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#include "trt_yolov8_classifier.h"
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namespace trt_yolov8 {
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using namespace nvinfer1;
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void trt_yolov8_classifier::batch_preprocess(std::vector<cv::Mat>& imgs, float* output, int dst_width, int dst_height) {
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for (size_t b = 0; b < imgs.size(); b++) {
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int h = imgs[b].rows;
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int w = imgs[b].cols;
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int m = std::min(h, w);
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int top = (h - m) / 2;
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int left = (w - m) / 2;
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cv::Mat img = imgs[b](cv::Rect(left, top, m, m));
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cv::resize(img, img, cv::Size(dst_width, dst_height), 0, 0, cv::INTER_LINEAR);
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cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
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img.convertTo(img, CV_32F, 1/255.0);
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std::vector<cv::Mat> channels(3);
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cv::split(img, channels);
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// CHW format
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for (int c = 0; c < 3; ++c) {
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int i = 0;
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for (int row = 0; row < dst_height; ++row) {
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for (int col = 0; col < dst_width; ++col) {
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output[b * 3 * dst_height * dst_width + c * dst_height * dst_width + i] =
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channels[c].at<float>(row, col);
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++i;
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}
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}
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}
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}
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}
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std::vector<float> trt_yolov8_classifier::softmax(float *prob, int n) {
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std::vector<float> res;
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float sum = 0.0f;
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float t;
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for (int i = 0; i < n; i++) {
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t = expf(prob[i]);
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res.push_back(t);
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sum += t;
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}
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for (int i = 0; i < n; i++) {
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res[i] /= sum;
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}
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return res;
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}
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std::vector<int> trt_yolov8_classifier::topk(const std::vector<float>& vec, int k) {
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std::vector<int> topk_index;
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std::vector<size_t> vec_index(vec.size());
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std::iota(vec_index.begin(), vec_index.end(), 0);
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std::sort(vec_index.begin(), vec_index.end(), [&vec](size_t index_1, size_t index_2) { return vec[index_1] > vec[index_2]; });
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int k_num = std::min<int>(vec.size(), k);
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for (int i = 0; i < k_num; ++i) {
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topk_index.push_back(vec_index[i]);
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}
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return topk_index;
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}
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void trt_yolov8_classifier::prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer, float** cpu_input_buffer, 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**)gpu_input_buffer, kBatchSize * 3 * kClsInputH * kClsInputW * sizeof(float)));
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CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * kOutputSize * sizeof(float)));
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*cpu_input_buffer = new float[kBatchSize * 3 * kClsInputH * kClsInputW];
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*output_buffer_host = new float[kBatchSize * kOutputSize];
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}
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void trt_yolov8_classifier::infer(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) {
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CUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * kClsInputH * kClsInputW * sizeof(float), cudaMemcpyHostToDevice, stream));
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context.enqueue(batchSize, buffers, stream, nullptr);
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CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream));
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cudaStreamSynchronize(stream);
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}
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void trt_yolov8_classifier::serialize_engine(unsigned int max_batchsize, float& gd, float& gw, std::string& wts_name, std::string& engine_name) {
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// Create builder
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IBuilder* builder = createInferBuilder(gLogger);
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IBuilderConfig* config = builder->createBuilderConfig();
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// Create model to populate the network, then set the outputs and create an engine
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IHostMemory *serialized_engine = nullptr;
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//engine = buildEngineYolov8Cls(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
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serialized_engine = buildEngineYolov8Cls(builder, config, DataType::kFLOAT, wts_name, gd, gw);
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assert(serialized_engine);
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// Save engine to file
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std::ofstream p(engine_name, std::ios::binary);
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if (!p) {
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std::cerr << "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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// Close everything down
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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 trt_yolov8_classifier::deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine, 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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trt_yolov8_classifier::trt_yolov8_classifier(std::string model_path) {
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if (model_path.empty()) {
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return;
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}
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cudaSetDevice(kGpuId);
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deserialize_engine(model_path, &runtime, &engine, &context);
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CUDA_CHECK(cudaStreamCreate(&stream));
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}
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trt_yolov8_classifier::~trt_yolov8_classifier() {
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cudaStreamDestroy(stream);
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delete context;
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delete engine;
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delete runtime;
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}
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void trt_yolov8_classifier::classify(std::vector<cv::Mat> images, std::vector<std::vector<Classification>>& classifications, int top_k) {
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// Prepare cpu and gpu buffers
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float* device_buffers[2];
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float* cpu_input_buffer = nullptr;
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float* output_buffer_host = nullptr;
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prepare_buffers(engine, &device_buffers[0], &device_buffers[1], &cpu_input_buffer, &output_buffer_host);
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// batch predict
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for (size_t i = 0; i < images.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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for (size_t j = i; j < i + kBatchSize && j < images.size(); j++) {
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img_batch.push_back(images[j]);
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}
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// Preprocess
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batch_preprocess(img_batch, cpu_input_buffer);
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// Run inference
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auto start = std::chrono::system_clock::now();
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infer(*context, stream, (void**)device_buffers, cpu_input_buffer, output_buffer_host, kBatchSize);
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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() << "ms" << std::endl;
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// Postprocess and get top-k result
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for (size_t b = 0; b < img_batch.size(); b++) {
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float* p = &output_buffer_host[b * kOutputSize];
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auto res = softmax(p, kOutputSize);
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auto topk_idx = topk(res, top_k);
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std::vector<Classification> classification;
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for (auto idx: topk_idx) {
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classification.push_back(Classification {idx, res[idx]});
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}
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classifications.push_back(classification);
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}
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}
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CUDA_CHECK(cudaFree(device_buffers[0]));
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CUDA_CHECK(cudaFree(device_buffers[1]));
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delete[] cpu_input_buffer;
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delete[] output_buffer_host;
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}
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bool trt_yolov8_classifier::wts_2_engine(std::string wts_name, std::string engine_name, std::string sub_type) {
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int is_p = 0;
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float gd = 0.0f, gw = 0.0f;
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int max_channels = 0;
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if (sub_type[0] == 'n') { // yolov8n
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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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} else if (sub_type[0] == 's') { // yolov8s
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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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} else if (sub_type[0] == 'm') { // yolov8m
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gd = 0.67;
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gw = 0.75;
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max_channels = 576;
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} else if (sub_type[0] == 'l') { // yolov8l
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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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} else if (sub_type[0] == 'x') { // yolov8x
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gd = 1.0;
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gw = 1.25;
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max_channels = 640;
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} else {
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return false; // not support
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}
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if (sub_type.size() == 2 && sub_type[1] == '6') { // yolov8n6/yolov8s6/yolov8m6/yolov8l6/yolov8x6
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is_p = 6;
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} else if (sub_type.size() == 2 && sub_type[1] == '2') { // yolov8n2/yolov8s2/yolov8m2/yolov8l2/yolov8x2
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is_p = 2;
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}
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serialize_engine(kBatchSize, gd, gw, wts_name, engine_name);
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return true;
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}
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}
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