260 lines
9.9 KiB
C++
260 lines
9.9 KiB
C++
#include <fstream>
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#include <iostream>
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#include <opencv2/opencv.hpp>
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#include "cuda_utils.h"
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#include "logging.h"
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#include "model.h"
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#include "postprocess.h"
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#include "preprocess.h"
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#include "utils.h"
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Logger gLogger;
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using namespace nvinfer1;
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const int kOutputSize = kMaxNumOutputBbox * sizeof(Detection) / sizeof(float) + 1;
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void serialize_engine(std::string& wts_name, std::string& engine_name, std::string& type, float& gd, float& gw,
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int& max_channels) {
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IBuilder* builder = createInferBuilder(gLogger);
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IBuilderConfig* config = builder->createBuilderConfig();
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IHostMemory* serialized_engine = nullptr;
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if (type == "n") {
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serialized_engine = buildEngineYolov10DetN(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
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} else if (type == "s") {
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serialized_engine = buildEngineYolov10DetS(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
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} else if (type == "m") {
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serialized_engine = buildEngineYolov10DetM(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
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} else if (type == "b" || type == "l") {
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serialized_engine = buildEngineYolov10DetBL(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
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} else if (type == "x") {
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serialized_engine = buildEngineYolov10DetX(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
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} else {
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std::cerr << "Unsupported type!" << std::endl;
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exit(0);
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}
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assert(serialized_engine);
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std::ofstream p(engine_name, std::ios::binary);
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if (!p) {
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std::cout << "could not open plan output file" << std::endl;
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assert(false);
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}
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p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());
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delete serialized_engine;
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delete config;
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delete builder;
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}
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void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine,
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IExecutionContext** context) {
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std::ifstream file(engine_name, std::ios::binary);
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if (!file.good()) {
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std::cerr << "read " << engine_name << " error!" << std::endl;
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assert(false);
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}
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size_t size = 0;
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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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char* serialized_engine = new char[size];
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assert(serialized_engine);
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file.read(serialized_engine, size);
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file.close();
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*runtime = createInferRuntime(gLogger);
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assert(*runtime);
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*engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
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assert(*engine);
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*context = (*engine)->createExecutionContext();
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assert(*context);
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delete[] serialized_engine;
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}
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void prepare_buffer(ICudaEngine* engine, float** input_buffer_device, float** output_buffer_device,
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float** output_buffer_host) {
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assert(engine->getNbBindings() == 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(kInputTensorName);
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const int outputIndex = engine->getBindingIndex(kOutputTensorName);
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assert(inputIndex == 0);
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assert(outputIndex == 1);
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// Create GPU buffers on device
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CUDA_CHECK(cudaMalloc((void**)input_buffer_device, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
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CUDA_CHECK(cudaMalloc((void**)output_buffer_device, kBatchSize * kOutputSize * sizeof(float)));
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*output_buffer_host = new float[kBatchSize * kOutputSize];
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}
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void infer(IExecutionContext& context, cudaStream_t& stream, void** buffers, float* output, int batchsize) {
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// infer on the batch asynchronously, and DMA output back to host
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auto start = std::chrono::system_clock::now();
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context.enqueueV2(buffers, stream, nullptr);
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CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost,
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stream));
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auto end = std::chrono::system_clock::now();
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std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count()
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<< "ms" << std::endl;
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CUDA_CHECK(cudaStreamSynchronize(stream));
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}
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bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, std::string& img_dir, std::string& type,
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float& gd, float& gw, int& max_channels) {
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if (argc < 4)
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return false;
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if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
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wts = std::string(argv[2]);
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engine = std::string(argv[3]);
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auto sub_type = std::string(argv[4]);
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if (sub_type[0] == 'n') {
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gd = 0.33;
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gw = 0.25;
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max_channels = 1024;
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type = "n";
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} else if (sub_type[0] == 's') {
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gd = 0.33;
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gw = 0.50;
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max_channels = 1024;
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type = "s";
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} else if (sub_type[0] == 'm') {
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gd = 0.67;
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gw = 0.75;
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max_channels = 768;
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type = "m";
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} else if (sub_type[0] == 'b') {
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gd = 0.67;
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gw = 1.0;
