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, Callable, Dict, Mapping, Optional, Sequence, Union

import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from torch.utils.tensorboard.summary import hparams

import pytorch_lightning as pl
from pytorch_lightning.core.saving import save_hparams_to_yaml
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities.cloud_io import get_filesystem
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__)

if _OMEGACONF_AVAILABLE:
    from omegaconf import Container, OmegaConf


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:

    .. 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:`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.

    """
    NAME_HPARAMS_FILE = "hparams.yaml"
    LOGGER_JOIN_CHAR = "-"

    def __init__(
        self,
        save_dir: str,
        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[str] = None,
        agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
        agg_default_func: Optional[Callable[[Sequence[float]], float]] = None,
        **kwargs,
    ):
        super().__init__(agg_key_funcs=agg_key_funcs, agg_default_func=agg_default_func)
        self._save_dir = save_dir
        self._name = name or ""
        self._version = version
        self._sub_dir = sub_dir
        self._log_graph = log_graph
        self._default_hp_metric = default_hp_metric
        self._prefix = prefix
        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 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) -> Optional[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.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

    @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)

    @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"

        metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)

        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)
                # 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

    @rank_zero_only
    def log_graph(self, model: "pl.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._apply_batch_transfer_handler(input_array)
                model._running_torchscript = True
                self.experiment.add_graph(model, input_array)
                model._running_torchscript = False
            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",
                )

    @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)

    @rank_zero_only
    def finalize(self, status: str) -> None:
        self.experiment.flush()
        self.experiment.close()
        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) -> int:
        """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):
        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, (torch.Tensor, np.ndarray)) and v.ndim > 1 else v for k, v in params.items()}

    def __getstate__(self):
        state = self.__dict__.copy()
        state["_experiment"] = None
        return state