IBN-Net (#362)

* IBN-Net

InstanceNorm2d

resnet50-ibna

resnet50-ibnb

* add ibnnet pytorch repo

ibnnet/CMakeLists.txt

@@ -0,0 +1,35 @@
+cmake_minimum_required(VERSION 2.6)
+
+project(IBNNet)
+
+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)
+
+file(GLOB SOURCE_FILES "*.h" "*.cpp")
+
+add_executable(ibnnet ${SOURCE_FILES})
+target_link_libraries(ibnnet nvinfer)
+target_link_libraries(ibnnet cudart)
+target_link_libraries(ibnnet ${OpenCV_LIBS})
+
+add_definitions(-O2 -pthread)
+

ibnnet/InferenceEngine.cpp

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+#include "InferenceEngine.h"
+
+namespace trt {
+
+   InferenceEngine::InferenceEngine(const EngineConfig &enginecfg): _engineCfg(enginecfg) { 
+
+        assert(_engineCfg.max_batch_size > 0);
+
+        CHECK(cudaSetDevice(_engineCfg.device_id));
+
+        _runtime = make_holder(nvinfer1::createInferRuntime(gLogger));
+        assert(_runtime);
+
+        _engine = make_holder(_runtime->deserializeCudaEngine(_engineCfg.trtModelStream.get(), _engineCfg.stream_size)); 
+        assert(_engine);
+
+        _context = make_holder(_engine->createExecutionContext());
+        assert(_context);
+
+        _inputSize = _engineCfg.max_batch_size * 3 * _engineCfg.input_h * _engineCfg.input_w * _depth;
+        _outputSize = _engineCfg.max_batch_size * _engineCfg.output_size * _depth; 
+
+        CHECK(cudaMallocHost((void**)&_data, _inputSize));
+        CHECK(cudaMallocHost((void**)&_prob, _outputSize));
+
+        _streamptr = std::shared_ptr<cudaStream_t>( new cudaStream_t, 
+            [](cudaStream_t* ptr){ 
+                cudaStreamDestroy(*ptr);
+                if(ptr != nullptr){ 
+                    delete ptr;
+                } 
+            });
+
+        CHECK(cudaStreamCreate(&*_streamptr.get()));
+
+        // Pointers to input and output device buffers to pass to engine.
+        // Engine requires exactly IEngine::getNbBindings() number of buffers.
+        assert(_engine->getNbBindings() == 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()
+        _inputIndex = _engine->getBindingIndex(_engineCfg.input_name);
+        _outputIndex = _engine->getBindingIndex(_engineCfg.output_name);
+        
+        // Create GPU buffers on device
+        CHECK(cudaMalloc(&_buffers[_inputIndex], _inputSize));
+        CHECK(cudaMalloc(&_buffers[_outputIndex], _outputSize));
+
+        _inputSize /= _engineCfg.max_batch_size;
+        _outputSize /= _engineCfg.max_batch_size; 
+
+    }
+
+    bool InferenceEngine::doInference(const int inference_batch_size, std::function<void(float*)> preprocessing) {
+        assert(inference_batch_size <= _engineCfg.max_batch_size);
+        preprocessing(_data);
+        CHECK(cudaSetDevice(_engineCfg.device_id));
+        CHECK(cudaMemcpyAsync(_buffers[_inputIndex], _data, inference_batch_size * _inputSize, cudaMemcpyHostToDevice, *_streamptr));
+        auto status = _context->enqueue(inference_batch_size, _buffers, *_streamptr, nullptr);
+        CHECK(cudaMemcpyAsync(_prob, _buffers[_outputIndex], inference_batch_size * _outputSize, cudaMemcpyDeviceToHost, *_streamptr));
+        CHECK(cudaStreamSynchronize(*_streamptr));
+        return status;
+    }
+
+    InferenceEngine::InferenceEngine(InferenceEngine &&other) noexcept: 
+        _engineCfg(other._engineCfg)
+        , _data(other._data)
+        , _prob(other._prob)
+        , _inputIndex(other._inputIndex) 
+        , _outputIndex(other._outputIndex)
+        , _inputSize(other._inputSize) 
+        , _outputSize(other._outputSize)
+        , _runtime(std::move(other._runtime))
+        , _engine(std::move(other._engine))
+        , _context(std::move(other._context))
+        , _streamptr(other._streamptr) { 
+
+        _buffers[0] = other._buffers[0];
+        _buffers[1] = other._buffers[1];
+        other._streamptr.reset();
+        other._data = nullptr;
+        other._prob = nullptr;
+        other._buffers[0] = nullptr; 
+        other._buffers[1] = nullptr; 
+    } 
+
+    InferenceEngine::~InferenceEngine() {  
+        CHECK(cudaFreeHost(_data));
+        CHECK(cudaFreeHost(_prob));
+        CHECK(cudaFree(_buffers[_inputIndex]));
+        CHECK(cudaFree(_buffers[_outputIndex]));
+    }
+}

