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import torch
import torch.nn as nn
from pytorch_lightning import LightningModule
import torch.nn.functional as F
from einops import rearrange
from itertools import chain
from torchmetrics import Accuracy, Precision, Recall, F1Score, AUROC, ConfusionMatrix, CohenKappa, AveragePrecision, MetricCollection
from osf.models.balanced_losses import FocalLoss, BalancedSoftmax
def _create_pred_metrics(num_classes: int) -> MetricCollection:
"""Create metrics that take preds (class indices) as input."""
metrics = {
"acc": Accuracy(task="multiclass", num_classes=num_classes, average="micro"),
"f1": F1Score(task="multiclass", num_classes=num_classes, average="macro"),
"f1_w": F1Score(task="multiclass", num_classes=num_classes, average="weighted"),
"rec_m": Recall(task="multiclass", num_classes=num_classes, average="macro"),
"kappa": CohenKappa(task="multiclass", num_classes=num_classes, weights="quadratic"),
}
return MetricCollection(metrics)
def _create_prob_metrics(num_classes: int) -> MetricCollection:
"""Create metrics that take probs (probabilities) as input."""
metrics = {
"auc": AUROC(task="multiclass", num_classes=num_classes, average="macro"),
"auprc": AveragePrecision(task="multiclass", num_classes=num_classes, average="macro"),
}
return MetricCollection(metrics)
def _create_perclass_pred_metrics(num_classes: int) -> MetricCollection:
"""Create per-class metrics that take preds as input."""
metrics = {
"acc_c": Accuracy(task="multiclass", num_classes=num_classes, average=None),
"prec_c": Precision(task="multiclass", num_classes=num_classes, average=None),
"rec_c": Recall(task="multiclass", num_classes=num_classes, average=None),
"f1_c": F1Score(task="multiclass", num_classes=num_classes, average=None),
"cm": ConfusionMatrix(task="multiclass", num_classes=num_classes, normalize=None),
}
return MetricCollection(metrics)
def _create_perclass_prob_metrics(num_classes: int) -> MetricCollection:
"""Create per-class metrics that take probs as input."""
metrics = {
"auc_c": AUROC(task="multiclass", num_classes=num_classes, average=None),
"auprc_c": AveragePrecision(task="multiclass", num_classes=num_classes, average=None),
}
return MetricCollection(metrics)
class SSLFineTuner(LightningModule):
def __init__(self,
backbones,
use_which_backbone,
config = None,
in_features: int = 256,
num_classes: int = 2,
epochs: int = 10,
dropout: float = 0.0,
lr: float = 1e-3,
weight_decay: float = 1e-4,
final_lr: float = 1e-5,
use_channel_bank: bool = True,
loss_type: str = "ce",
class_distribution: Optional[torch.Tensor] = None,
focal_gamma: float = 2.0,
focal_alpha: Optional[float | torch.Tensor] = None,
use_mean_pool: bool = False,
total_training_steps: int = None,
finetune_backbone: bool = False,
*args, **kwargs
) -> None:
super().__init__()
self.save_hyperparameters()
self.lr = lr
self.weight_decay = weight_decay
self.epochs = epochs
self.final_lr = final_lr
self.use_channel_bank = use_channel_bank
self.loss_type = loss_type
self.focal_gamma = focal_gamma
self.focal_alpha = focal_alpha
self.use_mean_pool = use_mean_pool
self.total_training_steps = total_training_steps
self.finetune_backbone = finetune_backbone
if loss_type == "ce":
self.criterion = None
elif loss_type == "focal":
alpha = focal_alpha
if alpha is None and class_distribution is not None:
class_dist = class_distribution.float()
total_samples = class_dist.sum()
alpha = total_samples / (num_classes * class_dist)
alpha = alpha / alpha.mean()
self.criterion = FocalLoss(alpha=alpha, gamma=focal_gamma, reduction="mean")
elif loss_type == "balanced_softmax":
self.criterion = BalancedSoftmax(class_distribution, reduction="mean")
else:
raise ValueError(f"Unknown loss_type: {loss_type}. Must be one of ['ce', 'focal', 'balanced_softmax']")
if isinstance(backbones, nn.ModuleDict):
self.backbones = backbones
else:
self.backbones = nn.ModuleDict(backbones)
self.config = config
self.use_which_backbone = use_which_backbone
self.backbone = self.backbones[self.use_which_backbone] if self.use_which_backbone != "fusion" else None
if self.use_which_backbone == "fusion":
for k in ("ecg", "resp", "elect"):
if k in self.backbones:
for p in self.backbones[k].parameters():
p.requires_grad = self.finetune_backbone
if not self.finetune_backbone:
self.backbones[k].eval()
else:
for p in self.backbone.parameters():
p.requires_grad = self.finetune_backbone
if not self.finetune_backbone:
self.backbone.eval()
if self.finetune_backbone:
print(f"[INFO] Full finetuning mode: backbone parameters are TRAINABLE")
if self.use_which_backbone == "fusion":
dims = [getattr(self.backbones[k], "out_dim", in_features)
for k in ("ecg", "resp", "elect") if k in self.backbones]
if len(dims) == 0:
raise ValueError("fusion requires at least one of {'ecg','resp','elect'} in backbones.")
