mirror of https://github.com/alibaba/MNN.git
71 lines
2.5 KiB
C++
71 lines
2.5 KiB
C++
//
|
|
// ShapeReduction.cpp
|
|
// MNN
|
|
//
|
|
// Created by MNN on 2019/01/10.
|
|
// Copyright © 2018, Alibaba Group Holding Limited
|
|
//
|
|
|
|
#include "core/Macro.h"
|
|
#include "core/SizeComputer.hpp"
|
|
#include "core/TensorUtils.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() || 2 == inputs.size());
|
|
MNN_ASSERT(1 == outputs.size());
|
|
|
|
auto output = outputs[0];
|
|
TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
|
|
auto reduce = op->main_as_ReductionParam();
|
|
output->buffer().type = inputs[0]->buffer().type;
|
|
if (nullptr == reduce->dim() && inputs.size() == 1) {
|
|
output->buffer().dimensions = 0;
|
|
return true;
|
|
}
|
|
std::set<int> reduceDimSet;
|
|
if (nullptr != reduce->dim()) {
|
|
for (int i = 0; i < reduce->dim()->size(); ++i) {
|
|
reduceDimSet.insert(reduce->dim()->data()[i]);
|
|
}
|
|
} else {
|
|
auto input1 = inputs[1];
|
|
auto size = input1->elementSize();
|
|
auto dims = input1->host<int32_t>();
|
|
for (int i = 0; i < size; ++i) {
|
|
reduceDimSet.insert(dims[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];
|
|
}
|
|
TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
|
|
|
|
return true;
|
|
}
|
|
};
|
|
|
|
REGISTER_SHAPE_INPUTS(ReductionComputer, OpType_Reduction, {1});
|
|
} // namespace MNN
|