f_log.write(
'epoch %2d, iter %4d: loss: %.8f | load time: %.4f | backward time: %.4f\n'
% (epoch + 1, iter + 1, loss_confmap.item(),
tic - tic_load, time.time() - tic))
if stacked_num is not None:
writer.add_scalar('loss/loss_all', loss_confmap.item(),
iter_count)
confmap_pred = confmap_preds[stacked_num -
1].cpu().detach().numpy()
else:
writer.add_scalar('loss/loss_all', loss_confmap.item(),
iter_count)
confmap_pred = confmap_preds.cpu().detach().numpy()
if 'mnet_cfg' in model_cfg:
chirp_amp_curr = chirp_amp(data.numpy()[0, :, 0, 0, :, :],
radar_configs['data_type'])
else:
chirp_amp_curr = chirp_amp(data.numpy()[0, :, 0, :, :],
radar_configs['data_type'])
# draw train images
fig_name = os.path.join(
train_viz_path,
'%03d_%010d_%06d.png' % (epoch + 1, iter_count, iter + 1))
img_path = image_paths[0][0]
visualize_train_img(fig_name, img_path, chirp_amp_curr,
confmap_pred[0, :n_class, 0, :, :],
confmap_gt[0, :n_class, 0, :, :])
if (iter + 1) % config_dict['train_cfg']['save_step'] == 0:
# validate current model
iter_ = iter_.next
process_tic = time.time()
for i in range(test_configs['test_stride']):
total_count += 1
res_final = post_process_single_frame(init_genConfmap.confmap,
dataset, config_dict)
cur_frame_id = start_frame_id
write_dets_results_single_frame(res_final, cur_frame_id,
save_path, dataset)
write_dets_results_single_frame(res_final, cur_frame_id,
output_path, dataset)
confmap_pred_0 = init_genConfmap.confmap
res_final_0 = res_final
#img_path = image_paths[i]
radar_input = chirp_amp(data.numpy()[0, :, i, :, :],
radar_configs['data_type'])
fig_name = os.path.join(test_res_dir, seq_name, 'rod_viz',
'%010d.jpg' % (cur_frame_id))
if confmap_gt is not None:
confmap_gt_0 = confmap_gt[0, :, i, :, :]
visualize_test_img(fig_name,
radar_input,
confmap_pred_0,
confmap_gt_0,
res_final_0,
dataset,
sybl=sybl)
else:
visualize_test_img_wo_gt(fig_name,
radar_input,
confmap_pred_0,