mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-22 13:30:11 +08:00
Jirka Borovec
b3fe17ddeb
* flushing loggers * flushing loggers * flushing loggers * flushing loggers * changelog * typo * fix trains * optimize imports * add logger test all * add logger test pickle * flake8 * fix benchmark * hanging loggers * try * del * all * cleaning
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
from argparse import Namespace
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import pytest
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import torch
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from pytorch_lightning.loggers import TensorBoardLogger
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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.mkdir("tb_versioning")
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root_dir.mkdir("version_0")
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root_dir.mkdir("version_1")
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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.mkdir("tb_versioning")
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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 = 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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tmpdir.mkdir("tb_versioning")
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expected_version = "2020-02-05-162402"
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logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning", version=expected_version)
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logger.log_hyperparams({"a": 1, "b": 2}) # Force data to be written
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assert logger.version == expected_version
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# Could also test existence of the directory but this fails
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# in the "minimum requirements" test setup
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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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assert logger.root_dir == tmpdir
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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 = {
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"float": 0.3,
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"int": 1,
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"FloatTensor": torch.tensor(0.1),
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"IntTensor": torch.tensor(1)
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}
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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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}
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logger.log_hyperparams(hparams)
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