torch版ResNet50(带有多输出)转c++ tensorrt_resnet50 tensorrt

第一个最简单的lenet示例请参考这篇文章

一.torch阶段 

测试图片:

torch代码:

# coding:utf-8
import torch
from torch import nn
from torch.nn import functional as F
import torchvision
import os
import struct
import time
import cv2
import numpy as np
 
def main():
    print('cuda device count: ', torch.cuda.device_count())
    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
    model = torchvision.models.resnet50(pretrained=True)
    # net.fc = nn.Linear(512, 2)
    model = model.to('cuda:0')
    model.eval()
    # print(model)
    st_time = time.time()
    nums = 10000
    for i in range(nums):
        input_ = torch.ones(1, 3, 224, 224).to('cuda:0')
 
        out = model(input_)
        # print('====out.shape:===', out.shape)#(1, 1000)
 
    end_time = time.time()
    print('==avge cost time{}'.format((end_time - st_time)/nums))
 
    # input_ = torch.ones(1, 3, 224, 224).to('cuda:0')
    # save_pth(model, input_)#存储.pth
    # save_onnx(input_, model)#存储.onnx方便可视化网络
    # get_wts(model)#提取key value权重
 
def save_pth(model, input_):
    conv1 = model.conv1(input_)
    print('===conv1.shape:', conv1.shape)
    # maxpool_1 = model.maxpool(conv1)
    # print('===maxpool_1.shape:', maxpool_1.shape)
    # layer1 = model.layer1(maxpool_1)
    # print('===layer1.shape:', layer1.shape)
    # layer2 = model.layer2(layer1)
    # print('===layer2.shape:', layer2.shape)
    # layer3 = model.layer3(layer2)
    # print('===layer3.shape:', layer3.shape)
    # layer4 = model.layer4(layer3)
    # print('===layer4.shape:', layer4.shape)
    # print('resnet50 out:', out.shape)
    torch.save(model, "resnet50.pth")
 
def get_wts(model):
    f = open("resnet50.wts", 'w')
    f.write("{}\n".format(len(model.state_dict().keys())))
    for k, v in model.state_dict().items():
        # print('key: ', k)#weight name
        # print('value: ', v.shape)#weight shape
        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")
 
def save_onnx(input_, model):
    # torch.onnx.export(model, input_, "./resnet50.onnx", verbose=True)
    torch.onnx.export(model,  # model being run
                      input_,  # model input (or a tuple for multiple inputs)
                      "./resnet50.onnx",
                      opset_version=10,
                      verbose=False,  # store the trained parameter weights inside the model file
                      training=False,
                      do_constant_folding=True,
                      input_names=['input'],
                      output_names=['output']
                      )
 
def test_real_img():
    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
    model = torchvision.models.resnet50(pretrained=True)
    # net.fc = nn.Linear(512, 2)
    model = model.to('cuda:0')
    model.eval()
    # print(model)
 
    img = cv2.imread('./test2.jpg')
    print('===img.shape', img.shape)
    img = cv2.resize(img, (224, 224))
    mean = np.array([0.406, 0.456, 0.485]).astype(np.float32)
    std = np.array([0.225, 0.224, 0.229]).astype(np.float32)
    img = (img / 255. - mean) / std
    img = np.expand_dims(img, axis=0)
    print('===img.shape', img.shape)
    img = np.transpose(img, (0, 3, 1, 2)).astype(np.float32)
 
    # img = np.ones((1, 3, 224, 224)).astype(np.float32)
    nums = 10000
    img = torch.from_numpy(img)
    st_time = time.time()
    for i in range(nums):
        with torch.no_grad():
            out = model(img.cuda())
    end_time = time.time()
    print('==avge cost time{}'.format((end_time - st_time) / nums))
    print('====out.shape:===', out.shape)  # (1, 1000)
    with open('./pytorch_result.txt', 'w', encoding='utf-8') as file:
        for i in range(1000):
            file.write(str(out.cpu().numpy()[0][i]) + '\n')
    torch_value, torch_index = torch.max(out, dim=1)
    print('====torch_value:===', torch_value)#13.8998
    print('====torch_index:===', torch_index)#285 Egyptian cat
    topk = 5
    topk_index = torch.argsort(out, dim=1, descending=True)[:, :topk]
    print('===topk_index:', topk_index)
    out = out.cpu().numpy()
    index = np.where(out == np.max(out))
    print('===index:===', index)
 
if __name__ == '__main__':
    # main()
    test_real_img()

