def test_testtube_pickle():
"""
Verify that pickling a trainer containing a test tube logger works
"""
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
save_dir = tutils.init_save_dir()
logger = tutils.get_test_tube_logger(False)
logger.log_hyperparams(hparams)
logger.save()
trainer_options = dict(max_nb_epochs=1,
train_percent_check=0.01,
logger=logger)
trainer = Trainer(**trainer_options)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
tutils.clear_save_dir()
def test_mlflow_logger():
"""
verify that basic functionality of mlflow logger works
"""
tutils.reset_seed()
try:
from pytorch_lightning.logging import MLFlowLogger
except ModuleNotFoundError:
return
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
root_dir = os.path.dirname(os.path.realpath(__file__))
mlflow_dir = os.path.join(root_dir, "mlruns")
logger = MLFlowLogger("test", f"file://{mlflow_dir}")
trainer_options = dict(max_nb_epochs=1,
train_percent_check=0.01,
logger=logger)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
print('result finished')
assert result == 1, "Training failed"
tutils.clear_save_dir()
def test_comet_logger(tmpdir, monkeypatch):
"""Verify that basic functionality of Comet.ml logger works."""
# prevent comet logger from trying to print at exit, since
# pytest's stdout/stderr redirection breaks it
import atexit
monkeypatch.setattr(atexit, "register", lambda _: None)
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
comet_dir = os.path.join(tmpdir, "cometruns")
# We test CometLogger in offline mode with local saves
logger = CometLogger(
save_dir=comet_dir,
project_name="general",
workspace="dummy-test",
)
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
train_percent_check=0.01,
logger=logger)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
print('result finished')
assert result == 1, "Training failed"
def test_wandb_pickle(tmpdir):
"""Verify that pickling trainer with wandb logger works."""
tutils.reset_seed()
wandb_dir = str(tmpdir)
logger = WandbLogger(save_dir=wandb_dir, anonymous=True)
assert logger is not None
def test_simple_cpu():
"""
Verify continue training session on CPU
:return:
"""
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
save_dir = tutils.init_save_dir()
# logger file to get meta
trainer_options = dict(
max_nb_epochs=1,
val_percent_check=0.1,
train_percent_check=0.1,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
tutils.clear_save_dir()
def test_mlflow_pickle():
"""
verify that pickling trainer with mlflow logger works
"""
tutils.reset_seed()
try:
from pytorch_lightning.logging import MLFlowLogger
except ModuleNotFoundError:
return
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
root_dir = os.path.dirname(os.path.realpath(__file__))
mlflow_dir = os.path.join(root_dir, "mlruns")
logger = MLFlowLogger("test", f"file://{mlflow_dir}")
trainer_options = dict(max_nb_epochs=1, logger=logger)
trainer = Trainer(**trainer_options)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
tutils.clear_save_dir()
def test_no_val_end_module(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
tutils.reset_seed()
class CurrentTestModel(LightningValidationStepMixin,
LightningTestModelBase):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
logger = tutils.get_test_tube_logger(tmpdir, False)
trainer_options = dict(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
tags_path = tutils.get_data_path(logger, path_dir=tmpdir)
tags_path = os.path.join(tags_path, 'meta_tags.csv')
model_2 = LightningTestModel.load_from_metrics(
weights_path=new_weights_path, tags_csv=tags_path)
model_2.eval()
def test_multiple_test_dataloader(tmpdir):
"""Verify multiple test_dataloader."""
