From 729fd0d53b24a6b76bea95ae1e1302680dae7858 Mon Sep 17 00:00:00 2001
From: Fredo Guan <fredo.guan@hotmail.com>
Date: Sat, 10 Dec 2022 21:04:33 -0800
Subject: [PATCH] Update davit.py

---
 timm/models/davit.py | 27 ++++++++++-----------------
 1 file changed, 10 insertions(+), 17 deletions(-)

diff --git a/timm/models/davit.py b/timm/models/davit.py
index b1f82e23..3cca1a3d 100644
--- a/timm/models/davit.py
+++ b/timm/models/davit.py
@@ -788,7 +788,7 @@ class DaViT(nn.Module):
         if global_pool is None:
             global_pool = self.head.global_pool.pool_type
         self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
-    
+    '''
     def forward_network(self, x : Tensor):
         size: Tuple[int, int] = (x.size(2), x.size(3))
         features = [x]
@@ -808,16 +808,16 @@ class DaViT(nn.Module):
     
     def forward_pyramid_features(self, x) -> List[Tensor]:
         x = self.forward_network(x)
-        '''
+        
         outs = []
         for i, out in enumerate(x):
             H, W = sizes[i]
             outs.append(out.view(-1, H, W, self.embed_dims[i]).permute(0, 3, 1, 2).contiguous())   
-        '''
+        
         return x
-
+    '''
     def forward_features(self, x):
-        x = self.forward_network(x)
+        x = self.stages(x)
         # take final feature and norm
         x = self.norms(x[-1].permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
         #H, W = sizes[-1]
@@ -834,7 +834,7 @@ class DaViT(nn.Module):
         
     def forward(self, x):
         return self.forward_classifier(x)
-
+'''
 class DaViTFeatures(DaViT):
 
     def __init__(self, *args, **kwargs):
@@ -843,7 +843,7 @@ class DaViTFeatures(DaViT):
     
     def forward(self, x) -> List[Tensor]:
         return self.forward_pyramid_features(x)
-
+'''
 def checkpoint_filter_fn(state_dict, model):
     """ Remap MSFT checkpoints -> timm """
     if 'head.norm.weight' in state_dict:
@@ -866,16 +866,11 @@ def checkpoint_filter_fn(state_dict, model):
 
 def _create_davit(variant, pretrained=False, **kwargs):
 
-    model_cls = DaViT
-    features_only = False
-    kwargs_filter = None
+    
 
     default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
     out_indices = kwargs.pop('out_indices', default_out_indices)
-    if kwargs.pop('features_only', False):
-        model_cls = DaViTFeatures
-        kwargs_filter = ('num_classes', 'global_pool')
-        features_only = True
+
     model = build_model_with_cfg(
         model_cls,
         variant,
@@ -883,9 +878,7 @@ def _create_davit(variant, pretrained=False, **kwargs):
         pretrained_filter_fn=checkpoint_filter_fn,
         feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
         **kwargs)
-    if features_only:
-        model.pretrained_cfg = pretrained_cfg_for_features(model.default_cfg)
-        model.default_cfg = model.pretrained_cfg  # backwards compat
+
     return model