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yolo.cpp
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yolo.cpp
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#include "yolo.hpp"
#include <atomic>
#include <mutex>
#include <queue>
#include <condition_variable>
#include <infer/trt_infer.hpp>
#include <common/ilogger.hpp>
#include <common/infer_controller.hpp>
#include <common/preprocess_kernel.cuh>
#include <common/monopoly_allocator.hpp>
#include <common/cuda_tools.hpp>
namespace Yolo{
using namespace cv;
using namespace std;
const char* type_name(Type type){
switch(type){
case Type::V5: return "YoloV5";
case Type::V3: return "YoloV3";
case Type::V7: return "YoloV7";
case Type::X: return "YoloX";
default: return "Unknow";
}
}
void decode_kernel_invoker(
float* predict, int num_bboxes, int num_classes, float confidence_threshold,
float* invert_affine_matrix, float* parray,
int max_objects, cudaStream_t stream
);
void nms_kernel_invoker(
float* parray, float nms_threshold, int max_objects, cudaStream_t stream
);
struct AffineMatrix{
float i2d[6]; // image to dst(network), 2x3 matrix
float d2i[6]; // dst to image, 2x3 matrix
void compute(const cv::Size& from, const cv::Size& to){
float scale_x = to.width / (float)from.width;
float scale_y = to.height / (float)from.height;
// 这里取min的理由是
// 1. M矩阵是 from * M = to的方式进行映射,因此scale的分母一定是from
// 2. 取最小,即根据宽高比,算出最小的比例,如果取最大,则势必有一部分超出图像范围而被裁剪掉,这不是我们要的
// **
float scale = std::min(scale_x, scale_y);
/**
这里的仿射变换矩阵实质上是2x3的矩阵,具体实现是
scale, 0, -scale * from.width * 0.5 + to.width * 0.5
0, scale, -scale * from.height * 0.5 + to.height * 0.5
这里可以想象成,是经历过缩放、平移、平移三次变换后的组合,M = TPS
例如第一个S矩阵,定义为把输入的from图像,等比缩放scale倍,到to尺度下
S = [
scale, 0, 0
0, scale, 0
0, 0, 1
]
P矩阵定义为第一次平移变换矩阵,将图像的原点,从左上角,移动到缩放(scale)后图像的中心上
P = [
1, 0, -scale * from.width * 0.5
0, 1, -scale * from.height * 0.5
0, 0, 1
]
T矩阵定义为第二次平移变换矩阵,将图像从原点移动到目标(to)图的中心上
T = [
1, 0, to.width * 0.5,
0, 1, to.height * 0.5,
0, 0, 1
]
通过将3个矩阵顺序乘起来,即可得到下面的表达式:
M = [
scale, 0, -scale * from.width * 0.5 + to.width * 0.5
0, scale, -scale * from.height * 0.5 + to.height * 0.5
0, 0, 1
]
去掉第三行就得到opencv需要的输入2x3矩阵
**/
/*
+ scale * 0.5 - 0.5 的主要原因是使得中心更加对齐,下采样不明显,但是上采样时就比较明显
参考:https://www.iteye.com/blog/handspeaker-1545126
*/
i2d[0] = scale; i2d[1] = 0; i2d[2] = -scale * from.width * 0.5 + to.width * 0.5 + scale * 0.5 - 0.5;
i2d[3] = 0; i2d[4] = scale; i2d[5] = -scale * from.height * 0.5 + to.height * 0.5 + scale * 0.5 - 0.5;
cv::Mat m2x3_i2d(2, 3, CV_32F, i2d);
cv::Mat m2x3_d2i(2, 3, CV_32F, d2i);
cv::invertAffineTransform(m2x3_i2d, m2x3_d2i);
}
cv::Mat i2d_mat(){
return cv::Mat(2, 3, CV_32F, i2d);
}
};
static float iou(const Box& a, const Box& b){
float cleft = max(a.left, b.left);
float ctop = max(a.top, b.top);
float cright = min(a.right, b.right);
float cbottom = min(a.bottom, b.bottom);
float c_area = max(cright - cleft, 0.0f) * max(cbottom - ctop, 0.0f);
if(c_area == 0.0f)
return 0.0f;
float a_area = max(0.0f, a.right - a.left) * max(0.0f, a.bottom - a.top);
