Fix default_cfgs

Author: brianhou0208

tests/test_models.py

@@ -53,7 +53,7 @@
     'vision_transformer', 'vision_transformer_sam', 'vision_transformer_hybrid', 'vision_transformer_relpos',
     'beit', 'mvitv2', 'eva', 'cait', 'xcit', 'volo', 'twins', 'deit', 'swin_transformer', 'swin_transformer_v2',
     'swin_transformer_v2_cr', 'maxxvit', 'efficientnet', 'mobilenetv3', 'levit', 'efficientformer', 'resnet',
-    'regnet', 'byobnet', 'byoanet', 'mlp_mixer', 'hiera', 'fastvit', 'hieradet_sam2', 'aimv2*'
+    'regnet', 'byobnet', 'byoanet', 'mlp_mixer', 'hiera', 'fastvit', 'hieradet_sam2', 'aimv2*', 'tnt',
 ]
 
 # transformer / hybrid models don't support full set of spatial / feature APIs and/or have spatial output.

timm/models/tnt.py

@@ -20,7 +20,7 @@
 from ._builder import build_model_with_cfg
 from ._features import feature_take_indices
 from ._manipulate import checkpoint
-from ._registry import register_model
+from ._registry import generate_default_cfgs, register_model
 
 
 __all__ = ['TNT']  # model_registry will add each entrypoint fn to this
@@ -450,11 +450,14 @@ def _cfg(url='', **kwargs):
         'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
         'first_conv': 'pixel_embed.proj', 'classifier': 'head',
+        'paper_ids': 'arXiv:2103.00112',
+        'paper_name': 'Transformer in Transformer',
+        'origin_url': 'https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tnt_pytorch',
         **kwargs
     }
 
 
-default_cfgs = {
+default_cfgs = generate_default_cfgs({
     'tnt_s_patch16_224.in1k': _cfg(
         # hf_hub_id='timm/',
         # url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
@@ -464,7 +467,7 @@ def _cfg(url='', **kwargs):
         # hf_hub_id='timm/',
         url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_b_82.9.pth.tar',
     ),
-}
+})
 
 
 def checkpoint_filter_fn(state_dict, model):