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tensorNet.cpp
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/*
* Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#include "tensorNet.h"
#include "randInt8Calibrator.h"
#include "cudaMappedMemory.h"
#include "cudaResize.h"
#include "filesystem.h"
#include "NvCaffeParser.h"
#include "NvInfer.h"
#include "NvInferPlugin.h"
#if NV_TENSORRT_MAJOR >= 5
#include "NvOnnxParser.h"
#include "NvUffParser.h"
// #include "NvInferPlugin.h"
#endif
#include <fstream>
#include <iostream>
#include <map>
#include <cassert>
#include <cublas_v2.h>
#include <cudnn.h>
#include <sstream>
#include <bits/stl_algo.h>
#include <bits/stl_numeric.h>
#define CHECK(status) \
do \
{ \
auto ret = (status); \
if (ret != 0) \
{ \
std::cout << "Cuda failure: " << ret << std::endl; \
abort(); \
} \
} while (0)
#if NV_TENSORRT_MAJOR > 1
# define CREATE_INFER_BUILDER nvinfer1::createInferBuilder
# define CREATE_INFER_RUNTIME nvinfer1::createInferRuntime
#else
# define CREATE_INFER_BUILDER createInferBuilder
# define CREATE_INFER_RUNTIME createInferRuntime
#endif
#define LOG_DOWNLOADER_TOOL " if loading a built-in model, maybe it wasn't downloaded before.\n\n" \
" Run the Model Downloader tool again and select it for download:\n\n" \
" $ cd <jetson-inference>/tools\n" \
" $ ./download-models.sh\n"
//---------------------------------------------------------------------
const char* precisionTypeToStr(precisionType type) {
switch (type) {
case TYPE_DISABLED:
return "DISABLED";
case TYPE_FASTEST:
return "FASTEST";
case TYPE_FP32:
return "FP32";
case TYPE_FP16:
return "FP16";
case TYPE_INT8:
return "INT8";
}
}
precisionType precisionTypeFromStr(const char* str) {
if (!str) return TYPE_DISABLED;
for (int n = 0; n < NUM_PRECISIONS; n++) {
if (strcasecmp(str, precisionTypeToStr((precisionType)n)) == 0)
return (precisionType)n;
}
return TYPE_DISABLED;
}
static inline nvinfer1::DataType precisionTypeToTRT(precisionType type) {
switch (type) {
case TYPE_FP16:
return nvinfer1::DataType::kHALF;
#if NV_TENSORRT_MAJOR >= 4
case TYPE_INT8:
return nvinfer1::DataType::kINT8;
#endif
}
return nvinfer1::DataType::kFLOAT;
}
static inline bool isFp16Enabled(nvinfer1::IBuilder* builder) {
#if NV_TENSORRT_MAJOR < 4
return builder->getHalf2Mode();
#else
return builder->getFp16Mode();
#endif
}
static inline bool isInt8Enabled(nvinfer1::IBuilder* builder) {
#if NV_TENSORRT_MAJOR >= 4
return builder->getInt8Mode();
#else
return false;
#endif
}
#if NV_TENSORRT_MAJOR >= 4
static inline const char* dataTypeToStr(nvinfer1::DataType type) {
switch (type) {
case nvinfer1::DataType::kFLOAT:
return "FP32";
case nvinfer1::DataType::kHALF:
return "FP16";
case nvinfer1::DataType::kINT8:
return "INT8";
case nvinfer1::DataType::kINT32:
return "INT32";
}
}
static inline const char* dimensionTypeToStr(nvinfer1::DimensionType type) {
switch (type) {
case nvinfer1::DimensionType::kSPATIAL:
return "SPATIAL";
case nvinfer1::DimensionType::kCHANNEL:
return "CHANNEL";
case nvinfer1::DimensionType::kINDEX:
return "INDEX";
case nvinfer1::DimensionType::kSEQUENCE:
return "SEQUENCE";
}
}
#endif
#if NV_TENSORRT_MAJOR > 1
static inline nvinfer1::Dims validateDims(const nvinfer1::Dims& dims) {
if (dims.nbDims == nvinfer1::Dims::MAX_DIMS) return dims;
nvinfer1::Dims dims_out = dims;
// TRT doesn't set the higher dims, so make sure they are 1