tutils.reset_seed()
class CurrentTestModel(LightningTestMultipleDataloadersMixin,
LightningTestModelBase):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
val_percent_check=0.1,
train_percent_check=0.2,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# verify there are 2 val loaders
assert len(trainer.get_test_dataloaders()) == 2, \
'Multiple test_dataloaders not initiated properly'
# make sure predictions are good for each test set
for dataloader in trainer.get_test_dataloaders():
tutils.run_prediction(dataloader, trainer.model)
# run the test method
trainer.test()
def test_running_test_pretrained_model(tmpdir):
tutils.reset_seed()
"""Verify test() on pretrained model"""
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_test_tube_logger(tmpdir, False)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(show_progress_bar=False,
max_num_epochs=4,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger.experiment,
trainer.checkpoint_callback.filepath,
module_class=LightningTestModel)
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_test_acc(new_trainer)
def test_load_model_from_checkpoint(tmpdir):
tutils.reset_seed()
"""Verify test() on pretrained model"""
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(
show_progress_bar=False,
max_num_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=True,
logger=False,
default_save_path=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = LightningTestModel.load_from_checkpoint(
os.path.join(trainer.checkpoint_callback.filepath,
"_ckpt_epoch_0.ckpt"))
# test that hparams loaded correctly
for k, v in vars(hparams).items():
assert getattr(pretrained_model.hparams, k) == v
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_test_acc(new_trainer)
def test_optimizer_return_options():
tutils.reset_seed()
trainer = Trainer()
model, hparams = tutils.get_model()
# single optimizer
opt_a = torch.optim.Adam(model.parameters(), lr=0.002)
opt_b = torch.optim.SGD(model.parameters(), lr=0.002)
optim, lr_sched = trainer.init_optimizers(opt_a)
assert len(optim) == 1 and len(lr_sched) == 0
# opt tuple
opts = (opt_a, opt_b)
optim, lr_sched = trainer.init_optimizers(opts)
assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
assert len(lr_sched) == 0
# opt list
opts = [opt_a, opt_b]
optim, lr_sched = trainer.init_optimizers(opts)
assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
assert len(lr_sched) == 0
# opt tuple of lists
opts = ([opt_a], ['lr_scheduler'])
optim, lr_sched = trainer.init_optimizers(opts)
assert len(optim) == 1 and len(lr_sched) == 1
assert optim[0] == opts[0][0] and lr_sched[0] == 'lr_scheduler'
def test_ddp_sampler_error(tmpdir):
"""
Make sure DDP + AMP work
:return:
"""
if not tutils.can_run_gpu_test():
return
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams, force_remove_distributed_sampler=True)
logger = tutils.get_test_tube_logger(tmpdir, True)
trainer = Trainer(
logger=logger,
show_progress_bar=False,
max_nb_epochs=1,
gpus=[0, 1],
distributed_backend='ddp',
use_amp=True
)
with pytest.warns(UserWarning):
trainer.get_dataloaders(model)
def test_multi_gpu_model_dp(tmpdir):
"""
Make sure DP works
:return:
"""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
model, hparams = tutils.get_model()
trainer_options = dict(
default_save_path=tmpdir,
show_progress_bar=False,
distributed_backend='dp',
max_nb_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus='-1'
)
tutils.run_model_test(trainer_options, model, hparams)
# test memory helper functions
memory.get_memory_profile('min_max')
def test_multiple_val_dataloader():
"""
Verify multiple val_dataloader
:return:
"""
tutils.reset_seed()
class CurrentTestModel(LightningValidationMultipleDataloadersMixin,
LightningTestModelBase):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
trainer_options = dict(
max_nb_epochs=1,
val_percent_check=0.1,
train_percent_check=1.0,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# verify training completed
assert result == 1
# verify there are 2 val loaders
assert len(trainer.get_val_dataloaders()) == 2, \
'Multiple val_dataloaders not initiated properly'
# make sure predictions are good for each val set
for dataloader in trainer.get_val_dataloaders():
tutils.run_prediction(dataloader, trainer.model)
def test_comet_logger(tmpdir):
"""Verify that basic functionality of Comet.ml logger works."""
tutils.reset_seed()
try:
from pytorch_lightning.logging import CometLogger
except ModuleNotFoundError:
return
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
comet_dir = os.path.join(tmpdir, "cometruns")
# We test CometLogger in offline mode with local saves
logger = CometLogger(
save_dir=comet_dir,
project_name="general",
workspace="dummy-test",
)
trainer_options = dict(max_num_epochs=1,
train_percent_check=0.01,
logger=logger)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
print('result finished')
assert result == 1, "Training failed"
def test_wandb_pickle(tmpdir):
"""Verify that pickling trainer with wandb logger works."""
tutils.reset_seed()
from pytorch_lightning.logging import WandbLogger
wandb_dir = str(tmpdir)
logger = WandbLogger(save_dir=wandb_dir, anonymous=True)
def test_running_test_after_fitting(tmpdir):
"""Verify test() on fitted model."""