ibnnet/InferenceEngine.h

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+/**************************************************************************
+ * Handle memory pre-alloc
+ * both on host(pinned memory, allow CUDA DMA) & device
+*************************************************************************/
+
+#pragma once
+
+#include <thread>
+#include <chrono>
+#include <memory>
+#include <functional>
+#include <opencv2/opencv.hpp>
+
+#include "utils.h"
+#include "holder.h"
+#include "logging.h"
+#include "NvInfer.h"
+#include "cuda_runtime_api.h"
+static Logger gLogger;
+
+namespace trt {
+
+    struct EngineConfig {
+        const char* input_name;
+        const char* output_name; 
+        std::shared_ptr<char> trtModelStream;
+        int max_batch_size; /* create engine */
+        int input_h;  
+        int input_w;
+        int output_size;
+        int stream_size;
+        int device_id;
+    };
+
+    class InferenceEngine {
+
+    public:
+        InferenceEngine(const EngineConfig &enginecfg);
+        InferenceEngine(InferenceEngine &&other) noexcept;
+        ~InferenceEngine();
+
+        InferenceEngine(const InferenceEngine &) = delete;
+        InferenceEngine& operator=(const InferenceEngine &) = delete;
+        InferenceEngine& operator=(InferenceEngine && other) = delete;
+
+        bool doInference(const int inference_batch_size, std::function<void(float*)> preprocessing);
+        float* getOutput() { return _prob; }
+        std::thread::id getThreadID() { return std::this_thread::get_id(); }
+
+    private:
+        EngineConfig _engineCfg;
+        float* _data{nullptr};
+        float* _prob{nullptr};
+
+        // Pointers to input and output device buffers to pass to engine.
+        // Engine requires exactly IEngine::getNbBindings() number of buffers.
+        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()
+        int _inputIndex;
+        int _outputIndex;
+
+        int _inputSize;
+        int _outputSize;
+
+        static constexpr std::size_t _depth{sizeof(float)};
+
+        TensorRTHolder<nvinfer1::IRuntime> _runtime{nullptr};
+        TensorRTHolder<nvinfer1::ICudaEngine> _engine{nullptr};
+        TensorRTHolder<nvinfer1::IExecutionContext> _context{nullptr};
+        std::shared_ptr<cudaStream_t> _streamptr;
+    };
+
+}
+

ibnnet/README.md

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+# IBN-Net
+
+An implementation of IBN-Net, proposed in ["Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net"](https://arxiv.org/abs/1807.09441), ECCV2018 by Xingang Pan, Ping Luo, Jianping Shi, Xiaoou Tang. 
+
+For the Pytorch implementation, you can refer to [IBN-Net](https://github.com/XingangPan/IBN-Net)
+
+## Features
+- InstanceNorm2d
+- bottleneck_ibn
+- Resnet50-IBNA
+- Resnet50-IBNB
+- Multi-thread inference
+
+## How to Run
+
+* 1. generate .wts
+
+  // for ibn-a
+  ```
+  python gen_wts.py a
+  ```
+  a file 'resnet50-ibna.wts' will be generated.
+
+  // for ibn-b
+  ```
+  python gen_wts.py b
+  ```
+  a file 'resnet50-ibnb.wts' will be generated.
+* 2. cmake and make
+
+  ```
+  mkdir build
+  cd build
+  cmake ..
+  make
+  ```
+* 3. build engine and run classification
+
+  // put resnet50-ibna.wts/resnet50-ibnb.wts into tensorrtx/ibnnet
+  
+  // go to tensorrtx/ibnnet
+  ```
+  ./ibnnet -s  // serialize model to plan file
+  ./ibnnet -d  // deserialize plan file and run inference
+  ```
+  

