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d581b00 | 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 | import torch
import torch.nn as nn
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from src.models.model import build_model
from src.data.loader import get_cross_dataset_loaders
def evaluate(model, loader, device, criterion):
model.eval()
all_preds, all_labels = [], []
total_loss = 0
with torch.no_grad():
for images, labels in loader:
images = images.to(device)
labels = labels.float().unsqueeze(1).to(device)
outputs = model(images)
loss = criterion(outputs, labels)
total_loss += loss.item()
preds = (torch.sigmoid(outputs) >= 0.5).float()
all_preds.extend(preds.cpu().numpy().flatten())
all_labels.extend(labels.cpu().numpy().flatten())
avg_loss = total_loss / len(loader)
acc = accuracy_score(all_labels, all_preds)
prec = precision_score(all_labels, all_preds)
rec = recall_score(all_labels, all_preds)
f1 = f1_score(all_labels, all_preds)
cm = confusion_matrix(all_labels, all_preds)
return avg_loss, acc, prec, rec, f1, cm
def train(epochs=10, batch_size=32, lr=1e-4):
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
print(f"Using device: {device}")
train_loader, test_loader = get_cross_dataset_loaders(batch_size=batch_size)
model = build_model().to(device)
for name, param in model.named_parameters():
if "layer4" in name or "fc" in name:
param.requires_grad = True
criterion = nn.BCEWithLogitsLoss()
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr)
scheduler = ReduceLROnPlateau(optimizer, patience=2)
best_loss = float("inf")
early_stop_patience = 3
no_improve_count = 0
for epoch in range(epochs):
model.train()
train_loss, correct, total = 0, 0, 0
for images, labels in train_loader:
images = images.to(device)
labels = labels.float().unsqueeze(1).to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
preds = (torch.sigmoid(outputs) >= 0.5).float()
correct += (preds == labels).sum().item()
total += labels.size(0)
train_acc = correct / total
avg_train_loss = train_loss / len(train_loader)
# Validate on unseen generators
val_loss, acc, prec, rec, f1, _ = evaluate(model, test_loader, device, criterion)
scheduler.step(val_loss)
print(f"Epoch {epoch+1}/{epochs} | "
f"Train Loss: {avg_train_loss:.4f} | Train Acc: {train_acc:.4f} | "
f"Unseen Test Acc: {acc:.4f} | F1: {f1:.4f}")
if val_loss < best_loss:
best_loss = val_loss
no_improve_count = 0
torch.save(model.state_dict(), "saved_models/cross_val_best.pth")
print(f" -> Best model saved")
else:
no_improve_count += 1
if no_improve_count >= early_stop_patience:
print(f"Early stopping at epoch {epoch+1}")
break
# Final evaluation
print("\n--- Final Evaluation on Unseen Generators ---")
_, acc, prec, rec, f1, cm = evaluate(model, test_loader, device, criterion)
print(f"Accuracy: {acc:.4f}")
print(f"Precision: {prec:.4f}")
print(f"Recall: {rec:.4f}")
print(f"F1 Score: {f1:.4f}")
print(f"Confusion Matrix:\n{cm}")
if __name__ == "__main__":
train() |