forked from jiuyuan/InfiniTensor
54 lines
2.0 KiB
C++
54 lines
2.0 KiB
C++
#pragma once
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#include "core/operator.h"
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namespace infini {
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/**
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* @brief See https://arxiv.org/abs/1502.03167 for the detail of batch
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* normalization.
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*
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*/
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class BatchNormObj : public OperatorObj {
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float momentum, eps;
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bool trainingMode;
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public:
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/**
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* @brief Construct a new BatchNorm object.
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*
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* @param graph The computation graph that this operator belongs to.
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* @param input The input tensor of BatchNorm. For image data, the input
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* shape is usually [N, C, H, W].
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* @param output The output tensor of BatchNorm, which should have the same
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* shape as the input tensor.
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* @param mean The mean tensor, which has a shape of [C].
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* @param var The var tensor, which has a shape of [C].
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* @param scale The scale tensor, which has a shape of [C].
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* @param bias The bias tensor, which has a shape of [C].
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* @param momentum Factor used in computing the running mean and variance.
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* Default is 0.9.
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* @param eps The epsilon value to use to avoid division by zero. Default is
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* 1e-5.
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* @param trainingMode Set to true when used for training.
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*/
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BatchNormObj(GraphObj *graph, Tensor input, Tensor output, Tensor mean,
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Tensor var, Tensor scale, Tensor bias, float momentum = 0.9,
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float eps = 1e-5, bool trainingMode = false);
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OP_CLONE(BatchNormObj);
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optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
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std::string toString() const override;
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// output size will be 3 when training
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int numInputs() const override { return 5; }
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int numOutputs() const override { return outputs.size(); }
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float getMomentum() const { return momentum; }
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float getEps() const { return eps; }
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bool getTrainingMode() const { return trainingMode; }
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private:
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vector<int> getWorkloadVector() const override;
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vector<int> getOpAttrVector() const override;
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vector<DataType> inferDataType(const TensorVec &inputs) const override;
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};
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} // namespace infini
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