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monitors.py
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from pytorch_lightning.callbacks import Callback
from pytorch_lightning.utilities.exceptions import MisconfigurationException
class MetricMonitor(Callback):
"""
A base class for all metric monitors. This callback is used by a pytorch
lightning trainer. It reports metrics to the clearml logger.
"""
def __init__(self, stage='train', metric=None, logger=None,
logging_interval=None, title=None, series=None):
"""
:param stage: a string. The values can only be "both", "train",
or "valid"
:param metric:
:param logger:
:param logging_interval:
:param title:
:param series: the legend used for the clearml polot. The default is
to use the smae string as the stage. However, there are cases when
we want to have a differnt label than the stage, such as "train" and
"train_no_dropout"
"""
if logging_interval not in (None, "step", "epoch"):
raise MisconfigurationException(
"monitors.py::MetricMonitor: logging_interval should be "
"`step` or `epoch` or `None`.")
if metric is None:
raise MisconfigurationException(
"monitors.py::MetricMonitor: metric is not specified")
if stage not in ('both', 'train', 'valid'):
raise MisconfigurationException(
f"monitors.py::MetricMonitor: input 'stage' argument = "
f"{stage}, which cannot be recognized")
self.logger = logger
self.metric = metric
self.logging_interval = logging_interval
self.stage = stage
self.title = title
self.series = series
def on_train_batch_end(self, trainer, pl_module, outputs, batch=None,
batch_idx=None, dataloader_idx=None):
"""
Report metrics in each iteration
:param trainer: pytorch lightning trainer
:param pl_module: model used, a LightningModule class
:param outputs: outputs from each iteration. It is from the return
of training_step() function defined in the model
:param batch: Not used but required as a inheried class of Callback
:param batch_idx: Not used but required as a inheried class of Callback
:param dataloader_idx: Not used but required as a inheried class of Callback
:return: None
"""
if 'train' in self.stage:
if self.logging_interval == "step":
series = self.series if self.series is not None else 'train'
self.logger.report_scalar(title=self.title, series=series,
value=outputs[self.metric],
iteration=trainer.global_step)
def on_train_epoch_end(self, trainer, pl_module):
if 'train' in self.stage:
if self.logging_interval == "epoch":
outputs = pl_module.train_epoch_outputs
series = self.series if self.series is not None else 'train'
self.logger.report_scalar(title=self.title, series=series,
value=outputs[self.metric],
iteration=trainer.current_epoch)
def on_validation_batch_end(self, trainer, pl_module, outputs, batch,
batch_idx, unused=0):
if 'valid' in self.stage:
if self.logging_interval == "step":
series = self.series if self.series is not None else 'valid'
self.logger.report_scalar(title=self.title, series=series,
value=outputs[self.metric],
iteration=trainer.global_step)
def on_validation_epoch_end(self, trainer, pl_module):
if 'valid' in self.stage:
if self.logging_interval == "epoch":
outputs = pl_module.valid_epoch_outputs
series = self.series if self.series is not None else 'valid'
self.logger.report_scalar(title=self.title,
series=series,
value=outputs[self.metric],
iteration=trainer.current_epoch
)
# Loss Monitors
class LossMonitor(MetricMonitor):
def __init__(self, stage='train', logger=None, logging_interval=None):
super(LossMonitor, self).__init__(stage=stage, metric="loss",
logger=logger,
logging_interval=logging_interval,
title=f'loss_by_{logging_interval}')
class LossNoDropoutMonitor(MetricMonitor):
def __init__(self, stage='train', logger=None, logging_interval=None):
super(LossNoDropoutMonitor, self).__init__(stage=stage,
metric="loss_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'loss_by_'
f'{logging_interval}',