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_test_tube_logger(tmpdir, False)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=4,
train_percent_check=0.4,
val_percent_check=0.2,
test_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer)
def test_running_test_without_val(tmpdir):
"""Verify `test()` works on a model with no `val_loader`."""
tutils.reset_seed()
class CurrentTestModel(LightningTestMixin, LightningTestModelBase):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
logger = tutils.get_test_tube_logger(tmpdir, False)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(show_progress_bar=False,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
test_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer)
def test_early_stopping_cpu_model(tmpdir):
"""Test each of the trainer options."""
tutils.reset_seed()
stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
trainer_options = dict(
default_save_path=tmpdir,
min_epochs=2,
early_stop_callback=stopping,
gradient_clip_val=1.0,
overfit_pct=0.20,
track_grad_norm=2,
print_nan_grads=True,
show_progress_bar=True,
logger=tutils.get_test_tube_logger(tmpdir),
train_percent_check=0.1,
val_percent_check=0.1,
)
model, hparams = tutils.get_model()
tutils.run_model_test(trainer_options,
model,
on_gpu=False,
early_stop=True)
# test freeze on cpu
model.freeze()
model.unfreeze()
def test_comet_pickle(tmpdir, monkeypatch):
"""Verify that pickling trainer with comet logger works."""
# prevent comet logger from trying to print at exit, since
# pytest's stdout/stderr redirection breaks it
import atexit
monkeypatch.setattr(atexit, "register", lambda _: None)
tutils.reset_seed()
try:
from pytorch_lightning.logging import CometLogger
except ModuleNotFoundError:
return
# hparams = tutils.get_hparams()
# model = LightningTestModel(hparams)
comet_dir = os.path.join(tmpdir, "cometruns")
# We test CometLogger in offline mode with local saves
logger = CometLogger(
save_dir=comet_dir,
project_name="general",
workspace="dummy-test",
)
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
logger=logger)
trainer = Trainer(**trainer_options)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
def test_mlflow_logger(tmpdir):
"""Verify that basic functionality of mlflow logger works."""
tutils.reset_seed()
try:
from pytorch_lightning.logging import MLFlowLogger
except ModuleNotFoundError:
return
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
mlflow_dir = os.path.join(tmpdir, "mlruns")
logger = MLFlowLogger("test",
tracking_uri=f"file:{os.sep * 2}{mlflow_dir}")
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
train_percent_check=0.01,
logger=logger)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
print('result finished')
assert result == 1, "Training failed"
def test_default_logger_callbacks_cpu_model(tmpdir):
"""
Test each of the trainer options
:return:
"""
tutils.reset_seed()
trainer_options = dict(default_save_path=tmpdir,
max_nb_epochs=1,
gradient_clip_val=1.0,
overfit_pct=0.20,
print_nan_grads=True,
show_progress_bar=False,
train_percent_check=0.01,
val_percent_check=0.01)
model, hparams = tutils.get_model()
tutils.run_model_test_no_loggers(trainer_options,
model,
hparams,
on_gpu=False)
# test freeze on cpu
model.freeze()
model.unfreeze()
def test_comet_pickle(tmpdir):
"""Verify that pickling trainer with comet logger works."""
tutils.reset_seed()
try:
from pytorch_lightning.logging import CometLogger
except ModuleNotFoundError:
return
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
comet_dir = os.path.join(tmpdir, "cometruns")
# We test CometLogger in offline mode with local saves
logger = CometLogger(
save_dir=comet_dir,
project_name="general",
workspace="dummy-test",
)
trainer_options = dict(max_num_epochs=1, logger=logger)
trainer = Trainer(**trainer_options)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
def test_cpu_restore_training(tmpdir):
"""Verify continue training session on CPU."""
