mirror of https://github.com/alibaba/MNN.git
100 lines
4.6 KiB
C++
100 lines
4.6 KiB
C++
//
|
|
// ConvolutionGroup.cpp
|
|
// MNN
|
|
//
|
|
// Created by MNN on 2018/08/06.
|
|
// Copyright © 2018, Alibaba Group Holding Limited
|
|
//
|
|
|
|
#include "backend/cpu/compute/ConvolutionGroup.hpp"
|
|
#include "backend/cpu/compute/CommonOptFunction.h"
|
|
#include "core/Macro.h"
|
|
#include "core/TensorUtils.hpp"
|
|
|
|
namespace MNN {
|
|
ConvolutionGroup::ConvolutionGroup(Backend *b, const std::vector<std::shared_ptr<Execution>> &subConvolution)
|
|
: MNN::Execution(b) {
|
|
mSubConvolution = subConvolution;
|
|
MNN_ASSERT(subConvolution.size() > 1);
|
|
|
|
mInputRaw.reset(new Tensor(4));
|
|
mInputUnit.reset(new Tensor(4, Tensor::CAFFE_C4));
|
|
mOutputRaw.reset(new Tensor(4));
|
|
mOutputUnit.reset(new Tensor(4, Tensor::CAFFE_C4));
|
|
|
|
mInputUnitWrap.push_back(mInputUnit.get());
|
|
mOutputUnitWrap.push_back(mOutputUnit.get());
|
|
}
|
|
|
|
ErrorCode ConvolutionGroup::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
|
|
auto ib = inputs[0]->buffer();
|
|
auto ob = outputs[0]->buffer();
|
|
::memcpy(mInputRaw->buffer().dim, ib.dim, ib.dimensions * sizeof(halide_dimension_t));
|
|
mInputRaw->buffer().dimensions = ib.dimensions;
|
|
mInputRaw->buffer().dim[0].extent = 1;
|
|
|
|
::memcpy(mInputUnit->buffer().dim, ib.dim, ib.dimensions * sizeof(halide_dimension_t));
|
|
mInputUnit->buffer().dimensions = ib.dimensions;
|
|
mInputUnit->buffer().dim[1].extent = ib.dim[1].extent / mSubConvolution.size();
|
|
mInputUnit->buffer().dim[0].extent = 1;
|
|
TensorUtils::getDescribe(mInputUnit.get())->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
|
|
TensorUtils::setLinearLayout(mInputUnit.get());
|
|
|
|
::memcpy(mOutputRaw->buffer().dim, ob.dim, ob.dimensions * sizeof(halide_dimension_t));
|
|
mOutputRaw->buffer().dimensions = ob.dimensions;
|
|
mOutputRaw->buffer().dim[0].extent = 1;
|
|
|
|
::memcpy(mOutputUnit->buffer().dim, ob.dim, ob.dimensions * sizeof(halide_dimension_t));
|
|
mOutputUnit->buffer().dimensions = ob.dimensions;
|
|
mOutputUnit->buffer().dim[1].extent = ob.dim[1].extent / mSubConvolution.size();
|
|
mOutputUnit->buffer().dim[0].extent = 1;
|
|
TensorUtils::getDescribe(mOutputUnit.get())->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
|
|
TensorUtils::setLinearLayout(mOutputUnit.get());
|
|
|
|
backend()->onAcquireBuffer(mOutputUnit.get(), Backend::DYNAMIC);
|
|
backend()->onAcquireBuffer(mInputUnit.get(), Backend::DYNAMIC);
|
|
backend()->onAcquireBuffer(mInputRaw.get(), Backend::DYNAMIC);
|
|
backend()->onAcquireBuffer(mOutputRaw.get(), Backend::DYNAMIC);
|
|
|
|
for (auto &iter : mSubConvolution) {
|
|
iter->onResize(mInputUnitWrap, mOutputUnitWrap);
|
|
}
|
|
|
|
backend()->onReleaseBuffer(mOutputUnit.get(), Backend::DYNAMIC);
|
|
backend()->onReleaseBuffer(mInputUnit.get(), Backend::DYNAMIC);
|
|
backend()->onReleaseBuffer(mInputRaw.get(), Backend::DYNAMIC);
|
|
backend()->onReleaseBuffer(mOutputRaw.get(), Backend::DYNAMIC);
|
|
|
|
return NO_ERROR;
|
|
}
|
|
|
|
ErrorCode ConvolutionGroup::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
|
|
auto input = inputs[0];
|
|
auto output = outputs[0];
|
|
int batch = input->buffer().dim[0].extent;
|
|
auto inputBatchSize = input->width() * input->height() * ALIGN_UP4(input->channel());
|
|
auto outputBatchSize = output->width() * output->height() * ALIGN_UP4(output->channel());
|
|
|
|
for (int b = 0; b < batch; ++b) {
|
|
auto srcOrigin = input->host<float>() + b * inputBatchSize;
|
|
auto dstOrigin = output->host<float>() + b * outputBatchSize;
|
|
|
|
MNNUnpackC4(mInputRaw->host<float>(), srcOrigin, input->width() * input->height(), input->channel());
|
|
int inputGroupSize = input->width() * input->height() * input->channel() / mSubConvolution.size();
|
|
int outputGroupSize = output->width() * output->height() * output->channel() / mSubConvolution.size();
|
|
int subInputChannel = input->channel() / mSubConvolution.size();
|
|
int subOutputChannel = output->channel() / mSubConvolution.size();
|
|
for (int group = 0; group < mSubConvolution.size(); ++group) {
|
|
MNNPackC4(mInputUnit->host<float>(), mInputRaw->host<float>() + group * inputGroupSize,
|
|
input->width() * input->height(), subInputChannel);
|
|
mSubConvolution[group]->onExecute(mInputUnitWrap, mOutputUnitWrap);
|
|
MNNUnpackC4(mOutputRaw->host<float>() + group * outputGroupSize, mOutputUnit->host<float>(),
|
|
output->width() * output->height(), subOutputChannel);
|
|
}
|
|
MNNPackC4(dstOrigin, mOutputRaw->host<float>(), output->width() * output->height(), output->channel());
|
|
}
|
|
|
|
return NO_ERROR;
|
|
}
|
|
} // namespace MNN
|