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Merged
rwightman merged 2 commits into from
Dec 30, 2025
+39 −9
Merged

A few misc weights to close out 2025 #2639

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940edf4
Add sbb vit 'dlittle' weights trained with NAdaMuon
rwightman Dec 28, 2025
8674fa4
Add csatv2_21m weights at 512 & 640 img size. Add layer-scale support…
rwightman Dec 30, 2025
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34 changes: 25 additions & 9 deletions timm/models/csatv2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
import torch.nn as nn
import torch.nn.functional as F

from timm.layers import trunc_normal_, DropPath, Mlp, LayerNorm2d, Attention, NormMlpClassifierHead
from timm.layers import trunc_normal_, DropPath, Mlp, LayerNorm2d, Attention, NormMlpClassifierHead, LayerScale, LayerScale2d
from timm.layers.grn import GlobalResponseNorm
from timm.models._builder import build_model_with_cfg
from timm.models._features import feature_take_indices
Expand Down Expand Up @@ -322,6 +322,7 @@ def __init__(
self,
dim: int,
drop_path: float = 0.,
ls_init_value: Optional[float] = None,
device=None,
dtype=None,
) -> None:
Expand All @@ -333,6 +334,7 @@ def __init__(
self.act = nn.GELU()
self.grn = GlobalResponseNorm(4 * dim, channels_last=True, **dd)
self.pwconv2 = nn.Linear(4 * dim, dim, **dd)
self.ls = LayerScale2d(dim, init_values=ls_init_value, **dd) if ls_init_value else nn.Identity()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.attn = SpatialAttention(**dd)

Expand All @@ -350,6 +352,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
attn = self.attn(x)
attn = F.interpolate(attn, size=x.shape[2:], mode='bilinear', align_corners=True)
x = x * attn
x = self.ls(x)

return shortcut + self.drop_path(x)

Expand Down Expand Up @@ -442,6 +445,7 @@ def __init__(
attn_drop: float = 0.,
proj_drop: float = 0.,
drop_path: float = 0.,
ls_init_value: Optional[float] = None,
device=None,
dtype=None,
) -> None:
Expand Down Expand Up @@ -470,10 +474,12 @@ def __init__(
proj_drop=proj_drop,
**dd,
)
self.ls1 = LayerScale(oup, init_values=ls_init_value, **dd) if ls_init_value else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

self.norm2 = nn.LayerNorm(oup, **dd)
self.mlp = Mlp(oup, hidden_dim, oup, act_layer=nn.GELU, drop=proj_drop, **dd)
self.ls2 = LayerScale(oup, init_values=ls_init_value, **dd) if ls_init_value else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

def forward(self, x: torch.Tensor) -> torch.Tensor:
Expand All @@ -484,7 +490,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
x_t = x_t.flatten(2).transpose(1, 2)
x_t = self.norm1(x_t)
x_t = self.pos_embed(x_t, (H, W))
x_t = self.attn(x_t)
x_t = self.ls1(self.attn(x_t))
x_t = x_t.transpose(1, 2).reshape(B, -1, H, W)
x = shortcut + self.drop_path1(x_t)
else:
Expand All @@ -493,15 +499,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
x_t = x.flatten(2).transpose(1, 2)
x_t = self.norm1(x_t)
x_t = self.pos_embed(x_t, (H, W))
x_t = self.attn(x_t)
x_t = self.ls1(self.attn(x_t))
x_t = x_t.transpose(1, 2).reshape(B, -1, H, W)
x = shortcut + self.drop_path1(x_t)

# MLP block
B, C, H, W = x.shape
shortcut = x
x_t = x.flatten(2).transpose(1, 2)
x_t = self.mlp(self.norm2(x_t))
x_t = self.ls2(self.mlp(self.norm2(x_t)))
x_t = x_t.transpose(1, 2).reshape(B, C, H, W)
x = shortcut + self.drop_path2(x_t)

Expand Down Expand Up @@ -545,6 +551,7 @@ def __init__(
transformer_depths: Tuple[int, ...] = (0, 0, 2, 2),
drop_path_rate: float = 0.0,
transformer_drop_path: bool = False,
ls_init_value: Optional[float] = None,
global_pool: str = 'avg',
device=None,
dtype=None,
Expand Down Expand Up @@ -590,9 +597,9 @@ def __init__(
# Downsample at start of stage (except first stage)
([nn.Conv2d(dims[i - 1], dim, kernel_size=2, stride=2, **dd)] if i > 0 else []) +
# Conv blocks
[Block(dim=dim, drop_path=next(dp_iter), **dd) for _ in range(depth - t_depth)] +
[Block(dim=dim, drop_path=next(dp_iter), ls_init_value=ls_init_value, **dd) for _ in range(depth - t_depth)] +
# Transformer blocks at end of stage
[TransformerBlock(inp=dim, oup=dim, drop_path=next(dp_iter), **dd) for _ in range(t_depth)] +
[TransformerBlock(inp=dim, oup=dim, drop_path=next(dp_iter), ls_init_value=ls_init_value, **dd) for _ in range(t_depth)] +
# Trailing LayerNorm (except last stage)
([LayerNorm2d(dim, eps=1e-6, **dd)] if i < len(depths) - 1 else [])
)
Expand Down Expand Up @@ -726,7 +733,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 512, 512),
'num_classes': 1000, 'input_size': (3, 512, 512), 'pool_size': (8, 8),
'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225),
'interpolation': 'bilinear', 'crop_pct': 1.0,
'classifier': 'head.fc', 'first_conv': [],
Expand All @@ -735,10 +742,19 @@ def _cfg(url='', **kwargs):


default_cfgs = generate_default_cfgs({
'csatv2': _cfg(
'csatv2.r512_in1k': _cfg(
hf_hub_id='timm/',
),
'csatv2_21m.sw_r640_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 640, 640),
interpolation='bicubic',
),
'csatv2_21m.sw_r512_in1k': _cfg(
hf_hub_id='timm/',
pool_size=(10, 10),
interpolation='bicubic',
),
'csatv2_21m': _cfg(),
})


Expand Down
14 changes: 14 additions & 0 deletions timm/models/vision_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2791,6 +2791,9 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
'vit_little_patch16_reg4_gap_256.sbb_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_dlittle_patch16_reg1_gap_256.sbb_nadamuon_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_medium_patch16_reg1_gap_256.sbb_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), crop_pct=0.95),
Expand Down Expand Up @@ -4324,6 +4327,17 @@ def vit_little_patch16_reg1_gap_256(pretrained: bool = False, **kwargs) -> Visio
return model


@register_model
def vit_dlittle_patch16_reg1_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
patch_size=16, embed_dim=320, depth=14, num_heads=5, init_values=1e-5, mlp_ratio=5.6,
class_token=False, no_embed_class=True, reg_tokens=1, global_pool='avg', attn_layer='diff',
)
model = _create_vision_transformer(
'vit_dlittle_patch16_reg1_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model


@register_model
def vit_little_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
Expand Down