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Added Focal Loss to yolo-layer

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AlexeyAB
2018-04-05 23:27:02 +03:00
parent be9d971ddb
commit 943f6e874b
5 changed files with 51 additions and 11 deletions
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40
src/yolo_layer.c
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@ -109,18 +109,40 @@ float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i
}
void delta_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat)
void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss)
{
int n;
if (delta[index]){
delta[index + stride*class] = 1 - output[index + stride*class];
if(avg_cat) *avg_cat += output[index + stride*class];
delta[index + stride*class_id] = 1 - output[index + stride*class_id];
if(avg_cat) *avg_cat += output[index + stride*class_id];
return;
}
for(n = 0; n < classes; ++n){
delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n];
if(n == class && avg_cat) *avg_cat += output[index + stride*n];
}
// Focal loss
if (focal_loss) {
// Focal Loss
float alpha = 0.5; // 0.25 or 0.5
//float gamma = 2; // hardcoded in many places of the grad-formula
int ti = index + stride*class_id;
float pt = output[ti] + 0.000000000000001F;
//float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832
float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss
for (n = 0; n < classes; ++n) {
delta[index + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]);
delta[index + stride*n] *= alpha*grad;
if (n == class_id) *avg_cat += output[index + stride*n];
}
}
else {
// default
for (n = 0; n < classes; ++n) {
delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n];
if (n == class_id && avg_cat) *avg_cat += output[index + stride*n];
}
}
}
static int entry_index(layer l, int batch, int location, int entry)
@ -196,7 +218,7 @@ void forward_yolo_layer(const layer l, network_state state)
int class = state.truth[best_t*(4 + 1) + b*l.truths + 4];
if (l.map) class = l.map[class];
int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0);
delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0, l.focal_loss);
box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
delta_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);
}
@ -236,7 +258,7 @@ void forward_yolo_layer(const layer l, network_state state)
int class = state.truth[t*(4 + 1) + b*l.truths + 4];
if (l.map) class = l.map[class];
int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat);
delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat, l.focal_loss);
++count;
++class_count;