Add cc-by-nc-4.0 license for metaclip, make note in quickgelu model def about pretrained_cfg mapping

Author: rwightman

timm/models/vision_transformer.py

@@ -1473,18 +1473,22 @@ def _cfg(url='', **kwargs):
     'vit_base_patch32_clip_224.metaclip_2pt5b': _cfg(
         hf_hub_id='facebook/metaclip-b32-fullcc2.5b',
         hf_hub_filename='metaclip_b32_fullcc2.5b.bin',
+        license='cc-by-nc-4.0',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
     'vit_base_patch16_clip_224.metaclip_2pt5b': _cfg(
         hf_hub_id='facebook/metaclip-b16-fullcc2.5b',
         hf_hub_filename='metaclip_b16_fullcc2.5b.bin',
+        license='cc-by-nc-4.0',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
     'vit_large_patch14_clip_224.metaclip_2pt5b': _cfg(
         hf_hub_id='facebook/metaclip-l14-fullcc2.5b',
         hf_hub_filename='metaclip_l14_fullcc2.5b.bin',
+        license='cc-by-nc-4.0',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
     'vit_huge_patch14_clip_224.metaclip_2pt5b': _cfg(
         hf_hub_id='facebook/metaclip-h14-fullcc2.5b',
         hf_hub_filename='metaclip_h14_fullcc2.5b.bin',
+        license='cc-by-nc-4.0',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
 
     'vit_base_patch32_clip_224.openai': _cfg(
@@ -2129,7 +2133,8 @@ def vit_base_patch32_clip_quickgelu_224(pretrained=False, **kwargs) -> VisionTra
         patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True,
         norm_layer=nn.LayerNorm, act_layer='quick_gelu')
     model = _create_vision_transformer(
-        'vit_base_patch32_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+        'vit_base_patch32_clip_224',  # map to non quickgelu pretrained_cfg intentionally
+        pretrained=pretrained, **dict(model_args, **kwargs))
     return model
 
 
@@ -2141,7 +2146,8 @@ def vit_base_patch16_clip_quickgelu_224(pretrained=False, **kwargs) -> VisionTra
         patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True,
         norm_layer=nn.LayerNorm, act_layer='quick_gelu')
     model = _create_vision_transformer(
-        'vit_base_patch16_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+        'vit_base_patch16_clip_224',  # map to non quickgelu pretrained_cfg intentionally
+        pretrained=pretrained, **dict(model_args, **kwargs))
     return model
 
 
@@ -2154,7 +2160,8 @@ def vit_large_patch14_clip_quickgelu_224(pretrained=False, **kwargs) -> VisionTr
         patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True,
         norm_layer=nn.LayerNorm, act_layer='quick_gelu')
     model = _create_vision_transformer(
-        'vit_large_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+        'vit_large_patch14_clip_224',  # map to non quickgelu pretrained_cfg intentionally
+        pretrained=pretrained, **dict(model_args, **kwargs))
     return model
 
 
@@ -2166,7 +2173,8 @@ def vit_large_patch14_clip_quickgelu_336(pretrained=False, **kwargs) -> VisionTr
         patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True,
         norm_layer=nn.LayerNorm, act_layer='quick_gelu')
     model = _create_vision_transformer(
-        'vit_large_patch14_clip_336', pretrained=pretrained, **dict(model_args, **kwargs))
+        'vit_large_patch14_clip_336',  # map to non quickgelu pretrained_cfg intentionally
+        pretrained=pretrained, **dict(model_args, **kwargs))
     return model
 
 
@@ -2178,7 +2186,8 @@ def vit_huge_patch14_clip_quickgelu_224(pretrained=False, **kwargs) -> VisionTra
         patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True,
         norm_layer=nn.LayerNorm, act_layer='quick_gelu')
     model = _create_vision_transformer(
-        'vit_huge_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+        'vit_huge_patch14_clip_224',  # map to non quickgelu pretrained_cfg intentionally
+        pretrained=pretrained, **dict(model_args, **kwargs))
     return model
 
 
@@ -2190,7 +2199,8 @@ def vit_huge_patch14_clip_quickgelu_378(pretrained=False, **kwargs) -> VisionTra
         patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True,
         norm_layer=nn.LayerNorm, act_layer='quick_gelu')
     model = _create_vision_transformer(
-        'vit_huge_patch14_clip_378', pretrained=pretrained, **dict(model_args, **kwargs))
+        'vit_huge_patch14_clip_378',  # map to non quickgelu pretrained_cfg intentionally
+        pretrained=pretrained, **dict(model_args, **kwargs))
     return model