182 lines
7.0 KiB
Python
182 lines
7.0 KiB
Python
import os
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import shutil
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import logging as log
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import warnings
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import numpy as np
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from .base import Callback
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class ModelCheckpoint(Callback):
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r"""
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Save the model after every epoch.
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Args:
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filepath: path to save the model file.
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Can contain named formatting options to be auto-filled.
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Example::
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# save epoch and val_loss in name
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ModelCheckpoint(filepath='{epoch:02d}-{val_loss:.2f}.hdf5')
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# saves file like: /path/epoch_2-val_loss_0.2.hdf5
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monitor (str): quantity to monitor.
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verbose (bool): verbosity mode, False or True.
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save_top_k (int): if `save_top_k == k`,
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the best k models according to
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the quantity monitored will be saved.
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if ``save_top_k == 0``, no models are saved.
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if ``save_top_k == -1``, all models are saved.
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Please note that the monitors are checked every `period` epochs.
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if ``save_top_k >= 2`` and the callback is called multiple
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times inside an epoch, the name of the saved file will be
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appended with a version count starting with `v0`.
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mode (str): one of {auto, min, max}.
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If ``save_top_k != 0``, the decision
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to overwrite the current save file is made
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based on either the maximization or the
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minimization of the monitored quantity. For `val_acc`,
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this should be `max`, for `val_loss` this should
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be `min`, etc. In `auto` mode, the direction is
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automatically inferred from the name of the monitored quantity.
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save_weights_only (bool): if True, then only the model's weights will be
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saved (`model.save_weights(filepath)`), else the full model
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is saved (`model.save(filepath)`).
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period (int): Interval (number of epochs) between checkpoints.
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Example::
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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# saves checkpoints to my_path whenever 'val_loss' has a new min
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checkpoint_callback = ModelCheckpoint(filepath='my_path')
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Trainer(checkpoint_callback=checkpoint_callback)
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"""
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def __init__(self, filepath, monitor: str = 'val_loss', verbose: bool = False,
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save_top_k: int = 1, save_weights_only: bool = False,
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mode: str = 'auto', period: int = 1, prefix: str = ''):
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super().__init__()
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if save_top_k and os.path.isdir(filepath) and len(os.listdir(filepath)) > 0:
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warnings.warn(
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f"Checkpoint directory {filepath} exists and is not empty with save_top_k != 0."
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"All files in this directory will be deleted when a checkpoint is saved!"
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)
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self.monitor = monitor
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self.verbose = verbose
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self.filepath = filepath
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os.makedirs(filepath, exist_ok=True)
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self.save_top_k = save_top_k
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self.save_weights_only = save_weights_only
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self.period = period
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self.epochs_since_last_check = 0
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self.prefix = prefix
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self.best_k_models = {}
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# {filename: monitor}
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self.kth_best_model = ''
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self.best = 0
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self.save_function = None
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mode_dict = {
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'min': (np.less, np.Inf, 'min'),
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'max': (np.greater, -np.Inf, 'max'),
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'auto': (np.greater, -np.Inf, 'max') if 'acc' in self.monitor or self.monitor.startswith('fmeasure')
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else (np.less, np.Inf, 'min'),
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}
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if mode not in mode_dict:
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warnings.warn(
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f'ModelCheckpoint mode {mode} is unknown, '
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'fallback to auto mode.', RuntimeWarning)
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mode = 'auto'
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self.monitor_op, self.kth_value, self.mode = mode_dict[mode]
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def _del_model(self, filepath):
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try:
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shutil.rmtree(filepath)
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except OSError:
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os.remove(filepath)
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def _save_model(self, filepath):
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# make paths
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os.makedirs(os.path.dirname(filepath), exist_ok=True)
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# delegate the saving to the model
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if self.save_function is not None:
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self.save_function(filepath)
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else:
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raise ValueError(".save_function() not set")
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def check_monitor_top_k(self, current):
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less_than_k_models = len(self.best_k_models) < self.save_top_k
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if less_than_k_models:
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return True
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return self.monitor_op(current, self.best_k_models[self.kth_best_model])
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def on_validation_end(self, trainer, pl_module):
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logs = trainer.callback_metrics
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epoch = trainer.current_epoch
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self.epochs_since_last_check += 1
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if self.save_top_k == 0:
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# no models are saved
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return
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if self.epochs_since_last_check >= self.period:
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self.epochs_since_last_check = 0
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filepath = f'{self.filepath}/{self.prefix}_ckpt_epoch_{epoch}.ckpt'
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version_cnt = 0
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while os.path.isfile(filepath):
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# this epoch called before
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filepath = f'{self.filepath}/{self.prefix}_ckpt_epoch_{epoch}_v{version_cnt}.ckpt'
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version_cnt += 1
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if self.save_top_k != -1:
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current = logs.get(self.monitor)
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if current is None:
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warnings.warn(
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f'Can save best model only with {self.monitor} available,'
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' skipping.', RuntimeWarning)
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else:
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if self.check_monitor_top_k(current):
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self._do_check_save(filepath, current, epoch)
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else:
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if self.verbose > 0:
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log.info(
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f'\nEpoch {epoch:05d}: {self.monitor}'
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f' was not in top {self.save_top_k}')
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else:
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if self.verbose > 0:
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log.info(f'\nEpoch {epoch:05d}: saving model to {filepath}')
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self._save_model(filepath)
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def _do_check_save(self, filepath, current, epoch):
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# remove kth
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if len(self.best_k_models) == self.save_top_k:
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delpath = self.kth_best_model
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self.best_k_models.pop(self.kth_best_model)
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self._del_model(delpath)
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self.best_k_models[filepath] = current
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if len(self.best_k_models) == self.save_top_k:
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# monitor dict has reached k elements
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_op = max if self.mode == 'min' else min
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self.kth_best_model = _op(self.best_k_models,
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key=self.best_k_models.get)
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self.kth_value = self.best_k_models[self.kth_best_model]
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_op = min if self.mode == 'min' else max
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self.best = _op(self.best_k_models.values())
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if self.verbose > 0:
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log.info(
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f'\nEpoch {epoch:05d}: {self.monitor} reached'
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f' {current:0.5f} (best {self.best:0.5f}), saving model to'
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f' {filepath} as top {self.save_top_k}')
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self._save_model(filepath)
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