swin v2 fixup for latest changes on norm_norm_norm / bits_and_tpu branch

timm/models/swin_transformer_v2_cr.py

@@ -37,7 +37,7 @@ import torch.utils.checkpoint as checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .fx_features import register_notrace_function
-from .helpers import build_model_with_cfg, overlay_external_default_cfg, named_apply
+from .helpers import build_model_with_cfg, named_apply
 from .layers import DropPath, Mlp, to_2tuple, _assert
 from .registry import register_model
 from .vision_transformer import checkpoint_filter_fn
@@ -754,29 +754,10 @@ def init_weights(module: nn.Module, name: str = ''):
             nn.init.zeros_(module.bias)
 
 
-def _create_swin_transformer_v2_cr(variant, pretrained=False, default_cfg=None, **kwargs):
-    if default_cfg is None:
-        default_cfg = deepcopy(default_cfgs[variant])
-    overlay_external_default_cfg(default_cfg, kwargs)
-    default_num_classes = default_cfg['num_classes']
-    default_img_size = default_cfg['input_size'][-2:]
-
-    num_classes = kwargs.pop('num_classes', default_num_classes)
-    img_size = kwargs.pop('img_size', default_img_size)
+def _create_swin_transformer_v2_cr(variant, pretrained=False, **kwargs):
     if kwargs.get('features_only', None):
         raise RuntimeError('features_only not implemented for Vision Transformer models.')
-
-    model = build_model_with_cfg(
-        SwinTransformerV2Cr,
-        variant,
-        pretrained,
-        default_cfg=default_cfg,
-        img_size=img_size,
-        num_classes=num_classes,
-        pretrained_filter_fn=checkpoint_filter_fn,
-        **kwargs
-    )
-
+    model = build_model_with_cfg(SwinTransformerV2Cr, variant, pretrained, **kwargs)
     return model