diff --git a/dygraph/paddleseg/models/backbones/resnet_vd.py b/dygraph/paddleseg/models/backbones/resnet_vd.py
index d7dfc66fd5dc44a6a27c04eea73dc692f857c61c..2876c753eb36f2da0f0bd955ba9c614aabf71616 100644
--- a/dygraph/paddleseg/models/backbones/resnet_vd.py
+++ b/dygraph/paddleseg/models/backbones/resnet_vd.py
@@ -363,7 +363,7 @@ class ResNet_vd(fluid.dygraph.Layer):
         y = self.pool2d_avg(y)
         y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
         y = self.out(y)
-        return y, feat_list
+        return feat_list
 
     # def init_weight(self, pretrained_model=None):
 
@@ -385,6 +385,7 @@ class ResNet_vd(fluid.dygraph.Layer):
                     pretrained_model))
 
 
+@manager.BACKBONES.add_component
 def ResNet18_vd(**args):
     model = ResNet_vd(layers=18, **args)
     return model
diff --git a/dygraph/paddleseg/models/fcn.py b/dygraph/paddleseg/models/fcn.py
index 87446e017d142aa15aa373b9c17976701576a387..2015609d5efbe60c16ae29d72d153ddf9272db42 100644
--- a/dygraph/paddleseg/models/fcn.py
+++ b/dygraph/paddleseg/models/fcn.py
@@ -70,11 +70,11 @@ class FCN(fluid.dygraph.Layer):
         self.model_pretrained = model_pretrained
         self.backbone_indices = backbone_indices
         if channels is None:
-            channels = backbone_channels[backbone_indices[0]]
+            channels = backbone_channels[0]
 
         self.backbone = backbone
         self.conv_last_2 = ConvBNLayer(
-            num_channels=backbone_channels[backbone_indices[0]],
+            num_channels=backbone_channels[0],
             num_filters=channels,
             filter_size=1,
             stride=1)
diff --git a/dygraph/paddleseg/utils/config.py b/dygraph/paddleseg/utils/config.py
index 270cabba6281980e79dfd1735756c49169e938ae..7be20929f3e2053eda0cb891e03a49c105c7de75 100644
--- a/dygraph/paddleseg/utils/config.py
+++ b/dygraph/paddleseg/utils/config.py
@@ -137,6 +137,12 @@ class Config(object):
             lr = self.learning_rate
             args = self.optimizer_args
             args.setdefault('momentum', 0.9)
+            weight_decay = args.get('weight_decay', None)
+            if weight_decay is not None:
+                args.pop('weight_decay')
+                regularization = fluid.regularizer.L2DecayRegularizer(
+                    regularization_coeff=weight_decay)
+            args.setdefault('regularization', None)
             return fluid.optimizer.Momentum(
                 lr, parameter_list=self.model.parameters(), **args)
         else: