Add mlp head support for convnext_large, add laion2b CLIP weights, prep fine-tuned weight tags

timm/models/convnext.py

@@ -397,6 +397,11 @@ def checkpoint_filter_fn(state_dict, model):
         if 'visual.head.proj.weight' in state_dict:
             out_dict['head.fc.weight'] = state_dict['visual.head.proj.weight']
             out_dict['head.fc.bias'] = torch.zeros(state_dict['visual.head.proj.weight'].shape[0])
+        elif 'visual.head.mlp.fc1.weight' in state_dict:
+            out_dict['head.pre_logits.fc.weight'] = state_dict['visual.head.mlp.fc1.weight']
+            out_dict['head.pre_logits.fc.bias'] = state_dict['visual.head.mlp.fc1.bias']
+            out_dict['head.fc.weight'] = state_dict['visual.head.mlp.fc2.weight']
+            out_dict['head.fc.bias'] = torch.zeros(state_dict['visual.head.mlp.fc2.weight'].shape[0])
         return out_dict
 
     import re
@@ -716,6 +721,16 @@ default_cfgs = generate_default_cfgs({
 
     'convnextv2_small.untrained': _cfg(),
 
+    # CLIP weights, fine-tuned on in1k or in12k + in1k
+    'convnext_base.clip_laion2b_augreg_ft_in1k': _cfg(
+        # hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0),
+    'convnext_base.clip_laiona_augreg_ft_in1k_384': _cfg(
+        # hf_hub_id='timm/',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
+
     # CLIP based weights, original image tower weights and fine-tunes
     'convnext_base.clip_laion2b': _cfg(
         hf_hub_id='laion/CLIP-convnext_base_w-laion2B-s13B-b82K',
@@ -742,6 +757,11 @@ default_cfgs = generate_default_cfgs({
         hf_hub_filename='open_clip_pytorch_model.bin',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
         input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, num_classes=640),
+    'convnext_large_mlp.clip_laion2b_augreg': _cfg(
+        hf_hub_id='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
+        input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=768),
 })
 
 
@@ -854,6 +874,13 @@ def convnext_large(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def convnext_large_mlp(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], head_hidden_size=1536, **kwargs)
+    model = _create_convnext('convnext_large_mlp', pretrained=pretrained, **model_args)
+    return model
+
+
 @register_model
 def convnext_xlarge(pretrained=False, **kwargs):
     model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)