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31e2456 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 | """The four models under test. They share encoders, differ in loss and Delta-t.
Model variants:
A: ECG-JEPA unimodal (I-JEPA self-prediction on ECG only)
B: cross-modal JEPA, delta_t = 0
C: symmetric InfoNCE (no predictor)
F: PhysioJEPA v1 (cross-modal JEPA, variable delta_t)
"""
from __future__ import annotations
from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
from torch import nn
from .dt_embed import DeltaTEmbedding
from .ecg_encoder import ECGPatchTokeniser
from .ema import EMA
from .masking import multi_block_mask_1d
from .ppg_encoder import PPGPatchTokeniser
from .vit import CrossAttentionPredictor, ViT1D
@dataclass
class ModelConfig:
ecg_patch: int = 50
ppg_patch: int = 25
d_model: int = 256
ecg_depth: int = 12
ppg_depth: int = 6
heads: int = 8
pred_depth: int = 4
max_tokens: int = 128
# ablation knobs
query_mode: str = "learned" # "learned" | "sinusoidal"
mask_ratio: float = 0.50
def _pool(x: torch.Tensor) -> torch.Tensor:
return x.mean(dim=1)
def _make_query_emb(cfg: ModelConfig) -> tuple[nn.Module | None, torch.Tensor | None]:
"""Returns either a learned nn.Parameter wrapped in a tiny Module, or a
fixed sinusoidal table buffer. Caller should index with positions.
"""
if cfg.query_mode == "sinusoidal":
import math
n_pos, d = cfg.max_tokens, cfg.d_model
pe = torch.zeros(n_pos, d)
pos = torch.arange(0, n_pos, dtype=torch.float32).unsqueeze(1)
div = torch.exp(torch.arange(0, d, 2, dtype=torch.float32) *
-(math.log(10_000.0) / d))
pe[:, 0::2] = torch.sin(pos * div)
pe[:, 1::2] = torch.cos(pos * div)
return None, pe # caller stores as buffer
return None, None # caller creates learned Parameter
class ECGOnlyEncoder(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.tok = ECGPatchTokeniser(patch_size=cfg.ecg_patch, d_model=cfg.d_model,
max_patches=cfg.max_tokens)
self.trunk = ViT1D(depth=cfg.ecg_depth, d_model=cfg.d_model, heads=cfg.heads)
def forward(self, ecg: torch.Tensor) -> torch.Tensor:
return self.trunk(self.tok(ecg)) # [B, N_e, d]
class PPGEncoder(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.tok = PPGPatchTokeniser(patch_size=cfg.ppg_patch, d_model=cfg.d_model,
max_patches=cfg.max_tokens)
self.trunk = ViT1D(depth=cfg.ppg_depth, d_model=cfg.d_model, heads=cfg.heads)
def forward(self, ppg: torch.Tensor) -> torch.Tensor:
return self.trunk(self.tok(ppg))
# ---------------------------------------------------------------------------
# Baseline A β ECG-JEPA unimodal (I-JEPA style self-prediction)
# ---------------------------------------------------------------------------
class BaselineA(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.cfg = cfg
self.ecg = ECGOnlyEncoder(cfg)
self.ecg_tgt = EMA(self.ecg)
self.predictor = CrossAttentionPredictor(
depth=cfg.pred_depth, d_model=cfg.d_model, heads=cfg.heads
)
_, sinpe = _make_query_emb(cfg)
if sinpe is None:
self.query_emb = nn.Parameter(torch.zeros(cfg.max_tokens, cfg.d_model))
nn.init.trunc_normal_(self.query_emb, std=0.02)
else:
self.register_buffer("query_emb", sinpe, persistent=False)
def step(self, batch: dict) -> dict:
ecg = batch["ecg"] # [B, 1, T]
b = ecg.shape[0]
n_ecg = ecg.shape[-1] // self.cfg.ecg_patch
ctx_idxs = []
tgt_idxs = []
for _ in range(b):
c, t = multi_block_mask_1d(n_ecg, n_targets=4, target_size_range=(4, 8),
mask_ratio=self.cfg.mask_ratio)
ctx_idxs.append(c)
tgt_idxs.append(t)
