127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
import pickle
|
|
from unittest.mock import patch, MagicMock
|
|
|
|
import torch
|
|
|
|
import tests.base.utils as tutils
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.loggers import NeptuneLogger
|
|
from tests.base import LightningTestModel
|
|
|
|
|
|
def test_neptune_logger(tmpdir):
|
|
"""Verify that basic functionality of neptune logger works."""
|
|
tutils.reset_seed()
|
|
|
|
hparams = tutils.get_default_hparams()
|
|
model = LightningTestModel(hparams)
|
|
logger = NeptuneLogger(offline_mode=True)
|
|
|
|
trainer_options = dict(
|
|
default_save_path=tmpdir,
|
|
max_epochs=1,
|
|
train_percent_check=0.05,
|
|
logger=logger
|
|
)
|
|
trainer = Trainer(**trainer_options)
|
|
result = trainer.fit(model)
|
|
|
|
assert result == 1, 'Training failed'
|
|
|
|
|
|
@patch('pytorch_lightning.loggers.neptune.neptune')
|
|
def test_neptune_online(neptune):
|
|
logger = NeptuneLogger(api_key='test', project_name='project')
|
|
neptune.init.assert_called_once_with(api_token='test', project_qualified_name='project')
|
|
|
|
assert logger.name == neptune.create_experiment().name
|
|
assert logger.version == neptune.create_experiment().id
|
|
|
|
|
|
@patch('pytorch_lightning.loggers.neptune.neptune')
|
|
def test_neptune_additional_methods(neptune):
|
|
logger = NeptuneLogger(offline_mode=True)
|
|
|
|
logger.log_metric('test', torch.ones(1))
|
|
neptune.create_experiment().log_metric.assert_called_once_with('test', torch.ones(1))
|
|
neptune.create_experiment().log_metric.reset_mock()
|
|
|
|
logger.log_metric('test', 1.0)
|
|
neptune.create_experiment().log_metric.assert_called_once_with('test', 1.0)
|
|
neptune.create_experiment().log_metric.reset_mock()
|
|
|
|
logger.log_metric('test', 1.0, step=2)
|
|
neptune.create_experiment().log_metric.assert_called_once_with('test', x=2, y=1.0)
|
|
neptune.create_experiment().log_metric.reset_mock()
|
|
|
|
logger.log_text('test', 'text')
|
|
neptune.create_experiment().log_metric.assert_called_once_with('test', 'text')
|
|
neptune.create_experiment().log_metric.reset_mock()
|
|
|
|
logger.log_image('test', 'image file')
|
|
neptune.create_experiment().log_image.assert_called_once_with('test', 'image file')
|
|
neptune.create_experiment().log_image.reset_mock()
|
|
|
|
logger.log_image('test', 'image file', step=2)
|
|
neptune.create_experiment().log_image.assert_called_once_with('test', x=2, y='image file')
|
|
neptune.create_experiment().log_image.reset_mock()
|
|
|
|
logger.log_artifact('file')
|
|
neptune.create_experiment().log_artifact.assert_called_once_with('file', None)
|
|
|
|
logger.set_property('property', 10)
|
|
neptune.create_experiment().set_property.assert_called_once_with('property', 10)
|
|
|
|
logger.append_tags('one tag')
|
|
neptune.create_experiment().append_tags.assert_called_once_with('one tag')
|
|
neptune.create_experiment().append_tags.reset_mock()
|
|
|
|
logger.append_tags(['two', 'tags'])
|
|
neptune.create_experiment().append_tags.assert_called_once_with('two', 'tags')
|
|
|
|
|
|
def test_neptune_pickle(tmpdir):
|
|
"""Verify that pickling trainer with neptune logger works."""
|
|
tutils.reset_seed()
|
|
|
|
logger = NeptuneLogger(offline_mode=True)
|
|
|
|
trainer_options = dict(
|
|
default_save_path=tmpdir,
|
|
max_epochs=1,
|
|
logger=logger
|
|
)
|
|
|
|
trainer = Trainer(**trainer_options)
|
|
pkl_bytes = pickle.dumps(trainer)
|
|
trainer2 = pickle.loads(pkl_bytes)
|
|
trainer2.logger.log_metrics({'acc': 1.0})
|
|
|
|
|
|
def test_neptune_leave_open_experiment_after_fit(tmpdir):
|
|
"""Verify that neptune experiment was closed after training"""
|
|
tutils.reset_seed()
|
|
|
|
hparams = tutils.get_default_hparams()
|
|
model = LightningTestModel(hparams)
|
|
|
|
def _run_training(logger):
|
|
logger._experiment = MagicMock()
|
|
|
|
trainer_options = dict(
|
|
default_save_path=tmpdir,
|
|
max_epochs=1,
|
|
train_percent_check=0.05,
|
|
logger=logger
|
|
)
|
|
trainer = Trainer(**trainer_options)
|
|
trainer.fit(model)
|
|
return logger
|
|
|
|
logger_close_after_fit = _run_training(NeptuneLogger(offline_mode=True))
|
|
assert logger_close_after_fit._experiment.stop.call_count == 1
|
|
|
|
logger_open_after_fit = _run_training(
|
|
NeptuneLogger(offline_mode=True, close_after_fit=False))
|
|
assert logger_open_after_fit._experiment.stop.call_count == 0
|