| | |
| | | #include "cuda.h" |
| | | #include "utils.h" |
| | | #include <stdio.h> |
| | | #include <assert.h> |
| | | #include <string.h> |
| | | #include <stdlib.h> |
| | | |
| | | int get_region_layer_locations(region_layer l) |
| | | { |
| | | return l.inputs / (l.classes+l.coords); |
| | | } |
| | | |
| | | region_layer make_region_layer(int batch, int inputs, int n, int classes, int coords, int rescore) |
| | | region_layer make_region_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore) |
| | | { |
| | | region_layer l = {0}; |
| | | l.type = REGION; |
| | | |
| | | |
| | | l.n = n; |
| | | l.batch = batch; |
| | | l.inputs = inputs; |
| | | l.classes = classes; |
| | | l.coords = coords; |
| | | l.rescore = rescore; |
| | | l.side = side; |
| | | assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs); |
| | | l.cost = calloc(1, sizeof(float)); |
| | | int outputs = inputs; |
| | | l.outputs = outputs; |
| | | 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); |
| | | #endif |
| | | l.outputs = l.inputs; |
| | | l.truths = l.side*l.side*(1+l.coords+l.classes); |
| | | l.output = calloc(batch*l.outputs, sizeof(float)); |
| | | l.delta = calloc(batch*l.outputs, sizeof(float)); |
| | | #ifdef GPU |
| | | l.output_gpu = cuda_make_array(l.output, batch*l.outputs); |
| | | l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); |
| | | #endif |
| | | |
| | | fprintf(stderr, "Region Layer\n"); |
| | | srand(0); |
| | |
| | | |
| | | void forward_region_layer(const region_layer l, network_state state) |
| | | { |
| | | int locations = get_region_layer_locations(l); |
| | | int locations = l.side*l.side; |
| | | int i,j; |
| | | memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); |
| | | for(i = 0; i < l.batch*locations; ++i){ |
| | | int index = i*(l.classes + l.coords); |
| | | int mask = (!state.truth || !state.truth[index]); |
| | | |
| | | for(j = 0; j < l.classes; ++j){ |
| | | l.output[index+j] = state.input[index+j]; |
| | | } |
| | | |
| | | softmax_array(l.output + index, l.classes, l.output + index); |
| | | index += l.classes; |
| | | |
| | | for(j = 0; j < l.coords; ++j){ |
| | | l.output[index+j] = mask*state.input[index+j]; |
| | | int index = i*((1+l.coords)*l.n + l.classes); |
| | | if(l.softmax){ |
| | | activate_array(l.output + index, l.n*(1+l.coords), LOGISTIC); |
| | | int offset = l.n*(1+l.coords); |
| | | softmax_array(l.output + index + offset, l.classes, |
| | | l.output + index + offset); |
| | | } |
| | | } |
| | | if(state.train){ |
| | | float avg_iou = 0; |
| | | float avg_cat = 0; |
| | | float avg_obj = 0; |
| | | float avg_anyobj = 0; |
| | | int count = 0; |
| | | *(l.cost) = 0; |
| | | int size = l.outputs * l.batch; |
| | | int size = l.inputs * l.batch; |
| | | memset(l.delta, 0, size * sizeof(float)); |
| | | for (i = 0; i < l.batch*locations; ++i) { |
| | | int offset = i*(l.classes+l.coords); |
| | | int bg = state.truth[offset]; |
| | | for (j = offset; j < offset+l.classes; ++j) { |
| | | //*(l.cost) += pow(state.truth[j] - l.output[j], 2); |
| | | //l.delta[j] = state.truth[j] - l.output[j]; |
| | | } |
| | | |
| | | box anchor = {0,0,.5,.5}; |
| | | box truth_code = {state.truth[j+0], state.truth[j+1], state.truth[j+2], state.truth[j+3]}; |
| | | box out_code = {l.output[j+0], l.output[j+1], l.output[j+2], l.output[j+3]}; |
| | | box out = decode_box(out_code, anchor); |
| | | box truth = decode_box(truth_code, anchor); |
| | | |
| | | if(bg) continue; |
| | | //printf("Box: %f %f %f %f\n", truth.x, truth.y, truth.w, truth.h); |
| | | //printf("Code: %f %f %f %f\n", truth_code.x, truth_code.y, truth_code.w, truth_code.h); |
| | | //printf("Pred : %f %f %f %f\n", out.x, out.y, out.w, out.h); |
| | | // printf("Pred Code: %f %f %f %f\n", out_code.x, out_code.y, out_code.w, out_code.h); |
| | | float iou = box_iou(out, truth); |
| | | avg_iou += iou; |
| | | ++count; |
| | | |
| | | /* |
| | | *(l.cost) += pow((1-iou), 2); |
| | | l.delta[j+0] = (state.truth[j+0] - l.output[j+0]); |
| | | l.delta[j+1] = (state.truth[j+1] - l.output[j+1]); |
| | | l.delta[j+2] = (state.truth[j+2] - l.output[j+2]); |
| | | l.delta[j+3] = (state.truth[j+3] - l.output[j+3]); |
| | | */ |
| | | |
| | | for (j = offset+l.classes; j < offset+l.classes+l.coords; ++j) { |
| | | //*(l.cost) += pow(state.truth[j] - l.output[j], 2); |
| | | //l.delta[j] = state.truth[j] - l.output[j]; |
| | | float diff = state.truth[j] - l.output[j]; |
| | | if (fabs(diff) < 1){ |
| | | l.delta[j] = diff; |
| | | *(l.cost) += .5*pow(state.truth[j] - l.output[j], 2); |
| | | } else { |
| | | l.delta[j] = (diff > 0) ? 1 : -1; |
| | | *(l.cost) += fabs(diff) - .5; |
| | | int index = i*((1+l.coords)*l.n + l.classes); |
| | | for(j = 0; j < l.n; ++j){ |
