MNN/source/backend/opencl/execution/image/RoiPoolingExecution.cpp

130 lines
5.0 KiB
C++

//
// RoiPoolingExecution.cpp
// MNN
//
// Created by MNN on 2019/02/28.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "backend/opencl/execution/image/RoiPoolingExecution.hpp"
#include "core/Macro.h"
#include <float.h>
#include "core/TensorUtils.hpp"
namespace MNN {
namespace OpenCL {
RoiPooling::RoiPooling(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend) : CommonExecution(backend, op) {
#ifdef LOG_VERBOSE
MNN_PRINT("start RoiPooling init !\n");
#endif
mUnits.resize(1);
auto &unit = mUnits[0];
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
auto roi = op->main_as_RoiParameters();
mPooledWidth = roi->pooledWidth();
mPooledHeight = roi->pooledHeight();
mSpatialScale = roi->spatialScale();
mAreadySetArg = false;
std::set<std::string> buildOptions;
std::string kernelName = "roi_pooling";
std::vector<int> roiShape = tensorShapeFormat(inputs[1]);
const int roiHeight = roiShape.at(1);
const int roiWidth = roiShape.at(2);
const int roiChannels = roiShape.at(3);
if (roiWidth == 5) {
buildOptions.emplace("-DROI_C1H1W5");
}else if(roiChannels == 5){
buildOptions.emplace("-DROI_C5H1W1");
}
unit.kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("roi_pooling", kernelName, buildOptions, mOpenCLBackend->getPrecision());
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(unit.kernel));
#ifdef LOG_VERBOSE
MNN_PRINT("end RoiPooling init !\n");
#endif
}
ErrorCode RoiPooling::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto &unit = mUnits[0];
Tensor *input = inputs[0];
Tensor *output = outputs[0];
Tensor *roi = inputs[1];
std::vector<int> inputShape = tensorShapeFormat(input);
std::vector<int> outputShape = tensorShapeFormat(output);
std::vector<int> roiShape = tensorShapeFormat(roi);
const int batch = outputShape.at(0);
const int outputHeight = outputShape.at(1);
const int outputWidth = outputShape.at(2);
const int channels = outputShape.at(3);
const int inputBatch = inputShape.at(0);
const int inputHeight = inputShape.at(1);
const int inputWidth = inputShape.at(2);
const int inputChannels = inputShape.at(3);
int channelBlocks = (channels + 3) / 4;
std::vector<uint32_t> mGWS{1, 1, 1, 1};
std::vector<uint32_t> mLWS{1, 1, 1, 1};
mGWS = {static_cast<uint32_t>(channelBlocks),
static_cast<uint32_t>(outputWidth),
static_cast<uint32_t>(batch * outputHeight),
};
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
ret |= unit.kernel->get().setArg(idx++, mGWS[0]);
ret |= unit.kernel->get().setArg(idx++, mGWS[1]);
ret |= unit.kernel->get().setArg(idx++, mGWS[2]);
ret |= unit.kernel->get().setArg(idx++, openCLImage(input));
ret |= unit.kernel->get().setArg(idx++, openCLImage(roi));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(inputHeight));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(inputWidth));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(inputBatch));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(outputHeight));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(outputWidth));
ret |= unit.kernel->get().setArg(idx++, static_cast<float>(mSpatialScale));
ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
MNN_CHECK_CL_SUCCESS(ret, "setArg RoiPoolExecution");
mLWS = roiPoolingLocalWS(mGWS, mMaxWorkGroupSize);
mOpenCLBackend->recordKernel3d(unit.kernel, mGWS, mLWS);
unit.globalWorkSize = {mGWS[0], mGWS[1], mGWS[2]};
unit.localWorkSize = {mLWS[0], mLWS[1], mLWS[2]};
return NO_ERROR;
}
std::vector<uint32_t> RoiPooling::roiPoolingLocalWS(const std::vector<uint32_t> &gws, const uint32_t maxWorkGroupSize) {
std::vector<uint32_t> lws(4, 0);
GpuType gpuType = mOpenCLBackend->getOpenCLRuntime()->getGpuType();
uint32_t deviceComputeUnits = mOpenCLBackend->getOpenCLRuntime()->deviceComputeUnits();
int coreNum = deviceComputeUnits;
for (int i = 0, totalSizeNow = 1; i < gws.size(); ++i) {
int remain = gws[i] % coreNum, groupSize = gws[i] / coreNum;
if (remain == 0) {
lws[i] = groupSize;
} else {
while(groupSize) {
int remain = gws[i] % groupSize;
if (remain == 0 && (i > 0 || groupSize <= maxWorkGroupSize)) {
lws[i] = groupSize;
break;
}
--groupSize;
}
}
lws[i] = std::max<uint32_t>(std::min<uint32_t>(lws[i], maxWorkGroupSize / totalSizeNow), 1);
totalSizeNow *= lws[i];
}
return lws;
}
using RoiPoolingCreator = TypedCreator<RoiPooling>;
REGISTER_OPENCL_OP_CREATOR(RoiPoolingCreator, OpType_ROIPooling, IMAGE);
} // namespace OpenCL
} // namespace MNN