MNN/source/shape/ShapeReduction.cpp

60 lines
1.8 KiB
C++

//
// ShapeReduction.cpp
// MNN
//
// Created by MNN on 2019/01/10.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "Macro.h"
#include "SizeComputer.hpp"
namespace MNN {
class ReductionComputer : public SizeComputer {
public:
virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {
MNN_ASSERT(1 == inputs.size());
MNN_ASSERT(1 == outputs.size());
auto output = outputs[0];
auto reduce = op->main_as_ReductionParam();
output->setType(reduce->dType());
if (nullptr == reduce->dim()) {
output->buffer().dimensions = 0;
return true;
}
std::set<int> reduceDimSet;
for (int i = 0; i < reduce->dim()->size(); ++i) {
reduceDimSet.insert(reduce->dim()->data()[i]);
}
auto input = inputs[0];
const int inputDimensions = input->dimensions();
if (reduceDimSet.find(-1) != reduceDimSet.end()) {
// dim set have -1 which mean applying reduction on last dimension
reduceDimSet.erase(-1);
reduceDimSet.insert(inputDimensions - 1);
}
std::vector<int> newDims;
for (int i = 0; i < inputDimensions; ++i) {
if (reduceDimSet.find(i) == reduceDimSet.end()) {
newDims.push_back(input->length(i));
} else if (reduce->keepDims()) {
newDims.push_back(1);
}
}
output->buffer().dimensions = (int)newDims.size();
for (int i = 0; i < newDims.size(); ++i) {
output->buffer().dim[i].extent = newDims[i];
output->buffer().dim[i].flags = 0;
}
return true;
}
};
REGISTER_SHAPE(ReductionComputer, OpType_Reduction);
} // namespace MNN