from argparse import Namespace import pytest import torch from pytorch_lightning.loggers import TensorBoardLogger def test_tensorboard_automatic_versioning(tmpdir): """Verify that automatic versioning works""" root_dir = tmpdir.mkdir("tb_versioning") root_dir.mkdir("version_0") root_dir.mkdir("version_1") logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning") assert logger.version == 2 def test_tensorboard_manual_versioning(tmpdir): """Verify that manual versioning works""" root_dir = tmpdir.mkdir("tb_versioning") root_dir.mkdir("version_0") root_dir.mkdir("version_1") root_dir.mkdir("version_2") logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning", version=1) assert logger.version == 1 def test_tensorboard_named_version(tmpdir): """Verify that manual versioning works for string versions, e.g. '2020-02-05-162402' """ tmpdir.mkdir("tb_versioning") expected_version = "2020-02-05-162402" logger = TensorBoardLogger(save_dir=tmpdir, name="tb_versioning", version=expected_version) logger.log_hyperparams({"a": 1, "b": 2}) # Force data to be written assert logger.version == expected_version # Could also test existence of the directory but this fails # in the "minimum requirements" test setup @pytest.mark.parametrize("name", ['', None]) def test_tensorboard_no_name(tmpdir, name): """Verify that None or empty name works""" logger = TensorBoardLogger(save_dir=tmpdir, name=name) assert logger.root_dir == tmpdir @pytest.mark.parametrize("step_idx", [10, None]) def test_tensorboard_log_metrics(tmpdir, step_idx): logger = TensorBoardLogger(tmpdir) metrics = { "float": 0.3, "int": 1, "FloatTensor": torch.tensor(0.1), "IntTensor": torch.tensor(1) } logger.log_metrics(metrics, step_idx) def test_tensorboard_log_hyperparams(tmpdir): logger = TensorBoardLogger(tmpdir) hparams = { "float": 0.3, "int": 1, "string": "abc", "bool": True, "dict": {'a': {'b': 'c'}}, "list": [1, 2, 3], "namespace": Namespace(foo=Namespace(bar='buzz')), "layer": torch.nn.BatchNorm1d } logger.log_hyperparams(hparams) def test_tensorboard_log_hparams_and_metrics(tmpdir): logger = TensorBoardLogger(tmpdir) hparams = { "float": 0.3, "int": 1, "string": "abc", "bool": True, "dict": {'a': {'b': 'c'}}, "list": [1, 2, 3], "namespace": Namespace(foo=Namespace(bar='buzz')), "layer": torch.nn.BatchNorm1d } metrics = {'abc': torch.tensor([0.54])} logger.log_hyperparams(hparams, metrics)