diff --git a/timm/models/davit.py b/timm/models/davit.py
index 7834546c..e0bb0aa0 100644
--- a/timm/models/davit.py
+++ b/timm/models/davit.py
@@ -382,8 +382,8 @@ class DaViT(nn.Module):
         attn_drop_rate=0.,
         img_size=224,
         num_classes=1000,
-        global_pool='avg',
-        features_only = False
+        global_pool='avg'#,
+        #features_only = False
     ):
         super().__init__()
 
@@ -399,7 +399,8 @@ class DaViT(nn.Module):
         self.num_features = embed_dims[-1]
         self.drop_rate=drop_rate
         self.grad_checkpointing = False
-        self._features_only = False
+        self._features_only = features_only
+        self.feature_info = []
 
         self.patch_embeds = nn.ModuleList([
             PatchEmbed(patch_size=patch_size if i == 0 else 2,
@@ -438,7 +439,10 @@ class DaViT(nn.Module):
                     for attention_id, attention_type in enumerate(attention_types)]
                 ) for layer_id, item in enumerate(block_param)
             ])
-            main_blocks.append(block)
+            
+            main_blocks.append((f'block.{block_id}', block))
+            
+            self.feature_info += [dict(num_ch=self.embed_dims[block_id], reduction = 2, module=f'block.{block_id}')]
         self.main_blocks = nn.ModuleList(main_blocks)
         
         '''
@@ -457,7 +461,7 @@ class DaViT(nn.Module):
         self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
         self.apply(self._init_weights)
         
-        self._update_forward_fn()
+        #self._update_forward_fn()
         
         #self.forward = self._get_forward_fn()
         '''
@@ -482,6 +486,7 @@ class DaViT(nn.Module):
         else:
             return self.forward_classification
     '''
+    
     @torch.jit.ignore
     def _update_forward_fn(self):
         if self._features_only == True: