lightning/tests/trainer/properties/log_dir.py

# Copyright The PyTorch Lightning 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 os

from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from tests.base.boring_model import BoringModel


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_logdir(tmpdir):
    """
    Tests that the path is correct when checkpoint and loggers are used
    """
    expected = os.path.join(tmpdir, 'lightning_logs', 'version_0')

    model = TestModel(expected)

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=2,
        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
    )

    assert trainer.log_dir == expected
    trainer.fit(model)
    assert trainer.log_dir == expected


def test_logdir_no_checkpoint_cb(tmpdir):
    """
    Tests that the path is correct with no checkpoint
    """
    expected = os.path.join(tmpdir, 'lightning_logs', 'version_0')
    model = TestModel(expected)

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=2,
        checkpoint_callback=False
    )

    assert trainer.log_dir == expected
    trainer.fit(model)
    assert trainer.log_dir == expected


def test_logdir_no_logger(tmpdir):
    """
    Tests that the path is correct even when there is no logger
    """
    expected = os.path.join(tmpdir)
    model = TestModel(expected)

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=2,
        logger=False,
        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
    )

    assert trainer.log_dir == expected
    trainer.fit(model)
    assert trainer.log_dir == expected


def test_logdir_no_logger_no_checkpoint(tmpdir):
    """
    Tests that the path is correct even when there is no logger
    """
    expected = os.path.join(tmpdir)
    model = TestModel(expected)

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=2,
        logger=False,
        checkpoint_callback=False
    )

    assert trainer.log_dir == expected
    trainer.fit(model)
    assert trainer.log_dir == expected


def test_logdir_custom_callback(tmpdir):
    """
    Tests that the path is correct even when there is a custom callback
    """
    expected = os.path.join(tmpdir, 'lightning_logs', 'version_0')
    model = TestModel(expected)

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=2,
        callbacks=[ModelCheckpoint(dirpath=os.path.join(tmpdir, 'ckpts'))],
    )

    assert trainer.log_dir == expected
    trainer.fit(model)
    assert trainer.log_dir == expected


def test_logdir_custom_logger(tmpdir):
    """
    Tests that the path is correct even when there is a custom logger
    """
    expected = os.path.join(tmpdir, 'custom_logs', 'version_0')
    model = TestModel(expected)

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=2,
        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
        logger=TensorBoardLogger(save_dir=tmpdir, name='custom_logs')
    )

    assert trainer.log_dir == expected
    trainer.fit(model)
    assert trainer.log_dir == expected