def setUp(self):
self.optimizer = mock.MagicMock()
self.optimizer.param_groups = [{'x': None}]
self.extension = extensions.LinearShift('x', self.value_range,
self.time_range,
self.optimizer)
self.interval = 2
self.trigger = training.get_trigger((self.interval, 'iteration'))
self.trainer = testing.get_trainer_with_mock_updater(self.trigger)
self.trainer.updater.get_optimizer.return_value = self.optimizer
def test_resume(self):
new_optimizer = mock.Mock()
new_optimizer.param_groups = [{'x': None}]
new_extension = extensions.LinearShift('x', self.value_range,
self.time_range, new_optimizer)
self.trainer.extend(self.extension)
self.trainer.run()
new_trainer = testing.get_trainer_with_mock_updater((5, 'iteration'))
new_trainer.extend(new_extension)
testing.save_and_load_pth(self.trainer, new_trainer)
new_extension.initialize(new_trainer)
self.assertEqual(new_optimizer.param_groups[0]['x'],
self.optimizer.param_groups[0]['x'])
self.assertIsInstance(new_optimizer.param_groups[0]['x'], float)
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 test_with_optimizer(self):
optimizer = mock.Mock()
optimizer.param_groups = [{'x': 0}]
extension = extensions.LinearShift('x', self.value_range,
self.time_range, optimizer)
self._run_trainer(extension, self.expect, optimizer)
def test_basic(self):
self.optimizer.param_groups[0]['x'] = 0
extension = extensions.LinearShift('x', self.value_range,
self.time_range)
self._run_trainer(extension, self.expect)
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()