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MNN/source/backend/opencl/core/ImageBufferConvertor.cpp

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//
// ImageBufferConvertor.cpp
// MNN
//
// Created by MNN on 2019/02/28.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "backend/opencl/core/ImageBufferConvertor.hpp"
namespace MNN {
namespace OpenCL {
bool convertNCHWBufferToImage(const Tensor *input, Tensor *output, cl::Kernel &bufferToImageKernel,
OpenCLRuntime *runtime, bool needWait) {
std::vector<int> outputShape = tensorShapeFormat(input);
uint32_t outputGlobalWorkSize[2] = {static_cast<uint32_t>(UP_DIV(outputShape[3], 4) * outputShape[2]),
static_cast<uint32_t>(outputShape[0] * outputShape[1])};
if (bufferToImageKernel.get() == nullptr) {
std::set<std::string> buildOptions;
bufferToImageKernel = runtime->buildKernel("buffer_to_image", "nchw_buffer_to_image", buildOptions);
}
uint32_t idx = 0;
bufferToImageKernel.setArg(idx++, outputGlobalWorkSize[0]);
bufferToImageKernel.setArg(idx++, outputGlobalWorkSize[1]);
bufferToImageKernel.setArg(idx++, openCLBuffer(input));
bufferToImageKernel.setArg(idx++, static_cast<uint32_t>(outputShape[1]));
bufferToImageKernel.setArg(idx++, static_cast<uint32_t>(outputShape[2]));
bufferToImageKernel.setArg(idx++, static_cast<uint32_t>(outputShape[3]));
bufferToImageKernel.setArg(idx++, openCLImage(output));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(bufferToImageKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(outputGlobalWorkSize[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(bufferToImageKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (true == needWait) {
event.wait();
}
return true;
}
bool convertNHWCBufferToImage(const Tensor *input, Tensor *output, cl::Kernel &bufferToImageKernel,
OpenCLRuntime *runtime, bool needWait) {
std::vector<int> outputShape = tensorShapeFormat(input);
uint32_t outputGlobalWorkSize[2] = {static_cast<uint32_t>(UP_DIV(outputShape[3], 4) * outputShape[2]),
static_cast<uint32_t>(outputShape[0] * outputShape[1])};
if (bufferToImageKernel.get() == nullptr) {
std::set<std::string> buildOptions;
bufferToImageKernel = runtime->buildKernel("buffer_to_image", "nhwc_buffer_to_image", buildOptions);
}
uint32_t idx = 0;
bufferToImageKernel.setArg(idx++, outputGlobalWorkSize[0]);
bufferToImageKernel.setArg(idx++, outputGlobalWorkSize[1]);
bufferToImageKernel.setArg(idx++, openCLBuffer(input));
bufferToImageKernel.setArg(idx++, static_cast<uint32_t>(outputShape[1]));
bufferToImageKernel.setArg(idx++, static_cast<uint32_t>(outputShape[2]));
bufferToImageKernel.setArg(idx++, static_cast<uint32_t>(outputShape[3]));
bufferToImageKernel.setArg(idx++, openCLImage(output));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(bufferToImageKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(outputGlobalWorkSize[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(bufferToImageKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (true == needWait) {
event.wait();
}
return true;
}
bool convertImageToNCHWBuffer(const Tensor *input, Tensor *output, cl::Kernel &imageToBufferKernel,
OpenCLRuntime *runtime, bool needWait) {
std::vector<int> inputShape = tensorShapeFormat(input);
uint32_t in_gws[2] = {static_cast<uint32_t>(UP_DIV(inputShape[3], 4) * inputShape[2]),
static_cast<uint32_t>(inputShape[0] * inputShape[1])};
if (imageToBufferKernel.get() == nullptr) {
std::set<std::string> buildOptions;
imageToBufferKernel = runtime->buildKernel("buffer_to_image", "image_to_nchw_buffer", buildOptions);
}
uint32_t idx = 0;
imageToBufferKernel.setArg(idx++, in_gws[0]);
imageToBufferKernel.setArg(idx++, in_gws[1]);
imageToBufferKernel.setArg(idx++, openCLBuffer(output));
imageToBufferKernel.setArg(idx++, static_cast<uint32_t>(inputShape[1]));
imageToBufferKernel.setArg(idx++, static_cast<uint32_t>(inputShape[2]));
