def main():
parser = argparse.ArgumentParser(description='PyTorch Tacotron 2 Training')
parser = parse_args(parser)
args, _ = parser.parse_known_args()
LOGGER.set_model_name("Tacotron2_PyT")
LOGGER.set_backends([
dllg.StdOutBackend(log_file=None,
logging_scope=dllg.TRAIN_ITER_SCOPE,
iteration_interval=1),
dllg.JsonBackend(log_file=os.path.join(
args.output_directory, args.log_file) if args.rank == 0 else None,
logging_scope=dllg.TRAIN_ITER_SCOPE,
iteration_interval=1)
])
LOGGER.timed_block_start("run")
LOGGER.register_metric(tags.TRAIN_ITERATION_LOSS,
metric_scope=dllg.TRAIN_ITER_SCOPE)
LOGGER.register_metric("iter_time", metric_scope=dllg.TRAIN_ITER_SCOPE)
LOGGER.register_metric("epoch_time", metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("run_time", metric_scope=dllg.RUN_SCOPE)
LOGGER.register_metric("val_iter_loss", metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_frames/sec",
metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_avg_frames/sec",
metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_avg_loss",
metric_scope=dllg.EPOCH_SCOPE)
log_hardware()
parser = parse_tacotron2_args(parser)
args = parser.parse_args()
log_args(args)
torch.backends.cudnn.enabled = args.cudnn_enabled
torch.backends.cudnn.benchmark = args.cudnn_benchmark
distributed_run = args.world_size > 1
if distributed_run:
init_distributed(args, args.world_size, args.rank, args.group_name)
os.makedirs(args.output_directory, exist_ok=True)
LOGGER.log(key=tags.RUN_START)
run_start_time = time.time()
model = get_tacotron2_model(args,
len(args.training_anchor_dirs),
is_training=True)
if not args.amp_run and distributed_run:
model = DDP(model)
model.restore_checkpoint(
os.path.join(args.output_directory, args.latest_checkpoint_file))
optimizer = torch.optim.Adam(model.parameters(),
lr=args.init_lr,
weight_decay=args.weight_decay)
if args.amp_run:
model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
if distributed_run:
model = DDP(model)
criterion = Tacotron2Loss()
collate_fn = TextMelCollate(args)
train_dataset = TextMelDataset(args, args.training_anchor_dirs)
train_loader = DataLoader(train_dataset,
num_workers=2,
shuffle=False,
batch_size=args.batch_size //
len(args.training_anchor_dirs),
pin_memory=False,
drop_last=True,
collate_fn=collate_fn)
# valate_dataset = TextMelDataset(args, args.validation_anchor_dirs)
model.train()
elapsed_epochs = model.get_elapsed_epochs()
epochs = args.epochs - elapsed_epochs
iteration = elapsed_epochs * len(train_loader)
LOGGER.log(key=tags.TRAIN_LOOP)
for epoch in range(1, epochs + 1):
LOGGER.epoch_start()
epoch_start_time = time.time()
epoch += elapsed_epochs
LOGGER.log(key=tags.TRAIN_EPOCH_START, value=epoch)
# used to calculate avg frames/sec over epoch
reduced_num_frames_epoch = 0
# used to calculate avg loss over epoch
train_epoch_avg_loss = 0.0
train_epoch_avg_frames_per_sec = 0.0
num_iters = 0
adjust_learning_rate(optimizer, epoch, args)
for i, batch in enumerate(train_loader):
print(f"Batch: {i}/{len(train_loader)} epoch {epoch}")
LOGGER.iteration_start()
iter_start_time = time.time()
LOGGER.log(key=tags.TRAIN_ITER_START, value=i)
# start = time.perf_counter()
optimizer.zero_grad()
x, y, num_frames = batch_to_gpu(batch)
y_pred = model(x)
loss = criterion(y_pred, y)
if distributed_run:
reduced_loss = reduce_tensor(loss.data, args.world_size).item()
reduced_num_frames = reduce_tensor(num_frames.data, 1).item()
