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SuperPoint TensorRT implementation (#1034)

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* adding tensorrt implementation of superpoint network.

* inference output result & todo list added to README
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Xiang Shin Wuu 2022-07-06 08:49:10 +05:30 committed by GitHub
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superpoint/CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 2.6)
project(SuperPointNet)
add_definitions(-std=c++11)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
find_package(CUDA REQUIRED)
include_directories(${PROJECT_SOURCE_DIR}/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# tensorrt
include_directories(/usr/include/x86_64-linux-gnu/)
link_directories(/usr/lib/x86_64-linux-gnu/)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -pthread -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED")
find_package(OpenCV)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(supernet ${PROJECT_SOURCE_DIR}/supernet.cpp ${PROJECT_SOURCE_DIR}/utils.cpp)
target_link_libraries(supernet nvinfer)
target_link_libraries(supernet cudart)
target_link_libraries(supernet ${OpenCV_LIBS})
add_definitions(-O2 -pthread)

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# SuperPoint
The PyTorch implementation is from [magicleap/SuperPointPretrainedNetwork.](https://github.com/magicleap/SuperPointPretrainedNetwork)
The pretrained models are from [magicleap/SuperPointPretrainedNetwork.](https://github.com/magicleap/SuperPointPretrainedNetwork)
## Config
- FP16/FP32 can be selected by the macro `USE_FP16` in supernet.cpp
- GPU id and batch size can be selected by the macro `DEVICE` & `BATCH_SIZE` in supernet.cpp
## How to Run
1.Generate .wts file from the baseline pytorch implementation of pretrained model. The following example described how to generate superpoint_v1.wts from pytorch implementation of superpoint_v1.
```
git clone https://github.com/xiang-wuu/SuperPointPretrainedNetwork
cd SuperPointPretrainedNetwork
git checkout deploy
// copy tensorrtx/superpoint/gen_wts.py to here(SuperPointPretrainedNetwork)
python gen_wts.py
// a file 'superpoint_v1.wts' will be generated.
// before running gen_wts.py python script make sure you cloned private fork and checkout to deploy branch.
```
2.Put .wts file into tensorrtx/superpoint, build and run
```
cd tensorrtx/superpoint
mkdir build
cd build
cmake ..
make
./supernet -s SuperPointPretrainedNetwork/superpoint_v1.wts // serialize model to plan file i.e. 'supernet.engine'
```
## Run Demo using SuperPointPretrainedNetwork Python Script
The live demo can be run by inffering TensorRT generated engine file or by the pre-trained pytorch weight file , the `demo_superpoint.py` script is modified to infer automatically by either using TensorRT or PyTorch based on the provided input weight file.
```
cd SuperPointPretrainedNetwork
python demo_superpoint.py assets/nyu_snippet.mp4 --cuda --weights_path tensorrtx/superpoint/build/supernet.engine
// provide absolute path to supernet.engine as input weight file
python demo_superpoint.py assets/nyu_snippet.mp4 --cuda --weights_path superpoint_v1.pth
// execute above command to infer using pytorch pre-trained weight files instead of tensorrt engine file.
```
## Output
As from the below result there is no significant difference in the inferred output!
<table>
<th>
PyTorch
</th>
<th>
TensorRT
</th>
<tr>
<td>
<img src="https://user-images.githubusercontent.com/107029401/177322379-2782ca66-bcac-4cf6-b6d3-e1b4d4a8e171.gif"/>
</td>
<td>
<img src="https://user-images.githubusercontent.com/107029401/177322387-c945b903-f233-4a43-bfd3-530c46f4f4db.gif"/>
</td>
</tr>
</table>
## TODO
- [ ] Optimizing post-processing using custom TensorRT layer.
- [ ] Benchmark validation for speed accuracy tradeoff with [hpatches](https://github.com/hpatches/hpatches-benchmark) dataset

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import torch
import struct
from model import SuperPointNet
model_name = "superpoint_v1"
net = SuperPointNet()
net.load_state_dict(torch.load("superpoint_v1.pth"))
net = net.cuda()
net.eval()
f = open(model_name + ".wts", "w")
f.write("{}\n".format(len(net.state_dict().keys())))
for k, v in net.state_dict().items():
vr = v.reshape(-1).cpu().numpy()
f.write("{} {}".format(k, len(vr)))
for vv in vr:
f.write(" ")
f.write(struct.pack(">f", float(vv)).hex())
f.write("\n")

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/*
* 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) noexcept 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

