修改加载latest model的方式，修改global_step计算，增加preserved参数，增加train_with_pretrained_model参数

utils.py

@@ -204,14 +204,29 @@ def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios=
         writer.add_audio(k, v, global_step, audio_sampling_rate)
 
 
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+def extract_digits(f):
+    digits = "".join(filter(str.isdigit, f))
+    return int(digits) if digits else -1
+
+
+def latest_checkpoint_path(dir_path, regex="G_[0-9]*.pth"):
     f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+    f_list.sort(key=lambda f: extract_digits(f))
     x = f_list[-1]
-    print(x)
+    print(f"latest_checkpoint_path:{x}")
     return x
 
 
+def oldest_checkpoint_path(dir_path, regex="G_[0-9]*.pth", preserved=4):
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    f_list.sort(key=lambda f: extract_digits(f))
+    if len(f_list) > preserved:
+        x = f_list[0]
+        print(f"oldest_checkpoint_path:{x}")
+        return x
+    return ""
+
+
 def plot_spectrogram_to_numpy(spectrogram):
     global MATPLOTLIB_FLAG
     if not MATPLOTLIB_FLAG:
@@ -288,6 +303,10 @@ def get_hparams(init=True):
                         help='finetune epochs')
     parser.add_argument('--cont', type=bool, default=False, help='whether to continue training on the latest checkpoint')
     parser.add_argument('--drop_speaker_embed', type=bool, default=False, help='whether to drop existing characters')
+    parser.add_argument('--train_with_pretrained_model', type=bool, default=True,
+                        help='whether to train with pretrained model')
+    parser.add_argument('--preserved', type=int, default=4,
+                        help='Number of preserved models')
 
     args = parser.parse_args()
     model_dir = os.path.join("./", args.model)
@@ -312,6 +331,8 @@ def get_hparams(init=True):
     hparams.max_epochs = args.max_epochs
     hparams.cont = args.cont
     hparams.drop_speaker_embed = args.drop_speaker_embed
+    hparams.train_with_pretrained_model = args.train_with_pretrained_model
+    hparams.preserved = args.preserved
     return hparams