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Prepping weight push, benchmarking.

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Ross Wightman 2024-05-10 14:14:06 -07:00
parent 2bfa5e5d74
commit 3582ca499e
3 changed files with 83 additions and 26 deletions
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24
timm/models/eva.py
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@ -718,10 +718,10 @@ def checkpoint_filter_fn(
continue continue
# FIXME here while import new weights, to remove # FIXME here while import new weights, to remove
# if k == 'cls_token': if k == 'cls_token':
# print('DEBUG: cls token -> reg') print('DEBUG: cls token -> reg')
# k = 'reg_token' k = 'reg_token'
# #v = v + state_dict['pos_embed'][0, :] #v = v + state_dict['pos_embed'][0, :]
if 'patch_embed.proj.weight' in k: if 'patch_embed.proj.weight' in k:
_, _, H, W = model.patch_embed.proj.weight.shape _, _, H, W = model.patch_embed.proj.weight.shape
@ -951,26 +951,26 @@ default_cfgs = generate_default_cfgs({
num_classes=0, num_classes=0,
), ),
'vit_medium_patch16_rope_reg1_gap_256.in1k': _cfg( 'vit_medium_patch16_rope_reg1_gap_256.sbb_in1k': _cfg(
#hf_hub_id='timm/', #hf_hub_id='timm/',
#file='vit_medium_gap1_rope-in1k-20230920-5.pth', file='vit_medium_gap1_rope-in1k-20230920-5.pth',
input_size=(3, 256, 256), crop_pct=0.95, input_size=(3, 256, 256), crop_pct=0.95,
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5) mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
), ),
'vit_mediumd_patch16_rope_reg1_gap_256.in1k': _cfg( 'vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k': _cfg(
#hf_hub_id='timm/', #hf_hub_id='timm/',
#file='vit_mediumd_gap1_rope-in1k-20230926-5.pth', file='vit_mediumd_gap1_rope-in1k-20230926-5.pth',
input_size=(3, 256, 256), crop_pct=0.95, input_size=(3, 256, 256), crop_pct=0.95,
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5) mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
), ),
'vit_betwixt_patch16_rope_reg4_gap_256.in1k': _cfg( 'vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k': _cfg(
#hf_hub_id='timm/', #hf_hub_id='timm/',
#file='vit_betwixt_gap4_rope-in1k-20231005-5.pth', file='vit_betwixt_gap4_rope-in1k-20231005-5.pth',
input_size=(3, 256, 256), crop_pct=0.95, input_size=(3, 256, 256), crop_pct=0.95,
), ),
'vit_base_patch16_rope_reg1_gap_256.in1k': _cfg( 'vit_base_patch16_rope_reg1_gap_256.sbb_in1k': _cfg(
#hf_hub_id='timm/', #hf_hub_id='timm/',
#file='vit_base_gap1_rope-in1k-20230930-5.pth', file='vit_base_gap1_rope-in1k-20230930-5.pth',
input_size=(3, 256, 256), crop_pct=0.95, input_size=(3, 256, 256), crop_pct=0.95,
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5) mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
), ),