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max_channels = 512;
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type = "b";
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} else if (sub_type[0] == 'l') {
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gd = 1.0;
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gw = 1.0;
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max_channels = 512;
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type = "l";
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} else if (sub_type[0] == 'x') {
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gd = 1.0;
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gw = 1.25;
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max_channels = 512;
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type = "x";
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} else {
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return false;
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}
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} else if (std::string(argv[1]) == "-d" && argc == 4) {
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engine = std::string(argv[2]);
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img_dir = std::string(argv[3]);
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} else {
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return false;
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}
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return true;
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}
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int main(int argc, char** argv) {
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// -s ../models/yolov10n.wts ../models/yolov10n.fp32.trt n
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// -d ../models/yolov10n.fp32.trt ../images
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cudaSetDevice(kGpuId);
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std::string wts_name = "";
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std::string engine_name = "";
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std::string img_dir;
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std::string type = "";
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float gd = 0.0f, gw = 0.0f;
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int max_channels = 0;
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if (!parse_args(argc, argv, wts_name, engine_name, img_dir, type, gd, gw, max_channels)) {
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std::cerr << "Arguments not right!" << std::endl;
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std::cerr << "./yolov10_det -s [.wts] [.engine] [n/s/m/b/l/x] // serialize model to "
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"plan file"
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<< std::endl;
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std::cerr << "./yolov10_det -d [.engine] ../samples // deserialize plan file and run inference" << std::endl;
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return -1;
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}
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// Create a model using the API directly and serialize it to a file
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if (!wts_name.empty()) {
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serialize_engine(wts_name, engine_name, type, gd, gw, max_channels);
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return 0;
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}
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// Deserialize the engine from file
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IRuntime* runtime = nullptr;
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ICudaEngine* engine = nullptr;
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IExecutionContext* context = nullptr;
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deserialize_engine(engine_name, &runtime, &engine, &context);
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cudaStream_t stream;
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CUDA_CHECK(cudaStreamCreate(&stream));
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cuda_preprocess_init(kMaxInputImageSize);
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// Prepare cpu and gpu buffers
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float* device_buffers[2];
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float* output_buffer_host = nullptr;
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// Read images from directory
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std::vector<std::string> file_names;
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if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
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std::cerr << "read_files_in_dir failed." << std::endl;
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return -1;
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}
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prepare_buffer(engine, &device_buffers[0], &device_buffers[1], &output_buffer_host);
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// batch predict
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for (size_t i = 0; i < file_names.size(); i += kBatchSize) {
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// Get a batch of images
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std::vector<cv::Mat> img_batch;
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std::vector<std::string> img_name_batch;
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for (size_t j = i; j < i + kBatchSize && j < file_names.size(); j++) {
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cv::Mat img = cv::imread(img_dir + "/" + file_names[j]);
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if (img.empty()) {
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std::cerr << "Fatal error: image cannot open!" << std::endl;
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return -1;
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}
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img_batch.push_back(img);
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img_name_batch.push_back(file_names[j]);
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}
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// Preprocess
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cuda_batch_preprocess(img_batch, device_buffers[0], kInputW, kInputH, stream);
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// Run inference
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infer(*context, stream, (void**)device_buffers, output_buffer_host, kBatchSize);
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// output_buffer_host保存前100个值到文件
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// std::ofstream out_file("../output.txt");
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// for (int i = 0; i < 100; i++) {
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// out_file << output_buffer_host[i] << std::endl;
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// }
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// out_file.close();
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std::vector<std::vector<Detection>> res_batch;
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batch_topk(res_batch, output_buffer_host, img_batch.size(), kOutputSize, kConfThresh);
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// print results
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for (size_t j = 0; j < res_batch.size(); j++) {
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for (size_t k = 0; k < res_batch[j].size(); k++) {
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std::cout << "image: " << img_name_batch[j] << ", bbox: " << res_batch[j][k].bbox[0] << ", "
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<< res_batch[j][k].bbox[1] << ", " << res_batch[j][k].bbox[2] << ", "
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<< res_batch[j][k].bbox[3] << ", conf: " << res_batch[j][k].conf
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<< ", class_id: " << res_batch[j][k].class_id << std::endl;
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}
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}
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// Draw bounding boxes
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draw_bbox(img_batch, res_batch);
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// Save images
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for (size_t j = 0; j < img_batch.size(); j++) {
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cv::imwrite("_" + img_name_batch[j], img_batch[j]);
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}
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}
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// Release stream and buffers
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cudaStreamDestroy(stream);
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CUDA_CHECK(cudaFree(device_buffers[0]));
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CUDA_CHECK(cudaFree(device_buffers[1]));
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cuda_preprocess_destroy();
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// Destroy the engine
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delete context;
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delete engine;
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delete runtime;
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return 0;
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}
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