223 lines
7.6 KiB
Python
223 lines
7.6 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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"""
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Test Tube Logger
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----------------
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"""
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from argparse import Namespace
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from typing import Any, Dict, Optional, Union
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
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from pytorch_lightning.utilities import _module_available
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from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
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_TESTTUBE_AVAILABLE = _module_available("test_tube")
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if _TESTTUBE_AVAILABLE:
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from test_tube import Experiment
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else:
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Experiment = None
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class TestTubeLogger(LightningLoggerBase):
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r"""
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Log to local file system in `TensorBoard <https://www.tensorflow.org/tensorboard>`_ format
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but using a nicer folder structure (see `full docs <https://williamfalcon.github.io/test-tube>`_).
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Install it with pip:
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.. code-block:: bash
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pip install test_tube
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.. code-block:: python
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from pytorch_lightning import Trainer
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from pytorch_lightning.loggers import TestTubeLogger
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logger = TestTubeLogger("tt_logs", name="my_exp_name")
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trainer = Trainer(logger=logger)
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Use the logger anywhere in your :class:`~pytorch_lightning.core.lightning.LightningModule` as follows:
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.. code-block:: python
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from pytorch_lightning import LightningModule
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class LitModel(LightningModule):
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def training_step(self, batch, batch_idx):
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# example
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self.logger.experiment.whatever_method_summary_writer_supports(...)
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def any_lightning_module_function_or_hook(self):
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self.logger.experiment.add_histogram(...)
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Args:
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save_dir: Save directory
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name: Experiment name. Defaults to ``'default'``.
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description: A short snippet about this experiment
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debug: If ``True``, it doesn't log anything.
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version: Experiment version. If version is not specified the logger inspects the save
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directory for existing versions, then automatically assigns the next available version.
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create_git_tag: If ``True`` creates a git tag to save the code used in this experiment.
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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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prefix: A string to put at the beginning of metric keys.
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"""
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__test__ = False
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LOGGER_JOIN_CHAR = '-'
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def __init__(
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self,
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save_dir: str,
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name: str = "default",
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description: Optional[str] = None,
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debug: bool = False,
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version: Optional[int] = None,
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create_git_tag: bool = False,
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log_graph: bool = False,
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prefix: str = '',
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):
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if Experiment is None:
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raise ImportError('You want to use `test_tube` logger which is not installed yet,'
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' install it with `pip install test-tube`.')
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super().__init__()
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self._save_dir = save_dir
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self._name = name
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self.description = description
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self.debug = debug
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self._version = version
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self.create_git_tag = create_git_tag
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self._log_graph = log_graph
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self._prefix = prefix
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self._experiment = None
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@property
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@rank_zero_experiment
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def experiment(self) -> Experiment:
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r"""
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Actual TestTube object. To use TestTube features in your
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:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
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Example::
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self.logger.experiment.some_test_tube_function()
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"""
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if self._experiment is not None:
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return self._experiment
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self._experiment = Experiment(
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save_dir=self.save_dir,
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name=self._name,
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debug=self.debug,
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version=self.version,
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description=self.description,
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create_git_tag=self.create_git_tag,
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rank=rank_zero_only.rank,
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)
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return self._experiment
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@rank_zero_only
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def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
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# TODO: HACK figure out where this is being set to true
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self.experiment.debug = self.debug
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params = self._convert_params(params)
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params = self._flatten_dict(params)
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self.experiment.argparse(Namespace(**params))
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@rank_zero_only
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def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
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# TODO: HACK figure out where this is being set to true
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metrics = self._add_prefix(metrics)
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self.experiment.debug = self.debug
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self.experiment.log(metrics, global_step=step)
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@rank_zero_only
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def log_graph(self, model: LightningModule, input_array=None):
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if self._log_graph:
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if input_array is None:
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input_array = model.example_input_array
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if input_array is not None:
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self.experiment.add_graph(
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model,
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model.transfer_batch_to_device(
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model.example_input_array, model.device)
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)
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else:
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rank_zero_warn('Could not log computational graph since the'
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' `model.example_input_array` attribute is not set'
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' or `input_array` was not given',
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UserWarning)
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@rank_zero_only
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def save(self) -> None:
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super().save()
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# TODO: HACK figure out where this is being set to true
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self.experiment.debug = self.debug
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self.experiment.save()
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@rank_zero_only
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def finalize(self, status: str) -> None:
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super().finalize(status)
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# TODO: HACK figure out where this is being set to true
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self.experiment.debug = self.debug
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self.save()
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self.close()
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@rank_zero_only
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def close(self) -> None:
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super().save()
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# TODO: HACK figure out where this is being set to true
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self.experiment.debug = self.debug
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if not self.debug:
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exp = self.experiment
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exp.close()
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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 name(self) -> str:
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if self._experiment is None:
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return self._name
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else:
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return self.experiment.name
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@property
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def version(self) -> int:
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if self._experiment is None:
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return self._version
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else:
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return self.experiment.version
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# Test tube experiments are not pickleable, so we need to override a few
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# methods to get DDP working. See
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# https://docs.python.org/3/library/pickle.html#handling-stateful-objects
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# for more info.
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def __getstate__(self) -> Dict[Any, Any]:
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state = self.__dict__.copy()
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state["_experiment"] = self.experiment.get_meta_copy()
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return state
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def __setstate__(self, state: Dict[Any, Any]):
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self._experiment = state["_experiment"].get_non_ddp_exp()
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del state["_experiment"]
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self.__dict__.update(state)
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