def test_savefun_and_writer_exclusive(self):
# savefun and writer arguments cannot be specified together.
def savefun(*args, **kwargs):
assert False
writer = extensions.snapshot_writers.SimpleWriter()
with pytest.raises(TypeError):
extensions.snapshot(savefun=savefun, writer=writer)
trainer = mock.MagicMock()
with pytest.raises(TypeError):
extensions.snapshot_object(trainer, savefun=savefun, writer=writer)
def test_clean_up_tempdir(self):
snapshot = extensions.snapshot_object(self.trainer, 'myfile.dat')
snapshot(self.trainer)
left_tmps = [
fn for fn in os.listdir('.') if fn.startswith('tmpmyfile.dat')
]
self.assertEqual(len(left_tmps), 0)
def test_save_file(self):
w = extensions.snapshot_writers.SimpleWriter()
snapshot = extensions.snapshot_object(self.trainer,
'myfile.dat',
writer=w)
snapshot(self.trainer)
self.assertTrue(os.path.exists('myfile.dat'))
def test_on_error(self):
class TheOnlyError(Exception):
pass
@training.make_extension(trigger=(1, 'iteration'), priority=100)
def exception_raiser(trainer):
raise TheOnlyError()
self.trainer.extend(exception_raiser)
snapshot = extensions.snapshot_object(self.trainer,
self.filename,
snapshot_on_error=True)
self.trainer.extend(snapshot)
self.assertFalse(os.path.exists(self.filename))
with self.assertRaises(TheOnlyError):
self.trainer.run()
self.assertTrue(os.path.exists(self.filename))
def create_trainer(
config_dict: Dict[str, Any],
output: Path,
):
# config
config = Config.from_dict(config_dict)
config.add_git_info()
assert_config(config)
output.mkdir(exist_ok=True, parents=True)
with (output / "config.yaml").open(mode="w") as f:
yaml.safe_dump(config.to_dict(), f)
# model
predictor = create_predictor(config.network)
model = Model(
loss_config=config.loss,
predictor=predictor,
local_padding_size=config.dataset.local_padding_size,
)
if config.train.weight_initializer is not None:
init_weights(model, name=config.train.weight_initializer)
device = torch.device("cuda")
model.to(device)
# dataset
_create_iterator = partial(
create_iterator,
batch_size=config.train.batchsize,
eval_batch_size=config.train.eval_batchsize,
num_processes=config.train.num_processes,
use_multithread=config.train.use_multithread,
)
datasets = create_dataset(config.dataset)
train_iter = _create_iterator(datasets["train"],
for_train=True,
for_eval=False)
test_iter = _create_iterator(datasets["test"],
for_train=False,
for_eval=False)
eval_iter = _create_iterator(datasets["eval"],
for_train=False,
for_eval=True)
valid_iter = None
if datasets["valid"] is not None:
valid_iter = _create_iterator(datasets["valid"],
for_train=False,
for_eval=True)
warnings.simplefilter("error", MultiprocessIterator.TimeoutWarning)
# optimizer
cp: Dict[str, Any] = copy(config.train.optimizer)
n = cp.pop("name").lower()
optimizer: Optimizer
if n == "adam":
optimizer = optim.Adam(model.parameters(), **cp)
elif n == "sgd":
optimizer = optim.SGD(model.parameters(), **cp)
else:
raise ValueError(n)
# updater
if not config.train.use_amp:
updater = StandardUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
else:
updater = AmpUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
# trainer
trigger_log = (config.train.log_iteration, "iteration")
trigger_eval = (config.train.eval_iteration, "iteration")
trigger_snapshot = (config.train.snapshot_iteration, "iteration")
trigger_stop = ((config.train.stop_iteration, "iteration")
if config.train.stop_iteration is not None else None)
trainer = Trainer(updater, stop_trigger=trigger_stop, out=output)
shift_ext = None
if config.train.linear_shift is not None:
shift_ext = extensions.LinearShift(**config.train.linear_shift)
if config.train.step_shift is not None:
shift_ext = extensions.StepShift(**config.train.step_shift)
if shift_ext is not None:
trainer.extend(shift_ext)
ext = extensions.Evaluator(test_iter, model, device=device)
trainer.extend(ext, name="test", trigger=trigger_log)
generator = Generator(
config=config,
predictor=predictor,
use_gpu=True,
max_batch_size=(config.train.eval_batchsize
if config.train.eval_batchsize is not None else
config.train.batchsize),
use_fast_inference=False,
)
generate_evaluator = GenerateEvaluator(
generator=generator,
time_length=config.dataset.time_length_evaluate,
local_padding_time_length=config.dataset.
local_padding_time_length_evaluate,
)
ext = extensions.Evaluator(eval_iter, generate_evaluator, device=device)
trainer.extend(ext, name="eval", trigger=trigger_eval)
if valid_iter is not None:
ext = extensions.Evaluator(valid_iter,
generate_evaluator,
device=device)
trainer.extend(ext, name="valid", trigger=trigger_eval)
if config.train.stop_iteration is not None:
saving_model_num = int(config.train.stop_iteration /
config.train.eval_iteration / 10)
else:
saving_model_num = 10
ext = extensions.snapshot_object(
predictor,
filename="predictor_{.updater.iteration}.pth",
n_retains=saving_model_num,
)
trainer.extend(
ext,
trigger=LowValueTrigger("eval/main/mcd", trigger=trigger_eval),
)
trainer.extend(extensions.FailOnNonNumber(), trigger=trigger_log)
trainer.extend(extensions.observe_lr(), trigger=trigger_log)
trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(
extensions.PrintReport(["iteration", "main/loss", "test/main/loss"]),
trigger=trigger_log,
)
trainer.extend(TensorboardReport(writer=SummaryWriter(Path(output))),
trigger=trigger_log)
if config.project.category is not None:
ext = WandbReport(
config_dict=config.to_dict(),
project_category=config.project.category,
project_name=config.project.name,
output_dir=output.joinpath("wandb"),
)
trainer.extend(ext, trigger=trigger_log)
(output / "struct.txt").write_text(repr(model))
if trigger_stop is not None:
trainer.extend(extensions.ProgressBar(trigger_stop))
ext = extensions.snapshot_object(
trainer,
filename="trainer_{.updater.iteration}.pth",
n_retains=1,
autoload=True,
)
trainer.extend(ext, trigger=trigger_snapshot)
return trainer
def create_trainer(
config_dict: Dict[str, Any],
output: Path,
):
# config
config = Config.from_dict(config_dict)
config.add_git_info()
output.mkdir(exist_ok=True, parents=True)
with (output / "config.yaml").open(mode="w") as f:
yaml.safe_dump(config.to_dict(), f)
# model
device = torch.device("cuda")
predictor = create_predictor(config.network)
model = Model(
model_config=config.model,
predictor=predictor,
local_padding_length=config.dataset.local_padding_length,
)
init_weights(model, "orthogonal")
model.to(device)
# dataset
_create_iterator = partial(
create_iterator,
batch_size=config.train.batchsize,
eval_batch_size=config.train.eval_batchsize,
num_processes=config.train.num_processes,
use_multithread=config.train.use_multithread,
)
datasets = create_dataset(config.dataset)
train_iter = _create_iterator(datasets["train"],
for_train=True,
for_eval=False)
test_iter = _create_iterator(datasets["test"],
for_train=False,
for_eval=False)
eval_iter = _create_iterator(datasets["eval"],
for_train=False,
for_eval=True)
warnings.simplefilter("error", MultiprocessIterator.TimeoutWarning)
# optimizer
cp: Dict[str, Any] = copy(config.train.optimizer)
n = cp.pop("name").lower()
optimizer: Optimizer
if n == "adam":
optimizer = optim.Adam(model.parameters(), **cp)
elif n == "sgd":
optimizer = optim.SGD(model.parameters(), **cp)
else:
raise ValueError(n)
# updater
use_amp = config.train.use_amp if config.train.use_amp is not None else amp_exist
if use_amp:
updater = AmpUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
else:
updater = StandardUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
# trainer
trigger_log = (config.train.log_iteration, "iteration")
trigger_eval = (config.train.eval_iteration, "iteration")
trigger_stop = ((config.train.stop_iteration, "iteration")
if config.train.stop_iteration is not None else None)
trainer = Trainer(updater, stop_trigger=trigger_stop, out=output)
writer = SummaryWriter(Path(output))
# # error at randint
# sample_data = datasets["train"][0]
# writer.add_graph(
# model,
# input_to_model=(
# sample_data["wave"].unsqueeze(0).to(device),
# sample_data["local"].unsqueeze(0).to(device),
# sample_data["speaker_id"].unsqueeze(0).to(device)
# if predictor.with_speaker
# else None,
# ),
# )
if config.train.multistep_shift is not None:
trainer.extend(
extensions.MultistepShift(**config.train.multistep_shift))
if config.train.step_shift is not None:
trainer.extend(extensions.StepShift(**config.train.step_shift))
ext = extensions.Evaluator(test_iter, model, device=device)
trainer.extend(ext, name="test", trigger=trigger_log)
generator = Generator(
config=config,
noise_schedule_config=NoiseScheduleModelConfig(start=1e-4,
stop=0.05,
num=50),
predictor=predictor,
sampling_rate=config.dataset.sampling_rate,
use_gpu=True,
)
generate_evaluator = GenerateEvaluator(
generator=generator,
local_padding_time_second=config.dataset.
