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_mixing_of_dataloader_options(tmpdir):
"""Verify that dataloaders can be passed to fit"""
tutils.reset_seed()
class CurrentTestModel(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)
fit_options = dict(val_dataloader=model._dataloader(train=False))
results = trainer.fit(model, **fit_options)
# fit model
trainer = Trainer(**trainer_options)
fit_options = dict(val_dataloader=model._dataloader(train=False),
test_dataloader=model._dataloader(train=False))
results = trainer.fit(model, **fit_options)
assert len(trainer.get_val_dataloaders()) == 1, \
f'`val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
assert len(trainer.get_test_dataloaders()) == 1, \
f'`test_dataloaders` not initiated properly, got {trainer.get_test_dataloaders()}'
def test_multiple_dataloaders_passed_to_fit(tmpdir):
""" Verify that multiple val & test dataloaders can be passed to fit """
tutils.reset_seed()
class CurrentTestModel(LightningTestModelBaseWithoutDataloader):
pass
hparams = tutils.get_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)
# train, multiple val and multiple test passed to fit
model = CurrentTestModel(hparams)
trainer = Trainer(**trainer_options)
fit_options = dict(train_dataloader=model._dataloader(train=True),
val_dataloader=[
model._dataloader(train=False),
model._dataloader(train=False)
],
test_dataloader=[
model._dataloader(train=False),
model._dataloader(train=False)
])
results = trainer.fit(model, **fit_options)
assert len(trainer.get_val_dataloaders()) == 2, \
f'Multiple `val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
assert len(trainer.get_test_dataloaders()) == 2, \
f'Multiple `test_dataloaders` not initiated properly, got {trainer.get_test_dataloaders()}'
def test_amp_gpu_ddp_slurm_managed():
"""
Make sure DDP + AMP work
:return:
"""
if not can_run_gpu_test():
return
reset_seed()
# simulate setting slurm flags
set_random_master_port()
os.environ['SLURM_LOCALID'] = str(0)
hparams = get_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(show_progress_bar=True,
max_nb_epochs=1,
gpus=[0],
distributed_backend='ddp',
use_amp=True)
save_dir = init_save_dir()
# exp file to get meta
logger = get_test_tube_logger(False)
# exp file to get weights
checkpoint = init_checkpoint_callback(logger)
# add these to the trainer options
trainer_options['checkpoint_callback'] = checkpoint
trainer_options['logger'] = logger
# fit model
trainer = Trainer(**trainer_options)
trainer.is_slurm_managing_tasks = True
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'amp + ddp model failed to complete'
# test root model address
assert trainer.resolve_root_node_address('abc') == 'abc'
assert trainer.resolve_root_node_address('abc[23]') == 'abc23'
assert trainer.resolve_root_node_address('abc[23-24]') == 'abc23'
assert trainer.resolve_root_node_address(
'abc[23-24, 45-40, 40]') == 'abc23'
# test model loading with a map_location
pretrained_model = load_model(logger.experiment, save_dir)
# test model preds
[
run_prediction(dataloader, pretrained_model)
for dataloader in trainer.get_test_dataloaders()
]
if trainer.use_ddp:
# on hpc this would work fine... but need to hack it for the purpose of the test
trainer.model = pretrained_model
trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers(
)
# test HPC loading / saving
trainer.hpc_save(save_dir, logger)
trainer.hpc_load(save_dir, on_gpu=True)
# test freeze on gpu
model.freeze()
model.unfreeze()
clear_save_dir()