for (int n = dims_out.nbDims; n < nvinfer1::Dims::MAX_DIMS; n++)
dims_out.d[n] = 1;
return dims_out;
}
#endif
const char* deviceTypeToStr(deviceType type) {
switch (type) {
case DEVICE_GPU:
return "GPU";
case DEVICE_DLA_0:
return "DLA_0";
case DEVICE_DLA_1:
return "DLA_1";
}
}
deviceType deviceTypeFromStr(const char* str) {
if (!str) return DEVICE_GPU;
for (int n = 0; n < NUM_DEVICES; n++) {
if (strcasecmp(str, deviceTypeToStr((deviceType)n)) == 0)
return (deviceType)n;
}
if (strcasecmp(str, "DLA") == 0) return DEVICE_DLA;
return DEVICE_GPU;
}
#if NV_TENSORRT_MAJOR >= 5
static inline nvinfer1::DeviceType deviceTypeToTRT(deviceType type) {
switch (type) {
case DEVICE_GPU:
return nvinfer1::DeviceType::kGPU;
//case DEVICE_DLA: return nvinfer1::DeviceType::kDLA;
# if NV_TENSORRT_MAJOR == 5 && NV_TENSORRT_MINOR == 0 && NV_TENSORRT_PATCH == 0
case DEVICE_DLA_0:
return nvinfer1::DeviceType::kDLA0;
case DEVICE_DLA_1:
return nvinfer1::DeviceType::kDLA1;
# else
case DEVICE_DLA_0:
return nvinfer1::DeviceType::kDLA;
case DEVICE_DLA_1:
return nvinfer1::DeviceType::kDLA;
# endif
}
}
#endif
const char* modelFormatToStr(modelFormat format) {
switch (format) {
case MODEL_CUSTOM:
return "custom";
case MODEL_CAFFE:
return "caffe";
case MODEL_ONNX:
return "ONNX";
case MODEL_UFF:
return "UFF";
}
}
modelFormat modelFormatFromStr(const char* str) {
if (!str) return MODEL_CUSTOM;
if (strcasecmp(str, "caffemodel") == 0 || strcasecmp(str, "caffe") == 0)
return MODEL_CAFFE;
else if (strcasecmp(str, "onnx") == 0)
return MODEL_ONNX;
else if (strcasecmp(str, "uff") == 0)
return MODEL_UFF;
return MODEL_CUSTOM;
}
//---------------------------------------------------------------------
// constructor
tensorNet::tensorNet() {
mEngine = NULL;
mInfer = NULL;
mContext = NULL;
mStream = NULL;
mWidth = 0;
mHeight = 0;
mInputSize = 0;
mMaxBatchSize = 0;
mInputCPU = NULL;
mInputCUDA = NULL;
mEnableDebug = false;
mEnableProfiler = false;
mModelFormat = MODEL_CUSTOM;
mPrecision = TYPE_FASTEST;
mDevice = DEVICE_GPU;
mAllowGPUFallback = false;
memset(mEvents, 0, sizeof(mEvents));
#if NV_TENSORRT_MAJOR < 2
memset(&mInputDims, 0, sizeof(Dims3));
#endif
}
// Destructor
tensorNet::~tensorNet() {
if (mEngine != NULL) {
mEngine->destroy();
mEngine = NULL;
}
if (mInfer != NULL) {
mInfer->destroy();
mInfer = NULL;
}
}
// EnableProfiler
void tensorNet::EnableProfiler() {
mEnableProfiler = true;
if (mContext != NULL) mContext->setProfiler(&gProfiler);
}
// EnableDebug
void tensorNet::EnableDebug() { mEnableDebug = true; }
// DetectNativePrecisions()
std::vector<precisionType> tensorNet::DetectNativePrecisions(
deviceType device) {
std::vector<precisionType> types;
Logger logger;
// create a temporary builder for querying the supported types
nvinfer1::IBuilder* builder = CREATE_INFER_BUILDER(logger);
if (!builder) {
printf(LOG_TRT
"QueryNativePrecisions() failed to create TensorRT IBuilder "
"instance\n");
return types;
}
#if NV_TENSORRT_MAJOR >= 5
if (device == DEVICE_DLA_0 || device == DEVICE_DLA_1)
builder->setFp16Mode(true);
builder->setDefaultDeviceType(deviceTypeToTRT(device));
#endif
// FP32 is supported on all platforms
types.push_back(TYPE_FP32);
// detect fast (native) FP16
if (builder->platformHasFastFp16()) types.push_back(TYPE_FP16);
#if NV_TENSORRT_MAJOR >= 4
// detect fast (native) INT8
if (builder->platformHasFastInt8()) types.push_back(TYPE_INT8);
#endif
// print out supported precisions (optional)
