mirror of https://github.com/alibaba/MNN.git
135 lines
4.5 KiB
C++
135 lines
4.5 KiB
C++
//
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// CropExecution.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 "execution/CropExecution.hpp"
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#include "Macro.h"
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#include "TensorUtils.hpp"
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#include "core/OpenCLBackend.hpp"
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#include "core/OpenCLRunningUtils.hpp"
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namespace MNN {
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namespace OpenCL {
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CropExecution::CropExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend)
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: Execution(backend) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("Start CropExecution init !\n");
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#endif
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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auto cropParam = op->main_as_Crop();
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mAxis = cropParam->axis();
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int offsetSize = cropParam->offset()->size();
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mOffsets.resize(offsetSize);
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for (int i = 0; i < offsetSize; ++i) {
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mOffsets[i] = cropParam->offset()->data()[i];
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}
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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if (mKernel.get() == nullptr) {
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std::set<std::string> buildOptions;
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std::string kernelName = "crop";
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mKernel = runtime->buildKernel("crop", kernelName, buildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(mKernel));
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}
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#ifdef LOG_VERBOSE
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MNN_PRINT("end CropExecution init !\n");
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#endif
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}
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ErrorCode CropExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("Start CropExecution onResize !\n");
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#endif
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#ifdef LOG_VERBOSE
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MNN_PRINT("end CropExecution onResize !\n");
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#endif
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return NO_ERROR;
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}
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ErrorCode CropExecution::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 CropExecution onExecute !\n");
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#endif
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Tensor *input = inputs[0];
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Tensor *input1 = inputs[1];
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Tensor *output = outputs[0];
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std::vector<int> inputShape = tensorShapeFormat(input);
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std::vector<int> outputShape = tensorShapeFormat(output);
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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 inputChannel = inputShape.at(3);
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const int outputBatch = 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 outputChannel = outputShape.at(3);
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const int inputDim = input->buffer().dimensions;
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std::vector<int> offsets(inputDim, 0);
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for (int i = 0; i < inputDim; ++i) {
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int cropOffset = 0;
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if (i >= mAxis) {
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if (mOffsets.size() == 1) {
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cropOffset = mOffsets[0];
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} else if (mOffsets.size() > 1) {
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cropOffset = mOffsets[i - mAxis];
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}
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MNN_ASSERT(input->buffer().dim[i].extent - cropOffset >= input1->buffer().dim[i].extent);
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}
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offsets[i] = cropOffset;
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}
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uint32_t outputGlobalWorkSize[2] = {static_cast<uint32_t>(UP_DIV(outputChannel, 4) * outputWidth),
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static_cast<uint32_t>(outputBatch * outputHeight)};
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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{
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uint32_t idx = 0;
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mKernel.setArg(idx++, outputGlobalWorkSize[0]);
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mKernel.setArg(idx++, outputGlobalWorkSize[1]);
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mKernel.setArg(idx++, openCLImage(input));
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mKernel.setArg(idx++, openCLImage(output));
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mKernel.setArg(idx++, inputHeight);
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mKernel.setArg(idx++, inputWidth);
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mKernel.setArg(idx++, offsets[0]); // offset n
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mKernel.setArg(idx++, offsets[2]); // offset h
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mKernel.setArg(idx++, offsets[3]); // offset w
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mKernel.setArg(idx++, offsets[1]); // offset c4
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mKernel.setArg(idx++, outputHeight);
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mKernel.setArg(idx++, outputWidth);
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}
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const std::vector<uint32_t> lws = {16, mMaxWorkGroupSize / 16};
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cl::Event event;
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std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
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for (size_t i = 0; i < lws.size(); ++i) {
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roundUpGroupWorkSize[i] = ROUND_UP(outputGlobalWorkSize[i], std::max((uint32_t)1, lws[i]));
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}
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runtime->commandQueue().enqueueNDRangeKernel(mKernel, cl::NullRange,
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cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
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cl::NDRange(lws[0], lws[1]), nullptr, &event);
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#ifdef LOG_VERBOSE
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MNN_PRINT("end CropExecution onExecute !\n");
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#endif
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return NO_ERROR;
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}
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OpenCLCreatorRegister<TypedCreator<CropExecution>> __crop_op(OpType_Crop);
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} // namespace OpenCL
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} // namespace MNN
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