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Add DenseNet121 (#460)

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* add: densenet

* update readme
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cmake_minimum_required(VERSION 3.10)
# set the project name
project(densenet)
add_definitions(-std=c++11)
# get main project dir to include common files
get_filename_component(MAIN_DIR ../ ABSOLUTE)
# When enabled the static version of the
# CUDA runtime library will be used in CUDA_LIBRARIES
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
# specify the C++ standard
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED True)
set(CMAKE_BUILD_TYPE Debug)
# include
# include and link cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# include and link tensorrt
include_directories(/usr/include/x86_64-linux-gnu)
link_directories(/usr/lib/x86_64-linux-gnu)
# add the executable
add_executable(densenet ${PROJECT_SOURCE_DIR}/densenet121.cpp)
target_link_libraries(densenet nvinfer)
target_link_libraries(densenet cudart)
add_definitions(-O2 -pthread)

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# Densenet121
The Pytorch implementation is [makaveli10/densenet](https://github.com/makaveli10/torchtrtz/tree/main/densenet).
The tensorrt implemenation is taken from [makaveli10/cpptensorrtz](https://github.com/makaveli10/cpptensorrtz/).
## How to Run
1. generate densenet121.wts from pytorch
```
git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/makaveli10/torchtrtz.git
// go to torchtrtz/densenet
// Enter these two commands to create densenet121.wts
$ python models.py
$ python gen_trtwts.py
```
2. build densenet and run
```
// put densenet121.wts into tensorrtx/densenet
// go to tensorrtx/densenet
mkdir build
cd build
cmake ..
make
sudo ./densenet -s // serialize model to file i.e. 'LPRnet.engine'
sudo ./densenet -d // deserialize model and run inference
```
3. Verify output from [torch impl](https://github.com/makaveli10/torchtrtz/blob/main/densenet/README.md)
TensorRT output[:5]:
```
[-0.587389, -0.329202, -1.83404, -1.89935, -0.928404]
```