ibnnet/gen_wts.py

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+import torch
+import os
+import sys
+import struct
+
+
+assert sys.argv[1] == "a" or sys.argv[1] == "b"
+model_name = "resnet50_ibn_" + sys.argv[1]
+
+net = torch.hub.load('XingangPan/IBN-Net', model_name, pretrained=True).to('cuda:0').eval()
+
+#verify
+#input = torch.ones(1, 3, 224, 224).to('cuda:0')
+#pixel_mean = torch.tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1).to('cuda:0')
+#pixel_std = torch.tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1).to('cuda:0')
+#input.sub_(pixel_mean).div_(pixel_std)
+#out = net(input)
+#print(out)
+
+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")
+
+

ibnnet/holder.h

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+#pragma once
+
+template <typename T>
+class TensorRTHolder {
+    T* holder;
+public:
+    explicit TensorRTHolder(T* holder_) : holder(holder_) {}
+    ~TensorRTHolder() {
+        if (holder)
+            holder->destroy();
+    }
+    TensorRTHolder(const TensorRTHolder&) = delete;
+    TensorRTHolder& operator=(const TensorRTHolder&) = delete;
+    TensorRTHolder(TensorRTHolder && rhs) noexcept{
+        holder = rhs.holder;
+        rhs.holder = nullptr;
+    }
+    TensorRTHolder& operator=(TensorRTHolder&& rhs) noexcept {
+        if (this == &rhs) {
+            return *this;
+        }
+        if (holder) holder->destroy();
+        holder = rhs.holder;
+        rhs.holder = nullptr;
+        return *this;
+    }
+    T* operator->() {
+        return holder;
+    }
+    T* get() { return holder; }
+    explicit operator bool() { return holder != nullptr; }
+    T& operator*() noexcept { return *holder; }
+};
+
+template <typename T>
+TensorRTHolder<T> make_holder(T* holder) {
+    return TensorRTHolder<T>(holder);
+}
+
+template <typename T>
+using TensorRTNonHolder = T*;