if len(set(dims)) != 1:
raise ValueError(f"Mean fusion requires equal output dims, got {dims}")
final_in_features = dims[0]
else:
final_in_features = getattr(self.backbone, "out_dim", in_features)
self.linear_layer = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(final_in_features, num_classes)
)
self.train_pred_metrics = _create_pred_metrics(num_classes)
self.val_pred_metrics = _create_pred_metrics(num_classes)
self.test_pred_metrics = _create_pred_metrics(num_classes)
self.train_prob_metrics = _create_prob_metrics(num_classes)
self.val_prob_metrics = _create_prob_metrics(num_classes)
self.test_prob_metrics = _create_prob_metrics(num_classes)
self.train_pred_metrics_c = _create_perclass_pred_metrics(num_classes)
self.val_pred_metrics_c = _create_perclass_pred_metrics(num_classes)
self.test_pred_metrics_c = _create_perclass_pred_metrics(num_classes)
self.train_prob_metrics_c = _create_perclass_prob_metrics(num_classes)
self.val_prob_metrics_c = _create_perclass_prob_metrics(num_classes)
self.test_prob_metrics_c = _create_perclass_prob_metrics(num_classes)
self.class_names = getattr(self.config, "class_names", [str(i) for i in range(num_classes)])
def on_train_epoch_start(self) -> None:
if not self.finetune_backbone:
if self.use_which_backbone == "fusion":
for k in ("ecg", "resp", "elect"):
if k in self.backbones:
self.backbones[k].eval()
else:
self.backbone.eval()
def training_step(self, batch, batch_idx):
loss, logits, y = self.shared_step(batch)
probs = logits.softmax(-1)
preds = logits.argmax(-1)
self.train_pred_metrics.update(preds, y)
self.train_prob_metrics.update(probs, y)
self.train_pred_metrics_c.update(preds, y)
self.train_prob_metrics_c.update(probs, y)
self.log("train_loss", loss, prog_bar=True, on_step=True, on_epoch=False, sync_dist=True)
return loss
def on_train_epoch_end(self):
pred_agg = self.train_pred_metrics.compute()
prob_agg = self.train_prob_metrics.compute()
self.log("train_acc", pred_agg["acc"], prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
self.log("train_f1", pred_agg["f1"], prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
self.log("train_auc", prob_agg["auc"], prog_bar=False, on_step=False, on_epoch=True, sync_dist=True)
self.log("train_auprc", prob_agg["auprc"], prog_bar=False, on_step=False, on_epoch=True, sync_dist=True)
pred_c = self.train_pred_metrics_c.compute()
prob_c = self.train_prob_metrics_c.compute()
cm = pred_c["cm"]
support = cm.sum(dim=1) if cm is not None else None
for i in range(len(pred_c["acc_c"])):
name = self.class_names[i] if i < len(self.class_names) else str(i)
self.log(f"train/acc_{name}", pred_c["acc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"train/prec_{name}", pred_c["prec_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"train/rec_{name}", pred_c["rec_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"train/f1_{name}", pred_c["f1_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"train/auc_{name}", prob_c["auc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"train/auprc_{name}", prob_c["auprc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
if support is not None:
self.log(f"train/support_{name}", support[i].to(pred_c["acc_c"][i].dtype), on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.train_pred_metrics.reset()
self.train_prob_metrics.reset()
self.train_pred_metrics_c.reset()
self.train_prob_metrics_c.reset()
def validation_step(self, batch, batch_idx):
loss, logits, y = self.shared_step(batch)
probs = logits.softmax(-1)
preds = logits.argmax(-1)
self.val_pred_metrics.update(preds, y)
self.val_prob_metrics.update(probs, y)
self.val_pred_metrics_c.update(preds, y)
self.val_prob_metrics_c.update(probs, y)
self.log("val_loss", loss, prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
return loss
def on_validation_epoch_end(self):
pred_agg = self.val_pred_metrics.compute()
prob_agg = self.val_prob_metrics.compute()
