From 06959d3912cd6fa7bdc82de2d53ea0aab08b4b3a Mon Sep 17 00:00:00 2001 From: BaofengZan Date: Mon, 3 Aug 2020 15:03:06 +0800 Subject: [PATCH] =?UTF-8?q?=E5=AE=9E=E7=8E=B0=E4=BA=86s/m/l/x=E7=89=88?= =?UTF-8?q?=E6=9C=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- yolov5/yolov5s.cpp | 872 +++++++++++++++++++++++++++++++-------------- 1 file changed, 597 insertions(+), 275 deletions(-) diff --git a/yolov5/yolov5s.cpp b/yolov5/yolov5s.cpp index 370d1de..0e4ae80 100644 --- a/yolov5/yolov5s.cpp +++ b/yolov5/yolov5s.cpp @@ -1,275 +1,597 @@ -#include -#include -#include "cuda_runtime_api.h" -#include "logging.h" -#include "common.hpp" - -#define USE_FP16 // comment out this if want to use FP32 -#define DEVICE 0 // GPU id -#define NMS_THRESH 0.5 -#define CONF_THRESH 0.4 -#define BATCH_SIZE 1 - -// stuff we know about the network and the input/output blobs -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 1000 boxes that conf >= 0.1 -const char* INPUT_BLOB_NAME = "data"; -const char* OUTPUT_BLOB_NAME = "prob"; -static Logger gLogger; -REGISTER_TENSORRT_PLUGIN(YoloPluginCreator); - -// Creat the engine using only the API and not any parser. -ICudaEngine* createEngine(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 weightMap = loadWeights("../yolov5s.wts"); - Weights emptywts{DataType::kFLOAT, nullptr, 0}; - - // yolov5 backbone - auto focus0 = focus(network, weightMap, *data, 3, 32, 3, "model.0"); - auto conv1 = convBnLeaky(network, weightMap, *focus0->getOutput(0), 64, 3, 2, 1, "model.1"); - auto bottleneck_CSP2 = bottleneckCSP(network, weightMap, *conv1->getOutput(0), 64, 64, 1, true, 1, 0.5, "model.2"); - auto conv3 = convBnLeaky(network, weightMap, *bottleneck_CSP2->getOutput(0), 128, 3, 2, 1, "model.3"); - auto bottleneck_csp4 = bottleneckCSP(network, weightMap, *conv3->getOutput(0), 128, 128, 3, true, 1, 0.5, "model.4"); - auto conv5 = convBnLeaky(network, weightMap, *bottleneck_csp4->getOutput(0), 256, 3, 2, 1, "model.5"); - auto bottleneck_csp6 = bottleneckCSP(network, weightMap, *conv5->getOutput(0), 256, 256, 3, true, 1, 0.5, "model.6"); - auto conv7 = convBnLeaky(network, weightMap, *bottleneck_csp6->getOutput(0), 512, 3, 2, 1, "model.7"); - auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), 512, 512, 5, 9, 13, "model.8"); - auto bottleneck_csp9 = bottleneckCSP(network, weightMap, *spp8->getOutput(0), 512, 512, 1, false, 1, 0.5, "model.9"); - - // yolov5 head - auto conv10 = convBnLeaky(network, weightMap, *bottleneck_csp9->getOutput(0), 256, 1, 1, 1, "model.10"); - - float *deval = reinterpret_cast(malloc(sizeof(float) * 256 * 2 * 2)); - for (int i = 0; i < 256 * 2 * 2; i++) { - deval[i] = 1.0; - } - Weights deconvwts11{DataType::kFLOAT, deval, 256 * 2 * 2}; - IDeconvolutionLayer* deconv11 = network->addDeconvolutionNd(*conv10->getOutput(0), 256, DimsHW{2, 2}, deconvwts11, emptywts); - deconv11->setStrideNd(DimsHW{2, 2}); - deconv11->setNbGroups(256); - weightMap["deconv11"] = deconvwts11; - - ITensor* inputTensors12[] = {deconv11->getOutput(0), bottleneck_csp6->getOutput(0)}; - auto cat12 = network->addConcatenation(inputTensors12, 2); - auto bottleneck_csp13 = bottleneckCSP(network, weightMap, *cat12->getOutput(0), 512, 256, 1, false, 1, 0.5, "model.13"); - auto conv14 = convBnLeaky(network, weightMap, *bottleneck_csp13->getOutput(0), 128, 1, 1, 1, "model.14"); - - Weights deconvwts15{DataType::kFLOAT, deval, 128 * 2 * 2}; - IDeconvolutionLayer* deconv15 = network->addDeconvolutionNd(*conv14->getOutput(0), 128, DimsHW{2, 2}, deconvwts15, emptywts); - deconv15->setStrideNd(DimsHW{2, 