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import torch
import torch.nn as nn
import torch.nn.functional as F
class MILHead(nn.Module):
"""
MIL Head supporting max / LogSumExp / top-k / attention-gated pooling.
Args:
d_in (int): token feature dim
num_classes (int): number of classes
pooling (str): 'max' | 'lsep' | 'topk' | 'attn'
lsep_temp (float): temperature for LogSumExp pooling
lsep_norm (bool): whether to subtract log(N_eff)
topk (int | None): fixed k for top-k pooling
topk_ratio (float): ratio for top-k if topk is None
attn_hidden (int): hidden dim for gated-attention
attn_dropout (float): dropout for gated-attention
attn_bag_mode (str): only used when pooling='attn'
- 'logit_pool': bag_logits = sum(attn * inst_logits)
- 'embed_cls' : bag_embed = sum(attn * feats), then bag_logits = bag_classifier(bag_embed)
"""
def __init__(
self,
d_in: int = 1024,
num_classes: int = 2,
pooling: str = "lsep",
lsep_temp: float = 1.0,
lsep_norm: bool = True,
topk: int = None,
topk_ratio: float = 0.1,
attn_hidden: int = 128,
attn_dropout: float = 0.0,
attn_bag_mode: str = "embed_cls",
):
super().__init__()
self.num_classes = num_classes
self.pooling = pooling.lower()
self.lsep_temp = float(lsep_temp)
self.lsep_norm = bool(lsep_norm)
self.topk = topk
self.topk_ratio = float(topk_ratio)
self.attn_bag_mode = attn_bag_mode.lower()
if self.pooling not in {"max", "lsep", "topk", "attn"}:
raise ValueError(f"Unknown pooling mode: {self.pooling}")
if self.attn_bag_mode not in {"logit_pool", "embed_cls"}:
raise ValueError(f"Unknown attn_bag_mode: {self.attn_bag_mode}")
# Instance-level classifier: shared across tokens
self.classifier = nn.Linear(d_in, num_classes)
# Attention pooling modules
if self.pooling == "attn":
print(
f"Using gated-attention pooling with hidden size {attn_hidden}, "
f"bag mode = {self.attn_bag_mode}"
)
self.attn_V = nn.Linear(d_in, attn_hidden, bias=True)
self.attn_U = nn.Linear(d_in, attn_hidden, bias=True)
self.attn_w = nn.Linear(attn_hidden, 1, bias=False)
self.attn_drop = nn.Dropout(attn_dropout)
# Decoupled bag classifier
if self.attn_bag_mode == "embed_cls":
self.bag_classifier = nn.Linear(d_in, num_classes)
else:
self.bag_classifier = None
else:
self.bag_classifier = None
@staticmethod
def _masked_fill_min(x, mask, fill_value=None):
"""Set masked positions to a very small number before max/topk."""
if mask is None:
return x
if fill_value is None:
fill_value = torch.finfo(x.dtype).min
return x.masked_fill(~mask, fill_value)
def _pool_max(self, logits, mask=None):
logits = self._masked_fill_min(
logits, mask.unsqueeze(-1) if mask is not None else None
)
bag = logits.max(dim=1).values
return bag
def _pool_lsep(self, logits, mask=None):
z = logits / self.lsep_temp
if mask is not None:
neg_inf = torch.finfo(z.dtype).min
z = z.masked_fill(~mask.unsqueeze(-1), neg_inf)
bag = torch.logsumexp(z, dim=1)
if self.lsep_norm:
if mask is None:
n = logits.size(1)
bag = bag - math.log(max(n, 1))
else:
n_eff = mask.sum(dim=1).clamp_min(1).unsqueeze(-1)
bag = bag - n_eff.log()
return bag
def _pool_topk(self, logits, mask=None):
B, N, C = logits.shape
if mask is not None:
logits = self._masked_fill_min(logits, mask.unsqueeze(-1))
n_eff = mask.sum(dim=1)
else:
n_eff = logits.new_full((B,), N, dtype=torch.long)
k = self.topk
if k is None:
k = torch.clamp((n_eff.float() * self.topk_ratio).long(), min=1)
else:
k = torch.clamp(torch.full_like(n_eff, k), min=1, max=N)
scores = logits.transpose(1, 2) # [B, C, N]
topk_vals = []
for b in range(B):
kb = int(min(int(k[b].item()), int(n_eff[b].item())))
vals, _ = scores[b].topk(kb, dim=-1) # [C, kb]
topk_vals.append(vals.mean(dim=-1)) # [C]
bag = torch.stack(topk_vals, dim=0) # [B, C]
return bag
def _compute_attn(self, feats, mask=None):
"""
feats: [B, N, D]
mask : [B, N]
returns:
attn: [B, N, 1]
"""
Vh = self.attn_V(feats) # [B, N, L]
Uh = self.attn_U(feats) # [B, N, L]
gate = torch.tanh(Vh) * torch.sigmoid(Uh)
gate = self.attn_drop(gate)
attn = self.attn_w(gate) # [B, N, 1]
if mask is not None:
neg_inf = torch.finfo(attn.dtype).min
attn = attn.masked_fill(~mask.unsqueeze(-1), neg_inf)
attn = F.softmax(attn, dim=1)
return attn
def _pool_attn(self, feats, logits, mask=None):
"""
Returns:
bag_logits: [B, C]
attn: [B, N, 1]
bag_embed: [B, D]
"""
attn = self._compute_attn(feats, mask) # [B, N, 1]
bag_embed = (attn * feats).sum(dim=1) # [B, D]
if self.attn_bag_mode == "logit_pool":
bag_logits = (attn * logits).sum(dim=1) # [B, C]
elif self.attn_bag_mode == "embed_cls":
bag_logits = self.bag_classifier(bag_embed) # [B, C]
else:
raise ValueError(f"Unknown attn_bag_mode: {self.attn_bag_mode}")
return bag_logits, attn, bag_embed
def forward(self, feats, mask=None):
"""
Args:
feats: [B, N, D]
mask: [B, N], True means valid token
Returns:
dict{
'inst_logits': [B, N, C],
'bag_logits': [B, C],
'attn': [B, N, 1] or None,
'bag_embed': [B, D] or None
}
"""
inst_logits = self.classifier(feats) # [B, N, C]
attn = None
bag_embed = None
if self.pooling == "max":
bag_logits = self._pool_max(inst_logits, mask)
elif self.pooling == "lsep":
bag_logits = self._pool_lsep(inst_logits, mask)
elif self.pooling == "topk":
bag_logits = self._pool_topk(inst_logits, mask)
elif self.pooling == "attn":
bag_logits, attn, bag_embed = self._pool_attn(feats, inst_logits, mask)
else:
raise ValueError(f"Unknown pooling mode: {self.pooling}")
return {
"inst_logits": inst_logits,
"bag_logits": bag_logits,
"attn": attn,
"bag_embed": bag_embed,
} |