self.log("val_acc", pred_agg["acc"], prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
self.log("val_f1", pred_agg["f1"], prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
self.log("val_f1_w", pred_agg["f1_w"], prog_bar=False, on_step=False, on_epoch=True, sync_dist=True)
self.log("val_rec_m", pred_agg["rec_m"], prog_bar=False, on_step=False, on_epoch=True, sync_dist=True)
self.log("val_auc", prob_agg["auc"], prog_bar=False, on_step=False, on_epoch=True, sync_dist=True)
self.log("val_auprc", prob_agg["auprc"], prog_bar=False, on_step=False, on_epoch=True, sync_dist=True)
self.log("val_kappa", pred_agg["kappa"], prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
pred_c = self.val_pred_metrics_c.compute()
prob_c = self.val_prob_metrics_c.compute()
cm = pred_c["cm"]
support = cm.sum(dim=1)
for i in range(len(pred_c["acc_c"])):
name = self.class_names[i] if i < len(self.class_names) else str(i)
self.log(f"val/acc_{name}", pred_c["acc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"val/prec_{name}", pred_c["prec_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"val/rec_{name}", pred_c["rec_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"val/f1_{name}", pred_c["f1_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"val/auc_{name}", prob_c["auc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"val/auprc_{name}", prob_c["auprc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"val/support_{name}", support[i].to(pred_c["acc_c"][i].dtype), on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.val_pred_metrics.reset()
self.val_prob_metrics.reset()
self.val_pred_metrics_c.reset()
self.val_prob_metrics_c.reset()
def test_step(self, batch, batch_idx):
loss, logits, y = self.shared_step(batch)
probs = logits.softmax(-1)
preds = logits.argmax(-1)
self.test_pred_metrics.update(preds, y)
self.test_prob_metrics.update(probs, y)
self.test_pred_metrics_c.update(preds, y)
self.test_prob_metrics_c.update(probs, y)
self.log("test_loss", loss, on_step=False, on_epoch=True, sync_dist=True)
return loss
def on_test_epoch_end(self):
pred_agg = self.test_pred_metrics.compute()
prob_agg = self.test_prob_metrics.compute()
self.log("test_acc", pred_agg["acc"], on_step=False, on_epoch=True, sync_dist=True)
self.log("test_f1", pred_agg["f1"], on_step=False, on_epoch=True, sync_dist=True)
self.log("test_f1_w", pred_agg["f1_w"], on_step=False, on_epoch=True, sync_dist=True)
self.log("test_rec_m", pred_agg["rec_m"], on_step=False, on_epoch=True, sync_dist=True)
self.log("test_auc", prob_agg["auc"], on_step=False, on_epoch=True, sync_dist=True)
self.log("test_auprc", prob_agg["auprc"], on_step=False, on_epoch=True, sync_dist=True)
self.log("test_kappa", pred_agg["kappa"], on_step=False, on_epoch=True, sync_dist=True)
pred_c = self.test_pred_metrics_c.compute()
prob_c = self.test_prob_metrics_c.compute()
cm = pred_c["cm"]
support = cm.sum(dim=1) if cm is not None else None
for i in range(len(pred_c["acc_c"])):
name = self.class_names[i] if i < len(self.class_names) else str(i)
self.log(f"test/acc_{name}", pred_c["acc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"test/prec_{name}", pred_c["prec_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"test/rec_{name}", pred_c["rec_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"test/f1_{name}", pred_c["f1_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"test/auc_{name}", prob_c["auc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.log(f"test/auprc_{name}", prob_c["auprc_c"][i], on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
if support is not None:
self.log(f"test/support_{name}", support[i].to(pred_c["acc_c"][i].dtype),
on_step=False, on_epoch=True, prog_bar=False, sync_dist=True)
self.test_pred_metrics.reset()
self.test_prob_metrics.reset()
self.test_pred_metrics_c.reset()
self.test_prob_metrics_c.reset()
