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