有关yolov4-tiny chunk部分的代码 #51
Comments
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@beizhengren 应该会有问题，你改过重新build下就好 |
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@enazoe 好嘞, 我先试试 |
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@enazoe 搞定了,谢谢! |
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@enazoe yolo-tensorrt/modules/chunk.cu Line 68 in cc405b0 最大的改动就是把 void Chunk::configurePlugin 中的代码移到了 configureWithFormat中. 完整的chunk_V2.cpp如下:
Click to expand#include <cmath>
#include <stdio.h>
#include <cassert>
#include <iostream>
#include "chunk_V2.h"
#include <cuda_runtime.h>
#define ASSERT(assertion) \
{ \
if (!(assertion)) \
{ \
std::cout<<"ASSERTION FAILED in " \
<<__FILE__<<":"<<__LINE__ \
<<std::endl; \
abort(); \
} \
}
#define NV_CUDA_CHECK(status) \
{ \
if (status != 0) \
{ \
std::cout << "Cuda failure: " << cudaGetErrorString(status) << " in file " << __FILE__ \
<< " at line " << __LINE__ << std::endl; \
abort(); \
} \
}
namespace nvinfer1
{
ChunkV2::ChunkV2()
{
}
ChunkV2::ChunkV2(const void* buffer, size_t size)
{
assert(size == sizeof(_n_size_split));
_n_size_split = *reinterpret_cast<const int*>(buffer);
}
ChunkV2::~ChunkV2()
{
}
int ChunkV2::getNbOutputs() const
{
return 2;
}
Dims ChunkV2::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
{
assert(nbInputDims == 1);
assert(index == 0 || index == 1);
return Dims3(inputs[0].d[0] / 2, inputs[0].d[1], inputs[0].d[2]);
}
int ChunkV2::initialize()
{
return 0;
}
void ChunkV2::terminate()
{
}
size_t ChunkV2::getWorkspaceSize(int maxBatchSize) const
{
return 0;
}
size_t ChunkV2::getSerializationSize() const
{
return sizeof(_n_size_split);
}
void ChunkV2::serialize(void *buffer)const
{
*reinterpret_cast<int*>(buffer) = _n_size_split;
}
bool ChunkV2::supportsFormat(DataType type, PluginFormat format) const
{
return ((type == DataType::kFLOAT || type == DataType::kHALF || type == DataType::kINT8) &&
(format == PluginFormat::kNCHW));
}
// Set plugin namespace
void ChunkV2::setPluginNamespace(const char* pluginNamespace)
{
_s_plugin_namespace = pluginNamespace;
}
const char* ChunkV2::getPluginNamespace() const
{
return _s_plugin_namespace.c_str();
}
// Configure the layer with input and output data types.
void ChunkV2::configureWithFormat(
const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs,
DataType type, PluginFormat format, int maxBatchSize){
_n_size_split = inputDims->d[0] / 2 * inputDims->d[1] * inputDims->d[2] *sizeof(float);
std::cerr << _n_size_split << std::endl;
ASSERT(format == PluginFormat::kNCHW);
ASSERT(type == DataType::kFLOAT || type == DataType::kHALF);
//mDataType = type;
ASSERT(inputDims[0].nbDims >= 1); // number of dimensions of the input tensor must be >=1
}
const char* ChunkV2::getPluginType()const
{
return "CHUNK_TRT";
}
const char* ChunkV2::getPluginVersion() const
{
return "1.0";
}
void ChunkV2::destroy()
{
delete this;
}
// Clone the plugin
IPluginV2* ChunkV2::clone() const
{
ChunkV2 *p = new ChunkV2();
p->_n_size_split = _n_size_split;
p->setPluginNamespace(_s_plugin_namespace.c_str());
return p;
}
int ChunkV2::enqueue(int batchSize,
const void* const* inputs,
void** outputs,
void* workspace,
cudaStream_t stream)
{
for (int b = 0; b < batchSize; ++b)
{
NV_CUDA_CHECK(cudaMemcpy((char*)outputs[0] + b * _n_size_split, (char*)inputs[0] + b * 2 * _n_size_split, _n_size_split, cudaMemcpyDeviceToDevice));
NV_CUDA_CHECK(cudaMemcpy((char*)outputs[1] + b * _n_size_split, (char*)inputs[0] + b * 2 * _n_size_split + _n_size_split, _n_size_split, cudaMemcpyDeviceToDevice));
}
return 0;
}
PluginFieldCollection ChunkV2PluginCreator::_fc{};
std::vector<PluginField> ChunkV2PluginCreator::_vec_plugin_attributes;
ChunkV2PluginCreator::ChunkV2PluginCreator()
{
_vec_plugin_attributes.clear();
_fc.nbFields = _vec_plugin_attributes.size();
_fc.fields = _vec_plugin_attributes.data();
}
const char* ChunkV2PluginCreator::getPluginName() const
{
return "CHUNK_TRT";
}
const char* ChunkV2PluginCreator::getPluginVersion() const
{
return "1.0";
}
const PluginFieldCollection* ChunkV2PluginCreator::getFieldNames()
{
return &_fc;
}
IPluginV2* ChunkV2PluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
{
ChunkV2* obj = new ChunkV2();
obj->setPluginNamespace(_s_name_space.c_str());
return obj;
}
IPluginV2* ChunkV2PluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
{
ChunkV2* obj = new ChunkV2(serialData,serialLength);
obj->setPluginNamespace(_s_name_space.c_str());
return obj;
}
void ChunkV2PluginCreator::setPluginNamespace(const char* libNamespace)
{
_s_name_space = libNamespace;
}
const char* ChunkV2PluginCreator::getPluginNamespace() const
{
return _s_name_space.c_str();
}
REGISTER_TENSORRT_PLUGIN(ChunkV2PluginCreator);
}//namespace nvinfer1 |
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@beizhengren 额，这个我不太清楚，fp16精度不用特意实现吧，fp32实现，序列化的时候会自动设成fp16 |
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@enazoe 哦哦哦, 这样啊. 那int8应该和fp16的实现过程差不多吧? 只是多了一步setInt8Calibrator |
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@beizhengren 是的，目前来看是这样的 |
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@enazoe |
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@enazoe yolo-tensorrt/modules/yolo.cpp Line 462 in 1110190 创建 half 的engine的时候 会执行下面的语句报错 yolo-tensorrt/modules/chunk.cu Line 68 in cc405b0 您有空能试一下吗?我尝试了很多办法,没有搞定. 非常感谢! |
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Hi. Anyone can explain this issue as English ? Actually, I have same problem in below line :( yolo-tensorrt/modules/chunk.cu Line 68 in cc405b0 My env;
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@enazoe 作者您好,
关于yolov4-tiny部分, 我想把IPluginV2IOExt 换成IPluginV2, 然后支持tensorrt5. 如下:
trt7:
trt5:
请问这样换完之后(版本暂且成为trt5), 是不是需要用trt5生成新的engine之后, 才能做推断.
trt5直接加载 原始的trt7的已经转好的模型 推断会不会有问题呢?
谢谢!
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