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_model_saving_loading():
"""
Tests use case where trainer saves the model, and user loads it from tags independently
:return:
"""
reset_seed()
hparams = get_hparams()
model = LightningTestModel(hparams)
save_dir = init_save_dir()
# logger file to get meta
logger = get_test_tube_logger(False)
logger.log_hyperparams(hparams)
logger.save()
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
clear_save_dir()
def test_no_val_end_module():
"""
Tests use case where trainer saves the model, and user loads it from tags independently
:return:
"""
reset_seed()
class CurrentTestModel(LightningValidationStepMixin,
LightningTestModelBase):
pass
hparams = get_hparams()
model = CurrentTestModel(hparams)
save_dir = init_save_dir()
# logger file to get meta
logger = get_test_tube_logger(False)
logger.log_hyperparams(hparams)
logger.save()
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'
# 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
clear_save_dir()
def test_no_val_end_module():
"""
Tests use case where trainer saves the model, and user loads it from tags independently
:return:
"""
class CurrentTestModel(LightningValidationStepMixin, LightningTestModelBase):
pass
hparams = get_hparams()
model = CurrentTestModel(hparams)
save_dir = init_save_dir()
# exp file to get meta
exp = get_exp(False)
exp.argparse(hparams)
exp.save()
trainer_options = dict(
max_nb_epochs=1,
cluster=SlurmCluster(),
experiment=exp,
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'
# save model
new_weights_path = os.path.join(save_dir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
tags_path = exp.get_data_path(exp.name, exp.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, on_gpu=False)
model_2.eval()
# make prediction
clear_save_dir()