evaluate_local_padding_time_second,
)
ext = extensions.Evaluator(eval_iter, generate_evaluator, device=device)
trainer.extend(ext, name="eval", trigger=trigger_eval)
if config.train.stop_iteration is not None:
saving_model_num = int(config.train.stop_iteration /
config.train.eval_iteration / 10)
else:
saving_model_num = 10
ext = extensions.snapshot_object(
predictor,
filename="predictor_{.updater.iteration}.pth",
n_retains=saving_model_num,
)
trainer.extend(
ext,
trigger=LowValueTrigger("eval/main/mcd", trigger=trigger_eval),
)
trainer.extend(extensions.FailOnNonNumber(), trigger=trigger_log)
trainer.extend(extensions.observe_lr(), trigger=trigger_log)
trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(
extensions.PrintReport(["iteration", "main/loss", "test/main/loss"]),
trigger=trigger_log,
)
trainer.extend(ext, trigger=TensorboardReport(writer=writer))
if config.project.category is not None:
ext = WandbReport(
config_dict=config.to_dict(),
project_category=config.project.category,
project_name=config.project.name,
output_dir=output.joinpath("wandb"),
)
trainer.extend(ext, trigger=trigger_log)
(output / "struct.txt").write_text(repr(model))
if trigger_stop is not None:
trainer.extend(extensions.ProgressBar(trigger_stop))
ext = extensions.snapshot_object(
trainer,
filename="trainer_{.updater.iteration}.pth",
n_retains=1,
autoload=True,
)
trainer.extend(ext, trigger=trigger_eval)
return trainer
def create_trainer(
config_dict: Dict[str, Any],
output: Path,
):
# config
config = Config.from_dict(config_dict)
config.add_git_info()
output.mkdir(exist_ok=True, parents=True)
with (output / "config.yaml").open(mode="w") as f:
yaml.safe_dump(config.to_dict(), f)
# model
networks = create_network(config.network)
model = Model(model_config=config.model, networks=networks)
if config.train.weight_initializer is not None:
init_weights(model, name=config.train.weight_initializer)
device = torch.device("cuda") if config.train.use_gpu else torch.device(
"cpu")
model.to(device)
# dataset
_create_iterator = partial(
create_iterator,
batch_size=config.train.batch_size,
eval_batch_size=config.train.eval_batch_size,
num_processes=config.train.num_processes,
use_multithread=config.train.use_multithread,
)
datasets = create_dataset(config.dataset)
train_iter = _create_iterator(datasets["train"], for_train=True)
test_iter = _create_iterator(datasets["test"], for_train=False)
eval_iter = _create_iterator(datasets["eval"], for_train=False)
warnings.simplefilter("error", MultiprocessIterator.TimeoutWarning)
# optimizer
optimizer = make_optimizer(config_dict=config.train.optimizer, model=model)
# updater
if not config.train.use_amp:
updater = StandardUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
else:
updater = AmpUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
# trainer
trigger_log = (config.train.log_iteration, "iteration")
trigger_eval = (config.train.eval_iteration, "iteration")
trigger_snapshot = (config.train.snapshot_iteration, "iteration")
trigger_stop = ((config.train.stop_iteration, "iteration")
if config.train.stop_iteration is not None else None)
trainer = Trainer(updater, stop_trigger=trigger_stop, out=output)
ext = extensions.Evaluator(test_iter, model, device=device)
trainer.extend(ext, name="test", trigger=trigger_log)
if config.train.stop_iteration is not None:
saving_model_num = int(config.train.stop_iteration /
config.train.eval_iteration / 10)
else:
saving_model_num = 10
ext = extensions.snapshot_object(
networks.predictor,
filename="predictor_{.updater.iteration}.pth",
n_retains=saving_model_num,
)
trainer.extend(
ext,
trigger=LowValueTrigger("test/main/loss", trigger=trigger_eval),
)
trainer.extend(extensions.FailOnNonNumber(), trigger=trigger_log)
trainer.extend(extensions.observe_lr(), trigger=trigger_log)
trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(
extensions.PrintReport(["iteration", "main/loss", "test/main/loss"]),
trigger=trigger_log,
)
ext = TensorboardReport(writer=SummaryWriter(Path(output)))
trainer.extend(ext, trigger=trigger_log)
if config.project.category is not None:
ext = WandbReport(
config_dict=config.to_dict(),
project_category=config.project.category,
project_name=config.project.name,
output_dir=output.joinpath("wandb"),
)
trainer.extend(ext, trigger=trigger_log)
(output / "struct.txt").write_text(repr(model))
if trigger_stop is not None:
trainer.extend(extensions.ProgressBar(trigger_stop))
ext = extensions.snapshot_object(
trainer,
filename="trainer_{.updater.iteration}.pth",
n_retains=1,
autoload=True,
)
trainer.extend(ext, trigger=trigger_snapshot)
return trainer
def create_trainer(
config_dict: Dict[str, Any],
output: Path,
):
# config
config = Config.from_dict(config_dict)
config.add_git_info()
output.mkdir(exist_ok=True, parents=True)
with (output / "config.yaml").open(mode="w") as f:
yaml.safe_dump(config.to_dict(), f)
# model
predictor = create_predictor(config.network)
model = Model(model_config=config.model, predictor=predictor)
if config.train.weight_initializer is not None:
init_weights(model, name=config.train.weight_initializer)
device = torch.device("cuda")
model.to(device)
# dataset
_create_iterator = partial(
create_iterator,
batch_size=config.train.batch_size,
num_processes=config.train.num_processes,
use_multithread=config.train.use_multithread,
)
datasets = create_dataset(config.dataset)
train_iter = _create_iterator(datasets["train"], for_train=True)
test_iter = _create_iterator(datasets["test"], for_train=False)
warnings.simplefilter("error", MultiprocessIterator.TimeoutWarning)
# optimizer
cp: Dict[str, Any] = copy(config.train.optimizer)
n = cp.pop("name").lower()
optimizer: Optimizer
if n == "adam":
optimizer = optim.Adam(model.parameters(), **cp)
elif n == "sgd":
optimizer = optim.SGD(model.parameters(), **cp)
else:
raise ValueError(n)
# updater
updater = StandardUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
converter=list_concat,
device=device,
)
# trainer
trigger_log = (config.train.log_iteration, "iteration")
trigger_eval = (config.train.snapshot_iteration, "iteration")
trigger_stop = ((config.train.stop_iteration, "iteration")
if config.train.stop_iteration is not None else None)
trainer = Trainer(updater, stop_trigger=trigger_stop, out=output)
writer = SummaryWriter(Path(output))
sample_data = datasets["train"][0]
writer.add_graph(
model,
input_to_model=(
[sample_data["f0"].to(device)],
[sample_data["phoneme"].to(device)],
[sample_data["phoneme_list"].to(device)],
([sample_data["speaker_id"].to(device)]
if predictor.with_speaker else None),
),
)
ext = extensions.Evaluator(test_iter,
model,
converter=list_concat,
device=device)
trainer.extend(ext, name="test", trigger=trigger_log)
if config.train.stop_iteration is not None:
saving_model_num = int(config.train.stop_iteration /
config.train.snapshot_iteration / 10)
else:
saving_model_num = 10
ext = extensions.snapshot_object(
predictor,
filename="predictor_{.updater.iteration}.pth",
n_retains=saving_model_num,
)
trainer.extend(
ext,
trigger=LowValueTrigger("test/main/loss", trigger=trigger_eval),
)
trainer.extend(extensions.FailOnNonNumber(), trigger=trigger_log)
trainer.extend(extensions.observe_lr(), trigger=trigger_log)
trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(
extensions.PrintReport(["iteration", "main/loss", "test/main/loss"]),
trigger=trigger_log,
)
ext = TensorboardReport(writer=writer)
trainer.extend(ext, trigger=trigger_log)
if config.project.category is not None:
ext = WandbReport(
config_dict=config.to_dict(),
project_category=config.project.category,
project_name=config.project.name,
output_dir=output.joinpath("wandb"),
)
trainer.extend(ext, trigger=trigger_log)
(output / "struct.txt").write_text(repr(model))
if trigger_stop is not None:
trainer.extend(extensions.ProgressBar(trigger_stop))
ext = extensions.snapshot_object(
trainer,
filename="trainer_{.updater.iteration}.pth",
n_retains=1,
autoload=True,
)
trainer.extend(ext, trigger=trigger_eval)
return trainer
def create_trainer(
config_dict: Dict[str, Any],
output: Path,
):
# config
config = Config.from_dict(config_dict)
config.add_git_info()
output.mkdir(parents=True)
with (output / "config.yaml").open(mode="w") as f:
yaml.safe_dump(config.to_dict(), f)
# model
device = torch.device("cuda")
networks = create_network(config.network)
model = Model(
model_config=config.model,
networks=networks,
local_padding_length=config.dataset.local_padding_length,
)
model.to(device)
if config.model.discriminator_input_type is not None:
discriminator_model = DiscriminatorModel(
model_config=config.model,
networks=networks,
local_padding_length=config.dataset.local_padding_length,
)
discriminator_model.to(device)
else:
discriminator_model = None
# dataset
def _create_iterator(dataset, for_train: bool):
return MultiprocessIterator(
dataset,
config.train.batchsize,
repeat=for_train,
shuffle=for_train,
n_processes=config.train.num_processes,
dataset_timeout=300,
)
datasets = create_dataset(config.dataset)
train_iter = _create_iterator(datasets["train"], for_train=True)
test_iter = _create_iterator(datasets["test"], for_train=False)
test_eval_iter = _create_iterator(datasets["test_eval"], for_train=False)
warnings.simplefilter("error", MultiprocessIterator.TimeoutWarning)
# optimizer
optimizer = create_optimizer(config.train.optimizer, model)
if config.train.discriminator_optimizer is not None:
discriminator_optimizer = create_optimizer(
config.train.discriminator_optimizer, discriminator_model)
else:
discriminator_optimizer = None
# updater
updater = Updater(
iterator=train_iter,
optimizer=optimizer,
discriminator_model=discriminator_model,
model=model,
discriminator_optimizer=discriminator_optimizer,
device=device,
)
# trainer
trigger_log = (config.train.log_iteration, "iteration")
trigger_snapshot = (config.train.snapshot_iteration, "iteration")
trigger_stop = ((config.train.stop_iteration, "iteration")
if config.train.stop_iteration is not None else None)
trainer = Trainer(updater, stop_trigger=trigger_stop, out=output)
if config.train.step_shift is not None:
trainer.extend(extensions.StepShift(**config.train.step_shift))
ext = extensions.Evaluator(test_iter, model, device=device)
trainer.extend(ext, name="test", trigger=trigger_log)
if discriminator_model is not None:
ext = extensions.Evaluator(test_iter,
discriminator_model,
device=device)
trainer.extend(ext, name="test", trigger=trigger_log)
generator = Generator(config=config,
predictor=networks.predictor,
use_gpu=True)
generate_evaluator = GenerateEvaluator(
generator=generator,
time_length=config.dataset.evaluate_time_second,
local_padding_time_length=config.dataset.
evaluate_local_padding_time_second,
)
ext = extensions.Evaluator(test_eval_iter,
generate_evaluator,
device=device)
trainer.extend(ext, name="eval", trigger=trigger_snapshot)
ext = extensions.snapshot_object(
networks.predictor, filename="predictor_{.updater.iteration}.pth")
trainer.extend(ext, trigger=trigger_snapshot)
# ext = extensions.snapshot_object(
# trainer, filename="trainer_{.updater.iteration}.pth"
# )
# trainer.extend(ext, trigger=trigger_snapshot)
# if networks.discriminator is not None:
# ext = extensions.snapshot_object(
# networks.discriminator, filename="discriminator_{.updater.iteration}.pth"
# )
# trainer.extend(ext, trigger=trigger_snapshot)
trainer.extend(extensions.FailOnNonNumber(), trigger=trigger_log)
trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(
extensions.PrintReport(["iteration", "main/loss", "test/main/loss"]),
trigger=trigger_log,
)
ext = TensorboardReport(writer=SummaryWriter(Path(output)))
trainer.extend(ext, trigger=trigger_log)
(output / "struct.txt").write_text(repr(model))
if discriminator_model is not None:
(output / "discriminator_struct.txt").write_text(
repr(discriminator_model))
if trigger_stop is not None:
trainer.extend(extensions.ProgressBar(trigger_stop))
return trainer
def train_phase(predictor, train, valid, args):
print('# classes:', train.n_classes)
print('# samples:')
print('-- train:', len(train))
print('-- valid:', len(valid))
# setup dataset iterators
train_iter = iterators.MultiprocessIterator(train, args.batchsize)
valid_iter = iterators.SerialIterator(valid,
args.batchsize,
repeat=False,
shuffle=True)
# setup a model
class_weight = None # NOTE: please set if you have..