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#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <opencv2/opencv.hpp>
#include <dirent.h>
#include "NvInfer.h"
#include "utils.h"
#include "cuda_runtime_api.h"
#include "logging.h"
//#define USE_FP16 // comment out this if want to use FP32
#define DEVICE 0 // GPU id
#define BATCH_SIZE 1 // currently, only support BATCH=1
// stuff we know about the network and the input/output blobs
static const int INPUT_H = 120;
static const int INPUT_W = 160;
const char *INPUT_BLOB_NAME = "data";
const char *OUTPUT_BLOB_NAME_1 = "semi";
const char *OUTPUT_BLOB_NAME_2 = "desc";
static Logger gLogger;
// create the engine using only the API and not any parser.
ICudaEngine *createEngine(IBuilder *builder, IBuilderConfig *config, std::string path, DataType dt)
{
INetworkDefinition *network = builder->createNetworkV2(0U);
// Create input tensor of shape { 3, INPUT_H, INPUT_W } with name INPUT_BLOB_NAME
ITensor *data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{1, INPUT_H, INPUT_W});
assert(data);
std::map<std::string, Weights> weightMap = loadWeights(path);
IConvolutionLayer *conv1a = network->addConvolutionNd(*data, 64, DimsHW{3, 3}, weightMap["conv1a.weight"], weightMap["conv1a.bias"]);
assert(conv1a);
conv1a->setStrideNd(DimsHW{1, 1});
conv1a->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu1 = network->addActivation(*conv1a->getOutput(0), ActivationType::kRELU);
assert(relu1);
IConvolutionLayer *conv1b = network->addConvolutionNd(*relu1->getOutput(0), 64, DimsHW{3, 3}, weightMap["conv1b.weight"], weightMap["conv1b.bias"]);
assert(conv1b);
conv1b->setStrideNd(DimsHW{1, 1});
conv1b->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu2 = network->addActivation(*conv1b->getOutput(0), ActivationType::kRELU);
assert(relu2);
IPoolingLayer *pool1 = network->addPoolingNd(*relu2->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
assert(pool1);
pool1->setStrideNd(DimsHW{2, 2});
IConvolutionLayer *conv2a = network->addConvolutionNd(*pool1->getOutput(0), 64, DimsHW{3, 3}, weightMap["conv2a.weight"], weightMap["conv2a.bias"]);
assert(conv2a);
conv2a->setStrideNd(DimsHW{1, 1});
conv2a->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu3 = network->addActivation(*conv2a->getOutput(0), ActivationType::kRELU);
assert(relu3);
IConvolutionLayer *conv2b = network->addConvolutionNd(*relu3->getOutput(0), 64, DimsHW{3, 3}, weightMap["conv2b.weight"], weightMap["conv2b.bias"]);
assert(conv2b);
conv2b->setStrideNd(DimsHW{1, 1});
conv2b->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu4 = network->addActivation(*conv2b->getOutput(0), ActivationType::kRELU);
assert(relu4);
IPoolingLayer *pool2 = network->addPoolingNd(*relu4->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
assert(pool2);
pool2->setStrideNd(DimsHW{2, 2});
IConvolutionLayer *conv3a = network->addConvolutionNd(*pool2->getOutput(0), 128, DimsHW{3, 3}, weightMap["conv3a.weight"], weightMap["conv3a.bias"]);
assert(conv3a);
conv3a->setStrideNd(DimsHW{1, 1});
conv3a->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu44 = network->addActivation(*conv3a->getOutput(0), ActivationType::kRELU);
assert(relu44);
IConvolutionLayer *conv3b = network->addConvolutionNd(*relu44->getOutput(0), 128, DimsHW{3, 3}, weightMap["conv3b.weight"], weightMap["conv3b.bias"]);
assert(conv3b);
conv3b->setStrideNd(DimsHW{1, 1});
conv3b->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu5 = network->addActivation(*conv3b->getOutput(0), ActivationType::kRELU);
assert(relu5);
IPoolingLayer *pool3 = network->addPoolingNd(*relu5->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
assert(pool3);
pool3->setStrideNd(DimsHW{2, 2});
IConvolutionLayer *conv4a = network->addConvolutionNd(*pool3->getOutput(0), 128, DimsHW{3, 3}, weightMap["conv4a.weight"], weightMap["conv4a.bias"]);
assert(conv4a);
conv4a->setStrideNd(DimsHW{1, 1});
conv4a->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu6 = network->addActivation(*conv4a->getOutput(0), ActivationType::kRELU);
assert(relu6);
IConvolutionLayer *conv4b = network->addConvolutionNd(*relu6->getOutput(0), 128, DimsHW{3, 3}, weightMap["conv4b.weight"], weightMap["conv4b.bias"]);
assert(conv4b);
conv4b->setStrideNd(DimsHW{1, 1});
conv4b->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu7 = network->addActivation(*conv4b->getOutput(0), ActivationType::kRELU);
assert(relu7);
IConvolutionLayer *convPa = network->addConvolutionNd(*relu7->getOutput(0), 256, DimsHW{3, 3}, weightMap["convPa.weight"], weightMap["convPa.bias"]);
assert(convPa);
convPa->setStrideNd(DimsHW{1, 1});
convPa->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu8 = network->addActivation(*convPa->getOutput(0), ActivationType::kRELU);
assert(relu8);
IConvolutionLayer *convPb = network->addConvolutionNd(*relu8->getOutput(0), 65, DimsHW{1, 1}, weightMap["convPb.weight"], weightMap["convPb.bias"]);
assert(convPb);
convPb->setStrideNd(DimsHW{1, 1});
IConvolutionLayer *convDa = network->addConvolutionNd(*relu7->getOutput(0), 256, DimsHW{3, 3}, weightMap["convDa.weight"], weightMap["convDa.bias"]);
assert(convDa);
convDa->setStrideNd(DimsHW{1, 1});
convDa->setPaddingNd(DimsHW{1, 1});
IActivationLayer *relu9 = network->addActivation(*convDa->getOutput(0), ActivationType::kRELU);
assert(relu9);
IConvolutionLayer *convDb = network->addConvolutionNd(*relu9->getOutput(0), 256, DimsHW{1, 1}, weightMap["convDb.weight"], weightMap["convDb.bias"]);
assert(convDb);
convDb->setStrideNd(DimsHW{1, 1});
convPb->getOutput(0)->setName(OUTPUT_BLOB_NAME_1);
std::cout << "set name out1" << std::endl;
network->markOutput(*convPb->getOutput(0));
convDb->getOutput(0)->setName(OUTPUT_BLOB_NAME_2);
std::cout << "set name out2" << std::endl;
network->markOutput(*convDb->getOutput(0));
// Build engine
builder->setMaxBatchSize(BATCH_SIZE);
config->setMaxWorkspaceSize(1 << 20);
#ifdef USE_FP16
config->setFlag(BuilderFlag::kFP16);
#endif
ICudaEngine *engine = builder->buildEngineWithConfig(*network, *config);
std::cout << "build out" << std::endl;
// Don't need the network any more
network->destroy();
// Release host memory
for (auto &mem : weightMap)
{
free((void *)(mem.second.values));
}
return engine;
}
// Creat the engine using only the API and not any parser.
void APIToModel(std::string path, 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(builder, config, path, DataType::kFLOAT);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->destroy();
builder->destroy();
}
int main(int argc, char **argv)
{
cudaSetDevice(DEVICE);
// create a model using the API directly and serialize it to a stream
char *trtModelStream{nullptr};
size_t size{0};
if (argc == 3 && std::string(argv[1]) == "-s")
{
IHostMemory *modelStream{nullptr};
APIToModel(std::string(argv[2]), &modelStream);
assert(modelStream != nullptr);
std::ofstream p("supernet.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 0;
}
else
{
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./supernet -s <path_to_.wts_file> // serialize model to plan file" << std::endl;
return -1;
}
return 0;
}

