def _test_loggers_pickle(tmpdir, monkeypatch, logger_class):
"""Verify that pickling trainer with logger works."""
_patch_comet_atexit(monkeypatch)
logger_args = _get_logger_args(logger_class, tmpdir)
logger = logger_class(**logger_args)
# this can cause pickle error if the experiment object is not picklable
# the logger needs to remove it from the state before pickle
_ = logger.experiment
# logger also has to avoid adding un-picklable attributes to self in .save
logger.log_metrics({"a": 1})
logger.save()
# test pickling loggers
pickle.dumps(logger)
trainer = Trainer(max_epochs=1, logger=logger)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
# make sure we restored properly
assert trainer2.logger.name == logger.name
assert trainer2.logger.save_dir == logger.save_dir
def test_loggers_pickle(neptune, tmpdir, monkeypatch, logger_class):
"""Verify that pickling trainer with logger works."""
_patch_comet_atexit(monkeypatch)
logger_args = _get_logger_args(logger_class, tmpdir)
logger = logger_class(**logger_args)
# this can cause pickle error if the experiment object is not picklable
# the logger needs to remove it from the state before pickle
_ = logger.experiment
# test pickling loggers
pickle.dumps(logger)
trainer = Trainer(
max_epochs=1,
logger=logger,
)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({'acc': 1.0})
# make sure we restord properly
assert trainer2.logger.name == logger.name
assert trainer2.logger.save_dir == logger.save_dir
def test_loggers_fit_test_all(tmpdir, monkeypatch):
"""Verify that basic functionality of all loggers."""
_test_loggers_fit_test(tmpdir, TensorBoardLogger)
with mock.patch("pytorch_lightning.loggers.comet.comet_ml"), mock.patch(
"pytorch_lightning.loggers.comet.CometOfflineExperiment"):
_patch_comet_atexit(monkeypatch)
_test_loggers_fit_test(tmpdir, CometLogger)
with mock.patch("pytorch_lightning.loggers.mlflow.mlflow"), mock.patch(
"pytorch_lightning.loggers.mlflow.MlflowClient"):
_test_loggers_fit_test(tmpdir, MLFlowLogger)
with mock.patch("pytorch_lightning.loggers.neptune.neptune",
new_callable=create_neptune_mock):
_test_loggers_fit_test(tmpdir, NeptuneLogger)
with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"):
_test_loggers_fit_test(tmpdir, TestTubeLogger)
with mock.patch("pytorch_lightning.loggers.wandb.wandb") as wandb:
wandb.run = None
wandb.init().step = 0
_test_loggers_fit_test(tmpdir, WandbLogger)
def test_logger_created_on_rank_zero_only(tmpdir, monkeypatch, logger_class):
""" Test that loggers get replaced by dummy loggers on global rank > 0"""
_patch_comet_atexit(monkeypatch)
try:
_test_logger_created_on_rank_zero_only(tmpdir, logger_class)
except (ImportError, ModuleNotFoundError):
pytest.xfail(f"multi-process test requires {logger_class.__class__} dependencies to be installed.")
def test_loggers_pickle_all(tmpdir, monkeypatch, logger_class):
""" Test that the logger objects can be pickled. This test only makes sense if the packages are installed. """
_patch_comet_atexit(monkeypatch)
try:
_test_loggers_pickle(tmpdir, monkeypatch, logger_class)
except (ImportError, ModuleNotFoundError):
pytest.xfail(f"pickle test requires {logger_class.__class__} dependencies to be installed.")
def test_logger_with_prefix_all(tmpdir, monkeypatch):
"""Test that prefix is added at the beginning of the metric keys."""