ibnnet/ibnnet.cpp

@@ -0,0 +1,197 @@
+#include "ibnnet.h"
+
+//#define USE_FP16
+
+namespace trt {
+
+    IBNNet::IBNNet(trt::EngineConfig &enginecfg, const IBN ibn) : _engineCfg(enginecfg) {
+        switch(ibn) {
+            case IBN::A:
+                _ibn = "a"; 
+                break;
+            case IBN::B:
+                _ibn = "b"; 
+                break;
+            case IBN::NONE:
+            default:
+                _ibn = "";
+                break;
+        }
+    }
+
+    // create the engine using only the API and not any parser.
+    ICudaEngine *IBNNet::createEngine(IBuilder* builder, IBuilderConfig* config) {
+        // resnet50-ibna, resnet50-ibnb, resnet50
+        assert(_ibn == "a" or _ibn == "b" or _ibn == "");
+        INetworkDefinition* network = builder->createNetworkV2(0U);
+
+        // Create input tensor of shape { 3, INPUT_H, INPUT_W } with name INPUT_BLOB_NAME
+        ITensor* data = network->addInput(_engineCfg.input_name, _dt, Dims3{3, _engineCfg.input_h, _engineCfg.input_w});
+        assert(data);
+
+        std::string path;
+        if(_ibn == "") {
+            path = "../resnet50.wts";
+        } else {
+            path = "../resnet50-ibn" + _ibn + ".wts";
+        }
+
+        std::map<std::string, Weights> weightMap = loadWeights(path);
+        Weights emptywts{DataType::kFLOAT, nullptr, 0};
+
+        std::map<std::string, std::vector<std::string>> ibn_layers{ 
+            { "a", {"a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "", "", ""}},
+            { "b", {"", "", "b", "", "", "","b", "", "", "", "", "", "", "", "", "",}},
+            { "", {16, ""}}};
+
+        const float mean[3] = {0.485, 0.456, 0.406}; // rgb
+        const float std[3] = {0.229, 0.224, 0.225};
+        ITensor* pre_input = MeanStd(network, weightMap, data, "", mean, std, false);
+
+        IConvolutionLayer* conv1 = network->addConvolutionNd(*pre_input, 64, DimsHW{7, 7}, weightMap["conv1.weight"], emptywts);
+        assert(conv1);
+        conv1->setStrideNd(DimsHW{2, 2});
+        conv1->setPaddingNd(DimsHW{3, 3});
+
+        IActivationLayer* relu1{nullptr};
+        if (_ibn == "b") {
+            IScaleLayer* bn1 = addInstanceNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
+            relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
+        } else {
+            IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
+            relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
+        }
+        assert(relu1);
+
+        // Add max pooling layer with stride of 2x2 and kernel size of 2x2.
+        IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
+        assert(pool1);
+        pool1->setStrideNd(DimsHW{2, 2});
+        pool1->setPaddingNd(DimsHW{1, 1});
+
+        IActivationLayer* x = bottleneck_ibn(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "layer1.0.", ibn_layers[_ibn][0]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.1.", ibn_layers[_ibn][1]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.2.", ibn_layers[_ibn][2]);
+
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 128, 2, "layer2.0.", ibn_layers[_ibn][3]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.1.", ibn_layers[_ibn][4]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.2.", ibn_layers[_ibn][5]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.3.", ibn_layers[_ibn][6]);
+
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 256, 2, "layer3.0.", ibn_layers[_ibn][7]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.1.", ibn_layers[_ibn][8]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.2.", ibn_layers[_ibn][9]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.3.", ibn_layers[_ibn][10]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.4.", ibn_layers[_ibn][11]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.5.", ibn_layers[_ibn][12]);
+
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 512, 2, "layer4.0.", ibn_layers[_ibn][13]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.1.", ibn_layers[_ibn][14]);
+        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.2.", ibn_layers[_ibn][15]);
+
+        IPoolingLayer* pool2 = network->addPoolingNd(*x->getOutput(0), PoolingType::kAVERAGE, DimsHW{7, 7});
+        assert(pool2);
+        pool2->setStrideNd(DimsHW{1, 1});
+        
+        IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]);
+        assert(fc1);
+
+        fc1->getOutput(0)->setName(_engineCfg.output_name);
+        std::cout << "set name out" << std::endl;
+        network->markOutput(*fc1->getOutput(0));
+
+        // Build engine
+        builder->setMaxBatchSize(_engineCfg.max_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;
+    }
+
+    bool IBNNet::serializeEngine() {
+        // Create builder
+        auto builder = make_holder(createInferBuilder(gLogger));
+        auto config = make_holder(builder->createBuilderConfig());
+        // Create model to populate the network, then set the outputs and create an engine
+        ICudaEngine *engine = createEngine(builder.get(), config.get());
+        assert(engine);
+
+        // Serialize the engine
+        TensorRTHolder<IHostMemory> modelStream = make_holder(engine->serialize());
+        assert(modelStream);
+
+        std::ofstream p("./ibnnet.engine", std::ios::binary | std::ios::out);
+        if (!p) {
+            std::cerr << "could not open plan output file" << std::endl;
+            return false;
+        }
+        p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
+
+        return true;
+    }
+
+    bool IBNNet::deserializeEngine() {
+        std::ifstream file("./ibnnet.engine", std::ios::binary | std::ios::in);
+        if (file.good()) {
+            file.seekg(0, file.end);
+            _engineCfg.stream_size = file.tellg();
+            file.seekg(0, file.beg);
+            _engineCfg.trtModelStream = std::shared_ptr<char>( new char[_engineCfg.stream_size], []( char* ptr ){ delete [] ptr; } );
+            assert(_engineCfg.trtModelStream.get());
+            file.read(_engineCfg.trtModelStream.get(), _engineCfg.stream_size);
+            file.close();
+    
+            _inferEngine = make_unique<trt::InferenceEngine>(_engineCfg);
+            return true;
+        }
+        return false;
+    }
+
+    void IBNNet::preprocessing(const cv::Mat& img, float* const data, const std::size_t stride) {
+        for (std::size_t i = 0; i < stride; ++i) { 
+            data[i] = img.at<cv::Vec3b>(i)[2] / 255.0; 
+            data[i + stride] = img.at<cv::Vec3b>(i)[1] / 255.0;
+            data[i + (stride<<1)] = img.at<cv::Vec3b>(i)[0] / 255.0;
+        }
+    }
+
+    bool IBNNet::inference(std::vector<cv::Mat> &input) {
+        if(_inferEngine != nullptr) {
+            const std::size_t stride = _engineCfg.input_w * _engineCfg.input_h;
+            return _inferEngine.get()->doInference(input.size(), 
+                [&](float* data) {
+                    for(const auto &img : input) {
+                        preprocessing(img, data, stride);
+                        data += 3 * stride;
+                    }
+                }
+            );
+        } else {
+            return false;
+        }
+    }
+
+    float* IBNNet::getOutput() { 
+        if(_inferEngine != nullptr) 
+            return _inferEngine.get()->getOutput(); 
+        return nullptr;
+    }
+
+    int IBNNet::getDeviceID() { 
+        return _engineCfg.device_id; 
+    }
+
+}

