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tensorboard.py
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tensorboard.py
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"""
TensorBoard
-----------
"""
import os
from argparse import Namespace
from typing import Optional, Dict, Union, Any
from warnings import warn
import torch
from pkg_resources import parse_version
from torch.utils.tensorboard import SummaryWriter
from pytorch_lightning import _logger as log
from pytorch_lightning.core.saving import save_hparams_to_yaml
from pytorch_lightning.loggers.base import LightningLoggerBase
from pytorch_lightning.utilities import rank_zero_only
class TensorBoardLogger(LightningLoggerBase):
r"""
Log to local file system in `TensorBoard <https://www.tensorflow.org/tensorboard>`_ format.
Implemented using :class:`~torch.utils.tensorboard.SummaryWriter`. Logs are saved to
``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes
preinstalled.
Example:
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.loggers import TensorBoardLogger
>>> logger = TensorBoardLogger("tb_logs", name="my_model")
>>> trainer = Trainer(logger=logger)
Args:
save_dir: Save directory
name: Experiment name. Defaults to ``'default'``. If it is the empty string then no per-experiment
subdirectory is used.
version: Experiment version. If version is not specified the logger inspects the save
directory for existing versions, then automatically assigns the next available version.
If it is a string then it is used as the run-specific subdirectory name,
otherwise ``'version_${version}'`` is used.
\**kwargs: Other arguments are passed directly to the :class:`SummaryWriter` constructor.
"""
NAME_HPARAMS_FILE = 'hparams.yaml'
def __init__(self,
save_dir: str,
name: Optional[str] = "default",
version: Optional[Union[int, str]] = None,
**kwargs):
super().__init__()
self.save_dir = save_dir
self._name = name
self._version = version
self._experiment = None
self.hparams = {}
self._kwargs = kwargs
@property
def root_dir(self) -> str:
"""
Parent directory for all tensorboard checkpoint subdirectories.
If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
and the checkpoint will be saved in "save_dir/version_dir"
"""
if self.name is None or len(self.name) == 0:
return self.save_dir
else:
return os.path.join(self.save_dir, self.name)
@property
def log_dir(self) -> str:
"""
The directory for this run's tensorboard checkpoint. By default, it is named
``'version_${self.version}'`` but it can be overridden by passing a string value
for the constructor's version parameter instead of ``None`` or an int.
"""
# create a pseudo standard path ala test-tube
version = self.version if isinstance(self.version, str) else f"version_{self.version}"
log_dir = os.path.join(self.root_dir, version)
return log_dir
@property
def experiment(self) -> SummaryWriter:
r"""
Actual tensorboard object. To use TensorBoard features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_tensorboard_function()
"""
if self._experiment is not None:
return self._experiment
os.makedirs(self.root_dir, exist_ok=True)
self._experiment = SummaryWriter(log_dir=self.log_dir, **self._kwargs)
return self._experiment
@rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace],
metrics: Optional[Dict[str, Any]] = None) -> None:
params = self._convert_params(params)
# store params to output
self.hparams.update(params)
# format params into the suitable for tensorboard
params = self._flatten_dict(params)
params = self._sanitize_params(params)
if parse_version(torch.__version__) < parse_version("1.3.0"):
warn(
f"Hyperparameter logging is not available for Torch version {torch.__version__}."
" Skipping log_hyperparams. Upgrade to Torch 1.3.0 or above to enable"
" hyperparameter logging."
)
else:
from torch.utils.tensorboard.summary import hparams
if metrics is None:
metrics = {}
exp, ssi, sei = hparams(params, metrics)
writer = self.experiment._get_file_writer()
writer.add_summary(exp)
writer.add_summary(ssi)
writer.add_summary(sei)
if metrics:
# necessary for hparam comparison with metrics
self.log_metrics(metrics)
@rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
for k, v in metrics.items():
if isinstance(v, torch.Tensor):
v = v.item()
self.experiment.add_scalar(k, v, step)
@rank_zero_only
def save(self) -> None:
super().save()
dir_path = self.log_dir
if not os.path.isdir(dir_path):
dir_path = self.save_dir
# prepare the file path
hparams_file = os.path.join(dir_path, self.NAME_HPARAMS_FILE)
# save the metatags file
save_hparams_to_yaml(hparams_file, self.hparams)
@rank_zero_only
def finalize(self, status: str) -> None:
self.save()
@property
def name(self) -> str:
return self._name
@property
def version(self) -> int:
if self._version is None:
self._version = self._get_next_version()
return self._version
def _get_next_version(self):
root_dir = os.path.join(self.save_dir, self.name)
if not os.path.isdir(root_dir):
log.warning('Missing logger folder: %s', root_dir)
return 0
existing_versions = []
for d in os.listdir(root_dir):
if os.path.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
existing_versions.append(int(d.split("_")[1]))
if len(existing_versions) == 0:
return 0
return max(existing_versions) + 1