prefix = "tmp"
# Comet
with mock.patch("pytorch_lightning.loggers.comet.comet_ml"), mock.patch(
"pytorch_lightning.loggers.comet.CometOfflineExperiment"):
_patch_comet_atexit(monkeypatch)
logger = _instantiate_logger(CometLogger,
save_dir=tmpdir,
prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log_metrics.assert_called_once_with(
{"tmp-test": 1.0}, epoch=None, step=0)
# MLflow
with mock.patch("pytorch_lightning.loggers.mlflow.mlflow"), mock.patch(
"pytorch_lightning.loggers.mlflow.MlflowClient"):
logger = _instantiate_logger(MLFlowLogger,
save_dir=tmpdir,
prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log_metric.assert_called_once_with(
ANY, "tmp-test", 1.0, ANY, 0)
# Neptune
with mock.patch("pytorch_lightning.loggers.neptune.neptune"):
logger = _instantiate_logger(NeptuneLogger,
api_key="test",
project="project",
save_dir=tmpdir,
prefix=prefix)
assert logger.experiment.__getitem__.call_count == 2
logger.log_metrics({"test": 1.0}, step=0)
assert logger.experiment.__getitem__.call_count == 3
logger.experiment.__getitem__.assert_called_with("tmp/test")
logger.experiment.__getitem__().log.assert_called_once_with(1.0)
# TensorBoard
with mock.patch("pytorch_lightning.loggers.tensorboard.SummaryWriter"):
logger = _instantiate_logger(TensorBoardLogger,
save_dir=tmpdir,
prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.add_scalar.assert_called_once_with(
"tmp-test", 1.0, 0)
# WandB
with mock.patch("pytorch_lightning.loggers.wandb.wandb") as wandb:
logger = _instantiate_logger(WandbLogger,
save_dir=tmpdir,
prefix=prefix)
wandb.run = None
wandb.init().step = 0
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log.assert_called_once_with({
"tmp-test": 1.0,
"trainer/global_step": 0
})
def test_loggers_save_dir_and_weights_save_path_all(tmpdir, monkeypatch, logger_class):
"""Test the combinations of save_dir, weights_save_path and default_root_dir."""
with contextlib.ExitStack() as stack:
for mgr in LOGGER_CTX_MANAGERS:
stack.enter_context(mgr)
_patch_comet_atexit(monkeypatch)
_test_loggers_save_dir_and_weights_save_path(tmpdir, CometLogger)
def test_loggers_save_dir_and_weights_save_path(wandb, tmpdir, monkeypatch,
logger_class):
""" Test the combinations of save_dir, weights_save_path and default_root_dir. """
_patch_comet_atexit(monkeypatch)
class TestLogger(logger_class):
# for this test it does not matter what these attributes are
# so we standardize them to make testing easier
@property
def version(self):
return 'version'
@property
def name(self):
return 'name'
model = EvalModelTemplate()
trainer_args = dict(
default_root_dir=tmpdir,
max_steps=1,
)
# no weights_save_path given
save_dir = tmpdir / 'logs'
weights_save_path = None
logger = TestLogger(**_get_logger_args(TestLogger, save_dir))
trainer = Trainer(**trainer_args,
logger=logger,
weights_save_path=weights_save_path)
trainer.fit(model)
assert trainer.weights_save_path == trainer.default_root_dir
assert trainer.checkpoint_callback.dirpath == os.path.join(
logger.save_dir, 'name', 'version', 'checkpoints')
assert trainer.default_root_dir == tmpdir
# with weights_save_path given, the logger path and checkpoint path should be different
save_dir = tmpdir / 'logs'
weights_save_path = tmpdir / 'weights'
logger = TestLogger(**_get_logger_args(TestLogger, save_dir))
trainer = Trainer(**trainer_args,
logger=logger,
weights_save_path=weights_save_path)
trainer.fit(model)
assert trainer.weights_save_path == weights_save_path
assert trainer.logger.save_dir == save_dir
assert trainer.checkpoint_callback.dirpath == weights_save_path / 'name' / 'version' / 'checkpoints'
assert trainer.default_root_dir == tmpdir
# no logger given
weights_save_path = tmpdir / 'weights'
trainer = Trainer(**trainer_args,
logger=False,
weights_save_path=weights_save_path)
trainer.fit(model)
assert trainer.weights_save_path == weights_save_path
assert trainer.checkpoint_callback.dirpath == weights_save_path / 'checkpoints'
assert trainer.default_root_dir == tmpdir
def test_logger_created_on_rank_zero_only(tmpdir, monkeypatch, logger_class):
"""Test that loggers get replaced by dummy loggers on global rank > 0."""