ibnnet/ibnnet.h

@@ -0,0 +1,45 @@
+#pragma once
+
+#include "utils.h"
+#include "holder.h"
+#include "layers.h"
+#include "InferenceEngine.h"
+#include <memory>
+#include <vector>
+#include <chrono>
+#include <opencv2/opencv.hpp>
+extern Logger gLogger;
+using namespace trtxapi;
+
+namespace trt {
+
+    enum IBN {
+        A, // resnet50-ibna,
+        B, // resnet50-ibnb,
+        NONE // resnet50
+    };
+
+    class IBNNet {
+    public:
+        IBNNet(trt::EngineConfig &enginecfg, const IBN ibn);
+        ~IBNNet() {};
+
+        bool serializeEngine(); /* create & serializeEngine */ 
+        bool deserializeEngine();
+        bool inference(std::vector<cv::Mat> &input); /* support batch inference */
+
+        float* getOutput(); 
+        int getDeviceID(); /* cuda deviceid */ 
+
+    private:
+        ICudaEngine *createEngine(IBuilder *builder, IBuilderConfig *config);
+        void preprocessing(const cv::Mat& img, float* const data, const std::size_t stride);
+
+    private:
+        trt::EngineConfig _engineCfg;
+        std::unique_ptr<trt::InferenceEngine> _inferEngine{nullptr};
+        std::string _ibn;
+        DataType _dt{DataType::kFLOAT};
+    };
+
+}

