TensorBoardLogger sub_dir parameter for grouping logs (#6195)
* fixed a small typo
* cleaning up
* added sub_dir argument to tensorboard and wrote test
* sub dir arg exclusively for tensorboard, linted
* resolving merge conflict
* resolved merge conflict
* resolved merge conflict
* resolved merge conflict
* resolve merge conflict before revert
* resolving merge conflict
* reverted to pre-lint
* added tensorboard sub_dir test
* pep8 formatting
* removed sub_dir arg from test_all function:
* updated feature description
* typo in doc description
* updated CHANGELOG
* Update pytorch_lightning/loggers/tensorboard.py
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
* swapped argument position
* added expandvars tests
* added expandvars
* removed model init
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix tests
* fix failed test
* Revert "fix failed test"
This reverts commit 50b34c66da.
* add env var to test
* fix typo in tests
* fix tests
* for test consistency
* fix typo
* fix typo 2
Co-authored-by: Ubuntu <azureuser@devhenrik.evuifrmjd4lepbj4relcwwu5va.ax.internal.cloudapp.net>
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roger Shieh <sh.rog@protonmail.ch>
This commit is contained in:
Jan-Henrik Lambrechts
GitHub
parent
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commit
608de6abf4
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@ -20,6 +20,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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- Added `clip_grad_by_value` support for TPUs ([#7025](https://github.com/PyTorchLightning/pytorch-lightning/pull/7025))
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- Added `sub_dir` parameter to `TensorBoardLogger` ([#6195](https://github.com/PyTorchLightning/pytorch-lightning/pull/6195))
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- Added correct `dataloader_idx` to batch transfer hooks ([#6241](https://github.com/PyTorchLightning/pytorch-lightning/pull/6241))
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@ -62,6 +62,9 @@ class TensorBoardLogger(LightningLoggerBase):
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directory for existing versions, then automatically assigns the next available version.
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If it is a string then it is used as the run-specific subdirectory name,
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otherwise ``'version_${version}'`` is used.
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sub_dir: Sub-directory to group TensorBoard logs. If a sub_dir argument is passed
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then logs are saved in ``/save_dir/version/sub_dir/``. Defaults to ``None`` in which
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logs are saved in ``/save_dir/version/``.
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log_graph: Adds the computational graph to tensorboard. This requires that
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the user has defined the `self.example_input_array` attribute in their
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model.
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@ -83,12 +86,14 @@ class TensorBoardLogger(LightningLoggerBase):
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log_graph: bool = False,
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default_hp_metric: bool = True,
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prefix: str = '',
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sub_dir: Optional[str] = None,
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**kwargs
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):
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super().__init__()
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self._save_dir = save_dir
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self._name = name or ''
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self._version = version
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self._sub_dir = sub_dir
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self._log_graph = log_graph
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self._default_hp_metric = default_hp_metric
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self._prefix = prefix
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@ -120,12 +125,20 @@ class TensorBoardLogger(LightningLoggerBase):
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# create a pseudo standard path ala test-tube
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version = self.version if isinstance(self.version, str) else f"version_{self.version}"
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log_dir = os.path.join(self.root_dir, version)
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if isinstance(self.sub_dir, str):
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log_dir = os.path.join(log_dir, self.sub_dir)
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log_dir = os.path.expandvars(log_dir)
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log_dir = os.path.expanduser(log_dir)
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return log_dir
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@property
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def save_dir(self) -> Optional[str]:
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return self._save_dir
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@property
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def sub_dir(self) -> Optional[str]:
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return self._sub_dir
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@property
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@rank_zero_experiment
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def experiment(self) -> SummaryWriter:
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@ -48,8 +48,8 @@ def _get_logger_args(logger_class, save_dir):
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return logger_args
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def _instantiate_logger(logger_class, save_idr, **override_kwargs):
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args = _get_logger_args(logger_class, save_idr)
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def _instantiate_logger(logger_class, save_dir, **override_kwargs):
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args = _get_logger_args(logger_class, save_dir)
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args.update(**override_kwargs)
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logger = logger_class(**args)
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return logger
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@ -368,38 +368,38 @@ def test_logger_with_prefix_all(tmpdir, monkeypatch):
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with mock.patch('pytorch_lightning.loggers.comet.comet_ml'), \
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mock.patch('pytorch_lightning.loggers.comet.CometOfflineExperiment'):
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_patch_comet_atexit(monkeypatch)
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logger = _instantiate_logger(CometLogger, save_idr=tmpdir, prefix=prefix)
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logger = _instantiate_logger(CometLogger, save_dir=tmpdir, prefix=prefix)
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logger.log_metrics({"test": 1.0}, step=0)
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logger.experiment.log_metrics.assert_called_once_with({"tmp-test": 1.0}, epoch=None, step=0)
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# MLflow
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with mock.patch('pytorch_lightning.loggers.mlflow.mlflow'), \
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mock.patch('pytorch_lightning.loggers.mlflow.MlflowClient'):
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logger = _instantiate_logger(MLFlowLogger, save_idr=tmpdir, prefix=prefix)
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logger = _instantiate_logger(MLFlowLogger, save_dir=tmpdir, prefix=prefix)
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logger.log_metrics({"test": 1.0}, step=0)
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logger.experiment.log_metric.assert_called_once_with(ANY, "tmp-test", 1.0, ANY, 0)
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# Neptune
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with mock.patch('pytorch_lightning.loggers.neptune.neptune'):
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logger = _instantiate_logger(NeptuneLogger, save_idr=tmpdir, prefix=prefix)
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logger = _instantiate_logger(NeptuneLogger, save_dir=tmpdir, prefix=prefix)
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logger.log_metrics({"test": 1.0}, step=0)
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logger.experiment.log_metric.assert_called_once_with("tmp-test", 1.0)
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# TensorBoard
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with mock.patch('pytorch_lightning.loggers.tensorboard.SummaryWriter'):
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logger = _instantiate_logger(TensorBoardLogger, save_idr=tmpdir, prefix=prefix)
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logger = _instantiate_logger(TensorBoardLogger, save_dir=tmpdir, prefix=prefix)
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logger.log_metrics({"test": 1.0}, step=0)
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logger.experiment.add_scalar.assert_called_once_with("tmp-test", 1.0, 0)
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# TestTube
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with mock.patch('pytorch_lightning.loggers.test_tube.Experiment'):
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logger = _instantiate_logger(TestTubeLogger, save_idr=tmpdir, prefix=prefix)
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logger = _instantiate_logger(TestTubeLogger, save_dir=tmpdir, prefix=prefix)
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logger.log_metrics({"test": 1.0}, step=0)
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logger.experiment.log.assert_called_once_with({"tmp-test": 1.0}, global_step=0)
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# WandB
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with mock.patch('pytorch_lightning.loggers.wandb.wandb') as wandb:
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logger = _instantiate_logger(WandbLogger, save_idr=tmpdir, prefix=prefix)
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logger = _instantiate_logger(WandbLogger, save_dir=tmpdir, prefix=prefix)
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wandb.run = None
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wandb.init().step = 0
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logger.log_metrics({"test": 1.0}, step=0)
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@ -119,6 +119,51 @@ def test_tensorboard_no_name(tmpdir, name):
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assert os.listdir(tmpdir / "version_0")
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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(
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default_root_dir=tmpdir,
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max_steps=1,
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)
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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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