| "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() |
|
|
