YOLOX环境的安装配置部署
旷世的最新作品，采用Anchor-free，效果所有yolo系列。本文主要记录该模型如何在C++环境下进行部署。
采用的方案是TensorRT，一种NVIDIA提供的推理部署框架，并且能在嵌入式平台上实现。
论文：https://arxiv.org/abs/2107.08430
代码：https://github.com/Megvii-BaseDetection/YOLOX
一、YOLOX配置环境
基本遵循github上安装指导：
git clone git@github.com:Megvii-BaseDetection/YOLOX.git
cd YOLOX
pip3 install -U pip && pip3 install -r requirements.txt
pip3 install -v -e .
git clone https://github.com/NVIDIA/apex
cd apex
pip3 install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
python tools/demo.py image -n yolox-s -c /path/to/your/yolox_s.pth.tar --path assets/dog.jpg --conf 0.3 --nms 0.65 --tsize 640 --save_result
我个人的测试环境如下：
- Ubuntu 16.04， python 3.6，PyTorch 1.9.0
- RTX 3060 Laptop显卡，Driver 470.42.01
- CUDA 11.1（显卡太新以至于11以下的不兼容）， cudnn-v8.0.4.30
- OpenCV 3.4.4
二、TensorRT配置
YOLOX的github提供了简单的教程，但是有几处比较容易出错，因此记录一下。
1. 安装TensorRT
-
安装方式的选择：尽管deb安装看起来相当简单的样子，但是仅仅限于你的CUDA同样采用deb包安装的情况下。而实际上，更加普遍的安装方式的run文件安装。因此，更推荐TAR方式进行安装。 -
版本选择：版本选择是有严格要求的，必须保证平台，CUDA版本，CUDNN版本，完全和要求一致才可以安装。相关的查询参考页面：文档页面中的TensorRT Support Matrix 选项。注意：Update版本不等于本身版本，比如我安装的CUDA 11.1版本，因此我只能选择TensorRT 7.2.1版本进行安装。 -
安装过程总结（以7.2.1为例）
2. 安装torch2trt
安装链接：torch2trt
安装命令：
git clone https://github.com/NVIDIA-AI-IOT/torch2trt
cd torch2trt
sudo python setup.py install --plugins
3. 权重转换
- 官网上仅仅提供
.pth.tar权重文件，因此需要利用自带的脚本去转化为TensorRT需要的``格式文件 - 默认的max_workspace_size较大，需要调小，否则会报错，这里也感谢作者团队的指导。参考我提问的#97
- 由于在个人笔记本上操作，因此显卡内存有限，对于yolo-x会报错溢出，我个人最高好像只能到yolo-l
三、TensorRT的C++部署
修改CMakeLists.txt为：
# cuda
include_directories(/usr/local/cuda-11.1/include)
link_directories(/usr/local/cuda-11.1/lib64)
# cudnn
include_directories(/home/chen/cuda/include)
link_directories(/home/chen/cuda/lib64)
# tensorrt
include_directories(/home/chen/DeepLearning/settings/TensorRT-7.2.1.6/include)
link_directories(/home/chen/DeepLearning/settings/TensorRT-7.2.1.6/lib)
测试，效果很棒
另外附加一个视频的demo
#include <fstream>
#include <iostream>
#include <sstream>
#include <numeric>
#include <chrono>
#include <vector>
#include <opencv2/opencv.hpp>
#include <dirent.h>
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
#define DEVICE 0 // GPU id
#define NMS_THRESH 0.65
#define BBOX_CONF_THRESH 0.3
using namespace nvinfer1;
// stuff we know about the network and the input/output blobs
static const int INPUT_W = 640;
static const int INPUT_H = 640;
const char* INPUT_BLOB_NAME = "input_0";
const char* OUTPUT_BLOB_NAME = "output_0";
static Logger gLogger;
cv::Mat static_resize(cv::Mat& img) {
float r = std::min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
// r = std::min(r, 1.0f);
int unpad_w = r * img.cols;
int unpad_h = r * img.rows;
cv::Mat re(unpad_h, unpad_w, CV_8UC3);
cv::resize(img, re, re.size());
cv::Mat out(INPUT_W, INPUT_H, CV_8UC3, cv::Scalar(114, 114, 114));
re.copyTo(out(cv::Rect(0, 0, re.cols, re.rows)));
return out;
}
struct Object
{
cv::Rect_<float> rect;
int label;
float prob;
};
struct GridAndStride
{
int grid0;
int grid1;
int stride;
};
static int generate_grids_and_stride(const int target_size, std::vector<int>& strides, std::vector<GridAndStride>& grid_strides)
{
for (auto stride : strides)
{
int num_grid = target_size / stride;
for (int g1 = 0; g1 < num_grid; g1++)
{
for (int g0 = 0; g0 < num_grid; g0++)
{
grid_strides.push_back((GridAndStride){g0, g1, stride});
}
}
}
}
static inline float intersection_area(const Object& a, const Object& b)
{
cv::Rect_<float> inter = a.rect & b.rect;
