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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 logging
import os
from argparse import Namespace
from typing import Any, Dict, Mapping, Optional, Union

import numpy as np
from lightning_utilities.core.imports import RequirementCache
from tensorboardX import SummaryWriter
from tensorboardX.summary import hparams
from torch import Tensor

import pytorch_lightning as pl
from lightning_lite.utilities.cloud_io import get_filesystem
from lightning_lite.utilities.types import _PATH
from pytorch_lightning.core.saving import save_hparams_to_yaml
from pytorch_lightning.loggers.logger import Logger, rank_zero_experiment
from pytorch_lightning.utilities.imports import _OMEGACONF_AVAILABLE
from pytorch_lightning.utilities.logger import _add_prefix, _convert_params, _flatten_dict
from pytorch_lightning.utilities.logger import _sanitize_params as _utils_sanitize_params
from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn

log = logging.getLogger(__name__)

_TENSORBOARD_AVAILABLE = RequirementCache("tensorboard")

if _OMEGACONF_AVAILABLE:
    from omegaconf import Container, OmegaConf


[docs]class TensorBoardLogger(Logger): r""" Log to local file system in `TensorBoard <https://www.tensorflow.org/tensorboard>`_ format. Implemented using :class:`~tensorboardX.SummaryWriter`. Logs are saved to ``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes preinstalled. Example: .. testcode:: 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). prefix: A string to put at the beginning of metric keys. sub_dir: Sub-directory to group TensorBoard logs. If a sub_dir argument is passed then logs are saved in ``/save_dir/name/version/sub_dir/``. Defaults to ``None`` in which logs are saved in ``/save_dir/name/version/``. \**kwargs: Additional arguments used by :class:`tensorboardX.SummaryWriter` can be passed as keyword arguments in this logger. To automatically flush to disk, `max_queue` sets the size of the queue for pending logs before flushing. `flush_secs` determines how many seconds elapses before flushing. Example: >>> import shutil, tempfile >>> tmp = tempfile.mkdtemp() >>> tbl = TensorBoardLogger(tmp) >>> tbl.log_hyperparams({"epochs": 5, "optimizer": "Adam"}) >>> tbl.log_metrics({"acc": 0.75}) >>> tbl.log_metrics({"acc": 0.9}) >>> tbl.finalize("success") >>> shutil.rmtree(tmp) """ NAME_HPARAMS_FILE = "hparams.yaml" LOGGER_JOIN_CHAR = "-" def __init__( self, save_dir: _PATH, name: Optional[str] = "lightning_logs", version: Optional[Union[int, str]] = None, log_graph: bool = False, default_hp_metric: bool = True, prefix: str = "", sub_dir: Optional[_PATH] = None, **kwargs: Any, ): super().__init__() save_dir = os.fspath(save_dir) self._save_dir = save_dir self._name = name or "" self._version = version self._sub_dir = None if sub_dir is None else os.fspath(sub_dir) if log_graph and not _TENSORBOARD_AVAILABLE: rank_zero_warn("You set `TensorBoardLogger(log_graph=True)` but `tensorboard` is not available.") self._log_graph = log_graph and _TENSORBOARD_AVAILABLE self._default_hp_metric = default_hp_metric self._prefix = prefix self._fs = get_filesystem(save_dir) self._experiment: Optional["SummaryWriter"] = None self.hparams: Union[Dict[str, Any], Namespace] = {} self._kwargs = kwargs @property def root_dir(self) -> str: """Parent directory for all tensorboard checkpoint subdirectories. If the experiment name parameter is an empty string, no experiment subdirectory is used and the checkpoint will be saved in "save_dir/version" """ 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) if isinstance(self.sub_dir, str): log_dir = os.path.join(log_dir, self.sub_dir) log_dir = os.path.expandvars(log_dir) log_dir = os.path.expanduser(log_dir) return log_dir @property def save_dir(self) -> str: """Gets the save directory where the TensorBoard experiments are saved. Returns: The local path to the save directory where the TensorBoard experiments are saved. """ return self._save_dir @property def sub_dir(self) -> Optional[str]: """Gets the sub directory where the TensorBoard experiments are saved. Returns: The local path to the sub directory where the TensorBoard experiments are saved. """ return self._sub_dir @property @rank_zero_experiment def experiment(self) -> SummaryWriter: r""" Actual tensorboard object. To use TensorBoard features in your :class:`~pytorch_lightning.core.module.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: """Record hyperparameters. TensorBoard logs with and without saved hyperparameters are incompatible, the hyperparameters are then not displayed in the TensorBoard. Please delete or move the previously saved logs to display the new ones with hyperparameters. Args: params: a dictionary-like container with the hyperparameters metrics: Dictionary with metric names as keys and measured quantities as values """ params = _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 = _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: Mapping[str, float], step: Optional[int] = None) -> None: assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0" metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR) for k, v in metrics.items(): if isinstance(v, Tensor): v = v.item() if isinstance(v, dict): self.experiment.add_scalars(k, v, step) else: try: self.experiment.add_scalar(k, v, step) # todo: specify the possible exception except Exception as ex: m = f"\n you tried to log {v} which is currently not supported. Try a dict or a scalar/tensor." raise ValueError(m) from ex
[docs] @rank_zero_only def log_graph(self, model: "pl.LightningModule", input_array: Optional[Tensor] = None) -> None: if not self._log_graph: return input_array = model.example_input_array if input_array is None else input_array if input_array is None: rank_zero_warn( "Could not log computational graph to TensorBoard: The `model.example_input_array` attribute" " is not set or `input_array` was not given." ) elif not isinstance(input_array, (Tensor, tuple)): rank_zero_warn( "Could not log computational graph to TensorBoard: The `input_array` or `model.example_input_array`" f" has type {type(input_array)} which can't be traced by TensorBoard. Make the input array a tuple" f" representing the positional arguments to the model's `forward()` implementation." ) else: input_array = model._on_before_batch_transfer(input_array) input_array = model._apply_batch_transfer_handler(input_array) with pl.core.module._jit_is_scripting(): self.experiment.add_graph(model, input_array)
[docs] @rank_zero_only def save(self) -> None: super().save() dir_path = self.log_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 and the log directory exists if self._fs.isdir(dir_path) and not self._fs.isfile(hparams_file): save_hparams_to_yaml(hparams_file, self.hparams)
[docs] @rank_zero_only def finalize(self, status: str) -> None: if self._experiment is not None: self.experiment.flush() self.experiment.close() if status == "success": # saving hparams happens independent of experiment manager self.save()
@property def name(self) -> str: """Get the name of the experiment. Returns: The name of the experiment. """ return self._name @property def version(self) -> Union[int, str]: """Get the experiment version. Returns: The experiment version if specified else the next version. """ if self._version is None: self._version = self._get_next_version() return self._version def _get_next_version(self) -> int: root_dir = self.root_dir try: listdir_info = self._fs.listdir(root_dir) except OSError: log.warning("Missing logger folder: %s", root_dir) return 0 existing_versions = [] for listing in listdir_info: 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 @staticmethod def _sanitize_params(params: Dict[str, Any]) -> Dict[str, Any]: params = _utils_sanitize_params(params) # logging of arrays with dimension > 1 is not supported, sanitize as string return {k: str(v) if isinstance(v, (Tensor, np.ndarray)) and v.ndim > 1 else v for k, v in params.items()} def __getstate__(self) -> Dict[str, Any]: state = self.__dict__.copy() state["_experiment"] = None return state

© Copyright Copyright (c) 2018-2022, Lightning AI et al... Revision 92fe1887.

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