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2fe488a | 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 | """Transformer operating on nodes to predict edges between nodes."""
from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint as grad_ckpt
class CrossAttentionBlock(nn.Module):
"""A single cross-attention block with MLP and residual connections."""
def __init__(
self,
hidden_dim: int = 64,
n_heads: int = 4,
mlp_ratio: float = 2.0,
dropout: float = 0.1,
):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_dim)
self.norm2 = nn.LayerNorm(hidden_dim)
self.cross_attn = nn.MultiheadAttention(
hidden_dim, n_heads, batch_first=True, dropout=dropout
)
mlp_hidden = int(hidden_dim * mlp_ratio)
self.mlp = nn.Sequential(
nn.Linear(hidden_dim, mlp_hidden),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(mlp_hidden, hidden_dim),
nn.Dropout(dropout),
)
def forward(
self,
q: torch.Tensor,
kv: torch.Tensor,
kv_mask: torch.Tensor | None = None,
) -> torch.Tensor:
"""Cross-attention with residual.
Parameters
----------
q : torch.Tensor
Query tensor, shape (B, N_q, D).
kv : torch.Tensor
Key/value tensor, shape (B, N_kv, D).
kv_mask : torch.Tensor, optional
Boolean mask for kv positions, shape (B, N_kv).
True = real position, False = padding (will be ignored).
"""
key_padding_mask = ~kv_mask if kv_mask is not None else None
attn_out, _ = self.cross_attn(
self.norm1(q), self.norm1(kv), self.norm1(kv),
key_padding_mask=key_padding_mask,
)
q = q + attn_out
q = q + self.mlp(self.norm2(q))
return q
class SimpleNodeTransformer(nn.Module):
"""Transformer for predicting edges between cell detections."""
def __init__(
self,
feat_dim: int = 33,
hidden_dim: int = 128,
n_heads: int = 4,
n_blocks: int = 4,
mlp_ratio: float = 2.0,
dropout: float = 0.3,
pair_chunk_size: int | None = 32,
):
super().__init__()
self.pair_chunk_size = pair_chunk_size
self.proj = nn.Linear(feat_dim, hidden_dim)
self.norm_in = nn.LayerNorm(hidden_dim)
self.blocks = nn.ModuleList([
CrossAttentionBlock(hidden_dim, n_heads, mlp_ratio, dropout)
for _ in range(n_blocks)
])
self.norm_out = nn.LayerNorm(hidden_dim)
# MLP for pairwise scoring: concatenated features + relative position
self.pair_mlp = nn.Sequential(
nn.Linear(hidden_dim * 2 + 3, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, hidden_dim // 2),
nn.GELU(),
nn.Linear(hidden_dim // 2, 1),
)
def forward(
self,
feat_t: torch.Tensor,
feat_t1: torch.Tensor,
coords_t: torch.Tensor,
coords_t1: torch.Tensor,
mask_t: torch.Tensor | None = None,
mask_t1: torch.Tensor | None = None,
) -> torch.Tensor:
"""
Predict edge logits between detections at consecutive frames.
Accepts both unbatched (N, D) and batched (B, N, D) inputs.
When unbatched, a batch dimension is added and removed automatically.
Parameters
----------
feat_t : torch.Tensor
Features at time t, shape (N_t, D) or (B, N_t, D).
feat_t1 : torch.Tensor
Features at time t+1, shape (N_t1, D) or (B, N_t1, D).
coords_t : torch.Tensor
Coordinates (z, y, x) at time t, shape (N_t, 3) or (B, N_t, 3).
coords_t1 : torch.Tensor
Coordinates (z, y, x) at time t+1, shape (N_t1, 3) or (B, N_t1, 3).
mask_t : torch.Tensor, optional
Boolean mask for t nodes, shape (B, N_t). True = real, False = pad.
mask_t1 : torch.Tensor, optional
Boolean mask for t+1 nodes, shape (B, N_t1). True = real, False = pad.
Returns
-------
torch.Tensor
Edge logits, shape (N_t, N_t1) or (B, N_t, N_t1).
"""
unbatched = feat_t.ndim == 2
if unbatched:
feat_t = feat_t.unsqueeze(0)
feat_t1 = feat_t1.unsqueeze(0)
coords_t = coords_t.unsqueeze(0)
coords_t1 = coords_t1.unsqueeze(0)
q = self.norm_in(self.proj(feat_t)) # (B, N_t, hidden)
k = self.norm_in(self.proj(feat_t1)) # (B, N_t1, hidden)
# Bi-directional cross-attention: t attends to t+1 and vice versa.
for block in self.blocks:
def _q_fn(
q: torch.Tensor, kv: torch.Tensor,
mask: torch.Tensor | None, _b: CrossAttentionBlock = block,
) -> torch.Tensor:
return _b(q, kv, kv_mask=mask)
def _k_fn(
k: torch.Tensor, kv: torch.Tensor,
mask: torch.Tensor | None, _b: CrossAttentionBlock = block,
) -> torch.Tensor:
return _b(k, kv, kv_mask=mask)
if torch.is_grad_enabled():
q = grad_ckpt(_q_fn, q, k, mask_t1, use_reentrant=False)
k = grad_ckpt(_k_fn, k, q, mask_t, use_reentrant=False)
else:
q = _q_fn(q, k, mask_t1)
k = _k_fn(k, q, mask_t)
q = self.norm_out(q) # (B, N_t, hidden)
k = self.norm_out(k) # (B, N_t1, hidden)
# Build pairwise logits in chunks over N_t to avoid O(N²) peak allocation.
# Full tensor (B, N_t, N_t1, 2*hidden+3) can be tens of GB for large N.
# Each chunk is grad-checkpointed: forward peak = B×chunk×N_t1×(2H+3),
# backward only re-stores tiny q_c / coords slice instead of all activations.
N_t = q.shape[1]
chunk = self.pair_chunk_size or N_t
chunks = []
pair_mlp = self.pair_mlp
for i in range(0, N_t, chunk):
q_c = q[:, i : i + chunk, :]
coords_c = coords_t[:, i : i + chunk, :]
def _chunk_fn(
qc: torch.Tensor,
kk: torch.Tensor,
cc: torch.Tensor,
cc1: torch.Tensor,
_pm: nn.Module = pair_mlp,
) -> torch.Tensor:
nc_i = qc.shape[1]
n1 = kk.shape[1]
qe = qc.unsqueeze(2).expand(-1, -1, n1, -1)
ke = kk.unsqueeze(1).expand(-1, nc_i, -1, -1)
rel = (cc.unsqueeze(2) - cc1.unsqueeze(1)) / 100.0
return _pm(torch.cat([qe, ke, rel], dim=-1)).squeeze(-1)
if torch.is_grad_enabled():
out = grad_ckpt(
_chunk_fn, q_c, k, coords_c, coords_t1, use_reentrant=False
)
else:
out = _chunk_fn(q_c, k, coords_c, coords_t1)
chunks.append(out)
logits = torch.cat(chunks, dim=1) # (B, N_t, N_t1)
if unbatched:
logits = logits.squeeze(0)
return logits
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