {
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int ch = i + c;
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if (ch < 0 || ch >= C) {
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continue;
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}
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auto c_out = e_out.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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auto c_mid = e_mid.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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auto c_out_g = e_out_g.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
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Eigen::array<int, 4>({{1, 1, H, W}}));
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i_x_g += ratio * c_out_g * c_out * i_x / c_mid;
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}
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}
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}
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}
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};
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template struct LRNGradFunctor<platform::CPUDeviceContext, float>;
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template struct LRNGradFunctor<platform::CPUDeviceContext, double>;
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class LRNOp : 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(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LRNOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of LRNOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("MidOut"),
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"MidOut(Out) of LRNOp should not be null.");
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auto x_dim = ctx->GetInputDim("X");
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PADDLE_ENFORCE_EQ(x_dim.size(), 4, "Input(X)'rank of LRNOp should be 4.");
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ctx->SetOutputDim("Out", x_dim);
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ctx->SetOutputDim("MidOut", x_dim);
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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template <typename T>
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class LRNOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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LRNOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"(Tensor) The input of LRN operator. "
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"It must be a 4D tenor with NCHW format.");
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AddOutput("Out",
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"(Tensor) The output of LRN operator, which is also the 4D "
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"tensor with NCHW format.");
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AddOutput("MidOut",
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"(Tensor) Middle result of LRN operator. It's computed in "
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"forward process and also used in backward process.");
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AddAttr<int>("n",
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"(int default 5) "
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"n is the \"adjacent\" kernel that maps "
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"at the same spatial position.")
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.SetDefault(5)
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.GreaterThan(0);
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AddAttr<T>("k",
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"(float, default 2.0) "
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"k is the bias.")
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.SetDefault(2.0)
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.GreaterThan(0.0);
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AddAttr<T>("alpha",
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"(float, default 0.0001) "
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"alpha is the scale number.")
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.SetDefault(0.0001)
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.GreaterThan(0.0);
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AddAttr<T>("beta",
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"(float, default 0.75) "
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"beta is the power number.")
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.SetDefault(0.75)
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.GreaterThan(0.0);
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AddComment(R"DOC(
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Local Response Normalization Operator.
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This operator comes from the paper:
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<<ImageNet Classification with Deep Convolutional Neural Networks>>.
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The original formula is:
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$$
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Output(i, x, y) = Input(i, x, y) / \left(
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k + \alpha \sum\limits^{\min(C, c + n/2)}_{j = \max(0, c - n/2)}
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(Input(j, x, y))^2
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\right)^{\beta}
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$$
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Function implementation:
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Inputs and outpus are in NCHW format, while input.shape.ndims() equals 4.
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And dimensions 0 ~ 3 represent batch size, feature maps, rows,
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and columns, respectively.
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Input and Output in the formula above is for each map(i) of one image, and
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Input(i, x, y), Output(i, x, y) represents an element in an image.
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C is the number of feature maps of one image. n is a hyper-parameter
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configured when operator is initialized. The sum in the denominator
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is the sum of the same positions in the neighboring maps.
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)DOC");
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}
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};
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class LRNOpGrad : 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(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasInput("MidOut"), "Input(MidOut) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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ctx->SetOutputDim(framework::GradVarName("X"), x_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(lrn, ops::LRNOp, ops::LRNOpMaker<float>, lrn_grad, ops::LRNOpGrad);
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REGISTER_OP_CPU_KERNEL(
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lrn, ops::LRNKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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lrn_grad, ops::LRNGradKernel<paddle::platform::CPUDeviceContext, float>);
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