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- #include "cuda_utils.h"
- #include "logging.h"
- #include "utils.h"
- #include "preprocess.h"
- #include "postprocess.h"
- #include "model.h"
-
- #include <iostream>
- #include <chrono>
- #include <cmath>
-
- using namespace nvinfer1;
-
- static Logger gLogger;
- const static int kOutputSize = kMaxNumOutputBbox * sizeof(Detection) / sizeof(float) + 1;
-
- bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, bool& is_p6, float& gd, float& gw, std::string& img_dir) {
- if (argc < 4) return false;
- if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
- wts = std::string(argv[2]);
- engine = std::string(argv[3]);
- auto net = std::string(argv[4]);
- if (net[0] == 'n') {
- gd = 0.33;
- gw = 0.25;
- } else if (net[0] == 's') {
- gd = 0.33;
- gw = 0.50;
- } else if (net[0] == 'm') {
- gd = 0.67;
- gw = 0.75;
- } else if (net[0] == 'l') {
- gd = 1.0;
- gw = 1.0;
- } else if (net[0] == 'x') {
- gd = 1.33;
- gw = 1.25;
- } else if (net[0] == 'c' && argc == 7) {
- gd = atof(argv[5]);
- gw = atof(argv[6]);
- } else {
- return false;
- }
- if (net.size() == 2 && net[1] == '6') {
- is_p6 = true;
- }
- } else if (std::string(argv[1]) == "-d" && argc == 4) {
- engine = std::string(argv[2]);
- img_dir = std::string(argv[3]);
- } else {
- return false;
- }
- return true;
- }
-
- void prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer, float** cpu_output_buffer) {
- assert(engine->getNbBindings() == 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(kInputTensorName);
- const int outputIndex = engine->getBindingIndex(kOutputTensorName);
- assert(inputIndex == 0);
- assert(outputIndex == 1);
- // Create GPU buffers on device
- CUDA_CHECK(cudaMalloc((void**)gpu_input_buffer, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
- CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * kOutputSize * sizeof(float)));
-
- *cpu_output_buffer = new float[kBatchSize * kOutputSize];
- }
-
- void infer(IExecutionContext& context, cudaStream_t& stream, void** gpu_buffers, float* output, int batchsize) {
- context.enqueue(batchsize, gpu_buffers, stream, nullptr);
- CUDA_CHECK(cudaMemcpyAsync(output, gpu_buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream));
- cudaStreamSynchronize(stream);
- }
-
- void serialize_engine(unsigned int max_batchsize, bool& is_p6, float& gd, float& gw, std::string& wts_name, std::string& engine_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 = nullptr;
- if (is_p6) {
- engine = build_det_p6_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
- } else {
- engine = build_det_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
- }
- assert(engine != nullptr);
-
- // Serialize the engine
- IHostMemory* serialized_engine = engine->serialize();
- assert(serialized_engine != nullptr);
-
- // Save engine to file
- std::ofstream p(engine_name, std::ios::binary);
- if (!p) {
- std::cerr << "Could not open plan output file" << std::endl;
- assert(false);
- }
- p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());
-
- // Close everything down
- engine->destroy();
- config->destroy();
- serialized_engine->destroy();
- builder->destroy();
- }
-
- void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine, IExecutionContext** context) {
- std::ifstream file(engine_name, std::ios::binary);
- if (!file.good()) {
- std::cerr << "read " << engine_name << " error!" << std::endl;
- assert(false);
- }
- size_t size = 0;
- file.seekg(0, file.end);
- size = file.tellg();
- file.seekg(0, file.beg);
- char* serialized_engine = new char[size];
- assert(serialized_engine);
- file.read(serialized_engine, size);
- file.close();
-
- *runtime = createInferRuntime(gLogger);
- assert(*runtime);
- *engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
- assert(*engine);
- *context = (*engine)->createExecutionContext();
- assert(*context);
- delete[] serialized_engine;
- }
-
- int main(int argc, char** argv) {
- cudaSetDevice(kGpuId);
-
- std::string wts_name = "";
- std::string engine_name = "";
- bool is_p6 = false;
- float gd = 0.0f, gw = 0.0f;
- std::string img_dir;
-
- if (!parse_args(argc, argv, wts_name, engine_name, is_p6, gd, gw, img_dir)) {
- std::cerr << "arguments not right!" << std::endl;
- std::cerr << "./yolov5_det -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file" << std::endl;
- std::cerr << "./yolov5_det -d [.engine] ../images // deserialize plan file and run inference" << std::endl;
- return -1;
- }
-
- // Create a model using the API directly and serialize it to a file
- if (!wts_name.empty()) {
- serialize_engine(kBatchSize, is_p6, gd, gw, wts_name, engine_name);
- return 0;
- }
-
- // Deserialize the engine from file
- IRuntime* runtime = nullptr;
- ICudaEngine* engine = nullptr;
- IExecutionContext* context = nullptr;
- deserialize_engine(engine_name, &runtime, &engine, &context);
- cudaStream_t stream;
- CUDA_CHECK(cudaStreamCreate(&stream));
-
- // Init CUDA preprocessing
- cuda_preprocess_init(kMaxInputImageSize);
-
- // Prepare cpu and gpu buffers
- float* gpu_buffers[2];
- float* cpu_output_buffer = nullptr;
- prepare_buffers(engine, &gpu_buffers[0], &gpu_buffers[1], &cpu_output_buffer);
-
- // Read images from directory
- std::vector<std::string> file_names;
- if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
- std::cerr << "read_files_in_dir failed." << std::endl;
- return -1;
- }
-
- // batch predict
- for (size_t i = 0; i < file_names.size(); i += kBatchSize) {
- // Get a batch of images
- std::vector<cv::Mat> img_batch;
- std::vector<std::string> img_name_batch;
- for (size_t j = i; j < i + kBatchSize && j < file_names.size(); j++) {
- cv::Mat img = cv::imread(img_dir + "/" + file_names[j]);
- img_batch.push_back(img);
- img_name_batch.push_back(file_names[j]);
- }
-
- // Preprocess
- cuda_batch_preprocess(img_batch, gpu_buffers[0], kInputW, kInputH, stream);
-
- // Run inference
- auto start = std::chrono::system_clock::now();
- infer(*context, stream, (void**)gpu_buffers, cpu_output_buffer, kBatchSize);
- auto end = std::chrono::system_clock::now();
- std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
-
- // NMS
- std::vector<std::vector<Detection>> res_batch;
- batch_nms(res_batch, cpu_output_buffer, img_batch.size(), kOutputSize, kConfThresh, kNmsThresh);
-
- // Draw bounding boxes
- draw_bbox(img_batch, res_batch);
-
- // Save images
- for (size_t j = 0; j < img_batch.size(); j++) {
- cv::imwrite("_" + img_name_batch[j], img_batch[j]);
- }
- }
-
- // Release stream and buffers
- cudaStreamDestroy(stream);
- CUDA_CHECK(cudaFree(gpu_buffers[0]));
- CUDA_CHECK(cudaFree(gpu_buffers[1]));
- delete[] cpu_output_buffer;
- cuda_preprocess_destroy();
- // 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 < kOutputSize; i++) {
- // std::cout << prob[i] << ", ";
- // if (i % 10 == 0) std::cout << std::endl;
- // }
- // std::cout << std::endl;
-
- return 0;
- }
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