| | |
| | | l.output[out_i++] = mask*state.input[in_i++]; |
| | | } |
| | | } |
| | | float avg_iou = 0; |
| | | int count = 0; |
| | | if(l.does_cost && state.train){ |
| | | int count = 0; |
| | | *(l.cost) = 0; |
| | | int size = get_detection_layer_output_size(l) * l.batch; |
| | | memset(l.delta, 0, size * sizeof(float)); |
| | |
| | | *(l.cost) += pow(state.truth[j] - l.output[j], 2); |
| | | l.delta[j] = state.truth[j] - l.output[j]; |
| | | } |
| | | |
| | | box truth; |
| | | truth.x = state.truth[j+0]; |
| | | truth.y = state.truth[j+1]; |
| | | truth.w = state.truth[j+2]; |
| | | truth.h = state.truth[j+3]; |
| | | truth.x = state.truth[j+0]/7; |
| | | truth.y = state.truth[j+1]/7; |
| | | truth.w = pow(state.truth[j+2], 2); |
| | | truth.h = pow(state.truth[j+3], 2); |
| | | box out; |
| | | out.x = l.output[j+0]; |
| | | out.y = l.output[j+1]; |
| | | out.w = l.output[j+2]; |
| | | out.h = l.output[j+3]; |
| | | out.x = l.output[j+0]/7; |
| | | out.y = l.output[j+1]/7; |
| | | out.w = pow(l.output[j+2], 2); |
| | | out.h = pow(l.output[j+3], 2); |
| | | |
| | | if(!(truth.w*truth.h)) continue; |
| | | l.delta[j+0] = (truth.x - out.x); |
| | | l.delta[j+1] = (truth.y - out.y); |
| | | l.delta[j+2] = (truth.w - out.w); |
| | | l.delta[j+3] = (truth.h - out.h); |
| | | *(l.cost) += pow((out.x - truth.x), 2); |
| | | *(l.cost) += pow((out.y - truth.y), 2); |
| | | *(l.cost) += pow((out.w - truth.w), 2); |
| | | *(l.cost) += pow((out.h - truth.h), 2); |
| | | |
| | | /* |
| | | l.delta[j+0] = .1 * (truth.x - out.x) / (49 * truth.w * truth.w); |
| | | l.delta[j+1] = .1 * (truth.y - out.y) / (49 * truth.h * truth.h); |
| | | l.delta[j+2] = .1 * (truth.w - out.w) / ( truth.w * truth.w); |
| | | l.delta[j+3] = .1 * (truth.h - out.h) / ( truth.h * truth.h); |
| | | |
| | | *(l.cost) += pow((out.x - truth.x)/truth.w/7., 2); |
| | | *(l.cost) += pow((out.y - truth.y)/truth.h/7., 2); |
| | | *(l.cost) += pow((out.w - truth.w)/truth.w, 2); |
| | | *(l.cost) += pow((out.h - truth.h)/truth.h, 2); |
| | | */ |
| | | float iou = box_iou(out, truth); |
| | | avg_iou += iou; |
| | | ++count; |
| | | dbox delta = diou(out, truth); |
| | | |
| | | l.delta[j+0] = 10 * delta.dx/7; |
| | | l.delta[j+1] = 10 * delta.dy/7; |
| | | l.delta[j+2] = 10 * delta.dw * 2 * sqrt(out.w); |
| | | l.delta[j+3] = 10 * delta.dh * 2 * sqrt(out.h); |
| | | |
| | | |
| | | *(l.cost) += pow((1-iou), 2); |
| | | l.delta[j+0] = 4 * (state.truth[j+0] - l.output[j+0]); |
| | | l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]); |
| | | l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]); |
| | | l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]); |
| | | if(0){ |
| | | for (j = offset; j < offset+classes; ++j) { |
| | | if(state.truth[j]) state.truth[j] = iou; |
| | | l.delta[j] = state.truth[j] - l.output[j]; |
| | | } |
| | | } |
| | | } |
| | | printf("Avg IOU: %f\n", avg_iou/count); |
| | | } |
| | | /* |
| | | int count = 0; |
| | | for(i = 0; i < l.batch*locations; ++i){ |
| | | for(j = 0; j < l.classes+l.background; ++j){ |
| | | printf("%f, ", l.output[count++]); |
| | | } |
| | | printf("\n"); |
| | | for(j = 0; j < l.coords; ++j){ |
| | | printf("%f, ", l.output[count++]); |
| | | } |
| | | printf("\n"); |
| | | } |
| | | */ |
| | | /* |
| | | if(l.background || 1){ |
| | | for(i = 0; i < l.batch*locations; ++i){ |
| | | int index = i*(l.classes+l.coords+l.background); |
| | | for(j= 0; j < l.classes; ++j){ |
| | | if(state.truth[index+j+l.background]){ |
| | | //dark_zone(l, j, index, state); |
| | | } |
| | | } |
| | | } |
| | | } |
| | | */ |
| | | } |
| | | |
| | | void backward_detection_layer(const detection_layer l, network_state state) |