139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.loggers import LoggerCollection, TensorBoardLogger
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from tests.helpers.boring_model import BoringModel
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class TestModel(BoringModel):
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def __init__(self, expected_log_dir):
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super().__init__()
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self.expected_log_dir = expected_log_dir
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def training_step(self, *args, **kwargs):
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assert self.trainer.log_dir == self.expected_log_dir
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return super().training_step(*args, **kwargs)
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def test_logdir(tmpdir):
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"""
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Tests that the path is correct when checkpoint and loggers are used
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"""
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expected = os.path.join(tmpdir, "lightning_logs", "version_0")
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model = TestModel(expected)
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trainer = Trainer(default_root_dir=tmpdir, max_steps=2, callbacks=[ModelCheckpoint(dirpath=tmpdir)])
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assert trainer.log_dir == expected
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trainer.fit(model)
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assert trainer.log_dir == expected
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def test_logdir_no_checkpoint_cb(tmpdir):
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"""
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Tests that the path is correct with no checkpoint
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"""
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expected = os.path.join(tmpdir, "lightning_logs", "version_0")
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model = TestModel(expected)
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trainer = Trainer(default_root_dir=tmpdir, max_steps=2, checkpoint_callback=False)
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assert trainer.log_dir == expected
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trainer.fit(model)
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assert trainer.log_dir == expected
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def test_logdir_no_logger(tmpdir):
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"""
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Tests that the path is correct even when there is no logger
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"""
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expected = os.path.join(tmpdir)
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model = TestModel(expected)
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trainer = Trainer(default_root_dir=tmpdir, max_steps=2, logger=False, callbacks=[ModelCheckpoint(dirpath=tmpdir)])
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assert trainer.log_dir == expected
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trainer.fit(model)
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assert trainer.log_dir == expected
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def test_logdir_no_logger_no_checkpoint(tmpdir):
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"""
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Tests that the path is correct even when there is no logger
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"""
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expected = os.path.join(tmpdir)
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model = TestModel(expected)
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trainer = Trainer(default_root_dir=tmpdir, max_steps=2, logger=False, checkpoint_callback=False)
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assert trainer.log_dir == expected
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trainer.fit(model)
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assert trainer.log_dir == expected
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def test_logdir_custom_callback(tmpdir):
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"""
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Tests that the path is correct even when there is a custom callback
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"""
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expected = os.path.join(tmpdir, "lightning_logs", "version_0")
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model = TestModel(expected)
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trainer = Trainer(
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default_root_dir=tmpdir, max_steps=2, callbacks=[ModelCheckpoint(dirpath=os.path.join(tmpdir, "ckpts"))]
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)
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assert trainer.log_dir == expected
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trainer.fit(model)
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assert trainer.log_dir == expected
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def test_logdir_custom_logger(tmpdir):
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"""
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Tests that the path is correct even when there is a custom logger
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"""
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expected = os.path.join(tmpdir, "custom_logs", "version_0")
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model = TestModel(expected)
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_steps=2,
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callbacks=[ModelCheckpoint(dirpath=tmpdir)],
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logger=TensorBoardLogger(save_dir=tmpdir, name="custom_logs"),
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)
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assert trainer.log_dir == expected
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trainer.fit(model)
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assert trainer.log_dir == expected
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def test_logdir_logger_collection(tmpdir):
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"""Tests that the logdir equals the default_root_dir when the logger is a LoggerCollection"""
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default_root_dir = tmpdir / "default_root_dir"
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save_dir = tmpdir / "save_dir"
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model = TestModel(default_root_dir)
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trainer = Trainer(
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default_root_dir=default_root_dir,
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max_steps=2,
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logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs")],
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)
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assert isinstance(trainer.logger, LoggerCollection)
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assert trainer.log_dir == default_root_dir
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trainer.fit(model)
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assert trainer.log_dir == default_root_dir
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