github
/
darknet
mirror of https://github.com/AlexeyAB/darknet.git
1
0
Fork 0
Code Issues Releases Wiki Activity

gaussian_yolo: added uc_normalizer and minor fix for iou_normalizer for GIoU.

Browse Source
pull/4302/head^2
AlexeyAB 6 years ago
parent 0cf4c16c9f
commit 0d30db35fc
1 changed files with 40 additions and 19 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 57
      src/gaussian_yolo_layer.c

57
src/gaussian_yolo_layer.c
Unescape View File

@ -140,7 +140,7 @@ box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int
return b;
}
float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate)
float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate)
{
box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride);
@ -157,6 +157,7 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind
float dx, dy, dw, dh;
if (iou_loss == MSE) {
// MSE
iou = all_ious.iou;
float tx = (truth.x*lw - i);
@ -171,6 +172,7 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind
}
else
{
// GIoU
iou = all_ious.giou;
// https://github.com/generalized-iou/g-darknet
@ -183,14 +185,9 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind
dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
// normalize iou weight
dx *= iou_normalizer;
dy *= iou_normalizer;
dw *= iou_normalizer;
dh *= iou_normalizer;
}
// Gaussian
float in_exp_x = dx / x[index+1*stride];
float in_exp_x_2 = pow(in_exp_x, 2);
float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const));
@ -223,15 +220,39 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind
delta[index + 7 * stride] = 0;
}
delta[index + 0*stride] += temp_x * in_exp_x * (1./x[index+1*stride]);
delta[index + 2*stride] += temp_y * in_exp_y * (1./x[index+3*stride]);
delta[index + 4*stride] += temp_w * in_exp_w * (1./x[index+5*stride]);
delta[index + 6*stride] += temp_h * in_exp_h * (1./x[index+7*stride]);
float delta_x = temp_x * in_exp_x * (1. / x[index + 1 * stride]);
float delta_y = temp_y * in_exp_y * (1. / x[index + 3 * stride]);
float delta_w = temp_w * in_exp_w * (1. / x[index + 5 * stride]);
float delta_h = temp_h * in_exp_h * (1. / x[index + 7 * stride]);
float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const));
float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const));
float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const));
float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const));
if (iou_loss != MSE) {
// normalize iou weight, for GIoU
delta_x *= iou_normalizer;
delta_y *= iou_normalizer;
delta_w *= iou_normalizer;
delta_h *= iou_normalizer;
}
// normalize Uncertainty weight
delta_ux *= uc_normalizer;
delta_uy *= uc_normalizer;
delta_uw *= uc_normalizer;
delta_uh *= uc_normalizer;
delta[index + 0 * stride] += delta_x;
delta[index + 2 * stride] += delta_y;
delta[index + 4 * stride] += delta_w;
delta[index + 6 * stride] += delta_h;
delta[index + 1*stride] += temp_x * (in_exp_x_2/x[index+1*stride] - 1./(x[index+1*stride]+sigma_const));
delta[index + 3*stride] += temp_y * (in_exp_y_2/x[index+3*stride] - 1./(x[index+3*stride]+sigma_const));
delta[index + 5*stride] += temp_w * (in_exp_w_2/x[index+5*stride] - 1./(x[index+5*stride]+sigma_const));
delta[index + 7*stride] += temp_h * (in_exp_h_2/x[index+7*stride] - 1./(x[index+7*stride]+sigma_const));
delta[index + 1 * stride] += delta_ux;
delta[index + 3 * stride] += delta_uy;
delta[index + 5 * stride] += delta_uw;
delta[index + 7 * stride] += delta_uh;
return iou;
}
@ -359,7 +380,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state)
int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0);
box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1);
delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1);
}
}
}
@ -388,7 +409,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state)
int mask_n = int_index(l.mask, best_n, l.n);
if(mask_n >= 0){
int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1);
float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1);
int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8);
avg_obj += l.output[obj_index];
@ -419,7 +440,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state)
if (iou > l.iou_thresh) {
int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1);
float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1);
int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8);
avg_obj += l.output[obj_index];

Write Preview
Loading…
Cancel
Save