return inter.area();
}
static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)
{
int i = left;
int j = right;
float p = faceobjects[(left + right) / 2].prob;
while (i <= j)
{
while (faceobjects[i].prob > p)
i++;
while (faceobjects[j].prob < p)
j--;
if (i <= j)
{
// swap
std::swap(faceobjects[i], faceobjects[j]);
i++;
j--;
}
}
#pragma omp parallel sections
{
#pragma omp section
{
if (left < j) qsort_descent_inplace(faceobjects, left, j);
}
#pragma omp section
{
if (i < right) qsort_descent_inplace(faceobjects, i, right);
}
}
}
static void qsort_descent_inplace(std::vector<Object>& objects)
{
if (objects.empty())
return;
qsort_descent_inplace(objects, 0, objects.size() - 1);
}
static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)
{
picked.clear();
const int n = faceobjects.size();
std::vector<float> areas(n);
for (int i = 0; i < n; i++)
{
areas[i] = faceobjects[i].rect.area();
}
for (int i = 0; i < n; i++)
{
const Object& a = faceobjects[i];
int keep = 1;
for (int j = 0; j < (int)picked.size(); j++)
{
const Object& b = faceobjects[picked[j]];
// intersection over union
float inter_area = intersection_area(a, b);
float union_area = areas[i] + areas[picked[j]] - inter_area;
// float IoU = inter_area / union_area
if (inter_area / union_area > nms_threshold)
keep = 0;
}
if (keep)
picked.push_back(i);
}
}
static void generate_yolox_proposals(std::vector<GridAndStride> grid_strides, float* feat_blob, float prob_threshold, std::vector<Object>& objects)
{
const int num_class = 80;
const int num_anchors = grid_strides.size();
for (int anchor_idx = 0; anchor_idx < num_anchors; anchor_idx++)
{
const int grid0 = grid_strides[anchor_idx].grid0;
const int grid1 = grid_strides[anchor_idx].grid1;
const int stride = grid_strides[anchor_idx].stride;
const int basic_pos = anchor_idx * 85;
// yolox/models/yolo_head.py decode logic
float x_center = (feat_blob[basic_pos+0] + grid0) * stride;
float y_center = (feat_blob[basic_pos+1] + grid1) * stride;
float w = exp(feat_blob[basic_pos+2]) * stride;
float h = exp(feat_blob[basic_pos+3]) * stride;
float x0 = x_center - w * 0.5f;
float y0 = y_center - h * 0.5f;
float box_objectness = feat_blob[basic_pos+4];
for (int class_idx = 0; class_idx < num_class; class_idx++)
{
float box_cls_score = feat_blob[basic_pos + 5 + class_idx];
float box_prob = box_objectness * box_cls_score;
if (box_prob > prob_threshold)
{
Object obj;
obj.rect.x = x0;
obj.rect.y = y0;
obj.rect.width = w;
obj.rect.height = h;
obj.label = class_idx;
obj.prob = box_prob;
objects.push_back(obj);
}
} // class loop
} // point anchor loop
}
float* blobFromImage(cv::Mat& img){
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
float* blob = new float[img.total()*3];
int channels = 3;
int img_h = 640;
int img_w = 640;
std::vector<float> mean = {0.485, 0.456, 0.406};
std::vector<float> std = {0.229, 0.224, 0.225};
for (size_t c = 0; c < channels; c++)
{
for (size_t h = 0; h < img_h; h++)
{
for (size_t w = 0; w < img_w; w++)
{
blob[c * img_w * img_h + h * img_w + w] =
(((float)img.at<cv::Vec3b>(h, w)[c]) / 255.0f - mean[c]) / std[c];
}
}
}
return blob;
}
static void decode_outputs(float* prob, std::vector<Object>& objects, float scale, const int img_w, const int img_h) {
std::vector<Object> proposals;
std::vector<int> strides = {8, 16, 32};
std::vector<GridAndStride> grid_strides;
generate_grids_and_stride(INPUT_W, strides, grid_strides);
