216 lines
9.6 KiB
C++
Executable File
216 lines
9.6 KiB
C++
Executable File
|
|
#include "trt_yolov8_detector.h"
|
|
|
|
namespace trt_yolov8 {
|
|
using namespace nvinfer1;
|
|
void trt_yolov8_detector::serialize_engine(std::string& wts_name, std::string& engine_name, int& is_p, std::string& sub_type, float& gd,
|
|
float& gw, int& max_channels) {
|
|
IBuilder* builder = createInferBuilder(gLogger);
|
|
IBuilderConfig* config = builder->createBuilderConfig();
|
|
IHostMemory* serialized_engine = nullptr;
|
|
|
|
if (is_p == 6) {
|
|
serialized_engine = buildEngineYolov8DetP6(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
|
|
} else if (is_p == 2) {
|
|
serialized_engine = buildEngineYolov8DetP2(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
|
|
} else {
|
|
serialized_engine = buildEngineYolov8Det(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
|
|
}
|
|
|
|
assert(serialized_engine);
|
|
std::ofstream p(engine_name, std::ios::binary);
|
|
if (!p) {
|
|
std::cout << "could not open plan output file" << std::endl;
|
|
assert(false);
|
|
}
|
|
p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());
|
|
|
|
delete serialized_engine;
|
|
delete config;
|
|
delete builder;
|
|
}
|
|
|
|
void trt_yolov8_detector::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;
|
|
}
|
|
|
|
void trt_yolov8_detector::prepare_buffer(ICudaEngine* engine, float** input_buffer_device, float** output_buffer_device,
|
|
float** output_buffer_host, float** decode_ptr_host, float** decode_ptr_device,
|
|
std::string cuda_post_process) {
|
|
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**)input_buffer_device, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
|
|
CUDA_CHECK(cudaMalloc((void**)output_buffer_device, kBatchSize * kOutputSize * sizeof(float)));
|
|
if (cuda_post_process == "c") {
|
|
*output_buffer_host = new float[kBatchSize * kOutputSize];
|
|
} else if (cuda_post_process == "g") {
|
|
if (kBatchSize > 1) {
|
|
std::cerr << "Do not yet support GPU post processing for multiple batches" << std::endl;
|
|
exit(0);
|
|
}
|
|
// Allocate memory for decode_ptr_host and copy to device
|
|
*decode_ptr_host = new float[1 + kMaxNumOutputBbox * bbox_element];
|
|
CUDA_CHECK(cudaMalloc((void**)decode_ptr_device, sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element)));
|
|
}
|
|
}
|
|
|
|
void trt_yolov8_detector::infer(IExecutionContext& context, cudaStream_t& stream, void** buffers, float* output, int batchsize,
|
|
float* decode_ptr_host, float* decode_ptr_device, int model_bboxes, std::string cuda_post_process) {
|
|
// infer on the batch asynchronously, and DMA output back to host
|
|
auto start = std::chrono::system_clock::now();
|
|
context.enqueue(batchsize, buffers, stream, nullptr);
|
|
if (cuda_post_process == "c") {
|
|
CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost,
|
|
stream));
|
|
/*
|
|
auto end = std::chrono::system_clock::now();
|
|
std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count()
|
|
<< "ms" << std::endl;*/
|
|
} else if (cuda_post_process == "g") {
|
|
CUDA_CHECK(
|
|
cudaMemsetAsync(decode_ptr_device, 0, sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element), stream));
|
|
cuda_decode((float*)buffers[1], model_bboxes, kConfThresh, decode_ptr_device, kMaxNumOutputBbox, stream);
|
|
cuda_nms(decode_ptr_device, kNmsThresh, kMaxNumOutputBbox, stream); //cuda nms
|
|
CUDA_CHECK(cudaMemcpyAsync(decode_ptr_host, decode_ptr_device,
|
|
sizeof(float) * (1 + kMaxNumOutputBbox * bbox_element), cudaMemcpyDeviceToHost,
|
|
stream));
|
|
/*
|
|
auto end = std::chrono::system_clock::now();
|
|
std::cout << "inference and gpu postprocess time: "
