373 lines
14 KiB
C++
373 lines
14 KiB
C++
#include <iostream>
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#include <chrono>
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#include "cuda_runtime_api.h"
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#include "logging.h"
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#include "common.hpp"
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#define DEVICE 0
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#define NET s // s m l x
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#define NETSTRUCT(str) createEngine_##str
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#define CREATENET(net) NETSTRUCT(net)
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#define STR1(x) #x
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#define STR2(x) STR1(x)
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// #define USE_FP16 // comment out this if want to use FP16
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#define CONF_THRESH 0.5
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#define BATCH_SIZE 1
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = 816;
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static const int INPUT_W = 672;
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static const int OUTPUT_SIZE = 672*816;
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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using namespace nvinfer1;
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static Logger gLogger;
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cv::Mat preprocess_img(cv::Mat& img) {
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int w, h, x, y;
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float r_w = INPUT_W / (img.cols*1.0);
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float r_h = INPUT_H / (img.rows*1.0);
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if (r_h > r_w) {
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w = INPUT_W;
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h = r_w * img.rows;
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x = 0;
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y = (INPUT_H - h) / 2;
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} else {
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w = r_h* img.cols;
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h = INPUT_H;
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x = (INPUT_W - w) / 2;
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y = 0;
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}
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cv::Mat re(h, w, CV_8UC3);
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cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC);
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cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
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re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
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return out;
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}
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ILayer* doubleConv(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, std::string lname, int midch){
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// Weights emptywts{DataType::kFLOAT, nullptr, 0};
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// int p = ksize / 2;
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// if (midch==NULL){
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// midch = outch;
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// }
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IConvolutionLayer* conv1 = network->addConvolutionNd(input, midch, DimsHW{ksize, ksize}, weightMap[lname + ".double_conv.0.weight"], weightMap[lname + ".double_conv.0.bias"]);
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conv1->setStrideNd(DimsHW{1, 1});
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conv1->setPaddingNd(DimsHW{1, 1});
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conv1->setNbGroups(1);
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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".double_conv.1", 0);
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IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
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IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + ".double_conv.3.weight"], weightMap[lname + ".double_conv.3.bias"]);
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conv2->setStrideNd(DimsHW{1, 1});
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conv2->setPaddingNd(DimsHW{1, 1});
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conv2->setNbGroups(1);
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IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".double_conv.4", 0);
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IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kLEAKY_RELU);
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assert(relu2);
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return relu2;
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}
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ILayer* down(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int p, std::string lname){
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IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{2, 2});
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assert(pool1);
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ILayer* dcov1 = doubleConv(network,weightMap,*pool1->getOutput(0),outch,3,lname+".maxpool_conv.1",outch);
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assert(dcov1);
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return dcov1;
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}
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ILayer* up(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input1, ITensor& input2, int resize, int outch, int midch, std::string lname){
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float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * resize * 2 * 2));
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for (int i = 0; i < resize * 2 * 2; i++) {
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deval[i] = 1.0;
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}
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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Weights deconvwts1{DataType::kFLOAT, deval, resize * 2 * 2};
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IDeconvolutionLayer* deconv1 = network->addDeconvolutionNd(input1, resize, DimsHW{2, 2}, deconvwts1, emptywts);
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deconv1->setStrideNd(DimsHW{2, 2});
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deconv1->setNbGroups(resize);
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weightMap["deconvwts."+lname] = deconvwts1;
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int diffx = input2.getDimensions().d[1]-deconv1->getOutput(0)->getDimensions().d[1];
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int diffy = input2.getDimensions().d[2]-deconv1->getOutput(0)->getDimensions().d[2];
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// IPoolingLayer* pool1 = network->addPooling(dcov1, PoolingType::kMAX, DimsHW{2, 2});
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// pool1->setStrideNd(DimsHW{2, 2});
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// dcov1->add_pading
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ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0),DimsHW{diffx / 2, diffy / 2},DimsHW{diffx - (diffx / 2), diffy - (diffy / 2)});