const uint32_t numTypes = types.size();
printf(LOG_TRT "native precisions detected for %s: ",
deviceTypeToStr(device));
for (uint32_t n = 0; n < numTypes; n++) {
printf("%s", precisionTypeToStr(types[n]));
if (n < numTypes - 1) printf(", ");
}
printf("\n");
builder->destroy();
return types;
}
// DetectNativePrecision
bool tensorNet::DetectNativePrecision(const std::vector<precisionType>& types,
precisionType type) {
const uint32_t numTypes = types.size();
for (uint32_t n = 0; n < numTypes; n++) {
if (types[n] == type) return true;
}
return false;
}
// DetectNativePrecision
bool tensorNet::DetectNativePrecision(precisionType precision,
deviceType device) {
std::vector<precisionType> types = DetectNativePrecisions(device);
return DetectNativePrecision(types, precision);
}
// Create an optimized GIE network from caffe prototxt and model file
bool tensorNet::ProfileModel(const std::string& deployFile, // name for caffe prototxt
const std::string& modelFile, // name for model
const char* input, const Dims3& inputDims,
const std::vector<std::string>& outputs, // network outputs
unsigned int maxBatchSize, // batch size - NB must be at least as large as the batch we want to run with
precisionType precision,
deviceType device, bool allowGPUFallback,
nvinfer1::IInt8Calibrator* calibrator,
std::ostream& gieModelStream) // output stream for the GIE model
{
// create API root class - must span the lifetime of the engine usage
nvinfer1::IBuilder* builder = CREATE_INFER_BUILDER(gLogger);
nvinfer1::INetworkDefinition* network = builder->createNetwork();
builder->setDebugSync(mEnableDebug);
builder->setMinFindIterations(3); // allow time for TX1 GPU to spin up
builder->setAverageFindIterations(2);
//mEnableFP16 = (mOverride16 == true) ? false : builder->platformHasFastFp16();
//printf(LOG_TRT "platform %s fast FP16 support\n", mEnableFP16 ? "has" : "does not have");
printf(LOG_TRT "device %s, loading %s %s\n", deviceTypeToStr(device), deployFile.c_str(), modelFile.c_str());
// parse the different types of model formats
if( mModelFormat == MODEL_CAFFE )
{
// parse the caffe model to populate the network, then set the outputs
nvcaffeparser1::ICaffeParser* parser = nvcaffeparser1::createCaffeParser();
nvinfer1::DataType modelDataType = (precision == TYPE_FP16) ? nvinfer1::DataType::kHALF : nvinfer1::DataType::kFLOAT; // import INT8 weights as FP32
const nvcaffeparser1::IBlobNameToTensor* blobNameToTensor =
parser->parse(deployFile.c_str(), // caffe deploy file
modelFile.c_str(), // caffe model file
*network, // network definition that the parser will populate
modelDataType);
if( !blobNameToTensor )
{
printf(LOG_TRT "device %s, failed to parse caffe network\n", deviceTypeToStr(device));
return false;
}
// the caffe file has no notion of outputs, so we need to manually say which tensors the engine should generate
const size_t num_outputs = outputs.size();
for( size_t n=0; n < num_outputs; n++ )
{
nvinfer1::ITensor* tensor = blobNameToTensor->find(outputs[n].c_str());
if( !tensor )
printf(LOG_TRT "failed to retrieve tensor for Output \"%s\"\n", outputs[n].c_str());
else
{
#if NV_TENSORRT_MAJOR >= 4
nvinfer1::Dims3 dims = static_cast<nvinfer1::Dims3&&>(tensor->getDimensions());
printf(LOG_TRT "retrieved Output tensor \"%s\": %ix%ix%i\n", tensor->getName(), dims.d[0], dims.d[1], dims.d[2]);
#endif
}
network->markOutput(*tensor);
}
//parser->destroy();
}
