add filter_wieght

model_zoo/official/cv/ssd/eval.py

@@ -78,6 +78,8 @@ if __name__ == '__main__':
     prefix = "ssd_eval.mindrecord"
     mindrecord_dir = config.mindrecord_dir
     mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
+    if args_opt.dataset == "voc":
+        config.coco_root = config.voc_root
     if not os.path.exists(mindrecord_file):
         if not os.path.isdir(mindrecord_dir):
             os.makedirs(mindrecord_dir)

model_zoo/official/cv/ssd/train.py

@@ -46,6 +46,7 @@ def main():
     parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size.")
     parser.add_argument("--save_checkpoint_epochs", type=int, default=10, 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("--filter_weight", type=bool, default=False, help="Filter weight parameters, default is False.")
     args_opt = parser.parse_args()
 
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
@@ -117,7 +118,8 @@ def main():
             if args_opt.pre_trained_epoch_size <= 0:
                 raise KeyError("pre_trained_epoch_size must be greater than 0.")
             param_dict = load_checkpoint(args_opt.pre_trained)
-            filter_checkpoint_parameter(param_dict)
+            if args_opt.filter_weight:
+                filter_checkpoint_parameter(param_dict)
             load_param_into_net(net, param_dict)
 
         lr = Tensor(get_lr(global_step=config.global_step,