generate_yolox_proposals(grid_strides, prob, BBOX_CONF_THRESH, proposals);
// std::cout << "num of boxes before nms: " << proposals.size() << std::endl;
qsort_descent_inplace(proposals);
std::vector<int> picked;
nms_sorted_bboxes(proposals, picked, NMS_THRESH);
int count = picked.size();
std::cout << "[INFO] Num of boxes: " << count << std::endl;
objects.resize(count);
for (int i = 0; i < count; i++)
{
objects[i] = proposals[picked[i]];
// adjust offset to original unpadded
float x0 = (objects[i].rect.x) / scale;
float y0 = (objects[i].rect.y) / scale;
float x1 = (objects[i].rect.x + objects[i].rect.width) / scale;
float y1 = (objects[i].rect.y + objects[i].rect.height) / scale;
// clip
x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);
objects[i].rect.x = x0;
objects[i].rect.y = y0;
objects[i].rect.width = x1 - x0;
objects[i].rect.height = y1 - y0;
}
}
const float color_list[80][3] =
{
{0.000, 0.447, 0.741},
{0.850, 0.325, 0.098},
{0.929, 0.694, 0.125},
{0.494, 0.184, 0.556},
{0.466, 0.674, 0.188},
{0.301, 0.745, 0.933},
{0.635, 0.078, 0.184},
{0.300, 0.300, 0.300},
{0.600, 0.600, 0.600},
{1.000, 0.000, 0.000},
{1.000, 0.500, 0.000},
{0.749, 0.749, 0.000},
{0.000, 1.000, 0.000},
{0.000, 0.000, 1.000},
{0.667, 0.000, 1.000},
{0.333, 0.333, 0.000},
{0.333, 0.667, 0.000},
{0.333, 1.000, 0.000},
{0.667, 0.333, 0.000},
{0.667, 0.667, 0.000},
{0.667, 1.000, 0.000},
{1.000, 0.333, 0.000},
{1.000, 0.667, 0.000},
{1.000, 1.000, 0.000},
{0.000, 0.333, 0.500},
{0.000, 0.667, 0.500},
{0.000, 1.000, 0.500},
{0.333, 0.000, 0.500},
{0.333, 0.333, 0.500},
{0.333, 0.667, 0.500},
{0.333, 1.000, 0.500},
{0.667, 0.000, 0.500},
{0.667, 0.333, 0.500},
{0.667, 0.667, 0.500},
{0.667, 1.000, 0.500},
{1.000, 0.000, 0.500},
{1.000, 0.333, 0.500},
{1.000, 0.667, 0.500},
{1.000, 1.000, 0.500},
{0.000, 0.333, 1.000},
{0.000, 0.667, 1.000},
{0.000, 1.000, 1.000},
{0.333, 0.000, 1.000},
{0.333, 0.333, 1.000},
{0.333, 0.667, 1.000},
{0.333, 1.000, 1.000},
{0.667, 0.000, 1.000},
{0.667, 0.333, 1.000},
{0.667, 0.667, 1.000},
{0.667, 1.000, 1.000},
{1.000, 0.000, 1.000},
{1.000, 0.333, 1.000},
{1.000, 0.667, 1.000},
{0.333, 0.000, 0.000},
{0.500, 0.000, 0.000},
{0.667, 0.000, 0.000},
{0.833, 0.000, 0.000},
{1.000, 0.000, 0.000},
{0.000, 0.167, 0.000},
{0.000, 0.333, 0.000},
{0.000, 0.500, 0.000},
{0.000, 0.667, 0.000},
{0.000, 0.833, 0.000},
{0.000, 1.000, 0.000},
{0.000, 0.000, 0.167},
{0.000, 0.000, 0.333},
{0.000, 0.000, 0.500},
{0.000, 0.000, 0.667},
{0.000, 0.000, 0.833},
{0.000, 0.000, 1.000},
{0.000, 0.000, 0.000},
{0.143, 0.143, 0.143},
{0.286, 0.286, 0.286},
{0.429, 0.429, 0.429},
{0.571, 0.571, 0.571},
{0.714, 0.714, 0.714},
{0.857, 0.857, 0.857},
{0.000, 0.447, 0.741},
{0.314, 0.717, 0.741},
{0.50, 0.5, 0}
};
static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
static 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"
};
cv::Mat image = bgr.clone();
for (size_t i = 0; i < objects.size(); i++)
{
const Object& obj = objects[i];
fprintf(stderr, "[OUTPUT] Label %s (%d), prob = %.5f at [%.2f,%.2f]; size = %.2f x %.2f\n", class_names[obj.label], obj.label, obj.prob,
obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
cv::Scalar color = cv::Scalar(color_list[obj.label][0], color_list[obj.label][1], color_list[obj.label][2]);
float c_mean = cv::mean(color)[0];
cv::Scalar txt_color;
if (c_mean > 0.5){
txt_color = cv::Scalar(0, 0, 0);
}else{
txt_color = cv::Scalar(255, 255, 255);
