fix avg ckpts

paddlespeech/dataset/s2t/avg_model.py

@@ -53,36 +53,34 @@ def average_checkpoints(dst_model="",
     paddle.set_device('cpu')
 
     val_scores = []
-    beat_val_scores = None
+    jsons = glob.glob(f'{ckpt_dir}/[!train]*.json')
-    selected_epochs = None
-
-    jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
     jsons = sorted(jsons, key=os.path.getmtime, reverse=True)
     for y in jsons:
         with open(y, 'r') as f:
             dic_json = json.load(f)
         loss = dic_json['val_loss']
         epoch = dic_json['epoch']
-        if epoch >= args.min_epoch and epoch <= args.max_epoch:
+        if epoch >= min_epoch and epoch <= max_epoch:
             val_scores.append((epoch, loss))
+    assert val_scores, f"Not find any valid checkpoints: {val_scores}"
     val_scores = np.array(val_scores)
 
-    if args.val_best:
+    if val_best:
         sort_idx = np.argsort(val_scores[:, 1])
         sorted_val_scores = val_scores[sort_idx]
     else:
         sorted_val_scores = val_scores
 
-    beat_val_scores = sorted_val_scores[:args.num, 1]
+    beat_val_scores = sorted_val_scores[:num, 1]
-    selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
+    selected_epochs = sorted_val_scores[:num, 0].astype(np.int64)
     avg_val_score = np.mean(beat_val_scores)
     print("selected val scores = " + str(beat_val_scores))
     print("selected epochs = " + str(selected_epochs))
     print("averaged val score = " + str(avg_val_score))
 
     path_list = [
-        args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
+        ckpt_dir + '/{}.pdparams'.format(int(epoch))
-        for epoch in sorted_val_scores[:args.num, 0]
+        for epoch in sorted_val_scores[:num, 0]
     ]
     print(path_list)