_patch_comet_atexit(monkeypatch)
try:
if logger_class is TestTubeLogger:
with pytest.deprecated_call(match="TestTubeLogger is deprecated since v1.5"):
_test_logger_created_on_rank_zero_only(tmpdir, logger_class)
else:
_test_logger_created_on_rank_zero_only(tmpdir, logger_class)
except (ImportError, ModuleNotFoundError):
pytest.xfail(f"multi-process test requires {logger_class.__class__} dependencies to be installed.")
def test_loggers_pickle_all(tmpdir, monkeypatch, logger_class):
"""Test that the logger objects can be pickled.
This test only makes sense if the packages are installed.
"""
_patch_comet_atexit(monkeypatch)
try:
if logger_class is TestTubeLogger:
with pytest.deprecated_call(match="TestTubeLogger is deprecated since v1.5"):
_test_loggers_pickle(tmpdir, monkeypatch, logger_class)
else:
_test_loggers_pickle(tmpdir, monkeypatch, logger_class)
except (ImportError, ModuleNotFoundError):
pytest.xfail(f"pickle test requires {logger_class.__class__} dependencies to be installed.")
def test_loggers_fit_test(wandb, neptune, tmpdir, monkeypatch, logger_class):
"""Verify that basic functionality of all loggers."""
os.environ['PL_DEV_DEBUG'] = '0'
_patch_comet_atexit(monkeypatch)
model = EvalModelTemplate()
class StoreHistoryLogger(logger_class):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.history = []
def log_metrics(self, metrics, step):
super().log_metrics(metrics, step)
self.history.append((step, metrics))
logger_args = _get_logger_args(logger_class, tmpdir)
logger = StoreHistoryLogger(**logger_args)
if logger_class == WandbLogger:
# required mocks for Trainer
logger.experiment.id = 'foo'
logger.experiment.project_name.return_value = 'bar'
trainer = Trainer(
max_epochs=1,
logger=logger,
limit_train_batches=0.2,
limit_val_batches=0.5,
fast_dev_run=True,
default_root_dir=tmpdir,
)
trainer.fit(model)
trainer.test()
log_metric_names = [(s, sorted(m.keys())) for s, m in logger.history]
if logger_class == TensorBoardLogger:
expected = [(0, ['hp_metric']), (0, ['epoch', 'train_some_val']),
(0, ['early_stop_on', 'epoch', 'val_acc']),
(0, ['hp_metric']), (1, ['epoch', 'test_acc',
'test_loss'])]
assert log_metric_names == expected
else:
expected = [(0, ['epoch', 'train_some_val']),
(0, ['early_stop_on', 'epoch', 'val_acc']),
(1, ['epoch', 'test_acc', 'test_loss'])]
assert log_metric_names == expected
def test_logger_with_prefix_all(tmpdir, monkeypatch):
"""
Test that prefix is added at the beginning of the metric keys.
