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LTF V2: multi-branch (baseline + Level-1 MIL on patch tokens + Level-2 MIL on CLS + fusion).
V1 code in LTF.py is unchanged; this module is a separate copy for version V2.
"""
from typing import Optional, Tuple
import torch
import torch.nn as nn
from model.LTF import LTFCore
from mil_module.mil_core import MILCore
def _clip_vit_resblock_modules(transformer) -> list:
"""Return the OpenAI CLIP ViT residual blocks as a list."""
rb = transformer.resblocks
if isinstance(rb, nn.Sequential):
return list(rb.children())
return list(rb)
def _count_clip_vit_depth(visual) -> int:
return len(_clip_vit_resblock_modules(visual.transformer))
class BagTokenShuffle(nn.Module):
"""Randomly permute tokens within each bag during training with probability p_shuffle."""
def forward(self, x, p_shuffle: float = 0.0, p_drop: float = 0.0, key_padding_mask=None, **kwargs):
del p_drop, key_padding_mask, kwargs
if (not self.training) or p_shuffle <= 0:
return x
if float(torch.rand((), device=x.device)) >= float(p_shuffle):
return x
B, N, D = x.shape
if N <= 1:
return x
perm = torch.rand(B, N, device=x.device).argsort(dim=1)
return x.gather(1, perm.unsqueeze(-1).expand(B, N, D))
class LTFCoreV2(nn.Module):
"""Baseline and hierarchical MIL branches with shared frozen CLIP features.
Level 1 pools patch tokens within local tiles and aggregates tile bags with a learned query.
Level 2 pools CLS tokens across views. Cross-Shuffle is applied only during training.
"""
def __init__(
self,
embed_dim=1024,
num_heads=8,
num_classes=2,
num_layers=1,
dropout=0.1,
tile_heads=2,
tile_layers=2,
use_global_image=True,
use_patch_transformer=True,
linear_classifier=False,
mil_heads=8,
fusion_hidden=512,
l1_cross_shuffle_p: float = 0.9,
l1_replace_ratio_range: Tuple[float, float] = (0.5, 0.9),
l1_with_replacement_p: float = 0.5,
# Level-2 CrossTokenShuffle
l2_cross_shuffle_p: float = 0.5,
l2_replace_ratio_range: Tuple[float, float] = (0.1, 0.5),
l2_with_replacement_p: float = 0.5,
bag_token_shuffle_p: float = 0.5,
cross_shuffle_min_keep_original: int = 0,
):
super().__init__()
self.embed_dim = embed_dim
self.use_global_image = use_global_image
self.num_classes = num_classes
self.l1_cross_shuffle_p = float(l1_cross_shuffle_p)
self.l1_replace_ratio_range = (float(l1_replace_ratio_range[0]), float(l1_replace_ratio_range[1]))
self.l1_with_replacement_p = float(l1_with_replacement_p)
self.l2_cross_shuffle_p = float(l2_cross_shuffle_p)
self.l2_replace_ratio_range = (float(l2_replace_ratio_range[0]), float(l2_replace_ratio_range[1]))
self.l2_with_replacement_p = float(l2_with_replacement_p)
self.bag_token_shuffle_p = float(bag_token_shuffle_p)
self.cross_shuffle_min_keep_original = int(cross_shuffle_min_keep_original)
self.baseline_core = LTFCore(
embed_dim=embed_dim,
num_heads=num_heads,
num_classes=num_classes,
num_layers=num_layers,
dropout=dropout,
tile_heads=tile_heads,
tile_layers=tile_layers,
use_global_image=use_global_image,
use_patch_transformer=use_patch_transformer,