#if NV_TENSORRT_MAJOR >= 5
else if( mModelFormat == MODEL_ONNX )
{
#if NV_TENSORRT_MAJOR >= 7
network->destroy();
network = builder->createNetworkV2(1U << (uint32_t)nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
if( !network )
{
printf(LOG_TRT "IBuilder::createNetworkV2(EXPLICIT_BATCH) failed\n");
return false;
}
#endif
nvonnxparser::IParser* parser = nvonnxparser::createParser(*network, gLogger);
if( !parser )
{
printf(LOG_TRT "failed to create nvonnxparser::IParser instance\n");
return false;
}
#if NV_TENSORRT_MAJOR == 5 && NV_TENSORRT_MINOR == 0
const int parserLogLevel = (int)nvinfer1::ILogger::Severity::kINFO;
#else
const int parserLogLevel = (int)nvinfer1::ILogger::Severity::kVERBOSE;
#endif
if( !parser->parseFromFile(modelFile.c_str(), parserLogLevel) )
{
printf(LOG_TRT "failed to parse ONNX model '%s'\n", modelFile.c_str());
return false;
}
//parser->destroy();
}
else if( mModelFormat == MODEL_UFF )
{
// create parser instance
nvuffparser::IUffParser* parser = nvuffparser::createUffParser();
if( !parser )
{
printf(LOG_TRT "failed to create UFF parser\n");
return false;
}
// register input
if( !parser->registerInput(input, inputDims, nvuffparser::UffInputOrder::kNCHW) )
{
printf(LOG_TRT "failed to register input '%s' for UFF model '%s'\n", input, modelFile.c_str());
return false;
}
// register outputs
/*const size_t numOutputs = outputs.size();
for( uint32_t n=0; n < numOutputs; n++ )
{
if( !parser->registerOutput(outputs[n].c_str()) )
printf(LOG_TRT "failed to register output '%s' for UFF model '%s'\n", outputs[n].c_str(), modelFile.c_str());
}*/
if( !parser->registerOutput("MarkOutput_0") )
printf(LOG_TRT "failed to register output '%s' for UFF model '%s'\n", "MarkOutput_0", modelFile.c_str());
// parse network
if( !parser->parse(modelFile.c_str(), *network, nvinfer1::DataType::kFLOAT) )
{
printf(LOG_TRT "failed to parse UFF model '%s'\n", modelFile.c_str());
return false;
}
//parser->destroy();
}
#endif
// build the engine
printf(LOG_TRT "device %s, configuring CUDA engine\n", deviceTypeToStr(device));
builder->setMaxBatchSize(maxBatchSize);
builder->setMaxWorkspaceSize(16 << 20);
// set up the builder for the desired precision
if( precision == TYPE_INT8 )
{
#if NV_TENSORRT_MAJOR >= 4
builder->setInt8Mode(true);
//builder->setFp16Mode(true); // TODO: experiment for benefits of both INT8/FP16
if( !calibrator )
{
// extract the dimensions of the network input blobs
std::map<std::string, nvinfer1::Dims3> inputDimensions;
for( int i=0, n=network->getNbInputs(); i < n; i++ )
{
nvinfer1::Dims dims = network->getInput(i)->getDimensions();
#if NV_TENSORRT_MAJOR >= 7
if( mModelType == MODEL_ONNX )
dims = shiftDims(dims); // change NCHW to CHW for EXPLICIT_BATCH
#endif
//nvinfer1::Dims3 dims = static_cast<nvinfer1::Dims3&&>(network->getInput(i)->getDimensions());
inputDimensions.insert(std::make_pair(network->getInput(i)->getName(), static_cast<nvinfer1::Dims3&&>(dims)));
std::cout << LOG_TRT << "retrieved Input tensor \"" << network->getInput(i)->getName() << "\": " << dims.d[0] << "x" << dims.d[1] << "x" << dims.d[2] << std::endl;
}
// default to random calibration
calibrator = new randInt8Calibrator(1, mCacheCalibrationPath, inputDimensions);
printf(LOG_TRT "warning: device %s using INT8 precision with RANDOM calibration\n", deviceTypeToStr(device));
}
builder->setInt8Calibrator(calibrator);
#else
printf(LOG_TRT "INT8 precision requested, and TensorRT %u.%u doesn't meet minimum version for INT8\n", NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR);
printf(LOG_TRT "please use minumum version of TensorRT 4.0 or newer for INT8 support\n");
return false;
#endif
}
else if( precision == TYPE_FP16 )
{
#if NV_TENSORRT_MAJOR < 4
builder->setHalf2Mode(true);
#else
builder->setFp16Mode(true);
#endif
}
// set the default device type
#if NV_TENSORRT_MAJOR >= 5
builder->setDefaultDeviceType(deviceTypeToTRT(device));
if( allowGPUFallback )
builder->allowGPUFallback(true);
#if !(NV_TENSORRT_MAJOR == 5 && NV_TENSORRT_MINOR == 0 && NV_TENSORRT_PATCH == 0)
if( device == DEVICE_DLA_0 )
builder->setDLACore(0);
else if( device == DEVICE_DLA_1 )
builder->setDLACore(1);
#endif
#else
if( device != DEVICE_GPU )
{
printf(LOG_TRT "device %s is not supported in TensorRT %u.%u\n", deviceTypeToStr(device), NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR);
return false;
}
#endif
// build CUDA engine
printf(LOG_TRT "device %s, building FP16: %s\n", deviceTypeToStr(device), isFp16Enabled(builder) ? "ON" : "OFF");
printf(LOG_TRT "device %s, building INT8: %s\n", deviceTypeToStr(device), isInt8Enabled(builder) ? "ON" : "OFF");
printf(LOG_TRT "device %s, building CUDA engine (this may take a few minutes the first time a network is loaded)\n", deviceTypeToStr(device));
nvinfer1::ICudaEngine* engine = builder->buildCudaEngine(*network);
if( !engine )
{
printf(LOG_TRT "device %s, failed to build CUDA engine\n", deviceTypeToStr(device));
return false;
}
printf(LOG_TRT "device %s, completed building CUDA engine\n", deviceTypeToStr(device));
// we don't need the network definition any more, and we can destroy the parser
network->destroy();
//parser->destroy();
// serialize the engine, then close everything down
#if NV_TENSORRT_MAJOR > 1
nvinfer1::IHostMemory* serMem = engine->serialize();
if( !serMem )
{
printf(LOG_TRT "device %s, failed to serialize CUDA engine\n", deviceTypeToStr(device));
return false;
}
gieModelStream.write((const char*)serMem->data(), serMem->size());
#else
engine->serialize(gieModelStream);
#endif
engine->destroy();
builder->destroy();
return true;
}
// FindFastestPrecision
precisionType tensorNet::FindFastestPrecision(deviceType device,
bool allowInt8) {
std::vector<precisionType> types = DetectNativePrecisions(device);
if (allowInt8 && DetectNativePrecision(types, TYPE_INT8))
return TYPE_INT8;
else if (DetectNativePrecision(types, TYPE_FP16))
return TYPE_FP16;
else
return TYPE_FP32;
}
// Create an optimized GIE network from caffe prototxt and model file
bool tensorNet::ProfileModel(
const std::string& deployFile, // name for caffe prototxt
const std::string& modelFile, // name for model
const std::vector<std::string>& outputs, // network outputs
unsigned int
maxBatchSize, // batch size - NB must be at least as large as the batch we want to run with
precisionType precision, deviceType device, bool allowGPUFallback,
nvinfer1::IInt8Calibrator* calibrator,
std::ostream& gieModelStream) // output stream for the GIE model
{
// create API root class - must span the lifetime of the engine usage
nvinfer1::IBuilder* builder = CREATE_INFER_BUILDER(gLogger);
nvinfer1::INetworkDefinition* network = builder->createNetwork();
builder->setDebugSync(mEnableDebug);
builder->setMinFindIterations(3); // allow time for TX1 GPU to spin up
builder->setAverageFindIterations(2);
