From 37ce374b42178edd579c42db288a4656b142991d Mon Sep 17 00:00:00 2001 From: wang-xinyu Date: Sun, 21 Jun 2020 22:58:23 +0800 Subject: [PATCH] add yolov5s --- yolov5/yolov5s.cpp | 549 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 549 insertions(+) create mode 100644 yolov5/yolov5s.cpp diff --git a/yolov5/yolov5s.cpp b/yolov5/yolov5s.cpp new file mode 100644 index 0000000..7a115f2 --- /dev/null +++ b/yolov5/yolov5s.cpp @@ -0,0 +1,549 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include "logging.h" +#include "yololayer.h" + +#define CHECK(status) \ + do\ + {\ + auto ret = (status);\ + if (ret != 0)\ + {\ + std::cerr << "Cuda failure: " << ret << std::endl;\ + abort();\ + }\ + } while (0) + + +#define USE_FP16 // comment out this if want to use FP32 +#define DEVICE 0 // GPU id +#define NMS_THRESH 0.5 +#define BBOX_CONF_THRESH 0.4 + +using namespace nvinfer1; + +// 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 = 1000 * 7 + 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); + +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; +} + +cv::Rect get_rect(cv::Mat& img, float bbox[4]) { + int l, r, t, b; + float r_w = INPUT_W / (img.cols * 1.0); + float r_h = INPUT_H / (img.rows * 1.0); + if (r_h > r_w) { + l = bbox[0] - bbox[2]/2.f; + r = bbox[0] + bbox[2]/2.f; + t = bbox[1] - bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2; + b = bbox[1] + bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2; + l = l / r_w; + r = r / r_w; + t = t / r_w; + b = b / r_w; + } else { + l = bbox[0] - bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2; + r = bbox[0] + bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2; + t = bbox[1] - bbox[3]/2.f; + b = bbox[1] + bbox[3]/2.f; + l = l / r_h; + r = r / r_h; + t = t / r_h; + b = b / r_h; + } + return cv::Rect(l, t, r-l, b-t); +} + +float iou(float lbox[4], float rbox[4]) { + float interBox[] = { + std::max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left + std::min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right + std::max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top + std::min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom + }; + + if(interBox[2] > interBox[3] || interBox[0] > interBox[1]) + return 0.0f; + + float interBoxS =(interBox[1]-interBox[0])*(interBox[3]-interBox[2]); + return interBoxS/(lbox[2]*lbox[3] + rbox[2]*rbox[3] -interBoxS); +} + +bool cmp(Yolo::Detection& a, Yolo::Detection& b) { + return a.det_confidence * a.class_confidence > b.det_confidence * b.class_confidence; +} + +void nms(std::vector& res, float *output, float nms_thresh = NMS_THRESH) { + std::map> m; + for (int i = 0; i < output[0] && i < 1000; i++) { + if (output[1 + 7 * i + 4] * output[1 + 7 * i + 6] <= BBOX_CONF_THRESH) continue; + Yolo::Detection det; + memcpy(&det, &output[1 + 7 * i], 7 * sizeof(float)); + if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector()); + m[det.class_id].push_back(det); + } + for (auto it = m.begin(); it != m.end(); it++) { + //std::cout << it->second[0].class_id << " --- " << std::endl; + auto& dets = it->second; + std::sort(dets.begin(), dets.end(), cmp); + for (size_t m = 0; m < dets.size(); ++m) { + auto& item = dets[m]; + res.push_back(item); + for (size_t n = m + 1; n < dets.size(); ++n) { + if (iou(item.bbox, dets[n].bbox) > nms_thresh) { + dets.erase(dets.begin()+n); + --n; + } + } + } + } +} + +// TensorRT weight files have a simple space delimited format: +// [type] [size] +std::map loadWeights(const std::string file) { + std::cout << "Loading weights: " << file << std::endl; + std::map weightMap; + + // Open weights file + std::ifstream input(file); + assert(input.is_open() && "Unable to load weight file."); + + // Read number of weight blobs + int32_t count; + input >> count; + assert(count > 0 && "Invalid weight map file."); + + while (count--) + { + Weights wt{DataType::kFLOAT, nullptr, 0}; + uint32_t size; + + // Read name and type of blob + std::string name; + input >> name >> std::dec >> size; + wt.type = DataType::kFLOAT; + + // Load blob + uint32_t* val = reinterpret_cast(malloc(sizeof(val) * size)); + for (uint32_t x = 0, y = size; x < y; ++x) + { + input >> std::hex >> val[x]; + } + wt.values = val; + + wt.count = size; + weightMap[name] = wt; + } + + return weightMap; +} + +IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map& weightMap, ITensor& input, std::string lname, float eps) { + float *gamma = (float*)weightMap[lname + ".weight"].values; + float *beta = (float*)weightMap[lname + ".bias"].values; + float *mean = (float*)weightMap[lname + ".running_mean"].values; + float *var = (float*)weightMap[lname + ".running_var"].values; + int len = weightMap[lname + ".running_var"].count; + + float *scval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + scval[i] = gamma[i] / sqrt(var[i] + eps); + } + Weights scale{DataType::kFLOAT, scval, len}; + + float *shval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps); + } + Weights shift{DataType::kFLOAT, shval, len}; + + float *pval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + pval[i] = 1.0; + } + Weights power{DataType::kFLOAT, pval, len}; + + weightMap[lname + ".scale"] = scale; + weightMap[lname + ".shift"] = shift; + weightMap[lname + ".power"] = power; + IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power); + assert(scale_1); + return scale_1; +} + +ILayer* convBnLeaky(INetworkDefinition *network, std::map& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname) { + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + int p = ksize / 2; + IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + ".conv.weight"], emptywts); + assert(conv1); + conv1->setStrideNd(DimsHW{s, s}); + conv1->setPaddingNd(DimsHW{p, p}); + conv1->setNbGroups(g); + IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn", 1e-4); + auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU); + lr->setAlpha(0.1); + return lr; +} + +ILayer* focus(INetworkDefinition *network, std::map& weightMap, ITensor& input, int inch, int outch, int ksize, std::string lname) { + ISliceLayer *s1 = network->addSlice(input, Dims3{0, 0, 0}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2}); + ISliceLayer *s2 = network->addSlice(input, Dims3{0, 1, 0}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2}); + ISliceLayer *s3 = network->addSlice(input, Dims3{0, 0, 1}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2}); + ISliceLayer *s4 = network->addSlice(input, Dims3{0, 1, 1}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2}); + ITensor* inputTensors[] = {s1->getOutput(0), s2->getOutput(0), s3->getOutput(0), s4->getOutput(0)}; + auto cat = network->addConcatenation(inputTensors, 4); + auto conv = convBnLeaky(network, weightMap, *cat->getOutput(0), outch, ksize, 1, 1, lname + ".conv"); + return conv; +} + +ILayer* bottleneck(INetworkDefinition *network, std::map& weightMap, ITensor& input, int c1, int c2, bool shortcut, int g, float e, std::string lname) { + auto cv1 = convBnLeaky(network, weightMap, input, (int)((float)c2 * e), 1, 1, 1, lname + ".cv1"); + auto cv2 = convBnLeaky(network, weightMap, *cv1->getOutput(0), c2, 3, 1, g, lname + ".cv2"); + if (shortcut && c1 == c2) { + auto ew = network->addElementWise(input, *cv2->getOutput(0), ElementWiseOperation::kSUM); + return ew; + } + return cv2; +} + +ILayer* bottleneckCSP(INetworkDefinition *network, std::map& weightMap, ITensor& input, int c1, int c2, int n, bool shortcut, int g, float e, std::string lname) { + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + int c_ = (int)((float)c2 * e); + auto cv1 = convBnLeaky(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1"); + auto cv2 = network->addConvolutionNd(input, c_, DimsHW{1, 1}, weightMap[lname + ".cv2.weight"], emptywts); + ITensor *y1 = cv1->getOutput(0); + for (int i = 0; i < n; i++) { + auto b = bottleneck(network, weightMap, *y1, c_, c_, shortcut, g, 1.0, lname + ".m." + std::to_string(i)); + y1 = b->getOutput(0); + } + auto cv3 = network->addConvolutionNd(*y1, c_, DimsHW{1, 1}, weightMap[lname + ".cv3.weight"], emptywts); + + ITensor* inputTensors[] = {cv3->getOutput(0), cv2->getOutput(0)}; + auto cat = network->addConcatenation(inputTensors, 2); + + IScaleLayer* bn = addBatchNorm2d(network, weightMap, *cat->getOutput(0), lname + ".bn", 1e-4); + auto lr = network->addActivation(*bn->getOutput(0), ActivationType::kLEAKY_RELU); + lr->setAlpha(0.1); + + auto cv4 = convBnLeaky(network, weightMap, *lr->getOutput(0), c2, 1, 1, 1, lname + ".cv4"); + return cv4; +} + +ILayer* SPP(INetworkDefinition *network, std::map& weightMap, ITensor& input, int c1, int c2, int k1, int k2, int k3, std::string lname) { + int c_ = c1 / 2; + auto cv1 = convBnLeaky(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1"); + + auto pool1 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{k1, k1}); + pool1->setPaddingNd(DimsHW{k1 / 2, k1 / 2}); + pool1->setStrideNd(DimsHW{1, 1}); + auto pool2 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{k2, k2}); + pool2->setPaddingNd(DimsHW{k2 / 2, k2 / 2}); + pool2->setStrideNd(DimsHW{1, 1}); + auto pool3 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{k3, k3}); + pool3->setPaddingNd(DimsHW{k3 / 2, k3 / 2}); + pool3->setStrideNd(DimsHW{1, 1}); + + ITensor* inputTensors[] = {cv1->getOutput(0), pool1->getOutput(0), pool2->getOutput(0), pool3->getOutput(0)}; + auto cat = network->addConcatenation(inputTensors, 