def load_model_path():
global model_path, model_config, device, learning_rate, reset_optimizer
try:
param = torch.load(model_path)
if 'model_config' in param and param['model_config'] != model_config:
model_config = param['model_config']
print('用model的設置，不要用：')
print(utils.dict2params('model_config'))
model_state = param['model_state']
optimizer_state = param['model_optimizer_state']
print('參數來自：', model_path)
param_loaded = True
except:
print('無先前參數')
param_loaded = False
model = PerformanceRNN(**model_config).to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
if param_loaded:
model.load_state_dict(model_state)
if not reset_optimizer:
optimizer.load_state_dict(optimizer_state)
return model, optimizer
def load_session():
global sess_path, model_config, device, learning_rate, reset_optimizer
try:
sess = torch.load(sess_path)
if 'model_config' in sess and sess['model_config'] != model_config:
model_config = sess['model_config']
print('Use session config instead:')
print(utils.dict2params(model_config))
model_state = sess['model_state']
optimizer_state = sess['model_optimizer_state']
print('Session is loaded from', sess_path)
sess_loaded = True
except:
print('New session')
sess_loaded = False
model = PerformanceRNN(**model_config).to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
if sess_loaded:
model.load_state_dict(model_state)
if not reset_optimizer:
optimizer.load_state_dict(optimizer_state)
return model, optimizer
event_dim = EventSeq.dim()
control_dim = ControlSeq.dim()
model_config = config.model
model_params = utils.params2dict(options.model_params)
model_config.update(model_params)
device = config.device
print('-' * 70)
print('Session path:', sess_path)
print('Dataset path:', data_path)
print('Saving interval:', saving_interval)
print('-' * 70)
print('Hyperparameters:', utils.dict2params(model_config))
print('Learning rate:', learning_rate)
print('Batch size:', batch_size)
print('Window size:', window_size)
print('Stride size:', stride_size)
print('Control ratio:', control_ratio)
print('Teacher forcing ratio:', teacher_forcing_ratio)
print('Random transposition:', use_transposition)
print('Reset optimizer:', reset_optimizer)
print('Enabling logging:', enable_logging)
print('Device:', device)
print('-' * 70)
# ========================================================================
# Load session and dataset
# ========================================================================