| | |
| | | l.output = calloc(batch*outputs, sizeof(float)); |
| | | l.delta = calloc(batch*outputs, sizeof(float)); |
| | | #ifdef GPU |
| | | l.output_gpu = cuda_make_array(0, batch*outputs); |
| | | l.delta_gpu = cuda_make_array(0, batch*outputs); |
| | | l.output_gpu = cuda_make_array(l.output, batch*outputs); |
| | | l.delta_gpu = cuda_make_array(l.delta, batch*outputs); |
| | | #endif |
| | | |
| | | fprintf(stderr, "Detection Layer\n"); |
| | |
| | | int size = get_detection_layer_output_size(l) * l.batch; |
| | | memset(l.delta, 0, size * sizeof(float)); |
| | | for (i = 0; i < l.batch*locations; ++i) { |
| | | int classes = l.objectness+l.classes; |
| | | int classes = (l.objectness || l.background)+l.classes; |
| | | int offset = i*(classes+l.coords); |
| | | for (j = offset; j < offset+classes; ++j) { |
| | | *(l.cost) += pow(state.truth[j] - l.output[j], 2); |
| | | l.delta[j] = state.truth[j] - l.output[j]; |
| | | if(l.background && j == offset) l.delta[j] *= .1; |
| | | } |
| | | |
| | | box truth; |
| | |
| | | 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]/7; |
| | | out.y = l.output[j+1]/7; |
| | |
| | | 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+2] = 4 * (state.truth[j+2] - l.output[j+2]); |
| | | l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]); |
| | | if(l.rescore){ |
| | | for (j = offset; j < offset+classes; ++j) { |
| | | if(state.truth[j]) state.truth[j] = iou; |
| | | l.delta[j] = state.truth[j] - l.output[j]; |
| | | if(l.objectness){ |
| | | state.truth[offset] = iou; |
| | | l.delta[offset] = state.truth[offset] - l.output[offset]; |
| | | } |
| | | else{ |
| | | for (j = offset; j < offset+classes; ++j) { |
| | | if(state.truth[j]) state.truth[j] = iou; |
| | | l.delta[j] = state.truth[j] - l.output[j]; |
| | | } |
| | | } |
| | | } |
| | | } |
| | |
| | | float scale = 1; |
| | | float latent_delta = 0; |
| | | if(l.joint) scale = state.input[in_i++]; |
| | | else if (l.objectness) state.delta[in_i++] = -l.delta[out_i++]; |
| | | else if (l.background) state.delta[in_i++] = scale*l.delta[out_i++]; |
| | | else if (l.objectness) state.delta[in_i++] += -l.delta[out_i++]; |
| | | else if (l.background) state.delta[in_i++] += scale*l.delta[out_i++]; |
| | | for(j = 0; j < l.classes; ++j){ |
| | | latent_delta += state.input[in_i]*l.delta[out_i]; |
| | | state.delta[in_i++] = scale*l.delta[out_i++]; |
| | | state.delta[in_i++] += scale*l.delta[out_i++]; |
| | | } |
| | | |
| | | if (l.objectness) { |
| | | |
| | | }else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i); |
| | | for(j = 0; j < l.coords; ++j){ |
| | | state.delta[in_i++] = l.delta[out_i++]; |
| | | for (j = 0; j < l.coords; ++j){ |
| | | state.delta[in_i++] += l.delta[out_i++]; |
| | | } |
| | | if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] = latent_delta; |
| | | if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] += latent_delta; |
| | | } |
| | | } |
| | | |
| | |
| | | cpu_state.truth = truth_cpu; |
| | | cpu_state.delta = delta_cpu; |
| | | |
| | | cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); |
| | | cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); |
| | | cuda_pull_array(state.delta, delta_cpu, l.batch*l.inputs); |
| | | cuda_pull_array(l.delta_gpu, l.delta, l.batch*outputs); |
| | | backward_detection_layer(l, cpu_state); |
| | | cuda_push_array(state.delta, delta_cpu, l.batch*l.inputs); |
| | | |
| | | if (truth_cpu) free(truth_cpu); |
| | | free(in_cpu); |
| | | free(delta_cpu); |
| | | } |