mirror of https://github.com/alibaba/MNN.git
135 lines
4.6 KiB
C++
135 lines
4.6 KiB
C++
//
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// RoiPoolingExecution.cpp
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// MNN
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//
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// Created by MNN on 2019/02/28.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/opencl/execution/image/RoiPoolingExecution.hpp"
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#include "core/Macro.h"
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#include <float.h>
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#include "core/TensorUtils.hpp"
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namespace MNN {
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namespace OpenCL {
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RoiPooling::RoiPooling(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend) : Execution(backend) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("start RoiPooling init !\n");
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#endif
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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auto roi = op->main_as_RoiParameters();
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mPooledWidth = roi->pooledWidth();
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mPooledHeight = roi->pooledHeight();
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mSpatialScale = roi->spatialScale();
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mAreadySetArg = false;
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std::set<std::string> buildOptions;
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std::string kernelName = "roi_pooling";
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mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("roi_pooling", kernelName, buildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(mKernel));
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#ifdef LOG_VERBOSE
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MNN_PRINT("end RoiPooling init !\n");
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#endif
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}
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ErrorCode RoiPooling::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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Tensor *input = inputs[0];
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Tensor *output = outputs[0];
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Tensor *roi = inputs[1];
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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std::vector<int> inputShape = tensorShapeFormat(input);
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std::vector<int> outputShape = tensorShapeFormat(output);
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std::vector<int> roiShape = tensorShapeFormat(roi);
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const int batch = outputShape.at(0);
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const int outputHeight = outputShape.at(1);
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const int outputWidth = outputShape.at(2);
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const int channels = outputShape.at(3);
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const int inputBatch = inputShape.at(0);
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const int inputHeight = inputShape.at(1);
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const int inputWidth = inputShape.at(2);
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const int inputChannels = inputShape.at(3);
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int channelBlocks = (channels + 3) / 4;
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mGWS = {static_cast<uint32_t>(channelBlocks),
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static_cast<uint32_t>(outputWidth),
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static_cast<uint32_t>(batch * outputHeight),
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};
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uint32_t idx = 0;
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mKernel.setArg(idx++, mGWS[0]);
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mKernel.setArg(idx++, mGWS[1]);
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mKernel.setArg(idx++, mGWS[2]);
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mKernel.setArg(idx++, openCLImage(input));
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mKernel.setArg(idx++, openCLImage(roi));
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mKernel.setArg(idx++, static_cast<int32_t>(inputHeight));
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mKernel.setArg(idx++, static_cast<int32_t>(inputWidth));
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mKernel.setArg(idx++, static_cast<int32_t>(channels));
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mKernel.setArg(idx++, static_cast<int32_t>(roiShape.at(1)));
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mKernel.setArg(idx++, static_cast<float>(mSpatialScale));
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mKernel.setArg(idx++, openCLImage(output));
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mLWS = roiPoolingLocalWS(mGWS, mMaxWorkGroupSize);
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return NO_ERROR;
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}
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std::vector<uint32_t> RoiPooling::roiPoolingLocalWS(const std::vector<uint32_t> &gws, const uint32_t maxWorkGroupSize) {
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std::vector<uint32_t> lws(4, 0);
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GpuType gpuType = mOpenCLBackend->getOpenCLRuntime()->getGpuType();
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uint32_t deviceComputeUnits = mOpenCLBackend->getOpenCLRuntime()->deviceComputeUnits();
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int coreNum = deviceComputeUnits;
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for (int i = 0, totalSizeNow = 1; i < gws.size(); ++i) {
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int remain = gws[i] % coreNum, groupSize = gws[i] / coreNum;
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if (remain == 0) {
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lws[i] = groupSize;
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} else {
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while(groupSize) {
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int remain = gws[i] % groupSize;
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if (remain == 0 && (i > 0 || groupSize <= maxWorkGroupSize)) {
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lws[i] = groupSize;
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break;
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}
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--groupSize;
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}
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}
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lws[i] = std::max<uint32_t>(std::min<uint32_t>(lws[i], maxWorkGroupSize / totalSizeNow), 1);
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totalSizeNow *= lws[i];
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}
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return lws;
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}
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ErrorCode RoiPooling::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("start RoiPooling onExecute !\n");
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#endif
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#ifdef ENABLE_OPENCL_TIME_PROFILER
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cl::Event event;
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run3DKernelDefault(mKernel, mGWS, mLWS,
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mOpenCLBackend->getOpenCLRuntime(), &event);
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int costTime = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
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MNN_PRINT("kernel cost:%d us RoiPooling\n",costTime);
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#else
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run3DKernelDefault(mKernel, mGWS, mLWS, mOpenCLBackend->getOpenCLRuntime());
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#endif
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#ifdef LOG_VERBOSE
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MNN_PRINT("end RoiPooling onExecute !\n");
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#endif
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return NO_ERROR;
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}
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OpenCLCreatorRegister<TypedCreator<RoiPooling>> __roi_pooling_op(OpType_ROIPooling, IMAGE);
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} // namespace OpenCL
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} // namespace MNN
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