| | | int prob_index = index + j*(1 + l.coords); |
| | | l.delta[prob_index] = (1./l.n)*(0-l.output[prob_index]); |
| | | if(l.softmax){ |
| | | l.delta[prob_index] = 1./(l.n*l.side)*(0-l.output[prob_index]); |
| | | } |
| | | //l.delta[j] = state.truth[j] - l.output[j]; |
| | | *(l.cost) += (1./l.n)*pow(l.output[prob_index], 2); |
| | | //printf("%f\n", l.output[prob_index]); |
| | | avg_anyobj += l.output[prob_index]; |
| | | } |
| | | |
| | | /* |
| | | if(l.rescore){ |
| | | for (j = offset; j < offset+l.classes; ++j) { |
| | | if(state.truth[j]) state.truth[j] = iou; |
| | | l.delta[j] = state.truth[j] - l.output[j]; |
| | | } |
| | | } |
| | | */ |
| | | int truth_index = i*(1 + l.coords + l.classes); |
| | | int best_index = -1; |
| | | float best_iou = 0; |
| | | float best_rmse = 4; |
| | | |
| | | int bg = !state.truth[truth_index]; |
| | | if(bg) { |
| | | continue; |
| | | } |
| | | |
| | | int class_index = index + l.n*(1+l.coords); |
| | | for(j = 0; j < l.classes; ++j) { |
| | | l.delta[class_index+j] = state.truth[truth_index+1+j] - l.output[class_index+j]; |
| | | *(l.cost) += pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2); |
| | | if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; |
| | | } |
| | | truth_index += l.classes + 1; |
| | | box truth = {state.truth[truth_index+0], state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3]}; |
| | | truth.x /= l.side; |
| | | truth.y /= l.side; |
| | | |
| | | for(j = 0; j < l.n; ++j){ |
| | | int out_index = index + j*(1+l.coords); |
| | | box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]}; |
| | | |
| | | out.x /= l.side; |
| | | out.y /= l.side; |
| | | if (l.sqrt){ |
| | | out.w = out.w*out.w; |
| | | out.h = out.h*out.h; |
| | | } |
| | | |
| | | float iou = box_iou(out, truth); |
| | | float rmse = box_rmse(out, truth); |
| | | if(best_iou > 0 || iou > 0){ |
| | | if(iou > best_iou){ |
| | | best_iou = iou; |
| | | best_index = j; |
| | | } |
| | | }else{ |
| | | if(rmse < best_rmse){ |
| | | best_rmse = rmse; |
| | | best_index = j; |
| | | } |
| | | } |
| | | } |
| | | //printf("%d", best_index); |
| | | int in_index = index + best_index*(1+l.coords); |
| | | *(l.cost) -= pow(l.output[in_index], 2); |
| | | *(l.cost) += pow(1-l.output[in_index], 2); |
| | | avg_obj += l.output[in_index]; |
| | | l.delta[in_index+0] = (1.-l.output[in_index]); |
| | | if(l.softmax){ |
| | | l.delta[in_index+0] = 5*(1.-l.output[in_index]); |
| | | } |
| | | //printf("%f\n", l.output[in_index]); |
| | | |
| | | l.delta[in_index+1] = 5*(state.truth[truth_index+0] - l.output[in_index+1]); |
| | | l.delta[in_index+2] = 5*(state.truth[truth_index+1] - l.output[in_index+2]); |
| | | if(l.sqrt){ |
| | | l.delta[in_index+3] = 5*(sqrt(state.truth[truth_index+2]) - l.output[in_index+3]); |
| | | l.delta[in_index+4] = 5*(sqrt(state.truth[truth_index+3]) - l.output[in_index+4]); |
| | | }else{ |
| | | l.delta[in_index+3] = 5*(state.truth[truth_index+2] - l.output[in_index+3]); |
| | | l.delta[in_index+4] = 5*(state.truth[truth_index+3] - l.output[in_index+4]); |
| | | } |
| | | |
| | | *(l.cost) += pow(1-best_iou, 2); |
| | | avg_iou += best_iou; |
| | | ++count; |
| | | if(l.softmax){ |
| | | gradient_array(l.output + index, l.n*(1+l.coords), LOGISTIC, l.delta + index); |
| | | } |
| | | } |
| | | printf("Avg IOU: %f\n", avg_iou/count); |
| | | printf("Avg IOU: %f, Avg Cat Pred: %f, Avg Obj: %f, Avg Any: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); |
| | | } |
| | | } |
| | | |
| | | void backward_region_layer(const region_layer l, network_state state) |
| | | { |
| | | axpy_cpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); |
| | | //copy_cpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1); |
| | | axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); |
| | | } |
| | | |
| | | #ifdef GPU |
| | |
| | | float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); |
| | | float *truth_cpu = 0; |
| | | if(state.truth){ |
| | | truth_cpu = calloc(l.batch*l.outputs, sizeof(float)); |
| | | cuda_pull_array(state.truth, truth_cpu, l.batch*l.outputs); |
| | | int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes); |
| | | truth_cpu = calloc(num_truth, sizeof(float)); |
| | | cuda_pull_array(state.truth, truth_cpu, num_truth); |
| | | } |
| | | cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); |
| | | network_state cpu_state; |
| | |
| | | cpu_state.input = in_cpu; |
| | | forward_region_layer(l, cpu_state); |
| | | cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); |
| | | cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); |
| | | cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); |
| | | free(cpu_state.input); |
| | | if(cpu_state.truth) free(cpu_state.truth); |
| | | } |