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121a325 7b33404 121a325 7b33404 121a325 7b33404 121a325 7b33404 121a325 | 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 | """
Code for baseline model to compare the classifier to
"""
from lightning import LightningModule
import torch
import torch.nn as nn
from .loss import calculate_loss, auprc_zeros_vs_ones_from_logits, auroc_zeros_vs_ones_from_logits
from .model import DimCompressor
class BaselineBindPredictor(LightningModule):
"""
Baseline predictor: simple MLP that just concatenates the embeddings and outputs per-token predictions.
"""
def __init__(
self,
# input_dim: int = 256, # OLD: single input dim
binder_input_dim: int = 1280, # NEW: TF (binder) original dim (e.g., 1280)
glm_input_dim: int = 256, # NEW: DNA/GLM original dim (e.g., 256)
compressed_dim: int = 256, # NEW: learnable compressed dim
hidden_dim: int = 256,
lr: float = 1e-4,
alpha: float = 20,
gamma: float = 20,
dropout: float = 0,
weight_decay: float = 0.01,
loss_type: str = "mixed"
):
# Init
super(BaselineBindPredictor, self).__init__()
self.save_hyperparameters()
# Learnable compressor for binder -> 256, then project to hidden
self.binder_compress = DimCompressor(binder_input_dim, out_dim=compressed_dim)
self.mlp = torch.nn.Sequential(
torch.nn.Linear(compressed_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, 1),
torch.nn.ReLU(),
)
def forward(self, binder_emb, glm_emb, binder_mask, glm_mask):
"""
binder_emb: (B, Lb, binder_input_dim)
glm_emb: (B, Lg, glm_input_dim)
Returns per-nucleotide logits for the GLM sequence: (B, Lg)
"""
# Binder: learnable compression → glm_input_dim
b = self.binder_compress(binder_emb) # (B, Lb, glm_input_dim)
# Concatenate target and binder. Concatenate on the length dimension
lg = glm_emb.shape[1]
concat_embeddings = torch.concat((glm_emb,b), dim=1) # (B, Lb + Lg, glm_input_dim)
# Run concatenated embeddings through MLP
logits = self.mlp(concat_embeddings) # (B, Lb + Lg, 1)
# Get only the DNA logits.
logits = logits[:,0:lg,:].squeeze(
-1
)
return logits
# ----- Lightning hooks -----
def training_step(self, batch, batch_idx):
"""
Training step taken by PyTorch-Lightning trainer. Uses batch returned by data collator.
Colator returns a dictionary with:
"binder_emb" # [B, Lb_max, Db]
"binder_kpm" # [B, Lb_max]
"glm_emb" # [B, Lg_max, Dg]
"glm_kpm" # [B, Lg_max]
"labels" # [B, Lg_max]
"ID"
"tr_sequence"
"dna_sequence"
}
"""
logits = self.forward(batch["binder_emb"], batch["glm_emb"], batch["binder_kpm"], batch["glm_kpm"])
loss = calculate_loss(
logits, batch["labels"], batch["binder_kpm"], batch["glm_kpm"], alpha=self.hparams.alpha, gamma=self.hparams.gamma, loss_type=self.hparams.loss_type
)
self.log(
"train/loss",
loss,
on_step=True,
on_epoch=True,
prog_bar=True,
batch_size=logits.size(0),
)
# ---- AUPRC and AUROC on labels in {0, >0.99} only ----
ap, n_pos, n_neg, precision, recall, thresholds = auprc_zeros_vs_ones_from_logits(
logits.detach(), batch["labels"], batch.get("glm_kpm"), pos_thresh=0.99
)
auc, n_pos, n_neg, tpr, fpr, thresolds, tp, fp = auroc_zeros_vs_ones_from_logits(
logits.detach(), batch["labels"], batch.get("glm_kpm"), pos_thresh=0.99
)
# per-batch AP (epoch-mean is a decent summary); sync across GPUs if using DDP
self.log("train/auprc_0v1",
ap if torch.isfinite(ap) else torch.tensor(0.0, device=ap.device),
on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=logits.size(0))
self.log("train/auroc_0v1",
auc if torch.isfinite(auc) else torch.tensor(0.0, device=auc.device),
on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=logits.size(0))
# (optional) also log class counts so you can sanity-check balance
self.log("train/n_pos_0v1", float(n_pos), on_step=False, on_epoch=True, sync_dist=True)
self.log("train/n_neg_0v1", float(n_neg), on_step=False, on_epoch=True, sync_dist=True)
return loss
def validation_step(self, batch, batch_idx):
