From 5f1c69cef58a7118c5efb2e90c2923f8bab396c9 Mon Sep 17 00:00:00 2001 From: triple-Mu Date: Sun, 8 Jan 2023 16:46:04 +0800 Subject: [PATCH] Add cpp infer demo --- .pre-commit-config.yaml | 2 +- csrc/config/detect_config.hpp | 135 ++++++++++++ csrc/end2end/CMakeLists.txt | 52 +++++ csrc/end2end/main.cpp | 74 +++++++ csrc/end2end/yolov8.hpp | 397 ++++++++++++++++++++++++++++++++++ infer.py | 3 +- 6 files changed, 660 insertions(+), 3 deletions(-) create mode 100644 csrc/config/detect_config.hpp create mode 100644 csrc/end2end/CMakeLists.txt create mode 100644 csrc/end2end/main.cpp create mode 100644 csrc/end2end/yolov8.hpp diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 632c816..23b552c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,4 +22,4 @@ repos: - id: fix-encoding-pragma args: ["--remove"] - id: mixed-line-ending - args: ["--fix=lf"] \ No newline at end of file + args: ["--fix=lf"] diff --git a/csrc/config/detect_config.hpp b/csrc/config/detect_config.hpp new file mode 100644 index 0000000..50399ea --- /dev/null +++ b/csrc/config/detect_config.hpp @@ -0,0 +1,135 @@ +// +// Created by ubuntu on 1/8/23. +// + +#ifndef YOLOV8_TENSORRT_CSRC_DETECT_CONFIG_H +#define YOLOV8_TENSORRT_CSRC_DETECT_CONFIG_H +#include "NvInfer.h" +#include "iostream" +#include "string" +#include +#include +#include + +#define CHECK(call) \ +do \ +{ \ + const cudaError_t error_code = call; \ + if (error_code != cudaSuccess) \ + { \ + printf("CUDA Error:\n"); \ + printf(" File: %s\n", __FILE__); \ + printf(" Line: %d\n", __LINE__); \ + printf(" Error code: %d\n", error_code); \ + printf(" Error text: %s\n", \ + cudaGetErrorString(error_code)); \ + exit(1); \ + } \ +} while (0) + +const int DEVICE = 0; + +class Logger : public nvinfer1::ILogger +{ +public: + nvinfer1::ILogger::Severity reportableSeverity; + + explicit Logger(nvinfer1::ILogger::Severity severity = nvinfer1::ILogger::Severity::kINFO) : + reportableSeverity(severity) + { + } + + void log(nvinfer1::ILogger::Severity severity, const char* msg) noexcept override + { + if (severity > reportableSeverity) + { + return; + } + switch (severity) + { + case nvinfer1::ILogger::Severity::kINTERNAL_ERROR: + std::cerr << "INTERNAL_ERROR: "; + break; + case nvinfer1::ILogger::Severity::kERROR: + std::cerr << "ERROR: "; + break; + case nvinfer1::ILogger::Severity::kWARNING: + std::cerr << "WARNING: "; + break; + case nvinfer1::ILogger::Severity::kINFO: + std::cerr << "INFO: "; + break; + default: + std::cerr << "VERBOSE: "; + break; + } + std::cerr << msg << std::endl; + } +}; + +inline int get_size_by_dims(const nvinfer1::Dims& dims) +{ + int size = 1; + for (int i = 0; i < dims.nbDims; i++) + { + size *= dims.d[i]; + } + return size; +} + +inline int DataTypeToSize(const nvinfer1::DataType& dataType) +{ + switch (dataType) + { + case nvinfer1::DataType::kFLOAT: + return sizeof(float); + case nvinfer1::DataType::kHALF: + return 2; + case nvinfer1::DataType::kINT8: + return sizeof(int8_t); + case nvinfer1::DataType::kINT32: + return sizeof(int32_t); + case nvinfer1::DataType::kBOOL: + return sizeof(bool); + default: + return sizeof(float); + } +} + +inline float clamp(const float val, const float minVal = 0.f, const float maxVal = 1280.f) +{ + assert(minVal <= maxVal); + return std::min(maxVal, std::max(minVal, val)); +} + +inline bool IsPathExist(const std::string& path) +{ + if (access(path.c_str(), 0) == F_OK) + { + return true; + } + return false; +} + +inline bool IsFile(const std::string& path) +{ + if (!IsPathExist(path)) + { + printf("%s:%d %s not