From 1b5afb45838e603fa6780762eb8cc59246dc2d81 Mon Sep 17 00:00:00 2001
From: IlyaOvodov <b@ovdv.ru>
Date: Tue, 08 May 2018 11:09:35 +0000
Subject: [PATCH] Output improvements for detector results: When printing detector results, output was done in random order, obfuscating results for interpreting. Now: 1. Text output includes coordinates of rects in (left,right,top,bottom in pixels) along with label and score 2. Text output is sorted by rect lefts to simplify finding appropriate rects on image 3. If several class probs are > thresh for some detection, the most probable is written first and coordinates for others are not repeated 4. Rects are imprinted in image in order by their best class prob, so most probable rects are always on top and not overlayed by less probable ones 5. Most probable label for rect is always written first Also: 6. Message about low GPU memory include required amount
---
src/yolo_v2_class.cpp | 107 ++++++++++++++++++++++++++++++-----------------------
1 files changed, 61 insertions(+), 46 deletions(-)
diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp
index f036b2a..03d581a 100644
--- a/src/yolo_v2_class.cpp
+++ b/src/yolo_v2_class.cpp
@@ -22,9 +22,16 @@
#define FRAMES 3
-struct detector_gpu_t{
- float **probs;
- box *boxes;
+#ifdef GPU
+void check_cuda(cudaError_t status) {
+ if (status != cudaSuccess) {
+ const char *s = cudaGetErrorString(status);
+ printf("CUDA Error Prev: %s\n", s);
+ }
+}
+#endif
+
+struct detector_gpu_t {
network net;
image images[FRAMES];
float *avg;
@@ -33,33 +40,36 @@
unsigned int *track_id;
};
-
-YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id)
+YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id)
{
+ wait_stream = 0;
int old_gpu_index;
#ifdef GPU
- cudaGetDevice(&old_gpu_index);
+ check_cuda( cudaGetDevice(&old_gpu_index) );
#endif
detector_gpu_ptr = std::make_shared<detector_gpu_t>();
- detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+ detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
#ifdef GPU
- cudaSetDevice(gpu_id);
+ //check_cuda( cudaSetDevice(cur_gpu_id) );
+ cuda_set_device(cur_gpu_id);
+ printf(" Used GPU %d \n", cur_gpu_id);
#endif
network &net = detector_gpu.net;
- net.gpu_index = gpu_id;
+ net.gpu_index = cur_gpu_id;
//gpu_index = i;
char *cfgfile = const_cast<char *>(cfg_filename.data());
char *weightfile = const_cast<char *>(weight_filename.data());
- net = parse_network_cfg(cfgfile);
+ net = parse_network_cfg_custom(cfgfile, 1);
if (weightfile) {
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
- net.gpu_index = gpu_id;
+ net.gpu_index = cur_gpu_id;
+ fuse_conv_batchnorm(net);
layer l = net.layers[net.n - 1];
int j;
@@ -68,22 +78,18 @@
for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float));
for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3);
- detector_gpu.boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
- detector_gpu.probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
- for (j = 0; j < l.w*l.h*l.n; ++j) detector_gpu.probs[j] = (float *)calloc(l.classes, sizeof(float));
-
detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int));
for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1;
#ifdef GPU
- cudaSetDevice(old_gpu_index);
+ check_cuda( cudaSetDevice(old_gpu_index) );
#endif
}
YOLODLL_API Detector::~Detector()
{
- detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+ detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
layer l = detector_gpu.net.layers[detector_gpu.net.n - 1];
free(detector_gpu.track_id);
@@ -92,14 +98,10 @@
for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]);
for (int j = 0; j < FRAMES; ++j) if(detector_gpu.images[j].data) free(detector_gpu.images[j].data);
- for (int j = 0; j < l.w*l.h*l.n; ++j) free(detector_gpu.probs[j]);
- free(detector_gpu.boxes);
- free(detector_gpu.probs);
-
int old_gpu_index;
#ifdef GPU
cudaGetDevice(&old_gpu_index);
- cudaSetDevice(detector_gpu.net.gpu_index);
+ cuda_set_device(detector_gpu.net.gpu_index);
#endif
free_network(detector_gpu.net);
@@ -109,12 +111,12 @@
#endif
}
-YOLODLL_API int Detector::get_net_width() {
- detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+YOLODLL_API int Detector::get_net_width() const {
+ detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
return detector_gpu.net.w;