logits = self.forward(batch["binder_emb"], batch["glm_emb"], batch["binder_kpm"], batch["glm_kpm"])
loss = calculate_loss(
logits, batch["labels"], batch["binder_kpm"], batch["glm_kpm"], alpha=self.hparams.alpha, gamma=self.hparams.gamma, loss_type=self.hparams.loss_type
)
self.log(
"val/loss",
loss,
on_step=False,
on_epoch=True,
prog_bar=True,
batch_size=logits.size(0),
)
# ---- AUPRC and AUROC on labels in {0, >0.99} only ----
ap, n_pos, n_neg, precision, recall, thresholds = auprc_zeros_vs_ones_from_logits(
logits.detach(), batch["labels"], batch.get("glm_kpm"), pos_thresh=0.99
)
auc, n_pos, n_neg, tpr, fpr, thresolds, tp, fp = auroc_zeros_vs_ones_from_logits(
logits.detach(), batch["labels"], batch.get("glm_kpm"), pos_thresh=0.99
)
# per-batch AP (epoch-mean is a decent summary); sync across GPUs if using DDP
self.log("val/auprc_0v1",
ap if torch.isfinite(ap) else torch.tensor(0.0, device=ap.device),
on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=logits.size(0))
self.log("val/auroc_0v1",
auc if torch.isfinite(auc) else torch.tensor(0.0, device=auc.device),
on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=logits.size(0))
return loss
def test_step(self, batch, batch_idx):
logits = self.forward(batch["binder_emb"], batch["glm_emb"], batch["binder_kpm"], batch["glm_kpm"])
loss = calculate_loss(
logits, batch["labels"], batch["binder_kpm"], batch["glm_kpm"], alpha=self.hparams.alpha, gamma=self.hparams.gamma, loss_type=self.hparams.loss_type
)
self.log(
"test/loss", loss, on_step=False, on_epoch=True, batch_size=logits.size(0)
)
# ---- AUPRC and AUROC on labels in {0, >0.99} only ----
ap, n_pos, n_neg, precision, recall, thresholds = auprc_zeros_vs_ones_from_logits(
logits.detach(), batch["labels"], batch.get("glm_kpm"), pos_thresh=0.99
)
auc, n_pos, n_neg, tpr, fpr, thresolds, tp, fp = auroc_zeros_vs_ones_from_logits(
logits.detach(), batch["labels"], batch.get("glm_kpm"), pos_thresh=0.99
)
# per-batch AP (epoch-mean is a decent summary); sync across GPUs if using DDP
self.log("test/auprc_0v1",
ap if torch.isfinite(ap) else torch.tensor(0.0, device=ap.device),
on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=logits.size(0))
self.log("test/auroc_0v1",
auc if torch.isfinite(auc) else torch.tensor(0.0, device=auc.device),
on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=logits.size(0))
return loss
def on_before_optimizer_step(self, optimizer):
# Compute global L2 norm of all parameter gradients (ignores None grads)
grads = []
for p in self.parameters():
if p.grad is not None:
# .detach() avoids autograd tracking; .float() avoids fp16 overflow in norms
grads.append(p.grad.detach().float().norm(2))
if grads:
total_norm = torch.norm(torch.stack(grads), p=2)
self.log("train/grad_norm", total_norm, on_step=True, prog_bar=False, logger=True)
def on_after_backward(self):
grads = [p.grad.detach().float().norm(2)
for p in self.parameters() if p.grad is not None]
if grads:
total_norm = torch.norm(torch.stack(grads), p=2)
self.log("train/grad_norm_back", total_norm, on_step=True, prog_bar=False)
def on_train_epoch_end(self):
if False:
if self.train_auc.compute() is not None:
self.log("train/auroc", self.train_auc.compute(), prog_bar=True)
self.train_auc.reset()
def on_validation_epoch_end(self):
if False:
if self.val_auc.compute() is not None:
self.log("val/auroc", self.val_auc.compute(), prog_bar=True)
self.val_auc.reset()
def on_test_epoch_end(self):
if False:
if self.test_auc.compute() is not None:
self.log("test/auroc", self.test_auc.compute(), prog_bar=True)
self.test_auc.reset()
def configure_optimizers(self):
# AdamW + cosine as a sensible default
opt = torch.optim.AdamW(
self.parameters(),
lr=self.hparams.lr,
weight_decay=self.hparams.weight_decay,
)
# Scheduler optional—comment out if you prefer fixed LR
sch = torch.optim.lr_scheduler.CosineAnnealingLR(
opt, T_max=max(self.trainer.max_epochs, 1)
)
return {
"optimizer": opt,
"lr_scheduler": {"scheduler": sch, "interval": "epoch"},
} |