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陈赣
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third_party/trt_yolov8/trt_yolov8_seg_detector.cpp
vendored
Executable file
272
third_party/trt_yolov8/trt_yolov8_seg_detector.cpp
vendored
Executable file
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#include "trt_yolov8_seg_detector.h"
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namespace trt_yolov8 {
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using namespace nvinfer1;
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cv::Rect trt_yolov8_seg_detector::get_downscale_rect(float bbox[4], float scale) {
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float left = bbox[0];
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float top = bbox[1];
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float right = bbox[0] + bbox[2];
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float bottom = bbox[1] + bbox[3];
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left = left < 0 ? 0 : left;
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top = top < 0 ? 0 : top;
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right = right > 640 ? 640 : right;
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bottom = bottom > 640 ? 640 : bottom;
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left /= scale;
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top /= scale;
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right /= scale;
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bottom /= scale;
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return cv::Rect(int(left), int(top), int(right - left), int(bottom - top));
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}
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std::vector<cv::Mat> trt_yolov8_seg_detector::process_mask(const float* proto, int proto_size, std::vector<Detection>& dets) {
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std::vector<cv::Mat> masks;
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for (size_t i = 0; i < dets.size(); i++) {
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cv::Mat mask_mat = cv::Mat::zeros(kInputH / 4, kInputW / 4, CV_32FC1);
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auto r = get_downscale_rect(dets[i].bbox, 4);
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for (int x = r.x; x < r.x + r.width; x++) {
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for (int y = r.y; y < r.y + r.height; y++) {
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float e = 0.0f;
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for (int j = 0; j < 32; j++) {
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e += dets[i].mask[j] * proto[j * proto_size / 32 + y * mask_mat.cols + x];
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}
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e = 1.0f / (1.0f + expf(-e));
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mask_mat.at<float>(y, x) = e;
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}
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}
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cv::resize(mask_mat, mask_mat, cv::Size(kInputW, kInputH));
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masks.push_back(mask_mat);
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}
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return masks;
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}
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void trt_yolov8_seg_detector::serialize_engine(std::string& wts_name, std::string& engine_name, std::string& sub_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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serialized_engine = buildEngineYolov8Seg(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
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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 trt_yolov8_seg_detector::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 trt_yolov8_seg_detector::prepare_buffer(ICudaEngine* engine, float** input_buffer_device, float** output_buffer_device,
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float** output_seg_buffer_device, float** output_buffer_host, float** output_seg_buffer_host,
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float** decode_ptr_host, float** decode_ptr_device, std::string cuda_post_process) {
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assert(engine->getNbBindings() == 3);
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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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const int outputIndex_seg = engine->getBindingIndex("proto");
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assert(inputIndex == 0);
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assert(outputIndex == 1);
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assert(outputIndex_seg == 2);
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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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CUDA_CHECK(cudaMalloc((void**)output_seg_buffer_device, kBatchSize * kOutputSegSize * 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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*output_seg_buffer_host = new float[kBatchSize * kOutputSegSize];
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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 trt_yolov8_seg_detector::infer(IExecutionContext& context, cudaStream_t& stream, void** buffers, float* output, float* output_seg,
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int batchsize, float* decode_ptr_host, float* decode_ptr_device, int model_bboxes,
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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.enqueue(batchsize, buffers, stream, nullptr);
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if (cuda_post_process == "c") {
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//std::cout << "kOutputSize:" << kOutputSize << std::endl;
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CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost,
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stream));
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//std::cout << "kOutputSegSize:" << kOutputSegSize << std::endl;
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CUDA_CHECK(cudaMemcpyAsync(output_seg, buffers[2], batchsize * kOutputSegSize * sizeof(float),
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cudaMemcpyDeviceToHost, stream));
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/*
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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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/*
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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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trt_yolov8_seg_detector::trt_yolov8_seg_detector(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 the engine from file
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deserialize_engine(model_path, &runtime, &engine, &context);
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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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}
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trt_yolov8_seg_detector::~trt_yolov8_seg_detector() {
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// Release stream and buffers
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cudaStreamDestroy(stream);
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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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}
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void trt_yolov8_seg_detector::detect(std::vector<cv::Mat> images, std::vector<std::vector<Detection>>& detections, std::vector<std::vector<cv::Mat>>& masks) {
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// Prepare cpu and gpu buffers
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float* device_buffers[3];
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float* output_buffer_host = nullptr;
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float* output_seg_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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prepare_buffer(engine, &device_buffers[0], &device_buffers[1], &device_buffers[2], &output_buffer_host,
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&output_seg_buffer_host, &decode_ptr_host, &decode_ptr_device, cuda_post_process);
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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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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, output_seg_buffer_host, kBatchSize,
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decode_ptr_host, decode_ptr_device, model_bboxes, cuda_post_process);
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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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for (size_t b = 0; b < img_batch.size(); b++) {
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auto& res = res_batch[b];
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auto mask = process_mask(&output_seg_buffer_host[b * kOutputSegSize], kOutputSegSize, res);
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masks.push_back(mask);
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}
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}
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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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// todo seg in gpu
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std::cerr << "seg_postprocess is not support in gpu right now" << std::endl;
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}
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// push back to return
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detections.insert(detections.end(), res_batch.begin(), res_batch.end());
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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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CUDA_CHECK(cudaFree(device_buffers[2]));
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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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delete[] output_seg_buffer_host;
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}
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bool trt_yolov8_seg_detector::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(wts_name, engine_name, sub_type, gd, gw, max_channels);
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return true;
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}
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}
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