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Update ViTamin model defs

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This commit is contained in:
Ross Wightman 2024-06-06 09:16:43 -07:00
parent cc8a03daac
commit 7d4ada6d16
1 changed files with 114 additions and 34 deletions
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148
timm/models/vitamin.py
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@ -308,34 +308,36 @@ def _cfg(url='', **kwargs):
default_cfgs = generate_default_cfgs({
'vitamin_small.datacomp1b_clip_ltt': _cfg(
'vitamin_small_224.datacomp1b_clip_ltt': _cfg(
hf_hub_id='jienengchen/ViTamin-S-LTT', num_classes=384),
'vitamin_small.datacomp1b_clip': _cfg(
'vitamin_small_224.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-S', num_classes=384),
'vitamin_base.datacomp1b_clip_ltt': _cfg(
'vitamin_base_224.datacomp1b_clip_ltt': _cfg(
hf_hub_id='jienengchen/ViTamin-B-LTT', num_classes=768),
'vitamin_base.datacomp1b_clip': _cfg(
'vitamin_base_224.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-B', num_classes=768),
'vitamin_large.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-224px', num_classes=1024),
'vitamin_large_256.datacomp1b_clip_l2': _cfg(
'vitamin_large_224.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-224px', num_classes=768),
'vitamin_large_256.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-256px', num_classes=768,
input_size=(3, 256, 256), crop_pct=1.0),
'vitamin_large_336.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-336px', num_classes=768,
input_size=(3, 336, 336), crop_pct=1.0),
'vitamin_large_384.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-384px', num_classes=768,
input_size=(3, 384, 384), crop_pct=1.0),
'vitamin_large2_224.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L2-224px', num_classes=1024),
'vitamin_large2_256.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L2-256px', num_classes=1024,
input_size=(3, 256, 256), crop_pct=1.0),
'vitamin_large_256.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-256px', num_classes=1024,
input_size=(3, 256, 256), crop_pct=1.0),
'vitamin_large_336.datacomp1b_clip_l2': _cfg(
'vitamin_large2_336.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L2-336px', num_classes=1024,
input_size=(3, 336, 336), crop_pct=1.0),
'vitamin_large_336.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-336px', num_classes=1024,
input_size=(3, 336, 336), crop_pct=1.0),
'vitamin_large_384.datacomp1b_clip_l2': _cfg(
'vitamin_large2_384.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L2-384px', num_classes=1024,
input_size=(3, 384, 384), crop_pct=1.0),
'vitamin_large_384.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-L-384px', num_classes=1024,
input_size=(3, 384, 384), crop_pct=1.0),
'vitamin_xlarge_256.datacomp1b_clip': _cfg(
hf_hub_id='jienengchen/ViTamin-XL-256px', num_classes=1152,
input_size=(3, 256, 256), crop_pct=1.0),
@ -349,12 +351,12 @@ default_cfgs = generate_default_cfgs({
@register_model
def vitamin_small(pretrained=False, **kwargs) -> VisionTransformer:
def vitamin_small_224(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg = VitCfg(
embed_dim=(64, 128, 384),
depths=(2, 4, 1),
stem_width=64,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -364,17 +366,17 @@ def vitamin_small(pretrained=False, **kwargs) -> VisionTransformer:
embed_dim=384, depth=14, num_heads=6, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', embed_cfg=embed_cfg
)
model = _create_vitamin('vitamin_small', pretrained=pretrained, **dict(model_args, **kwargs))
model = _create_vitamin('vitamin_small_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vitamin_base(pretrained=False, **kwargs) -> VisionTransformer:
def vitamin_base_224(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg = VitCfg(
embed_dim=(128, 256, 768),
depths=(2, 4, 1),
stem_width=128,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -383,17 +385,17 @@ def vitamin_base(pretrained=False, **kwargs) -> VisionTransformer:
model_args = dict(
embed_dim=768, depth=14, num_heads=12, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', embed_cfg=embed_cfg)
model = _create_vitamin('vitamin_base', pretrained=pretrained, **dict(model_args, **kwargs))
model = _create_vitamin('vitamin_base_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vitamin_large(pretrained=False, **kwargs) -> VisionTransformer:
def vitamin_large_224(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg = VitCfg(
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -403,7 +405,7 @@ def vitamin_large(pretrained=False, **kwargs) -> VisionTransformer:
embed_dim=1024, depth=31, num_heads=16, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', embed_cfg=embed_cfg,
)
model = _create_vitamin('vitamin_large', pretrained=pretrained, **dict(model_args, **kwargs))
model = _create_vitamin('vitamin_large_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@ -413,7 +415,7 @@ def vitamin_large_256(pretrained=False, **kwargs) -> VisionTransformer:
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -432,7 +434,7 @@ def vitamin_large_336(pretrained=False, **kwargs) -> VisionTransformer:
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -452,7 +454,7 @@ def vitamin_large_384(pretrained=False, **kwargs) -> VisionTransformer:
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -465,13 +467,91 @@ def vitamin_large_384(pretrained=False, **kwargs) -> VisionTransformer:
return model
@register_model
def vitamin_large2_224(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg = VitCfg(
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
head_type='1d',
)
model_args = dict(
embed_dim=1024, depth=31, num_heads=16, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', embed_cfg=embed_cfg,
)
model = _create_vitamin('vitamin_large2_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vitamin_large2_256(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg = VitCfg(
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
head_type='1d',
)
model_args = dict(
img_size=256, embed_dim=1024, depth=31, num_heads=16, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', embed_cfg=embed_cfg)
model = _create_vitamin('vitamin_large2_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vitamin_large2_336(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg = VitCfg(
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
head_type='1d',
)
model_args = dict(
img_size=336, embed_dim=1024, depth=31, num_heads=16, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', embed_cfg=embed_cfg
)
model = _create_vitamin('vitamin_large2_336', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vitamin_large2_384(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg = VitCfg(
embed_dim=(160, 320, 1024),
depths=(2, 4, 1),
stem_width=160,
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
head_type='1d',
)
model_args = dict(
img_size=384, embed_dim=1024, depth=31, num_heads=16, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', embed_cfg=embed_cfg)
model = _create_vitamin('vitamin_large2_384', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vitamin_xlarge_256(pretrained=False, **kwargs) -> VisionTransformer:
embed_cfg=VitCfg(
embed_dim=(192, 384, 1152),
depths=(2, 4, 1),
stem_width=192,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -491,7 +571,7 @@ def vitamin_xlarge_336(pretrained=False, **kwargs) -> VisionTransformer:
embed_dim=(192, 384, 1152),
depths=(2, 4, 1),
stem_width=192,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),
@ -500,7 +580,7 @@ def vitamin_xlarge_336(pretrained=False, **kwargs) -> VisionTransformer:
model_args = dict(
img_size=336, embed_dim=1152, depth=32, num_heads=16, mlp_layer=GeGluMlp, mlp_ratio=2.,
class_token=False, global_pool='avg', pos_embed='none', embed_cfg=embed_cfg)
model = _create_vitamin('vitamin_xlarge_336', pretrained=pretrained, **dict(model_args, **kwargs))
model = _create_vitamin('vitamin_xlarge_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@ -510,7 +590,7 @@ def vitamin_xlarge_384(pretrained=False, **kwargs) -> VisionTransformer:
embed_dim=(192, 384, 1152),
depths=(2, 4, 1),
stem_width=192,
conv_cfg = VitConvCfg(
conv_cfg=VitConvCfg(
norm_layer='layernorm2d',
norm_eps=1e-6,
),