float b_area = max(0.0f, b.right - b.left) * max(0.0f, b.bottom - b.top);
return c_area / (a_area + b_area - c_area);
}
static BoxArray cpu_nms(BoxArray& boxes, float threshold){
std::sort(boxes.begin(), boxes.end(), [](BoxArray::const_reference a, BoxArray::const_reference b){
return a.confidence > b.confidence;
});
BoxArray output;
output.reserve(boxes.size());
vector<bool> remove_flags(boxes.size());
for(int i = 0; i < boxes.size(); ++i){
if(remove_flags[i]) continue;
auto& a = boxes[i];
output.emplace_back(a);
for(int j = i + 1; j < boxes.size(); ++j){
if(remove_flags[j]) continue;
auto& b = boxes[j];
if(b.class_label == a.class_label){
if(iou(a, b) >= threshold)
remove_flags[j] = true;
}
}
}
return output;
}
using ControllerImpl = InferController
<
Mat, // input
BoxArray, // output
tuple<string, int>, // start param
AffineMatrix // additional
>;
class InferImpl : public Infer, public ControllerImpl{
public:
/** 要求在InferImpl里面执行stop,而不是在基类执行stop **/
virtual ~InferImpl(){
stop();
}
virtual bool startup(
const string& file, Type type, int gpuid,
float confidence_threshold, float nms_threshold,
NMSMethod nms_method, int max_objects,
bool use_multi_preprocess_stream
){
if(type == Type::V5 || type == Type::V3 || type == Type::V7){
normalize_ = CUDAKernel::Norm::alpha_beta(1 / 255.0f, 0.0f, CUDAKernel::ChannelType::Invert);
}else if(type == Type::X){
//float mean[] = {0.485, 0.456, 0.406};
//float std[] = {0.229, 0.224, 0.225};
//normalize_ = CUDAKernel::Norm::mean_std(mean, std, 1/255.0f, CUDAKernel::ChannelType::Invert);
normalize_ = CUDAKernel::Norm::None();
}else{
INFOE("Unsupport type %d", type);
}
use_multi_preprocess_stream_ = use_multi_preprocess_stream;
confidence_threshold_ = confidence_threshold;
nms_threshold_ = nms_threshold;
nms_method_ = nms_method;
max_objects_ = max_objects;
return ControllerImpl::startup(make_tuple(file, gpuid));
}
virtual void worker(promise<bool>& result) override{
string file = get<0>(start_param_);
int gpuid = get<1>(start_param_);
TRT::set_device(gpuid);
auto engine = TRT::load_infer(file);
if(engine == nullptr){
INFOE("Engine %s load failed", file.c_str());
result.set_value(false);
return;
}
engine->print();
const int MAX_IMAGE_BBOX = max_objects_;
const int NUM_BOX_ELEMENT = 7; // left, top, right, bottom, confidence, class, keepflag
TRT::Tensor affin_matrix_device(TRT::DataType::Float);
TRT::Tensor output_array_device(TRT::DataType::Float);
int max_batch_size = engine->get_max_batch_size();
auto input = engine->tensor("images");
auto output = engine->tensor("output");
int num_classes = output->size(2) - 5;
input_width_ = input->size(3);
input_height_ = input->size(2);
tensor_allocator_ = make_shared<MonopolyAllocator<TRT::Tensor>>(max_batch_size * 2);
stream_ = engine->get_stream();
gpu_ = gpuid;
result.set_value(true);
input->resize_single_dim(0, max_batch_size).to_gpu();
affin_matrix_device.set_stream(stream_);
// 这里8个值的目的是保证 8 * sizeof(float) % 32 == 0
affin_matrix_device.resize(max_batch_size, 8).to_gpu();
// 这里的 1 + MAX_IMAGE_BBOX结构是,counter + bboxes ...
