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198a76d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | """MoR-CNN: Mixture-of-Recursions applied to a convolutional vision model.
Ports the LLM MoR idea (llm-pipeline) to 2D medical imaging. The mapping:
entry block -> pretrained CNN stem (unique weights)
shared recursive -> ``RecursiveConvBlock`` stack reused at every recursion
core
depth router -> per-slice ``DepthRouter`` (expert choice over slices)
recursion emb -> learned per-recursion embedding added before the core
slice freeze -> a slice that stops routing keeps its current state
At inference the router spends full recursion depth only on the "hard" slices
(those most likely to be abnormal), so FLOPs scale with content — the lever the
competition's Efficiency Track scores.
"""
from __future__ import annotations
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
import timm
HAS_TIMM = True
except ImportError: # pragma: no cover - optional dependency
HAS_TIMM = False
class DepthScore(nn.Module):
"""Per-slice continuation score: pooled feature -> scalar logit.
Mirrors ``_DepthScore`` in llm-pipeline's MoR router, but over a global
pooled per-slice feature instead of a per-token hidden state.
"""
def __init__(self, dim: int, hidden: int, init_bias: float = 0.0):
super().__init__()
self.in_proj = nn.Linear(dim, hidden)
self.out_proj = nn.Linear(hidden, 1)
with torch.no_grad():
self.out_proj.bias.fill_(init_bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.out_proj(F.silu(self.in_proj(x))).squeeze(-1)
class DepthRouter(nn.Module):
"""Expert-choice depth router over slices.
One ``DepthScore`` head per recursion. Each recursion also adds a learned
recursion embedding so the router can condition on how deep a slice
already is (the vision analogue of ``rec_emb`` in the LLM MoR).
"""
def __init__(
self,
dim: int,
hidden: int,
n_recursions: int,
capacities: list[float],
init_bias: float = 0.0,
warmup_steps: int = 0,
):
super().__init__()
self.n_recursions = n_recursions
self.capacities = capacities
self.warmup_steps = warmup_steps
self.heads = nn.ModuleList(
[DepthScore(dim, hidden, init_bias) for _ in range(n_recursions)]
)
self.rec_emb = nn.Parameter(torch.zeros(n_recursions, dim))
def capacity(self, r: int, step: int) -> float:
"""Slice fraction kept at recursion ``r``, ramping from 1.0 during warmup."""
target = self.capacities[r]
if self.warmup_steps > 0 and step < self.warmup_steps:
t = step / self.warmup_steps
return 1.0 - (1.0 - target) * t
return target
def forward(self, x: torch.Tensor, r: int) -> torch.Tensor:
# x: [S, dim] pooled per-slice features
return self.heads[r](x + self.rec_emb[r])
class RecursiveConvBlock(nn.Module):
"""A shared inverted-residual conv block reused at every recursion.
Depthwise-separable (MobileNet-v2 style) so the recursive core stays cheap
while the stem does the heavy feature extraction.
"""
def __init__(self, dim: int):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.pw1 = nn.Conv2d(dim, dim * 2, 1)
self.dw = nn.Conv2d(dim * 2, dim * 2, 3, padding=1, groups=dim * 2)
self.pw2 = nn.Conv2d(dim * 2, dim, 1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: [N, dim, h, w]
identity = x
x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
x = self.pw2(F.gelu(self.dw(F.gelu(self.pw1(x)))))
return identity + x
class SliceAttentionPool(nn.Module):
"""Weight slices by learned relevance before aggregating into a study vector."""
def __init__(self, dim: int):
super().__init__()
self.query = nn.Parameter(torch.randn(dim))
self.scale = dim ** -0.5
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: [S, dim]
w = F.softmax(x @ self.query * self.scale, dim=0)
return (w.unsqueeze(1) * x).sum(0)
class ChannelLayerNorm(nn.Module):
"""LayerNorm over the channel dim of a [N, C, H, W] feature map."""
def __init__(self, dim: int):
super().__init__()
self.norm = nn.LayerNorm(dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
class MoRCNN(nn.Module):
"""Pretrained CNN stem + MoR recursive core + per-slice depth router."""
def __init__(
self,
*,
stem_name: str = "convnext_tiny",
pretrained: bool = True,
n_recursions: int = 3,
capacities: tuple[float, ...] = (1.0, 2.0 / 3.0, 1.0 / 3.0),
router_hidden: int = 128,
core_blocks: int = 2,
n_classes: int = 12,
router_warmup_steps: int = 0,
router_init_bias: float = 0.0,
):
super().__init__()
if not HAS_TIMM:
raise ImportError("timm is required for a pretrained stem")
self.n_recursions = n_recursions
self.capacities = list(capacities)
assert len(self.capacities) == n_recursions
self.stem = timm.create_model(
stem_name, pretrained=pretrained, features_only=True, num_classes=0
)
out_dim = self.stem.feature_info.channels()[-1]
self.core = nn.Sequential(*[RecursiveConvBlock(out_dim) for _ in range(core_blocks)])
self.router = DepthRouter(
out_dim,
router_hidden,
n_recursions,
self.capacities,
init_bias=router_init_bias,
warmup_steps=router_warmup_steps,
)
self.rec_emb = nn.Parameter(torch.zeros(n_recursions, out_dim))
self.exit = nn.Sequential(
ChannelLayerNorm(out_dim),
nn.Conv2d(out_dim, out_dim, 1),
nn.GELU(),
)
self.pool = SliceAttentionPool(out_dim)
self.head = nn.Sequential(
nn.Linear(out_dim, out_dim * 2),
nn.GELU(),
nn.Linear(out_dim * 2, n_classes),
)
def _run_core(self, feat: torch.Tensor, r: int) -> torch.Tensor:
emb = self.rec_emb[r].view(1, -1, 1, 1)
return self.core(feat + emb)
def _global_pool(self, feat: torch.Tensor) -> torch.Tensor:
return feat.mean(dim=(2, 3))
def forward(
self, x: torch.Tensor, step: int = 0
) -> tuple[torch.Tensor, list[float]]:
"""Forward one study.
Args:
x: [S, 3, H, W] sampled slices of a single study.
step: current optimizer step, used for router warmup.
Returns:
(logits [n_classes], route_stats) where route_stats is the active
slice fraction at each recursion.
"""
S = x.size(0)
feat = self.stem(x)[-1] # [S, C, h, w]
C = feat.size(1)
pooled = self._global_pool(feat)
stats: list[float] = []
for r in range(self.n_recursions):
probs = torch.sigmoid(self.router(pooled, r))
cap = self.router.capacity(r, step)
if r == self.n_recursions - 1:
active = torch.ones(S, dtype=torch.bool, device=x.device)
else:
k = max(1, int(round(S * cap)))
active = torch.zeros(S, dtype=torch.bool, device=x.device)
active[torch.topk(probs, k).indices] = True
stats.append(active.float().mean().item())
if active.all():
feat = self._run_core(feat, r)
else:
idx = active.nonzero(as_tuple=False).squeeze(1)
updated = self._run_core(feat[idx], r)
feat = feat.clone()
feat[idx] = updated
pooled = self._global_pool(feat)
slice_feats = self.exit(feat) # [S, C, h, w]
slice_feats = self._global_pool(slice_feats) # [S, C]
study = self.pool(slice_feats) # [C]
logits = self.head(study) # [n_classes]
return logits, stats
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