ibnnet/layers.cpp

@@ -0,0 +1,210 @@
+#include "layers.h"
+
+namespace trtxapi {
+
+    ITensor* MeanStd(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor* input, const std::string lname, const float* mean, const float* std, const bool div255) {
+        if(div255) {
+            Weights Div_225{ DataType::kFLOAT, nullptr, 3 };
+            float *wgt = reinterpret_cast<float*>(malloc(sizeof(float) * 3));
+            std::fill_n(wgt, 3, 255.0f); 
+            Div_225.values = wgt;
+            weightMap[lname + ".div"] = Div_225;
+            IConstantLayer* d = network->addConstant(Dims3{ 3, 1, 1 }, Div_225);
+            input = network->addElementWise(*input, *d->getOutput(0), ElementWiseOperation::kDIV)->getOutput(0);
+        }
+        Weights Mean{ DataType::kFLOAT, nullptr, 3 };
+        Mean.values = mean;
+        IConstantLayer* m = network->addConstant(Dims3{ 3, 1, 1 }, Mean);
+        IElementWiseLayer* sub_mean = network->addElementWise(*input, *m->getOutput(0), ElementWiseOperation::kSUB);
+        if (std != nullptr) {
+            Weights Std{ DataType::kFLOAT, nullptr, 3 };
+            Std.values = std;
+            IConstantLayer* s = network->addConstant(Dims3{ 3, 1, 1 }, Std);
+            IElementWiseLayer* std_mean = network->addElementWise(*sub_mean->getOutput(0), *s->getOutput(0), ElementWiseOperation::kDIV);
+            return std_mean->getOutput(0);
+        } else {
+            return sub_mean->getOutput(0);
+        }
+    }
+
+    IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, const std::string lname, const 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;
+
+        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 wscale{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 wshift{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 wpower{DataType::kFLOAT, pval, len};
+
+        weightMap[lname + ".scale"] = wscale;
+        weightMap[lname + ".shift"] = wshift;
+        weightMap[lname + ".power"] = wpower;
+        IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, wshift, wscale, wpower);
+        assert(scale_1);
+        return scale_1;
+    }
+
+    IScaleLayer* addInstanceNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, const std::string lname, const float eps) {
+
+        int len = weightMap[lname + ".weight"].count;
+
+        IReduceLayer* reduce1 = network->addReduce(input, 
+            ReduceOperation::kAVG,
+            6, 
+            true);
+        assert(reduce1);
+
+        IElementWiseLayer* ew1 = network->addElementWise(input, 
+            *reduce1->getOutput(0),
+            ElementWiseOperation::kSUB);  
+        assert(ew1);
+
+        const static float pval1[3]{0.0, 1.0, 2.0};   
+        Weights wshift1{DataType::kFLOAT, pval1, 1};
+        Weights wscale1{DataType::kFLOAT, pval1+1, 1};
+        Weights wpower1{DataType::kFLOAT, pval1+2, 1};
+
+        IScaleLayer* scale1 = network->addScale(
+            *ew1->getOutput(0), 
+            ScaleMode::kUNIFORM,
+            wshift1,  
+            wscale1,  
+            wpower1); 
+        assert(scale1);
+
+        IReduceLayer* reduce2 = network->addReduce(
+            *scale1->getOutput(0), 
+            ReduceOperation::kAVG,
+            6, 
+            true);
+        assert(reduce2);
+
+        const static float pval2[3]{eps, 1.0, 0.5}; 
+        Weights wshift2{DataType::kFLOAT, pval2, 1};
+        Weights wscale2{DataType::kFLOAT, pval2+1, 1};
+        Weights wpower2{DataType::kFLOAT, pval2+2, 1};
+        
+        IScaleLayer* scale2 = network->addScale(
+            *reduce2->getOutput(0), 
+            ScaleMode::kUNIFORM,
+            wshift2,  
+            wscale2,  
+            wpower2);
+        assert(scale2);
+
+        IElementWiseLayer* ew2 = network->addElementWise(*ew1->getOutput(0), 
+            *scale2->getOutput(0),
+            ElementWiseOperation::kDIV); 
+        assert(ew2);
+
+        float* pval3 = reinterpret_cast<float*>(malloc(sizeof(float) * len));
+        std::fill_n(pval3, len, 1.0); 
+        Weights wpower3{DataType::kFLOAT, pval3, len};
+        weightMap[lname + ".power3"] = wpower3;
+
+        IScaleLayer* scale3 = network->addScale(
+            *ew2->getOutput(0), 
+            ScaleMode::kCHANNEL,
+            weightMap[lname + ".bias"], 
+            weightMap[lname + ".weight"],  
+            wpower3); 
+        assert(scale3);
+        return scale3;
+    }
+
+    IConcatenationLayer* addIBN(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, const std::string lname) {
+        Dims spliteDims = input.getDimensions();
+        ISliceLayer *split1 = network->addSlice(input, 
+            Dims3{0, 0, 0}, 
+            Dims3{spliteDims.d[0]/2, spliteDims.d[1], spliteDims.d[2]}, 
+            Dims3{1, 1, 1});
+        assert(split1);
+
+        ISliceLayer *split2 = network->addSlice(input, 
+            Dims3{spliteDims.d[0]/2, 0, 0}, 
+            Dims3{spliteDims.d[0]/2, spliteDims.d[1], spliteDims.d[2]}, 
+            Dims3{1, 1, 1});
+        assert(split2);
+
+        auto in1 = addInstanceNorm2d(network, weightMap, *split1->getOutput(0), lname + "IN", 1e-5);
+        auto bn1 = addBatchNorm2d(network, weightMap, *split2->getOutput(0), lname + "BN", 1e-5);
+
+        ITensor* tensor1[] = {in1->getOutput(0), bn1->getOutput(0)};
+        auto cat1 = network->addConcatenation(tensor1, 2);
+        assert(cat1);
+        return cat1;
+    }
+
+    IActivationLayer* bottleneck_ibn(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, const int inch, const int outch, const int stride, const std::string lname, const std::string ibn) {
+        Weights emptywts{DataType::kFLOAT, nullptr, 0};
+
+        IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{1, 1}, weightMap[lname + "conv1.weight"], emptywts);
+        assert(conv1);
+
+        IActivationLayer* relu1{nullptr};
+        if (ibn == "a") {
+            IConcatenationLayer* bn1 = addIBN(network, weightMap, *conv1->getOutput(0), lname + "bn1.");
+            relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
+            assert(relu1);
+        } else {
+            IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5);
+            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);
+
+        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);
+
+        IElementWiseLayer* ew1;
+        if (stride != 1 || inch != outch * 4) {
+            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);
+            ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
+        } else {
+            ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
+        }
+    
+        IActivationLayer* relu3{nullptr};
+        if (ibn == "b") {
+            IScaleLayer* in1 = addInstanceNorm2d(network, weightMap, *ew1->getOutput(0), lname + "IN", 1e-5);
+            relu3 = network->addActivation(*in1->getOutput(0), ActivationType::kRELU);
+        } else {
+            relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
+        }
+
+        assert(relu3);
+        return relu3;
+    }
+
+}

