competition update

2025-07-02 12:18:09 -07:00
parent 9e17716a4a
commit 77dbcf868f
2615 changed files with 1648116 additions and 125 deletions
--- a/language_model/wenet/bin/average_model.py
+++ b/language_model/wenet/bin/average_model.py
@@ -0,0 +1,78 @@
+# Copyright 2019 Mobvoi Inc. All Rights Reserved.
+# Author: di.wu@mobvoi.com (DI WU)
+import os
+import argparse
+import glob
+
+import yaml
+import numpy as np
+import torch
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='average model')
+    parser.add_argument('--dst_model', required=True, help='averaged model')
+    parser.add_argument('--src_path',
+                        required=True,
+                        help='src model path 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)
+    checkpoints = []
+    val_scores = []
+    if args.val_best:
+        yamls = glob.glob('{}/[!train]*.yaml'.format(args.src_path))
+        for y in yamls:
+            with open(y, 'r') as f:
+                dic_yaml = yaml.load(f, Loader=yaml.FullLoader)
+                loss = dic_yaml['cv_loss']
+                epoch = dic_yaml['epoch']
+                if epoch >= args.min_epoch and epoch <= args.max_epoch:
+                    val_scores += [[epoch, loss]]
+        val_scores = np.array(val_scores)
+        sort_idx = np.argsort(val_scores[:, -1])
+        sorted_val_scores = val_scores[sort_idx][::1]
+        print("best val scores = " + str(sorted_val_scores[:args.num, 1]))
+        print("selected epochs = " +
+              str(sorted_val_scores[:args.num, 0].astype(np.int64)))
+        path_list = [
+            args.src_path + '/{}.pt'.format(int(epoch))
+            for epoch in sorted_val_scores[:args.num, 0]
+        ]
+    else:
+        path_list = glob.glob('{}/[!avg][!final]*.pt'.format(args.src_path))
+        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('Processing {}'.format(path))
+        states = torch.load(path, map_location=torch.device('cpu'))
+        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:
+            # pytorch 1.6 use true_divide instead of /=
+            avg[k] = torch.true_divide(avg[k], num)
+    print('Saving to {}'.format(args.dst_model))
+    torch.save(avg, args.dst_model)