else:
reduced_loss = loss.item()
reduced_num_frames = num_frames.item()
if np.isnan(reduced_loss):
raise Exception("loss is NaN")
LOGGER.log(key=tags.TRAIN_ITERATION_LOSS, value=reduced_loss)
train_epoch_avg_loss += reduced_loss
num_iters += 1
# accumulate number of frames processed in this epoch
reduced_num_frames_epoch += reduced_num_frames
if args.amp_run:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.grad_clip_thresh)
else:
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), args.grad_clip_thresh)
optimizer.step()
iteration += 1
LOGGER.log(key=tags.TRAIN_ITER_STOP, value=i)
iter_stop_time = time.time()
iter_time = iter_stop_time - iter_start_time
frames_per_sec = reduced_num_frames / iter_time
train_epoch_avg_frames_per_sec += frames_per_sec
LOGGER.log(key="train_iter_frames/sec", value=frames_per_sec)
LOGGER.log(key="iter_time", value=iter_time)
LOGGER.iteration_stop()
LOGGER.log(key=tags.TRAIN_EPOCH_STOP, value=epoch)
epoch_stop_time = time.time()
epoch_time = epoch_stop_time - epoch_start_time
LOGGER.log(key="train_epoch_frames/sec",
value=(reduced_num_frames_epoch / epoch_time))
LOGGER.log(key="train_epoch_avg_frames/sec",
value=(train_epoch_avg_frames_per_sec /
num_iters if num_iters > 0 else 0.0))
LOGGER.log(key="train_epoch_avg_loss",
value=(train_epoch_avg_loss /
num_iters if num_iters > 0 else 0.0))
LOGGER.log(key="epoch_time", value=epoch_time)
LOGGER.log(key=tags.EVAL_START, value=epoch)
# validate(model, criterion, valate_dataset, iteration, collate_fn, distributed_run, args)
LOGGER.log(key=tags.EVAL_STOP, value=epoch)
# Store latest checkpoint in each epoch
model.elapse_epoch()
checkpoint_path = os.path.join(args.output_directory,
args.latest_checkpoint_file)
torch.save(model.state_dict(), checkpoint_path)
# Plot alignemnt
if epoch % args.epochs_per_alignment == 0 and args.rank == 0:
alignments = y_pred[3].data.numpy()
index = np.random.randint(len(alignments))
plot_alignment(
alignments[index].transpose(0, 1), # [enc_step, dec_step]
os.path.join(args.output_directory,
f"align_{epoch:04d}_{iteration}.png"),
info=
f"{datetime.now().strftime('%Y-%m-%d %H:%M')} Epoch={epoch:04d} Iteration={iteration} Average loss={train_epoch_avg_loss/num_iters:.5f}"
)
# Save checkpoint
if epoch % args.epochs_per_checkpoint == 0 and args.rank == 0:
checkpoint_path = os.path.join(args.output_directory,
f"checkpoint_{epoch:04d}.pt")
print(
f"Saving model and optimizer state at epoch {epoch:04d} to {checkpoint_path}"
)
torch.save(model.state_dict(), checkpoint_path)
# Save evaluation
# save_sample(model, args.tacotron2_checkpoint, args.phrase_path,
# os.path.join(args.output_directory, f"sample_{epoch:04d}_{iteration}.wav"), args.sampling_rate)
LOGGER.epoch_stop()
run_stop_time = time.time()
run_time = run_stop_time - run_start_time
LOGGER.log(key="run_time", value=run_time)
LOGGER.log(key=tags.RUN_FINAL)
print("training time", run_stop_time - run_start_time)
LOGGER.timed_block_stop("run")
if args.rank == 0:
LOGGER.finish()
def main():
parser = argparse.ArgumentParser(description='PyTorch Tacotron 2 Training')
parser = parse_args(parser)
args, _ = parser.parse_known_args()
LOGGER.set_model_name("Tacotron2_PyT")
LOGGER.set_backends([
dllg.StdOutBackend(log_file=None,
logging_scope=dllg.TRAIN_ITER_SCOPE,
iteration_interval=1),
dllg.JsonBackend(log_file=args.log_file if args.rank == 0 else None,
