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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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import torch.nn as nn
from . import callback_register, callback_config_register
from typing import Dict, List
import os
from pytorch_lightning.callbacks import ModelCheckpoint
[docs]@callback_config_register('checkpoint')
class CheckpointCallbackConfig(object):
"""Config for CheckpointCallback
Config Example:
>>> {
>>> // default checkpoint configure
>>> "_name": "checkpoint",
>>> "config": {
>>> "monitor": "*@*", // monitor which metrics or log value
>>> "save_top_k": 3, //save top k
>>> "mode": "*@*", //"max" or "min" select topk min or max checkpoint, min for loss, max for acc
>>> "save_last": true, // always save last checkpoint
>>> "auto_insert_metric_name": true, //the save file name with or not metric name
>>> "every_n_train_steps": null, // Number of training steps between checkpoints.
>>> "every_n_epochs": 1, //Number of epochs between checkpoints.
>>> "save_on_train_epoch_end": false,// Whether to run checkpointing at the end of the training epoch. If this is False, then the check runs at the end of the validation.
>>> "save_weights_only": false, //whether save other status like optimizer, etc.
>>> }
>>> }
"""
def __init__(self, config: Dict):
super(CheckpointCallbackConfig, self).__init__()
config = config['config']
self.monitor = config['monitor']
self.save_last = config['save_last']
self.save_top_k = config['save_top_k']
self.mode = config['mode']
self.auto_insert_metric_name = config['auto_insert_metric_name']
self.every_n_train_steps = config['every_n_train_steps']
self.every_n_epochs = config['every_n_epochs']
self.save_on_train_epoch_end = config['save_on_train_epoch_end']
self.save_weights_only = config['save_weights_only']
[docs]@callback_register('checkpoint')
class CheckpointCallback(object):
"""Save checkpoint decided by config
"""
def __init__(self, config: CheckpointCallbackConfig):
super().__init__()
self.config = config
def __call__(self, rt_config: Dict)->ModelCheckpoint:
"""get the ModelCheckpoint object
Args:
rt_config: runtime config, include save_dir, and the checkpoint path name
Returns:
ModelCheckpoint object
"""
dirpath = os.path.join(rt_config.get('save_dir', ''), rt_config.get("name", ''))
return ModelCheckpoint(dirpath=dirpath, **self.config.__dict__)