Update davit.py

timm/models/davit.py

@@ -383,6 +383,7 @@ class DaViT(nn.Module):
         img_size=224,
         num_classes=1000,
         global_pool='avg'
+        features_only = False
     ):
         super().__init__()
 
@@ -398,7 +399,7 @@ class DaViT(nn.Module):
         self.num_features = embed_dims[-1]
         self.drop_rate=drop_rate
         self.grad_checkpointing = False
-        
+        self.features_only = False
 
         self.patch_embeds = nn.ModuleList([
             PatchEmbed(patch_size=patch_size if i == 0 else 2,
@@ -567,14 +568,19 @@ class DaViT(nn.Module):
         return x
     
     def forward_head(self, x, pre_logits: bool = False):
-        
         return self.head(x, pre_logits=pre_logits)
         
-    def forward(self, x):
+    def forward_classification(self, x):
         x = self.forward_features(x)
         x = self.forward_head(x)
         return x
     
+    def forward(self, x):
+        if self.features_only == True:
+            return forward_features_full(x)
+        else:
+            return forward(x)
+
 def checkpoint_filter_fn(state_dict, model):
     """ Remap MSFT checkpoints -> timm """
     if 'head.norm.weight' in state_dict: