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import logging
import types
from collections import OrderedDict
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
try:
import timm
from timm.layers import RotAttentionPool2d
from timm.layers import AttentionPool2d as AbsAttentionPool2d
from timm.layers import Mlp, to_2tuple
from timm.layers import AttentionRope, RotaryEmbeddingCat
except ImportError:
timm = None
class TimmModel(nn.Module):
"""timm model adapter"""
def __init__(
self,
model_name: str,
embed_dim: int,
image_size: Union[int, Tuple[int, int]] = 224,
pool: str = "avg",
proj: str = "linear",
proj_bias: bool = False,
drop: float = 0.0,
drop_path: Optional[float] = None,
patch_drop: Optional[float] = None,
init_values: Optional[float] = None,
qk_norm: bool = False,
use_rope: bool = False,
rope_keep_ape: bool = False,
dynamic_img_size: bool = False,
norm_pre: bool = False,
pretrained: bool = False,
output_tokens: bool = False,
):
super().__init__()
if timm is None:
raise RuntimeError(
"Please install the latest timm (`pip install timm`) to use timm based models."
)
self.image_size = to_2tuple(image_size)
self.output_tokens = output_tokens
timm_kwargs = {}
if drop_path is not None:
timm_kwargs["drop_path_rate"] = drop_path
if patch_drop is not None:
timm_kwargs["patch_drop_rate"] = patch_drop
if init_values is not None:
timm_kwargs["init_values"] = init_values
if qk_norm:
timm_kwargs["qk_norm"] = True
if dynamic_img_size:
timm_kwargs["dynamic_img_size"] = True
if use_rope:
class _AttentionRopeNoPrefix(AttentionRope):
"""AttentionRope with num_prefix_tokens=0 for models without cls token."""
def __init__(self, *args, **kwargs):
kwargs["num_prefix_tokens"] = 0
super().__init__(*args, **kwargs)
timm_kwargs["attn_layer"] = _AttentionRopeNoPrefix
if not rope_keep_ape:
timm_kwargs["pos_embed"] = "none"
custom_pool = pool in ("abs_attn", "rot_attn")
if proj:
assert proj in ("linear", "mlp", "none")
extra_proj = proj in ("linear", "mlp")
if not extra_proj and not custom_pool:
proj_dim = 0 if proj == "none" else embed_dim
self.trunk = timm.create_model(
model_name,
num_classes=proj_dim,
global_pool=pool,
pretrained=pretrained,
**timm_kwargs,
)
prev_chs = embed_dim
else:
self.trunk = timm.create_model(
model_name,
pretrained=pretrained,
**timm_kwargs,
)
feat_size = self.trunk.default_cfg.get("pool_size", None)
feature_ndim = 1 if not feat_size else 2
if custom_pool:
assert feature_ndim == 2
self.trunk.reset_classifier(0, global_pool="")
else:
reset_kwargs = dict(global_pool=pool) if pool else {}
self.trunk.reset_classifier(0, **reset_kwargs)
prev_chs = self.trunk.num_features
head_layers = OrderedDict()
if pool == "abs_attn":
head_layers["pool"] = AbsAttentionPool2d(
prev_chs, feat_size=feat_size, out_features=embed_dim
)
prev_chs = embed_dim
elif pool == "rot_attn":
head_layers["pool"] = RotAttentionPool2d(prev_chs, out_features=embed_dim)
prev_chs = embed_dim
if proj == "linear":
head_layers["drop"] = nn.Dropout(drop)
head_layers["proj"] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)
elif proj == "mlp":
head_layers["mlp"] = Mlp(
prev_chs,
2 * embed_dim,
embed_dim,
drop=(drop, 0),
bias=(True, proj_bias),
)
self.head = nn.Sequential(head_layers)
if (
norm_pre
and hasattr(self.trunk, "norm_pre")
and isinstance(self.trunk.norm_pre, nn.Identity)
):
self.trunk.norm_pre = nn.LayerNorm(self.trunk.embed_dim)
logging.info(
f"Replaced norm_pre Identity with LayerNorm({self.trunk.embed_dim})"
)
self._has_rope = use_rope
if use_rope:
self._setup_rope()
def _setup_rope(self):
"""Inject 2D Rotary Position Embedding into the timm trunk."""