exist\n", __FILE__, __LINE__, path.c_str()); + return false; + } + struct stat buffer; + return (stat(path.c_str(), &buffer) == 0 && S_ISREG(buffer.st_mode)); +} + +inline bool IsFolder(const std::string& path) +{ + if (!IsPathExist(path)) + { + return false; + } + struct stat buffer; + return (stat(path.c_str(), &buffer) == 0 && S_ISDIR(buffer.st_mode)); +} + +#endif //YOLOV8_TENSORRT_CSRC_DETECT_CONFIG_H diff --git a/csrc/end2end/CMakeLists.txt b/csrc/end2end/CMakeLists.txt new file mode 100644 index 0000000..bf23c3a --- /dev/null +++ b/csrc/end2end/CMakeLists.txt @@ -0,0 +1,52 @@ +cmake_minimum_required(VERSION 2.8.12) + +set(CMAKE_CUDA_ARCHITECTURES 60 61 62 70 72 75 86) +set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc) + +project(yolov8 LANGUAGES CXX CUDA) + +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O3 -g") +set(CMAKE_CXX_STANDARD 14) +set(CMAKE_BUILD_TYPE Release) +option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) + +# CUDA +find_package(CUDA REQUIRED) +message(STATUS "CUDA Libs: \n${CUDA_LIBRARIES}\n") +message(STATUS "CUDA Headers: \n${CUDA_INCLUDE_DIRS}\n") + +# OpenCV +find_package(OpenCV REQUIRED) +message(STATUS "OpenCV Libs: \n${OpenCV_LIBS}\n") +message(STATUS "OpenCV Libraries: \n${OpenCV_LIBRARIES}\n") +message(STATUS "OpenCV Headers: \n${OpenCV_INCLUDE_DIRS}\n") + +# TensorRT +set(TensorRT_INCLUDE_DIRS /usr/include/x86_64-linux-gnu) +set(TensorRT_LIBRARIES /usr/lib/x86_64-linux-gnu) + + +message(STATUS "TensorRT Libs: \n${TensorRT_LIBRARIES}\n") +message(STATUS "TensorRT Headers: \n${TensorRT_INCLUDE_DIRS}\n") + +list(APPEND INCLUDE_DIRS + ${CUDA_INCLUDE_DIRS} + ${OpenCV_INCLUDE_DIRS} + ${TensorRT_INCLUDE_DIRS} + ../config + ) + +list(APPEND ALL_LIBS + ${CUDA_LIBRARIES} + ${OpenCV_LIBRARIES} + ${TensorRT_LIBRARIES} + ) + +include_directories(${INCLUDE_DIRS}) + +add_executable(${PROJECT_NAME} + main.cpp + ) + +target_link_directories(${PROJECT_NAME} PUBLIC ${ALL_LIBS}) +target_link_libraries(${PROJECT_NAME} PRIVATE nvinfer nvinfer_plugin cudart ${OpenCV_LIBS}) diff --git a/csrc/end2end/main.cpp b/csrc/end2end/main.cpp new file mode 100644 index 0000000..e996d89 --- /dev/null +++ b/csrc/end2end/main.cpp @@ -0,0 +1,74 @@ +// +// Created by ubuntu on 1/8/23. +// +#include "yolov8.hpp" +int main(int argc, char** argv) +{ + cudaSetDevice(DEVICE); + + const std::string engine_file_path{ argv[1] }; + const std::string path{ argv[2] }; + std::vector imagePathList; + bool isVideo{ false }; + if (IsFile(path)) + { + std::string suffix = path.substr(path.find_last_of('.') + 1); + if (suffix == "jpg") + { + imagePathList.push_back(path); + } + else if (suffix == "mp4") + { + isVideo = true; + } + } + else if (IsFolder(path)) + { + cv::glob(path + "/*.jpg", imagePathList); + } + + auto* yolov8 = new YOLOv8(engine_file_path); + yolov8->make_pipe(true); + + if (isVideo) + { + cv::VideoCapture cap(path); + cv::Mat image; + + while (cap.isOpened()) + { + cap >> image; + yolov8->copy_from_Mat(image); + yolov8->infer(); + std::vector objs; + yolov8->postprocess(objs); + draw_objects(image, objs); + if (cv::waitKey(1) == 'q') + { + break; + } + + } + cv::destroyAllWindows(); + } + else + { + for (auto& path : imagePathList) + { + cv::Mat image = cv::imread(path); + yolov8->copy_from_Mat(image); + auto start = std::chrono::system_clock::now(); + yolov8->infer(); + auto end = std::chrono::system_clock::now(); + auto tc = std::chrono::duration_cast(end - start).count() / 1000.f; + + printf("infer %-20s\tcost %2.4f ms\n", path.c_str(), tc); + + std::vector objs; + yolov8->postprocess(objs); + draw_objects(image, objs); + cv::waitKey(0); + } + } + return 