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PaddleSpeech/paddlespeech/text/exps/ernie_linear/avg_model.py

113 lines
3.5 KiB

#!/usr/bin/env python3
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import argparse
import glob
import json
import os
import numpy as np
import paddle
def main(args):
paddle.set_device('cpu')
val_scores = []
beat_val_scores = []
selected_epochs = []
if args.val_best:
jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
for y in jsons:
with open(y, 'r') as f:
dict_json = json.load(f)
loss = dict_json['F1']
epoch = dict_json['epoch']
if epoch >= args.min_epoch and epoch <= args.max_epoch:
val_scores.append((epoch, loss))
val_scores = np.array(val_scores)
sort_idx = np.argsort(-val_scores[:, 1])
sorted_val_scores = val_scores[sort_idx]
path_list = [
args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
for epoch in sorted_val_scores[:args.num, 0]
]
beat_val_scores = sorted_val_scores[:args.num, 1]
selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
print("best val scores = " + str(beat_val_scores))
print("selected epochs = " + str(selected_epochs))
else:
path_list = glob.glob(f'{args.ckpt_dir}/[!avg][!final]*.pdparams')
path_list = sorted(path_list, key=os.path.getmtime)
path_list = path_list[-args.num:]
print(path_list)
avg = None
num = args.num
assert num == len(path_list)
for path in path_list:
print(f'Processing {path}')
states = paddle.load(path)
if avg is None:
avg = states
else:
for k in avg.keys():
avg[k] += states[k]
# average
for k in avg.keys():
if avg[k] is not None:
avg[k] /= num
paddle.save(avg, args.dst_model)
print(f'Saving to {args.dst_model}')
meta_path = os.path.splitext(args.dst_model)[0] + '.avg.json'
with open(meta_path, 'w') as f:
data = json.dumps({
"avg_ckpt": args.dst_model,
"ckpt": path_list,
"epoch": selected_epochs.tolist(),
"val_loss": beat_val_scores.tolist(),
})
f.write(data + "\n")
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='average model')
parser.add_argument('--dst_model', required=True, help='averaged model')
parser.add_argument(
'--ckpt_dir', required=True, help='ckpt model dir for average')
parser.add_argument(
'--val_best', action="store_true", help='averaged model')
parser.add_argument(
'--num', default=5, type=int, help='nums for averaged model')
parser.add_argument(
'--min_epoch',
default=0,
type=int,
help='min epoch used for averaging model')
parser.add_argument(
'--max_epoch',
default=65536, # Big enough
type=int,
help='max epoch used for averaging model')
args = parser.parse_args()
print(args)
main(args)