//mEnableFP16 = (mOverride16 == true) ? false : builder->platformHasFastFp16();
//printf(LOG_GIE "platform %s fast FP16 support\n", mEnableFP16 ? "has" : "does not have");
printf(LOG_GIE "device %s, loading %s %s\n", deviceTypeToStr(device),
deployFile.c_str(), modelFile.c_str());
// parse the different types of model formats
if (mModelFormat == MODEL_CAFFE) {
// parse the caffe model to populate the network, then set the outputs
nvcaffeparser1::ICaffeParser* parser = nvcaffeparser1::createCaffeParser();
nvinfer1::DataType modelDataType =
(precision == TYPE_FP16)
? nvinfer1::DataType::kHALF
: nvinfer1::DataType::kFLOAT; // import INT8 weights as FP32
const nvcaffeparser1::IBlobNameToTensor* blobNameToTensor = parser->parse(
deployFile.c_str(), // caffe deploy file
modelFile.c_str(), // caffe model file
*network, // network definition that the parser will populate
modelDataType);
if (!blobNameToTensor) {
printf(LOG_GIE "device %s, failed to parse caffe network\n",
deviceTypeToStr(device));
return false;
}
// the caffe file has no notion of outputs, so we need to manually say which tensors the engine should generate
const size_t num_outputs = outputs.size();
for (size_t n = 0; n < num_outputs; n++) {
nvinfer1::ITensor* tensor = blobNameToTensor->find(outputs[n].c_str());
if (!tensor)
printf(LOG_GIE "failed to retrieve tensor for Output \"%s\"\n",
outputs[n].c_str());
else {
#if NV_TENSORRT_MAJOR >= 4
nvinfer1::Dims3 dims =
static_cast<nvinfer1::Dims3&&>(tensor->getDimensions());
printf(LOG_GIE "retrieved Output tensor \"%s\": %ix%ix%i\n",
tensor->getName(), dims.d[0], dims.d[1], dims.d[2]);
#endif
}
network->markOutput(*tensor);
}
//parser->destroy();
}
#if NV_TENSORRT_MAJOR >= 5
else if (mModelFormat == MODEL_ONNX) {
nvonnxparser::IParser* parser =
nvonnxparser::createParser(*network, gLogger);
if (!parser) {
printf(LOG_TRT "failed to create nvonnxparser::IParser instance\n");
return false;
}
if (!parser->parseFromFile(modelFile.c_str(),
(int)nvinfer1::ILogger::Severity::kWARNING)) {
printf(LOG_TRT "failed to parse ONNX model '%s'\n", modelFile.c_str());
return false;
}
//parser->destroy();
}
#endif
#if NV_TENSORRT_MAJOR >= 4
// extract the dimensions of the network input blobs
std::map<std::string, nvinfer1::Dims3> inputDimensions;
for (int i = 0, n = network->getNbInputs(); i < n; i++) {
nvinfer1::Dims3 dims =
static_cast<nvinfer1::Dims3&&>(network->getInput(i)->getDimensions());
inputDimensions.insert(
std::make_pair(network->getInput(i)->getName(), dims));
std::cout << LOG_TRT << "retrieved Input tensor \""
<< network->getInput(i)->getName() << "\": " << dims.d[0] << "x"
<< dims.d[1] << "x" << dims.d[2] << std::endl;
}
#endif
// build the engine
printf(LOG_GIE "device %s, configuring CUDA engine\n",
deviceTypeToStr(device));
builder->setMaxBatchSize(maxBatchSize);
builder->setMaxWorkspaceSize(16 << 20);
// set up the builder for the desired precision
if (precision == TYPE_INT8) {
#if NV_TENSORRT_MAJOR >= 4
builder->setInt8Mode(true);
//builder->setFp16Mode(true); // TODO: experiment for benefits of both INT8/FP16
if (!calibrator) {
calibrator =
new randInt8Calibrator(1, mCacheCalibrationPath, inputDimensions);
printf(
LOG_TRT
"warning: device %s using INT8 precision with RANDOM calibration\n",
deviceTypeToStr(device));
}
builder->setInt8Calibrator(calibrator);
#else
printf(LOG_TRT
"INT8 precision requested, and TensorRT %u.%u doesn't meet minimum "
