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6ec5f7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | """ELF slide encoder: interpolate → LayerNorm → 8-head ABMIL."""
from __future__ import annotations
from collections import OrderedDict
from pathlib import Path
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
DEFAULT_REPO_ID = "luoxd96/ELF"
WEIGHTS_FILE = "elf_slide_encoder.pth"
class BatchedABMIL(nn.Module):
def __init__(self, dim: int):
super().__init__()
self.attention_a = nn.Sequential(nn.Linear(dim, dim), nn.Tanh())
self.attention_b = nn.Sequential(nn.Linear(dim, dim), nn.Sigmoid())
self.attention_c = nn.Linear(dim, 1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.attention_c(self.attention_a(x) * self.attention_b(x))
class ELFSlideEncoder(nn.Module):
def __init__(self, embed_dim: int = 768, num_heads: int = 8):
super().__init__()
if embed_dim % num_heads != 0:
raise ValueError(f"embed_dim ({embed_dim}) must be divisible by num_heads ({num_heads})")
self.embed_dim = embed_dim
self.num_heads = num_heads
self.norm = nn.LayerNorm(embed_dim)
self.attn = nn.ModuleList(
[BatchedABMIL(embed_dim // num_heads) for _ in range(num_heads)]
)
def forward(self, x: torch.Tensor, lens: Optional[torch.Tensor] = None):
"""
Args:
x: ``[B, N, C]`` patch features (``C`` in {768, 1024, 1280, 1536}).
lens: ``[B]`` native ``C`` per item; defaults to ``x.shape[-1]``.
Returns:
features_dim: ``[B, C]`` — ``softmax(ᾱ)ᵀ X``
features: ``[B, 768]`` — ``softmax(ᾱ)ᵀ X_768``
attention: ``[B, 1, N]``
"""
if x.ndim != 3:
raise ValueError(f"expected [B, N, C], got {tuple(x.shape)}")
batch, n_tiles, feat_dim = x.shape
if lens is None:
lens = torch.full((batch,), feat_dim, dtype=torch.long, device=x.device)
x768 = []
for i in range(batch):
c = int(lens[i].item())
x768.append(
F.interpolate(x[i, :, :c].unsqueeze(0), size=self.embed_dim, mode="linear", align_corners=True).squeeze(0)
)
x768 = self.norm(torch.stack(x768, dim=0))
head_dim = self.embed_dim // self.num_heads
heads = x768.view(batch, n_tiles, head_dim, self.num_heads)
logits = torch.stack([self.attn[h](heads[:, :, :, h]) for h in range(self.num_heads)], dim=-1)
attn = F.softmax(logits.mean(dim=-1).transpose(1, 2), dim=-1)
feat_768 = torch.bmm(attn, x768)[:, 0]
feat_native = torch.stack(
[torch.bmm(attn[i : i + 1], x[i : i + 1, :, : int(lens[i].item())])[0, 0] for i in range(batch)]
)
return feat_native, feat_768, attn
@classmethod
def from_pretrained(
cls,
repo_id: str = DEFAULT_REPO_ID,
filename: str = WEIGHTS_FILE,
device: Union[str, torch.device] = "cpu",
embed_dim: int = 768,
num_heads: int = 8,
) -> "ELFSlideEncoder":
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id=repo_id, filename=filename)
return load_encoder(path, device=device, embed_dim=embed_dim, num_heads=num_heads)
def preprocess_patch_features(features: torch.Tensor, foundation_model: Optional[str] = None) -> torch.Tensor:
x = features.float()
if (foundation_model or "").lower() == "virchow2" and x.shape[-1] >= 2560:
x = 0.5 * (x[..., :1280] + x[..., 1280:2560])
return x
def _unwrap_state_dict(raw) -> OrderedDict:
if isinstance(raw, dict) and "state_dict" in raw:
raw = raw["state_dict"]
return OrderedDict((k[7:] if k.startswith("module.") else k, v) for k, v in raw.items())
def extract_inference_weights(state_dict: dict) -> OrderedDict:
keys = list(state_dict.keys())
prefix = ""
if any(k.startswith("momentum_enc.") for k in keys):
prefix = "momentum_enc."
keep = ("norm.", "attn.")
out = OrderedDict(
(k[len(prefix) :], v)
for k, v in state_dict.items()
if k.startswith(prefix) and k[len(prefix) :].startswith(keep)
)
if not out:
raise KeyError(f"no norm/attn weights found; prefixes={sorted({k.split('.')[0] for k in keys})[:12]}")
return out
def load_encoder(
checkpoint: Union[str, Path],
device: Union[str, torch.device] = "cpu",
embed_dim: int = 768,
num_heads: int = 8,
) -> ELFSlideEncoder:
ckpt = torch.load(str(checkpoint), map_location="cpu", weights_only=False)
weights = extract_inference_weights(_unwrap_state_dict(ckpt))
model = ELFSlideEncoder(embed_dim=embed_dim, num_heads=num_heads)
missing, unexpected = model.load_state_dict(weights, strict=True)
if missing or unexpected:
raise RuntimeError(f"load mismatch missing={missing} unexpected={unexpected}")
return model.to(device).eval()
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