From 0e3f0f1c608085add43905e473c4a4f6da23f697 Mon Sep 17 00:00:00 2001 From: dcarron <daniel.carron@idiap.ch> Date: Thu, 13 Jul 2023 11:56:59 +0200 Subject: [PATCH] Custom TensorBoardLogger --- .../loggers/custom_tensorboard_logger.py | 78 +++++++++++++++++++ src/ptbench/engine/trainer.py | 4 +- 2 files changed, 81 insertions(+), 1 deletion(-) create mode 100644 src/ptbench/engine/loggers/custom_tensorboard_logger.py diff --git a/src/ptbench/engine/loggers/custom_tensorboard_logger.py b/src/ptbench/engine/loggers/custom_tensorboard_logger.py new file mode 100644 index 00000000..f229a055 --- /dev/null +++ b/src/ptbench/engine/loggers/custom_tensorboard_logger.py @@ -0,0 +1,78 @@ +import os + +from typing import Any, Optional, Union + +from lightning.fabric.utilities.types import _PATH +from lightning.pytorch.loggers import TensorBoardLogger + + +class CustomTensorboardLogger(TensorBoardLogger): + r"""Custom implementation implementation of + lightning.pytorch.loggers.TensorBoardLogger. + + Allows us to put all logs inside the same directory, instead of a separate "version_n" directory which is the default behaviour. + + Parameters + ---------- + + 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. + """ + + 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, + name, + version, + log_graph, + default_hp_metric, + prefix, + sub_dir, + ) + + @property + def log_dir(self) -> str: + return os.path.join(self.save_dir, self.name) diff --git a/src/ptbench/engine/trainer.py b/src/ptbench/engine/trainer.py index 0236aca7..6dfc1584 100644 --- a/src/ptbench/engine/trainer.py +++ b/src/ptbench/engine/trainer.py @@ -156,8 +156,10 @@ def run( # Save model summary _, no_of_parameters = save_model_summary(output_folder, model) + from .loggers.custom_tensorboard_logger import CustomTensorboardLogger + log_dir = "logs" - tensorboard_logger = lightning.pytorch.loggers.TensorBoardLogger( + tensorboard_logger = CustomTensorboardLogger( output_folder, log_dir, ) -- GitLab