335 lines
11 KiB
Python
335 lines
11 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 logging
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import operator
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import os
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from argparse import Namespace
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from unittest import mock
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import numpy as np
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import pytest
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import torch
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import yaml
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from omegaconf import OmegaConf
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from pytorch_lightning import Trainer
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from pytorch_lightning.loggers import TensorBoardLogger
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from pytorch_lightning.utilities.imports import _compare_version
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from tests.helpers import BoringModel
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@pytest.mark.skipif(
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_compare_version("tensorboard", operator.ge, "2.6.0"), reason="cannot import EventAccumulator in >= 2.6.0"
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)
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def test_tensorboard_hparams_reload(tmpdir):
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from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
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class CustomModel(BoringModel):
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def __init__(self, b1=0.5, b2=0.999):
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super().__init__()
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self.save_hyperparameters()
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trainer = Trainer(max_steps=1, default_root_dir=tmpdir)
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model = CustomModel()
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assert trainer.log_dir == trainer.logger.log_dir
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trainer.fit(model)
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assert trainer.log_dir == trainer.logger.log_dir
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folder_path = trainer.log_dir
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# make sure yaml is there
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with open(os.path.join(folder_path, "hparams.yaml")) as file:
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# The FullLoader parameter handles the conversion from YAML
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# scalar values to Python the dictionary format
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yaml_params = yaml.safe_load(file)
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assert yaml_params["b1"] == 0.5
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assert yaml_params["b2"] == 0.999
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assert len(yaml_params.keys()) == 2
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# verify artifacts
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assert len(os.listdir(os.path.join(folder_path, "checkpoints"))) == 1
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# verify tb logs
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event_acc = EventAccumulator(folder_path)
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event_acc.Reload()
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hparams_data = b'\x12\x1f"\x06\n\x02b1 \x03"\x06\n\x02b2 \x03*\r\n\x0b\x12\thp_metric'
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assert event_acc.summary_metadata["_hparams_/experiment"].plugin_data.plugin_name == "hparams"
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assert event_acc.summary_metadata["_hparams_/experiment"].plugin_data.content == hparams_data
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def test_tensorboard_automatic_versioning(tmpdir):
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"""Verify that automatic versioning works."""
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root_dir = tmpdir / "tb_versioning"
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root_dir.mkdir()
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(root_dir / "version_0").mkdir()
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(root_dir / "version_1").mkdir()
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logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning")
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assert logger.version == 2
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def test_tensorboard_manual_versioning(tmpdir):
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"""Verify that manual versioning works."""
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root_dir = tmpdir / "tb_versioning"
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root_dir.mkdir()
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(root_dir / "version_0").mkdir()
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(root_dir / "version_1").mkdir()
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(root_dir / "version_2").mkdir()
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logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning", version=1)
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assert logger.version == 1
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def test_tensorboard_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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name = "tb_versioning"
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(tmpdir / name).mkdir()
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expected_version = "2020-02-05-162402"
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logger = TensorBoardLogger(save_dir=tmpdir, name=name, version=expected_version)
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logger.log_hyperparams({"a": 1, "b": 2, 123: 3, 3.5: 4, 5j: 5}) # Force data to be written
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assert logger.version == expected_version
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assert os.listdir(tmpdir / name) == [expected_version]
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assert os.listdir(tmpdir / name / expected_version)
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@pytest.mark.parametrize("name", ["", None])
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def test_tensorboard_no_name(tmpdir, name):
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"""Verify that None or empty name works."""
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logger = TensorBoardLogger(save_dir=tmpdir, name=name)
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logger.log_hyperparams({"a": 1, "b": 2, 123: 3, 3.5: 4, 5j: 5}) # Force data to be written
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assert logger.root_dir == tmpdir
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assert os.listdir(tmpdir / "version_0")
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@mock.patch.dict(os.environ, {})
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def test_tensorboard_log_sub_dir(tmpdir):
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class TestLogger(TensorBoardLogger):
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# for reproducibility
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@property
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def version(self):
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return "version"
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@property
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def name(self):
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return "name"
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trainer_args = dict(default_root_dir=tmpdir, max_steps=1)
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# no sub_dir specified
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save_dir = tmpdir / "logs"
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logger = TestLogger(save_dir)
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trainer = Trainer(**trainer_args, logger=logger)
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assert trainer.logger.log_dir == os.path.join(save_dir, "name", "version")
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# sub_dir specified
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logger = TestLogger(save_dir, sub_dir="sub_dir")
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trainer = Trainer(**trainer_args, logger=logger)
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assert trainer.logger.log_dir == os.path.join(save_dir, "name", "version", "sub_dir")
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# test home dir (`~`) handling
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save_dir = "~/tmp"
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explicit_save_dir = os.path.expanduser(save_dir)
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logger = TestLogger(save_dir, sub_dir="sub_dir")
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trainer = Trainer(**trainer_args, logger=logger)
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assert trainer.logger.log_dir == os.path.join(explicit_save_dir, "name", "version", "sub_dir")
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# test env var (`$`) handling
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test_env_dir = "some_directory"
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os.environ["test_env_dir"] = test_env_dir
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save_dir = "$test_env_dir/tmp"
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explicit_save_dir = f"{test_env_dir}/tmp"
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logger = TestLogger(save_dir, sub_dir="sub_dir")
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trainer = Trainer(**trainer_args, logger=logger)
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assert trainer.logger.log_dir == os.path.join(explicit_save_dir, "name", "version", "sub_dir")
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@pytest.mark.parametrize("step_idx", [10, None])
