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inceptionv3 migrated to trt7

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This commit is contained in:
wang-xinyu 2020-11-01 16:09:53 +08:00
parent df45e75182
commit be6c1165b1
5 changed files with 555 additions and 389 deletions
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1
README.md
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@ -15,7 +15,6 @@ All the models are implemented in pytorch or mxnet first, and export a weights f
- `21 Sep 2020`. [BaofengZan](https://github.com/BaofengZan) added hrnet classification and step by step tutorial(Chinese).
- `16 Sep 2020`. [hwh-hit](https://github.com/hwh-hit) added ufld(Ultra-Fast-Lane-Detection, ECCV2020).
- `13 Sep 2020`. Add crnn, and got 1000fps on GTX1080.
- `7 Sep 2020`. Implement retinaface(mobilenet0.25), and got 333fps on GTX1080.
- `28 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) added a tutorial for compiling and running tensorrtx on windows.
- `16 Aug 2020`. [upczww](https://github.com/upczww) added a python wrapper for yolov5.
- `14 Aug 2020`. Update yolov5 to v3.0 release.

9
inceptionv3/CMakeLists.txt
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@ -9,8 +9,13 @@ set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
include_directories(${PROJECT_SOURCE_DIR}/include)
include_directories(/usr/local/cuda-9.0/targets/aarch64-linux/include)
link_directories(/usr/local/cuda-9.0/targets/aarch64-linux/lib)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# tensorrt
include_directories(/usr/include/x86_64-linux-gnu/)
link_directories(/usr/lib/x86_64-linux-gnu/)
add_executable(inception ${PROJECT_SOURCE_DIR}/inception_v3.cpp)
target_link_libraries(inception nvinfer)

356
inceptionv3/common.h
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@ -1,356 +0,0 @@
#ifndef _TRT_COMMON_H_
#define _TRT_COMMON_H_
#include "NvInfer.h"
#include "NvOnnxConfig.h"
#include "NvOnnxParser.h"
#include <cuda_runtime_api.h>
#include <algorithm>
#include <cassert>
#include <fstream>
#include <iostream>
#include <iterator>
#include <map>
#include <memory>
#include <numeric>
#include <string>
#include <vector>
#include <cstring>
#include <cmath>
using namespace std;
#define CHECK(status) \
do \
{ \
auto ret = (status); \
if (ret != 0) \
{ \
std::cout << "Cuda failure: " << ret; \
abort(); \
} \
} while (0)
constexpr long double operator"" _GB(long double val) { return val * (1 << 30); }
constexpr long double operator"" _MB(long double val) { return val * (1 << 20); }
constexpr long double operator"" _KB(long double val) { return val * (1 << 10); }
// These is necessary if we want to be able to write 1_GB instead of 1.0_GB.
// Since the return type is signed, -1_GB will work as expected.
constexpr long long int operator"" _GB(long long unsigned int val) { return val * (1 << 30); }
constexpr long long int operator"" _MB(long long unsigned int val) { return val * (1 << 20); }
constexpr long long int operator"" _KB(long long unsigned int val) { return val * (1 << 10); }
// Logger for TensorRT info/warning/errors
class Logger : public nvinfer1::ILogger
{
public:
Logger(): Logger(Severity::kWARNING) {}
Logger(Severity severity): reportableSeverity(severity) {}
void log(Severity severity, const char* msg) override
{
// suppress messages with severity enum value greater than the reportable
if (severity > reportableSeverity) return;
switch (severity)
{
case Severity::kINTERNAL_ERROR: std::cerr << "INTERNAL_ERROR: "; break;
case Severity::kERROR: std::cerr << "ERROR: "; break;
case Severity::kWARNING: std::cerr << "WARNING: "; break;
case Severity::kINFO: std::cerr << "INFO: "; break;
default: std::cerr << "UNKNOWN: "; break;
}
std::cerr << msg << std::endl;
}
Severity reportableSeverity{Severity::kWARNING};
};
// Locate path to file, given its filename or filepath suffix and possible dirs it might lie in
// Function will also walk back MAX_DEPTH dirs from CWD to check for such a file path
inline std::string locateFile(const std::string& filepathSuffix, const std::vector<std::string>& directories)
{
const int MAX_DEPTH{10};
bool found{false};
std::string filepath;
for (auto& dir : directories)
{
filepath = dir + filepathSuffix;
for (int i = 0; i < MAX_DEPTH && !found; i++)
{
std::ifstream checkFile(filepath);
found = checkFile.is_open();
if (found) break;
filepath = "../" + filepath; // Try again in parent dir
}
if (found)
{
break;
}
filepath.clear();
}
if (filepath.empty()) {
std::string directoryList = std::accumulate(directories.begin() + 1, directories.end(), directories.front(),
[](const std::string& a, const std::string& b) { return a + "\n\t" + b; });
throw std::runtime_error("Could not find " + filepathSuffix + " in data directories:\n\t" + directoryList);
}
return filepath;
}
inline void readPGMFile(const std::string& fileName, uint8_t* buffer, int inH, int inW)
{
std::ifstream infile(fileName, std::ifstream::binary);
assert(infile.is_open() && "Attempting to read from a file that is not open.");
std::string magic, h, w, max;
infile >> magic >> h >> w >> max;
infile.seekg(1, infile.cur);
infile.read(reinterpret_cast<char*>(buffer), inH * inW);
}
namespace samples_common
{
inline void* safeCudaMalloc(size_t memSize)
{
void* deviceMem;
CHECK(cudaMalloc(&deviceMem, memSize));
if (deviceMem == nullptr)
{
std::cerr << "Out of memory" << std::endl;
exit(1);
}
return deviceMem;
}
inline bool isDebug()
{
return (std::getenv("TENSORRT_DEBUG") ? true : false);
}
struct InferDeleter
{
template <typename T>
void operator()(T* obj) const
{
if (obj) {
obj->destroy();
}
}
};
template <typename T>
inline std::shared_ptr<T> infer_object(T* obj)
{
if (!obj) {
throw std::runtime_error("Failed to create object");
}
return std::shared_ptr<T>(obj, InferDeleter());
}
template <class Iter>
inline std::vector<size_t> argsort(Iter begin, Iter end, bool reverse = false)
{
std::vector<size_t> inds(end - begin);
std::iota(inds.begin(), inds.end(), 0);
if (reverse) {
std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) {
return begin[i2] < begin[i1];
});
}
else
{
std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) {
return begin[i1] < begin[i2];
});
}
return inds;
}
inline bool readReferenceFile(const std::string& fileName, std::vector<std::string>& refVector)
{
std::ifstream infile(fileName);
if (!infile.is_open()) {
cout << "ERROR: readReferenceFile: Attempting to read from a file that is not open." << endl;
return false;
}
std::string line;
while (std::getline(infile, line)) {
if (line.empty()) continue;
refVector.push_back(line);
}
infile.close();
return true;
}
template <typename result_vector_t>
inline std::vector<std::string> classify(const vector<string>& refVector, const result_vector_t& output, const size_t topK)
{
auto inds = samples_common::argsort(output.cbegin(), output.cend(), true);
std::vector<std::string> result;
for (size_t k = 0; k < topK; ++k) {
result.push_back(refVector[inds[k]]);
}
return result;
}
//...LG returns top K indices, not values.
