update

timm/models/__init__.py

@@ -8,6 +8,7 @@ from .convmixer import *
 from .convnext import *
 from .crossvit import *
 from .cspnet import *
+from .davit import *
 from .deit import *
 from .densenet import *
 from .dla import *

timm/models/davit.py

@@ -22,12 +22,17 @@ from typing import Tuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import timm
+from .helpers import build_model_with_cfg
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_, SelectAdaptivePool2d, ClassifierHead, Mlp
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .layers import DropPath, to_2tuple, trunc_normal_, SelectAdaptivePool2d, ClassifierHead, Mlp
 from collections import OrderedDict
 import torch.utils.checkpoint as checkpoint
+from .pretrained import generate_default_cfgs
+from .registry import register_model
 
 
+__all__ = ['DaViT']
+
 
 
 
@@ -553,6 +558,31 @@ class DaViT(nn.Module):
         return model
     
     
+    
+    def _cfg(url='', **kwargs): # not sure how this should be set up
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
+        'crop_pct': 0.875, 'interpolation': 'bilinear',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'patch_embeds.0.proj', 'classifier': 'head.fc',
+        **kwargs
+    }
+    
+    
+    
+    default_cfgs = generate_default_cfgs({
+    
+    'davit_tiny.msft_in1k': _cfg(
+        url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/untagged-b2178bcf50f43d660d99/davit_tiny_ed28dd55.pth.tar"),
+    'davit_small.msft_in1k': _cfg(
+        url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/untagged-b2178bcf50f43d660d99/davit_small_d1ecf281.pth.tar"),
+    'davit_base.msft_in1k': _cfg(
+        url="https://github.com/fffffgggg54/pytorch-image-models/releases/download/untagged-b2178bcf50f43d660d99/davit_base_67d9ac26.pth.tar"),
+})
+
+    
+    
     @register_model
     def davit_tiny(pretrained=False, **kwargs):
         model_kwargs = dict(depths=(1, 1, 3, 1), embed_dims=(96, 192, 384, 768),