diff --git a/dygraph/paddleseg/utils/utils.py b/dygraph/paddleseg/utils/utils.py
index 02f7d3b7f9b9f3c8f2674e8ee0dcf7f52730b236..8b4a731ae0312b9eb1748f51a6046db834519d3e 100644
--- a/dygraph/paddleseg/utils/utils.py
+++ b/dygraph/paddleseg/utils/utils.py
@@ -12,28 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import contextlib
 import os
 import numpy as np
 import math
 import cv2
-import tempfile
 import paddle.fluid as fluid
-from urllib.parse import urlparse, unquote
 
-import filelock
-
-import paddleseg.env as segenv
-from paddleseg.utils import logger
-from paddleseg.utils.download import download_file_and_uncompress
-
-
-@contextlib.contextmanager
-def generate_tempdir(directory: str = None, **kwargs):
-    '''Generate a temporary directory'''
-    directory = segenv.TMP_HOME if not directory else directory
-    with tempfile.TemporaryDirectory(dir=directory, **kwargs) as _dir:
-        yield _dir
+from . import logger
 
 
 def seconds_to_hms(seconds):
@@ -44,21 +29,21 @@ def seconds_to_hms(seconds):
     return hms_str
 
 
+def load_entire_model(model, pretrained):
+
+    if pretrained is not None:
+        if os.path.exists(pretrained):
+            load_pretrained_model(model, pretrained)
+        else:
+            raise Exception('Pretrained model is not found: {}'.format(
+                pretrained))
+    else:
+        logger.warning('Not all pretrained parameters of {} to load, '\
+             'training from scratch or a pretrained backbone'.format(model.__class__.__name__))
+
 def load_pretrained_model(model, pretrained_model):
     if pretrained_model is not None:
         logger.info('Load pretrained model from {}'.format(pretrained_model))
-        # download pretrained model from url
-        if urlparse(pretrained_model).netloc:
-            pretrained_model = unquote(pretrained_model)
-            savename = pretrained_model.split('/')[-1].split('.')[0]
-            with generate_tempdir() as _dir:
-                with filelock.FileLock(os.path.join(segenv.TMP_HOME, savename)):
-                    pretrained_model = download_file_and_uncompress(
-                        pretrained_model,
-                        savepath=_dir,
-                        extrapath=segenv.PRETRAINED_MODEL_HOME,
-                        extraname=savename)
-
         if os.path.exists(pretrained_model):
             ckpt_path = os.path.join(pretrained_model, 'model')
             try: