# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Callback for saving checkpoint.
"""
import os
import mindspore
from mindnlp.abc import Callback
[docs]class CheckpointCallback(Callback):
"""
Save checkpoint of the model. save the current Trainer state at the end of each epoch, which can be used to
resume previous operations.
Continue training a sample code using the most recent epoch
Args:
save_path (str): The path to save the state. A specific path needs to be specified,
such as 'checkpoints/chtp.pt'. Default: None.
epochs (int): Save a checkpoint file every n epochs.
keep_checkpoint_max (int): Save checkpoint files at most. Default:5.
"""
def __init__(self, save_path=None, epochs=None, keep_checkpoint_max=5):
if save_path is not None:
os.makedirs(save_path, exist_ok=True)
else:
os.makedirs(os.path.expanduser('~'), exist_ok=True)
self.save_path = save_path
self.epochs = epochs
self.keep_checkpoint_max = keep_checkpoint_max
self.checkpoint_nums = 0
# to do
# self.steps = steps
# if (self.epochs is not None) & (self.steps is not None):
# raise ValueError("The parameter epochs and steps cannot be assigned at the same time,\
# you can only keep one of them.")
# elif (self.epochs is None) & (self.steps is None):
# raise ValueError("The parameter epochs and steps both are None,\
# you must assign one of them.")
[docs] def train_begin(self, run_context):
"""
Notice the file saved path of checkpoints at the beginning of training.
Args:
run_context (RunContext): Information about the model.
"""
if self.epochs is None:
print('For saving checkpoints, epoch cannont be `None` !')
print(f"\nThe train will start from the checkpoint saved in {self.save_path}.\n")
[docs] def train_epoch_end(self, run_context):
"""
Save checkpoint every n epochs at the end of the epoch.
Args:
run_context (RunContext): Information about the model.
"""
if self.checkpoint_nums == self.keep_checkpoint_max:
print('The maximum number of stored checkpoints has been reached.')
return
if self.epochs is None:
return
if (run_context.cur_epoch_nums % self.epochs != 0) & (run_context.cur_epoch_nums != run_context.epochs):
return
model = run_context.network
ckpt_name = type(model).__name__ + '_epoch_' + str(run_context.cur_epoch_nums-1) + '.ckpt'
mindspore.save_checkpoint(model, self.save_path + '/' + ckpt_name)
self.checkpoint_nums += 1
print(f"Checkpoint: {ckpt_name} has been saved in epoch:{run_context.cur_epoch_nums - 1}.")