Source code for pytorch_lightning.loggers.tensorboard
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TensorBoard Logger
------------------
"""
import os
from argparse import Namespace
from typing import Any, Dict, Optional, Union
from warnings import warn
import torch
from torch.utils.tensorboard import SummaryWriter
from torch.utils.tensorboard.summary import hparams
from pytorch_lightning import _logger as log
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.core.saving import save_hparams_to_yaml
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import rank_zero_only, rank_zero_warn
from pytorch_lightning.utilities.cloud_io import get_filesystem
from pytorch_lightning.utilities.exceptions import MisconfigurationException
try:
from omegaconf import Container, OmegaConf
except ImportError:
OMEGACONF_AVAILABLE = False
else:
OMEGACONF_AVAILABLE = True
[docs]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_${version}'`` is used.
log_graph: Adds the computational graph to tensorboard. This requires that
the user has defined the `self.example_input_array` attribute in their
model.
default_hp_metric: Enables a placeholder metric with key `hp_metric` when `log_hyperparams` is
called without a metric (otherwise calls to log_hyperparams without a metric are ignored).
\**kwargs: Additional arguments like `comment`, `filename_suffix`, etc. used by
:class:`SummaryWriter` can be passed as keyword arguments in this logger.
"""
NAME_HPARAMS_FILE = 'hparams.yaml'
def __init__(
self,
save_dir: str,
name: Optional[str] = "default",
version: Optional[Union[int, str]] = None,
log_graph: bool = False,
default_hp_metric: bool = True,
**kwargs
):
super().__init__()
self._save_dir = save_dir
self._name = name or ''
self._version = version
self._log_graph = log_graph
self._default_hp_metric = default_hp_metric
self._fs = get_filesystem(save_dir)
self._experiment = None
self.hparams = {}
self._kwargs = kwargs
@property
def root_dir(self) -> str:
"""
Parent directory for all tensorboard checkpoint subdirectories.
If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
and the checkpoint will be saved in "save_dir/version_dir"
"""
if self.name is None or len(self.name) == 0:
return self.save_dir
else:
return os.path.join(self.save_dir, self.name)
@property
def log_dir(self) -> str:
"""
The directory for this run's tensorboard checkpoint. By default, it is named
``'version_${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 save_dir(self) -> Optional[str]:
return self._save_dir
@property
@rank_zero_experiment
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
assert rank_zero_only.rank == 0, 'tried to init log dirs in non global_rank=0'
if self.root_dir:
self._fs.makedirs(self.root_dir, exist_ok=True)
self._experiment = SummaryWriter(log_dir=self.log_dir, **self._kwargs)
return self._experiment
[docs] @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
if OMEGACONF_AVAILABLE and isinstance(params, Container):
self.hparams = OmegaConf.merge(self.hparams, params)
else:
self.hparams.update(params)
# format params into the suitable for tensorboard
params = self._flatten_dict(params)
params = self._sanitize_params(params)
if metrics is None:
if self._default_hp_metric:
metrics = {"hp_metric": -1}
elif not isinstance(metrics, dict):
metrics = {"hp_metric": metrics}
if metrics:
self.log_metrics(metrics, 0)
exp, ssi, sei = hparams(params, metrics)
writer = self.experiment._get_file_writer()
writer.add_summary(exp)
writer.add_summary(ssi)
writer.add_summary(sei)
[docs] @rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'
for k, v in metrics.items():
if isinstance(v, torch.Tensor):
v = v.item()
if isinstance(v, dict):
self.experiment.add_scalars(k, v, step)
else:
try:
self.experiment.add_scalar(k, v, step)
except Exception as e:
m = f'\n you tried to log {v} which is not currently supported. Try a dict or a scalar/tensor.'
type(e)(e.message + m)
[docs] @rank_zero_only
def log_graph(self, model: LightningModule, input_array=None):
if self._log_graph:
if input_array is None:
input_array = model.example_input_array
if input_array is not None:
input_array = model.transfer_batch_to_device(input_array, model.device)
self.experiment.add_graph(model, input_array)
else:
rank_zero_warn('Could not log computational graph since the'
' `model.example_input_array` attribute is not set'
' or `input_array` was not given',
UserWarning)
[docs] @rank_zero_only
def save(self) -> None:
super().save()
dir_path = self.log_dir
if not self._fs.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 if it doesn't exist
if not os.path.isfile(hparams_file):
save_hparams_to_yaml(hparams_file, self.hparams)
[docs] @rank_zero_only
def finalize(self, status: str) -> None:
self.experiment.flush()
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 self._fs.isdir(root_dir):
log.warning('Missing logger folder: %s', root_dir)
return 0
existing_versions = []
for listing in self._fs.listdir(root_dir):
d = listing["name"]
bn = os.path.basename(d)
if self._fs.isdir(d) and bn.startswith("version_"):
dir_ver = bn.split("_")[1].replace('/', '')
existing_versions.append(int(dir_ver))
if len(existing_versions) == 0:
return 0
return max(existing_versions) + 1
def __getstate__(self):
state = self.__dict__.copy()
state["_experiment"] = None
return state
