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")