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ncnn_yolov6.cpp
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ncnn_yolov6.cpp
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//
// Created by DefTruth on 2022/6/25.
//
#include "ncnn_yolov6.h"
#include "lite/utils.h"
using ncnncv::NCNNYOLOv6;
NCNNYOLOv6::NCNNYOLOv6(const std::string &_param_path,
const std::string &_bin_path,
unsigned int _num_threads,
int _input_height,
int _input_width) :
log_id(_param_path.data()), param_path(_param_path.data()),
bin_path(_bin_path.data()), num_threads(_num_threads),
input_height(_input_height), input_width(_input_width)
{
net = new ncnn::Net();
// init net, change this setting for better performance.
net->opt.use_fp16_arithmetic = false;
net->opt.use_vulkan_compute = false; // default
// setup Focus in yolov5
// net->register_custom_layer("YoloV5Focus", YoloV5Focus_layer_creator);
net->load_param(param_path);
net->load_model(bin_path);
#ifdef LITENCNN_DEBUG
this->print_debug_string();
#endif
}
NCNNYOLOv6::~NCNNYOLOv6()
{
if (net) delete net;
net = nullptr;
}
void NCNNYOLOv6::transform(const cv::Mat &mat_rs, ncnn::Mat &in)
{
// BGR NHWC -> RGB NCHW
in = ncnn::Mat::from_pixels(mat_rs.data, ncnn::Mat::PIXEL_BGR2RGB, input_width, input_height);
in.substract_mean_normalize(mean_vals, norm_vals);
}
// letterbox
void NCNNYOLOv6::resize_unscale(const cv::Mat &mat, cv::Mat &mat_rs,
int target_height, int target_width,
YOLOv6ScaleParams &scale_params)
{
if (mat.empty()) return;
int img_height = static_cast<int>(mat.rows);
int img_width = static_cast<int>(mat.cols);
mat_rs = cv::Mat(target_height, target_width, CV_8UC3,
cv::Scalar(114, 114, 114));
// scale ratio (new / old) new_shape(h,w)
float w_r = (float) target_width / (float) img_width;
float h_r = (float) target_height / (float) img_height;
float r = std::min(w_r, h_r);
// compute padding
int new_unpad_w = static_cast<int>((float) img_width * r); // floor
int new_unpad_h = static_cast<int>((float) img_height * r); // floor
int pad_w = target_width - new_unpad_w; // >=0
int pad_h = target_height - new_unpad_h; // >=0
int dw = pad_w / 2;
int dh = pad_h / 2;
// resize with unscaling
cv::Mat new_unpad_mat;
// cv::Mat new_unpad_mat = mat.clone(); // may not need clone.
cv::resize(mat, new_unpad_mat, cv::Size(new_unpad_w, new_unpad_h));
new_unpad_mat.copyTo(mat_rs(cv::Rect(dw, dh, new_unpad_w, new_unpad_h)));
// record scale params.
scale_params.r = r;
scale_params.dw = dw;
scale_params.dh = dh;
scale_params.new_unpad_w = new_unpad_w;
scale_params.new_unpad_h = new_unpad_h;
scale_params.flag = true;
}
void NCNNYOLOv6::detect(const cv::Mat &mat, std::vector<types::Boxf> &detected_boxes,
float score_threshold, float iou_threshold,
unsigned int topk, unsigned int nms_type)
{
if (mat.empty()) return;
int img_height = static_cast<int>(mat.rows);
int img_width = static_cast<int>(mat.cols);
// resize & unscale
cv::Mat mat_rs;
YOLOv6ScaleParams scale_params;
this->resize_unscale(mat, mat_rs, input_height, input_width, scale_params);
// 1. make input tensor
ncnn::Mat input;
this->transform(mat_rs, input);
// 2. inference & extract
auto extractor = net->create_extractor();
extractor.set_light_mode(false); // default
extractor.set_num_threads(num_threads);
extractor.input("image_arrays", input);
// 3.rescale & exclude.
std::vector<types::Boxf> bbox_collection;
this->generate_bboxes(scale_params, bbox_collection, extractor, score_threshold, img_height, img_width);
// 4. hard|blend|offset nms with topk.