logging_scope=dllg.TRAIN_ITER_SCOPE,
iteration_interval=1)
])
LOGGER.timed_block_start("run")
LOGGER.register_metric(tags.TRAIN_ITERATION_LOSS,
metric_scope=dllg.TRAIN_ITER_SCOPE)
LOGGER.register_metric("iter_time", metric_scope=dllg.TRAIN_ITER_SCOPE)
LOGGER.register_metric("epoch_time", metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("run_time", metric_scope=dllg.RUN_SCOPE)
LOGGER.register_metric("val_iter_loss", metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_items/sec",
metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_avg_loss",
metric_scope=dllg.EPOCH_SCOPE)
log_hardware()
model_name = args.model_name
parser = models.parse_model_args(model_name, parser)
parser.parse_args()
args = parser.parse_args()
log_args(args)
torch.backends.cudnn.enabled = args.cudnn_enabled
torch.backends.cudnn.benchmark = args.cudnn_benchmark
distributed_run = args.world_size > 1
if distributed_run:
init_distributed(args, args.world_size, args.rank, args.group_name)
LOGGER.log(key=tags.RUN_START)
run_start_time = time.time()
model_config = models.get_model_config(model_name, args)
model = models.get_model(model_name,
model_config,
to_fp16=args.fp16_run,
to_cuda=True)
epoch_start = 0
if args.resume:
resume_model_path = args.resume_tacotron2_path if args.model_name == "Tacotron2" else args.resume_waveglow_path
checkpoint = torch.load(resume_model_path, map_location='cpu')
epoch_start = checkpoint["epoch"]
state_dict = checkpoint['state_dict']
if checkpoint_from_distributed(state_dict):
state_dict = unwrap_distributed(state_dict)
model.load_state_dict(state_dict)
print("restore model %s" % resume_model_path)
if distributed_run:
model = DDP(model)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay)
if args.fp16_run:
optimizer = FP16_Optimizer(
optimizer, dynamic_loss_scale=args.dynamic_loss_scaling)
try:
sigma = args.sigma
except AttributeError:
sigma = None
criterion = loss_functions.get_loss_function(model_name, sigma)
try:
n_frames_per_step = args.n_frames_per_step
except AttributeError:
n_frames_per_step = None
collate_fn = data_functions.get_collate_function(model_name,
n_frames_per_step)
trainset = data_functions.get_data_loader(model_name, args.dataset_path,
args.training_files, args)
train_sampler = DistributedSampler(trainset) if distributed_run else None
train_loader = DataLoader(trainset,
num_workers=1,
shuffle=False,
sampler=train_sampler,
batch_size=args.batch_size,
pin_memory=False,
drop_last=True,
collate_fn=collate_fn)
valset = data_functions.get_data_loader(model_name, args.dataset_path,
args.validation_files, args)
batch_to_gpu = data_functions.get_batch_to_gpu(model_name)
iteration = 0
model.train()
LOGGER.log(key=tags.TRAIN_LOOP)
for epoch in range(epoch_start, args.epochs):
LOGGER.epoch_start()
epoch_start_time = time.time()
LOGGER.log(key=tags.TRAIN_EPOCH_START, value=epoch)
# used to calculate avg items/sec over epoch
reduced_num_items_epoch = 0
# used to calculate avg loss over epoch
train_epoch_avg_loss = 0.0
num_iters = 0
# if overflow at the last iteration then do not save checkpoint
overflow = False
for i, batch in enumerate(train_loader):
LOGGER.iteration_start()
iter_start_time = time.time()
LOGGER.log(key=tags.TRAIN_ITER_START, value=i)
print("Batch: {}/{} epoch {}".format(i, len(train_loader), epoch))
start = time.perf_counter()
adjust_learning_rate(epoch, optimizer, args.learning_rate,
args.anneal_steps, args.anneal_factor)