output_array_device.resize(max_batch_size, 1 + MAX_IMAGE_BBOX * NUM_BOX_ELEMENT).to_gpu();
vector<Job> fetch_jobs;
while(get_jobs_and_wait(fetch_jobs, max_batch_size)){
int infer_batch_size = fetch_jobs.size();
input->resize_single_dim(0, infer_batch_size);
for(int ibatch = 0; ibatch < infer_batch_size; ++ibatch){
auto& job = fetch_jobs[ibatch];
auto& mono = job.mono_tensor->data();
if(mono->get_stream() != stream_){
// synchronize preprocess stream finish
checkCudaRuntime(cudaStreamSynchronize(mono->get_stream()));
}
affin_matrix_device.copy_from_gpu(affin_matrix_device.offset(ibatch), mono->get_workspace()->gpu(), 6);
input->copy_from_gpu(input->offset(ibatch), mono->gpu(), mono->count());
job.mono_tensor->release();
}
engine->forward(false);
output_array_device.to_gpu(false);
for(int ibatch = 0; ibatch < infer_batch_size; ++ibatch){
auto& job = fetch_jobs[ibatch];
float* image_based_output = output->gpu<float>(ibatch);
float* output_array_ptr = output_array_device.gpu<float>(ibatch);
auto affine_matrix = affin_matrix_device.gpu<float>(ibatch);
checkCudaRuntime(cudaMemsetAsync(output_array_ptr, 0, sizeof(int), stream_));
decode_kernel_invoker(image_based_output, output->size(1), num_classes, confidence_threshold_, affine_matrix, output_array_ptr, MAX_IMAGE_BBOX, stream_);
if(nms_method_ == NMSMethod::FastGPU){
nms_kernel_invoker(output_array_ptr, nms_threshold_, MAX_IMAGE_BBOX, stream_);
}
}
output_array_device.to_cpu();
for(int ibatch = 0; ibatch < infer_batch_size; ++ibatch){
float* parray = output_array_device.cpu<float>(ibatch);
int count = min(MAX_IMAGE_BBOX, (int)*parray);
auto& job = fetch_jobs[ibatch];
auto& image_based_boxes = job.output;
for(int i = 0; i < count; ++i){
float* pbox = parray + 1 + i * NUM_BOX_ELEMENT;
int label = pbox[5];
int keepflag = pbox[6];
if(keepflag == 1){
image_based_boxes.emplace_back(pbox[0], pbox[1], pbox[2], pbox[3], pbox[4], label);
}
}
if(nms_method_ == NMSMethod::CPU){
image_based_boxes = cpu_nms(image_based_boxes, nms_threshold_);
}
job.pro->set_value(image_based_boxes);
}
fetch_jobs.clear();
}
stream_ = nullptr;
tensor_allocator_.reset();
INFO("Engine destroy.");
}
virtual bool preprocess(Job& job, const Mat& image) override{
if(tensor_allocator_ == nullptr){
INFOE("tensor_allocator_ is nullptr");
return false;
}
if(image.empty()){
INFOE("Image is empty");
return false;
}
job.mono_tensor = tensor_allocator_->query();
if(job.mono_tensor == nullptr){
INFOE("Tensor allocator query failed.");
return false;
}
CUDATools::AutoDevice auto_device(gpu_);
auto& tensor = job.mono_tensor->data();
TRT::CUStream preprocess_stream = nullptr;
if(tensor == nullptr){
// not init
tensor = make_shared<TRT::Tensor>();
tensor->set_workspace(make_shared<TRT::MixMemory>());
if(use_multi_preprocess_stream_){
checkCudaRuntime(cudaStreamCreate(&preprocess_stream));
// owner = true, stream needs to be free during deconstruction
tensor->set_stream(preprocess_stream, true);
}else{
preprocess_stream = stream_;
// owner = false, tensor ignored the stream
tensor->set_stream(preprocess_stream, false);
}
}
Size input_size(input_width_, input_height_);
job.additional.compute(image.size(), input_size);
preprocess_stream = tensor->get_stream();
tensor->resize(1, 3, input_height_, input_width_);
size_t size_image = image.cols * image.rows * 3;
size_t size_matrix = iLogger::upbound(sizeof(job.additional.d2i), 32);
auto workspace = tensor->get_workspace();
uint8_t* gpu_workspace = (uint8_t*)workspace->gpu(size_matrix + size_image);
float* affine_matrix_device = (float*)gpu_workspace;
uint8_t* image_device = size_matrix + gpu_workspace;
uint8_t* cpu_workspace = (uint8_t*)workspace->cpu(size_matrix + size_image);