ibnnet/layers.h

@@ -0,0 +1,46 @@
+#pragma once
+
+#include <map>
+#include <math.h>
+#include <assert.h>
+#include "NvInfer.h"
+#include "cuda_runtime_api.h"
+using namespace nvinfer1;
+
+namespace trtxapi {
+
+    ITensor* MeanStd(INetworkDefinition *network, 
+        std::map<std::string, Weights>& weightMap, 
+        ITensor* input, 
+        const std::string lname,
+        const float* mean, 
+        const float* std, 
+        const bool div255);
+
+    IScaleLayer* addBatchNorm2d(INetworkDefinition *network, 
+        std::map<std::string, Weights>& weightMap, 
+        ITensor& input, 
+        const std::string lname, 
+        const float eps);
+
+    IScaleLayer* addInstanceNorm2d(INetworkDefinition *network, 
+        std::map<std::string, Weights>& weightMap, 
+        ITensor& input, 
+        const std::string lname, 
+        const float eps);
+
+    IConcatenationLayer* addIBN(INetworkDefinition *network, 
+        std::map<std::string, Weights>& weightMap, 
+        ITensor& input, 
+        const std::string lname);
+
+    IActivationLayer* bottleneck_ibn(INetworkDefinition *network, 
+        std::map<std::string, Weights>& weightMap, 
+        ITensor& input, 
+        const int inch, 
+        const int outch,
+        const int stride, 
+        const std::string lname, 
+        const std::string ibn);
+
+}