num_heads = self.trunk.blocks[0].attn.num_heads
head_dim = self.trunk.embed_dim // num_heads
self.trunk.patch_embed.strict_img_size = False
self.rope = RotaryEmbeddingCat(
dim=head_dim,
max_res=max(self.image_size),
in_pixels=True,
)
def _block_forward_rope(block_self, x, rope=None, attn_mask=None):
x = x + block_self.drop_path1(
block_self.ls1(
block_self.attn(block_self.norm1(x), rope=rope, attn_mask=attn_mask)
)
)
x = x + block_self.drop_path2(
block_self.ls2(block_self.mlp(block_self.norm2(x)))
)
return x
for blk in self.trunk.blocks:
blk.forward = types.MethodType(_block_forward_rope, blk)
timm_model_ref = self
_num_prefix = getattr(self.trunk, "num_prefix_tokens", 0)
def _forward_features_rope(trunk_self, x, attn_mask=None):
from torch.utils.checkpoint import checkpoint
from timm.layers import resample_abs_pos_embed
ps = trunk_self.patch_embed.patch_size
grid_shape = [x.shape[2] // ps[0], x.shape[3] // ps[1]]
x = trunk_self.patch_embed(x)
if x.ndim == 4:
x = x.reshape(x.shape[0], -1, x.shape[-1])
if hasattr(trunk_self, "pos_embed") and trunk_self.pos_embed is not None:
if x.shape[1] != trunk_self.pos_embed.shape[1]:
x = x + resample_abs_pos_embed(
trunk_self.pos_embed, grid_shape, num_prefix_tokens=_num_prefix
)
else:
x = x + trunk_self.pos_embed
x = trunk_self.pos_drop(x)
x = trunk_self.norm_pre(x)
rot_pos_embed = timm_model_ref.rope.get_embed(shape=grid_shape)
_sdpa_mask = None
if attn_mask is not None:
_sdpa_mask = torch.zeros_like(attn_mask, dtype=x.dtype)
_sdpa_mask.masked_fill_(~attn_mask, float("-inf"))
_sdpa_mask = _sdpa_mask.unsqueeze(1).unsqueeze(2)
for blk in trunk_self.blocks:
if trunk_self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(
blk,
x,
rope=rot_pos_embed,
attn_mask=_sdpa_mask,
use_reentrant=False,
)
else:
x = blk(x, rope=rot_pos_embed, attn_mask=_sdpa_mask)
x = trunk_self.norm(x)
return x
self.trunk.forward_features = types.MethodType(
_forward_features_rope, self.trunk
)
def _setup_dynamic_pos_embed(self):
"""Patch forward_features for variable-resolution pos_embed interpolation (non-RoPE)."""
self.trunk.patch_embed.strict_img_size = False
_num_prefix = getattr(self.trunk, "num_prefix_tokens", 0)
def _forward_features_dynamic(trunk_self, x, patch_valid_mask=None):
from torch.utils.checkpoint import checkpoint
from timm.layers import resample_abs_pos_embed
ps = trunk_self.patch_embed.patch_size
grid_shape = [x.shape[2] // ps[0], x.shape[3] // ps[1]]
x = trunk_self.patch_embed(x)
if x.ndim == 4:
x = x.reshape(x.shape[0], -1, x.shape[-1])
if hasattr(trunk_self, "pos_embed") and trunk_self.pos_embed is not None:
if x.shape[1] != trunk_self.pos_embed.shape[1]:
x = x + resample_abs_pos_embed(
trunk_self.pos_embed, grid_shape, num_prefix_tokens=_num_prefix
)
else:
x = x + trunk_self.pos_embed
x = trunk_self.pos_drop(x)
x = trunk_self.norm_pre(x)
_sdpa_mask = None
if patch_valid_mask is not None:
_sdpa_mask = torch.zeros_like(patch_valid_mask, dtype=x.dtype)
_sdpa_mask.masked_fill_(~patch_valid_mask, float("-inf"))
_sdpa_mask = _sdpa_mask.unsqueeze(1).unsqueeze(2)
for blk in trunk_self.blocks:
if trunk_self.grad_checkpointing and not torch.jit.is_scripting():
if _sdpa_mask is not None:
x = checkpoint(
blk, x, attn_mask=_sdpa_mask, use_reentrant=False
)
else:
x = checkpoint(blk, x, use_reentrant=False)
else:
x = blk(x, attn_mask=_sdpa_mask)
x = trunk_self.norm(x)
return x
self.trunk.forward_features = types.MethodType(
_forward_features_dynamic, self.trunk
)
def _setup_1d_forward(self):
"""Patch forward_features for NaFlex 1D mode (SigLIP2 style)."""
