add tinyclip 8m

Author: gau-nernst

timm/models/vision_transformer.py

@@ -1015,6 +1015,8 @@ def checkpoint_filter_fn(
         return _convert_openai_clip(state_dict, model)
     elif 'module.visual.class_embedding' in state_dict:
         return _convert_openai_clip(state_dict, model, prefix='module.visual.')
+    elif '_image_encoder.module.visual.class_embedding' in state_dict:
+        return _convert_openai_clip(state_dict, model, prefix='_image_encoder.module.visual.')
 
     if "mask_token" in state_dict:
         state_dict = _convert_dinov2(state_dict, model)
@@ -1735,6 +1737,10 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
         input_size=(3, 384, 384),
         num_classes=0),
 
+    'vit_8m_patch16_tinyclip_224.yfcc15m': _cfg(
+        url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M.pt',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
+
     'vit_medium_patch16_reg4_256': _cfg(
         input_size=(3, 256, 256)),
     'vit_medium_patch16_reg4_gap_256': _cfg(
@@ -2621,6 +2627,14 @@ def vit_so400m_patch14_siglip_384(pretrained: bool = False, **kwargs) -> VisionT
     return model
 
 
+@register_model
+def vit_8m_patch16_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    model_args = dict(embed_dim=256, depth=10, num_heads=4, pre_norm=True, norm_layer=nn.LayerNorm)
+    model = _create_vision_transformer(
+        'vit_8m_patch16_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 @register_model
 def vit_medium_patch16_reg4_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
     model_args = dict(