2}); - deconv15->setNbGroups(128); - - ITensor* inputTensors16[] = {deconv15->getOutput(0), bottleneck_csp4->getOutput(0)}; - auto cat16 = network->addConcatenation(inputTensors16, 2); - auto bottleneck_csp17 = bottleneckCSP(network, weightMap, *cat16->getOutput(0), 256, 128, 1, false, 1, 0.5, "model.17"); - 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 = convBnLeaky(network, weightMap, *bottleneck_csp17->getOutput(0), 128, 3, 2, 1, "model.18"); - ITensor* inputTensors19[] = {conv18->getOutput(0), conv14->getOutput(0)}; - auto cat19 = network->addConcatenation(inputTensors19, 2); - auto bottleneck_csp20 = bottleneckCSP(network, weightMap, *cat19->getOutput(0), 256, 256, 1, false, 1, 0.5, "model.20"); - 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 = convBnLeaky(network, weightMap, *bottleneck_csp20->getOutput(0), 256, 3, 2, 1, "model.21"); - ITensor* inputTensors22[] = {conv21->getOutput(0), conv10->getOutput(0)}; - auto cat22 = network->addConcatenation(inputTensors22, 2); - auto bottleneck_csp23 = bottleneckCSP(network, weightMap, *cat22->getOutput(0), 512, 512, 1, false, 1, 0.5, "model.23"); - 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"]); - - auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); - const PluginFieldCollection* pluginData = creator->getFieldNames(); - IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); - ITensor* inputTensors_yolo[] = {det2->getOutput(0), det1->getOutput(0), det0->getOutput(0)}; - auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj); - - yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); - network->markOutput(*yolo->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 = createEngine(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)); - cudaStreamSynchronize(stream); - - // Release stream and buffers - cudaStreamDestroy(stream); - CHECK(cudaFree(buffers[inputIndex])); - CHECK(cudaFree(buffers[outputIndex])); -} - -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}; - - if (argc == 2 && std::string(argv[1]) == "-s") { - IHostMemory* modelStream{nullptr}; - APIToModel(BATCH_SIZE, &modelStream); - assert(modelStream != nullptr); - std::ofstream p("yolov5s.engine", std::ios::binary); - if (!p) { - std::cerr << "could not open plan output file" << std::endl; - return -1; - } - p.write(reinterpret_cast(modelStream->data()), modelStream->size()); - modelStream->destroy(); - return 0; - } else if (argc == 3 && std::string(argv[1]) == "-d") { - std::ifstream file("yolov5s.engine", 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 << "./yolov5s -s // serialize model to plan file" << std::endl; - std::cerr << "./yolov5s -d ../samples // deserialize plan file and run inference" << std::endl; - return -1; - } - - std::vector 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); - for (int i = 0; i < INPUT_H * INPUT_W; i++) { - data[b * 3 * INPUT_H * INPUT_W + i] = pr_img.at(i)[2] / 255.0; - data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = pr_img.at(i)[1] / 255.0; - data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = pr_img.at(i)[0] / 255.0; - } - } - - // 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(end - start).count() << "ms" << std::endl; - std::vector> batch_res(fcount); - for (int b = 0; b < fcount; b++) { - auto& res = batch_res[b]; - nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH); - } - for (int b = 0; b < fcount; b++) { - auto& res = batch_res[b]; - //std::cout << res.size() << std::endl; - cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]); - for (size_t j = 0; j < res.size(); j++) { - cv::Rect r = get_rect(img, res[j].bbox); - cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2); - cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2); - } - cv::imwrite("_" + file_names[f - fcount + 1 + b], img); - } - fcount = 0; - } - - // Destroy the engine - context->destroy(); - engine->destroy(); - runtime->destroy(); - - // Print histogram of the output distribution - //std::cout << "\nOutput:\n\n"; - //for (unsigned int i = 0; i < OUTPUT_SIZE; i++) - //{ - // std::cout << prob[i] << ", "; - // if (i % 10 == 0) std::cout << std::endl; - //} - //std::cout << std::endl; - - return 0; -} +#include +#include +#include "cuda_runtime_api.h" +#include "logging.h" +#include "common.hpp" + +#define USE_FP16 // comment out this if want to use FP32 +#define DEVICE 0 // GPU id +#define NMS_THRESH 0.4 +#define CONF_THRESH 0.5 +#define BATCH_SIZE 1 + +#define NET x // s m l x +#define NETSTRUCT(str) createEngine_##str +#define CREATENET(net) NETSTRUCT(net) + +// stuff we know about the network and the input/output blobs +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 1000 boxes that conf >= 0.1 +const char* INPUT_BLOB_NAME = "data"; +const char* OUTPUT_BLOB_NAME = "prob"; +static Logger gLogger; +REGISTER_TENSORRT_PLUGIN(YoloPluginCreator); + +// Creat the engine using only the API and not any parser. +ICudaEngine* createEngine_s(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 weightMap = loadWeights("E:\\LearningCodes\\tensorrtx\\yolov5V2\\yolov5V2\\weights\\yolov5sV2_1.wts"); + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + + // yolov5 backbone + auto focus0 = focus(network, weightMap, *data, 3, 32, 3, "model.0"); + auto conv1 = convBnLeaky(network, weightMap, *focus0->getOutput(0), 64, 3, 2, 1, "model.1"); + auto bottleneck_CSP2 = bottleneckCSP(network, weightMap, *conv1->getOutput(0), 64, 64, 1, true, 1, 0.5, "model.2"); + auto conv3 = convBnLeaky(network, weightMap, *bottleneck_CSP2->getOutput(0), 128, 3, 2, 1, "model.3"); + auto bottleneck_csp4 = bottleneckCSP(network, weightMap, *conv3->getOutput(0), 128, 128, 3, true, 1, 0.5, "model.4"); + auto conv5 = convBnLeaky(network, weightMap, *bottleneck_csp4->getOutput(0), 256, 3, 2, 1, "model.5"); + auto bottleneck_csp6 = bottleneckCSP(network, weightMap, *conv5->getOutput(0), 256, 256, 3, true, 1, 0.5, "model.6"); + auto conv7 = convBnLeaky(network, weightMap, *bottleneck_csp6->getOutput(0), 512, 3, 2, 1, "model.7"); + auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), 512, 512, 5, 9, 13, "model.8"); + + // yolov5 head + auto bottleneck_csp9 = bottleneckCSP(network, weightMap, *spp8->getOutput(0), 512, 512, 1, false, 1, 0.5, "model.9"); + auto conv10 = convBnLeaky(network, weightMap, *bottleneck_csp9->getOutput(0), 256, 1, 1, 1, "model.10"); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 256 * 2 * 2)); + for (int i = 0; i < 256 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts11{DataType::kFLOAT, deval, 256 * 2 * 2}; + IDeconvolutionLayer* deconv11 = network->addDeconvolutionNd(*conv10->getOutput(0), 256, DimsHW{2, 2}, deconvwts11, emptywts); + deconv11->setStrideNd(DimsHW{2, 2}); + deconv11->setNbGroups(256); + weightMap["deconv11"] = deconvwts11; + + ITensor* inputTensors12[] = {deconv11->getOutput(0), bottleneck_csp6->getOutput(0)}; + auto cat12 = network->addConcatenation(inputTensors12, 2); + auto bottleneck_csp13 = bottleneckCSP(network, weightMap, *cat12->getOutput(0), 512, 256, 1, false, 1, 0.5, "model.13"); + auto conv14 = convBnLeaky(network, weightMap, *bottleneck_csp13->getOutput(0), 128, 1, 1, 1, "model.14"); + + Weights deconvwts15{DataType::kFLOAT, deval, 128 * 2 * 2}; + IDeconvolutionLayer* deconv15 = network->addDeconvolutionNd(*conv14->getOutput(0), 128, DimsHW{2, 2}, deconvwts15, emptywts); + deconv15->setStrideNd(DimsHW{2, 2}); + deconv15->setNbGroups(128); + //weightMap["deconv15"] = deconvwts15; + + ITensor* inputTensors16[] = {deconv15->getOutput(0), bottleneck_csp4->getOutput(0)}; + auto cat16 = network->addConcatenation(inputTensors16, 2); + auto bottleneck_csp17 = bottleneckCSP(network, weightMap, *cat16->getOutput(0), 256, 128, 1, false, 1, 0.5, "model.17"); + IConvolutionLayer* conv18 = 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 conv19 = convBnLeaky(network, weightMap, *bottleneck_csp17->getOutput(0), 128, 3, 2, 1, "model.18"); + ITensor* inputTensors20[] = {conv19->getOutput(0), conv14->getOutput(0)}; + auto cat20 = network->addConcatenation(inputTensors20, 2); + auto bottleneck_csp21 = bottleneckCSP(network, weightMap, *cat20->getOutput(0), 256, 256, 1, false, 1, 0.5, "model.20"); + IConvolutionLayer* conv22 = network->addConvolutionNd(*bottleneck_csp21->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]); + + auto conv23 = convBnLeaky(network, weightMap, *bottleneck_csp21->getOutput(0), 256, 3, 2, 1, "model.21"); + ITensor* inputTensors24[] = {conv23->getOutput(0), conv10->getOutput(0)}; + auto cat24 = network->addConcatenation(inputTensors24, 2); + auto bottleneck_csp25 = bottleneckCSP(network, weightMap, *cat24->getOutput(0), 512, 512, 1, false, 1, 0.5, "model.23"); + IConvolutionLayer* conv26 = network->addConvolutionNd(*bottleneck_csp25->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]); + + auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); + const PluginFieldCollection* pluginData = creator->getFieldNames(); + IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + ITensor* inputTensors_yolo[] = {conv26->getOutput(0), conv22->getOutput(0), conv18->getOutput(0)}; + auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj); + + yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*yolo->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; +} + +ICudaEngine* createEngine_m(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 weightMap = loadWeights("E:\\LearningCodes\\tensorrtx\\yolov5V2\\yolov5V2\\weights\\yolov5mV2.wts"); + Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; + + /* ------ yolov5 backbone------ */ + auto focus0 = focus(network, weightMap, *data, 3, 48, 3, "model.0"); + auto conv1 = convBnLeaky(network, weightMap, *focus0->getOutput(0), 96, 3, 2, 1, "model.1"); + auto bottleneck_CSP2 = bottleneckCSP(network, weightMap, *conv1->getOutput(0), 96, 96, 2, true, 1, 0.5, "model.2"); + auto conv3 = convBnLeaky(network, weightMap, *bottleneck_CSP2->getOutput(0), 192, 3, 2, 1, "model.3"); + auto bottleneck_csp4 = bottleneckCSP(network, weightMap, *conv3->getOutput(0), 192, 192, 6, true, 1, 0.5, "model.4"); + auto conv5 = convBnLeaky(network, weightMap, *bottleneck_csp4->getOutput(0), 384, 3, 2, 1, "model.5"); + auto bottleneck_csp6 = bottleneckCSP(network, weightMap, *conv5->getOutput(0), 384, 384, 6, true, 1, 0.5, "model.6"); + auto conv7 = convBnLeaky(network, weightMap, *bottleneck_csp6->getOutput(0), 768, 3, 2, 1, "model.7"); + auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), 768, 768, 5, 9, 13, "model.8"); + /* ------ yolov5 head ------ */ + auto bottleneck_csp9 = bottleneckCSP(network, weightMap, *spp8->getOutput(0), 768, 768, 2, false, 1, 