imageToBufferKernel.setArg(idx++, static_cast<uint32_t>(inputShape[3]));
imageToBufferKernel.setArg(idx++, openCLImage(input));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(imageToBufferKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(in_gws[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(imageToBufferKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (true == needWait) {
event.wait();
}
return true;
}
bool convertNC4HW4BufferToImage(const Tensor *input, Tensor *output, cl::Kernel &bufferToImageKernel,
OpenCLRuntime *runtime, bool needWait) {
uint32_t outputGlobalWorkSize[2] = {static_cast<uint32_t>(UP_DIV(input->channel(), 4) * input->width()),
static_cast<uint32_t>(input->batch() * input->height())};
if (bufferToImageKernel.get() == nullptr) {
std::set<std::string> buildOptions;
bufferToImageKernel = runtime->buildKernel("buffer_to_image", "nc4hw4_buffer_to_image", buildOptions);
}
uint32_t idx = 0;
int outputImageShape[2] = {input->height(), input->width()};
bufferToImageKernel.setArg(idx++, outputGlobalWorkSize[0]);
bufferToImageKernel.setArg(idx++, outputGlobalWorkSize[1]);
bufferToImageKernel.setArg(idx++, openCLBuffer(input));
bufferToImageKernel.setArg(idx++, sizeof(outputImageShape), outputImageShape);
bufferToImageKernel.setArg(idx++, UP_DIV(input->channel(), 4));
bufferToImageKernel.setArg(idx++, openCLImage(output));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(bufferToImageKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(outputGlobalWorkSize[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(bufferToImageKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (true == needWait) {
event.wait();
}
return true;
}
/**
* @brief convert image to nc/4hwc%4 buffer.
* @param input input tensor.
* @param output output tensor.
* @param bufferToImageKernel opencl kernel reference.
* @param runtime opencl runtime instance pointer.
* @param needWait whether need wait opencl complete before return or not, default false.
* @return true if success, false otherwise.
*/
bool convertImageToNC4HW4Buffer(const Tensor *input, Tensor *output, cl::Kernel &imageToBufferKernel,
OpenCLRuntime *runtime, bool needWait) {
auto inputShape = tensorShapeFormat(input);
uint32_t in_gws[2] = {static_cast<uint32_t>(UP_DIV(inputShape.at(3), 4) * inputShape.at(2)),
static_cast<uint32_t>(inputShape.at(0) * inputShape.at(1))};
if (imageToBufferKernel.get() == nullptr) {
std::set<std::string> buildOptions;
imageToBufferKernel = runtime->buildKernel("buffer_to_image", "image_to_nc4hw4_buffer", buildOptions);
}
uint32_t idx = 0;
int outputImageShape[2] = {inputShape.at(1), inputShape.at(2)};
imageToBufferKernel.setArg(idx++, in_gws[0]);
imageToBufferKernel.setArg(idx++, in_gws[1]);
imageToBufferKernel.setArg(idx++, openCLBuffer(output));
imageToBufferKernel.setArg(idx++, sizeof(outputImageShape), outputImageShape);
imageToBufferKernel.setArg(idx++, static_cast<uint32_t>(UP_DIV(inputShape.at(3), 4)));
imageToBufferKernel.setArg(idx++, openCLImage(input));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(imageToBufferKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(in_gws[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(imageToBufferKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (true == needWait) {
event.wait();
}
return true;
}
bool convertImageToNHWCBuffer(const Tensor *input, Tensor *output, cl::Kernel &imageToBufferKernel,
OpenCLRuntime *runtime, bool needWait) {
std::vector<int> inputShape = tensorShapeFormat(input);
uint32_t in_gws[2] = {static_cast<uint32_t>(UP_DIV(inputShape[3], 4) * inputShape[2]),
static_cast<uint32_t>(inputShape[0] * inputShape[1])};
if (imageToBufferKernel.get() == nullptr) {
std::set<std::string> buildOptions;
imageToBufferKernel = runtime->buildKernel("buffer_to_image", "image_to_nhwc_buffer", buildOptions);
}