"version for INT8\n",
NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR);
printf(LOG_TRT
"please use minumum version of TensorRT 4.0 or newer for INT8 "
"support\n");
return false;
#endif
} else if (precision == TYPE_FP16) {
#if NV_TENSORRT_MAJOR < 4
builder->setHalf2Mode(true);
#else
builder->setFp16Mode(true);
#endif
}
// set the default device type
#if NV_TENSORRT_MAJOR >= 5
builder->setDefaultDeviceType(deviceTypeToTRT(device));
if (allowGPUFallback) builder->allowGPUFallback(true);
# if !(NV_TENSORRT_MAJOR == 5 && NV_TENSORRT_MINOR == 0 && \
NV_TENSORRT_PATCH == 0)
if (device == DEVICE_DLA_0)
builder->setDLACore(0);
else if (device == DEVICE_DLA_1)
builder->setDLACore(1);
# endif
#else
if (device != DEVICE_GPU) {
printf(LOG_TRT "device %s is not supported in TensorRT %u.%u\n",
deviceTypeToStr(device), NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR);
return false;
}
#endif
// build CUDA engine
printf(LOG_TRT "device %s, building FP16: %s\n", deviceTypeToStr(device),
isFp16Enabled(builder) ? "ON" : "OFF");
printf(LOG_TRT "device %s, building INT8: %s\n", deviceTypeToStr(device),
isInt8Enabled(builder) ? "ON" : "OFF");
printf(LOG_GIE
"device %s, building CUDA engine (this may take a few minutes the "
"first time a network is loaded)\n",
deviceTypeToStr(device));
nvinfer1::ICudaEngine* engine = builder->buildCudaEngine(*network);
if (!engine) {
printf(LOG_GIE "device %s, failed to build CUDA engine\n",
deviceTypeToStr(device));
return false;
}
printf(LOG_GIE "device %s, completed building CUDA engine\n",
deviceTypeToStr(device));
// we don't need the network definition any more, and we can destroy the parser
network->destroy();
//parser->destroy();
// serialize the engine, then close everything down
#if NV_TENSORRT_MAJOR > 1
nvinfer1::IHostMemory* serMem = engine->serialize();
if (!serMem) {
printf(LOG_GIE "device %s, failed to serialize CUDA engine\n",
deviceTypeToStr(device));
return false;
}
gieModelStream.write((const char*)serMem->data(), serMem->size());
#else
engine->serialize(gieModelStream);
#endif
engine->destroy();
builder->destroy();
return true;
}
// LoadNetwork
bool tensorNet::LoadNetwork(const char* prototxt_path, const char* model_path,
const char* mean_path, const char* input_blob,
const char* output_blob, uint32_t maxBatchSize,
precisionType precision, deviceType device,
bool allowGPUFallback,
nvinfer1::IInt8Calibrator* calibrator,
cudaStream_t stream) {
std::vector<std::string> outputs;
outputs.push_back(output_blob);
return LoadNetwork(prototxt_path, model_path, mean_path, input_blob, outputs,
maxBatchSize, precision, device, allowGPUFallback);
}
// LoadNetwork
bool tensorNet::LoadNetwork(const char* prototxt_path_, const char* model_path_,
const char* mean_path, const char* input_blob,
const std::vector<std::string>& output_blobs,
uint32_t maxBatchSize, precisionType precision,
deviceType device, bool allowGPUFallback,
nvinfer1::IInt8Calibrator* calibrator,
cudaStream_t stream) {
if (/*!prototxt_path_ ||*/ !model_path_) return false;
#if NV_TENSORRT_MAJOR >= 4
printf(LOG_GIE "TensorRT version %u.%u.%u\n", NV_TENSORRT_MAJOR,
NV_TENSORRT_MINOR, NV_TENSORRT_PATCH);
#else
printf(LOG_GIE "TensorRT version %u.%u\n", NV_TENSORRT_MAJOR,
NV_TENSORRT_MINOR);
#endif
/*
* verify the prototxt and model paths
*/
const std::string model_path = locateFile(model_path_);
const std::string prototxt_path =
