Vb.main(save_dir, prj_dir, 'bDNN', 'train', dev='/gpu:' + gpu_no) gs.freeze_graph(prj_dir + '/logs/bDNN', prj_dir + '/saved_model/graph/bDNN', 'model_1/logits,model_1/labels') if mode == 2: set_path = ps.PathSetting(prj_dir, 'DNN', save_dir) logs_dir = set_path.logs_dir os.system("rm -rf " + logs_dir + '/train') os.system("rm -rf " + logs_dir + '/valid') os.system("mkdir " + logs_dir + '/train') os.system("mkdir " + logs_dir + '/valid') Vd.main(save_dir, prj_dir, 'DNN', 'train', dev='/gpu:' + gpu_no) gs.freeze_graph(prj_dir + '/logs/DNN', prj_dir + '/saved_model/graph/DNN', 'model_1/soft_pred,model_1/raw_labels') if mode == 3: set_path = ps.PathSetting(prj_dir, 'LSTM', save_dir) logs_dir = set_path.logs_dir os.system("rm -rf " + logs_dir + '/train') os.system("rm -rf " + logs_dir + '/valid') os.system("mkdir " + logs_dir + '/train') os.system("mkdir " + logs_dir + '/valid') Vl.main(save_dir, prj_dir, 'LSTM', 'train', dev='/gpu:' + gpu_no)
Vb.main(prj_dir, 'bDNN', 'train') gs.freeze_graph(prj_dir + '/logs/bDNN', prj_dir + '/saved_model/graph/bDNN', 'model_1/logits,model_1/labels') # gs.freeze_graph(prj_dir + '/saved_model/temp', prj_dir + '/saved_model/temp', 'model_1/soft_pred,model_1/raw_labels') if mode == 2: set_path = ps.PathSetting(prj_dir, 'DNN') logs_dir = set_path.logs_dir os.system("rm -rf " + logs_dir + '/train') os.system("rm -rf " + logs_dir + '/valid') os.system("mkdir " + logs_dir + '/train') os.system("mkdir " + logs_dir + '/valid') Vd.main(prj_dir, 'DNN', 'train') gs.freeze_graph(prj_dir + '/logs/DNN', prj_dir + '/saved_model/graph/DNN', 'model_1/soft_pred,model_1/raw_labels') # gs.freeze_graph(prj_dir + '/saved_model/temp', prj_dir + '/saved_model/temp', 'model_1/soft_pred,model_1/raw_labels') if mode == 3: set_path = ps.PathSetting(prj_dir, 'LSTM') logs_dir = set_path.logs_dir os.system("rm -rf " + logs_dir + '/train') os.system("rm -rf " + logs_dir + '/valid') os.system("mkdir " + logs_dir + '/train') os.system("mkdir " + logs_dir + '/valid') Vl.main(prj_dir, 'LSTM', 'train')
Vb.test_config(c_test_dir=data_dir, c_norm_dir=norm_dir, c_initial_logs_dir=model_dir, c_batch_size_eval=batch_size, c_data_len=data_len) pred, label = Vb.main() elif mode == 2: Vd.test_config(c_test_dir=data_dir, c_norm_dir=norm_dir, c_initial_logs_dir=model_dir, c_batch_size_eval=batch_size, c_data_len=data_len) pred, label = Vd.main() elif mode == 3: Vl.test_config(c_test_dir=data_dir, c_norm_dir=norm_dir, c_initial_logs_dir=model_dir, c_batch_size_eval=batch_size, c_data_len=data_len) pred, label = Vl.main() sio.savemat('./result/pred.mat', {'pred': pred}) sio.savemat('./result/label.mat', {'label': label}) print("done")
os.system("rm -rf " + save_dir) os.system("mkdir " + save_dir) os.system("mkdir " + save_dir + '/train') os.system("mkdir " + save_dir + '/valid') os.system( "matlab -r \"try acoustic_feat_ex(\'%s\',\'%s\'); catch; end; quit\"" % (train_data_dir, train_save_dir)) os.system( "matlab -r \"try acoustic_feat_ex(\'%s\',\'%s\'); catch; end; quit\"" % (valid_data_dir, valid_save_dir)) train_norm_dir = save_dir + '/train/global_normalize_factor.mat' test_norm_dir = prj_dir + '/norm_data/global_normalize_factor.mat' os.system("cp %s %s" % (train_norm_dir, test_norm_dir)) if mode == 0: logs_dir = prj_dir + '/logs' os.system("rm -rf " + logs_dir + '/train') os.system("rm -rf " + logs_dir + '/valid') os.system("mkdir " + logs_dir + '/train') os.system("mkdir " + logs_dir + '/valid') Vd.train_config(save_dir + '/train', save_dir + '/valid', prj_dir + '/logs', batch_size, train_step, 'train') Vd.main() # os.system("rm -rf") print("done")