Cleanup ijepa models, they're just gap (global-avg-pool) models w/o heads. fc-norm conversion was wrong, gigantic should have been giant

timm/models/vision_transformer.py

@@ -950,17 +950,6 @@ def _convert_dinov2(state_dict, model):
     return out_dict
 
 
-def _convert_ijepa(state_dict, model):
-    out_dict = {}
-    for k, v in state_dict['encoder'].items():
-        if k.startswith('module.'):
-            k = k[7:]
-        if k.startswith('norm.'):
-            k = 'fc_norm.' + k[5:]
-        out_dict[k] = v
-    return out_dict
-
-
 def checkpoint_filter_fn(
         state_dict,
         model,
@@ -973,6 +962,7 @@ def checkpoint_filter_fn(
     out_dict = {}
     state_dict = state_dict.get('model', state_dict)
     state_dict = state_dict.get('state_dict', state_dict)
+    prefix = ''
 
     if 'visual.class_embedding' in state_dict:
         return _convert_openai_clip(state_dict, model)
@@ -981,13 +971,17 @@ def checkpoint_filter_fn(
         state_dict = _convert_dinov2(state_dict, model)
 
     if "encoder" in state_dict:
-        state_dict = _convert_ijepa(state_dict, model)
+        state_dict = state_dict['encoder']
+        prefix = 'module.'
 
     if 'visual.trunk.pos_embed' in state_dict:
         # convert an OpenCLIP model with timm vision encoder
-        prefix = 'visual.trunk.'
-        state_dict = {k[len(prefix):]: v for k, v in state_dict.items() if k.startswith(prefix)}
         # FIXME remap final nn.Linear if it exists outside of the timm .trunk (ie in visual.head.proj)
+        prefix = 'visual.trunk.'
+
+    if prefix:
+        # filter on & remove prefix string from keys
+        state_dict = {k[len(prefix):]: v for k, v in state_dict.items() if k.startswith(prefix)}
 
     for k, v in state_dict.items():
         if 'patch_embed.proj.weight' in k:
@@ -1529,23 +1523,23 @@ default_cfgs = generate_default_cfgs({
         license='cc-by-nc-4.0',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
 
-    'vit_huge_patch14_ijepa_224.in1k': _cfg(
+    'vit_huge_patch14_gap_224.in1k_ijepa': _cfg(
         url='https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.14-300e.pth.tar',
         # hf_hub_id='timm/',
         license='cc-by-nc-4.0',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    'vit_huge_patch14_ijepa_224.in22k': _cfg(
+    'vit_huge_patch14_gap_224.in22k_ijepa': _cfg(
         url='https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.h.14-900e.pth.tar',
         # hf_hub_id='timm/',
         license='cc-by-nc-4.0',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    'vit_huge_patch16_ijepa_448.in1k': _cfg(
+    'vit_huge_patch16_gap_448.in1k_ijepa': _cfg(
         url='https://dl.fbaipublicfiles.com/ijepa/IN1K-vit.h.16-448px-300e.pth.tar',
         # hf_hub_id='timm/',
         license='cc-by-nc-4.0',
         input_size=(3, 448, 448), crop_pct=1.0,
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
-    'vit_gigantic_patch16_ijepa_224.in22k': _cfg(
+    'vit_giant_patch16_gap_224.in22k_ijepa': _cfg(
         url='https://dl.fbaipublicfiles.com/ijepa/IN22K-vit.g.16-600e.pth.tar',
         # hf_hub_id='timm/',
         license='cc-by-nc-4.0',
@@ -1856,7 +1850,7 @@ def vit_medium_patch16_gap_384(pretrained=False, **kwargs) -> VisionTransformer:
 
 @register_model
 def vit_base_patch16_gap_224(pretrained=False, **kwargs) -> VisionTransformer:
-    """ ViT-Base (ViT-B/16) w/o class token, w/ avg-pool @ 256x256
+    """ ViT-Base (ViT-B/16) w/o class token, w/ avg-pool @ 224x224
     """
     model_args = dict(
         patch_size=16, embed_dim=768, depth=12, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
@@ -1865,6 +1859,40 @@ def vit_base_patch16_gap_224(pretrained=False, **kwargs) -> VisionTransformer:
     return model
 
 
+@register_model
+def vit_huge_patch14_gap_224(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-Huge model (ViT-H/14) w/ no class token, avg pool
+    """
+    model_args = dict(
+        patch_size=14, embed_dim=1280, depth=32, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
+    model = _create_vision_transformer(
+        'vit_huge_patch14_gap_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_huge_patch16_gap_448(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-Huge model (ViT-H/16) w/ no class token, avg pool @ 448x448
+    """
+    model_args = dict(
+        patch_size=16, embed_dim=1280, depth=32, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
+    model = _create_vision_transformer(
+        'vit_huge_patch16_gap_448', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_giant_patch16_gap_224(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-Giant (little-gg) model (ViT-g/16) w/ no class token, avg pool
+    """
+    model_args = dict(
+        patch_size=16, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=48/11,
+        class_token=False, global_pool='avg', fc_norm=False)
+    model = _create_vision_transformer(
+        'vit_giant_patch16_gap_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 @register_model
 def vit_base_patch32_clip_224(pretrained=False, **kwargs) -> VisionTransformer:
     """ ViT-B/32 CLIP image tower @ 224x224
@@ -2190,37 +2218,6 @@ def vit_giant_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
     return model
 
 
-@register_model
-def vit_huge_patch14_ijepa_224(pretrained=False, **kwargs) -> VisionTransformer:
-    """ ViT-Huge model (ViT-H/14) from `I-JEPA` - https://arxiv.org/abs/2301.08243
-    """
-    model_args = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, class_token=False, global_pool='avg')
-    model = _create_vision_transformer(
-        'vit_huge_patch14_ijepa_224', pretrained=pretrained, **dict(model_args, **kwargs))
-    return model
-
-
-@register_model
-def vit_huge_patch16_ijepa_448(pretrained=False, **kwargs) -> VisionTransformer:
-    """ ViT-Huge model (ViT-H/16) from `I-JEPA` - https://arxiv.org/abs/2301.08243
-    """
-    model_args = dict(
-        patch_size=16, embed_dim=1280, depth=32, num_heads=16, class_token=False, global_pool='avg', img_size=448)
-    model = _create_vision_transformer(
-        'vit_huge_patch16_ijepa_448', pretrained=pretrained, **dict(model_args, **kwargs))
-    return model
-
-
-@register_model
-def vit_gigantic_patch16_ijepa_224(pretrained=False, **kwargs) -> VisionTransformer:
-    """ ViT-Gigantic (big-G) model (ViT-G/16) from `I-JEPA - https://arxiv.org/abs/2301.08243
-    """
-    model_args = dict(patch_size=16, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16)
-    model = _create_vision_transformer(
-        'vit_gigantic_patch16_ijepa_224', pretrained=pretrained, **dict(model_args, **kwargs))
-    return model
-
-
 @register_model
 def vit_base_patch16_siglip_224(pretrained=False, **kwargs) -> VisionTransformer:
     model_args = dict(