template <typename T>
inline vector<size_t> topK(const vector<T> inp, const size_t k)
{
vector<size_t> result;
std::vector<size_t> inds = samples_common::argsort(inp.cbegin(), inp.cend(), true);
result.assign(inds.begin(), inds.begin()+k);
return result;
}
template <typename T>
inline bool readASCIIFile(const string& fileName, const size_t size, vector<T>& out)
{
std::ifstream infile(fileName);
if (!infile.is_open()) {
cout << "ERROR readASCIIFile: Attempting to read from a file that is not open." << endl;
return false;
}
out.clear();
out.reserve(size);
out.assign(std::istream_iterator<T>(infile), std::istream_iterator<T>());
infile.close();
return true;
}
template <typename T>
inline bool writeASCIIFile(const string& fileName, const vector<T>& in)
{
std::ofstream outfile(fileName);
if (!outfile.is_open()) {
cout << "ERROR: writeASCIIFile: Attempting to write to a file that is not open." << endl;
return false;
}
for (auto fn : in) {
outfile << fn << " ";
}
outfile.close();
return true;
}
inline void print_version()
{
//... This can be only done after statically linking this support into parserONNX.library
#if 0
std::cout << "Parser built against:" << std::endl;
std::cout << " ONNX IR version: " << nvonnxparser::onnx_ir_version_string(onnx::IR_VERSION) << std::endl;
#endif
std::cout << " TensorRT version: "
<< NV_TENSORRT_MAJOR << "."
<< NV_TENSORRT_MINOR << "."
<< NV_TENSORRT_PATCH << "."
<< NV_TENSORRT_BUILD << std::endl;
}
inline string getFileType(const string& filepath)
{
return filepath.substr(filepath.find_last_of(".") + 1);
}
inline string toLower(const string& inp)
{
string out = inp;
std::transform(out.begin(), out.end(), out.begin(), ::tolower);
return out;
}
inline unsigned int getElementSize(nvinfer1::DataType t)
{
switch (t)
{
case nvinfer1::DataType::kINT32: return 4;
case nvinfer1::DataType::kFLOAT: return 4;
case nvinfer1::DataType::kHALF: return 2;
case nvinfer1::DataType::kINT8: return 1;
}
throw std::runtime_error("Invalid DataType.");
return 0;
}
inline int64_t volume(const nvinfer1::Dims& d)
{
return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies<int64_t>());
}
// Struct to maintain command-line arguments.
struct Args
{
bool runInInt8 = false;
};
// Populates the Args struct with the provided command-line parameters.
inline void parseArgs(Args& args, int argc, char* argv[])
{
if (argc >= 1)
{
for (int i = 1; i < argc; ++i)
{
if (!strcmp(argv[i], "--int8")) args.runInInt8 = true;
}
}
}
template <int C, int H, int W>
struct PPM
{
std::string magic, fileName;
int h, w, max;
uint8_t buffer[C * H * W];
};
struct BBox
{
float x1, y1, x2, y2;
};
template <int C, int H, int W>
inline void writePPMFileWithBBox(const std::string& filename, PPM<C, H, W>& ppm, const BBox& bbox)
{
std::ofstream outfile("./" + filename, std::ofstream::binary);
assert(!outfile.fail());
outfile << "P6" << "\n" << ppm.w << " " << ppm.h << "\n" << ppm.max << "\n";
auto round = [](float x) -> int { return int(std::floor(x + 0.5f)); };
const int x1 = std::min(std::max(0, round(int(bbox.x1))), W - 1);
