Add pretrained param load log

pdseg/train.py

@@ -261,6 +261,7 @@ def train(cfg):
     elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL):
         print('Pretrained model dir:', cfg.TRAIN.PRETRAINED_MODEL)
         load_vars = []
+        load_fail_vars = []
 
         def var_shape_matched(var, shape):
             """
@@ -271,12 +272,7 @@ def train(cfg):
             if var_exist:
                 var_shape = parse_shape_from_file(
                     os.path.join(cfg.TRAIN.PRETRAINED_MODEL, var.name))
-                if var_shape == shape:
-                    return True
-                else:
-                    print(
-                        "Variable[{}] shape does not match current network, skip"
-                        " to load it.".format(var.name))
+                return var_shape == shape
             return False
 
         for x in train_prog.list_vars():
@@ -285,13 +281,22 @@ def train(cfg):
                     x.name).get_tensor().shape())
                 if var_shape_matched(x, shape):
                     load_vars.append(x)
+                else:
+                    load_fail_vars.append(x)
         if cfg.MODEL.FP16:
             # If open FP16 training mode, load FP16 var separate
             load_fp16_vars(exe, cfg.TRAIN.PRETRAINED_MODEL, train_prog)
         else:
             fluid.io.load_vars(
                 exe, dirname=cfg.TRAIN.PRETRAINED_MODEL, vars=load_vars)
-        print("Pretrained model loaded successfully!")
+        for var in load_vars:
+            print("Parameter[{}] loaded sucessfully!".format(var.name))
+        for var in load_fail_vars:
+            print("Parameter[{}] shape does not match current network, skip"
+                  " to load it.".format(var.name))
+        print("{}/{} pretrained parameters loaded successfully!".format(
+            len(load_vars),
+            len(load_vars) + len(load_fail_vars)))
     else:
         print('Pretrained model dir {} not exists, training from scratch...'.
               format(cfg.TRAIN.PRETRAINED_MODEL))