lossfun = partial(softmax_cross_entropy,
normalize=False,
class_weight=class_weight)
device = torch.device(args.gpu)
model = Classifier(predictor, lossfun=lossfun)
model.to(device)
# setup an optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=max(args.decay, 0))
# setup a trainer
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
model,
device=device)
trainer = training.Trainer(updater, (args.iteration, 'iteration'),
out=args.out)
frequency = max(args.iteration //
20, 1) if args.frequency == -1 else max(1, args.frequency)
stop_trigger = triggers.EarlyStoppingTrigger(
monitor='validation/main/loss',
max_trigger=(args.iteration, 'iteration'),
check_trigger=(frequency, 'iteration'),
patients=np.inf if args.pinfall == -1 else max(1, args.pinfall))
trainer = training.Trainer(updater, stop_trigger, out=args.out)
# setup a visualizer
transforms = {
'x': lambda x: x,
'y': lambda x: np.argmax(x, axis=0),
't': lambda x: x
}
cmap = np.array([[0, 0, 0], [0, 0, 1]])
cmaps = {'x': None, 'y': cmap, 't': cmap}
clims = {'x': 'minmax', 'y': None, 't': None}
visualizer = ImageVisualizer(transforms=transforms,
cmaps=cmaps,
clims=clims)
# setup a validator
valid_file = os.path.join('validation', 'iter_{.updater.iteration:08}.png')
trainer.extend(Validator(valid_iter,
model,
valid_file,
visualizer=visualizer,
n_vis=20,
device=args.gpu),
trigger=(frequency, 'iteration'))
# trainer.extend(DumpGraph(model, 'main/loss'))
trainer.extend(extensions.snapshot(
filename='snapshot_iter_{.updater.iteration:08}.pth'),
trigger=(frequency, 'iteration'))
trainer.extend(extensions.snapshot_object(
predictor, 'predictor_iter_{.updater.iteration:08}.pth'),
trigger=(frequency, 'iteration'))
log_keys = [
'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy'
]
trainer.extend(LogReport(keys=log_keys))
# setup log ploter
if extensions.PlotReport.available():
for plot_key in ['loss', 'accuracy']:
plot_keys = [
key for key in log_keys
if key.split('/')[-1].startswith(plot_key)
]
trainer.extend(
extensions.PlotReport(plot_keys,
'iteration',
file_name=plot_key + '.png',
trigger=(frequency, 'iteration')))
trainer.extend(
PrintReport(['iteration'] + log_keys + ['elapsed_time'], n_step=100))
trainer.extend(extensions.ProgressBar())
if args.resume:
trainer.load_state_dict(torch.load(args.resume))
# train
trainer.run()
def add_snapshot_object(target, name):
ext = extensions.snapshot_object(target,
filename=name +
"_{.updater.iteration}.pth")
trainer.extend(ext, trigger=trigger_snapshot)
def create_trainer(
config_dict: Dict[str, Any],
output: Path,
):
# config
config = Config.from_dict(config_dict)
config.add_git_info()
output.mkdir(parents=True)
with (output / 'config.yaml').open(mode='w') as f:
yaml.safe_dump(config.to_dict(), f)
# model
networks = create_network(config.network)
model = Model(model_config=config.model, networks=networks)
device = torch.device('cuda')
model.to(device)
# dataset
def _create_iterator(dataset, for_train: bool):
return MultiprocessIterator(
dataset,
config.train.batchsize,
repeat=for_train,
shuffle=for_train,
n_processes=config.train.num_processes,
dataset_timeout=60,
)
datasets = create_dataset(config.dataset)
train_iter = _create_iterator(datasets['train'], for_train=True)
test_iter = _create_iterator(datasets['test'], for_train=False)
train_test_iter = _create_iterator(datasets['train_test'], for_train=False)
warnings.simplefilter('error', MultiprocessIterator.TimeoutWarning)
# optimizer
cp: Dict[str, Any] = copy(config.train.optimizer)
n = cp.pop('name').lower()
if n == 'adam':
optimizer = optim.Adam(model.parameters(), **cp)
elif n == 'sgd':
optimizer = optim.SGD(model.parameters(), **cp)
else:
raise ValueError(n)
# updater
updater = StandardUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
# trainer
trigger_log = (config.train.log_iteration, 'iteration')
trigger_snapshot = (config.train.snapshot_iteration, 'iteration')
trigger_stop = (
config.train.stop_iteration,
