mirrors/pytorch-image-models
1
0
Fork 0
mirror of https://github.com/huggingface/pytorch-image-models.git synced 2025-06-03 15:01:08 +08:00
Code Issues Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
Ross Wightman 2023-11-02 20:22:04 -07:00 committed by Ross Wightman
parent 6894ec7edc
commit 96bd162ddb
1 changed files with 16 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
timm/models/vision_transformer.py
View File

@ -1473,18 +1473,22 @@ default_cfgs = generate_default_cfgs({
'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