unet.cpp

#include <iostream>
#include <chrono>
#include "cuda_runtime_api.h"
#include "logging.h"
#include "common.hpp"
#define DEVICE 0
#define NET s  // s m l x
#define NETSTRUCT(str) createEngine_##str
#define CREATENET(net) NETSTRUCT(net)
#define STR1(x) #x
#define STR2(x) STR1(x)
// #define USE_FP16  // comment out this if want to use FP16
#define CONF_THRESH 0.5
#define BATCH_SIZE 1
#define BILINEAR true
// stuff we know about the network and the input/output blobs
static const int INPUT_H = 816;
static const int INPUT_W = 672;
static const int OUTPUT_SIZE = 672*816;

const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";

using namespace nvinfer1;

static Logger gLogger;


cv::Mat preprocess_img(cv::Mat& img) {
    int w, h, x, y;
    float r_w = INPUT_W / (img.cols*1.0);
    float r_h = INPUT_H / (img.rows*1.0);
    if (r_h > r_w) {
        w = INPUT_W;
        h = r_w * img.rows;
        x = 0;
        y = (INPUT_H - h) / 2;
    } else {
        w = r_h* img.cols;
        h = INPUT_H;
        x = (INPUT_W - w) / 2;
        y = 0;
    }
    cv::Mat re(h, w, CV_8UC3);
    cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC);
    cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
    re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
    return out;
}



ILayer* doubleConv(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, std::string lname, int midch){
    // Weights emptywts{DataType::kFLOAT, nullptr, 0};
    // int p = ksize / 2;
    // if (midch==NULL){
    //     midch = outch;
    // }
    IConvolutionLayer* conv1 = network->addConvolutionNd(input, midch, DimsHW{ksize, ksize}, weightMap[lname + ".double_conv.0.weight"], weightMap[lname + ".double_conv.0.bias"]);
    conv1->setStrideNd(DimsHW{1, 1});
    conv1->setPaddingNd(DimsHW{1, 1});
    conv1->setNbGroups(1);
    IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".double_conv.1", 0);
    IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
    IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + ".double_conv.3.weight"], weightMap[lname + ".double_conv.3.bias"]);
    conv2->setStrideNd(DimsHW{1, 1});
    conv2->setPaddingNd(DimsHW{1, 1});
    conv2->setNbGroups(1);
    IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".double_conv.4", 0);
    IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kLEAKY_RELU);
    assert(relu2);
    return relu2;
}

ILayer* down(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input,  int outch, int p, std::string lname){
    
    IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{2, 2});
    assert(pool1);
    ILayer* dcov1 = doubleConv(network,weightMap,*pool1->getOutput(0),outch,3,lname+".maxpool_conv.1",outch);
    assert(dcov1);
    return dcov1;
}

ILayer* up(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input1,  ITensor& input2, int resize,  int outch, int midch, std::string lname){
    float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * resize * 2 * 2));
    for (int i = 0; i < resize * 2 * 2; i++) {
        deval[i] = 1.0;
    }

    if (BILINEAR){
        // add upsample bilinear
        IResizeLayer* deconv1 = network->addResize(input1);
        auto outdims = input2.getDimensions();
        deconv1->setOutputDimensions(outdims);
        deconv1->setResizeMode(ResizeMode::kLINEAR);
        deconv1->setAlignCorners(true);

        int diffx = input2.getDimensions().d[1]-deconv1->getOutput(0)->getDimensions().d[1];
        int diffy = input2.getDimensions().d[2]-deconv1->getOutput(0)->getDimensions().d[2];

        ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0),DimsHW{diffx / 2, diffy / 2},DimsHW{diffx - (diffx / 2), diffy - (diffy / 2)});
        // dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2});
        ITensor* inputTensors[] = {&input2,pad1->getOutput(0)};
        auto cat = network->addConcatenation(inputTensors, 2);
        assert(cat);
        if (midch==64){
            ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),outch,3,lname+".conv",outch);
            assert(dcov1);
            return dcov1;
        }else{
            int midch1 = outch/2;
            ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),midch1,3,lname+".conv",outch);
            assert(dcov1);
            return dcov1;
        }
    }else{
        Weights emptywts{DataType::kFLOAT, nullptr, 0};
        Weights deconvwts1{DataType::kFLOAT, deval, resize * 2 * 2};
        IDeconvolutionLayer* deconv1 = network->addDeconvolutionNd(input1, resize, DimsHW{2, 2}, deconvwts1, emptywts);
        deconv1->setStrideNd(DimsHW{2, 2});
        deconv1->setNbGroups(resize);
        weightMap["deconvwts."+lname] = deconvwts1;

        int diffx = input2.getDimensions().d[1]-deconv1->getOutput(0)->getDimensions().d[1];
        int diffy = input2.getDimensions().d[2]-deconv1->getOutput(0)->getDimensions().d[2];

        ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0),DimsHW{diffx / 2, diffy / 2},DimsHW{diffx - (diffx / 2), diffy - (diffy / 2)});
        // dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2});
        ITensor* inputTensors[] = {&input2,pad1->getOutput(0)};
        auto cat = network->addConcatenation(inputTensors, 2);
        assert(cat);
        if (midch==64){
            ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),outch,3,lname+".conv",outch);
            assert(dcov1);
            return dcov1;
        }else{
            int midch1 = outch/2;
            ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),midch1,3,lname+".conv",outch);
            assert(dcov1);
            return dcov1;
        }
    }


}

ILayer* outConv(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input,  int outch, std::string lname){
    // Weights emptywts{DataType::kFLOAT, nullptr, 0};

    IConvolutionLayer* conv1 = network->addConvolutionNd(input, 1, DimsHW{1, 1}, weightMap[lname + ".conv.weight"], weightMap[lname + ".conv.bias"]);
    assert(conv1);
    conv1->setStrideNd(DimsHW{1, 1});
    conv1->setPaddingNd(DimsHW{0, 0});
    conv1->setNbGroups(1);
    return conv1;
}



ICudaEngine* createEngine_l(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
    INetworkDefinition* network = builder->createNetworkV2(0U);

    // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
    ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
    assert(data);

    std::map<std::string, Weights> weightMap = loadWeights("/home/sycv/workplace/pengyuzhou/tensorrtx/unet/unet_816_672.wts");
    Weights emptywts{DataType::kFLOAT, nullptr, 0};

    // build network
    auto x1 = doubleConv(network,weightMap,*data,64,3,"inc",64);
    auto x2 = down(network,weightMap,*x1->getOutput(0),128,1,"down1");
    auto x3 = down(network,weightMap,*x2->getOutput(0),256,1,"down2");
    auto x4 = down(network,weightMap,*x3->getOutput(0),512,1,"down3");
    auto x5 = down(network,weightMap,*x4->getOutput(0),512,1,"down4");
    ILayer* x6 = up(network,weightMap,*x5->getOutput(0),*x4->getOutput(0),512,512,512,"up1");
    ILayer* x7 = up(network,weightMap,*x6->getOutput(0),*x3->getOutput(0),256,256,256,"up2");
    ILayer* x8 = up(network,weightMap,*x7->getOutput(0),*x2->getOutput(0),128,128,128,"up3");
    ILayer* x9 = up(network,weightMap,*x8->getOutput(0),*x1->getOutput(0),64,64,64,"up4");
    ILayer* x10 = outConv(network,weightMap,*x9->getOutput(0),OUTPUT_SIZE,"outc");
    std::cout << "set name out" << std::endl;
    x10->getOutput(0)->setName(OUTPUT_BLOB_NAME);
    network->markOutput(*x10->getOutput(0));

    // Build engine
    builder->setMaxBatchSize(maxBatchSize);
    config->setMaxWorkspaceSize(16 * (1 << 20));  // 16MB
#ifdef USE_FP16
    config->setFlag(BuilderFlag::kFP16);
#endif
    std::cout << "Building engine, please wait for a while..." << std::endl;
    ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
    std::cout << "Build engine successfully!" << 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;
}


void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
    // Create builder
    IBuilder* builder = createInferBuilder(gLogger);
    IBuilderConfig* config = builder->createBuilderConfig();

    // Create model to populate the network, then set the outputs and create an engine
    // ICudaEngine* engine = (CREATENET(NET))(maxBatchSize, builder, config, DataType::kFLOAT);
    ICudaEngine* engine = createEngine_l(maxBatchSize, builder, config, DataType::kFLOAT);
    assert(engine != nullptr);

    // Serialize the engine
    (*modelStream) = engine->serialize();

    // Close everything down
    engine->destroy();
    builder->destroy();
}

void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
    const ICudaEngine& engine = context.getEngine();

    // Pointers to input and output device buffers to pass to engine.
    // Engine requires exactly IEngine::getNbBindings() number of buffers.
    assert(engine.getNbBindings() == 2);
    void* buffers[2];

    // In order to bind the buffers, we need to know the names of the input and output tensors.
    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);

    // Create GPU buffers on device
    CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
    CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));

    // Create stream
    cudaStream_t stream;
    CHECK(cudaStreamCreate(&stream));

    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
    context.enqueue(batchSize, buffers, stream, nullptr);
    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
    //流同步：通过cudaStreamSynchronize()来协调。
    cudaStreamSynchronize(stream);