'iteration') if config.train.stop_iteration is not None else None
trainer = Trainer(updater, stop_trigger=trigger_stop, out=output)
ext = extensions.Evaluator(test_iter, model, device=device)
trainer.extend(ext, name='test', trigger=trigger_log)
ext = extensions.Evaluator(train_test_iter, model, device=device)
trainer.extend(ext, name='train', trigger=trigger_log)
ext = extensions.snapshot_object(
networks.predictor, filename='predictor_{.updater.iteration}.npz')
trainer.extend(ext, trigger=trigger_snapshot)
trainer.extend(extensions.FailOnNonNumber(), trigger=trigger_log)
trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(extensions.PrintReport(
['iteration', 'main/loss', 'test/main/loss']),
trigger=trigger_log)
ext = TensorboardReport(writer=SummaryWriter(Path(output)))
trainer.extend(ext, trigger=trigger_log)
(output / 'struct.txt').write_text(repr(model))
if trigger_stop is not None:
trainer.extend(extensions.ProgressBar(trigger_stop))
return trainer
def create_trainer(
config_dict: Dict[str, Any],
output: Path,
):
# config
config = Config.from_dict(config_dict)
config.add_git_info()
output.mkdir(exist_ok=True, parents=True)
with (output / "config.yaml").open(mode="w") as f:
yaml.safe_dump(config.to_dict(), f)
# model
networks = create_network(config.network)
model = Model(config=config.model, networks=networks)
init_orthogonal(model)
device = torch.device("cuda")
model.to(device)
# dataset
_create_iterator = partial(
create_iterator,
batch_size=config.train.batch_size,
eval_batch_size=config.train.eval_batch_size,
num_processes=config.train.num_processes,
use_multithread=config.train.use_multithread,
)
datasets = create_dataset(config.dataset)
train_iter = _create_iterator(datasets["train"],
for_train=True,
for_eval=False)
test_iter = _create_iterator(datasets["test"],
for_train=False,
for_eval=False)
valid_iter = None
if datasets["valid"] is not None:
valid_iter = _create_iterator(datasets["valid"],
for_train=False,
for_eval=True)
warnings.simplefilter("error", MultiprocessIterator.TimeoutWarning)
# optimizer
cp: Dict[str, Any] = copy(config.train.optimizer)
n = cp.pop("name").lower()
optimizer: Optimizer
if n == "adam":
optimizer = optim.Adam(model.parameters(), **cp)
elif n == "sgd":
optimizer = optim.SGD(model.parameters(), **cp)
elif n == "ranger":
optimizer = Ranger(model.parameters(), **cp)
else:
raise ValueError(n)
# updater
if not config.train.use_amp:
updater = StandardUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
else:
updater = AmpUpdater(
iterator=train_iter,
optimizer=optimizer,
model=model,
device=device,
)
# trainer
trigger_log = (config.train.log_iteration, "iteration")
trigger_eval = (config.train.eval_iteration, "iteration")
trigger_stop = ((config.train.stop_iteration, "iteration")
if config.train.stop_iteration is not None else None)
trainer = Trainer(updater, stop_trigger=trigger_stop, out=output)
ext = extensions.Evaluator(test_iter, model, device=device)
trainer.extend(ext, name="test", trigger=trigger_log)
if valid_iter is not None:
ext = extensions.Evaluator(valid_iter, model, device=device)
trainer.extend(ext, name="valid", trigger=trigger_eval)
if config.train.stop_iteration is not None:
saving_model_num = int(config.train.stop_iteration /
config.train.eval_iteration / 10)
else:
saving_model_num = 10
for field in dataclasses.fields(Networks):
ext = extensions.snapshot_object(
getattr(networks, field.name),
filename=field.name + "_{.updater.iteration}.pth",
n_retains=saving_model_num,
)
trainer.extend(
ext,
trigger=HighValueTrigger(
("valid/main/phoneme_accuracy"
if valid_iter is not None else "test/main/phoneme_accuracy"),
trigger=trigger_eval,
),
)
trainer.extend(extensions.FailOnNonNumber(), trigger=trigger_log)
trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(
extensions.PrintReport(["iteration", "main/loss", "test/main/loss"]),
trigger=trigger_log,
)
ext = TensorboardReport(writer=SummaryWriter(Path(output)))
trainer.extend(ext, trigger=trigger_log)