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
test_logger_version = 10
logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
trainer_options = dict(
max_epochs=8,
val_check_interval=0.50,
val_percent_check=0.2,
train_percent_check=0.2,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1)
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
real_global_epoch = trainer.current_epoch
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
# wipe-out trainer and model
# retrain with not much data... this simulates picking training back up after slurm
# we want to see if the weights come back correctly
new_logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
trainer_options = dict(
max_epochs=2,
val_check_interval=0.50,
val_percent_check=0.2,
train_percent_check=0.2,
logger=new_logger,
checkpoint_callback=ModelCheckpoint(tmpdir),
)
trainer = Trainer(**trainer_options)
model = LightningTestModel(hparams)
# set the epoch start hook so we can predict before the model does the full training
def assert_good_acc():
assert trainer.current_epoch == real_global_epoch
assert trainer.current_epoch >= 0
# if model and state loaded correctly, predictions will be good even though we
# haven't trained with the new loaded model
trainer.model.eval()
for dataloader in trainer.get_val_dataloaders():
tutils.run_prediction(dataloader, trainer.model)
model.on_train_start = assert_good_acc
# by calling fit again, we trigger training, loading weights from the cluster
# and our hook to predict using current model before any more weight updates
trainer.fit(model)
def test_model_freeze_unfreeze():
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
model.freeze()
model.unfreeze()
def test_wandb_logger(tmpdir):
"""Verify that basic functionality of wandb logger works."""
tutils.reset_seed()
from pytorch_lightning.logging import WandbLogger
wandb_dir = os.path.join(tmpdir, "wandb")
logger = WandbLogger(save_dir=wandb_dir, anonymous=True)
def test_model_saving_loading():
"""
Tests use case where trainer saves the model, and user loads it from tags independently
:return:
"""
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
save_dir = tutils.init_save_dir()
# logger file to get meta
logger = tutils.get_test_tube_logger(False)
trainer_options = dict(
max_nb_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(save_dir)
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
# make a prediction
for dataloader in model.test_dataloader():
for batch in dataloader:
break
x, y = batch
x = x.view(x.size(0), -1)
# generate preds before saving model
model.eval()
pred_before_saving = model(x)
# save model
new_weights_path = os.path.join(save_dir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
tags_path = logger.experiment.get_data_path(logger.experiment.name, logger.experiment.version)
tags_path = os.path.join(tags_path, 'meta_tags.csv')
model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path,
tags_csv=tags_path)
model_2.eval()
# make prediction
# assert that both predictions are the same
new_pred = model_2(x)
assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
tutils.clear_save_dir()
def test_single_gpu_batch_parse():
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
trainer = Trainer()
# batch is just a tensor
batch = torch.rand(2, 3)
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
# tensor list
batch = [torch.rand(2, 3), torch.rand(2, 3)]
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0].device.index == 0 and batch[0].type(
) == 'torch.cuda.FloatTensor'
assert batch[1].device.index == 0 and batch[1].type(
) == 'torch.cuda.FloatTensor'
# tensor list of lists
batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0][0].device.index == 0 and batch[0][0].type(
) == 'torch.cuda.FloatTensor'
assert batch[0][1].device.index == 0 and batch[0][1].type(
) == 'torch.cuda.FloatTensor'
# tensor dict
batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0]['a'].device.index == 0 and batch[0]['a'].type(
) == 'torch.cuda.FloatTensor'
assert batch[0]['b'].device.index == 0 and batch[0]['b'].type(
) == 'torch.cuda.FloatTensor'
# tuple of tensor list and list of tensor dict
batch = ([torch.rand(2, 3) for _ in range(2)], [{
'a': torch.rand(2, 3),
'b': torch.rand(2, 3)
} for _ in range(2)])
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0][0].device.index == 0 and batch[0][0].type(
) == 'torch.cuda.FloatTensor'
assert batch[1][0]['a'].device.index == 0
assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
assert batch[1][0]['b'].device.index == 0
assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
def test_running_test_pretrained_model_ddp():
"""Verify test() on pretrained model"""
if not tutils.can_run_gpu_test():
return
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
save_dir = tutils.init_save_dir()
# exp file to get meta
logger = tutils.get_test_tube_logger(False)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
show_progress_bar=False,
max_nb_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend='ddp'
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
exp = logger.experiment
logging.info(os.listdir(exp.get_data_path(exp.name, exp.version)))
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger.experiment,
trainer.checkpoint_callback.filepath,
module_class=LightningTestModel)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
for dataloader in model.test_dataloader():
tutils.run_prediction(dataloader, pretrained_model)
tutils.clear_save_dir()
def test_cpu_model(tmpdir):
"""Make sure model trains on CPU."""
tutils.reset_seed()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
logger=tutils.get_test_tube_logger(tmpdir),
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.4)
model, hparams = tutils.get_model()
tutils.run_model_test(trainer_options, model, on_gpu=False)