0; +} diff --git a/csrc/end2end/yolov8.hpp b/csrc/end2end/yolov8.hpp new file mode 100644 index 0000000..7160805 --- /dev/null +++ b/csrc/end2end/yolov8.hpp @@ -0,0 +1,397 @@ +// +// Created by ubuntu on 1/8/23. +// +#include "detect_config.hpp" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "NvInfer.h" +#include "NvInferPlugin.h" +#include "NvInferRuntimeCommon.h" + +static const int INPUT_W = 640; +static const int INPUT_H = 640; +static const int NUM_INPUT = 1; +static const int NUM_OUTPUT = 4; + +static const int NUM_BINDINGS = NUM_INPUT + NUM_OUTPUT; +const cv::Scalar PAD_COLOR = { 114, 114, 114 }; +const cv::Scalar RECT_COLOR = cv::Scalar(0, 0, 255); +const cv::Scalar TXT_COLOR = cv::Scalar(255, 255, 255); + +const char* INPUT = "images"; +const char* NUM_DETS = "num_dets"; +const char* BBOXES = "bboxes"; +const char* SCORES = "scores"; +const char* LABELS = "labels"; + +const char* CLASS_NAMES[] = { "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", + "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", + "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", + "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", + "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", + "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", + "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", + "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", + "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", + "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", + "toothbrush" }; + +const unsigned int COLORS[80][3] = { + { 0, 114, 189 }, + { 217, 83, 25 }, + { 237, 177, 32 }, + { 126, 47, 142 }, + { 119, 172, 48 }, + { 77, 190, 238 }, + { 162, 20, 47 }, + { 76, 76, 76 }, + { 153, 153, 153 }, + { 255, 0, 0 }, + { 255, 128, 0 }, + { 191, 191, 0 }, + { 0, 255, 0 }, + { 0, 0, 255 }, + { 170, 0, 255 }, + { 85, 85, 0 }, + { 85, 170, 0 }, + { 85, 255, 0 }, + { 170, 85, 0 }, + { 170, 170, 0 }, + { 170, 255, 0 }, + { 255, 85, 0 }, + { 255, 170, 0 }, + { 255, 255, 0 }, + { 0, 85, 128 }, + { 0, 170, 128 }, + { 0, 255, 128 }, + { 85, 0, 128 }, + { 85, 85, 128 }, + { 85, 170, 128 }, + { 85, 255, 128 }, + { 170, 0, 128 }, + { 170, 85, 128 }, + { 170, 170, 128 }, + { 170, 255, 128 }, + { 255, 0, 128 }, + { 255, 85, 128 }, + { 255, 170, 128 }, + { 255, 255, 128 }, + { 0, 85, 255 }, + { 0, 170, 255 }, + { 0, 255, 255 }, + { 85, 0, 255 }, + { 85, 85, 255 }, + { 85, 170, 255 }, + { 85, 255, 255 }, + { 170, 0, 255 }, + { 170, 85, 255 }, + { 170, 170, 255 }, + { 170, 255, 255 }, + { 255, 0, 255 }, + { 255, 85, 255 }, + { 255, 170, 255 }, + { 85, 0, 0 }, + { 128, 0, 0 }, + { 170, 0, 0 }, + { 212, 0, 0 }, + { 255, 0, 0 }, + { 0, 43, 0 }, + { 0, 85, 0 }, + { 0, 128, 0 }, + { 0, 170, 0 }, + { 0, 212, 0 }, + { 0, 255, 0 }, + { 0, 0, 43 }, + { 0, 0, 85 }, + { 0, 0, 128 }, + { 0, 0, 170 }, + { 0, 0, 212 }, + { 0, 0, 255 }, + { 0, 0, 0 }, + { 36, 36, 36 }, + { 73, 73, 73 }, + { 109, 109, 109 }, + { 146, 146, 146 }, + { 182, 182, 182 }, + { 219, 219, 219 }, + { 0, 114, 189 }, + { 80, 183, 189 }, + { 128, 128, 0 } +}; + + + +struct Object +{ + cv::Rect_ rect; + int label = 0; + float prob = 0.0; +}; + +class YOLOv8 +{ +public: + explicit YOLOv8(const std::string& engine_file_path); + ~YOLOv8(); + + void make_pipe(bool warmup = true); + void copy_from_Mat(const cv::Mat& image); + void infer(); + void postprocess(std::vector& objs); + + size_t in_size = 1 * 3 * INPUT_W * INPUT_H; + float w; + float h; + float ratio = 1.0f; + float dw = 0.f; + float dh = 