this->nms(bbox_collection, detected_boxes, iou_threshold, topk, nms_type);
}
void NCNNYOLOv6::generate_anchors(const int target_height,
const int target_width,
std::vector<int> &strides,
std::vector<YOLOv6Anchor> &anchors)
{
for (auto stride: strides)
{
int num_grid_w = target_width / stride;
int num_grid_h = target_height / stride;
for (int g1 = 0; g1 < num_grid_h; ++g1)
{
for (int g0 = 0; g0 < num_grid_w; ++g0)
{
YOLOv6Anchor anchor;
anchor.grid0 = g0;
anchor.grid1 = g1;
anchor.stride = stride;
anchors.push_back(anchor);
}
}
}
}
static inline float sigmoid(float x)
{
return static_cast<float>(1.f / (1.f + std::exp(-x)));
}
void NCNNYOLOv6::generate_bboxes(const YOLOv6ScaleParams &scale_params,
std::vector<types::Boxf> &bbox_collection,
ncnn::Extractor &extractor,
float score_threshold, int img_height,
int img_width)
{
ncnn::Mat outputs;
extractor.extract("outputs", outputs); // (1,n=?,85=5+80=cxcy+cwch+obj_conf+cls_conf)
const unsigned int num_anchors = outputs.h;
const unsigned int num_classes = outputs.w - 5;
std::vector<YOLOv6Anchor> anchors;
std::vector<int> strides = {8, 16, 32}; // might have stride=64
this->generate_anchors(input_height, input_width, strides, anchors);
float r_ = scale_params.r;
int dw_ = scale_params.dw;
int dh_ = scale_params.dh;
bbox_collection.clear();
unsigned int count = 0;
for (unsigned int i = 0; i < num_anchors; ++i)
{
const float *offset_obj_cls_ptr =
(float *) outputs.data + (i * (num_classes + 5)); // row ptr
float obj_conf = sigmoid(offset_obj_cls_ptr[4]);
if (obj_conf < score_threshold) continue; // filter first.
float cls_conf = sigmoid(offset_obj_cls_ptr[5]);
unsigned int label = 0;
for (unsigned int j = 0; j < num_classes; ++j)
{
float tmp_conf = sigmoid(offset_obj_cls_ptr[j + 5]);
if (tmp_conf > cls_conf)
{
cls_conf = tmp_conf;
label = j;
}
} // argmax
float conf = obj_conf * cls_conf; // cls_conf (0.,1.)
if (conf < score_threshold) continue; // filter
const int grid0 = anchors.at(i).grid0;
const int grid1 = anchors.at(i).grid1;
const int stride = anchors.at(i).stride;
float dx = offset_obj_cls_ptr[0];
float dy = offset_obj_cls_ptr[1];
float dw = offset_obj_cls_ptr[2];
float dh = offset_obj_cls_ptr[3];
float cx = (dx + (float) grid0) * (float) stride;
float cy = (dy + (float) grid1) * (float) stride;
float w = std::exp(dw) * (float) stride;
float h = std::exp(dh) * (float) stride;
float x1 = ((cx - w / 2.f) - (float) dw_) / r_;
float y1 = ((cy - h / 2.f) - (float) dh_) / r_;
float x2 = ((cx + w / 2.f) - (float) dw_) / r_;
float y2 = ((cy + h / 2.f) - (float) dh_) / r_;
types::Boxf box;
box.x1 = std::max(0.f, x1);
box.y1 = std::max(0.f, y1);
box.x2 = std::min(x2, (float) img_width - 1.f);
box.y2 = std::min(y2, (float) img_height - 1.f);
box.score = conf;
box.label = label;
box.label_text = class_names[label];
box.flag = true;
bbox_collection.push_back(box);
count += 1; // limit boxes for nms.
if (count > max_nms)
break;
}
#if LITENCNN_DEBUG
std::cout << "detected num_anchors: " << num_anchors << "\n";
std::cout << "generate_bboxes num: " << bbox_collection.size() << "\n";
#endif
}
void NCNNYOLOv6::nms(std::vector<types::Boxf> &input, std::vector<types::Boxf> &output,
float iou_threshold, unsigned int topk,
unsigned int nms_type)
{
if (nms_type == NMS::BLEND) lite::utils::blending_nms(input, output, iou_threshold, topk);
else if (nms_type == NMS::OFFSET) lite::utils::offset_nms(input, output, iou_threshold, topk);
else lite::utils::hard_nms(input, output, iou_threshold, topk);
}
void NCNNYOLOv6::print_debug_string()
{
std::cout << "LITENCNN_DEBUG LogId: " << log_id << "\n";
input_indexes = net->input_indexes();
output_indexes = net->output_indexes();
#ifdef NCNN_STRING
input_names = net->input_names();
output_names = net->output_names();
#endif
std::cout << "=============== Input-Dims ==============\n";
for (int i = 0; i < input_indexes.size(); ++i)
{
std::cout << "Input: ";
auto tmp_in_blob = net->blobs().at(input_indexes.at(i));
#ifdef NCNN_STRING
std::cout << input_names.at(i) << ": ";
#endif
std::cout << "shape: c=" << tmp_in_blob.shape.c
<< " h=" << tmp_in_blob.shape.h << " w=" << tmp_in_blob.shape.w << "\n";
}
std::cout << "=============== Output-Dims ==============\n";
for (int i = 0; i < output_indexes.size(); ++i)
{
auto tmp_out_blob = net->blobs().at(output_indexes.at(i));
std::cout << "Output: ";
#ifdef NCNN_STRING
std::cout << output_names.at(i) << ": ";
#endif
std::cout << "shape: c=" << tmp_out_blob.shape.c
<< " h=" << tmp_out_blob.shape.h << " w=" << tmp_out_blob.shape.w << "\n";
}
std::cout << "========================================\n";
}