yolov5-tensorrt导出dll的一些文件

// Detection.cpp : 定义 DLL 的导出函数。 // #include "pch.h" #pragma once #include "Detection.h" using namespace std; static const int INPUT_H = Yolo::INPUT_H; static const int INPUT_W = Yolo::INPUT_W; static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1; // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1 const char* INPUT_BLOB_NAME = "data"; const char* OUTPUT_BLOB_NAME = "prob"; static Logger gLogger; static int get_width(int x, float gw, int divisor = 8) { //return math.ceil(x / divisor) * divisor if (int(x * gw) % divisor == 0) { return int(x * gw); } return (int(x * gw / divisor) + 1) * divisor; } static int get_depth(int x, float gd) { if (x == 1) { return 1; } else { return round(x * gd) > 1 ? round(x * gd) : 1; } } ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) { 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(wts_name); /* ------ yolov5 backbone------ */ auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0"); auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1"); auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2"); auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3"); auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4"); auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5"); auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6"); auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7"); auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, 9, 13, "model.8"); /* ------ yolov5 head ------ */ auto bottleneck_csp9 = C3(network, weightMap, *spp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.9"); auto conv10 = convBlock(network, weightMap, *bottleneck_csp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10"); auto upsample11 = network->addResize(*conv10->getOutput(0)); assert(upsample11); upsample11->setResizeMode(ResizeMode::kNEAREST); upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions()); ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) }; auto cat12 = network->addConcatenation(inputTensors12, 2); auto bottleneck_csp13 = C3(network, weightMap, *cat12->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.13"); auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14"); auto upsample15 = network->addResize(*conv14->getOutput(0)); assert(upsample15); upsample15->setResizeMode(ResizeMode::kNEAREST); upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions()); ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) }; auto cat16 = network->addConcatenation(inputTensors16, 2); auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17"); // yolo layer 0 IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]); auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18"); ITensor* inputTensors19[] = { conv18->getOutput(0), conv14->getOutput(0) }; auto cat19 = network->addConcatenation(inputTensors19, 2); auto bottleneck_csp20 = C3(network, weightMap, *cat19->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.20"); //yolo layer 1 IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]); auto conv21 = convBlock(network, weightMap, *bottleneck_csp20->getOutput(0), get_width(512, gw), 3, 2, 1, "model.21"); ITensor* inputTensors22[] = { conv21->getOutput(0), conv10->getOutput(0) }; auto cat22 = network->addConcatenation(inputTensors22, 2); auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23"); //下面三行是修改 IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]); std::vector<IConvolutionLayer*> dets = { det0, det1, det2 }; std::string lname = "yolov5sexp8best"; auto yolo = addYoLoLayer(network, weightMap, lname, dets ); yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); network->markOutput(*yolo->getOutput(0)); // Build engine builder->setMaxBatchSize(maxBatchSize); config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB #if defined(USE_FP16) config->setFlag(BuilderFlag::kFP16); #elif defined(USE_INT8) std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl; assert(builder->platformHasFastInt8()); config->setFlag(BuilderFlag::kINT8); Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME); config->setInt8Calibrator(calibrator); #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, float& gd, float& gw, std::string& wts_name) { // 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 = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name); assert(engine != nullptr); // Serialize the engine (*modelStream) = engine->serialize(); // Close everythin