def shared_step(self, batch):
context = torch.no_grad() if not self.finetune_backbone else torch.enable_grad()
with context:
psg = batch['psg']
if self.use_which_backbone == 'ecg':
x = psg[:, 0:1, :]
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'resp':
x = psg[:, 1:5, :]
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'elect':
x = psg[:, 5:, :]
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'all':
x = psg
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'fusion':
feats_list = []
if 'ecg' in self.backbones:
x_ecg = psg[:, 0:1, :]
f_ecg = self._get_features(self.backbones['ecg'], x_ecg)
feats_list.append(f_ecg)
if 'resp' in self.backbones:
x_resp = psg[:, 1:5, :]
f_resp = self._get_features(self.backbones['resp'], x_resp)
feats_list.append(f_resp)
if 'elect' in self.backbones:
x_elect = psg[:, 5:, :]
f_elect = self._get_features(self.backbones['elect'], x_elect)
feats_list.append(f_elect)
feats = torch.stack(feats_list, dim=0).mean(dim=0)
else:
raise ValueError(f"Unknown use_which_backbone: {self.use_which_backbone}")
y = batch["label"]
feats = feats.view(feats.size(0), -1)
logits = self.linear_layer(feats)
y = y.squeeze(1).long()
if self.criterion is None:
loss = F.cross_entropy(logits, y)
else:
loss = self.criterion(logits, y)
return loss, logits, y
def _get_features(self, backbone, x):
"""Get features from backbone. Uses mean pooling if use_mean_pool=True."""
if self.use_mean_pool:
if hasattr(backbone, 'forward_encoding_mean_pool'):
return backbone.forward_encoding_mean_pool(x)
elif hasattr(backbone, 'forward_avg_pool'):
return backbone.forward_avg_pool(x)
return backbone(x)
def configure_optimizers(self):
if self.finetune_backbone:
if self.use_which_backbone == "fusion":
backbone_params = chain(*[self.backbones[k].parameters()
for k in ("ecg", "resp", "elect") if k in self.backbones])
else:
backbone_params = self.backbone.parameters()
params = chain(backbone_params, self.linear_layer.parameters())
else:
params = self.linear_layer.parameters()
optimizer = torch.optim.AdamW(
params,
lr=self.lr,
weight_decay=self.weight_decay,
)
if self.total_training_steps is not None and self.total_training_steps > 0:
warmup_steps = int(0.1 * self.total_training_steps)
cosine_steps = self.total_training_steps - warmup_steps
warmup_scheduler = torch.optim.lr_scheduler.LinearLR(
optimizer,
start_factor=0.1,
end_factor=1.0,
total_iters=warmup_steps
)
cosine_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=cosine_steps,
eta_min=self.final_lr
)
scheduler = torch.optim.lr_scheduler.SequentialLR(
optimizer,
schedulers=[warmup_scheduler, cosine_scheduler],
milestones=[warmup_steps]
)
return [optimizer], [{"scheduler": scheduler, "interval": "step"}]
else:
return [optimizer]
class SSLVitalSignsRegressor(SSLFineTuner):
"""SSL Finetuner for vital signs regression (HR, SPO2). Uses MSE loss."""
def __init__(self,
backbones,
use_which_backbone,
config = None,
in_features: int = 256,
num_classes: int = 1,
target_names: list = None,
dropout: float = 0.0,
**kwargs
) -> None:
kwargs['loss_type'] = 'ce'
super().__init__(
backbones=backbones,
use_which_backbone=use_which_backbone,
config=config,
in_features=in_features,
num_classes=2,
dropout=dropout,
**kwargs
)
self.num_targets = num_classes
self.target_names = target_names or [f"target_{i}" for i in range(num_classes)]
self.criterion = nn.MSELoss()
in_feat = self.linear_layer[1].in_features
self.linear_layer = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(in_feat, num_classes)
)
del self.train_pred_metrics, self.val_pred_metrics, self.test_pred_metrics
del self.train_prob_metrics, self.val_prob_metrics, self.test_prob_metrics
del self.train_pred_metrics_c, self.val_pred_metrics_c, self.test_pred_metrics_c
del self.train_prob_metrics_c, self.val_prob_metrics_c, self.test_prob_metrics_c
def shared_step(self, batch):
"""Override: regression loss instead of classification."""