const int x2 = std::min(std::max(0, round(int(bbox.x2))), W - 1);
const int y1 = std::min(std::max(0, round(int(bbox.y1))), H - 1);
const int y2 = std::min(std::max(0, round(int(bbox.y2))), H - 1);
for (int x = x1; x <= x2; ++x)
{
// bbox top border
ppm.buffer[(y1 * ppm.w + x) * 3] = 255;
ppm.buffer[(y1 * ppm.w + x) * 3 + 1] = 0;
ppm.buffer[(y1 * ppm.w + x) * 3 + 2] = 0;
// bbox bottom border
ppm.buffer[(y2 * ppm.w + x) * 3] = 255;
ppm.buffer[(y2 * ppm.w + x) * 3 + 1] = 0;
ppm.buffer[(y2 * ppm.w + x) * 3 + 2] = 0;
}
for (int y = y1; y <= y2; ++y)
{
// bbox left border
ppm.buffer[(y * ppm.w + x1) * 3] = 255;
ppm.buffer[(y * ppm.w + x1) * 3 + 1] = 0;
ppm.buffer[(y * ppm.w + x1) * 3 + 2] = 0;
// bbox right border
ppm.buffer[(y * ppm.w + x2) * 3] = 255;
ppm.buffer[(y * ppm.w + x2) * 3 + 1] = 0;
ppm.buffer[(y * ppm.w + x2) * 3 + 2] = 0;
}
outfile.write(reinterpret_cast<char*>(ppm.buffer), ppm.w * ppm.h * 3);
}
} // namespace samples_common
#endif // _TRT_COMMON_H_

75
inceptionv3/inception_v3.cpp
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@ -1,12 +1,24 @@
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "common.h"
#include "logging.h"
#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <cmath>
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
// stuff we know about the network and the input/output blobs
static const int INPUT_H = 299;
@ -99,10 +111,10 @@ IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, W
IActivationLayer* basicConv2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, DimsHW ksize, int s, DimsHW p, std::string lname) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolution(input, outch, ksize, weightMap[lname + "conv.weight"], emptywts);
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, ksize, weightMap[lname + "conv.weight"], emptywts);
assert(conv1);
conv1->setStride(DimsHW{s, s});
conv1->setPadding(p);
conv1->setStrideNd(DimsHW{s, s});
conv1->setPaddingNd(p);
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn", 1e-3);
@ -122,10 +134,10 @@ IConcatenationLayer* inceptionA(INetworkDefinition *network, std::map<std::strin
relu3 = basicConv2d(network, weightMap, *relu3->getOutput(0), 96, DimsHW{3, 3}, 1, DimsHW{1, 1}, lname + "branch3x3dbl_2.");
relu3 = basicConv2d(network, weightMap, *relu3->getOutput(0), 96, DimsHW{3, 3}, 1, DimsHW{1, 1}, lname + "branch3x3dbl_3.");
IPoolingLayer* pool1 = network->addPooling(input, PoolingType::kAVERAGE, DimsHW{3, 3});
IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kAVERAGE, DimsHW{3, 3});
assert(pool1);
pool1->setStride(DimsHW{1, 1});
pool1->setPadding(DimsHW{1, 1});
pool1->setStrideNd(DimsHW{1, 1});
pool1->setPaddingNd(DimsHW{1, 1});
pool1->setAverageCountExcludesPadding(false);
IActivationLayer* relu4 = basicConv2d(network, weightMap, *pool1->getOutput(0), pool_proj, DimsHW{1, 1}, 1, DimsHW{0, 0}, lname + "branch_pool.");
@ -142,9 +154,9 @@ IConcatenationLayer* inceptionB(INetworkDefinition *network, std::map<std::strin