其中:get_wts用于生成16进制权重文件，resnet50.wts，后续tensorrt载入模型权重。

save_onnx用于生成resnet50.onnx文件，可视化网络结构。

结果:

查找imageNet 索引285所对应的标签为:

生成.txt截图如下:

二.tensorrt转换阶段

2.1序列化生成.engine阶段

1.文件代码结构图

其中resnet50.wts是torch阶段生成的，resnet50.engine是本阶段要生成的。

2.代码:

logging.h

/*
 * 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

Resnet50Serial.cpp

#include <map>
#include <chrono>
#include <fstream>
#include <string>
#include "NvInfer.h"
#include "logging.h"
#include "cuda_runtime_api.h"
#include <NvInferRuntimeCommon.h>
#include "common.hpp"
#include <opencv2/opencv.hpp>
#include <limits.h>
 
static Logger gLogger;
#define DEVICE 0//gpu id
#define BATCH_SIZE 1
static const int INPUT_H = 224;
static const int INPUT_W = 224;
// static const int BATCH_SIZE=32;
static const int OUTPUT_SIZE=1000;
static const int INFER_NUMS=10000;
const char* INPUT_BLOB_NAME = "image";
const char* OUTPUT_BLOB_NAME1 = "output1";
const char* OUTPUT_BLOB_NAME2 = "output2";
 
using namespace nvinfer1;
using namespace std;
 
#define CHECK(status) \
    do\
    {\
        auto ret = (status);\
        if (ret != 0)\
        {\
            std::cerr << "Cuda failure: " << ret << endl;\
            abort();\
        }\
    } while (0)
 
map<string, Weights> loadWeights(const string file)
{
    cout << "Loading weights: " << file << endl;
    map<string, Weights> weightMap;
 
    // Open weights file
    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
        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;
}
 
//输出每一个维度
void debug_print(ITensor* input_tensor, string head)
{
    cout<<"==head:"<<head<<":";
    for(int i = 0; i<input_tensor->getDimensions().nbDims; i++)
    {
        cout<<input_tensor->getDimensions().d[i]<<" ";
    }
    cout<<endl;
}
ICudaEngine* createEngine(const char* weightPath, unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
{
    //开始定义网络 0U无符号整型0
    INetworkDefinition* network =  builder->createNetworkV2(0U);
    ITensor* input = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
    assert(input);
 
    map<string, Weights> weightMap = loadWeights(weightPath);//载入权重放入weightMap
    auto id_323 = convBnRelu(network, weightMap, *input, 64, 7, 2, 3,"conv1", "bn1", false);
    // debug_print(id_323->getOutput(0), "id_323");//debug
    
    IPoolingLayer* pool1 = network->addPoolingNd(*id_323->getOutput(0), PoolingType::kMAX, DimsHW{3,3});
    assert(pool1);
    pool1->setStrideNd(DimsHW{2, 2});
    pool1->setPaddingNd(DimsHW{1, 1});
    // debug_print(pool1->getOutput(0), " pool1");//debug
 
    auto id_336 = bottleneck(network, weightMap, *pool1->getOutput(0), 64, 1, "layer1.0", false);
    // debug_print(id_336->getOutput(0), "id_336");//debug
    auto id_346 = bottleneck(network, weightMap, *id_336->getOutput(0), 64, 1, "layer1.1", true);
    // debug_print(id_346->getOutput(0), "id_346");//debug
    auto id_356 = bottleneck(network, weightMap, *id_346->getOutput(0), 64, 1, "layer1.2", true);
    // debug_print(id_356->getOutput(0), "id_356");//debug
 
    auto id_368 = bottleneck(network, weightMap, *id_356->getOutput(0), 128, 2, "layer2.0", false);
    // debug_print(id_368->getOutput(0), "id_368");//debug
    auto id_378 = bottleneck(network, weightMap, *id_368->getOutput(0), 128, 1, "layer2.1", true);
    // debug_print(id_378->getOutput(0), "id_378");//debug    
    auto id_388 = bottleneck(network, weightMap, *id_378->getOutput(0), 128, 1, "layer2.2", true);
    // debug_print(id_388->getOutput(0), "id_388");//debug
    auto id_398 = bottleneck(network, weightMap, *id_388->getOutput(0), 128, 1, "layer2.3", true);
    // debug_print(id_398->getOutput(0), "id_398");//debug  
 