series='no_dropout')
# LogAUC0.001_0.1 Monitors
class LogAUC0_001to0_1Monitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(LogAUC0_001to0_1Monitor, self).__init__(stage=stage,
metric="logAUC_0.001_0.1",
logger=logger,
logging_interval=logging_interval,
title=f'logAUC_0.001_0.1_by_{logging_interval}')
class LogAUC0_001to0_1NoDropoutMonitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(LogAUC0_001to0_1NoDropoutMonitor, self).__init__(stage=stage,
metric="logAUC_0.001_0.1_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'logAUC_0.001_0.1_by_{logging_interval}',
series='no_dropout')
# LogAUC_0.001_1 Monitors
class LogAUC0_001to1Monitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(LogAUC0_001to1Monitor, self).__init__(stage=stage,
metric="logAUC_0.001_1",
logger=logger,
logging_interval=logging_interval,
title=f'logAUC_0.001_1_by_{logging_interval}')
class LogAUC0_001to1NoDropoutMonitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(LogAUC0_001to1NoDropoutMonitor, self).__init__(stage=stage,
metric="logAUC_0.001_1_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'logAUC_0.001_1_by_{logging_interval}',
series='no_dropout')
# AUC
class AUCMonitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(AUCMonitor, self).__init__(stage=stage,
metric="AUC",
logger=logger,
logging_interval=logging_interval,
title=f'AUC_by_{logging_interval}')
class AUCNoDropoutMonitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(AUCNoDropoutMonitor, self).__init__(stage=stage,
metric="AUC_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'AUC_by_{logging_interval}',
series='no_dropout')
# PPV Monitors
class PPVMonitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(PPVMonitor, self).__init__(stage=stage, metric="ppv",
logger=logger,
logging_interval=logging_interval,
title=f'PPV_by_{logging_interval}')
class PPVNoDropoutMonitor(MetricMonitor):
def __init__(self, stage='valid', logger=None, logging_interval=None):
super(PPVNoDropoutMonitor, self).__init__(stage=stage,
metric="ppv_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'PPV_by_{logging_interval}',
series='no_dropout')
class AccuracyMonitor(MetricMonitor):
def __init__(self, stage='valid', logger =None, logging_interval=None):
super(AccuracyMonitor, self).__init__(stage=stage,
metric="accuracy",
logger=logger,
logging_interval=logging_interval,
title=f'accuracy_by_'
f'{logging_interval}')
class AccuracyNoDropoutMonitor(MetricMonitor):
def __init__(self, stage='valid', logger =None, logging_interval=None):
super(AccuracyNoDropoutMonitor, self).__init__(stage=stage,
metric="accuracy_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'accuracy_by_'
f'{logging_interval}',
series='no_dropout')
# RMSE
class RMSEMonitor(MetricMonitor):
def __init__(self, stage='valid', logger =None, logging_interval=None):
super(RMSEMonitor, self).__init__(stage=stage,
metric="RMSE",
logger=logger,
logging_interval=logging_interval,
title=f'RMSE_by_'
f'{logging_interval}')
class RMSENoDropoutMonitor(MetricMonitor):
def __init__(self, stage='valid', logger =None, logging_interval=None):
super(RMSENoDropoutMonitor, self).__init__(stage=stage,
metric="RMSE_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'RMSE_by_'
f'{logging_interval}',
series='no_dropout')
# F1 score
class F1ScoreMonitor(MetricMonitor):
def __init__(self, stage='valid', logger =None, logging_interval=None):
super(F1ScoreMonitor, self).__init__(stage=stage,
metric="f1_score",
logger=logger,
logging_interval=logging_interval,
title=f'f1_score_by_'
f'{logging_interval}')
class F1ScoreNoDropoutMonitor(MetricMonitor):
def __init__(self, stage='valid', logger =None, logging_interval=None):
super(F1ScoreNoDropoutMonitor, self).__init__(stage=stage,
metric="f1_score_no_dropout",
logger=logger,
logging_interval=logging_interval,
title=f'f1_score_by_'
f'{logging_interval}',
series='no_dropout')