83
timm/models/vision_transformer.py
View File

@ -428,6 +428,7 @@ class VisionTransformer(nn.Module):
act_layer: Optional[LayerType] = None, act_layer: Optional[LayerType] = None,
block_fn: Type[nn.Module] = Block, block_fn: Type[nn.Module] = Block,
mlp_layer: Type[nn.Module] = Mlp, mlp_layer: Type[nn.Module] = Mlp,
repr_size = False,
) -> None: ) -> None:
""" """
Args: Args:
@ -536,6 +537,14 @@ class VisionTransformer(nn.Module):
) )
else: else:
self.attn_pool = None self.attn_pool = None
if repr_size:
repr_size = self.embed_dim if isinstance(repr_size, bool) else repr_size
self.repr = nn.Sequential(nn.Linear(self.embed_dim, repr_size), nn.Tanh())
embed_dim = repr_size
print(self.repr)
else:
self.repr = nn.Identity()
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity() self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate) self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
@ -752,6 +761,7 @@ class VisionTransformer(nn.Module):
x = x[:, self.num_prefix_tokens:].mean(dim=1) x = x[:, self.num_prefix_tokens:].mean(dim=1)
elif self.global_pool: elif self.global_pool:
x = x[:, 0] # class token x = x[:, 0] # class token
x = self.repr(x)
x = self.fc_norm(x) x = self.fc_norm(x)
x = self.head_drop(x) x = self.head_drop(x)
return x if pre_logits else self.head(x) return x if pre_logits else self.head(x)
@ -1790,23 +1800,40 @@ default_cfgs = {
license='mit', license='mit',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512), mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
'vit_wee_patch16_reg1_gap_256': _cfg( 'vit_wee_patch16_reg1_gap_256.sbb_in1k': _cfg(
#file='', #file='',
input_size=(3, 256, 256), crop_pct=0.95), input_size=(3, 256, 256), crop_pct=0.95),
'vit_little_patch16_reg4_gap_256': _cfg( 'vit_pwee_patch16_reg1_gap_256.sbb_in1k': _cfg(
#file='', file='./vit_pwee-in1k-8.pth',
input_size=(3, 256, 256), crop_pct=0.95), input_size=(3, 256, 256), crop_pct=0.95),
'vit_medium_patch16_reg1_gap_256': _cfg( 'vit_little_patch16_reg4_gap_256.sbb_in1k': _cfg(
#file='vit_medium_gap1-in1k-20231118-8.pth', file='vit_little_patch16-in1k-8a.pth',
input_size=(3, 256, 256), crop_pct=0.95), input_size=(3, 256, 256), crop_pct=0.95),
'vit_medium_patch16_reg4_gap_256': _cfg( 'vit_medium_patch16_reg1_gap_256.sbb_in1k': _cfg(
#file='vit_medium_gap4-in1k-20231115-8.pth', file='vit_medium_gap1-in1k-20231118-8.pth',
input_size=(3, 256, 256), crop_pct=0.95), input_size=(3, 256, 256), crop_pct=0.95),
'vit_betwixt_patch16_reg1_gap_256': _cfg( 'vit_medium_patch16_reg4_gap_256.sbb_in1k': _cfg(
#file='vit_betwixt_gap1-in1k-20231121-8.pth', file='vit_medium_gap4-in1k-20231115-8.pth',
input_size=(3, 256, 256), crop_pct=0.95), input_size=(3, 256, 256), crop_pct=0.95),
'vit_betwixt_patch16_reg4_gap_256': _cfg( 'vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k': _cfg(
#file='vit_betwixt_gap4-in1k-20231106-8.pth', file='vit_mp_patch16_reg4-in1k-5a.pth',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_mediumd_patch16_reg4_gap_256.sbb_in12k': _cfg(
file='vit_mp_patch16_reg4-in12k-8.pth',
num_classes=11821,
input_size=(3, 256, 256), crop_pct=0.95),
'vit_betwixt_patch16_reg1_gap_256.sbb_in1k': _cfg(
file='vit_betwixt_gap1-in1k-20231121-8.pth',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k': _cfg(
file='vit_betwixt_patch16_reg4-ft-in1k-8b.pth',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_betwixt_patch16_reg4_gap_256.sbb_in1k': _cfg(
file='vit_betwixt_gap4-in1k-20231106-8.pth',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_betwixt_patch16_reg4_gap_256.sbb_in12k': _cfg(
file='vit_betwixt_gap4-in12k-8.pth',
num_classes=11821,
input_size=(3, 256, 256), crop_pct=0.95), input_size=(3, 256, 256), crop_pct=0.95),
'vit_base_patch16_reg4_gap_256': _cfg( 'vit_base_patch16_reg4_gap_256': _cfg(
input_size=(3, 256, 256)), input_size=(3, 256, 256)),
@ -1906,6 +1933,14 @@ def vit_small_patch16_224(pretrained: bool = False, **kwargs) -> VisionTransform
return model return model
@register_model
def vit_small_patch16_gap_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/16)
"""
model_args = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, global_pool='avg', class_token=False, repr_size=True)
model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model @register_model
def vit_small_patch16_384(pretrained: bool = False, **kwargs) -> VisionTransformer: def vit_small_patch16_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" ViT-Small (ViT-S/16) """ ViT-Small (ViT-S/16)
@ -2755,10 +2790,21 @@ def vit_so400m_patch14_siglip_384(pretrained: bool = False, **kwargs) -> VisionT
def vit_wee_patch16_reg1_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer: def vit_wee_patch16_reg1_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict( model_args = dict(
patch_size=16, embed_dim=256, depth=14, num_heads=4, init_values=1e-5, mlp_ratio=5, patch_size=16, embed_dim=256, depth=14, num_heads=4, init_values=1e-5, mlp_ratio=5,
class_token=False, no_embed_class=True, reg_tokens=1, global_pool='avg',
)
model = _create_vision_transformer(
'vit_wee_patch16_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_pwee_patch16_reg1_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=256, depth=16, num_heads=4, init_values=1e-5, mlp_ratio=5,
class_token=False, no_embed_class=True, reg_tokens=1, global_pool='avg', block_fn=ParallelScalingBlock, class_token=False, no_embed_class=True, reg_tokens=1, global_pool='avg', block_fn=ParallelScalingBlock,
) )
model = _create_vision_transformer( model = _create_vision_transformer(
'vit_medium_patch16_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs)) 'vit_pwee_patch16_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model return model
@ -2769,7 +2815,7 @@ def vit_little_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> Visio
class_token=False, no_embed_class=True, reg_tokens=4, global_pool='avg', class_token=False, no_embed_class=True, reg_tokens=4, global_pool='avg',
) )
model = _create_vision_transformer( model = _create_vision_transformer(
'vit_medium_patch16_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs)) 'vit_little_patch16_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model return model
@ -2795,6 +2841,17 @@ def vit_medium_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> Visio
return model return model
@register_model
def vit_mediumd_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=512, depth=20, num_heads=8, init_values=1e-5,
class_token=False, no_embed_class=True, reg_tokens=4, global_pool='avg',
)
model = _create_vision_transformer(
'vit_mediumd_patch16_reg4_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model @register_model
def vit_betwixt_patch16_reg1_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer: def vit_betwixt_patch16_reg1_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict( model_args = dict(

2
timm/version.py
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@ -1 +1 @@
__version__ = '1.0.0.dev0' __version__ = '1.0.1.dev0'