linear_classifier=linear_classifier,
)
mil_kw = dict(
attn_hidden=min(256, embed_dim),
attn_dropout=dropout,
attn_bag_mode="embed_cls",
)
self.mil_level1 = MILCore(
d_in=embed_dim,
num_classes=num_classes,
mil_head_pooling="attn",
mil_head_kwargs=mil_kw,
use_cross_shuffle=True,
trans=BagTokenShuffle(),
allow_trans_token_drop=False,
)
self.mil_level2 = MILCore(
d_in=embed_dim,
num_classes=num_classes,
mil_head_pooling="attn",
mil_head_kwargs=mil_kw,
use_cross_shuffle=True,
trans=BagTokenShuffle(),
allow_trans_token_drop=False,
)
self.level1_query = nn.Parameter(torch.zeros(1, 1, embed_dim))
mh = mil_heads if embed_dim % mil_heads == 0 else 8
while embed_dim % mh != 0:
mh -= 1
if mh < 1:
mh = 1
self.level1_aggregate_attn = nn.MultiheadAttention(
embed_dim, mh, dropout=dropout, batch_first=True
)
self.head_level1 = nn.Linear(embed_dim, num_classes)
self.head_level2 = nn.Linear(embed_dim, num_classes)
classifier_in_dim = embed_dim * 2 if use_global_image else embed_dim
fusion_in = embed_dim + embed_dim + classifier_in_dim
self.fusion_head = nn.Sequential(
nn.Linear(fusion_in, fusion_hidden),
nn.ReLU(),
nn.Dropout(p=dropout),
nn.Linear(fusion_hidden, num_classes),
)
nn.init.trunc_normal_(self.level1_query, std=0.02)
@staticmethod
def _sample_replace_ratio(low: float, high: float, device: torch.device) -> float:
lo, hi = min(low, high), max(low, high)
lo = max(0.0, lo)
hi = min(1.0, hi)
if hi <= lo:
return float(lo)
u = torch.rand((), device=device)
return float(lo + (hi - lo) * u)
def _apply_patch_transformer(self, x):
"""x: (B, N, T, D) -> same shape after optional token-level encoder."""
B_2, N_2, T_2, D_2 = x.shape
xf = x.reshape(B_2 * N_2, T_2, D_2)
if self.baseline_core.patch_transformer is not None:
xf = self.baseline_core.patch_transformer(xf)
return xf.reshape(B_2, N_2, T_2, D_2)
def _forward_baseline_from_xfeat(self, x_feat):
"""Run LTF classifier heads on processed tokens (same as LTFCore tail)."""
B_2, N_2, T_2, D_2 = x_feat.shape
bc = self.baseline_core
new_cls = x_feat[:, :, 0, :]
new_x = x_feat[:, :, 1:, :]
x_flat = new_x.reshape(-1, D_2)
x_result = bc.patch_classifier(x_flat)
x_result = x_result.reshape(B_2, N_2, T_2 - 1, 1)
main_feat = new_cls[:, 0]
tile_feats = new_cls[:, 1:]
cls_token = bc.cls_token.expand(B_2, 1, -1)
feature_tile = torch.cat([cls_token, tile_feats], dim=1)
feature_encoded = bc.tile_transformer(feature_tile)
cls_out = feature_encoded[:, 0]
if bc.use_global_image:
concat_feat = torch.cat([main_feat, cls_out], dim=1)
else:
concat_feat = cls_out
logits_b = bc.classifier(concat_feat)
jpeg_b = torch.sigmoid(bc.jpeg_estimate(cls_out))
return logits_b, x_result, jpeg_b, concat_feat
def forward(
self,
x,
labels: Optional[torch.Tensor] = None,
x_mil: Optional[torch.Tensor] = None,
):
"""Run the branches on token features shaped [B, N, T, D].
N must include the global view and at least one local tile. Optional x_mil
provides alternate MIL tokens with the same shape. Binary labels enable
training-time Cross-Shuffle; the baseline uses the original x features.
