supportfunction of incremental training

example/ssd_coco2017/train.py

@@ -87,7 +87,7 @@ def main():
     parser.add_argument("--dataset", type=str, default="coco", help="Dataset, defalut is coco.")
     parser.add_argument("--epoch_size", type=int, default=70, help="Epoch size, default is 70.")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
-    parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path.")
+    parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained Checkpoint file path.")
     parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.")
     parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
     args_opt = parser.parse_args()
@@ -157,8 +157,8 @@ def main():
         opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, 0.9, 0.0001, loss_scale)
         net = TrainingWrapper(net, opt, loss_scale)
 
-        if args_opt.checkpoint_path != "":
-            param_dict = load_checkpoint(args_opt.checkpoint_path)
+        if args_opt.pre_trained:
+            param_dict = load_checkpoint(args_opt.pre_trained)
             load_param_into_net(net, param_dict)
 
         callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]

example/yolov3_coco2017/train.py

@@ -70,7 +70,7 @@ def main():
     parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
     parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
-    parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
+    parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained checkpoint file path")
     parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.")
     parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
     parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_train",
@@ -138,8 +138,8 @@ def main():
         opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
         net = TrainingWrapper(net, opt, loss_scale)
 
-        if args_opt.checkpoint_path != "":
-            param_dict = load_checkpoint(args_opt.checkpoint_path)
+        if args_opt.pre_trained:
+            param_dict = load_checkpoint(args_opt.pre_trained)
             load_param_into_net(net, param_dict)
 
         callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]