Add explicit GAP (avg pool) variants of other SigLIP models.

Author: rwightman

timm/models/vision_transformer.py

@@ -1778,6 +1778,35 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
         input_size=(3, 384, 384),
         num_classes=0),
 
+    'vit_base_patch16_siglip_gap_224.webli': _cfg(
+        hf_hub_id='timm/ViT-B-16-SigLIP',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        num_classes=0),
+    'vit_base_patch16_siglip_gap_256.webli': _cfg(
+        hf_hub_id='timm/ViT-B-16-SigLIP-256',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        input_size=(3, 256, 256),
+        num_classes=0),
+    'vit_base_patch16_siglip_gap_384.webli': _cfg(
+        hf_hub_id='timm/ViT-B-16-SigLIP-384',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        input_size=(3, 384, 384),
+        num_classes=0),
+    'vit_base_patch16_siglip_gap_512.webli': _cfg(
+        hf_hub_id='timm/ViT-B-16-SigLIP-512',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        input_size=(3, 512, 512),
+        num_classes=0),
+    'vit_large_patch16_siglip_gap_256.webli': _cfg(
+        hf_hub_id='timm/ViT-L-16-SigLIP-256',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        input_size=(3, 256, 256),
+        num_classes=0),
+    'vit_large_patch16_siglip_gap_384.webli': _cfg(
+        hf_hub_id='timm/ViT-L-16-SigLIP-384',
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        input_size=(3, 384, 384),
+        num_classes=0),
     'vit_so400m_patch14_siglip_gap_224.webli': _cfg(
         hf_hub_id='timm/ViT-SO400M-14-SigLIP',
         hf_hub_filename='open_clip_pytorch_model.bin',
@@ -2803,8 +2832,75 @@ def vit_so400m_patch14_siglip_384(pretrained: bool = False, **kwargs) -> VisionT
     return model
 
 
+@register_model
+def vit_base_patch16_siglip_gap_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
+    model_args = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='avg', fc_norm=False,
+    )
+    model = _create_vision_transformer(
+        'vit_base_patch16_siglip_gap_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_base_patch16_siglip_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
+    model_args = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='avg', fc_norm=False,
+    )
+    model = _create_vision_transformer(
+        'vit_base_patch16_siglip_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_base_patch16_siglip_gap_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
+    model_args = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='avg', fc_norm=False,
+    )
+    model = _create_vision_transformer(
+        'vit_base_patch16_siglip_gap_384', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_base_patch16_siglip_gap_512(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
+    model_args = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='avg', fc_norm=False,
+    )
+    model = _create_vision_transformer(
+        'vit_base_patch16_siglip_gap_512', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_large_patch16_siglip_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
+    model_args = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, class_token=False, global_pool='avg', fc_norm=False,
+    )
+    model = _create_vision_transformer(
+        'vit_large_patch16_siglip_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_large_patch16_siglip_gap_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
+    model_args = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, class_token=False, global_pool='avg', fc_norm=False,
+    )
+    model = _create_vision_transformer(
+        'vit_large_patch16_siglip_gap_384', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 @register_model
 def vit_so400m_patch14_siglip_gap_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
     model_args = dict(
         patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362,
         class_token=False, global_pool='avg', fc_norm=False,
@@ -2816,6 +2912,7 @@ def vit_so400m_patch14_siglip_gap_224(pretrained: bool = False, **kwargs) -> Vis
 
 @register_model
 def vit_so400m_patch14_siglip_gap_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
     model_args = dict(
         patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362,
         class_token=False, global_pool='avg', fc_norm=False,
@@ -2827,6 +2924,7 @@ def vit_so400m_patch14_siglip_gap_384(pretrained: bool = False, **kwargs) -> Vis
 
 @register_model
 def vit_so400m_patch14_siglip_gap_448(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
     model_args = dict(
         patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362,
         class_token=False, global_pool='avg', fc_norm=False,
@@ -2838,6 +2936,7 @@ def vit_so400m_patch14_siglip_gap_448(pretrained: bool = False, **kwargs) -> Vis
 
 @register_model
 def vit_so400m_patch14_siglip_gap_896(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
     model_args = dict(
         patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362,
         class_token=False, global_pool='avg', fc_norm=False,