)
loss_net = NetWithLossClass(model, hparams)
lr = get_lr(hparams.optimizer_params["lr"], hparams.nepochs,
step_size_per_epoch)
lr = Tensor(lr)
if args.checkpoint != '':
param_dict = load_checkpoint(args.pre_trained_model_path)
load_param_into_net(model, param_dict)
print('Successfully loading the pre-trained model')
weights = model.trainable_params()
optimizer = Adam(weights, learning_rate=lr, loss_scale=1024.)
train_net = TrainOneStepCell(loss_net, optimizer)
model = Model(train_net)
lr_cb = Monitor(lr)
callback_list = [lr_cb]
if args.is_distributed:
ckpt_path = os.path.join(args.checkpoint_dir,
'ckpt_' + str(get_rank()) + '/')
else:
ckpt_path = args.checkpoint_dir
config_ck = CheckpointConfig(save_checkpoint_steps=step_size_per_epoch,
keep_checkpoint_max=10)
ckpt_cb = ModelCheckpoint(prefix='wavenet',
directory=ckpt_path,
config=config_ck)
callback_list.append(ckpt_cb)
model.train(hparams.nepochs, data_loaders, callbacks=callback_list)
step_size_per_epoch)
lr = Tensor(lr)
if args.checkpoint != '':
param_dict = load_checkpoint(args.pre_trained_model_path)
load_param_into_net(model, param_dict)
print('Successfully loading the pre-trained model')
weights = model.trainable_params()
optimizer = Adam(weights, learning_rate=lr, loss_scale=1024.)
train_net = TrainOneStepCell(loss_net, optimizer)
model = Model(train_net)
lr_cb = Monitor(lr)
callback_list = [lr_cb]
if args.is_distributed:
ckpt_path = os.path.join(args.checkpoint_dir,
'ckpt_' + str(get_rank()) + '/')
else:
ckpt_path = args.checkpoint_dir
config_ck = CheckpointConfig(save_checkpoint_steps=step_size_per_epoch,
keep_checkpoint_max=10)
ckpt_cb = ModelCheckpoint(prefix='wavenet',
directory=ckpt_path,
config=config_ck)
callback_list.append(ckpt_cb)
model.train(hparams.nepochs,
data_loaders,
callbacks=callback_list,
dataset_sink_mode=False)