ibnnet/logging.h

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

ibnnet/main.cpp

@@ -0,0 +1,98 @@
+#include <thread>
+#include <vector>
+#include <memory>
+#include "ibnnet.h"
+#include "InferenceEngine.h"
+
+// stuff we know about the network and the input/output blobs
+static const int MAX_BATCH_SIZE = 4;
+static const int INPUT_H = 224;
+static const int INPUT_W = 224;
+static const int OUTPUT_SIZE = 1000;
+static const int DEVICE_ID = 0;
+const char* INPUT_BLOB_NAME = "data";
+const char* OUTPUT_BLOB_NAME = "prob";
+extern Logger gLogger;
+
+void run_infer(std::shared_ptr<trt::IBNNet> model) {
+
+    CHECK(cudaSetDevice(model->getDeviceID()));
+
+    if(!model->deserializeEngine()) {
+        std::cout << "DeserializeEngine Failed." << std::endl;
+        return;
+    }
+
+    /* support batch input data */
+    std::vector<cv::Mat> input;
+    input.emplace_back( cv::Mat(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(255,255,255)) ) ;
+
+    /* run inference */
+    model->inference(input); 
+
+    /* get output data from cudaMalloc */
+    float* prob = model->getOutput();
+
+    /* print output */
+    std::cout << "\nOutput from thread_id: " << std::this_thread::get_id() << std::endl;
+    if( prob != nullptr ) { 
+        for (size_t batch_idx = 0; batch_idx < input.size(); ++batch_idx) {
+            for (int p = 0; p < OUTPUT_SIZE; ++p) {
+                std::cout<< prob[batch_idx+p] << " ";
+                if ((p+1) % 10 == 0) {
+                    std::cout << std::endl;
+                }
+            }
+        }
+    }
+}
+
+int main(int argc, char** argv) {
+
+    trt::EngineConfig engineCfg { 
+        INPUT_BLOB_NAME,
+        OUTPUT_BLOB_NAME,
+        nullptr,
+        MAX_BATCH_SIZE,
+        INPUT_H,
+        INPUT_W,
+        OUTPUT_SIZE,
+        0,
+        DEVICE_ID};
+
+    if (argc == 2 && std::string(argv[1]) == "-s") {
+        std::cout << "Serializling Engine" << std::endl;
+        trt::IBNNet ibnnet{engineCfg, trt::IBN::A}; 
+        ibnnet.serializeEngine();
+        return 0;
+    } else if (argc == 2 && std::string(argv[1]) == "-d") {
+
+        /* 
+         * Support multi thread inference (mthreads>1)
+         * Each thread holds their own CudaEngine
+         * They can run on different cuda device through trt::EngineConfig setting
+        */
+        int mthreads = 1; 
+        std::vector<std::thread> workers;
+        std::vector<std::shared_ptr<trt::IBNNet>> models;
+
+        for(int i = 0; i < mthreads; ++i) {
+            models.emplace_back( std::make_shared<trt::IBNNet>(engineCfg, trt::IBN::A) ); // For IBNB: trt::IBN::B
+        }
+
+        for(int i = 0; i < mthreads; ++i) {
+            workers.emplace_back( std::thread(run_infer, models[i]) );
+        }
+
+        for(auto & worker : workers) {
+            worker.join();
+        } 
+
+        return 0;
+    } else {
+        std::cerr << "arguments not right!" << std::endl;
+        std::cerr << "./ibnnet -s  // serialize model to plan file" << std::endl;
+        std::cerr << "./ibnnet -d  // deserialize plan file and run inference" << std::endl;
+        return -1;
+    }
+}

ibnnet/utils.cpp

@@ -0,0 +1,39 @@
+#include "utils.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;
+}

ibnnet/utils.h

@@ -0,0 +1,30 @@
+#pragma once
+
+#include <map>
+#include "NvInfer.h"
+#include "cuda_runtime_api.h"
+#include "assert.h"
+#include <fstream>
+#include <iostream>
+#include <memory>
+
+using namespace nvinfer1;
+
+#define CHECK(status)                             \
+    do                                            \
+    {                                             \
+        auto ret = (status);                      \
+        if (ret != 0)                             \
+        {                                         \
+            std::cout << "Cuda failure: " << ret; \
+            abort();                              \
+        }                                         \
+    } while (0)
+
+template<typename T, typename... Args>
+std::unique_ptr<T> make_unique(Args&&... args) {
+    return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
+}
+
+std::map<std::string, Weights> loadWeights(const std::string file);
+