Update experimental vit model configs

Author: rwightman

timm/models/vision_transformer.py

@@ -1723,7 +1723,12 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
         input_size=(3, 256, 256)),
     'vit_medium_patch16_reg4_gap_256': _cfg(
         input_size=(3, 256, 256)),
-    'vit_base_patch16_reg8_gap_256': _cfg(input_size=(3, 256, 256)),
+    'vit_base_patch16_reg4_gap_256': _cfg(
+        input_size=(3, 256, 256)),
+    'vit_so150m_patch16_reg4_gap_256': _cfg(
+        input_size=(3, 256, 256)),
+    'vit_so150m_patch16_reg4_map_256': _cfg(
+        input_size=(3, 256, 256)),
 }
 
 _quick_gelu_cfgs = [
@@ -2623,13 +2628,35 @@ def vit_medium_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> Visio
 
 
 @register_model
-def vit_base_patch16_reg8_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
+def vit_base_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
     model_args = dict(
         patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False,
-        no_embed_class=True, global_pool='avg', reg_tokens=8,
+        no_embed_class=True, global_pool='avg', reg_tokens=4,
+    )
+    model = _create_vision_transformer(
+        'vit_base_patch16_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_so150m_patch16_reg4_map_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    model_args = dict(
+        patch_size=16, embed_dim=896, depth=18, num_heads=14, mlp_ratio=2.572,
+        class_token=False, reg_tokens=4, global_pool='map',
+    )
+    model = _create_vision_transformer(
+        'vit_so150m_patch16_reg4_map_256', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_so150m_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    model_args = dict(
+        patch_size=16, embed_dim=896, depth=18, num_heads=14, mlp_ratio=2.572,
+        class_token=False, reg_tokens=4, global_pool='avg', fc_norm=False,
     )
     model = _create_vision_transformer(
-        'vit_base_patch16_reg8_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
+        'vit_so150m_patch16_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
     return model