"""
x_feat = self._apply_patch_transformer(x)
if x_mil is not None:
if x_mil.shape != x.shape:
raise ValueError(f"x_mil shape {tuple(x_mil.shape)} != x shape {tuple(x.shape)}")
x_mil_feat = self._apply_patch_transformer(x_mil)
else:
x_mil_feat = x_feat
logits_baseline, patch_logits, jpeg_pred, concat_feat = self._forward_baseline_from_xfeat(
x_feat
)
B, N, T, D = x_mil_feat.shape
if N < 2:
raise ValueError(f"LTFCoreV2 expects N>=2 (global + tiles), got N={N}")
Nt = N - 1
train_shuffle = self.training and labels is not None
p_bag_shuffle = self.bag_token_shuffle_p if train_shuffle else 0.0
patch_only = x_mil_feat[:, 1:, 1:, :].reshape(B * Nt, T - 1, D)
do1 = (
train_shuffle
and float(torch.rand((), device=x.device)) < self.l1_cross_shuffle_p
)
rr1 = self._sample_replace_ratio(*self.l1_replace_ratio_range, x.device)
wr1 = bool(float(torch.rand((), device=x.device)) < self.l1_with_replacement_p)
y1 = labels.unsqueeze(1).expand(-1, Nt).reshape(-1) if labels is not None else None
mk = self.cross_shuffle_min_keep_original
mil1 = self.mil_level1(
patch_only,
y=y1,
return_dict=True,
do_cross_shuffle=do1,
replace_ratio=rr1,
with_replacement=wr1,
detach_inserted=True,
cross_shuffle_min_keep=mk,
p_shuffle=p_bag_shuffle,
)
be = mil1["mil_head"]["bag_embed"]
if be is None:
raise RuntimeError("MIL level1 expected bag_embed (use attn + embed_cls).")
bag_seq = be.reshape(B, Nt, D)
q = self.level1_query.expand(B, -1, -1)
agg_out, _ = self.level1_aggregate_attn(q, bag_seq, bag_seq)
level1_emb = agg_out.squeeze(1)
cls_seq = x_mil_feat[:, :, 0, :]
do2 = (
train_shuffle
and float(torch.rand((), device=x.device)) < self.l2_cross_shuffle_p
)
rr2 = self._sample_replace_ratio(*self.l2_replace_ratio_range, x.device)
wr2 = bool(float(torch.rand((), device=x.device)) < self.l2_with_replacement_p)
mil2 = self.mil_level2(
cls_seq,
y=labels,
return_dict=True,
do_cross_shuffle=do2,
replace_ratio=rr2,
with_replacement=wr2,
detach_inserted=True,
cross_shuffle_min_keep=mk,
p_shuffle=p_bag_shuffle,
)
be2 = mil2["mil_head"]["bag_embed"]
if be2 is None:
raise RuntimeError("MIL level2 expected bag_embed (use attn + embed_cls).")
level2_emb = be2
logits_level1 = self.head_level1(level1_emb)
logits_level2 = self.head_level2(level2_emb)
fusion_in = torch.cat([level1_emb, level2_emb, concat_feat], dim=-1)
logits_fusion = self.fusion_head(fusion_in)
return {
"logits_baseline": logits_baseline,
"logits_level1": logits_level1,
"logits_level2": logits_level2,
"logits_fusion": logits_fusion,
"patch_logits": patch_logits,
"jpeg_pred": jpeg_pred,
}
class LTFNetV2(nn.Module):
"""Frozen CLIP visual + LTFCoreV2. Input: (B, N, 3, H, W)."""
def __init__(
self,
clip_visual,
num_heads=8,
num_classes=2,
num_layers=1,
dropout=0.2,
tile_heads=2,
tile_layers=2,
use_global_image=True,
use_patch_transformer=True,
linear_classifier=False,
mil_heads=8,
fusion_hidden=512,
l1_cross_shuffle_p: float = 0.9,
l1_replace_ratio_range: Tuple[float, float] = (0.5, 0.9),
l1_with_replacement_p: float = 0.5,
l2_cross_shuffle_p: float = 0.5,
l2_replace_ratio_range: Tuple[float, float] = (0.1, 0.5),
l2_with_replacement_p: float = 0.5,
bag_token_shuffle_p: float = 0.5,
cross_shuffle_min_keep_original: int = 0,
use_clip_intermediate_for_mil: bool = False,
clip_mil_stop_after_block_1based: int = 12,
):
super().__init__()
self.visual = clip_visual
self.dtype = self.visual.conv1.weight.dtype
for p in self.visual.parameters():
p.requires_grad = False
self.visual.eval()
self.input_resolution = self.visual.input_resolution
self.embed_dim = self.visual.conv1.weight.shape[0]