    auto id_410 = bottleneck(network, weightMap, *id_398->getOutput(0), 256, 2, "layer3.0", false);
    // debug_print(id_410->getOutput(0), "id_410");//debug
    auto id_420 = bottleneck(network, weightMap, *id_410->getOutput(0), 256, 1, "layer3.1", true);
    // debug_print(id_420->getOutput(0), "id_420");//debug    
    auto id_430 = bottleneck(network, weightMap, *id_420->getOutput(0), 256, 1, "layer3.2", true);
    // debug_print(id_430->getOutput(0), "id_430");//debug
    auto id_440 = bottleneck(network, weightMap, *id_430->getOutput(0), 256, 1, "layer3.3", true);
    // debug_print(id_440->getOutput(0), "id_440");//debug
    auto id_450 = bottleneck(network, weightMap, *id_440->getOutput(0), 256, 1, "layer3.4", true);
    // debug_print(id_450->getOutput(0), "id_450");//debug    
    auto id_460 = bottleneck(network, weightMap, *id_450->getOutput(0), 256, 1, "layer3.5", true);
    // debug_print(id_460->getOutput(0), "id_460");//debug
 
    auto id_472 = bottleneck(network, weightMap, *id_460->getOutput(0), 512, 2, "layer4.0", false);
    // debug_print(id_472->getOutput(0), "id_472");//debug
    auto id_482 = bottleneck(network, weightMap, *id_472->getOutput(0), 512, 1, "layer4.1", true);
    // debug_print(id_482->getOutput(0), "id_482");//debug    
    auto id_492 = bottleneck(network, weightMap, *id_482->getOutput(0), 512, 1, "layer4.2", true);
    IPoolingLayer* pool2 = network->addPoolingNd(*id_492->getOutput(0), PoolingType::kAVERAGE, DimsHW{7,7});
    assert(pool2);
    // debug_print(pool2->getOutput(0), "pool2");//debug
    
    IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]);
    assert(fc1);
    // debug_print(fc1->getOutput(0), "fc1");//debug
    IActivationLayer* fc1_relu = network->addActivation(*fc1->getOutput(0), ActivationType::kRELU);
    assert(fc1_relu);
    // //分类层
    // ISoftMaxLayer *prob = network->addSoftMax(*fc1->getOutput(0));
    // assert(prob);
 
    fc1->getOutput(0)->setName(OUTPUT_BLOB_NAME1);
    fc1_relu->getOutput(0)->setName(OUTPUT_BLOB_NAME2);
    network->markOutput(*fc1->getOutput(0));
    network->markOutput(*fc1_relu->getOutput(0));
 
    //构造engine
    builder->setMaxBatchSize(maxBatchSize);
    config->setMaxWorkspaceSize(1<<20);
    ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
    
    //放入engine 所以network可以销毁了
    network->destroy();
    // 释放资源
    for (auto& mem : weightMap)
    {
        free((void*) (mem.second.values));
    }
 
    return engine;
}
void APIToModel(const char* weightPath, unsigned int maxBatchSize, IHostMemory** modelStream)
{
    //创建builder
    IBuilder* builder = createInferBuilder(gLogger);//网络入口　类似pytorch的model
    IBuilderConfig* config = builder->createBuilderConfig();
 
    //创建模型　搭建网络层
    ICudaEngine* engine = createEngine(weightPath, maxBatchSize, builder, config, DataType::kFLOAT);
    assert(engine!=nullptr);
 
    //序列化engine
    (*modelStream)= engine->serialize();
 
    //销毁对象   
    engine->destroy();
    config->destroy();
    builder->destroy();
 
}
 
int main(int args, char **argv)
{   
    //序列化模型为.engine文件
    string engine_name = "./resnet50.engine";
    const char* weightPath = "./resnet50.wts";
    IHostMemory* modelStream{nullptr};//modelStream是一块内存区域，用来保存序列化文件
    APIToModel(weightPath, BATCH_SIZE, &modelStream);
    assert(modelStream!=nullptr);
    //变换为.engine文件
    ofstream p(engine_name);
    if (!p)
    {
        std::cerr<<"can not open plan file"<<endl;
        return -1;
    }
    p.write(reinterpret_cast<const char *>(modelStream->data()), modelStream->size());
    p.close();
    //销毁对象
    modelStream->destroy();   
    
    return 0;
}

common.hpp

#ifndef COMMON_HPP
#define COMMON_HPP
 
#include <map>
#include <chrono>
#include <fstream>
#include <vector>
#include <dirent.h>
#include <math.h>
#include <assert.h>
#include "NvInfer.h"
#include "logging.h"
#include "cuda_runtime_api.h"
 
using namespace nvinfer1;
 
