| | |
|---|
| | | int top = (y-h/2)*im.h; |
|---|
| | | int bot = (y+h/2)*im.h; |
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| | | draw_box(im, left, top, right, bot, red, green, blue); |
|---|
| | | draw_box(im, left+1, top+1, right+1, bot+1, red, green, blue); |
|---|
| | | draw_box(im, left-1, top-1, right-1, bot-1, red, green, blue); |
|---|
| | | } |
|---|
| | | } |
|---|
| | | } |
|---|
| | |
|---|
| | | float loss = train_network(net, train); |
|---|
| | | |
|---|
| | | //TODO |
|---|
| | | #ifdef GPU |
|---|
| | | float *out = get_network_output_gpu(net); |
|---|
| | | #else |
|---|
| | | float *out = get_network_output(net); |
|---|
| | | #endif |
|---|
| | | image im = float_to_image(net.w, net.h, 3, train.X.vals[127]); |
|---|
| | | image copy = copy_image(im); |
|---|
| | | draw_localization(copy, &(out[63*80])); |
|---|
| | |
|---|
| | | avg_loss = avg_loss*.9 + loss*.1; |
|---|
| | | printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs); |
|---|
| | | if(i == 100){ |
|---|
| | | net.learning_rate *= 10; |
|---|
| | | //net.learning_rate *= 10; |
|---|
| | | } |
|---|
| | | if(i%100==0){ |
|---|
| | | char buff[256]; |
|---|
| | |
|---|
| | | float y = (pred.vals[j][ci + 1] + row)/num_boxes; |
|---|
| | | float w = pred.vals[j][ci + 2]; //* distance_from_edge(row, num_boxes); |
|---|
| | | float h = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes); |
|---|
| | | w = pow(w, 1); |
|---|
| | | h = pow(h, 1); |
|---|
| | | w = pow(w, 2); |
|---|
| | | h = pow(h, 2); |
|---|
| | | float prob = scale*pred.vals[j][k+class+background+nuisance]; |
|---|
| | | if(prob < threshold) continue; |
|---|
| | | printf("%d %d %f %f %f %f %f\n", offset + j, class, prob, x, y, w, h); |
|---|
