"A `Callback` that saves tracked metrics into a persistent file."
from ..torch_core import *
from ..basic_data import DataBunch
from ..callback import *
from ..basic_train import Learner, LearnerCallback
from time import time
from fastprogress.fastprogress import format_time
__all__ = ['CSVLogger']
class CSVLogger(LearnerCallback):
"A `LearnerCallback` that saves history of metrics while training `learn` into CSV `filename`."
def __init__(self, learn:Learner, filename: str = 'history', append: bool = False):
super().__init__(learn)
self.filename,self.path,self.append = filename,self.learn.path/f'{filename}.csv',append
self.add_time = True
def read_logged_file(self):
"Read the content of saved file"
return pd.read_csv(self.path)
def on_train_begin(self, **kwargs: Any) -> None:
"Prepare file with metric names."
self.path.parent.mkdir(parents=True, exist_ok=True)
self.file = self.path.open('a') if self.append else self.path.open('w')
self.file.write(','.join(self.learn.recorder.names[:(None if self.add_time else -1)]) + '\n')
def on_epoch_begin(self, **kwargs:Any)->None:
if self.add_time: self.start_epoch = time()
def on_epoch_end(self, epoch: int, smooth_loss: Tensor, last_metrics: MetricsList, **kwargs: Any) -> bool:
"Add a line with `epoch` number, `smooth_loss` and `last_metrics`."
last_metrics = ifnone(last_metrics, [])
stats = [str(stat) if isinstance(stat, int) else '#na#' if stat is None else f'{stat:.6f}'
for name, stat in zip(self.learn.recorder.names, [epoch, smooth_loss] + last_metrics)]
if self.add_time: stats.append(format_time(time() - self.start_epoch))
str_stats = ','.join(stats)
self.file.write(str_stats + '\n')
def on_train_end(self, **kwargs: Any) -> None:
"Close the file."
self.file.close()
