262 lines
9.5 KiB
Python
262 lines
9.5 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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TensorBoard Logger
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------------------
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"""
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import os
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from argparse import Namespace
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from typing import Any, Dict, Optional, Union
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from torch.utils.tensorboard.summary import hparams
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from pytorch_lightning import _logger as log
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.core.saving import save_hparams_to_yaml
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from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
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from pytorch_lightning.utilities import rank_zero_only, rank_zero_warn, _OMEGACONF_AVAILABLE
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from pytorch_lightning.utilities.cloud_io import get_filesystem
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if _OMEGACONF_AVAILABLE:
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from omegaconf import Container, OmegaConf
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class TensorBoardLogger(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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Implemented using :class:`~torch.utils.tensorboard.SummaryWriter`. Logs are saved to
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``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes
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preinstalled.
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Example:
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.loggers import TensorBoardLogger
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>>> logger = TensorBoardLogger("tb_logs", name="my_model")
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>>> trainer = Trainer(logger=logger)
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Args:
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save_dir: Save directory
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name: Experiment name. Defaults to ``'default'``. If it is the empty string then no per-experiment
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subdirectory is used.
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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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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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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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default_hp_metric: Enables a placeholder metric with key `hp_metric` when `log_hyperparams` is
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called without a metric (otherwise calls to log_hyperparams without a metric are ignored).
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prefix: A string to put at the beginning of metric keys.
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\**kwargs: Additional arguments like `comment`, `filename_suffix`, etc. used by
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:class:`SummaryWriter` can be passed as keyword arguments in this logger.
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"""
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NAME_HPARAMS_FILE = 'hparams.yaml'
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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: Optional[str] = "default",
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version: Optional[Union[int, str]] = None,
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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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**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._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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self._fs = get_filesystem(save_dir)
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self._experiment = None
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self.hparams = {}
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self._kwargs = kwargs
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@property
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def root_dir(self) -> str:
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"""
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Parent directory for all tensorboard checkpoint subdirectories.
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If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
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and the checkpoint will be saved in "save_dir/version_dir"
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"""
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if self.name is None or len(self.name) == 0:
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return self.save_dir
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else:
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return os.path.join(self.save_dir, self.name)
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@property
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def log_dir(self) -> str:
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"""
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The directory for this run's tensorboard checkpoint. By default, it is named
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``'version_${self.version}'`` but it can be overridden by passing a string value
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for the constructor's version parameter instead of ``None`` or an int.
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"""
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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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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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@rank_zero_experiment
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def experiment(self) -> SummaryWriter:
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r"""
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Actual tensorboard object. To use TensorBoard 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_tensorboard_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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assert rank_zero_only.rank == 0, 'tried to init log dirs in non global_rank=0'
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if self.root_dir:
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self._fs.makedirs(self.root_dir, exist_ok=True)
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self._experiment = SummaryWriter(log_dir=self.log_dir, **self._kwargs)
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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],
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metrics: Optional[Dict[str, Any]] = None) -> None:
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params = self._convert_params(params)
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# store params to output
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if _OMEGACONF_AVAILABLE and isinstance(params, Container):
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self.hparams = OmegaConf.merge(self.hparams, params)
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else:
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self.hparams.update(params)
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# format params into the suitable for tensorboard
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params = self._flatten_dict(params)
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params = self._sanitize_params(params)
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if metrics is None:
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if self._default_hp_metric:
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metrics = {"hp_metric": -1}
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elif not isinstance(metrics, dict):
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metrics = {"hp_metric": metrics}
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if metrics:
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self.log_metrics(metrics, 0)
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exp, ssi, sei = hparams(params, metrics)
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writer = self.experiment._get_file_writer()
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writer.add_summary(exp)
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writer.add_summary(ssi)
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writer.add_summary(sei)
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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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assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'
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metrics = self._add_prefix(metrics)
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for k, v in metrics.items():
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if isinstance(v, torch.Tensor):
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v = v.item()
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if isinstance(v, dict):
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self.experiment.add_scalars(k, v, step)
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else:
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try:
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self.experiment.add_scalar(k, v, step)
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# todo: specify the possible exception
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except Exception as ex:
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m = f'\n you tried to log {v} which is not currently supported. Try a dict or a scalar/tensor.'
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type(ex)(ex.message + m)
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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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input_array = model.transfer_batch_to_device(input_array, model.device)
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self.experiment.add_graph(model, input_array)
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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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dir_path = self.log_dir
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if not self._fs.isdir(dir_path):
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dir_path = self.save_dir
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# prepare the file path
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hparams_file = os.path.join(dir_path, self.NAME_HPARAMS_FILE)
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# save the metatags file if it doesn't exist
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if not os.path.isfile(hparams_file):
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save_hparams_to_yaml(hparams_file, self.hparams)
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@rank_zero_only
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def finalize(self, status: str) -> None:
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self.experiment.flush()
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self.save()
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@property
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def name(self) -> str:
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return self._name
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@property
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def version(self) -> int:
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if self._version is None:
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self._version = self._get_next_version()
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return self._version
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def _get_next_version(self):
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root_dir = os.path.join(self.save_dir, self.name)
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if not self._fs.isdir(root_dir):
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log.warning('Missing logger folder: %s', root_dir)
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return 0
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existing_versions = []
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for listing in self._fs.listdir(root_dir):
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d = listing["name"]
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bn = os.path.basename(d)
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if self._fs.isdir(d) and bn.startswith("version_"):
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dir_ver = bn.split("_")[1].replace('/', '')
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existing_versions.append(int(dir_ver))
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if len(existing_versions) == 0:
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return 0
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return max(existing_versions) + 1
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def __getstate__(self):
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state = self.__dict__.copy()
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state["_experiment"] = None
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return state
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