if config.project.category is not None:
ext = WandbReport(
config_dict=config.to_dict(),
project_category=config.project.category,
project_name=config.project.name,
output_dir=output.joinpath("wandb"),
)
trainer.extend(ext, trigger=trigger_log)
(output / "struct.txt").write_text(repr(model))
if trigger_stop is not None:
trainer.extend(extensions.ProgressBar(trigger_stop))
ext = extensions.snapshot_object(
trainer,
filename="trainer_{.updater.iteration}.pth",
n_retains=1,
autoload=True,
)
trainer.extend(ext, trigger=trigger_eval)
return trainer
def train_phase(generator, train, valid, args):
print('# samples:')
print('-- train:', len(train))
print('-- valid:', len(valid))
# setup dataset iterators
train_iter = iterators.SerialIterator(train, args.batchsize)
valid_iter = iterators.SerialIterator(valid, args.batchsize,
repeat=False, shuffle=True)
# setup a model
model = Regressor(generator,
activation=torch.tanh,
lossfun=F.l1_loss,
accfun=F.l1_loss)
discriminator = build_discriminator()
discriminator.save_args(os.path.join(args.out, 'discriminator.json'))
device = torch.device(args.gpu)
model.to(device)
discriminator.to(device)
# setup an optimizer
optimizer_G = torch.optim.Adam(model.parameters(),
lr=args.lr,
betas=(args.beta, 0.999),
weight_decay=max(args.decay, 0))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=args.lr,
betas=(args.beta, 0.999),
weight_decay=max(args.decay, 0))
# setup a trainer
updater = DCGANUpdater(
iterator=train_iter,
optimizer={
'gen': optimizer_G,
'dis': optimizer_D,
},
model={
'gen': model,
'dis': discriminator,
},
alpha=args.alpha,
device=args.gpu,
)
frequency = max(args.iteration//80, 1) if args.frequency == -1 else max(1, args.frequency)
stop_trigger = triggers.EarlyStoppingTrigger(monitor='validation/main/loss',
max_trigger=(args.iteration, 'iteration'),
check_trigger=(frequency, 'iteration'),
patients=np.inf if args.pinfall == -1 else max(1, args.pinfall))
trainer = training.Trainer(updater, stop_trigger, out=args.out)
# shift lr
trainer.extend(
extensions.LinearShift('lr', (args.lr, 0.0),
(args.iteration//2, args.iteration),
optimizer=optimizer_G))
trainer.extend(
extensions.LinearShift('lr', (args.lr, 0.0),
(args.iteration//2, args.iteration),
optimizer=optimizer_D))
# setup a visualizer
transforms = {'x': lambda x: x, 'y': lambda x: x, 't': lambda x: x}
clims = {'x': (-1., 1.), 'y': (-1., 1.), 't': (-1., 1.)}
visualizer = ImageVisualizer(transforms=transforms,
cmaps=None,
clims=clims)
# setup a validator
valid_file = os.path.join('validation', 'iter_{.updater.iteration:08}.png')
trainer.extend(Validator(valid_iter, model, valid_file,
visualizer=visualizer, n_vis=20,
device=args.gpu),
trigger=(frequency, 'iteration'))
# trainer.extend(DumpGraph('loss_gen', filename='generative_loss.dot'))
# trainer.extend(DumpGraph('loss_cond', filename='conditional_loss.dot'))
# trainer.extend(DumpGraph('loss_dis', filename='discriminative_loss.dot'))
trainer.extend(extensions.snapshot(filename='snapshot_iter_{.updater.iteration:08}.pth'),
trigger=(frequency, 'iteration'))
trainer.extend(extensions.snapshot_object(generator, 'generator_iter_{.updater.iteration:08}.pth'),
trigger=(frequency, 'iteration'))
trainer.extend(extensions.snapshot_object(discriminator, 'discriminator_iter_{.updater.iteration:08}.pth'),
trigger=(frequency, 'iteration'))
log_keys = ['loss_gen', 'loss_cond', 'loss_dis',
'validation/main/accuracy']
trainer.extend(LogReport(keys=log_keys, trigger=(100, 'iteration')))
# setup log ploter
if extensions.PlotReport.available():
for plot_key in ['loss', 'accuracy']:
plot_keys = [key for key in log_keys if key.split('/')[-1].startswith(plot_key)]
trainer.extend(
extensions.PlotReport(plot_keys,
'iteration', file_name=plot_key + '.png',
trigger=(frequency, 'iteration')) )
trainer.extend(PrintReport(['iteration'] + log_keys + ['elapsed_time'], n_step=1))
trainer.extend(extensions.ProgressBar())
if args.resume:
trainer.load_state_dict(torch.load(args.resume))
# train
trainer.run()
