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140 lines
4.6 KiB
140 lines
4.6 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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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(framework::OpProto* proto, framework::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(framework::GradVarName("MidOut")),
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"Input(MidOut@GRAD) 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(lrn, ops::LRNKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(lrn_grad,
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ops::LRNGradKernel<paddle::platform::CPUPlace, float>);
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