0.5, "model.9"); + auto conv10 = convBnLeaky(network, weightMap, *bottleneck_csp9->getOutput(0), 384, 1, 1, 1, "model.10"); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 384 * 2 * 2)); + for (int i = 0; i < 384 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts11{ DataType::kFLOAT, deval, 384 * 2 * 2 }; + IDeconvolutionLayer* deconv11 = network->addDeconvolutionNd(*conv10->getOutput(0), 384, DimsHW{ 2, 2 }, deconvwts11, emptywts); + deconv11->setStrideNd(DimsHW{ 2, 2 }); + deconv11->setNbGroups(384); + weightMap["deconv11"] = deconvwts11; + ITensor* inputTensors12[] = { deconv11->getOutput(0), bottleneck_csp6->getOutput(0) }; + auto cat12 = network->addConcatenation(inputTensors12, 2); + + auto bottleneck_csp13 = bottleneckCSP(network, weightMap, *cat12->getOutput(0), 768, 384, 2, false, 1, 0.5, "model.13"); + + auto conv14 = convBnLeaky(network, weightMap, *bottleneck_csp13->getOutput(0), 192, 1, 1, 1, "model.14"); + + Weights deconvwts15{ DataType::kFLOAT, deval, 192 * 2 * 2 }; + IDeconvolutionLayer* deconv15 = network->addDeconvolutionNd(*conv14->getOutput(0), 192, DimsHW{ 2, 2 }, deconvwts15, emptywts); + deconv15->setStrideNd(DimsHW{ 2, 2 }); + deconv15->setNbGroups(192); + + ITensor* inputTensors16[] = { deconv15->getOutput(0), bottleneck_csp4->getOutput(0) }; + auto cat16 = network->addConcatenation(inputTensors16, 2); + + auto bottleneck_csp17 = bottleneckCSP(network, weightMap, *cat16->getOutput(0), 384, 192, 2, false, 1, 0.5, "model.17"); + + //yolo layer 1 + IConvolutionLayer* conv18 = 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 conv19 = convBnLeaky(network, weightMap, *bottleneck_csp17->getOutput(0), 192, 3, 2, 1, "model.18"); + + ITensor* inputTensors20[] = { conv19->getOutput(0), conv14->getOutput(0) }; + auto cat20 = network->addConcatenation(inputTensors20, 2); + + auto bottleneck_csp21 = bottleneckCSP(network, weightMap, *cat20->getOutput(0), 384, 384, 2, false, 1, 0.5, "model.20"); + + //yolo layer 2 + IConvolutionLayer* conv22 = network->addConvolutionNd(*bottleneck_csp21->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]); + + auto conv23 = convBnLeaky(network, weightMap, *bottleneck_csp21->getOutput(0), 384, 3, 2, 1, "model.21"); + + ITensor* inputTensors24[] = { conv23->getOutput(0), conv10->getOutput(0) }; + auto cat24 = network->addConcatenation(inputTensors24, 2); + + auto bottleneck_csp25 = bottleneckCSP(network, weightMap, *cat24->getOutput(0), 768, 768, 2, false, 1, 0.5, "model.23"); + + // yolo layer 3 + IConvolutionLayer* conv26 = network->addConvolutionNd(*bottleneck_csp25->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]); + + auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); + const PluginFieldCollection* pluginData = creator->getFieldNames(); + IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + ITensor* inputTensors_yolo[] = { conv26->getOutput(0), conv22->getOutput(0), conv18->getOutput(0) }; + auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj); + + yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*yolo->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; +} + +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 weightMap = loadWeights("E:\\LearningCodes\\tensorrtx\\yolov5V2\\yolov5V2\\weights\\yolov5lV2.wts"); + Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; + + /* ------ yolov5 backbone------ */ + auto focus0 = focus(network, weightMap, *data, 3, 64, 3, "model.0"); + auto conv1 = convBnLeaky(network, weightMap, *focus0->getOutput(0), 128, 3, 2, 1, "model.1"); + auto bottleneck_CSP2 = bottleneckCSP(network, weightMap, *conv1->getOutput(0), 128, 128, 3, true, 1, 0.5, "model.2"); + auto conv3 = convBnLeaky(network, weightMap, *bottleneck_CSP2->getOutput(0), 256, 3, 2, 1, "model.3"); + auto bottleneck_csp4 = bottleneckCSP(network, weightMap, *conv3->getOutput(0), 256, 256, 9, true, 1, 0.5, "model.4"); + auto conv5 = convBnLeaky(network, weightMap, *bottleneck_csp4->getOutput(0), 512, 3, 2, 1, "model.5"); + auto bottleneck_csp6 = bottleneckCSP(network, weightMap, *conv5->getOutput(0), 512, 512, 9, true, 1, 0.5, "model.6"); + auto conv7 = convBnLeaky(network, weightMap, *bottleneck_csp6->getOutput(0), 1024, 3, 2, 1, "model.7"); + auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), 1024, 1024, 5, 9, 13, "model.8"); + + /* ------ yolov5 head ------ */ + auto bottleneck_csp9 = bottleneckCSP(network, weightMap, *spp8->getOutput(0), 1024, 1024, 3, false, 1, 0.5, "model.9"); + auto conv10 = convBnLeaky(network, weightMap, *bottleneck_csp9->getOutput(0), 512, 1, 1, 1, "model.10"); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 512 * 2 * 2)); + for (int i = 0; i < 512 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts11{ DataType::kFLOAT, deval, 512 * 2 * 2 }; + IDeconvolutionLayer* deconv11 = network->addDeconvolutionNd(*conv10->getOutput(0), 512, DimsHW{ 2, 2 }, deconvwts11, emptywts); + deconv11->setStrideNd(DimsHW{ 2, 2 }); + deconv11->setNbGroups(512); + weightMap["deconv11"] = deconvwts11; + + ITensor* inputTensors12[] = { deconv11->getOutput(0), bottleneck_csp6->getOutput(0) }; + auto cat12 = network->addConcatenation(inputTensors12, 2); + auto bottleneck_csp13 = bottleneckCSP(network, weightMap, *cat12->getOutput(0), 1024, 512, 3, false, 1, 0.5, "model.13"); + auto conv14 = convBnLeaky(network, weightMap, *bottleneck_csp13->getOutput(0), 256, 1, 1, 1, "model.14"); + + Weights deconvwts15{ DataType::kFLOAT, deval, 256 * 2 * 2 }; + IDeconvolutionLayer* deconv15 = network->addDeconvolutionNd(*conv14->getOutput(0), 256, DimsHW{ 2, 2 }, deconvwts15, emptywts); + deconv15->setStrideNd(DimsHW{ 2, 2 }); + deconv15->setNbGroups(256); + ITensor* inputTensors16[] = { deconv15->getOutput(0), bottleneck_csp4->getOutput(0) }; + auto cat16 = network->addConcatenation(inputTensors16, 2); + + auto bottleneck_csp17 = bottleneckCSP(network, weightMap, *cat16->getOutput(0), 512, 256, 3, false, 1, 0.5, "model.17"); + + //yolo layer 1 + IConvolutionLayer* conv18 = 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 conv19 = convBnLeaky(network, weightMap, *bottleneck_csp17->getOutput(0), 256, 3, 2, 1, "model.18"); + + // yolo layer 2 + ITensor* inputTensors20[] = { conv19->getOutput(0), conv14->getOutput(0) }; + auto cat20 = network->addConcatenation(inputTensors20, 2); + + auto bottleneck_csp21 = bottleneckCSP(network, weightMap, *cat20->getOutput(0), 512, 512, 3, false, 1, 0.5, "model.20"); + + //yolo layer 3 + IConvolutionLayer* conv22 = network->addConvolutionNd(*bottleneck_csp21->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]); + + auto conv23 = convBnLeaky(network, weightMap, *bottleneck_csp21->getOutput(0), 512, 3, 2, 1, "model.21"); + + ITensor* inputTensors24[] = { conv23->getOutput(0), conv10->getOutput(0) }; + auto cat24 = network->addConcatenation(inputTensors24, 2); + + auto bottleneck_csp25 = bottleneckCSP(network, weightMap, *cat24->getOutput(0), 1024, 1024, 3, false, 1, 0.5, "model.23"); + + IConvolutionLayer* conv26 = network->addConvolutionNd(*bottleneck_csp25->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]); + + auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); + const