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scrfd.cpp
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scrfd.cpp
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#include "scrfd.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 Scrfd{
using namespace cv;
using namespace std;
void decode_kernel_invoker(
float* predict, int num_bboxes, float confidence_threshold,
float nms_threshold, float* invert_affine_matrix, float* parray,
int max_objects, float* prior,
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;
float scale = std::min(scale_x, scale_y);
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;
// 有了i2d矩阵,我们求其逆矩阵,即可得到d2i(用以解码时还原到原始图像分辨率上)
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);
}
};
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, int gpuid, float confidence_threshold, float nms_threshold){
float mean[] = {127.5, 127.5, 127.5};
float std[] = {128.0, 128.0, 128.0};
normalize_ = CUDAKernel::Norm::mean_std(mean, std, 1.0f);
confidence_threshold_ = confidence_threshold;
nms_threshold_ = nms_threshold;
return ControllerImpl::startup(make_tuple(file, gpuid));
}
size_t compute_prior_size(int input_width, int input_height, const vector<int>& strides={8, 16, 32}, int num_anchor_per_stage=2){
int input_area = input_width * input_height;
size_t total = 0;
for(int s : strides){
total += input_area / s / s * num_anchor_per_stage;
}
return total;
}
void init_prior_box(TRT::Tensor& prior, int input_width, int input_height){
vector<int> strides{8, 16, 32};
vector<vector<float>> min_sizes{
vector<float>({16.0f, 32.0f }),
vector<float>({64.0f, 128.0f}),
vector<float>({256.0f, 512.0f})
};
prior.resize(1, compute_prior_size(input_width, input_height, strides), 4).to_cpu();
int prior_row = 0;
for(int istride = 0; istride < strides.size(); ++istride){
int stride = strides[istride];
auto anchor_sizes = min_sizes[istride];
int feature_map_width = input_width / stride;
int feature_map_height = input_height / stride;
for(int y = 0; y < feature_map_height; ++y){
for(int x = 0; x < feature_map_width; ++x){
for(int isize = 0; isize < anchor_sizes.size(); ++isize){
float anchor_size = anchor_sizes[isize];
float dense_cx = x * stride;
float dense_cy = y * stride;
float s_kx = stride;
float s_ky = stride;
float* prow = prior.cpu<float>(0, prior_row++);
prow[0] = dense_cx;
prow[1] = dense_cy;
prow[2] = s_kx;
prow[3] = s_ky;
}
}
}
}
prior.to_gpu();
}
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 = 1024;
const int NUM_BOX_ELEMENT = 16; // left, top, right, bottom, confidence, keepflag(1keep,0ignore), landmark(x, y) * 5
TRT::Tensor affin_matrix_device(TRT::DataType::Float);
TRT::Tensor output_array_device(TRT::DataType::Float);
TRT::Tensor prior(TRT::DataType::Float);
int max_batch_size = engine->get_max_batch_size();
auto input = engine->input();
auto output = engine->output();
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);
init_prior_box(prior, input_width_, input_height_);
input->resize_single_dim(0, max_batch_size).to_gpu();
output->resize(max_batch_size, prior.size(1), 15).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();
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), confidence_threshold_, nms_threshold_, affine_matrix,
output_array_ptr, MAX_IMAGE_BBOX, prior.gpu<float>(),
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 keepflag = pbox[5];
if(keepflag == 1){
Box box;
box.left = pbox[0];
box.top = pbox[1];
box.right = pbox[2];
box.bottom = pbox[3];
box.confidence = pbox[4];
memcpy(box.landmark, pbox + 6, sizeof(box.landmark));
image_based_boxes.emplace_back(box);
}
}
job.pro->set_value(image_based_boxes);
}
fetch_jobs.clear();
}
stream_ = nullptr;
tensor_allocator_.reset();
INFOV("Engine destroy.");
}
virtual bool preprocess(Job& job, const Mat& image) override{
if(tensor_allocator_ == nullptr){
INFOE("tensor_allocator_ is nullptr");
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();
if(tensor == nullptr){
// not init
tensor = make_shared<TRT::Tensor>();
tensor->set_workspace(make_shared<TRT::MixMemory>());
}
Size input_size(input_width_, input_height_);
job.additional.compute(image.size(), input_size);
tensor->set_stream(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, stream_));
checkCudaRuntime(cudaMemcpyAsync(affine_matrix_device, affine_matrix_host, sizeof(job.additional.d2i), cudaMemcpyHostToDevice, 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, 0,
normalize_, 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;
TRT::CUStream stream_ = nullptr;
CUDAKernel::Norm normalize_;
};
tuple<cv::Mat, Box> crop_face_and_landmark(const cv::Mat& image, const Box& box, float scale_box){
float padding_x = (scale_box - 1) * box.width() * 0.5f;
float padding_y = (scale_box - 1) * box.height() * 0.5f;
int left = std::round(box.left - padding_x);
int top = std::round(box.top - padding_y);
int right = std::round(box.right + padding_x);
int bottom = std::round(box.bottom + padding_y);
Rect rbox(left, top, right-left, bottom-top);
rbox = rbox & Rect(0, 0, image.cols, image.rows);
auto box_copy = box;
for(int i = 0; i < 10; ++i){
if(i % 2 == 0){
// x
box_copy.landmark[i] -= left;
}else{
box_copy.landmark[i] -= top;
}
}
box_copy.left -= left;
box_copy.top -= top;
box_copy.right -= left;
box_copy.bottom -= top;
if(rbox.width < 1 || rbox.height < 1)
return make_tuple(Mat(), box_copy);
return make_tuple(image(rbox).clone(), box_copy);
}
shared_ptr<Infer> create_infer(const string& engine_file, int gpuid, float confidence_threshold, float nms_threshold){
shared_ptr<InferImpl> instance(new InferImpl());
if(!instance->startup(engine_file, gpuid, confidence_threshold, nms_threshold)){
instance.reset();
}
return instance;
}
};