 
IScaleLayer* addBatchNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,std::string bnname,float eps)
{
    float* gamma= (float*)weightMap[bnname+".weight"].values;
    float* beta=(float*)weightMap[bnname+".bias"].values;
    float* mean=(float*)weightMap[bnname+".running_mean"].values;
    float* var=(float*)weightMap[bnname+".running_var"].values;
    int length = weightMap[bnname+".running_var"].count;
    float* scval = reinterpret_cast<float *>(malloc(sizeof(float)*length));
    for (int i=0;i<length;i++)
    {   
        scval[i] = gamma[i]/sqrt(var[i]+eps);
    }
    Weights scale{ DataType::kFLOAT, scval, length};//实例化一个weights scale 存放scval指针
 
    float* shavl = reinterpret_cast<float *>(malloc(sizeof(float)*length));
    for (int i=0;i<length;i++)
    {   
        shavl[i] = beta[i]-mean[i]*gamma[i]/sqrt(var[i]+eps);
    }
    Weights shift{ DataType::kFLOAT, shavl, length};//实例化一个weights shift 存放shavl指针
    
    float* pval = reinterpret_cast<float *>(malloc(sizeof(float)*length));
    for (int i=0;i<length;i++)
    {   
        pval[i] = 1.0;
    }
    Weights power{ DataType::kFLOAT, pval, length};//实例化一个weights power 存放pval指针
 
    weightMap[bnname+".scale"] = scale;
    weightMap[bnname+".shift"] = shift;
    weightMap[bnname+".power"] = power;
    IScaleLayer* scale_1 = network->addScale(input,ScaleMode::kCHANNEL, shift, scale, power);
    assert(scale_1);
    return scale_1;
 
}
 
IActivationLayer* convBnRelu(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input,int outch, int ksize, int s,int p,std::string convname,std::string bnname,bool bias = false)
{
    Weights emptywts{ DataType::kFLOAT, nullptr, 0};//实例化一个空weights  emptywts　空指针　长度为0
    //卷积层
    IConvolutionLayer* conv1;//先定义指针
    if (!bias)
    {
        conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize,ksize}, weightMap[convname+".weight"],emptywts);
    }
    else
    {
        conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize,ksize}, weightMap[convname+".weight"],weightMap[convname+".bias"]);
    }    
    //设置步长
    assert(conv1);
    conv1->setStrideNd(DimsHW{s, s});
    conv1->setPaddingNd(DimsHW{p, p});
    IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), bnname, 1e-5);
    assert(bn1);
    //激活层
    IActivationLayer* relu = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
    assert(relu);
    return relu;
}
 
IActivationLayer* bottleneck(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int stride, std::string lname, bool shortcut_clean)
{
    Weights emptywts{ DataType::kFLOAT, nullptr, 0};//实例化一个空weights  emptywts　空指针　长度为0
    IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{1,1}, weightMap[lname+".conv1.weight"], emptywts);
    assert(conv1);
    IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname+".bn1", 1e-5);
    assert(bn1);
    IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
    assert(relu1);
 
    IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3,3}, weightMap[lname+".conv2.weight"], emptywts);
    assert(conv2);
    conv2->setStrideNd(DimsHW{stride, stride});
    conv2->setPaddingNd(DimsHW{1, 1});
    IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname+".bn2", 1e-5);
    assert(bn2);
    IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
    assert(relu2);
    IConvolutionLayer* conv3 = network->addConvolutionNd(*relu2->getOutput(0), outch*4, DimsHW{1,1}, weightMap[lname+".conv3.weight"], emptywts);
    assert(conv3);
    IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname+".bn3", 1e-5);
    assert(bn3);
 
    IElementWiseLayer *ew1;
    if (!shortcut_clean)
    {
        IConvolutionLayer* conv4 = network->addConvolutionNd(input, outch*4, DimsHW{1,1}, weightMap[lname+".downsample.0.weight"], emptywts);
        assert(conv4);
        conv4->setStrideNd(DimsHW{stride, stride});
        IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname+".downsample.1", 1e-5);
        assert(bn4);
        ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
    }
    else
    {
        ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
    }
    assert(ew1);
    IActivationLayer* relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
    assert(relu3);
    return relu3;
 