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// dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2});
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ITensor* inputTensors[] = {&input2,pad1->getOutput(0)};
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auto cat = network->addConcatenation(inputTensors, 2);
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assert(cat);
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if (midch==64){
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ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),outch,3,lname+".conv",outch);
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assert(dcov1);
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return dcov1;
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}else{
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int midch1 = outch/2;
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ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),midch1,3,lname+".conv",outch);
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assert(dcov1);
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return dcov1;
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}
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// assert(dcov1);
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// return dcov1;
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}
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ILayer* outConv(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, std::string lname){
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// Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer* conv1 = network->addConvolutionNd(input, 1, DimsHW{1, 1}, weightMap[lname + ".conv.weight"], weightMap[lname + ".conv.bias"]);
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assert(conv1);
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conv1->setStrideNd(DimsHW{1, 1});
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conv1->setPaddingNd(DimsHW{0, 0});
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conv1->setNbGroups(1);
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return conv1;
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}
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ICudaEngine* createEngine_l(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
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INetworkDefinition* network = builder->createNetworkV2(0U);
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// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
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ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights("/home/sycv/workplace/pengyuzhou/tensorrtx/unet/unet_816_672.wts");
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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// build network
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auto x1 = doubleConv(network,weightMap,*data,64,3,"inc",64);
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auto x2 = down(network,weightMap,*x1->getOutput(0),128,1,"down1");
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auto x3 = down(network,weightMap,*x2->getOutput(0),256,1,"down2");
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auto x4 = down(network,weightMap,*x3->getOutput(0),512,1,"down3");
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auto x5 = down(network,weightMap,*x4->getOutput(0),512,1,"down4");
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ILayer* x6 = up(network,weightMap,*x5->getOutput(0),*x4->getOutput(0),512,512,512,"up1");
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ILayer* x7 = up(network,weightMap,*x6->getOutput(0),*x3->getOutput(0),256,256,256,"up2");
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ILayer* x8 = up(network,weightMap,*x7->getOutput(0),*x2->getOutput(0),128,128,128,"up3");
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ILayer* x9 = up(network,weightMap,*x8->getOutput(0),*x1->getOutput(0),64,64,64,"up4");
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ILayer* x10 = outConv(network,weightMap,*x9->getOutput(0),OUTPUT_SIZE,"outc");
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std::cout << "set name out" << std::endl;
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x10->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*x10->getOutput(0));
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// Build engine
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builder->setMaxBatchSize(maxBatchSize);
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config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
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#ifdef USE_FP16
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config->setFlag(BuilderFlag::kFP16);
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#endif
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std::cout << "Building engine, please wait for a while..." << std::endl;
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ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
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std::cout << "Build engine successfully!" << std::endl;
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// Don't need the network any more
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network->destroy();
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// Release host memory
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for (auto& mem : weightMap)
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{
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free((void*)(mem.second.values));
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}
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return engine;
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}
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void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
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// Create builder
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IBuilder* builder = createInferBuilder(gLogger);
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IBuilderConfig* config = builder->createBuilderConfig();
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// Create model to populate the network, then set the outputs and create an engine
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// ICudaEngine* engine = (CREATENET(NET))(maxBatchSize, builder, config, DataType::kFLOAT);
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ICudaEngine* engine = createEngine_l(maxBatchSize, builder, config, DataType::kFLOAT);
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assert(engine != nullptr);
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// Serialize the engine
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(*modelStream) = engine->serialize();
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// Close everything down
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engine->destroy();
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builder->destroy();
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}
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void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
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const ICudaEngine& engine = context.getEngine();
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// Pointers to input and output device buffers to pass to engine.
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// Engine requires exactly IEngine::getNbBindings() number of buffers.
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assert(engine.getNbBindings() == 2);
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void* buffers[2];
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// In order to bind the buffers, we need to know the names of the input and output tensors.
