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142 lines
4.7 KiB
142 lines
4.7 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", R"DOC(
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(Tensor) The input of LRN operator. It must be a 4D tenor with NCHW format.
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)DOC");
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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", R"Doc(
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(Tensor)Middle result of lrn op.It's computed in forward process
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and also used in backward process.
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)Doc");
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AddAttr<int>("n", R"DOC(
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(int, default 5)n is “adjacent” kernel maps at the same spatial position.
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)DOC")
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.SetDefault(5)
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.GreaterThan(0);
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AddAttr<T>("k", R"DOC(
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(float, default 2.0)k is the bias.
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)DOC")
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.SetDefault(2.0)
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.GreaterThan(0.0);
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AddAttr<T>("alpha", R"DOC(
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(float, default 0.0001)alpha is the scale number.
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)DOC")
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.SetDefault(0.0001)
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.GreaterThan(0.0);
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AddAttr<T>("beta", R"DOC(
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(float, default 0.75)beta is the power number.
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)DOC")
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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.
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This Function 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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Input(i, x, y)
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Output(i, x, y) = ----------------------------------------------
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-- upper
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(k + alpha * > (Input(j, x, y))^2) ^ (beta)
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-- j = lower
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upper is `min(C, c + n/2)`
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lower if `max(0, c - n/2)`
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Function implementation:
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inputs and outpus is NCHW format, while input.shape.ndims() is equal 4.
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And the meaning of each dimension(0-3) is respectively batch size,
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feature maps, rows and columns.
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Input and Output in the above formula 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, and n is a hyper-parameters
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is configured when Function is initialized. The sum in the denominator
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is the sum of the same position 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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