mirror of https://github.com/alibaba/MNN.git
118 lines
4.3 KiB
C++
118 lines
4.3 KiB
C++
//
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// GridSampleBufExecution.cpp
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// MNN
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//
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// Created by MNN on 2021/08/11.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#ifndef MNN_OPENCL_BUFFER_CLOSED
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#include "backend/opencl/execution/buffer/GridSampleBufExecution.hpp"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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namespace OpenCL {
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GridSampleBufExecution::GridSampleBufExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend)
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: Execution(backend) {
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mPaddingMode = op->main_as_GridSample()->paddingMode();
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if (op->main_as_GridSample()->alignCorners()) {
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mAlignCorners = 1;
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}
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else {
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mAlignCorners = 0;
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}
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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auto gridSampleParam = op->main_as_GridSample();
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std::set<std::string> buildOptions;
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if (op->main_as_GridSample()->mode() == 0) {
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mKernelName = "bilinear_buf";
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mKernel = runtime->buildKernel("grid_sample_buf", mKernelName, buildOptions);
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}
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else {
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mKernelName = "nearest_buf";
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mKernel = runtime->buildKernel("grid_sample_buf", mKernelName, buildOptions);
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}
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(mKernel));
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}
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ErrorCode GridSampleBufExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto inputTensor = inputs[0];
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auto gridTensor = inputs[1];
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auto outputTensor = outputs[0];
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auto runtime = ((OpenCLBackend *)backend())->getOpenCLRuntime();
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const int batches = inputTensor->buffer().dim[0].extent;
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const int channels = inputTensor->buffer().dim[1].extent;
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const int inH = inputTensor->buffer().dim[2].extent;
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const int inW = inputTensor->buffer().dim[3].extent;
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const int channelC4 = UP_DIV(channels, 4);
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const int outH = outputTensor->buffer().dim[2].extent;
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const int outW = outputTensor->buffer().dim[3].extent;
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mGlobalWorkSize = {
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static_cast<uint32_t>(channelC4),
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static_cast<uint32_t>(outW),
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static_cast<uint32_t>(outH * batches)
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};
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MNN_ASSERT(outW > 0 && outH > 0);
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uint32_t idx = 0;
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mKernel.setArg(idx++, mGlobalWorkSize[0]);
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mKernel.setArg(idx++, mGlobalWorkSize[1]);
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mKernel.setArg(idx++, mGlobalWorkSize[2]);
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mKernel.setArg(idx++, openCLBuffer(inputTensor));
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mKernel.setArg(idx++, openCLBuffer(gridTensor));
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mKernel.setArg(idx++, openCLBuffer(outputTensor));
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mKernel.setArg(idx++, static_cast<uint32_t>(inH));
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mKernel.setArg(idx++, static_cast<uint32_t>(inW));
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mKernel.setArg(idx++, static_cast<uint32_t>(outH));
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mKernel.setArg(idx++, static_cast<uint32_t>(outW));
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mKernel.setArg(idx++, static_cast<uint32_t>(channelC4));
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mKernel.setArg(idx++, mPaddingMode);
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mKernel.setArg(idx++, mAlignCorners);
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mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runtime, mKernelName, mKernel).first;
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return NO_ERROR;
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}
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ErrorCode GridSampleBufExecution::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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#ifdef ENABLE_OPENCL_TIME_PROFILER
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cl::Event event;
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run3DKernelDefault(mKernel, mGlobalWorkSize, mLocalWorkSize,
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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 GridSample\n", costTime);
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#else
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run3DKernelDefault(mKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
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#endif
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return NO_ERROR;
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}
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class GridSampleBufCreator :public OpenCLBackend::Creator {
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public:
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virtual ~GridSampleBufCreator() = default;
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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const MNN::Op *op, Backend *backend) const override {
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if (op->main_as_GridSample()->mode() != 0 && op->main_as_GridSample()->mode() != 1) {
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MNN_PRINT("openCL buffer not support interpolate type: %d, fallback to cpu\n", op->main_as_GridSample()->mode());
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return nullptr;
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}
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return new GridSampleBufExecution(inputs, op, backend);
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}
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};
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OpenCLCreatorRegister<GridSampleBufCreator> __GridSampleBuf_op_(OpType_GridSample, BUFFER);
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} // namespace OpenCL
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} // namespace MNN
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#endif // MNN_OPENCL_BUFFER_CLOSED
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