relu2 = basicConv2d(network, weightMap, *relu2->getOutput(0), 96, DimsHW{3, 3}, 1, DimsHW{1, 1}, lname + "branch3x3dbl_2.");
relu2 = basicConv2d(network, weightMap, *relu2->getOutput(0), 96, DimsHW{3, 3}, 2, DimsHW{0, 0}, lname + "branch3x3dbl_3.");
IPoolingLayer* pool1 = network->addPooling(input, PoolingType::kMAX, DimsHW{3, 3});
IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{3, 3});
assert(pool1);
pool1->setStride(DimsHW{2, 2});
pool1->setStrideNd(DimsHW{2, 2});
ITensor* inputTensors[] = {relu1->getOutput(0), relu2->getOutput(0), pool1->getOutput(0)};
IConcatenationLayer* cat1 = network->addConcatenation(inputTensors, 3);
@ -166,10 +178,10 @@ IConcatenationLayer* inceptionC(INetworkDefinition *network, std::map<std::strin
relu3 = basicConv2d(network, weightMap, *relu3->getOutput(0), c7, DimsHW{7, 1}, 1, DimsHW{3, 0}, lname + "branch7x7dbl_4.");
relu3 = basicConv2d(network, weightMap, *relu3->getOutput(0), 192, DimsHW{1, 7}, 1, DimsHW{0, 3}, lname + "branch7x7dbl_5.");
IPoolingLayer* pool1 = network->addPooling(input, PoolingType::kAVERAGE, DimsHW{3, 3});
IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kAVERAGE, DimsHW{3, 3});
assert(pool1);
pool1->setStride(DimsHW{1, 1});
pool1->setPadding(DimsHW{1, 1});
pool1->setStrideNd(DimsHW{1, 1});
pool1->setPaddingNd(DimsHW{1, 1});
pool1->setAverageCountExcludesPadding(false);
IActivationLayer* relu4 = basicConv2d(network, weightMap, *pool1->getOutput(0), 192, DimsHW{1, 1}, 1, DimsHW{0, 0}, lname + "branch_pool.");
@ -188,9 +200,9 @@ IConcatenationLayer* inceptionD(INetworkDefinition *network, std::map<std::strin
relu2 = basicConv2d(network, weightMap, *relu2->getOutput(0), 192, DimsHW{7, 1}, 1, DimsHW{3, 0}, lname + "branch7x7x3_3.");
relu2 = basicConv2d(network, weightMap, *relu2->getOutput(0), 192, DimsHW{3, 3}, 2, DimsHW{0, 0}, lname + "branch7x7x3_4.");
IPoolingLayer* pool1 = network->addPooling(input, PoolingType::kMAX, DimsHW{3, 3});
IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{3, 3});
assert(pool1);
pool1->setStride(DimsHW{2, 2});
pool1->setStrideNd(DimsHW{2, 2});
ITensor* inputTensors[] = {relu1->getOutput(0), relu2->getOutput(0), pool1->getOutput(0)};
IConcatenationLayer* cat1 = network->addConcatenation(inputTensors, 3);
@ -216,10 +228,10 @@ IConcatenationLayer* inceptionE(INetworkDefinition *network, std::map<std::strin
IConcatenationLayer* cat2 = network->addConcatenation(inputTensors1, 2);
assert(cat2);
IPoolingLayer* pool1 = network->addPooling(input, PoolingType::kAVERAGE, DimsHW{3, 3});
IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kAVERAGE, DimsHW{3, 3});
assert(pool1);
pool1->setStride(DimsHW{1, 1});
pool1->setPadding(DimsHW{1, 1});
pool1->setStrideNd(DimsHW{1, 1});
pool1->setPaddingNd(DimsHW{1, 1});
pool1->setAverageCountExcludesPadding(false);
IActivationLayer* relu4 = basicConv2d(network, weightMap, *pool1->getOutput(0), 192, DimsHW{1, 1}, 1, DimsHW{0, 0}, lname + "branch_pool.");
@ -230,9 +242,9 @@ IConcatenationLayer* inceptionE(INetworkDefinition *network, std::map<std::strin
}
// Creat the engine using only the API and not any parser.