0.f; + std::array, NUM_OUTPUT> out_sizes{}; + std::array outputs{}; +private: + nvinfer1::ICudaEngine* engine = nullptr; + nvinfer1::IRuntime* runtime = nullptr; + nvinfer1::IExecutionContext* context = nullptr; + cudaStream_t stream = nullptr; + std::array buffs{}; + Logger gLogger{ nvinfer1::ILogger::Severity::kERROR }; + +}; + +YOLOv8::YOLOv8(const std::string& engine_file_path) +{ + std::ifstream file(engine_file_path, std::ios::binary); + assert(file.good()); + file.seekg(0, std::ios::end); + auto size = file.tellg(); + std::ostringstream fmt; + + file.seekg(0, std::ios::beg); + char* trtModelStream = new char[size]; + assert(trtModelStream); + file.read(trtModelStream, size); + file.close(); + initLibNvInferPlugins(&this->gLogger, ""); + this->runtime = nvinfer1::createInferRuntime(this->gLogger); + assert(this->runtime != nullptr); + + this->engine = this->runtime->deserializeCudaEngine(trtModelStream, size); + assert(this->engine != nullptr); + + this->context = this->engine->createExecutionContext(); + + assert(this->context != nullptr); + cudaStreamCreate(&this->stream); + +} + +YOLOv8::~YOLOv8() +{ + this->engine->destroy(); + this->context->destroy(); + this->runtime->destroy(); + cudaStreamDestroy(this->stream); + for (auto& ptr : this->buffs) + { + CHECK(cudaFree(ptr)); + } + + for (auto& ptr : this->outputs) + { + CHECK(cudaFree(ptr)); + } + +} +void YOLOv8::make_pipe(bool warmup) +{ + const nvinfer1::Dims input_dims = this->engine->getBindingDimensions( + this->engine->getBindingIndex(INPUT) + ); + this->in_size = get_size_by_dims(input_dims); + CHECK(cudaMalloc(&this->buffs[0], this->in_size * sizeof(float))); + + this->context->setBindingDimensions(0, input_dims); + const int32_t num_dets_idx = this->engine->getBindingIndex(NUM_DETS); + const nvinfer1::Dims num_dets_dims = this->context->getBindingDimensions(num_dets_idx); + this->out_sizes[num_dets_idx - NUM_INPUT].first = get_size_by_dims(num_dets_dims); + this->out_sizes[num_dets_idx - NUM_INPUT].second = DataTypeToSize( + this->engine->getBindingDataType(num_dets_idx)); + + const int32_t bboxes_idx = this->engine->getBindingIndex(BBOXES); + const nvinfer1::Dims bboxes_dims = this->context->getBindingDimensions(bboxes_idx); + + this->out_sizes[bboxes_idx - NUM_INPUT].first = get_size_by_dims(bboxes_dims); + this->out_sizes[bboxes_idx - NUM_INPUT].second = DataTypeToSize( + this->engine->getBindingDataType(bboxes_idx)); + + const int32_t scores_idx = this->engine->getBindingIndex(SCORES); + const nvinfer1::Dims scores_dims = this->context->getBindingDimensions(scores_idx); + this->out_sizes[scores_idx - NUM_INPUT].first = get_size_by_dims(scores_dims); + this->out_sizes[scores_idx - NUM_INPUT].second = DataTypeToSize( + this->engine->getBindingDataType(scores_idx)); + + const int32_t labels_idx = this->engine->getBindingIndex(LABELS); + const nvinfer1::Dims labels_dims = this->context->getBindingDimensions(labels_idx); + this->out_sizes[labels_idx - NUM_INPUT].first = get_size_by_dims(labels_dims); + this->out_sizes[labels_idx - NUM_INPUT].second = DataTypeToSize( + this->engine->getBindingDataType(labels_idx)); + + for (int i = 0; i < NUM_OUTPUT; i++) + { + const int osize = this->out_sizes[i].first * out_sizes[i].second; + CHECK(cudaHostAlloc(&this->outputs[i], osize, 0)); + CHECK(cudaMalloc(&this->buffs[NUM_INPUT + i], osize)); + } + if (warmup) + { + for (int i = 0; i < 10; i++) + { + size_t isize = this->in_size * sizeof(float); + auto* tmp = new float[isize]; + + CHECK(cudaMemcpyAsync(this->buffs[0], + tmp, + isize, + cudaMemcpyHostToDevice, + this->stream)); + this->infer(); + } + printf("model