"""
prefix = 'tmp'
# Comet
with mock.patch('pytorch_lightning.loggers.comet.comet_ml'), \
mock.patch('pytorch_lightning.loggers.comet.CometOfflineExperiment'):
_patch_comet_atexit(monkeypatch)
logger = _instantiate_logger(CometLogger, save_idr=tmpdir, prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log_metrics.assert_called_once_with({"tmp-test": 1.0}, epoch=None, step=0)
# MLflow
with mock.patch('pytorch_lightning.loggers.mlflow.mlflow'), \
mock.patch('pytorch_lightning.loggers.mlflow.MlflowClient'):
logger = _instantiate_logger(MLFlowLogger, save_idr=tmpdir, prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log_metric.assert_called_once_with(ANY, "tmp-test", 1.0, ANY, 0)
# Neptune
with mock.patch('pytorch_lightning.loggers.neptune.neptune'):
logger = _instantiate_logger(NeptuneLogger, save_idr=tmpdir, prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log_metric.assert_called_once_with("tmp-test", x=0, y=1.0)
# TensorBoard
with mock.patch('pytorch_lightning.loggers.tensorboard.SummaryWriter'):
logger = _instantiate_logger(TensorBoardLogger, save_idr=tmpdir, prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.add_scalar.assert_called_once_with("tmp-test", 1.0, 0)
# TestTube
with mock.patch('pytorch_lightning.loggers.test_tube.Experiment'):
logger = _instantiate_logger(TestTubeLogger, save_idr=tmpdir, prefix=prefix)
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log.assert_called_once_with({"tmp-test": 1.0}, global_step=0)
# WandB
with mock.patch('pytorch_lightning.loggers.wandb.wandb') as wandb:
logger = _instantiate_logger(WandbLogger, save_idr=tmpdir, prefix=prefix)
wandb.run = None
wandb.init().step = 0
logger.log_metrics({"test": 1.0}, step=0)
logger.experiment.log.assert_called_once_with({'tmp-test': 1.0}, step=0)
def test_logger_created_on_rank_zero_only(neptune, tmpdir, monkeypatch,
logger_class):
""" Test that loggers get replaced by dummy loggers on global rank > 0"""
_patch_comet_atexit(monkeypatch)
logger_args = _get_logger_args(logger_class, tmpdir)
logger = logger_class(**logger_args)
model = EvalModelTemplate()
trainer = Trainer(
logger=logger,
default_root_dir=tmpdir,
distributed_backend='ddp_cpu',
num_processes=2,
max_steps=1,
checkpoint_callback=True,
callbacks=[RankZeroLoggerCheck()],
)
result = trainer.fit(model)
assert result == 1
def test_loggers_save_dir_and_weights_save_path_all(tmpdir, monkeypatch):
"""Test the combinations of save_dir, weights_save_path and default_root_dir."""
_test_loggers_save_dir_and_weights_save_path(tmpdir, TensorBoardLogger)
with mock.patch("pytorch_lightning.loggers.comet.comet_ml"), mock.patch(
"pytorch_lightning.loggers.comet.CometOfflineExperiment"):
_patch_comet_atexit(monkeypatch)
_test_loggers_save_dir_and_weights_save_path(tmpdir, CometLogger)
with mock.patch("pytorch_lightning.loggers.mlflow.mlflow"), mock.patch(
"pytorch_lightning.loggers.mlflow.MlflowClient"):
_test_loggers_save_dir_and_weights_save_path(tmpdir, MLFlowLogger)
with mock.patch("pytorch_lightning.loggers.test_tube.Experiment"):
_test_loggers_save_dir_and_weights_save_path(tmpdir, TestTubeLogger)
with mock.patch("pytorch_lightning.loggers.wandb.wandb"):
_test_loggers_save_dir_and_weights_save_path(tmpdir, WandbLogger)
def test_loggers_fit_test_all(tmpdir, monkeypatch):
""" Verify that basic functionality of all loggers. """
_test_loggers_fit_test(tmpdir, TensorBoardLogger)
with mock.patch('pytorch_lightning.loggers.comet.comet_ml'), \
mock.patch('pytorch_lightning.loggers.comet.CometOfflineExperiment'):
_patch_comet_atexit(monkeypatch)
_test_loggers_fit_test(tmpdir, CometLogger)
with mock.patch('pytorch_lightning.loggers.mlflow.mlflow'), \
mock.patch('pytorch_lightning.loggers.mlflow.MlflowClient'):
_test_loggers_fit_test(tmpdir, MLFlowLogger)
with mock.patch('pytorch_lightning.loggers.neptune.neptune'):
_test_loggers_fit_test(tmpdir, NeptuneLogger)
with mock.patch('pytorch_lightning.loggers.test_tube.Experiment'):
_test_loggers_fit_test(tmpdir, TestTubeLogger)
with mock.patch('pytorch_lightning.loggers.wandb.wandb'):
_test_loggers_fit_test(tmpdir, WandbLogger)