| | |
|---|
| | | int classes = 20; |
|---|
| | | int background = 0; |
|---|
| | | int nuisance = 1; |
|---|
| | | int num_output = 7*7*(4+classes+background+nuisance); |
|---|
| | | int num_boxes = 7; |
|---|
| | | int per_box = 4+classes+background+nuisance; |
|---|
| | | int num_output = num_boxes*num_boxes*per_box; |
|---|
| | | |
|---|
| | | int m = plist->size; |
|---|
| | | int i = 0; |
|---|
| | |
|---|
| | | matrix pred = network_predict_data(net, val); |
|---|
| | | int j, k, class; |
|---|
| | | for(j = 0; j < pred.rows; ++j){ |
|---|
| | | for(k = 0; k < pred.cols; k += classes+4+background+nuisance){ |
|---|
| | | for(k = 0; k < pred.cols; k += per_box){ |
|---|
| | | float scale = 1.; |
|---|
| | | int index = k/per_box; |
|---|
| | | int row = index / num_boxes; |
|---|
| | | int col = index % num_boxes; |
|---|
| | | if (nuisance) scale = 1.-pred.vals[j][k]; |
|---|
| | | for (class = 0; class < classes; ++class){ |
|---|
| | | int ci = k+classes+background+nuisance; |
|---|
| | | float left = pred.vals[j][ci + 0]; |
|---|
| | | float right = pred.vals[j][ci + 1]; |
|---|
| | | float top = pred.vals[j][ci + 2]; |
|---|
| | | float bot = pred.vals[j][ci + 3]; |
|---|
| | | printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, scale*pred.vals[j][k+class+background+nuisance], left, right, top, bot); |
|---|
| | | float y = (pred.vals[j][ci + 0] + row)/num_boxes; |
|---|
| | | float x = (pred.vals[j][ci + 1] + col)/num_boxes; |
|---|
| | | float h = pred.vals[j][ci + 2]; |
|---|
| | | float w = pred.vals[j][ci + 3]; |
|---|
| | | printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, scale*pred.vals[j][k+class+background+nuisance], y, x, h, w); |
|---|
| | | } |
|---|
| | | } |
|---|
| | | } |
|---|