# All sequences same B but variable ctx/tgt lengths β process per-sample
# then pack. For efficiency use a padded approach.
tok = self.ecg.tok(ecg) # [B, N, d]
trunk = self.ecg.trunk
# context forward: apply trunk on full sequence then gather ctx/tgt tokens
full_ctx = trunk(tok) # [B, N, d]
tgt_full = self.ecg_tgt.target.trunk(self.ecg_tgt.target.tok(ecg)).detach()
L_self = torch.tensor(0.0, device=ecg.device)
total = 0
for i in range(b):
q = self.query_emb[tgt_idxs[i]].unsqueeze(0) # [1, n_t, d]
ctx_tokens = full_ctx[i : i + 1, ctx_idxs[i], :]
pred = self.predictor(q, ctx_tokens).squeeze(0)
tgt_v = tgt_full[i, tgt_idxs[i], :]
L_self = L_self + F.l1_loss(pred, tgt_v, reduction="mean")
total += 1
L_self = L_self / max(total, 1)
return {"loss": L_self, "L_self": L_self.detach(), "L_cross": torch.tensor(0.0),
"z_ecg": _pool(full_ctx.detach())}
def targets(self):
return [(self.ecg, self.ecg_tgt)]
# ---------------------------------------------------------------------------
# Shared cross-modal backbone for Baselines B, C, and E3 PhysioJEPA
# ---------------------------------------------------------------------------
class CrossModalBackbone(nn.Module):
"""Dual online encoders + two EMA targets + cross-attention predictor + Ξt emb."""
def __init__(self, cfg: ModelConfig, use_predictor: bool = True, use_delta_t: bool = True):
super().__init__()
self.cfg = cfg
self.use_predictor = use_predictor
self.use_delta_t = use_delta_t
self.ecg = ECGOnlyEncoder(cfg)
self.ppg = PPGEncoder(cfg)
self.ecg_tgt = EMA(self.ecg)
self.ppg_tgt = EMA(self.ppg)
if use_predictor:
self.predictor = CrossAttentionPredictor(
depth=cfg.pred_depth, d_model=cfg.d_model, heads=cfg.heads
)
_, sinpe = _make_query_emb(cfg)
if sinpe is None:
self.query_emb = nn.Parameter(torch.zeros(cfg.max_tokens, cfg.d_model))
nn.init.trunc_normal_(self.query_emb, std=0.02)
else:
self.register_buffer("query_emb", sinpe, persistent=False)
if use_delta_t:
self.dt_emb = DeltaTEmbedding(d_model=cfg.d_model)
def encode_ctx(self, ecg: torch.Tensor) -> torch.Tensor:
return self.ecg(ecg)
def encode_ppg_target(self, ppg: torch.Tensor) -> torch.Tensor:
with torch.no_grad():
return self.ppg_tgt.target(ppg).detach()
def predict_ppg(self, z_ecg: torch.Tensor, n_ppg_tokens: int,
dt_seconds: torch.Tensor | None) -> torch.Tensor:
b = z_ecg.shape[0]
q = self.query_emb[:n_ppg_tokens].unsqueeze(0).expand(b, -1, -1)
ctx = z_ecg
if self.use_delta_t and dt_seconds is not None:
dt_tok = self.dt_emb(dt_seconds).unsqueeze(1) # [B, 1, d]
ctx = torch.cat([ctx, dt_tok], dim=1)
return self.predictor(q, ctx)
def targets(self):
return [(self.ecg, self.ecg_tgt), (self.ppg, self.ppg_tgt)]
# ---------------------------------------------------------------------------
# Baseline B β symmetric cross-modal JEPA, Ξt = 0
# ---------------------------------------------------------------------------
class BaselineB(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.cfg = cfg
self.bb = CrossModalBackbone(cfg, use_predictor=True, use_delta_t=False)
def step(self, batch: dict) -> dict:
ecg, ppg = batch["ecg"], batch["ppg"]
z_ecg = self.bb.encode_ctx(ecg) # [B, N_e, d]
z_ppg_tgt = self.bb.encode_ppg_target(ppg) # [B, N_p, d]
n_ppg = z_ppg_tgt.shape[1]
z_pred = self.bb.predict_ppg(z_ecg, n_ppg, dt_seconds=None)
L_cross = F.l1_loss(z_pred, z_ppg_tgt)
# auxiliary self-prediction on ECG (I-JEPA style) β same code path as BaselineA
n_ecg = z_ecg.shape[1]
b = z_ecg.shape[0]
tok = self.bb.ecg.tok(ecg)
full_ctx = self.bb.ecg.trunk(tok)
tgt_full = self.bb.ecg_tgt.target.trunk(self.bb.ecg_tgt.target.tok(ecg)).detach()
L_self = torch.tensor(0.0, device=ecg.device)
for i in range(b):
c, t = multi_block_mask_1d(n_ecg, n_targets=4, target_size_range=(4, 8), mask_ratio=self.cfg.mask_ratio)
if len(t) == 0:
continue
q = self.bb.query_emb[t].unsqueeze(0)
ctx_tokens = full_ctx[i : i + 1, c, :]
pred = self.bb.predictor(q, ctx_tokens).squeeze(0)
tgt_v = tgt_full[i, t, :]
L_self = L_self + F.l1_loss(pred, tgt_v)
L_self = L_self / max(b, 1)
loss = L_cross + 0.3 * L_self
return {"loss": loss, "L_cross": L_cross.detach(), "L_self": L_self.detach(),
"z_ecg": _pool(z_ecg.detach()), "z_ppg": _pool(z_ppg_tgt.detach()),
"z_pred": _pool(z_pred.detach())}
def targets(self):
return self.bb.targets()
# ---------------------------------------------------------------------------
# Baseline C β symmetric InfoNCE (no predictor)
# ---------------------------------------------------------------------------
class BaselineC(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.cfg = cfg
self.ecg = ECGOnlyEncoder(cfg)
self.ppg = PPGEncoder(cfg)
self.ecg_head = nn.Linear(cfg.d_model, cfg.d_model)
self.ppg_head = nn.Linear(cfg.d_model, cfg.d_model)
