File size: 5,534 Bytes
9f16c4c | 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 | import os, sys, json, math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.amp import autocast, GradScaler
from sklearn.metrics import (accuracy_score, precision_score, recall_score,
f1_score, roc_auc_score, matthews_corrcoef)
from collections import Counter
import numpy as np
from model_v2 import PeptEdgeV2, count_parameters
from data_utils import load_genpept_data, get_dataloaders
def evaluate(model, loader, device):
model.eval()
all_preds, all_labels, all_probs = [], [], []
with torch.no_grad():
for x, y in loader:
x, y = x.to(device), y.to(device)
with autocast(device_type='cuda'):
logits = model(x)
probs = F.softmax(logits, dim=1)
preds = logits.argmax(dim=1)
all_preds.append(preds.cpu())
all_labels.append(y.cpu())
all_probs.append(probs.cpu())
preds = torch.cat(all_preds).numpy()
labels = torch.cat(all_labels).numpy()
probs = torch.cat(all_probs).numpy()
return {
'accuracy': float(accuracy_score(labels, preds)),
'precision': float(precision_score(labels, preds, zero_division=0)),
'recall': float(recall_score(labels, preds, zero_division=0)),
'specificity': float(recall_score(labels, 1 - preds, zero_division=0)),
'f1': float(f1_score(labels, preds, zero_division=0)),
'auc': float(roc_auc_score(labels, probs[:, 1])),
'mcc': float(matthews_corrcoef(labels, preds)),
}
def train():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Device: {device}')
sequences, labels = load_genpept_data()
train_loader, val_loader, test_loader = get_dataloaders(
sequences, labels, batch_size=64, max_len=200
)
model = PeptEdgeV2(
vocab_size=21, max_len=200,
d_model=192, n_heads=6, num_layers=5,
ff_dim=384, num_classes=2,
dropout=0.25, sd_prob=0.05,
).to(device)
total_params = count_parameters(model)
print(f'Params: {total_params:,}')
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=5e-5)
warmup = 15
total_epochs = 100
def lr_lambda(step):
if step < warmup:
return step / warmup
return 0.5 * (1 + math.cos(math.pi * (step - warmup) / (total_epochs - warmup)))
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
scaler = GradScaler('cuda')
best_val_f1 = 0
best_state = None
patience_counter = 0
history = []
ckpt_dir = 'checkpoints'
os.makedirs(ckpt_dir, exist_ok=True)
print(f'\n{"Ep":>3} | {"Loss":>7} | {"Acc":>6} | {"F1":>6} | {"AUC":>6} | {"MCC":>6} | Best | {"LR":>8}')
print('-' * 55)
for epoch in range(total_epochs):
model.train()
total_loss = 0
for x, y in train_loader:
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
with autocast(device_type='cuda'):
logits = model(x)
loss = criterion(logits, y)
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
scaler.step(optimizer)
scaler.update()
total_loss += loss.item()
train_loss = total_loss / len(train_loader)
val_metrics = evaluate(model, val_loader, device)
scheduler.step()
is_best = val_metrics['f1'] > best_val_f1
if is_best:
best_val_f1 = val_metrics['f1']
best_state = model.state_dict().copy()
torch.save({
'epoch': epoch, 'model_state_dict': best_state,
'val_metrics': val_metrics, 'config': {'d_model': 192, 'n_heads': 6, 'num_layers': 5, 'ff_dim': 384, 'dropout': 0.25},
'total_params': total_params,
}, f'{ckpt_dir}/best_model.pt')
patience_counter = 0
else:
patience_counter += 1
history.append({'epoch': epoch+1, 'train_loss': train_loss, **val_metrics})
if epoch < 5 or (epoch+1) % 3 == 0 or is_best:
print(f'{epoch+1:>3} | {train_loss:>7.4f} | {val_metrics["accuracy"]:>6.4f} | '
f'{val_metrics["f1"]:>6.4f} | {val_metrics["auc"]:>6.4f} | '
f'{val_metrics["mcc"]:>6.4f} | {"*" if is_best else " "} | {scheduler.get_last_lr()[0]:>8.2e}')
if patience_counter >= 30:
print(f'Early stop at epoch {epoch+1}')
break
model.load_state_dict(best_state)
test_metrics = evaluate(model, test_loader, device)
print('\n' + '='*55)
print('TEST SET RESULTS')
print('='*55)
for k, v in test_metrics.items():
print(f' {k}: {v:.4f}')
print(f' params: {total_params:,}')
results = {
'test_metrics': test_metrics,
'total_params': total_params,
'best_val_f1': best_val_f1,
'history': history,
}
with open('results/final_results.json', 'w') as f:
json.dump(results, f, indent=2, default=str)
return test_metrics, total_params
if __name__ == '__main__':
metrics, params = train()
sota_f1 = 0.883
our_f1 = metrics['f1']
print(f'\nSOTA (ESM-2 LoRA 650M): {sota_f1:.2%} F1')
print(f'PeptEdgeV2 ({params:,} params): {our_f1:.2%} F1')
print(f'Δ: {our_f1 - sota_f1:+.2%}')
|