_num_prefix = getattr(self.trunk, "num_prefix_tokens", 0)
def _forward_features_1d(
trunk_self, x, patch_valid_mask=None, spatial_shapes=None
):
from torch.utils.checkpoint import checkpoint
conv = trunk_self.patch_embed.proj
D = conv.weight.shape[0]
x = torch.nn.functional.linear(
x.to(conv.weight.dtype), conv.weight.reshape(D, -1), conv.bias
)
if (
hasattr(trunk_self, "pos_embed")
and trunk_self.pos_embed is not None
and spatial_shapes is not None
):
pos_embed = trunk_self.pos_embed
base_n = pos_embed.shape[1]
base_grid = int(base_n**0.5)
pos_2d = (
pos_embed.reshape(1, base_grid, base_grid, -1)
.permute(0, 3, 1, 2)
.float()
)
B, sl, D_emb = x.shape
pos_resized = torch.zeros(B, sl, D_emb, device=x.device, dtype=x.dtype)
for i in range(B):
gh, gw = spatial_shapes[i].tolist()
pe = torch.nn.functional.interpolate(
pos_2d, size=(gh, gw), mode="bilinear", align_corners=False
)
pe = pe.squeeze(0).permute(1, 2, 0).reshape(gh * gw, -1).to(x.dtype)
n_patches = gh * gw
pos_resized[i, :n_patches] = pe
if n_patches < sl:
pos_resized[i, n_patches:] = pe[0]
x = x + pos_resized
elif hasattr(trunk_self, "pos_embed") and trunk_self.pos_embed is not None:
x = x + trunk_self.pos_embed
x = trunk_self.pos_drop(x)
x = trunk_self.norm_pre(x)
_sdpa_mask = None
if patch_valid_mask is not None:
_sdpa_mask = torch.zeros_like(patch_valid_mask, dtype=x.dtype)
_sdpa_mask.masked_fill_(~patch_valid_mask, float("-inf"))
_sdpa_mask = _sdpa_mask.unsqueeze(1).unsqueeze(2)
for blk in trunk_self.blocks:
if trunk_self.grad_checkpointing and not torch.jit.is_scripting():
if _sdpa_mask is not None:
x = checkpoint(
blk, x, attn_mask=_sdpa_mask, use_reentrant=False
)
else:
x = checkpoint(blk, x, use_reentrant=False)
else:
x = blk(x, attn_mask=_sdpa_mask)
x = trunk_self.norm(x)
return x
self.trunk._forward_features_1d = types.MethodType(
_forward_features_1d, self.trunk
)
self._has_1d_forward = True
def forward_patch_features(self, x):
"""Forward pass returning per-patch features (before pooling/projection)."""
return self.trunk.forward_features(x)
def forward(self, x, patch_valid_mask=None, spatial_shapes=None):
if spatial_shapes is not None and getattr(self, "_has_1d_forward", False):
patch_features = self.trunk._forward_features_1d(
x, patch_valid_mask=patch_valid_mask, spatial_shapes=spatial_shapes
)
elif patch_valid_mask is not None and self._has_rope:
patch_features = self.trunk.forward_features(x, attn_mask=patch_valid_mask)
elif patch_valid_mask is not None:
patch_features = self.trunk.forward_features(
x, patch_valid_mask=patch_valid_mask
)
else:
patch_features = self.trunk.forward_features(x)
if patch_valid_mask is not None:
mask_f = patch_valid_mask.unsqueeze(-1).to(
patch_features.dtype
)
patch_features = patch_features * mask_f
self._cached_patch_features = patch_features
if (
patch_valid_mask is not None
and getattr(self.trunk, "global_pool", "") == "avg"
):
pooled = patch_features.sum(dim=1) / mask_f.sum(dim=1).clamp(min=1)
pooled = (
self.trunk.fc_norm(pooled) if hasattr(self.trunk, "fc_norm") else pooled
)
elif (
patch_valid_mask is not None
and getattr(self.trunk, "attn_pool", None) is not None
):
attn_mask = torch.zeros(
patch_valid_mask.shape,
dtype=patch_features.dtype,
device=patch_features.device,
)
attn_mask.masked_fill_(~patch_valid_mask.bool(), float("-inf"))
attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)
pooled = self.trunk.attn_pool(patch_features, attn_mask=attn_mask)
pooled = (
self.trunk.fc_norm(pooled) if hasattr(self.trunk, "fc_norm") else pooled
)
else:
pooled = self.trunk.forward_head(patch_features)
pooled = self.head(pooled)
if self.output_tokens:
return pooled, patch_features
return pooled
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