model.zero_grad()
x, y, num_items = batch_to_gpu(batch)
if args.fp16_run:
y_pred = model(fp32_to_fp16(x))
loss = criterion(fp16_to_fp32(y_pred), y)
else:
y_pred = model(x)
loss = criterion(y_pred, y)
if distributed_run:
reduced_loss = reduce_tensor(loss.data, args.world_size).item()
reduced_num_items = reduce_tensor(num_items.data, 1).item()
else:
reduced_loss = loss.item()
reduced_num_items = num_items.item()
if np.isnan(reduced_loss):
raise Exception("loss is NaN")
LOGGER.log(key=tags.TRAIN_ITERATION_LOSS, value=reduced_loss)
train_epoch_avg_loss += reduced_loss
num_iters += 1
# accumulate number of items processed in this epoch
reduced_num_items_epoch += reduced_num_items
if args.fp16_run:
optimizer.backward(loss)
grad_norm = optimizer.clip_master_grads(args.grad_clip_thresh)
else:
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), args.grad_clip_thresh)
optimizer.step()
overflow = optimizer.overflow if args.fp16_run else False
iteration += 1
LOGGER.log(key=tags.TRAIN_ITER_STOP, value=i)
iter_stop_time = time.time()
iter_time = iter_stop_time - iter_start_time
LOGGER.log(key="train_iter_items/sec",
value=(reduced_num_items / iter_time))
LOGGER.log(key="iter_time", value=iter_time)
LOGGER.iteration_stop()
LOGGER.log(key=tags.TRAIN_EPOCH_STOP, value=epoch)
epoch_stop_time = time.time()
epoch_time = epoch_stop_time - epoch_start_time
LOGGER.log(key="train_epoch_items/sec",
value=(reduced_num_items_epoch / epoch_time))
LOGGER.log(key="train_epoch_avg_loss",
value=(train_epoch_avg_loss /
num_iters if num_iters > 0 else 0.0))
LOGGER.log(key="epoch_time", value=epoch_time)
LOGGER.log(key=tags.EVAL_START, value=epoch)
validate(model, criterion, valset, iteration, args.batch_size,
args.world_size, collate_fn, distributed_run, args.rank,
batch_to_gpu, args.fp16_run)
LOGGER.log(key=tags.EVAL_STOP, value=epoch)
if not overflow and (epoch % args.epochs_per_checkpoint
== 0) and args.rank == 0:
checkpoint_path = os.path.join(
args.output_directory,
"checkpoint_{}_{}".format(model_name, epoch))
save_checkpoint(model, epoch, model_config, checkpoint_path)
save_sample(
model_name, model, args.waveglow_checkpoint,
args.tacotron2_checkpoint, args.phrase_path,
os.path.join(args.output_directory,
"sample_{}_{}.wav".format(model_name, iteration)),
args.sampling_rate, args.fp16_run)
LOGGER.epoch_stop()
run_stop_time = time.time()
run_time = run_stop_time - run_start_time
LOGGER.log(key="run_time", value=run_time)
LOGGER.log(key=tags.RUN_FINAL)
print("training time", run_stop_time - run_start_time)
LOGGER.timed_block_stop("run")
if args.rank == 0:
LOGGER.finish()
def main():
parser = argparse.ArgumentParser(description='PyTorch Tacotron 2 Training')
parser = parse_args(parser)
args, _ = parser.parse_known_args()
LOGGER.set_model_name("Tacotron2_PyT")
LOGGER.set_backends([
dllg.StdOutBackend(log_file=None,
logging_scope=dllg.TRAIN_ITER_SCOPE,
iteration_interval=1),
dllg.JsonBackend(log_file=args.log_file if args.rank == 0 else None,
logging_scope=dllg.TRAIN_ITER_SCOPE,
iteration_interval=1)
])
LOGGER.timed_block_start("run")
LOGGER.register_metric(tags.TRAIN_ITERATION_LOSS,
metric_scope=dllg.TRAIN_ITER_SCOPE)
LOGGER.register_metric("iter_time", metric_scope=dllg.TRAIN_ITER_SCOPE)
LOGGER.register_metric("epoch_time", metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("run_time", metric_scope=dllg.RUN_SCOPE)