    // Release stream and buffers
    cudaStreamDestroy(stream);
    CHECK(cudaFree(buffers[inputIndex]));
    CHECK(cudaFree(buffers[outputIndex]));
}

struct  Detection{        
    float mask[INPUT_W*INPUT_H*1];
    };

float sigmoid(float x)
{
    return (1 / (1 + exp(-x)));
}

void process_cls_result(Detection &res, float *output) {    
    for(int i=0;i<INPUT_W*INPUT_H*1;i++){
        res.mask[i] = sigmoid(*(output+i));
        }
    }

int main(int argc, char** argv) {
    cudaSetDevice(DEVICE);
    // create a model using the API directly and serialize it to a stream
    char *trtModelStream{nullptr};
    size_t size{0};
    std::string engine_name = "unet.engine";
    if (argc == 2 && std::string(argv[1]) == "-s") {
        IHostMemory* modelStream{nullptr};
        APIToModel(BATCH_SIZE, &modelStream);
        assert(modelStream != nullptr);
        std::ofstream p(engine_name, std::ios::binary);
        if (!p) {
            std::cerr << "could not open plan output file" << std::endl;
            return -1;
        }
        p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
        modelStream->destroy();
        return 0;
    } else if (argc == 3 && std::string(argv[1]) == "-d") {
        std::ifstream file(engine_name, std::ios::binary);
        if (file.good()) {
            file.seekg(0, file.end);
            size = file.tellg();
            file.seekg(0, file.beg);
            trtModelStream = new char[size];
            assert(trtModelStream);
            file.read(trtModelStream, size);
            file.close();
        }
    } else {
        std::cerr << "arguments not right!" << std::endl;
        std::cerr << "./unet -s  // serialize model to plan file" << std::endl;
        std::cerr << "./unet -d ../samples  // deserialize plan file and run inference" << std::endl;
        return -1;
    }

    std::vector<std::string> file_names;
    if (read_files_in_dir(argv[2], file_names) < 0) {
        std::cout << "read_files_in_dir failed." << std::endl;
        return -1;
    }

    // prepare input data ---------------------------
    static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
    //for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
    //    data[i] = 1.0;
    static float prob[BATCH_SIZE * OUTPUT_SIZE];
    IRuntime* runtime = createInferRuntime(gLogger);
    assert(runtime != nullptr);
    ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
    assert(engine != nullptr);
    IExecutionContext* context = engine->createExecutionContext();
    assert(context != nullptr);
    delete[] trtModelStream;

    int fcount = 0;
    for (int f = 0; f < (int)file_names.size(); f++) {
        fcount++;
        if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
        for (int b = 0; b < fcount; b++) {
            cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]);
            if (img.empty()) continue;
            cv::Mat pr_img = preprocess_img(img); // letterbox BGR to RGB
            // cv::imwrite("s_o" + file_names[f - fcount + 1 + b] + "_unet.jpg", pr_img); 
            int i = 0;
            for (int row = 0; row < INPUT_H; ++row) {
                uchar* uc_pixel = pr_img.data + row * pr_img.step;
                for (int col = 0; col < INPUT_W; ++col) {
                    data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
                    data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
                    data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
                    uc_pixel += 3;
                    ++i;
                }
            }
        }

        // Run inference
        auto start = std::chrono::system_clock::now();
        doInference(*context, data, prob, BATCH_SIZE);
        auto end = std::chrono::system_clock::now();
        std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;

        

        std::vector<Detection> batch_res(fcount);
        for (int b = 0; b < fcount; b++) {
            auto& res = batch_res[b];
            process_cls_result(res, &prob[b * OUTPUT_SIZE]);
        }

        std::cout << fcount << std::endl;

        for (int b = 0; b < fcount; b++) {
            auto& res = batch_res[b];
            float * mask = res.mask;
            cv::Mat mask_mat = cv::Mat(INPUT_H,INPUT_W,CV_8UC1); 
            uchar *ptmp = NULL;
            for(int i =0; i< INPUT_H ;i++){
                ptmp = mask_mat.ptr<uchar>(i);
                for(int j=0;j<INPUT_W;j++){
                    float * pixcel = mask+i*INPUT_W+j;
                    // std::cout << *pixcel << std::endl;
                    if(*pixcel > CONF_THRESH){
                        ptmp[j] = 255;
                    }
                    else{
                        ptmp[j]=0;
                    } 
                }            
            }

            cv::imwrite("s_" + file_names[f - fcount + 1 + b] + "_unet.jpg", mask_mat); 
        }
        fcount = 0;
    }

    // Destroy the engine
    context->destroy();
    engine->destroy();
    runtime->destroy();

    return 0;
}