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType dt)
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
{
INetworkDefinition* network = builder->createNetwork();
INetworkDefinition* network = builder->createNetworkV2(0U);
// Create input tensor of shape { 1, 1, 32, 32 } with name INPUT_BLOB_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
@ -253,13 +265,13 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
IActivationLayer* relu1 = basicConv2d(network, weightMap, *scale1->getOutput(0), 32, DimsHW{3, 3}, 2, DimsHW{0, 0}, "Conv2d_1a_3x3.");
relu1 = basicConv2d(network, weightMap, *relu1->getOutput(0), 32, DimsHW{3, 3}, 1, DimsHW{0, 0}, "Conv2d_2a_3x3.");
relu1 = basicConv2d(network, weightMap, *relu1->getOutput(0), 64, DimsHW{3, 3}, 1, DimsHW{1, 1}, "Conv2d_2b_3x3.");
IPoolingLayer* pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
assert(pool1);
pool1->setStride(DimsHW{2, 2});
pool1->setStrideNd(DimsHW{2, 2});
relu1 = basicConv2d(network, weightMap, *pool1->getOutput(0), 80, DimsHW{1, 1}, 1, DimsHW{0, 0}, "Conv2d_3b_1x1.");
relu1 = basicConv2d(network, weightMap, *relu1->getOutput(0), 192, DimsHW{3, 3}, 1, DimsHW{0, 0}, "Conv2d_4a_3x3.");
pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
pool1->setStride(DimsHW{2, 2});
pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
pool1->setStrideNd(DimsHW{2, 2});
auto cat1 = inceptionA(network, weightMap, *pool1->getOutput(0), "Mixed_5b.", 32);
cat1 = inceptionA(network, weightMap, *cat1->getOutput(0), "Mixed_5c.", 64);
@ -273,7 +285,7 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
cat1 = inceptionE(network, weightMap, *cat1->getOutput(0), "Mixed_7b.");
cat1 = inceptionE(network, weightMap, *cat1->getOutput(0), "Mixed_7c.");
IPoolingLayer* pool2 = network->addPooling(*cat1->getOutput(0), PoolingType::kAVERAGE, DimsHW{8, 8});
IPoolingLayer* pool2 = network->addPoolingNd(*cat1->getOutput(0), PoolingType::kAVERAGE, DimsHW{8, 8});
assert(pool2);
IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]);
@ -285,8 +297,8 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
// Build engine
builder->setMaxBatchSize(maxBatchSize);
builder->setMaxWorkspaceSize(1 << 20);
ICudaEngine* engine = builder->buildCudaEngine(*network);
config->setMaxWorkspaceSize(1 << 20);
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
std::cout << "build out" << std::endl;
// Don't need the network any more
@ -305,9 +317,10 @@ void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
{
// Create builder
IBuilder* builder = createInferBuilder(gLogger);
IBuilderConfig* config = builder->createBuilderConfig();
// Create model to populate the network, then set the outputs and create an engine
ICudaEngine* engine = createEngine(maxBatchSize, builder, DataType::kFLOAT);
ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
assert(engine != nullptr);
// Serialize the engine
@ -316,6 +329,7 @@ void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
// Close everything down
engine->destroy();
builder->destroy();
config->destroy();
}
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
@ -370,7 +384,7 @@ int main(int argc, char** argv)
APIToModel(1, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("inception.engine");
std::ofstream p("inception.engine", std::ios::binary);
if (!p)
{
std::cerr << "could not open plan output file" << std::endl;
@ -396,7 +410,7 @@ int main(int argc, char** argv)
// Subtract mean from image
float data[3 * INPUT_H * INPUT_W];
static float data[3 * INPUT_H * INPUT_W];
for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
data[i] = 1.0;
@ -406,9 +420,10 @@ int main(int argc, char** argv)
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
// Run inference
float prob[OUTPUT_SIZE];
static float prob[OUTPUT_SIZE];
for (int i = 0; i < 100; i++) {
auto start = std::chrono::system_clock::now();
doInference(*context, data, prob, 1);

503
inceptionv3/logging.h Normal file
View File

@ -0,0 +1,503 @@
/*
* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef TENSORRT_LOGGING_H
#define TENSORRT_LOGGING_H
#include "NvInferRuntimeCommon.h"
#include <cassert>
#include <ctime>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <sstream>
#include <string>
using Severity = nvinfer1::ILogger::Severity;
class LogStreamConsumerBuffer : public std::stringbuf
{
public:
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
: mOutput(stream)
, mPrefix(prefix)
, mShouldLog(shouldLog)
{
}
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
: mOutput(other.mOutput)
{
}
~LogStreamConsumerBuffer()
{
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
// if the pointer to the beginning is not equal to the pointer to the current position,
// call putOutput() to log the output to the stream
if (pbase() != pptr())
{
putOutput();
}
}
// synchronizes the stream buffer and returns 0 on success
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
// resetting the buffer and flushing the stream
virtual int sync()
{
putOutput();
return 0;
}
void putOutput()
{
if (mShouldLog)
{
// prepend timestamp
std::time_t timestamp = std::time(nullptr);
tm* tm_local = std::localtime(&timestamp);
std::cout << "[";
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
// std::stringbuf::str() gets the string contents of the buffer
// insert the buffer contents pre-appended by the appropriate prefix into the stream
mOutput << mPrefix << str();
// set the buffer to empty
str("");
// flush the stream
mOutput.flush();
}
}
void setShouldLog(bool shouldLog)
{
mShouldLog = shouldLog;
}
private:
std::ostream& mOutput;
std::string mPrefix;
bool mShouldLog;
};
//!