|
|
<< std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;*/
|
|
}
|
|
|
|
CUDA_CHECK(cudaStreamSynchronize(stream));
|
|
}
|
|
|
|
void trt_yolov8_detector::detect(std::vector<cv::Mat> images, std::vector<std::vector<Detection>>& detections) {
|
|
// Prepare cpu and gpu buffers
|
|
float* device_buffers[2];
|
|
float* output_buffer_host = nullptr;
|
|
float* decode_ptr_host = nullptr;
|
|
float* decode_ptr_device = nullptr;
|
|
|
|
prepare_buffer(engine, &device_buffers[0], &device_buffers[1], &output_buffer_host, &decode_ptr_host,
|
|
&decode_ptr_device, cuda_post_process);
|
|
// batch predict
|
|
for (size_t i = 0; i < images.size(); i += kBatchSize) {
|
|
// Get a batch of images
|
|
std::vector<cv::Mat> img_batch;
|
|
for (size_t j = i; j < i + kBatchSize && j < images.size(); j++) {
|
|
img_batch.push_back(images[j]);
|
|
}
|
|
// Preprocess
|
|
cuda_batch_preprocess(img_batch, device_buffers[0], kInputW, kInputH, stream);
|
|
// Run inference
|
|
infer(*context, stream, (void**)device_buffers, output_buffer_host, kBatchSize, decode_ptr_host,
|
|
decode_ptr_device, model_bboxes, cuda_post_process);
|
|
std::vector<std::vector<Detection>> res_batch;
|
|
if (cuda_post_process == "c") {
|
|
// NMS
|
|
batch_nms(res_batch, output_buffer_host, img_batch.size(), kOutputSize, kConfThresh, kNmsThresh);
|
|
} else if (cuda_post_process == "g") {
|
|
//Process gpu decode and nms results
|
|
batch_process(res_batch, decode_ptr_host, img_batch.size(), bbox_element, img_batch);
|
|
}
|
|
|
|
// push back to return
|
|
detections.insert(detections.end(), res_batch.begin(), res_batch.end());
|
|
}
|
|
|
|
CUDA_CHECK(cudaFree(device_buffers[0]));
|
|
CUDA_CHECK(cudaFree(device_buffers[1]));
|
|
CUDA_CHECK(cudaFree(decode_ptr_device));
|
|
delete[] decode_ptr_host;
|
|
delete[] output_buffer_host;
|
|
}
|
|
|
|
trt_yolov8_detector::trt_yolov8_detector(std::string model_path) {
|
|
if (model_path.empty()) {
|
|
return;
|
|
}
|
|
|
|
cudaSetDevice(kGpuId);
|
|
// Deserialize the engine from file
|
|
deserialize_engine(model_path, &runtime, &engine, &context);
|
|
CUDA_CHECK(cudaStreamCreate(&stream));
|
|
cuda_preprocess_init(kMaxInputImageSize);
|
|
auto out_dims = engine->getBindingDimensions(1);
|
|
model_bboxes = out_dims.d[0];
|
|
}
|
|
|
|
trt_yolov8_detector::~trt_yolov8_detector() {
|
|
// Release stream and buffers
|
|
cudaStreamDestroy(stream);
|
|
cuda_preprocess_destroy();
|
|
// Destroy the engine
|
|
delete context;
|
|
delete engine;
|
|
delete runtime;
|
|
}
|
|
|
|
bool trt_yolov8_detector::wts_2_engine(std::string wts_name, std::string engine_name, std::string sub_type) {
|
|
int is_p = 0;
|
|
float gd = 0.0f, gw = 0.0f;
|
|
int max_channels = 0;
|
|
|
|
if (sub_type[0] == 'n') { // yolov8n
|
|
gd = 0.33;
|
|
gw = 0.25;
|
|
max_channels = 1024;
|
|
} else if (sub_type[0] == 's') { // yolov8s
|
|
gd = 0.33;
|
|
gw = 0.50;
|
|
max_channels = 1024;
|
|
} else if (sub_type[0] == 'm') { // yolov8m
|
|
gd = 0.67;
|
|
gw = 0.75;
|
|
max_channels = 576;
|
|
} else if (sub_type[0] == 'l') { // yolov8l
|
|
gd = 1.0;
|
|
gw = 1.0;
|
|
max_channels = 512;
|
|
} else if (sub_type[0] == 'x') { // yolov8x
|
|
gd = 1.0;
|
|
gw = 1.25;
|
|
max_channels = 640;
|
|
} else {
|
|
return false; // not support
|
|
}
|
|
|
|
if (sub_type.size() == 2 && sub_type[1] == '6') { // yolov8n6/yolov8s6/yolov8m6/yolov8l6/yolov8x6
|
|
is_p = 6;
|
|
} else if (sub_type.size() == 2 && sub_type[1] == '2') { // yolov8n2/yolov8s2/yolov8m2/yolov8l2/yolov8x2
|
|
is_p = 2;
|
|
}
|
|
|
|
serialize_engine(wts_name, engine_name, is_p, sub_type, gd, gw, max_channels);
|
|
return true;
|
|
}
|
|
} |