mirror of https://github.com/alibaba/MNN.git
117 lines
4.8 KiB
C++
117 lines
4.8 KiB
C++
//
|
|
// InterpBufExecution.cpp
|
|
// MNN
|
|
//
|
|
// Created by MNN on 2019/02/28.
|
|
// Copyright © 2018, Alibaba Group Holding Limited
|
|
//
|
|
|
|
#ifndef MNN_OPENCL_BUFFER_CLOSED
|
|
|
|
#include "backend/opencl/execution/buffer/InterpBufExecution.hpp"
|
|
|
|
namespace MNN {
|
|
namespace OpenCL {
|
|
|
|
InterpBufExecution::InterpBufExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend) : CommonExecution(backend, op) {
|
|
mUnits.resize(1);
|
|
auto &unit = mUnits[0];
|
|
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
|
|
auto runtime = mOpenCLBackend->getOpenCLRuntime();
|
|
auto interpParam = op->main_as_Interp();
|
|
mCordTransform[0] = interpParam->widthScale();
|
|
mCordTransform[1] = interpParam->widthOffset();
|
|
mCordTransform[2] = interpParam->heightScale();
|
|
mCordTransform[3] = interpParam->heightOffset();
|
|
|
|
std::set<std::string> buildOptions;
|
|
if (op->main_as_Interp()->resizeType() == 1) {
|
|
mKernelName = "nearest_buf";
|
|
unit.kernel = runtime->buildKernel("interp_buf", mKernelName, buildOptions, mOpenCLBackend->getPrecision());
|
|
} else if(op->main_as_Interp()->resizeType() == 4) {
|
|
mKernelName = "nearest_buf";
|
|
buildOptions.emplace("-DUSE_ROUND");
|
|
unit.kernel = runtime->buildKernel("interp_buf", mKernelName, buildOptions, mOpenCLBackend->getPrecision());
|
|
}else {
|
|
mKernelName = "bilinear_buf";
|
|
unit.kernel = runtime->buildKernel("interp_buf", mKernelName, buildOptions, mOpenCLBackend->getPrecision());
|
|
}
|
|
|
|
mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
|
|
}
|
|
|
|
ErrorCode InterpBufExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
|
|
auto &unit = mUnits[0];
|
|
Tensor *input = inputs[0];
|
|
Tensor *output = outputs[0];
|
|
auto runtime = ((OpenCLBackend *)backend())->getOpenCLRuntime();
|
|
|
|
std::vector<int> inputShape = tensorShapeFormat(input);
|
|
std::vector<int> outputShape = tensorShapeFormat(output);
|
|
|
|
const int inputBatch = input->batch();
|
|
const int inputHeight = input->height();
|
|
const int inputWidth = input->width();
|
|
const int inputChannels = input->channel();
|
|
|
|
const int channelBlocks = UP_DIV(inputChannels, 4);
|
|
|
|
const int outputHeight = output->height();
|
|
const int outputWidth = output->width();
|
|
|
|
mGWS = {static_cast<uint32_t>(channelBlocks),
|
|
static_cast<uint32_t>(outputWidth),
|
|
static_cast<uint32_t>(outputHeight * inputBatch)};
|
|
|
|
MNN_ASSERT(outputHeight > 0 && outputWidth > 0);
|
|
|
|
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++, openCLBuffer(input));
|
|
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
|
|
ret |= unit.kernel->get().setArg(idx++, mCordTransform[2]);
|
|
ret |= unit.kernel->get().setArg(idx++, mCordTransform[0]);
|
|
ret |= unit.kernel->get().setArg(idx++, mCordTransform[3]);
|
|
ret |= unit.kernel->get().setArg(idx++, mCordTransform[1]);
|
|
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>(outputHeight));
|
|
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(outputWidth));
|
|
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(inputBatch));
|
|
MNN_CHECK_CL_SUCCESS(ret, "setArg InterpBufExecution");
|
|
|
|
mLWS = localWS3DDefault(mGWS, mMaxWorkGroupSize, runtime, mKernelName, unit.kernel, mOpenCLBackend->getCLTuneLevel(), "interp_buf").first;
|
|
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;
|
|
|
|
}
|
|
|
|
class InterpBufCreator : public OpenCLBackend::Creator {
|
|
public:
|
|
virtual ~InterpBufCreator() = default;
|
|
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs, const MNN::Op *op, Backend *backend) const override {
|
|
for (int i = 0; i < inputs.size(); ++i) {
|
|
TensorUtils::setTensorSupportPack(inputs[i], false);
|
|
}
|
|
for (int i = 0; i < outputs.size(); ++i) {
|
|
TensorUtils::setTensorSupportPack(outputs[i], false);
|
|
}
|
|
if(op->main_as_Interp()->resizeType() == 3) {
|
|
MNN_PRINT("openCL buffer not support interp type:%d, fallback to cpu\n", op->main_as_Interp()->resizeType());
|
|
return nullptr;
|
|
}
|
|
return new InterpBufExecution(inputs, op, backend);
|
|
}
|
|
};
|
|
|
|
REGISTER_OPENCL_OP_CREATOR(InterpBufCreator, OpType_Interp, BUFFER);
|
|
|
|
} // namespace OpenCL
|
|
} // namespace MNN
|
|
#endif /* MNN_OPENCL_BUFFER_CLOSED */
|