uint32_t idx = 0;
imageToBufferKernel.setArg(idx++, in_gws[0]);
imageToBufferKernel.setArg(idx++, in_gws[1]);
imageToBufferKernel.setArg(idx++, openCLBuffer(output));
imageToBufferKernel.setArg(idx++, static_cast<uint32_t>(inputShape[1]));
imageToBufferKernel.setArg(idx++, static_cast<uint32_t>(inputShape[2]));
imageToBufferKernel.setArg(idx++, static_cast<uint32_t>(inputShape[3]));
imageToBufferKernel.setArg(idx++, openCLImage(input));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(imageToBufferKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(in_gws[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(imageToBufferKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (true == needWait) {
event.wait();
}
return true;
}
bool ImageBufferConvertor::convertImageToBuffer(const Tensor *image, const OpenCLBufferFormat type, Tensor *buffer,
bool needWait) {
#ifdef LOG_VERBOSE
MNN_PRINT("start convertImageToBuffer !\n");
#endif
auto formattedBufferShape = tensorShapeFormat(image);
auto runtime = mOpenCLRuntime;
std::string kernelName;
if (type == NHWC_BUFFER) {
kernelName = "image_to_nhwc_buffer";
} else if (type == NCHW_BUFFER) {
kernelName = "image_to_nchw_buffer";
} else if (type == CONV2D_FILTER) {
kernelName = "conv2d_filter_image_to_buffer";
} else if (type == ARGUMENT) {
kernelName = "arg_image_to_buffer";
} else {
MNN_PRINT("not support such type !!! \n");
}
if (mImageToBufferKernel.get() == nullptr || mImageToBufferKernelName != kernelName) {
mImageToBufferKernelName = kernelName;
std::set<std::string> buildOptions;
mImageToBufferKernel = runtime->buildKernel("buffer_to_image", kernelName, buildOptions);
}
std::vector<size_t> gws;
getImageShape(formattedBufferShape, type, &gws);
uint32_t idx = 0;
mImageToBufferKernel.setArg(idx++, gws[0]);
mImageToBufferKernel.setArg(idx++, gws[1]);
mImageToBufferKernel.setArg(idx++, openCLBuffer(buffer));
if (type == CONV2D_FILTER) {
const int channelHeightWidthSumSize =
buffer->buffer().dim[1].extent * buffer->buffer().dim[2].extent * buffer->buffer().dim[3].extent;
const int heightWidthSumSize = buffer->buffer().dim[2].extent * buffer->buffer().dim[3].extent;
int kernelShape[2] = {buffer->buffer().dim[2].extent, buffer->buffer().dim[3].extent};
mImageToBufferKernel.setArg(idx++, static_cast<uint32_t>(buffer->buffer().dim[0].extent));
mImageToBufferKernel.setArg(idx++, sizeof(kernelShape), kernelShape);
mImageToBufferKernel.setArg(idx++, static_cast<uint32_t>(channelHeightWidthSumSize));
mImageToBufferKernel.setArg(idx++, static_cast<uint32_t>(heightWidthSumSize));
} else if (type == ARGUMENT) {
mImageToBufferKernel.setArg(idx++, static_cast<uint32_t>(buffer->buffer().dim[0].extent));
} else {
mImageToBufferKernel.setArg(idx++, static_cast<uint32_t>(formattedBufferShape[1]));
mImageToBufferKernel.setArg(idx++, static_cast<uint32_t>(formattedBufferShape[2]));
mImageToBufferKernel.setArg(idx++, static_cast<uint32_t>(formattedBufferShape[3]));
}
mImageToBufferKernel.setArg(idx++, openCLImage(image));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(mImageToBufferKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(gws[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(mImageToBufferKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (needWait) {
event.wait();
}
#ifdef LOG_VERBOSE
MNN_PRINT("end convertImageToBuffer !\n");
#endif
return true;
}
bool ImageBufferConvertor::convertBufferToImage(const Tensor *buffer, const OpenCLBufferFormat type, Tensor *image,
bool needWait) {
#ifdef LOG_VERBOSE
MNN_PRINT("start convertBufferToImage !\n");
#endif
auto formattedBufferShape = tensorShapeFormat(buffer);
std::vector<size_t> imageShape;
getImageShape(formattedBufferShape, type, &imageShape);
uint32_t gws[2] = {static_cast<uint32_t>(imageShape[0]), static_cast<uint32_t>(imageShape[1])};
auto runtime = mOpenCLRuntime;
std::string kernelName;