LOGGER.register_metric("val_iter_loss", metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_items/sec",
metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_avg_items/sec",
metric_scope=dllg.EPOCH_SCOPE)
LOGGER.register_metric("train_epoch_avg_loss",
metric_scope=dllg.EPOCH_SCOPE)
log_hardware()
# Restore training from checkpoint logic
checkpoint = None
start_epoch = 0
model_name = args.model_name
parser = models.parse_model_args(model_name, parser)
parser.parse_args()
args = parser.parse_args()
log_args(args)
torch.backends.cudnn.enabled = args.cudnn_enabled
torch.backends.cudnn.benchmark = args.cudnn_benchmark
num_gpus = torch.cuda.device_count()
print("gpus", num_gpus)
distributed_run = num_gpus > 1
if distributed_run:
init_distributed(args, args.world_size, args.rank, args.group_name)
LOGGER.log(key=tags.RUN_START)
run_start_time = time.time()
# Restore training from checkpoint logic
if args.restore_from:
print('Restoring from {} checkpoint'.format(args.restore_from))
checkpoint = torch.load(args.restore_from, map_location='cpu')
start_epoch = checkpoint['epoch'] + 1
model_config = checkpoint['config']
model = models.get_model(model_name, model_config, to_cuda=True)
new_state_dict = {}
for key, value in checkpoint['state_dict'].items():
new_key = key.replace('module.', '')
new_state_dict[new_key] = value
model_dict = new_state_dict
if args.warm_start:
ignore_layers = ['embedding.weight']
print('Warm start')
if len(ignore_layers) > 0:
model_dict = {
k: v
for k, v in model_dict.items() if k not in ignore_layers
}
dummy_dict = model.state_dict()
dummy_dict.update(model_dict)
model_dict = dummy_dict
model.load_state_dict(model_dict)
else:
model_config = models.get_model_config(model_name, args)
model = models.get_model(model_name, model_config, to_cuda=True)
print("model configured")
#model.cuda(4)
model.cuda()
# if not args.amp_run and distributed_run:
# model = DDP(model ,delay_allreduce=True)
#
#
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay)
# Restore training from checkpoint logic
if checkpoint and 'optimizer_state_dict' in checkpoint and not args.warm_start: # TODO: think about this more
print('Restoring optimizer state')
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
if args.amp_run:
model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
print("amp initialized")
model = DDP(model, delay_allreduce=True)
print("ddpmodel")
try:
sigma = args.sigma
except AttributeError:
sigma = None
print("train starting")
criterion = loss_functions.get_loss_function(model_name, sigma)
try:
n_frames_per_step = args.n_frames_per_step
except AttributeError:
n_frames_per_step = None
print("data loading start")
collate_fn = data_functions.get_collate_function(model_name,
n_frames_per_step)
trainset = data_functions.get_data_loader(model_name, args.training_files,
args)
train_sampler = DistributedSampler(trainset) if distributed_run else None
print("train loader started")
train_loader = DataLoader(trainset,
num_workers=1,
shuffle=False,
sampler=train_sampler,
batch_size=args.batch_size,
pin_memory=False,
drop_last=True,
collate_fn=collate_fn)
valset = data_functions.get_data_loader(model_name, args.validation_files,
args)
batch_to_gpu = data_functions.get_batch_to_gpu(model_name)
iteration = 0
model.train()
LOGGER.log(key=tags.TRAIN_LOOP)
# Restore training from checkpoint logic
if start_epoch >= args.epochs:
print('Checkpoint epoch {} >= total epochs {}'.format(