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def test_tensorboard_log_metrics(tmpdir, step_idx):
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logger = TensorBoardLogger(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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def test_tensorboard_log_hyperparams(tmpdir):
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logger = TensorBoardLogger(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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"namespace": Namespace(foo=Namespace(bar="buzz")),
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"layer": torch.nn.BatchNorm1d,
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"tensor": torch.empty(2, 2, 2),
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"array": np.empty([2, 2, 2]),
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}
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logger.log_hyperparams(hparams)
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def test_tensorboard_log_hparams_and_metrics(tmpdir):
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logger = TensorBoardLogger(tmpdir, default_hp_metric=False)
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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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"namespace": Namespace(foo=Namespace(bar="buzz")),
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"layer": torch.nn.BatchNorm1d,
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"tensor": torch.empty(2, 2, 2),
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"array": np.empty([2, 2, 2]),
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}
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metrics = {"abc": torch.tensor([0.54])}
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logger.log_hyperparams(hparams, metrics)
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def test_tensorboard_log_omegaconf_hparams_and_metrics(tmpdir):
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logger = TensorBoardLogger(tmpdir, default_hp_metric=False)
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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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# "namespace": Namespace(foo=Namespace(bar="buzz")),
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# "layer": torch.nn.BatchNorm1d,
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}
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hparams = OmegaConf.create(hparams)
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metrics = {"abc": torch.tensor([0.54])}
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logger.log_hyperparams(hparams, metrics)
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@pytest.mark.parametrize("example_input_array", [None, torch.rand(2, 32)])
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def test_tensorboard_log_graph(tmpdir, example_input_array):
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"""test that log graph works with both model.example_input_array and if array is passed externaly."""
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model = BoringModel()
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if example_input_array is not None:
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model.example_input_array = None
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logger = TensorBoardLogger(tmpdir, log_graph=True)
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logger.log_graph(model, example_input_array)
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def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
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"""test that log graph throws warning if model.example_input_array is None."""
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model = BoringModel()
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model.example_input_array = None
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logger = TensorBoardLogger(tmpdir, log_graph=True)
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with pytest.warns(
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UserWarning,
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match="Could not log computational graph since the `model.example_input_array`"
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" attribute is not set or `input_array` was not given",
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):
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logger.log_graph(model)
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@mock.patch("pytorch_lightning.loggers.TensorBoardLogger.log_metrics")
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def test_tensorboard_with_accummulated_gradients(mock_log_metrics, tmpdir):
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"""Tests to ensure that tensorboard log properly when accumulated_gradients > 1."""
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.indexes = []
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def training_step(self, *args):
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self.log("foo", 1, on_step=True, on_epoch=True)
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if not self.trainer.fit_loop._should_accumulate():
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if self.trainer.logger_connector.should_update_logs:
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self.indexes.append(self.trainer.global_step)
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return super().training_step(*args)
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model = TestModel()
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model.training_epoch_end = None
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logger_0 = TensorBoardLogger(tmpdir, default_hp_metric=False)
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=12,
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limit_val_batches=0,
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max_epochs=3,
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accumulate_grad_batches=2,
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logger=[logger_0],
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log_every_n_steps=3,
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)
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trainer.fit(model)
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calls = [m[2] for m in mock_log_metrics.mock_calls]
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count_epochs = [c["step"] for c in calls if "foo_epoch" in c["metrics"]]
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assert count_epochs == [5, 11, 17]
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count_steps = [c["step"] for c in calls if "foo_step" in c["metrics"]]
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assert count_steps == model.indexes
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@mock.patch("pytorch_lightning.loggers.tensorboard.SummaryWriter")
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def test_tensorboard_finalize(summary_writer, tmpdir):
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"""Test that the SummaryWriter closes in finalize."""
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logger = TensorBoardLogger(save_dir=tmpdir)
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logger.finalize("any")
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summary_writer().flush.assert_called()
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summary_writer().close.assert_called()
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def test_tensorboard_save_hparams_to_yaml_once(tmpdir):
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model = BoringModel()
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logger = TensorBoardLogger(save_dir=tmpdir, default_hp_metric=False)
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trainer = Trainer(max_steps=1, default_root_dir=tmpdir, logger=logger)
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assert trainer.log_dir == trainer.logger.log_dir
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trainer.fit(model)
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hparams_file = "hparams.yaml"
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assert os.path.isfile(os.path.join(trainer.log_dir, hparams_file))
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assert not os.path.isfile(os.path.join(tmpdir, hparams_file))
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@mock.patch("pytorch_lightning.loggers.tensorboard.log")
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def test_tensorboard_with_symlink(log, tmpdir):
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"""Tests a specific failure case when tensorboard logger is used with empty name, symbolic link ``save_dir``,
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and relative paths."""
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os.chdir(tmpdir) # need to use relative paths
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source = os.path.join(".", "lightning_logs")
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dest = os.path.join(".", "sym_lightning_logs")
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os.makedirs(source, exist_ok=True)
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os.symlink(source, dest)
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logger = TensorBoardLogger(save_dir=dest, name="")
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_ = logger.version
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log.warning.assert_not_called()
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def test_tensorboard_missing_folder_warning(tmpdir, caplog):
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"""Verify that the logger throws a warning for invalid directory."""
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name = "fake_dir"
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logger = TensorBoardLogger(save_dir=tmpdir, name=name)
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with caplog.at_level(logging.WARNING):
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assert logger.version == 0
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assert "Missing logger folder:" in caplog.text
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