129 lines
4.2 KiB
Python
129 lines
4.2 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 unittest.mock import MagicMock
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import pytest
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.core.saving import load_hparams_from_yaml
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from pytorch_lightning.loggers import CSVLogger
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from pytorch_lightning.loggers.csv_logs import ExperimentWriter
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from tests.helpers.datamodules import ClassifDataModule
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from tests.helpers.simple_models import ClassificationModel
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def test_file_logger_automatic_versioning(tmpdir):
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"""Verify that automatic versioning works."""
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root_dir = tmpdir.mkdir("exp")
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root_dir.mkdir("version_0")
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root_dir.mkdir("version_1")
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logger = CSVLogger(save_dir=tmpdir, name="exp")
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assert logger.version == 2
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def test_file_logger_manual_versioning(tmpdir):
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"""Verify that manual versioning works."""
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root_dir = tmpdir.mkdir("exp")
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root_dir.mkdir("version_0")
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root_dir.mkdir("version_1")
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root_dir.mkdir("version_2")
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logger = CSVLogger(save_dir=tmpdir, name="exp", version=1)
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assert logger.version == 1
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def test_file_logger_named_version(tmpdir):
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"""Verify that manual versioning works for string versions, e.g. '2020-02-05-162402'."""
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exp_name = "exp"
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tmpdir.mkdir(exp_name)
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expected_version = "2020-02-05-162402"
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logger = CSVLogger(save_dir=tmpdir, name=exp_name, version=expected_version)
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logger.log_hyperparams({"a": 1, "b": 2})
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logger.save()
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assert logger.version == expected_version
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assert os.listdir(tmpdir / exp_name) == [expected_version]
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assert os.listdir(tmpdir / exp_name / expected_version)
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@pytest.mark.parametrize("name", ["", None])
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def test_file_logger_no_name(tmpdir, name):
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"""Verify that None or empty name works."""
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logger = CSVLogger(save_dir=tmpdir, name=name)
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logger.save()
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assert os.path.normpath(logger.root_dir) == tmpdir # use os.path.normpath to handle trailing /
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assert os.listdir(tmpdir / "version_0")
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@pytest.mark.parametrize("step_idx", [10, None])
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def test_file_logger_log_metrics(tmpdir, step_idx):
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logger = CSVLogger(tmpdir)
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metrics = {"float": 0.3, "int": 1, "FloatTensor": torch.tensor(0.1), "IntTensor": torch.tensor(1)}
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logger.log_metrics(metrics, step_idx)
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logger.save()
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path_csv = os.path.join(logger.log_dir, ExperimentWriter.NAME_METRICS_FILE)
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with open(path_csv) as fp:
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lines = fp.readlines()
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assert len(lines) == 2
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assert all(n in lines[0] for n in metrics)
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def test_file_logger_log_hyperparams(tmpdir):
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logger = CSVLogger(tmpdir)
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hparams = {
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"float": 0.3,
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"int": 1,
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"string": "abc",
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"bool": True,
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"dict": {"a": {"b": "c"}},
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"list": [1, 2, 3],
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"layer": torch.nn.BatchNorm1d,
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}
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logger.log_hyperparams(hparams)
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logger.save()
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path_yaml = os.path.join(logger.log_dir, ExperimentWriter.NAME_HPARAMS_FILE)
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params = load_hparams_from_yaml(path_yaml)
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assert all(n in params for n in hparams)
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def test_fit_csv_logger(tmpdir):
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dm = ClassifDataModule()
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model = ClassificationModel()
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logger = CSVLogger(save_dir=tmpdir)
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trainer = Trainer(default_root_dir=tmpdir, max_steps=10, logger=logger, log_every_n_steps=1)
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trainer.fit(model, datamodule=dm)
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metrics_file = os.path.join(logger.log_dir, ExperimentWriter.NAME_METRICS_FILE)
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assert os.path.isfile(metrics_file)
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def test_flush_n_steps(tmpdir):
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logger = CSVLogger(tmpdir, flush_logs_every_n_steps=2)
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metrics = {"float": 0.3, "int": 1, "FloatTensor": torch.tensor(0.1), "IntTensor": torch.tensor(1)}
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logger.save = MagicMock()
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logger.log_metrics(metrics, step=0)
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logger.save.assert_not_called()
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logger.log_metrics(metrics, step=1)
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logger.save.assert_called_once()
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