PluginFieldCollection* pluginData = creator->getFieldNames(); + IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + ITensor* inputTensors_yolo[] = { conv26->getOutput(0), conv22->getOutput(0), conv18->getOutput(0) }; + auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj); + + yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*yolo->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; +} + +ICudaEngine* createEngine_x(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 weightMap = loadWeights("E:\\LearningCodes\\tensorrtx\\yolov5V2\\yolov5V2\\weights\\yolov5xV2.wts"); + Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; + + /* ------ yolov5 backbone------ */ + auto focus0 = focus(network, weightMap, *data, 3, 80, 3, "model.0"); + auto conv1 = convBnLeaky(network, weightMap, *focus0->getOutput(0), 160, 3, 2, 1, "model.1"); + auto bottleneck_CSP2 = bottleneckCSP(network, weightMap, *conv1->getOutput(0), 160, 160, 4, true, 1, 0.5, "model.2"); + auto conv3 = convBnLeaky(network, weightMap, *bottleneck_CSP2->getOutput(0), 320, 3, 2, 1, "model.3"); + auto bottleneck_csp4 = bottleneckCSP(network, weightMap, *conv3->getOutput(0), 320, 320, 12, true, 1, 0.5, "model.4"); + auto conv5 = convBnLeaky(network, weightMap, *bottleneck_csp4->getOutput(0), 640, 3, 2, 1, "model.5"); + auto bottleneck_csp6 = bottleneckCSP(network, weightMap, *conv5->getOutput(0), 640, 640, 12, true, 1, 0.5, "model.6"); + auto conv7 = convBnLeaky(network, weightMap, *bottleneck_csp6->getOutput(0), 1280, 3, 2, 1, "model.7"); + auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), 1280, 1280, 5, 9, 13, "model.8"); + + /* ------- yolov5 head ------- */ + auto bottleneck_csp9 = bottleneckCSP(network, weightMap, *spp8->getOutput(0), 1280, 1280, 4, false, 1, 0.5, "model.9"); + auto conv10 = convBnLeaky(network, weightMap, *bottleneck_csp9->getOutput(0), 640, 1, 1, 1, "model.10"); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 640 * 2 * 2)); + for (int i = 0; i < 640 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts11{ DataType::kFLOAT, deval, 640 * 2 * 2 }; + IDeconvolutionLayer* deconv11 = network->addDeconvolutionNd(*conv10->getOutput(0), 640, DimsHW{ 2, 2 }, deconvwts11, emptywts); + deconv11->setStrideNd(DimsHW{ 2, 2 }); + deconv11->setNbGroups(640); + weightMap["deconv11"] = deconvwts11; + + ITensor* inputTensors12[] = { deconv11->getOutput(0), bottleneck_csp6->getOutput(0) }; + auto cat12 = network->addConcatenation(inputTensors12, 2); + + auto bottleneck_csp13 = bottleneckCSP(network, weightMap, *cat12->getOutput(0), 1280, 640, 4, false, 1, 0.5, "model.13"); + auto conv14 = convBnLeaky(network, weightMap, *bottleneck_csp13->getOutput(0), 320, 1, 1, 1, "model.14"); + + Weights deconvwts15{ DataType::kFLOAT, deval, 320 * 2 * 2 }; + IDeconvolutionLayer* deconv15 = network->addDeconvolutionNd(*conv14->getOutput(0), 320, DimsHW{ 2, 2 }, deconvwts15, emptywts); + deconv15->setStrideNd(DimsHW{ 2, 2 }); + deconv15->setNbGroups(320); + ITensor* inputTensors16[] = { deconv15->getOutput(0), bottleneck_csp4->getOutput(0) }; + auto cat16 = network->addConcatenation(inputTensors16, 2); + + auto bottleneck_csp17 = bottleneckCSP(network, weightMap, *cat16->getOutput(0), 640, 320, 4, false, 1, 0.5, "model.17"); + + // yolo layer 1 + IConvolutionLayer* conv18 = 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 conv19 = convBnLeaky(network, weightMap, *bottleneck_csp17->getOutput(0), 320, 3, 2, 1, "model.18"); + + ITensor* inputTensors20[] = { conv19->getOutput(0), conv14->getOutput(0) }; + auto