4); + + auto cv2 = convBnLeaky(network, weightMap, *cat->getOutput(0), c2, 1, 1, 1, lname + ".cv2"); + return cv2; +} + +// 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 bottleneck2 = bottleneck(network, weightMap, *conv1->getOutput(0), 64, 64, true, 1, 0.5, "model.2"); + auto conv3 = convBnLeaky(network, weightMap, *bottleneck2->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, 2, true, 1, 0.5, "model.9"); + // yolov5 head + auto bottleneck_csp10 = bottleneckCSP(network, weightMap, *bottleneck_csp9->getOutput(0), 512, 512, 1, false, 1, 0.5, "model.10"); + IConvolutionLayer* conv11 = network->addConvolutionNd(*bottleneck_csp10->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.11.weight"], weightMap["model.11.bias"]); + + float *deval = reinterpret_cast(malloc(sizeof(float) * 512 * 2 * 2)); + for (int i = 0; i < 512 * 2 * 2; i++) { + deval[i] = 1.0; + } + Weights deconvwts12{DataType::kFLOAT, deval, 512 * 2 * 2}; + IDeconvolutionLayer* deconv12 = network->addDeconvolutionNd(*bottleneck_csp10->getOutput(0), 512, DimsHW{2, 2}, deconvwts12, emptywts); + deconv12->setStrideNd(DimsHW{2, 2}); + deconv12->setNbGroups(512); + weightMap["deconv12"] = deconvwts12; + + ITensor* inputTensors13[] = {deconv12->getOutput(0), bottleneck_csp6->getOutput(0)}; + auto cat13 = network->addConcatenation(inputTensors13, 2); + auto conv14 = convBnLeaky(network, weightMap, *cat13->getOutput(0), 256, 1, 1, 1, "model.14"); + auto bottleneck_csp15 = bottleneckCSP(network, weightMap, *conv14->getOutput(0), 256, 256, 1, false, 1, 0.5, "model.15"); + IConvolutionLayer* conv16 = network->addConvolutionNd(*bottleneck_csp15->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.16.weight"], weightMap["model.16.bias"]); + + Weights deconvwts17{DataType::kFLOAT, deval, 256 * 2 * 2}; + IDeconvolutionLayer* deconv17 = network->addDeconvolutionNd(*bottleneck_csp15->getOutput(0), 256, DimsHW{2, 2}, deconvwts17, emptywts); + deconv17->setStrideNd(DimsHW{2, 2}); + deconv17->setNbGroups(256); + ITensor* inputTensors18[] = {deconv17->getOutput(0), bottleneck_csp4->getOutput(0)}; + auto cat18 = network->addConcatenation(inputTensors18, 2); + auto conv19 = convBnLeaky(network, weightMap, *cat18->getOutput(0), 128, 1, 1, 1, "model.19"); + auto bottleneck_csp20 = bottleneckCSP(network, weightMap, *conv19->getOutput(0), 128, 128, 1, false, 1, 0.5, "model.20"); + IConvolutionLayer* conv21 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.21.weight"], weightMap["model.21.bias"]); + + auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1"); + const PluginFieldCollection* pluginData = creator->getFieldNames(); + IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData); + ITensor* inputTensors_yolo[] = {conv11->getOutput(0), conv16->getOutput(0), conv21->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 read_files_in_dir(const char *p_dir_name, std::vector &file_names) { + DIR *p_dir = opendir(p_dir_name); + if (p_dir == nullptr) { + return -1; + } + + struct dirent* p_file = nullptr; + while ((p_file = readdir(p_dir)) != nullptr) { + if (strcmp(p_file->d_name, ".") != 0 && + strcmp(p_file->d_name, "..") != 0) { + //std::string cur_file_name(p_dir_name); + //cur_file_name += "/"; + //cur_file_name += p_file->d_name; + std::string cur_file_name(p_file->d_name); + file_names.push_back(cur_file_name); + } + } + + closedir(p_dir); + return 0; +} + +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(1, &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 --------------------------- + float data[3 * INPUT_H * INPUT_W]; + //for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) + // data[i] = 1.0; + static float prob[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 (auto f: file_names) { + fcount++; + std::cout << fcount << " " << f << std::endl; + cv::Mat img = cv::imread(std::string(argv[2]) + "/" + f); + if (img.empty()) continue; + cv::Mat pr_img = preprocess_img(img); + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + data[i] = pr_img.at(i)[2] / 255.0; + data[i + INPUT_H * INPUT_W] = pr_img.at(i)[1] / 255.0; + data[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, 1); + auto end = std::chrono::system_clock::now(); + std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; + std::vector res; + nms(res, prob); + for (int i=0; i<20; i++) { + std::cout << prob[i] << ","; + } + std::cout << res.size() << std::endl; + for (size_t j = 0; j < res.size(); j++) { + float *p = (float*)&res[j]; + for (size_t k = 0; k < 7; k++) { + std::cout << p[k] << ", "; + } + std::cout << std::endl; + 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("_" + f, img); + } + + // 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; +}