Source code for catalyst.contrib.dl.callbacks.alchemy_logger

# flake8: noqa
# @TODO: code formatting issue for 20.07 release
from typing import Dict, List

from alchemy import Logger

from catalyst import utils
from catalyst.core.callback import (
from catalyst.core.runner import IRunner

[docs]class AlchemyLogger(Callback): """Logger callback, translates ``runner.*_metrics`` to Alchemy. Read about Alchemy here Example: .. code-block:: python from catalyst.dl import SupervisedRunner, AlchemyLogger runner = SupervisedRunner() runner.train( model=model, criterion=criterion, optimizer=optimizer, loaders=loaders, logdir=logdir, num_epochs=num_epochs, verbose=True, callbacks={ "logger": AlchemyLogger( token="...", # your Alchemy token project="your_project_name", experiment="your_experiment_name", group="your_experiment_group_name", ) } ) Powered by Catalyst.Ecosystem. """
[docs] def __init__( self, metric_names: List[str] = None, log_on_batch_end: bool = True, log_on_epoch_end: bool = True, **logging_params, ): """ Args: metric_names (List[str]): list of metric names to log, if none - logs everything log_on_batch_end (bool): logs per-batch metrics if set True log_on_epoch_end (bool): logs per-epoch metrics if set True """ super().__init__( order=CallbackOrder.logging, node=CallbackNode.master, scope=CallbackScope.experiment, ) self.metrics_to_log = metric_names self.log_on_batch_end = log_on_batch_end self.log_on_epoch_end = log_on_epoch_end if not (self.log_on_batch_end or self.log_on_epoch_end): raise ValueError("You have to log something!") if (self.log_on_batch_end and not self.log_on_epoch_end) or ( not self.log_on_batch_end and self.log_on_epoch_end ): self.batch_log_suffix = "" self.epoch_log_suffix = "" else: self.batch_log_suffix = "_batch" self.epoch_log_suffix = "_epoch" self.logger = Logger(**logging_params)
def __del__(self): """@TODO: Docs. Contribution is welcome.""" self.logger.close() def _log_metrics( self, metrics: Dict[str, float], step: int, mode: str, suffix="" ): if self.metrics_to_log is None: metrics_to_log = sorted(metrics.keys()) else: metrics_to_log = self.metrics_to_log for name in metrics_to_log: if name in metrics: metric_name = f"{name}/{mode}{suffix}" metric_value = metrics[name] self.logger.log_scalar( name=metric_name, value=metric_value, step=step, )
[docs] def on_batch_end(self, runner: IRunner): """Translate batch metrics to Alchemy.""" if self.log_on_batch_end: mode = runner.loader_name metrics = runner.batch_metrics self._log_metrics( metrics=metrics, step=runner.global_sample_step, mode=mode, suffix=self.batch_log_suffix, )
[docs] def on_loader_end(self, runner: IRunner): """Translate loader metrics to Alchemy.""" if self.log_on_epoch_end: mode = runner.loader_name metrics = runner.loader_metrics self._log_metrics( metrics=metrics, step=runner.global_epoch, mode=mode, suffix=self.epoch_log_suffix, )
[docs] def on_epoch_end(self, runner: IRunner): """Translate epoch metrics to Alchemy.""" extra_mode = "_base" splitted_epoch_metrics = utils.split_dict_to_subdicts( dct=runner.epoch_metrics, prefixes=list(runner.loaders.keys()), extra_key=extra_mode, ) if self.log_on_epoch_end: self._log_metrics( metrics=splitted_epoch_metrics[extra_mode], step=runner.global_epoch, mode=extra_mode, suffix=self.epoch_log_suffix, )
__all__ = ["AlchemyLogger"]