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_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_test_acc(trainer)
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_running_test_without_val(tmpdir):
tutils.reset_seed()
"""Verify test() works on a model with no val_loader"""
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_nb_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_test_acc(trainer)
def test_running_test_pretrained_model_dp():
tutils.reset_seed()
"""Verify test() on pretrained model"""
if not tutils.can_run_gpu_test():
return
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)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
show_progress_bar=True,
max_nb_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend='dp'
)
# 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)
tutils.clear_save_dir()