switch (type) {
case CONV2D_FILTER:
kernelName = "conv2d_filter_buffer_to_image";
break;
case CONV2D1x1_OPT_FILTER:
kernelName = "conv2d1x1_opt_filter_buffer_to_image";
break;
case DW_CONV2D_FILTER:
kernelName = "dw_filter_buffer_to_image";
break;
case NHWC_BUFFER:
kernelName = "nhwc_buffer_to_image";
break;
case NCHW_BUFFER:
kernelName = "nchw_buffer_to_image";
break;
case ARGUMENT:
kernelName = "arg_buffer_to_image";
break;
default:
break;
}
if (mBufferToImageKernel.get() == nullptr || mBufferToImageKernelName != kernelName) {
mBufferToImageKernelName = kernelName;
std::set<std::string> buildOptions;
mBufferToImageKernel = runtime->buildKernel("buffer_to_image", kernelName, buildOptions);
}
uint32_t idx = 0;
mBufferToImageKernel.setArg(idx++, gws[0]);
mBufferToImageKernel.setArg(idx++, gws[1]);
mBufferToImageKernel.setArg(idx++, openCLBuffer(buffer));
if (type == CONV2D_FILTER) {
const int channelHeightWidthSumSize =
buffer->buffer().dim[1].extent * buffer->buffer().dim[2].extent * buffer->buffer().dim[3].extent;
const int heightWidthSumSize = buffer->buffer().dim[2].extent * buffer->buffer().dim[3].extent;
int kernelShape[2] = {buffer->buffer().dim[2].extent, buffer->buffer().dim[3].extent};
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(buffer->buffer().dim[0].extent));
mBufferToImageKernel.setArg(idx++, sizeof(kernelShape),kernelShape);
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(channelHeightWidthSumSize));
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(heightWidthSumSize));
} else if (type == DW_CONV2D_FILTER) {
const int heightWidthSumSize = buffer->buffer().dim[2].extent * buffer->buffer().dim[3].extent;
int kernelShape[4] = {buffer->buffer().dim[0].extent, buffer->buffer().dim[1].extent, buffer->buffer().dim[2].extent, buffer->buffer().dim[3].extent};
mBufferToImageKernel.setArg(idx++, sizeof(kernelShape),kernelShape);
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(heightWidthSumSize));
} else if (type == ARGUMENT) {
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(buffer->buffer().dim[0].extent));
} else if(type == CONV2D1x1_OPT_FILTER){
const int channelHeightWidthSumSize =
buffer->buffer().dim[1].extent * buffer->buffer().dim[2].extent * buffer->buffer().dim[3].extent;
const int heightWidthSumSize = buffer->buffer().dim[2].extent * buffer->buffer().dim[3].extent;
int kernelShape[2] = {buffer->buffer().dim[2].extent, buffer->buffer().dim[3].extent};
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(buffer->buffer().dim[1].extent));
mBufferToImageKernel.setArg(idx++, sizeof(kernelShape),kernelShape);
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(channelHeightWidthSumSize));
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(heightWidthSumSize));
}else {
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(formattedBufferShape[1]));
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(formattedBufferShape[2]));
mBufferToImageKernel.setArg(idx++, static_cast<uint32_t>(formattedBufferShape[3]));
}
mBufferToImageKernel.setArg(idx++, openCLImage(image));
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(mBufferToImageKernel));
const std::vector<uint32_t> lws = {16, std::max((uint32_t)1, maxWorkGroupSize / 16)};
cl::Event event;
cl_int error;
std::vector<uint32_t> roundUpGroupWorkSize(lws.size());
for (size_t i = 0; i < lws.size(); ++i) {
roundUpGroupWorkSize[i] = ROUND_UP(gws[i], lws[i]);
}
error = runtime->commandQueue().enqueueNDRangeKernel(mBufferToImageKernel, cl::NullRange,
cl::NDRange(roundUpGroupWorkSize[0], roundUpGroupWorkSize[1]),
cl::NDRange(lws[0], lws[1]), nullptr, &event);
MNN_CHECK_CL_SUCCESS(error);
if (needWait) {
event.wait();
}
#ifdef LOG_VERBOSE
MNN_PRINT("end convertBufferToImage !\n");
#endif
return true;
}
} // namespace OpenCL
} // namespace MNN
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