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#include "NvInfer.h"
#include "cuda_runtime_api.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 = 224;
static const int INPUT_W = 224;
static const int OUTPUT_SIZE = 1000;
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
using namespace nvinfer1;
static Logger gLogger;
// Load weights from files shared with TensorRT samples.
// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
std::map<std::string, Weights> loadWeights(const std::string file)
{
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--)
{
Weights wt{DataType::kFLOAT, nullptr, 0};
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x)
{
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
float *gamma = (float*)weightMap[lname + ".weight"].values;
float *beta = (float*)weightMap[lname + ".bias"].values;
float *mean = (float*)weightMap[lname + ".running_mean"].values;
float *var = (float*)weightMap[lname + ".running_var"].values;
int len = weightMap[lname + ".running_var"].count;
std::cout << "len " << len << std::endl;
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
scval[i] = gamma[i] / sqrt(var[i] + eps);
}
Weights scale{DataType::kFLOAT, scval, len};
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
}
Weights shift{DataType::kFLOAT, shval, len};
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
pval[i] = 1.0;
}
Weights power{DataType::kFLOAT, pval, len};
weightMap[lname + ".scale"] = scale;
weightMap[lname + ".shift"] = shift;
weightMap[lname + ".power"] = power;
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1);
return scale_1;
}
IConvolutionLayer* addDenseLayer(INetworkDefinition* network, ITensor* input, std::map<std::string, Weights>& weightMap, std::string lname, float eps)
{
// add Batchnorm
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *input, lname + ".norm1", eps);
// add relu
IActivationLayer* relu1 = network -> addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
// add conv
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network -> addConvolutionNd(*relu1->getOutput(0), 128, DimsHW{1, 1}, weightMap[lname + ".conv1.weight"], emptywts);
assert(conv1);
conv1 -> setStrideNd(DimsHW{1, 1});
// add Batchnorm
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv1 -> getOutput(0), lname + ".norm2", eps);
// add relu
IActivationLayer* relu2 = network -> addActivation(*bn2->getOutput(0), ActivationType::kRELU);
assert(relu2);
// add conv
IConvolutionLayer* conv2 = network -> addConvolutionNd(*relu2->getOutput(0), 32, DimsHW{3, 3}, weightMap[lname + ".conv2.weight"], emptywts);
assert(conv2);
conv2 -> setStrideNd(DimsHW{1, 1});
conv2 -> setPaddingNd(DimsHW{1, 1});
return conv2;
}
IPoolingLayer* addTransition(INetworkDefinition* network, ITensor& input, std::map<std::string, Weights>& weightMap, int outch, std::string lname, float eps)
{
// add batch norm
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap,input, lname + ".norm", eps);
// add relu activation
IActivationLayer* relu1 = network -> addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
// add convolution layer
// empty weights for no bias
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network -> addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{1, 1}, weightMap[lname + ".conv.weight"], emptywts);
assert(conv1);
conv1 -> setStrideNd(DimsHW{1, 1});
// add pooling
IPoolingLayer* pool1 = network->addPoolingNd(*conv1->getOutput(0), PoolingType::kAVERAGE, DimsHW{2, 2});
assert(pool1);
pool1 -> setStrideNd(DimsHW{2, 2});
pool1 -> setPaddingNd(DimsHW{0,0});
return pool1;
}
IConcatenationLayer* addDenseBlock(INetworkDefinition* network, ITensor* input, std::map<std::string, Weights>& weightMap, int numDenseLayers, std::string lname, float eps)
{
IConvolutionLayer* c{nullptr};
IConcatenationLayer* concat{nullptr};
ITensor* inputTensors[numDenseLayers+1];
inputTensors[0] = input;
c = addDenseLayer(network, input, weightMap, lname + ".denselayer" + std::to_string(1), eps);
int i;
for(i=1; i<numDenseLayers; i++)
{
// inch += 32;
inputTensors[i] = c -> getOutput(0);
concat = network -> addConcatenation(inputTensors, i+1);
assert(concat);
c = addDenseLayer(network, concat->getOutput(0), weightMap, lname + ".denselayer" + std::to_string(i+1), eps);
}
inputTensors[numDenseLayers] = c -> getOutput(0);
concat = network -> addConcatenation(inputTensors, numDenseLayers+1);
assert(concat);
return concat;
}
/**
* Uses the TensorRT API to create the network engine.
**/
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
{
// Initialize NetworkDefinition
INetworkDefinition* network = builder -> createNetworkV2(0U);
auto data = network -> addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
assert(data);
std::map<std::string, Weights> weightMap = loadWeights("../densenet121.wts");
Weights emptywts{DataType::kFLOAT, nullptr, 0};
auto conv0 = network -> addConvolutionNd(*data, 64, DimsHW{7, 7}, weightMap["features.conv0.weight"], emptywts);
assert(conv0);
conv0 -> setStrideNd(DimsHW{2, 2});
conv0 -> setPaddingNd(DimsHW{3, 3});
auto norm0 = addBatchNorm2d(network, weightMap, *conv0 -> getOutput(0), "features.norm0", 1e-5);
auto relu0 = network -> addActivation(*norm0 -> getOutput(0), ActivationType::kRELU);
assert(relu0);
auto pool0 = network -> addPoolingNd(*relu0 -> getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
assert(pool0);
pool0 -> setStrideNd(DimsHW{2, 2});
pool0 -> setPaddingNd(DimsHW{1, 1});
auto dense1 = addDenseBlock(network, pool0 -> getOutput(0), weightMap, 6, "features.denseblock1", 1e-5);
auto transition1 = addTransition(network, *dense1 -> getOutput(0), weightMap, 128, "features.transition1", 1e-5);
auto dense2 = addDenseBlock(network, transition1 -> getOutput(0), weightMap, 12, "features.denseblock2", 1e-5);
auto transition2 = addTransition(network, *dense2 -> getOutput(0), weightMap, 256, "features.transition2", 1e-5);
auto dense3 = addDenseBlock(network, transition2 -> getOutput(0), weightMap, 24, "features.denseblock3", 1e-5);
auto transition3 = addTransition(network, *dense3 -> getOutput(0), weightMap, 512, "features.transition3", 1e-5);
auto dense4 = addDenseBlock(network, transition3 -> getOutput(0), weightMap, 16, "features.denseblock4", 1e-5);
auto bn5 = addBatchNorm2d(network, weightMap, *dense4 -> getOutput(0), "features.norm5", 1e-5);
auto relu5 = network -> addActivation(*bn5 -> getOutput(0), ActivationType::kRELU);
// adaptive average pool => pytorch (F.adaptive_avg_pool2d(input, (1, 1)))
auto pool5 = network -> addPoolingNd(*relu5 -> getOutput(0), PoolingType::kAVERAGE, DimsHW{7,7});
auto fc1 = network -> addFullyConnected(*pool5 -> getOutput(0), 1000, weightMap["classifier.weight"], weightMap["classifier.bias"]);
assert(fc1);
// set ouput blob name
fc1 -> getOutput(0) -> setName(OUTPUT_BLOB_NAME);
std::cout << "set name out" << std::endl;
// mark the output
network -> markOutput(*fc1 -> getOutput(0));
// set batchsize and workspace size
builder -> setMaxBatchSize(maxBatchSize);
config -> setMaxWorkspaceSize(1 << 28); // 256 MiB
// build engine
ICudaEngine* engine = builder -> buildEngineWithConfig(*network, *config);
std::cout << "build out" << std::endl;
// destroy
network -> destroy();
// fere host mem
for(auto& mem: weightMap)
{
free((void*)(mem.second.values));
}
return engine;
}
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, config, DataType::kFLOAT);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->destroy();
builder->destroy();
config->destroy();
}
/**
* Performs inference on the given input and
* writes the output from device to host memory.
**/
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
{
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
assert(engine.getNbBindings() == 2);
void* buffers[2];
// In order to bind the buffers, we need to know the names of the input and output tensors.
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueue(batchSize, buffers, stream, nullptr);
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
// Release stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
int main(int argc, char** argv)
{
if (argc != 2) {
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./densenet -s // serialize model to plan file" << std::endl;
std::cerr << "./densenet -d // deserialize plan file and run inference" << std::endl;
return -1;
}
// create a model using the API directly and serialize it to a stream
char *trtModelStream{nullptr};
size_t size{0};
if (std::string(argv[1]) == "-s") {
IHostMemory* modelStream{nullptr};
APIToModel(1, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("densenet.engine", std::ios::binary);
if (!p)
{
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 1;
} else if (std::string(argv[1]) == "-d") {
std::ifstream file("densenet.engine", std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
} else {
return -1;
}
// Subtract mean from image
static float data[3 * INPUT_H * INPUT_W];
for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
data[i] = 1.0;
IRuntime* runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
// Run inference
static float prob[OUTPUT_SIZE];
for (int i = 0; i < 100; i++) {
auto start = std::chrono::system_clock::now();
doInference(*context, data, prob, 1);
auto end = std::chrono::system_clock::now();
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
}
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
// Print histogram of the output distribution
std::cout << "\nOutput:\n\n";
for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
{
std::cout << prob[i] << ", ";
if (i % 10 == 0) std::cout << i / 10 << std::endl;
}
std::cout << std::endl;
return 0;
}

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/*
* Copyright (c) 2021, 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)
, mPrefix(other.mPrefix)
, mShouldLog(other.mShouldLog)
{
}
~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