}   
 
ILayer* ResBlock(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname)
{
    Weights emptywts{ DataType::kFLOAT, nullptr, 0};//实例化一个空weights  emptywts　空指针　长度为0
    IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{1,1}, weightMap[lname+".conv1.weight"], emptywts);
    assert(conv1);
    conv1->setStrideNd(DimsHW{stride, stride});
    IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname+".bn1", 1e-5);
    assert(bn1);
    IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
    assert(relu1);
 
    IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3,3}, weightMap[lname+".conv2.weight"], emptywts);
    assert(conv2);
    conv2->setStrideNd(DimsHW{stride, stride});
    conv2->setPaddingNd(DimsHW{1, 1});
    IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname+".bn2", 1e-5);
    assert(bn2);
    IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
    assert(relu2);
 
    IConvolutionLayer* conv3 = network->addConvolutionNd(*relu2->getOutput(0), inch, DimsHW{1,1}, weightMap[lname+".conv3.weight"], emptywts);
    assert(conv3);
    conv3->setStrideNd(DimsHW{stride, stride});
    IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname+".bn3", 1e-5);
    assert(bn3);
    IElementWiseLayer* ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
    
    assert(ew1);
    IActivationLayer* relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
    assert(relu3);
    return relu3;
}
 
ILayer* liteResBlock(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int stride, std::string lname)
{
    Weights emptywts{ DataType::kFLOAT, nullptr, 0};//实例化一个空weights  emptywts　空指针　长度为0
    IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{3,3}, weightMap[lname+".conv1.weight"], emptywts);
    assert(conv1);
    conv1->setStrideNd(DimsHW{stride, stride});
    conv1->setPaddingNd(DimsHW{1, 1});
    IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname+".bn1", 1e-5);
    assert(bn1);
    IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
    assert(relu1);
 
    IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3,3}, weightMap[lname+".conv2.weight"], emptywts);
    assert(conv2);
    conv2->setStrideNd(DimsHW{stride, stride});
    conv2->setPaddingNd(DimsHW{1, 1});
    IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname+".bn2", 1e-5);
    assert(bn2);
    IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
    assert(relu2);
 
    IElementWiseLayer* ew1 = network->addElementWise(input, *bn2->getOutput(0), ElementWiseOperation::kSUM);
    
    assert(ew1);
    IActivationLayer* relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
    assert(relu3);
    return relu3;
}
#endif

CMakeLists.txt

cmake_minimum_required(VERSION 2.6)
 
project(resnet)
 
add_definitions(-std=c++11)
 
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
 
find_package(OpenCV REQUIRED)
include_directories(OpenCV_INCLUDE_DIRS)
 
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/)
 
add_executable(Resnet50Serial ${PROJECT_SOURCE_DIR}/Resnet50Serial.cpp)
 
target_link_libraries(Resnet50Serial nvinfer)
target_link_libraries(Resnet50Serial cudart)
target_link_libraries(Resnet50Serial ${OpenCV_LIBS})
#add_executable(resnext50 ${PROJECT_SOURCE_DIR}/resnext50_32x4d.cpp)
#target_link_libraries(resnext50 nvinfer)
#target_link_libraries(resnext50 cudart)
 
add_definitions(-O2 -pthread)

即可生成.engine文件,而如果要量化为fp16,只需要增加:

builder->setHalf2Mode(true);

就可以.

下面这句话用来判断是否支持fp16.

bool useFp16 = builder->platformHasFastFp16();

2.2反序列化推理阶段

1.文件代码结构图

其中resnet50.engine是上一阶段生成的,logging.h和上一阶段一样。

2.代码:

main.cpp

#include <complex>
#include <fstream>
#include <iostream>
#include "Resnet50Classify.h"
#include <vector>
#include <algorithm>
using namespace std;
 
bool cmp(int x,int y)
{
    return x>y;
}
 
template<typename T>
vector<int>  sort_indexes(const vector<T>  & v, bool reverse=false) {
 
    // initialize original index locations
    vector<int>  idx(v.size());
    for (int i = 0; i != idx.size(); ++i) idx[i] = i;
    