warmup 10 times\n"); + + } +} + +void YOLOv8::copy_from_Mat(const cv::Mat& image) +{ + float height = image.rows; + float width = image.cols; + + float r = std::min(INPUT_H / height, INPUT_W / width); + + int padw = (int)std::round(width * r); + int padh = (int)std::round(height * r); + + cv::Mat tmp; + if ((int)width != padw || (int)height != padh) + { + cv::resize(image, tmp, cv::Size(padw, padh)); + } + else + { + tmp = image.clone(); + } + + float _dw = INPUT_W - padw; + float _dh = INPUT_H - padh; + + _dw /= 2.0f; + _dh /= 2.0f; + int top = int(std::round(_dh - 0.1f)); + int bottom = int(std::round(_dh + 0.1f)); + int left = int(std::round(_dw - 0.1f)); + int right = int(std::round(_dw + 0.1f)); + cv::copyMakeBorder(tmp, tmp, top, bottom, left, right, cv::BORDER_CONSTANT, PAD_COLOR); + cv::dnn::blobFromImage(tmp, + tmp, + 1 / 255.f, + cv::Size(), + cv::Scalar(0, 0, 0), + true, + false, + CV_32F); + CHECK(cudaMemcpyAsync(this->buffs[0], + tmp.ptr(), + this->in_size * sizeof(float), + cudaMemcpyHostToDevice, + this->stream)); + + this->ratio = 1 / r; + this->dw = _dw; + this->dh = _dh; + this->w = width; + this->h = height; +} + +void YOLOv8::infer() +{ + this->context->enqueueV2(buffs.data(), this->stream, nullptr); + for (int i = 0; i < NUM_OUTPUT; i++) + { + const int osize = this->out_sizes[i].first * out_sizes[i].second; + CHECK(cudaMemcpyAsync(this->outputs[i], + this->buffs[NUM_INPUT + i], + osize, + cudaMemcpyDeviceToHost, + this->stream)); + } + cudaStreamSynchronize(this->stream); + +} + +void YOLOv8::postprocess(std::vector& objs) +{ + int* num_dets = static_cast(this->outputs[0]); + auto* boxes = static_cast(this->outputs[1]); + auto* scores = static_cast(this->outputs[2]); + int* labels = static_cast(this->outputs[3]); + for (int i = 0; i < num_dets[0]; i++) + { + Object obj; + float x0 = (boxes[i * 4]) - this->dw; + float y0 = (boxes[i * 4 + 1]) - this->dh; + float x1 = (boxes[i * 4 + 2]) - this->dw; + float y1 = (boxes[i * 4 + 3]) - this->dh; + + x0 = clamp(x0 * this->ratio, 0.f, this->w); + y0 = clamp(y0 * this->ratio, 0.f, this->h); + x1 = clamp(x1 * this->ratio, 0.f, this->w); + y1 = clamp(y1 * this->ratio, 0.f, this->h); + obj.rect.x = x0; + obj.rect.y = y0; + obj.rect.width = x1 - x0; + obj.rect.height = y1 - y0; + obj.prob = scores[i]; + obj.label = labels[i]; + + objs.push_back(obj); + + } +} + +static void draw_objects(const cv::Mat& image, const std::vector& objs) +{ + cv::Mat img = image.clone(); + for (auto& obj : objs) + { + cv::Scalar color = cv::Scalar(COLORS[obj.label][0], COLORS[obj.label][1], COLORS[obj.label][2]); + cv::rectangle(img, obj.rect, color, 2); + + char text[256]; + sprintf(text, "%s %.1f%%", CLASS_NAMES[obj.label], obj.prob * 100); + + int baseLine = 0; + cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.4, 1, &baseLine); + + int x = (int)obj.rect.x; + int y = (int)obj.rect.y + 1; + + if (y > img.rows) + y = img.rows; + + cv::rectangle(img, cv::Rect(x, y, label_size.width, label_size.height + baseLine), RECT_COLOR, -1); + + cv::putText(img, text, cv::Point(x, y + label_size.height), + cv::FONT_HERSHEY_SIMPLEX, 0.4, TXT_COLOR, 1); + } + + cv::imshow("results", img); +} diff --git a/infer.py b/infer.py index 8cd70b7..8dbe99c 100644 --- a/infer.py +++ b/infer.py @@ -1,3 +1,4 @@ +from models import TRTModule, TRTProfilerV0 # isort:skip import argparse import os import random @@ -7,8 +8,6 @@ import cv2 import numpy as np import torch -from models import TRTModule, TRTProfilerV0 - os.environ['CUDA_MODULE_LOADING'] = 'LAZY' random.seed(0)