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Add support for yolov4x-mish.

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Changes:
- add parameters nms_kind, nms_thresh, new_coords to yolo layer and darknet parser
- added diou nms, new method to compute the BBs
- created test for yolov4x-mish called yolo4x

Tested, all tests work.
Problem to solve: little loss in mAP of yolo4x

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-11-23 11:25:52 +01:00
parent 86478f9384
commit 702791e41a
10 changed files with 1571 additions and 31 deletions
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src/Yolo.cpp
+54 -14
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@@ -11,7 +11,7 @@
namespace tk { namespace dnn {
Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights, int n_masks, float scale_xy) :
Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights, int n_masks, float scale_xy, double nms_thresh, nmsKind_t nsm_kind, int new_coords) :
Layer(net) {
this->final = true;
@@ -19,6 +19,9 @@ Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights, int n_
this->num = num;
this->n_masks = n_masks;
this->scaleXY = scale_xy;
this->nms_thresh = nms_thresh;
this->nsm_kind = nsm_kind;
this->new_coords = new_coords;
// load anchors
if(fname_weights != "") {
@@ -59,12 +62,21 @@ int entry_index(int batch, int location, int entry,
entry*input_dim.w*input_dim.h + loc;
}
Yolo::box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) {
Yolo::box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, int new_coords) {
Yolo::box b;
b.x = (i + x[index + 0*stride]) / lw;
b.y = (j + x[index + 1*stride]) / lh;
b.w = exp(x[index + 2*stride]) * biases[2*n] / w;
b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
if(new_coords == 0){
b.x = (i + x[index + 0*stride]) / lw;
b.y = (j + x[index + 1*stride]) / lh;
b.w = exp(x[index + 2*stride]) * biases[2*n] / w;
b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
}
else{
b.x = (i + x[index + 0 * stride] * 2 - 0.5) / lw;
b.y = (j + x[index + 1 * stride] * 2 - 0.5) / lh;
b.w = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w;
b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h;
}
return b;
}
@@ -75,7 +87,10 @@ dnnType* Yolo::infer(dataDim_t &dim, dnnType* srcData) {
for (int b = 0; b < dim.n; ++b){
for(int n = 0; n < n_masks; ++n){
int index = entry_index(b, n*dim.w*dim.h, 0, classes, input_dim, output_dim);
activationLOGISTICForward(srcData + index, dstData + index, 2*dim.w*dim.h);
if (new_coords == 1)
activationLOGISTICForward(srcData + index, dstData + index, 4*dim.w*dim.h);
else
activationLOGISTICForward(srcData + index, dstData + index, 2*dim.w*dim.h);
if (this->scaleXY != 1) scalAdd(dstData + index, 2 * dim.w*dim.h, this->scaleXY, -0.5*(this->scaleXY - 1), 1);
@@ -116,7 +131,7 @@ void correct_yolo_boxes(Yolo::detection *dets, int n, int w, int h, int netw, in
}
}
int Yolo::computeDetections(Yolo::detection *dets, int &ndets, int netw, int neth, float thresh) {
int Yolo::computeDetections(Yolo::detection *dets, int &ndets, int netw, int neth, float thresh, int new_coords) {
if(predictions == nullptr)
predictions = new dnnType[output_dim.tot()];
@@ -140,7 +155,7 @@ int Yolo::computeDetections(Yolo::detection *dets, int &ndets, int netw, int net
if(objectness <= thresh) continue;
int box_index = entry_index(0, n*lw*lh + i, 0, classes, input_dim, output_dim);
dets[count].bbox = get_yolo_box(predictions, bias_h, mask_h[n], box_index, col, row, lw, lh, netw, neth, lw*lh);
dets[count].bbox = get_yolo_box(predictions, bias_h, mask_h[n], box_index, col, row, lw, lh, netw, neth, lw*lh, new_coords);
dets[count].objectness = objectness;
dets[count].classes = classes;
for(j = 0; j < classes; ++j){
@@ -193,6 +208,32 @@ float yolo_box_iou(Yolo::box a, Yolo::box b)
return yolo_box_intersection(a, b)/yolo_box_union(a, b);
}
void box_c(const Yolo::box a, const Yolo::box b, float& top, float& bot, float& left, float& right) {
top = std::min(a.y - a.h / 2, b.y - b.h / 2);
bot = std::max(a.y + a.h / 2, b.y + b.h / 2);
left = std::min(a.x - a.w / 2, b.x - b.w / 2);
right = std::max(a.x + a.w / 2, b.x + b.w / 2);
}
// https://github.com/Zzh-tju/DIoU-darknet
// https://arxiv.org/abs/1911.08287
float yolo_box_diou(const Yolo::box a, const Yolo::box b, const float nms_thresh=0.6)
{
float top, bot, left, right;
box_c(a, b, top, bot, left, right);
float w = right - left;
float h = bot - top;
float c = w * w + h * h;
float iou = yolo_box_iou(a, b);
if (c == 0)
return iou;
float d = (a.x - b.x) * (a.x - b.x) + (a.y - b.y) * (a.y - b.y);
float u = pow(d / c, nms_thresh);
float diou_term = u;
return iou - diou_term;
}
int yolo_nms_comparator(const void *pa, const void *pb)
{
Yolo::detection a = *(Yolo::detection *)pa;
@@ -219,8 +260,7 @@ Yolo::detection *Yolo::allocateDetections(int nboxes, int classes) {
return dets;
}
void Yolo::mergeDetections(Yolo::detection *dets, int ndets, int classes) {
double nms_thresh = 0.45;
void Yolo::mergeDetections(Yolo::detection *dets, int ndets, int classes, double nms_thresh, nmsKind_t nsm_kind) {
int total = ndets;
int i, j, k;
@@ -246,13 +286,13 @@ void Yolo::mergeDetections(Yolo::detection *dets, int ndets, int classes) {
box a = dets[i].bbox;
for(j = i+1; j < total; ++j){
box b = dets[j].bbox;
if (yolo_box_iou(a, b) > nms_thresh){
if (nsm_kind == GREEDY_NMS && yolo_box_iou(a, b) > nms_thresh)
dets[j].prob[k] = 0;
else if (nsm_kind == DIOU_NMS && yolo_box_diou(a, b, nms_thresh) > nms_thresh)
dets[j].prob[k] = 0;
}
}
}
}
}
}}