//! \class LogStreamConsumerBase
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
//!
class LogStreamConsumerBase
{
public:
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
: mBuffer(stream, prefix, shouldLog)
{
}
protected:
LogStreamConsumerBuffer mBuffer;
};
//!
//! \class LogStreamConsumer
//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
//! Please do not change the order of the parent classes.
//!
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
{
public:
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
//! Reportable severity determines if the messages are severe enough to be logged.
LogStreamConsumer(Severity reportableSeverity, Severity severity)
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
, std::ostream(&mBuffer) // links the stream buffer with the stream
, mShouldLog(severity <= reportableSeverity)
, mSeverity(severity)
{
}
LogStreamConsumer(LogStreamConsumer&& other)
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
, std::ostream(&mBuffer) // links the stream buffer with the stream
, mShouldLog(other.mShouldLog)
, mSeverity(other.mSeverity)
{
}
void setReportableSeverity(Severity reportableSeverity)
{
mShouldLog = mSeverity <= reportableSeverity;
mBuffer.setShouldLog(mShouldLog);
}
private:
static std::ostream& severityOstream(Severity severity)
{
return severity >= Severity::kINFO ? std::cout : std::cerr;
}
static std::string severityPrefix(Severity severity)
{
switch (severity)
{
case Severity::kINTERNAL_ERROR: return "[F] ";
case Severity::kERROR: return "[E] ";
case Severity::kWARNING: return "[W] ";
case Severity::kINFO: return "[I] ";
case Severity::kVERBOSE: return "[V] ";
default: assert(0); return "";
}
}
bool mShouldLog;
Severity mSeverity;
};
//! \class Logger
//!
//! \brief Class which manages logging of TensorRT tools and samples
//!
//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
//! and supports logging two types of messages:
//!
//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
//! - Test pass/fail messages
//!
//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
//!
//! In the future, this class could be extended to support dumping test results to a file in some standard format
//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
//!
//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
//! library and messages coming from the sample.
//!
//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
//! object.
class Logger : public nvinfer1::ILogger
{
public:
Logger(Severity severity = Severity::kWARNING)
: mReportableSeverity(severity)
{
}
//!
//! \enum TestResult
//! \brief Represents the state of a given test
//!
enum class TestResult
{
kRUNNING, //!< The test is running
kPASSED, //!< The test passed
kFAILED, //!< The test failed
kWAIVED //!< The test was waived
};
//!
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
//! \return The nvinfer1::ILogger associated with this Logger
//!
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
//! we can eliminate the inheritance of Logger from ILogger
//!
nvinfer1::ILogger& getTRTLogger()
{
return *this;
}
//!
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
//!
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
//! inheritance from nvinfer1::ILogger
//!
void log(Severity severity, const char* msg) override
{
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
}
//!
//! \brief Method for controlling the verbosity of logging output
//!
//! \param severity The logger will only emit messages that have severity of this level or higher.
//!
void setReportableSeverity(Severity severity)
{
mReportableSeverity = severity;
}
//!
//! \brief Opaque handle that holds logging information for a particular test
//!
//! This object is an opaque handle to information used by the Logger to print test results.
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
//! with Logger::reportTest{Start,End}().
//!
class TestAtom
{
public:
TestAtom(TestAtom&&) = default;
private:
friend class Logger;
TestAtom(bool started, const std::string& name, const std::string& cmdline)
: mStarted(started)
, mName(name)
, mCmdline(cmdline)
{
}
bool mStarted;
std::string mName;
std::string mCmdline;
};
//!