float* affine_matrix_host = (float*)cpu_workspace;
uint8_t* image_host = size_matrix + cpu_workspace;
//checkCudaRuntime(cudaMemcpyAsync(image_host, image.data, size_image, cudaMemcpyHostToHost, stream_));
// speed up
memcpy(image_host, image.data, size_image);
memcpy(affine_matrix_host, job.additional.d2i, sizeof(job.additional.d2i));
checkCudaRuntime(cudaMemcpyAsync(image_device, image_host, size_image, cudaMemcpyHostToDevice, preprocess_stream));
checkCudaRuntime(cudaMemcpyAsync(affine_matrix_device, affine_matrix_host, sizeof(job.additional.d2i), cudaMemcpyHostToDevice, preprocess_stream));
CUDAKernel::warp_affine_bilinear_and_normalize_plane(
image_device, image.cols * 3, image.cols, image.rows,
tensor->gpu<float>(), input_width_, input_height_,
affine_matrix_device, 114,
normalize_, preprocess_stream
);
return true;
}
virtual vector<shared_future<BoxArray>> commits(const vector<Mat>& images) override{
return ControllerImpl::commits(images);
}
virtual std::shared_future<BoxArray> commit(const Mat& image) override{
return ControllerImpl::commit(image);
}
private:
int input_width_ = 0;
int input_height_ = 0;
int gpu_ = 0;
float confidence_threshold_ = 0;
float nms_threshold_ = 0;
int max_objects_ = 1024;
NMSMethod nms_method_ = NMSMethod::FastGPU;
TRT::CUStream stream_ = nullptr;
bool use_multi_preprocess_stream_ = false;
CUDAKernel::Norm normalize_;
};
shared_ptr<Infer> create_infer(
const string& engine_file, Type type, int gpuid,
float confidence_threshold, float nms_threshold,
NMSMethod nms_method, int max_objects,
bool use_multi_preprocess_stream
){
shared_ptr<InferImpl> instance(new InferImpl());
if(!instance->startup(
engine_file, type, gpuid, confidence_threshold,
nms_threshold, nms_method, max_objects, use_multi_preprocess_stream)
){
instance.reset();
}
return instance;
}
void image_to_tensor(const cv::Mat& image, shared_ptr<TRT::Tensor>& tensor, Type type, int ibatch){
CUDAKernel::Norm normalize;
if(type == Type::V5 || type == Type::V3 || type == Type::V7){
normalize = CUDAKernel::Norm::alpha_beta(1 / 255.0f, 0.0f, CUDAKernel::ChannelType::Invert);
}else if(type == Type::X){
//float mean[] = {0.485, 0.456, 0.406};
//float std[] = {0.229, 0.224, 0.225};
//normalize_ = CUDAKernel::Norm::mean_std(mean, std, 1/255.0f, CUDAKernel::ChannelType::Invert);
normalize = CUDAKernel::Norm::None();
}else{
INFOE("Unsupport type %d", type);
}
Size input_size(tensor->size(3), tensor->size(2));
AffineMatrix affine;
affine.compute(image.size(), input_size);
size_t size_image = image.cols * image.rows * 3;
size_t size_matrix = iLogger::upbound(sizeof(affine.d2i), 32);
auto workspace = tensor->get_workspace();
uint8_t* gpu_workspace = (uint8_t*)workspace->gpu(size_matrix + size_image);
float* affine_matrix_device = (float*)gpu_workspace;
uint8_t* image_device = size_matrix + gpu_workspace;
uint8_t* cpu_workspace = (uint8_t*)workspace->cpu(size_matrix + size_image);
float* affine_matrix_host = (float*)cpu_workspace;
uint8_t* image_host = size_matrix + cpu_workspace;
auto stream = tensor->get_stream();
memcpy(image_host, image.data, size_image);
memcpy(affine_matrix_host, affine.d2i, sizeof(affine.d2i));
checkCudaRuntime(cudaMemcpyAsync(image_device, image_host, size_image, cudaMemcpyHostToDevice, stream));
checkCudaRuntime(cudaMemcpyAsync(affine_matrix_device, affine_matrix_host, sizeof(affine.d2i), cudaMemcpyHostToDevice, stream));
CUDAKernel::warp_affine_bilinear_and_normalize_plane(
image_device, image.cols * 3, image.cols, image.rows,
tensor->gpu<float>(ibatch), input_size.width, input_size.height,
affine_matrix_device, 114,
normalize, stream
);
tensor->synchronize();
}
};