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gaussian_yolo: added uc_normalizer and minor fix for iou_normalizer f…
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…or GIoU.
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@AlexeyAB
AlexeyAB committed Nov 16, 2019
1 parent 0cf4c16 commit 0d30db3
Showing 1 changed file with 40 additions and 19 deletions.
59 changes: 40 additions & 19 deletions src/gaussian_yolo_layer.c
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Original file line number Diff line number Diff line change
Expand Up @@ -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);

Expand All @@ -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);
Expand All @@ -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
Expand All @@ -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));
Expand Down Expand Up @@ -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]);

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));
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] += delta_ux;
delta[index + 3 * stride] += delta_uy;
delta[index + 5 * stride] += delta_uw;
delta[index + 7 * stride] += delta_uh;
return iou;
}

Expand Down Expand Up @@ -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);
}
}
}
Expand Down Expand Up @@ -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];
Expand Down Expand Up @@ -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];
Expand Down

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