locateFile(prototxt_path_ != NULL ? prototxt_path_ : "");
const std::string model_ext = fileExtension(model_path_);
const modelFormat model_fmt = modelFormatFromStr(model_ext.c_str());
printf(LOG_TRT "detected model format - %s (extension '.%s')\n",
modelFormatToStr(model_fmt), model_ext.c_str());
if (model_fmt == MODEL_CUSTOM || model_fmt == MODEL_UFF) {
printf(LOG_TRT "model format '%s' not supported by jetson-inference\n",
modelFormatToStr(model_fmt));
return false;
}
#if NV_TENSORRT_MAJOR < 5
else if (model_fmt == MODEL_ONNX) {
printf(LOG_TRT
"importing ONNX models is not supported in TensorRT %u.%u (version "
">= 5.0 required)\n",
NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR);
return false;
}
#endif
else if (model_fmt == MODEL_CAFFE && !prototxt_path_) {
printf(LOG_TRT
"attempted to load caffe model without specifying prototxt file\n");
return false;
}
mModelFormat = model_fmt;
/*
* if the precision is left unspecified, detect the fastest
*/
printf(LOG_TRT "desired precision specified for %s: %s\n",
deviceTypeToStr(device), precisionTypeToStr(precision));
if (precision == TYPE_DISABLED) {
printf(LOG_TRT "skipping network specified with precision TYPE_DISABLE\n");
printf(LOG_TRT "please specify a valid precision to create the network\n");
return false;
} else if (precision == TYPE_FASTEST) {
if (!calibrator)
printf(LOG_TRT
"requested fasted precision for device %s without providing valid "
"calibrator, disabling INT8\n",
deviceTypeToStr(device));
precision = FindFastestPrecision(device, (calibrator != NULL));
printf(LOG_TRT "selecting fastest native precision for %s: %s\n",
deviceTypeToStr(device), precisionTypeToStr(precision));
} else {
if (!DetectNativePrecision(precision, device)) {
printf(LOG_TRT "precision %s is not supported for device %s\n",
precisionTypeToStr(precision), deviceTypeToStr(device));
return false;
}
if (precision == TYPE_INT8 && !calibrator)
printf(
LOG_TRT
"warning: device %s using INT8 precision with RANDOM calibration\n",
deviceTypeToStr(device));
}
/*
* attempt to load network from cache before profiling with tensorRT
*/
std::stringstream gieModelStream;
gieModelStream.seekg(0, gieModelStream.beg);
char cache_prefix[512];
char cache_path[512];
sprintf(cache_prefix, "%s.%u.%u.%s.%s", model_path.c_str(), maxBatchSize,
(uint32_t)allowGPUFallback, deviceTypeToStr(device),
precisionTypeToStr(precision));
sprintf(cache_path, "%s.calibration", cache_prefix);
mCacheCalibrationPath = cache_path;
sprintf(cache_path, "%s.engine", cache_prefix);
mCacheEnginePath = cache_path;
printf(LOG_GIE "attempting to open engine cache file %s\n",
mCacheEnginePath.c_str());
std::ifstream cache(mCacheEnginePath);
if (!cache) {
printf(LOG_GIE
"cache file not found, profiling network model on device %s\n",
deviceTypeToStr(device));
if (!ProfileModel(prototxt_path, model_path, output_blobs, maxBatchSize,
precision, device, allowGPUFallback, calibrator,
gieModelStream)) {
printf("device %s, failed to load %s\n", deviceTypeToStr(device),
model_path.c_str());
return 0;
}
printf(LOG_GIE "network profiling complete, writing engine cache to %s\n",
mCacheEnginePath.c_str());
std::ofstream outFile;
outFile.open(mCacheEnginePath);
outFile << gieModelStream.rdbuf();
outFile.close();
gieModelStream.seekg(0, gieModelStream.beg);
printf(LOG_GIE "device %s, completed writing engine cache to %s\n",
deviceTypeToStr(device), mCacheEnginePath.c_str());