}
-YOLODLL_API int Detector::get_net_height() {
- detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+YOLODLL_API int Detector::get_net_height() const {
+ detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
return detector_gpu.net.h;
}
@@ -172,13 +174,15 @@
YOLODLL_API std::vector<bbox_t> Detector::detect(image_t img, float thresh, bool use_mean)
{
-
- detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+ detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
network &net = detector_gpu.net;
int old_gpu_index;
#ifdef GPU
cudaGetDevice(&old_gpu_index);
- cudaSetDevice(net.gpu_index);
+ if(cur_gpu_id != old_gpu_index)
+ cudaSetDevice(net.gpu_index);
+
+ net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait
#endif
//std::cout << "net.gpu_index = " << net.gpu_index << std::endl;
@@ -211,17 +215,21 @@
l.output = detector_gpu.avg;
detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES;
}
+ //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0);
+ //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms);
- get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0);
- if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms);
- //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
+ int nboxes = 0;
+ int letterbox = 0;
+ float hier_thresh = 0.5;
+ detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
+ if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
std::vector<bbox_t> bbox_vec;
- for (size_t i = 0; i < (l.w*l.h*l.n); ++i) {
- box b = detector_gpu.boxes[i];
- int const obj_id = max_index(detector_gpu.probs[i], l.classes);
- float const prob = detector_gpu.probs[i][obj_id];
+ for (size_t i = 0; i < nboxes; ++i) {
+ box b = dets[i].bbox;
+ int const obj_id = max_index(dets[i].prob, l.classes);
+ float const prob = dets[i].prob[obj_id];
if (prob > thresh)
{
@@ -238,26 +246,28 @@
}
}
+ free_detections(dets, nboxes);
if(sized.data)
free(sized.data);
#ifdef GPU
- cudaSetDevice(old_gpu_index);
+ if (cur_gpu_id != old_gpu_index)
+ cudaSetDevice(old_gpu_index);
#endif
return bbox_vec;
}
-YOLODLL_API std::vector<bbox_t> Detector::tracking(std::vector<bbox_t> cur_bbox_vec, int const frames_story)
+YOLODLL_API std::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history,
+ int const frames_story, int const max_dist)
{
- detector_gpu_t &det_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+ detector_gpu_t &det_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
bool prev_track_id_present = false;
for (auto &i : prev_bbox_vec_deque)
if (i.size() > 0) prev_track_id_present = true;
if (!prev_track_id_present) {
- //track_id = 1;
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
prev_bbox_vec_deque.push_front(cur_bbox_vec);
@@ -273,17 +283,20 @@
for (size_t m = 0; m < cur_bbox_vec.size(); ++m) {
bbox_t const& k = cur_bbox_vec[m];
if (i.obj_id == k.obj_id) {
- unsigned int cur_dist = sqrt(((float)i.x - k.x)*((float)i.x - k.x) + ((float)i.y - k.y)*((float)i.y - k.y));
- if (cur_dist < 100 && (k.track_id == 0 || dist_vec[m] > cur_dist)) {
+ float center_x_diff = (float)(i.x + i.w/2) - (float)(k.x + k.w/2);
+ float center_y_diff = (float)(i.y + i.h/2) - (float)(k.y + k.h/2);
+ unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff*center_y_diff);
+ if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) {
dist_vec[m] = cur_dist;
cur_index = m;
}
}
}
- bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), [&](bbox_t const& b) { return b.track_id == i.track_id; });
+ bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(),
+ [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; });
- if (cur_index >= 0 && track_id_absent) {
+ if (cur_index >= 0 && track_id_absent){
cur_bbox_vec[cur_index].track_id = i.track_id;
cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2;
cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2;
@@ -295,8 +308,10 @@
if (cur_bbox_vec[i].track_id == 0)
cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
- prev_bbox_vec_deque.push_front(cur_bbox_vec);
- if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
+ if (change_history) {
+ prev_bbox_vec_deque.push_front(cur_bbox_vec);
+ if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
+ }
return cur_bbox_vec;
}
\ No newline at end of file
--
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