InfiniTensor/include/operators/batch_norm.h

54 lines
2.0 KiB
C++

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