134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
# Copyright The Lightning AI team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import pytest
|
|
from lightning.pytorch import Trainer
|
|
from lightning.pytorch.callbacks import ModelCheckpoint
|
|
from lightning.pytorch.demos.boring_classes import BoringModel
|
|
from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger
|
|
|
|
|
|
class TestModel(BoringModel):
|
|
def __init__(self, expected_log_dir):
|
|
super().__init__()
|
|
self.expected_log_dir = expected_log_dir
|
|
|
|
def training_step(self, *args, **kwargs):
|
|
assert self.trainer.log_dir == self.expected_log_dir
|
|
return super().training_step(*args, **kwargs)
|
|
|
|
|
|
def test_log_dir(tmp_path):
|
|
"""Tests that the path is correct when checkpoint and loggers are used."""
|
|
expected = str(tmp_path / "lightning_logs" / "version_0")
|
|
|
|
model = TestModel(expected)
|
|
|
|
trainer = Trainer(default_root_dir=tmp_path, max_steps=2, callbacks=[ModelCheckpoint(dirpath=tmp_path)])
|
|
|
|
assert trainer.log_dir == expected
|
|
trainer.fit(model)
|
|
assert trainer.log_dir == expected
|
|
|
|
|
|
def test_log_dir_no_checkpoint_cb(tmp_path):
|
|
"""Tests that the path is correct with no checkpoint."""
|
|
expected = str(tmp_path / "lightning_logs" / "version_0")
|
|
model = TestModel(expected)
|
|
|
|
trainer = Trainer(default_root_dir=tmp_path, max_steps=2, enable_checkpointing=False)
|
|
|
|
assert trainer.log_dir == expected
|
|
trainer.fit(model)
|
|
assert trainer.log_dir == expected
|
|
|
|
|
|
def test_log_dir_no_logger(tmp_path):
|
|
"""Tests that the path is correct even when there is no logger."""
|
|
expected = str(tmp_path)
|
|
model = TestModel(expected)
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmp_path, max_steps=2, logger=False, callbacks=[ModelCheckpoint(dirpath=tmp_path)]
|
|
)
|
|
|
|
assert trainer.log_dir == expected
|
|
trainer.fit(model)
|
|
assert trainer.log_dir == expected
|
|
|
|
|
|
def test_log_dir_no_logger_no_checkpoint(tmp_path):
|
|
"""Tests that the path is correct even when there is no logger."""
|
|
expected = str(tmp_path)
|
|
model = TestModel(expected)
|
|
|
|
trainer = Trainer(default_root_dir=tmp_path, max_steps=2, logger=False, enable_checkpointing=False)
|
|
|
|
assert trainer.log_dir == expected
|
|
trainer.fit(model)
|
|
assert trainer.log_dir == expected
|
|
|
|
|
|
def test_log_dir_custom_callback(tmp_path):
|
|
"""Tests that the path is correct even when there is a custom callback."""
|
|
expected = str(tmp_path / "lightning_logs" / "version_0")
|
|
model = TestModel(expected)
|
|
|
|
trainer = Trainer(default_root_dir=tmp_path, max_steps=2, callbacks=[ModelCheckpoint(dirpath=(tmp_path / "ckpts"))])
|
|
|
|
assert trainer.log_dir == expected
|
|
trainer.fit(model)
|
|
assert trainer.log_dir == expected
|
|
|
|
|
|
def test_log_dir_custom_logger(tmp_path):
|
|
"""Tests that the path is correct even when there is a custom logger."""
|
|
expected = str(tmp_path / "custom_logs" / "version_0")
|
|
model = TestModel(expected)
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmp_path,
|
|
max_steps=2,
|
|
callbacks=[ModelCheckpoint(dirpath=tmp_path)],
|
|
logger=TensorBoardLogger(save_dir=tmp_path, name="custom_logs"),
|
|
)
|
|
|
|
assert trainer.log_dir == expected
|
|
trainer.fit(model)
|
|
assert trainer.log_dir == expected
|
|
|
|
|
|
def test_log_dir_multiple_loggers(tmp_path):
|
|
"""Tests that the logdir equals the default_root_dir when trainer has multiple loggers."""
|
|
default_root_dir = tmp_path / "default_root_dir"
|
|
save_dir = tmp_path / "save_dir"
|
|
expected = str(tmp_path / "save_dir" / "custom_logs" / "version_0")
|
|
model = TestModel(expected)
|
|
trainer = Trainer(
|
|
default_root_dir=default_root_dir,
|
|
max_steps=2,
|
|
logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs"), CSVLogger(tmp_path)],
|
|
)
|
|
|
|
assert trainer.log_dir == expected
|
|
trainer.fit(model)
|
|
assert trainer.log_dir == expected
|
|
|
|
|
|
@pytest.mark.parametrize("logger_cls", [CSVLogger, TensorBoardLogger])
|
|
def test_log_dir_from_logger_log_dir(logger_cls, tmp_path):
|
|
logger = logger_cls(tmp_path / "log_dir")
|
|
trainer = Trainer(default_root_dir=tmp_path, logger=logger)
|
|
assert trainer.log_dir == logger.log_dir
|