self.use_clip_intermediate_for_mil = bool(use_clip_intermediate_for_mil)
self.clip_mil_stop_after_block_1based = int(clip_mil_stop_after_block_1based)
if self.use_clip_intermediate_for_mil:
n_bl = _count_clip_vit_depth(self.visual)
stop = self.clip_mil_stop_after_block_1based
if stop < 1 or stop > n_bl:
raise ValueError(
f"use_clip_intermediate_for_mil requires 1 <= v2_clip_mil_stop_after_block_1based "
f"<= {n_bl} (ViT depth), got {stop}"
)
self.core = LTFCoreV2(
embed_dim=self.embed_dim,
num_heads=num_heads,
num_classes=num_classes,
num_layers=num_layers,
dropout=dropout,
tile_heads=tile_heads,
tile_layers=tile_layers,
use_global_image=use_global_image,
use_patch_transformer=use_patch_transformer,
linear_classifier=linear_classifier,
mil_heads=mil_heads,
fusion_hidden=fusion_hidden,
l1_cross_shuffle_p=l1_cross_shuffle_p,
l1_replace_ratio_range=l1_replace_ratio_range,
l1_with_replacement_p=l1_with_replacement_p,
l2_cross_shuffle_p=l2_cross_shuffle_p,
l2_replace_ratio_range=l2_replace_ratio_range,
l2_with_replacement_p=l2_with_replacement_p,
bag_token_shuffle_p=bag_token_shuffle_p,
cross_shuffle_min_keep_original=cross_shuffle_min_keep_original,
)
def _stem_to_lnd(self, x_bchw: torch.Tensor) -> torch.Tensor:
"""Convert images shaped [B, 3, H, W] to CLIP transformer input [L, N, D]."""
x = x_bchw.type(self.dtype)
x = self.visual.conv1(x)
x = x.reshape(x.shape[0], x.shape[1], -1)
x = x.permute(0, 2, 1)
cls = self.visual.class_embedding.to(x.dtype) + torch.zeros(
x.shape[0], 1, x.shape[2], dtype=x.dtype, device=x.device
)
x = torch.cat([cls, x], dim=1)
x = x + self.visual.positional_embedding.to(x.dtype)
x = self.visual.ln_pre(x)
return x.permute(1, 0, 2)
def _visual_tokens_from_imgs(
self, imgs_bchw: torch.Tensor
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Return final-layer tokens and optional intermediate-layer MIL tokens.
The second result is None when intermediate MIL features are disabled.
"""
x = self._stem_to_lnd(imgs_bchw)
if not self.use_clip_intermediate_for_mil:
x = self.visual.transformer(x)
return x.permute(1, 0, 2), None
blocks = _clip_vit_resblock_modules(self.visual.transformer)
stop = self.clip_mil_stop_after_block_1based
for i in range(stop):
x = blocks[i](x)
x_mil = x.permute(1, 0, 2).clone()
for i in range(stop, len(blocks)):
x = blocks[i](x)
return x.permute(1, 0, 2), x_mil
def get_tokens(self, x) -> torch.Tensor:
"""Return final ViT tokens shaped [B, T, D], without separating intermediate features."""
x = self._stem_to_lnd(x)
x = self.visual.transformer(x)
return x.permute(1, 0, 2)
def encode_image(self, image) -> torch.Tensor:
return self.visual(image.type(self.dtype))
def forward(self, x: torch.Tensor, labels: Optional[torch.Tensor] = None, chunk_size=256):
chunk_size = getattr(self, "eval_chunk_size", chunk_size)
B, N, C, H, W = x.shape
imgs = x.reshape(B * N, C, H, W)
feats_chunks = []
mil_chunks = []
with torch.no_grad():
for start in range(0, imgs.size(0), chunk_size):
end = min(start + chunk_size, imgs.size(0))
f_tok, m_tok = self._visual_tokens_from_imgs(imgs[start:end])
feats_chunks.append(f_tok)
if m_tok is not None:
mil_chunks.append(m_tok)
feats_token = torch.cat(feats_chunks, dim=0)
T = feats_token.shape[-2]
D = feats_token.shape[-1]
feats = feats_token.reshape(B, N, T, D).float()
if not mil_chunks:
return self.core(feats, labels=labels, x_mil=None)
mil_token = torch.cat(mil_chunks, dim=0).reshape(B, N, T, D).float()
return self.core(feats, labels=labels, x_mil=mil_token)
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