cat20 = network->addConcatenation(inputTensors20, 2); + + auto bottleneck_csp21 = bottleneckCSP(network, weightMap, *cat20->getOutput(0), 640, 640, 4, false, 1, 0.5, "model.20"); + + // yolo layer 2 + IConvolutionLayer* conv22 = network->addConvolutionNd(*bottleneck_csp21->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]); + + auto conv23 = convBnLeaky(network, weightMap, *bottleneck_csp21->getOutput(0), 640, 3, 2, 1, "model.21"); + + ITensor* inputTensors24[] = { conv23->getOutput(0), conv10->getOutput(0) }; + auto cat24 = network->addConcatenation(inputTensors24, 2); + + auto bottleneck_csp25 = bottleneckCSP(network, weightMap, *cat24->getOutput(0), 1280, 1280, 4, false, 1, 0.5, "model.23"); + + // yolo layer 3 + IConvolutionLayer* conv26 = network->addConvolutionNd(*bottleneck_csp25->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]); + + auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); + const PluginFieldCollection* pluginData = creator->getFieldNames(); + IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + ITensor* inputTensors_yolo[] = { conv26->getOutput(0), conv22->getOutput(0), conv18->getOutput(0) }; + auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj); + + yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*yolo->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(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)); + cudaStreamSynchronize(stream); + + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); +} + +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}; + if (argc == 2 && std::string(argv[1]) == "-s") { + IHostMemory* modelStream{nullptr}; + APIToModel(BATCH_SIZE, &modelStream); + assert(modelStream != nullptr); + std::ofstream p("yolov5sv2.engine", std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + modelStream->destroy(); + return 0; + } else if (argc == 3 && std::string(argv[1]) == "-d") { + std::ifstream file("yolov5sv2.engine", 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 << "./yolov5s -s // serialize model to plan file" << std::endl; + std::cerr << "./yolov5s -d ../samples // deserialize plan file and run inference" << std::endl; + return -1; + } + + std::vector 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 + 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] = uc_pixel[2] / 255; + data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = uc_pixel[1] / 255.0; + data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = 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(end - start).count() << "ms" << std::endl; + std::vector> batch_res(fcount); + for (int b = 0; b < fcount; b++) { + auto& res = batch_res[b]; + nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH); + } + for (int b = 0; b < fcount; b++) { + auto& res = batch_res[b]; + //std::cout << res.size() << std::endl; + cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]); + for (size_t j = 0; j < res.size(); j++) { + cv::Rect r = get_rect(img, res[j].bbox); + cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2); + cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2); + } + cv::imwrite("_" + file_names[f - fcount + 1 + b], img); + } + fcount = 0; + } + + // Destroy the engine + context->destroy(); + engine->destroy(); + runtime->destroy(); + + // Print histogram of the output distribution + //std::cout << "\nOutput:\n\n"; + //for (unsigned int i = 0; i < OUTPUT_SIZE; i++) + //{ + // std::cout << prob[i] << ", "; + // if (i % 10 == 0) std::cout << std::endl; + //} + //std::cout << std::endl; + + return 0; +}