start_epoch, args.epochs))
else:
for epoch in range(start_epoch, args.epochs):
LOGGER.epoch_start()
epoch_start_time = time.time()
LOGGER.log(key=tags.TRAIN_EPOCH_START, value=epoch)
# used to calculate avg items/sec over epoch
reduced_num_items_epoch = 0
# used to calculate avg loss over epoch
train_epoch_avg_loss = 0.0
train_epoch_avg_items_per_sec = 0.0
num_iters = 0
# if overflow at the last iteration then do not save checkpoint
overflow = False
for i, batch in enumerate(train_loader):
print("Batch: {}/{} epoch {}".format(i, len(train_loader),
epoch))
LOGGER.iteration_start()
iter_start_time = time.time()
LOGGER.log(key=tags.TRAIN_ITER_START, value=i)
start = time.perf_counter()
adjust_learning_rate(epoch, optimizer, args.learning_rate,
args.anneal_steps, args.anneal_factor)
model.zero_grad()
x, y, num_items = batch_to_gpu(batch)
y_pred = model(x)
loss = criterion(y_pred, y)
if distributed_run:
reduced_loss = reduce_tensor(loss.data,
args.world_size).item()
reduced_num_items = reduce_tensor(num_items.data, 1).item()
else:
reduced_loss = loss.item()
reduced_num_items = num_items.item()
if np.isnan(reduced_loss):
raise Exception("loss is NaN")
LOGGER.log(key=tags.TRAIN_ITERATION_LOSS, value=reduced_loss)
train_epoch_avg_loss += reduced_loss
num_iters += 1
# accumulate number of items processed in this epoch
reduced_num_items_epoch += reduced_num_items
if args.amp_run:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.grad_clip_thresh)
else:
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), args.grad_clip_thresh)
optimizer.step()
iteration += 1
LOGGER.log(key=tags.TRAIN_ITER_STOP, value=i)
iter_stop_time = time.time()
iter_time = iter_stop_time - iter_start_time
items_per_sec = reduced_num_items / iter_time
train_epoch_avg_items_per_sec += items_per_sec
LOGGER.log(key="train_iter_items/sec", value=items_per_sec)
LOGGER.log(key="iter_time", value=iter_time)
LOGGER.iteration_stop()
LOGGER.log(key=tags.TRAIN_EPOCH_STOP, value=epoch)
epoch_stop_time = time.time()
epoch_time = epoch_stop_time - epoch_start_time
LOGGER.log(key="train_epoch_items/sec",
value=(reduced_num_items_epoch / epoch_time))
LOGGER.log(key="train_epoch_avg_items/sec",
value=(train_epoch_avg_items_per_sec /
num_iters if num_iters > 0 else 0.0))
LOGGER.log(key="train_epoch_avg_loss",
value=(train_epoch_avg_loss /
num_iters if num_iters > 0 else 0.0))
LOGGER.log(key="epoch_time", value=epoch_time)
LOGGER.log(key=tags.EVAL_START, value=epoch)
validate(model, criterion, valset, iteration, args.batch_size,
args.world_size, collate_fn, distributed_run, args.rank,
batch_to_gpu)
LOGGER.log(key=tags.EVAL_STOP, value=epoch)
if (epoch % args.epochs_per_checkpoint == 0) and args.rank == 0:
checkpoint_path = os.path.join(
args.output_directory,
"checkpoint_{}_{}".format(model_name, epoch))
save_checkpoint(model, epoch, model_config, optimizer,
checkpoint_path)
save_sample(
model_name, model, args.waveglow_checkpoint,
args.tacotron2_checkpoint, args.phrase_path,
os.path.join(
args.output_directory,
"sample_{}_{}.wav".format(model_name, iteration)),
args.sampling_rate)
LOGGER.epoch_stop()
run_stop_time = time.time()
run_time = run_stop_time - run_start_time
LOGGER.log(key="run_time", value=run_time)
LOGGER.log(key=tags.RUN_FINAL)
print("training time", run_stop_time - run_start_time)
LOGGER.timed_block_stop("run")
if args.rank == 0:
LOGGER.finish()