    // sort indexes based on comparing values in v
    if(reverse)
    {
        sort(idx.begin(), idx.end(),
        [& v](int i1, int i2) {return v[i1] > v[i2];});
    }else{
        sort(idx.begin(), idx.end(),
        [& v](int i1, int i2) {return v[i1] <  v[i2];});
    }
 
  return idx;
}
 
void get_index_value(int OUTPUT_SIZE, float *prob, vector<float>& res){
	// res[0] = 1;
	// res[1] = 0.9898978;
    float maxp =  INT_MIN;
    int index = 0;
    for (int i = 0; i < OUTPUT_SIZE; i++)
    {         
        if(prob[i]>maxp){
            maxp = prob[i];
            index = i;
        }
    }   
    res[0] = index;
    res[1] = maxp;
}
 
vector<int>  topk_index(int OUTPUT_SIZE, float* prob, vector<float>& ProbIndex){
    vector<int> sorted_indx;
    sorted_indx = sort_indexes(ProbIndex, true);
    return sorted_indx;
}   
 
int main(int argc, char** argv){
    if( argc != 2)
    {
      cout<<"图片路径没有输入"<<endl;
      return -1;
    }
    ResNet50* model = new ResNet50();
    //开始推理, 模拟推理10000次，存储推理结果
    const char* enginePath = "./resnet50.engine";
    model->InferenceInit(enginePath);//将引擎文件载入显卡，反序列化好环境并启动cuda核
    const char* imgPath = argv[1];
    cout<<"=====main cv::CV_VERSION:===="<<CV_VERSION<<endl;
    auto start = chrono::system_clock::now();//开始时间
    model->preProcess(imgPath);//图像预处理  
    for (int i = 0; i < model->INFER_NUMS; i++)
    {               
        // std::cout<<"data[i]:"<<data[i]<<std::endl;
        model->doInference(model->data, model->prob1, model->prob2, model->batchSize); //开始推理
    }
    auto end = chrono::system_clock::now();//结束时间
    std::cout << chrono::duration_cast<chrono::milliseconds>(end - start).count() << "ms" << std::endl;
 
    cout<<"====model->prob1:"<<model->prob1<<endl;//打印地址
    cout<<"====model->prob2:"<<model->prob2<<endl;//打印地址
    cout<<"========================================"<<endl;   
 
    vector<float>res1(2, 0);
    get_index_value(model->OUTPUT_SIZE, model->prob1, res1);
 
    vector<float>res2(2, 0);
    get_index_value(model->OUTPUT_SIZE, model->prob2, res2);
 
    for(int i=0; i<2; i++){
        cout<<"===res1[i]:==="<<res1[i]<<endl;//打印最大值的索引
        cout<<"===res2[i]:==="<<res2[i]<<endl;//打印最大值
    }
    cout<<"========================================"<<endl;
    
    ofstream trt_result("./fc_and_relu.txt");
    int topk = 100;
    for (int i = 0; i < topk; i++)
    {   
        trt_result<<model->prob1[i];
        trt_result<<",";
        trt_result<<model->prob2[i]<<endl;
        cout<<"===model->prob1[i]==="<<model->prob1[i]<<endl;
        cout<<"===model->prob2[i]==="<<model->prob2[i]<<endl;
    }  
    trt_result.close();
    // vector<float> ProbIndex(model->prob1, model->prob1 + model->OUTPUT_SIZE);
    // vector<int> sorted_indx;
    // vector<int> res;
    // sorted_indx = sort_indexes(ProbIndex, true);
    // vector<float> ProbIndex1(model->prob1, model->prob1 + model->OUTPUT_SIZE);
    // vector<float> ProbIndex2(model->prob2, model->prob2 + model->OUTPUT_SIZE);
    // vector<int> sorted_indx1;
    // vector<int> sorted_indx2;
    // sorted_indx1 = topk_index(model->OUTPUT_SIZE, model->prob1, ProbIndex1);
    // sorted_indx2 = topk_index(model->OUTPUT_SIZE, model->prob2, ProbIndex2);
    // for (int i = 0; i < topk; i++)
    // {   
    //     cout<<"===sorted_indx1[i]==="<<sorted_indx1[i]<<endl;
    //     cout<<"===sorted_indx2[i]==="<<sorted_indx2[i]<<endl;
    // }   
    delete model;
    model = nullptr;
    return 0;
}