def test_no_val_end_module(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
class CurrentTestModel(LightTrainDataloader, LightValidationStepMixin,
TestModelBase):
pass
hparams = tutils.get_default_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
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_checkpoint(
checkpoint_path=new_weights_path, tags_csv=tags_path)
model_2.eval()
def test_no_val_end_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
monkeypatch.setenv('TORCH_HOME', tmpdir)
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
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
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, 'hparams.yaml')
ckpt_path = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}' if url_ckpt else new_weights_path
model_2 = EvalModelTemplate.load_from_checkpoint(checkpoint_path=ckpt_path,
hparams_file=hparams_path)
model_2.eval()
def test_loading_meta_tags(tmpdir):
""" test for backward compatibility to meta_tags.csv """
tutils.reset_seed()
hparams = EvalModelTemplate.get_default_hparams()
# save tags
logger = tutils.get_default_logger(tmpdir)
logger.log_hyperparams(Namespace(some_str='a_str', an_int=1, a_float=2.0))
logger.log_hyperparams(hparams)
logger.save()
# load hparams
path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(path_expt_dir,
TensorBoardLogger.NAME_HPARAMS_FILE)
hparams = load_hparams_from_yaml(hparams_path)
# save as legacy meta_tags.csv
tags_path = os.path.join(path_expt_dir, 'meta_tags.csv')
save_hparams_to_tags_csv(tags_path, hparams)
tags = load_hparams_from_tags_csv(tags_path)
assert hparams == tags
def test_no_val_end_module(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir)
)
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
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, 'hparams.yaml')
model_2 = EvalModelTemplate.load_from_checkpoint(
checkpoint_path=new_weights_path,
hparams_file=hparams_path
)
model_2.eval()
def test_no_val_module(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
trainer = Trainer(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
# fit model
result = trainer.fit(model)
# training 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)
# assert ckpt has hparams
ckpt = torch.load(new_weights_path)
assert CHECKPOINT_KEY_MODULE_ARGS in ckpt.keys(
), 'module_arguments missing from checkpoints'
# load new model
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, 'hparams.yaml')
model_2 = EvalModelTemplate.load_from_checkpoint(
checkpoint_path=new_weights_path, hparams_file=hparams_path)
model_2.eval()
def test_model_saving_loading(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_testtube_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'
# make a prediction
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
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(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_checkpoint(
checkpoint_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
def test_running_test_pretrained_model_distrib(tmpdir, backend):
"""Verify `test()` on pretrained model."""
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
progress_bar_refresh_rate=0,
max_epochs=2,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend=backend,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger,
trainer.checkpoint_callback.dirpath,
module_class=LightningTestModel)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tutils.run_prediction(dataloader, pretrained_model)
def test_loading_meta_tags(tmpdir):
hparams = tutils.get_default_hparams()
# save tags
logger = tutils.get_default_logger(tmpdir)
logger.log_hyperparams(Namespace(some_str='a_str', an_int=1, a_float=2.0))
logger.log_hyperparams(hparams)
logger.save()
# load tags
path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir)
tags_path = os.path.join(path_expt_dir, 'meta_tags.csv')
tags = load_hparams_from_tags_csv(tags_path)
assert tags.batch_size == 32 and tags.hidden_dim == 1000
def test_loading_yaml(tmpdir):
tutils.reset_seed()
hparams = EvalModelTemplate.get_default_hparams()
# save tags
logger = tutils.get_default_logger(tmpdir)
logger.log_hyperparams(Namespace(some_str='a_str', an_int=1, a_float=2.0))
logger.log_hyperparams(hparams)
logger.save()
# load hparams
path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(path_expt_dir, 'hparams.yaml')
tags = load_hparams_from_yaml(hparams_path)
assert tags['batch_size'] == 32 and tags['hidden_dim'] == 1000