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#include "utils.h"
#include <dirent.h>
#include <string.h>
// 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;
}
int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names)
{
DIR *p_dir = opendir(p_dir_name);
if (p_dir == nullptr)
{
return -1;
}
struct dirent *p_file = nullptr;
while ((p_file = readdir(p_dir)) != nullptr)
{
if (strcmp(p_file->d_name, ".") != 0 &&
strcmp(p_file->d_name, "..") != 0)
{
// std::string cur_file_name(p_dir_name);
// cur_file_name += "/";
// cur_file_name += p_file->d_name;
std::string cur_file_name(p_file->d_name);
file_names.push_back(cur_file_name);
}
}
closedir(p_dir);
return 0;
}
void tokenize(const std::string &str, std::vector<std::string> &tokens, const std::string &delimiters)
{
// Skip delimiters at beginning.
std::string::size_type lastPos = str.find_first_not_of(delimiters, 0);
// Find first non-delimiter.
std::string::size_type pos = str.find_first_of(delimiters, lastPos);
while (std::string::npos != pos || std::string::npos != lastPos)
{
// Found a token, add it to the vector.
tokens.push_back(str.substr(lastPos, pos - lastPos));
// Skip delimiters.
lastPos = str.find_first_not_of(delimiters, pos);
// Find next non-delimiter.
pos = str.find_first_of(delimiters, lastPos);
}
}

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#pragma once
#include <map>
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "assert.h"
#include <fstream>
#include <iostream>
#include <memory>
#include <vector>
#include <opencv2/opencv.hpp>
using namespace nvinfer1;
#define CHECK(status) \
do \
{ \
auto ret = (status); \
if (ret != 0) \
{ \
std::cout << "Cuda failure: " << ret; \
abort(); \
} \
} while (0)
int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names);
std::map<std::string, Weights> loadWeights(const std::string file);
void tokenize(const std::string &str, std::vector<std::string> &tokens, const std::string &delimiters = ",");