//! \brief Define a test for logging
//!
//! \param[in] name The name of the test. This should be a string starting with
//! "TensorRT" and containing dot-separated strings containing
//! the characters [A-Za-z0-9_].
//! For example, "TensorRT.sample_googlenet"
//! \param[in] cmdline The command line used to reproduce the test
//
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
//!
static TestAtom defineTest(const std::string& name, const std::string& cmdline)
{
return TestAtom(false, name, cmdline);
}
//!
//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
//! as input
//!
//! \param[in] name The name of the test
//! \param[in] argc The number of command-line arguments
//! \param[in] argv The array of command-line arguments (given as C strings)
//!
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
{
auto cmdline = genCmdlineString(argc, argv);
return defineTest(name, cmdline);
}
//!
//! \brief Report that a test has started.
//!
//! \pre reportTestStart() has not been called yet for the given testAtom
//!
//! \param[in] testAtom The handle to the test that has started
//!
static void reportTestStart(TestAtom& testAtom)
{
reportTestResult(testAtom, TestResult::kRUNNING);
assert(!testAtom.mStarted);
testAtom.mStarted = true;
}
//!
//! \brief Report that a test has ended.
//!
//! \pre reportTestStart() has been called for the given testAtom
//!
//! \param[in] testAtom The handle to the test that has ended
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
//! TestResult::kFAILED, TestResult::kWAIVED
//!
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
{
assert(result != TestResult::kRUNNING);
assert(testAtom.mStarted);
reportTestResult(testAtom, result);
}
static int reportPass(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kPASSED);
return EXIT_SUCCESS;
}
static int reportFail(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kFAILED);
return EXIT_FAILURE;
}
static int reportWaive(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kWAIVED);
return EXIT_SUCCESS;
}
static int reportTest(const TestAtom& testAtom, bool pass)
{
return pass ? reportPass(testAtom) : reportFail(testAtom);
}
Severity getReportableSeverity() const
{
return mReportableSeverity;
}
private:
//!
//! \brief returns an appropriate string for prefixing a log message with the given severity
//!
static const char* severityPrefix(Severity severity)
{
switch (severity)
{
case Severity::kINTERNAL_ERROR: return "[F] ";
case Severity::kERROR: return "[E] ";
case Severity::kWARNING: return "[W] ";
case Severity::kINFO: return "[I] ";
case Severity::kVERBOSE: return "[V] ";
default: assert(0); return "";
}
}
//!
//! \brief returns an appropriate string for prefixing a test result message with the given result
//!
static const char* testResultString(TestResult result)
{
switch (result)
{
case TestResult::kRUNNING: return "RUNNING";
case TestResult::kPASSED: return "PASSED";
case TestResult::kFAILED: return "FAILED";
case TestResult::kWAIVED: return "WAIVED";
default: assert(0); return "";
}
}
//!
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
//!
static std::ostream& severityOstream(Severity severity)
{
return severity >= Severity::kINFO ? std::cout : std::cerr;
}
//!
//! \brief method that implements logging test results
//!
static void reportTestResult(const TestAtom& testAtom, TestResult result)
{
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
<< testAtom.mCmdline << std::endl;
}
//!
//! \brief generate a command line string from the given (argc, argv) values
//!
static std::string genCmdlineString(int argc, char const* const* argv)
{
std::stringstream ss;
for (int i = 0; i < argc; i++)
{
if (i > 0)
ss << " ";
ss << argv[i];
}
return ss.str();
}
Severity mReportableSeverity;
};
namespace
{
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
//!
//! Example usage:
//!
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
//!
//! Example usage:
//!
//! LOG_INFO(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_INFO(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
//!
//! Example usage:
//!
//! LOG_WARN(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_WARN(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
//!
//! Example usage:
//!
//! LOG_ERROR(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
// ("fatal" severity)
//!
//! Example usage:
//!
//! LOG_FATAL(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
}
} // anonymous namespace
#endif // TENSORRT_LOGGING_H