}
cv::rectangle(image, obj.rect, color * 255, 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);
cv::Scalar txt_bk_color = color * 0.7 * 255;
int x = obj.rect.x;
int y = obj.rect.y + 1;
//int y = obj.rect.y - label_size.height - baseLine;
if (y > image.rows)
y = image.rows;
//if (x + label_size.width > image.cols)
//x = image.cols - label_size.width;
cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
txt_bk_color, -1);
cv::putText(image, text, cv::Point(x, y + label_size.height),
cv::FONT_HERSHEY_SIMPLEX, 0.4, txt_color, 1);
}
// cv::imwrite("det_res.jpg", image);
// fprintf(stderr, "save vis file\n");
cv::imshow("image", image);
cv::waitKey(1);
}
void doInference(IExecutionContext& context, float* input, float* output, const int output_size, cv::Size input_shape) {
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
assert(engine.getNbBindings() == 2);
void* buffers[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(INPUT_BLOB_NAME);
assert(engine.getBindingDataType(inputIndex) == nvinfer1::DataType::kFLOAT);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
assert(engine.getBindingDataType(outputIndex) == nvinfer1::DataType::kFLOAT);
int mBatchSize = engine.getMaxBatchSize();
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], 3 * input_shape.height * input_shape.width * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], output_size*sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, 3 * input_shape.height * input_shape.width * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueue(1, buffers, stream, nullptr);
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], output_size * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
// Release stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
int main(int argc, char** argv) {
cudaSetDevice(DEVICE);
// create a model using the API directly and serialize it to a stream
char *trtModelStream{nullptr};
size_t size{0};
if (argc == 2 ) {
const std::string engine_file_path {argv[1]};
std::ifstream file(engine_file_path, std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
} else {
std::cerr << "[ERRO] Arguments not right!" << std::endl;
return -1;
}
IRuntime* runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
auto out_dims = engine->getBindingDimensions(1);
auto output_size = 1;
for(int j=0;j<out_dims.nbDims;j++) {
output_size *= out_dims.d[j];
}
static float* prob = new float[output_size];
cv:: VideoCapture cap = cv::VideoCapture(0);
cap.set(cv::CAP_PROP_FRAME_WIDTH, 1920);
cap.set(cv::CAP_PROP_FRAME_HEIGHT, 1080);
cv::Mat frame;
while(cap.isOpened())
{
cap.read(frame);
if(frame.empty())
{
std::cout << "[ERRO] Read frame failed!" << std::endl;
break;
}
int img_w = frame.cols;
int img_h = frame.rows;
cv::Mat pr_img = static_resize(frame);
float* blob;
blob = blobFromImage(pr_img);
float scale = std::min(INPUT_W / (frame.cols*1.0), INPUT_H / (frame.rows*1.0));
// run inference
auto start = std::chrono::system_clock::now();
doInference(*context, blob, prob, output_size, pr_img.size());
auto end = std::chrono::system_clock::now();
std::cout << "[INFO] Cost Time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
std::vector<Object> objects;
decode_outputs(prob, objects, scale, img_w, img_h);
draw_objects(frame, objects);
}
// destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}
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