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// Note that indices are guaranteed to be less than IEngine::getNbBindings()
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const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
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const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
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// Create GPU buffers on device
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CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
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CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
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// Create stream
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cudaStream_t stream;
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CHECK(cudaStreamCreate(&stream));
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// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
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CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
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context.enqueue(batchSize, buffers, stream, nullptr);
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CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
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//流同步：通过cudaStreamSynchronize()来协调。
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cudaStreamSynchronize(stream);
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// Release stream and buffers
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cudaStreamDestroy(stream);
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CHECK(cudaFree(buffers[inputIndex]));
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CHECK(cudaFree(buffers[outputIndex]));
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}
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struct Detection{
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float mask[INPUT_W*INPUT_H*1];
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};
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float sigmoid(float x)
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{
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return (1 / (1 + exp(-x)));
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}
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void process_cls_result(Detection &res, float *output) {
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for(int i=0;i<INPUT_W*INPUT_H*1;i++){
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res.mask[i] = sigmoid(*(output+i));
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}
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}
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int main(int argc, char** argv) {
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cudaSetDevice(DEVICE);
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// create a model using the API directly and serialize it to a stream
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char *trtModelStream{nullptr};
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size_t size{0};
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std::string engine_name = "unet.engine";
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if (argc == 2 && std::string(argv[1]) == "-s") {
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IHostMemory* modelStream{nullptr};
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APIToModel(BATCH_SIZE, &modelStream);
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assert(modelStream != nullptr);
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std::ofstream p(engine_name, std::ios::binary);
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if (!p) {
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std::cerr << "could not open plan output file" << std::endl;
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return -1;
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}
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p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
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modelStream->destroy();
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return 0;
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} else if (argc == 3 && std::string(argv[1]) == "-d") {
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std::ifstream file(engine_name, std::ios::binary);
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if (file.good()) {
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file.seekg(0, file.end);
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size = file.tellg();
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file.seekg(0, file.beg);
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trtModelStream = new char[size];
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assert(trtModelStream);
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file.read(trtModelStream, size);
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file.close();
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}
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} else {
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std::cerr << "arguments not right!" << std::endl;
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std::cerr << "./unet -s // serialize model to plan file" << std::endl;
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std::cerr << "./unet -d ../samples // deserialize plan file and run inference" << std::endl;
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return -1;
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}
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std::vector<std::string> file_names;
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if (read_files_in_dir(argv[2], file_names) < 0) {
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std::cout << "read_files_in_dir failed." << std::endl;
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return -1;
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}
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// prepare input data ---------------------------
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static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
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//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
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// data[i] = 1.0;
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static float prob[BATCH_SIZE * OUTPUT_SIZE];
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IRuntime* runtime = createInferRuntime(gLogger);
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assert(runtime != nullptr);
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ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
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assert(engine != nullptr);
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IExecutionContext* context = engine->createExecutionContext();
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assert(context != nullptr);
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delete[] trtModelStream;
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int fcount = 0;
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for (int f = 0; f < (int)file_names.size(); f++) {
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fcount++;
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if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
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for (int b = 0; b < fcount; b++) {
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cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]);
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if (img.empty()) continue;
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cv::Mat pr_img = preprocess_img(img); // letterbox BGR to RGB
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// cv::imwrite("s_o" + file_names[f - fcount + 1 + b] + "_unet.jpg", pr_img);
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int i = 0;
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for (int row = 0; row < INPUT_H; ++row) {
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uchar* uc_pixel = pr_img.data + row * pr_img.step;
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for (int col = 0; col < INPUT_W; ++col) {
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data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
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data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
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data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
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uc_pixel += 3;
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++i;
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}
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}
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}
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// Run inference
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auto start = std::chrono::system_clock::now();
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doInference(*context, data, prob, BATCH_SIZE);
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auto end = std::chrono::system_clock::now();
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std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
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std::vector<Detection> batch_res(fcount);
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for (int b = 0; b < fcount; b++) {
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auto& res = batch_res[b];
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process_cls_result(res, &prob[b * OUTPUT_SIZE]);
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}
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std::cout << fcount << std::endl;
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for (int b = 0; b < fcount; b++) {
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auto& res = batch_res[b];
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float * mask = res.mask;
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cv::Mat mask_mat = cv::Mat(INPUT_H,INPUT_W,CV_8UC1);
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uchar *ptmp = NULL;
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for(int i =0; i< INPUT_H ;i++){
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ptmp = mask_mat.ptr<uchar>(i);
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for(int j=0;j<INPUT_W;j++){
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float * pixcel = mask+i*INPUT_W+j;
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// std::cout << *pixcel << std::endl;
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if(*pixcel > CONF_THRESH){
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ptmp[j] = 255;
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}
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else{
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ptmp[j]=0;
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}
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}
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}
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cv::imwrite("s_" + file_names[f - fcount + 1 + b] + "_unet.jpg", mask_mat);
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}
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fcount = 0;
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}
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// Destroy the engine
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context->destroy();
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engine->destroy();
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runtime->destroy();
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return 0;
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}
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