context = torch.no_grad() if not self.finetune_backbone else torch.enable_grad()
with context:
psg = batch['psg']
if self.use_which_backbone == 'ecg':
x = psg[:, 0:1, :]
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'resp':
x = psg[:, 1:5, :]
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'elect':
x = psg[:, 5:, :]
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'all':
x = psg
feats = self._get_features(self.backbone, x)
elif self.use_which_backbone == 'fusion':
feats_list = []
if 'ecg' in self.backbones:
f_ecg = self._get_features(self.backbones['ecg'], psg[:, 0:1, :])
feats_list.append(f_ecg)
if 'resp' in self.backbones:
f_resp = self._get_features(self.backbones['resp'], psg[:, 1:5, :])
feats_list.append(f_resp)
if 'elect' in self.backbones:
f_elect = self._get_features(self.backbones['elect'], psg[:, 5:, :])
feats_list.append(f_elect)
feats = torch.stack(feats_list, dim=0).mean(dim=0)
else:
raise ValueError(f"Unknown use_which_backbone: {self.use_which_backbone}")
y = batch["label"].float() # [B, num_targets]
feats = feats.view(feats.size(0), -1)
preds = self.linear_layer(feats) # [B, num_targets]
loss = self.criterion(preds, y)
return loss, preds, y
def training_step(self, batch, batch_idx):
"""Override: regression metrics."""
loss, preds, y = self.shared_step(batch)
with torch.no_grad():
for i, name in enumerate(self.target_names):
mae = F.l1_loss(preds[:, i], y[:, i])
self.log(f"train_{name}_mae", mae, on_step=False, on_epoch=True, sync_dist=True)
self.log("train_loss", loss, prog_bar=True, on_step=True, on_epoch=False, sync_dist=True)
return loss
def on_train_epoch_end(self):
"""Override: no classification metrics to compute."""
pass
def validation_step(self, batch, batch_idx):
"""Override: regression metrics."""
loss, preds, y = self.shared_step(batch)
for i, name in enumerate(self.target_names):
mae = F.l1_loss(preds[:, i], y[:, i])
self.log(f"val_{name}_mae", mae, on_step=False, on_epoch=True, sync_dist=True)
overall_mae = F.l1_loss(preds, y)
self.log("val_loss", loss, prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
self.log("val_mae", overall_mae, prog_bar=True, on_step=False, on_epoch=True, sync_dist=True)
return loss
def on_validation_epoch_end(self):
"""Override: no classification metrics to compute."""
pass
def test_step(self, batch, batch_idx):
"""Override: regression metrics."""
loss, preds, y = self.shared_step(batch)
for i, name in enumerate(self.target_names):
p, t = preds[:, i], y[:, i]
mae = F.l1_loss(p, t)
mse = F.mse_loss(p, t)
rmse = torch.sqrt(mse)
self.log(f"test_{name}_mae", mae, on_step=False, on_epoch=True, sync_dist=True)
self.log(f"test_{name}_mse", mse, on_step=False, on_epoch=True, sync_dist=True)
self.log(f"test_{name}_rmse", rmse, on_step=False, on_epoch=True, sync_dist=True)
overall_mae = F.l1_loss(preds, y)
overall_mse = F.mse_loss(preds, y)
self.log("test_loss", loss, on_step=False, on_epoch=True, sync_dist=True)
self.log("test_mae", overall_mae, on_step=False, on_epoch=True, sync_dist=True)
self.log("test_mse", overall_mse, on_step=False, on_epoch=True, sync_dist=True)
return loss
def on_test_epoch_end(self):
"""Override: no classification metrics to compute."""
pass
class SupervisedVitalSignsRegressor(SSLVitalSignsRegressor):
"""Supervised from-scratch regression. Equivalent to SSLVitalSignsRegressor with finetune_backbone=True."""
def __init__(self,
backbones,
use_which_backbone,
epochs: int = 100,
**kwargs
):
kwargs['finetune_backbone'] = True
super().__init__(
backbones=backbones,
use_which_backbone=use_which_backbone,
epochs=epochs,
**kwargs
)
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