278 lines
11 KiB
C++
278 lines
11 KiB
C++
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#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, float& gd, float& gw, int& max_channels,
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std::string& type) {
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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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serialized_engine = buildEngineYolo11Det(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels, type);
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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, float** decode_ptr_host, float** decode_ptr_device,
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std::string cuda_post_process) {
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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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if (cuda_post_process == "c") {
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*output_buffer_host = new float[kBatchSize * kOutputSize];
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} else if (cuda_post_process == "g") {
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if (kBatchSize > 1) {
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std::cerr << "Do not yet support GPU post processing for multiple batches" << std::endl;
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exit(0);
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}
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// Allocate memory for decode_ptr_host and copy to device
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*decode_ptr_host = new float[1 + kMaxNumOutputBbox * bbox_element];
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CUDA_CHECK(cudaMalloc((void**)decode_ptr_device, sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element)));
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}
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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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float* decode_ptr_host, float* decode_ptr_device, int model_bboxes, std::string cuda_post_process) {
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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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if (cuda_post_process == "c") {
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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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} else if (cuda_post_process == "g") {
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CUDA_CHECK(
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cudaMemsetAsync(decode_ptr_device, 0, sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element), stream));
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cuda_decode((float*)buffers[1], model_bboxes, kConfThresh, decode_ptr_device, kMaxNumOutputBbox, stream);
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cuda_nms(decode_ptr_device, kNmsThresh, kMaxNumOutputBbox, stream); //cuda nms
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CUDA_CHECK(cudaMemcpyAsync(decode_ptr_host, decode_ptr_device,
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sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element), cudaMemcpyDeviceToHost,
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stream));
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auto end = std::chrono::system_clock::now();
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std::cout << "inference and gpu postprocess time: "
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<< std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
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}
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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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std::string& cuda_post_process, 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)) {
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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 = 0.50;
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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 = 0.50;
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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 = 0.50;
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gw = 1.00;
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max_channels = 512;
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type = "m";
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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 = 1.50;
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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 == 5) {
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engine = std::string(argv[2]);
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img_dir = std::string(argv[3]);
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cuda_post_process = std::string(argv[4]);
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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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// yolo11_det -s ../models/yolo11n.wts ../models/yolo11n.fp32.trt n
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// yolo11_det -d ../models/yolo11n.fp32.trt ../images c
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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 cuda_post_process;
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std::string type;
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int model_bboxes;
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float gd = 0, gw = 0;
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int max_channels = 0;
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if (!parse_args(argc, argv, wts_name, engine_name, img_dir, type, cuda_post_process, gd, gw, max_channels)) {
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std::cerr << "Arguments not right!" << std::endl;
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std::cerr << "./yolo11_det -s [.wts] [.engine] [n/s/m/l/x] // serialize model to "
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"plan file"
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<< std::endl;
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std::cerr << "./yolo11_det -d [.engine] ../images [c/g]// deserialize plan file and run inference"
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<< 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, gd, gw, max_channels, type);
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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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auto out_dims = engine->getBindingDimensions(1);
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model_bboxes = out_dims.d[0];
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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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float* decode_ptr_host = nullptr;
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float* decode_ptr_device = 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, &decode_ptr_host,
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&decode_ptr_device, cuda_post_process);
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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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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, decode_ptr_host,
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decode_ptr_device, model_bboxes, cuda_post_process);
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// 保存output_buffer_host的前100个值，一行一个
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// std::ofstream out("../models/output.txt");
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// for (int j = 0; j < 100; j++) {
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// out << output_buffer_host[j] << std::endl;
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// }
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// out.close();
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std::vector<std::vector<Detection>> res_batch;
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if (cuda_post_process == "c") {
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// NMS
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batch_nms(res_batch, output_buffer_host, img_batch.size(), kOutputSize, kConfThresh, kNmsThresh);
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} else if (cuda_post_process == "g") {
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//Process gpu decode and nms results
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batch_process(res_batch, decode_ptr_host, img_batch.size(), bbox_element, img_batch);
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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_CHECK(cudaFree(decode_ptr_device));
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delete[] decode_ptr_host;
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delete[] output_buffer_host;
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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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// Print histogram of the output distribution
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//std::cout << "\nOutput:\n\n";
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//for (unsigned int i = 0; i < kOutputSize; i++)
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//{
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// std::cout << prob[i] << ", ";
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// if (i % 10 == 0) std::cout << std::endl;
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//}
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//std::cout << std::endl;
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return 0;
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}
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