# Standard CLIP-style temperature init: physical Ο β 0.07 β multiplier β 14.3.
# The earlier init log_tau=0 made multiplier=1, leaving logits β [-1, 1] which
# gives loss β ln(B) = uninformative ceiling.
self.log_tau = nn.Parameter(torch.log(torch.tensor(1.0 / 0.07)))
def step(self, batch: dict) -> dict:
ecg, ppg = batch["ecg"], batch["ppg"]
z_ecg = F.normalize(self.ecg_head(_pool(self.ecg(ecg))), dim=-1)
z_ppg = F.normalize(self.ppg_head(_pool(self.ppg(ppg))), dim=-1)
tau = torch.clamp(self.log_tau.exp(), 0.01, 100.0)
logits = tau * z_ecg @ z_ppg.t()
b = z_ecg.shape[0]
labels = torch.arange(b, device=ecg.device)
loss = 0.5 * (F.cross_entropy(logits, labels) + F.cross_entropy(logits.t(), labels))
return {"loss": loss, "L_cross": loss.detach(), "L_self": torch.tensor(0.0),
"z_ecg": z_ecg.detach(), "z_ppg": z_ppg.detach(),
"z_pred": z_ppg.detach(), "tau": tau.detach()}
def targets(self):
return [] # no EMA β pure contrastive
# ---------------------------------------------------------------------------
# E3 β PhysioJEPA v1 (variable Ξt cross-modal JEPA)
# ---------------------------------------------------------------------------
class PhysioJEPA(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.cfg = cfg
self.bb = CrossModalBackbone(cfg, use_predictor=True, use_delta_t=True)
def step(self, batch: dict) -> dict:
ecg, ppg = batch["ecg"], batch["ppg"]
dt = batch["dt_seconds"] # [B]
z_ecg = self.bb.encode_ctx(ecg)
z_ppg_tgt = self.bb.encode_ppg_target(ppg)
n_ppg = z_ppg_tgt.shape[1]
z_pred = self.bb.predict_ppg(z_ecg, n_ppg, dt_seconds=dt)
L_cross = F.l1_loss(z_pred, z_ppg_tgt)
# auxiliary ECG self-prediction
n_ecg = z_ecg.shape[1]
b = z_ecg.shape[0]
tok = self.bb.ecg.tok(ecg)
full_ctx = self.bb.ecg.trunk(tok)
tgt_full = self.bb.ecg_tgt.target.trunk(self.bb.ecg_tgt.target.tok(ecg)).detach()
L_self = torch.tensor(0.0, device=ecg.device)
for i in range(b):
c, t = multi_block_mask_1d(n_ecg, n_targets=4, target_size_range=(4, 8), mask_ratio=self.cfg.mask_ratio)
if len(t) == 0:
continue
q = self.bb.query_emb[t].unsqueeze(0)
ctx_tokens = full_ctx[i : i + 1, c, :]
pred = self.bb.predictor(q, ctx_tokens).squeeze(0)
tgt_v = tgt_full[i, t, :]
L_self = L_self + F.l1_loss(pred, tgt_v)
L_self = L_self / max(b, 1)
loss = L_cross + 0.3 * L_self
return {"loss": loss, "L_cross": L_cross.detach(), "L_self": L_self.detach(),
"z_ecg": _pool(z_ecg.detach()), "z_ppg": _pool(z_ppg_tgt.detach()),
"z_pred": _pool(z_pred.detach()), "dt": dt.detach()}
def targets(self):
return self.bb.targets()
MODEL_REGISTRY = {"A": BaselineA, "B": BaselineB, "C": BaselineC, "F": PhysioJEPA}
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