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train_clf.py — InceptionV3 binary classification on BDAPPV (panel / no panel).
Usage:
python train_clf.py --provider google
python train_clf.py --provider ign
python train_clf.py --provider google --checkpoint weights/inception_google_best.pth
"""
import argparse
import os
import torch
from tqdm import tqdm
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision.models import inception_v3, Inception_V3_Weights
from datasets import load_dataset
from dataset import ClassificationDataset
# ── Metrics ───────────────────────────────────────────────────────────────────
def accuracy_f1(logits, labels, threshold=0.0):
preds = (logits > threshold).float()
correct = (preds == labels).float().mean().item()
tp = (preds * labels).sum().item()
fp = (preds * (1 - labels)).sum().item()
fn = ((1 - preds) * labels).sum().item()
prec = tp / max(tp + fp, 1e-6)
rec = tp / max(tp + fn, 1e-6)
f1 = 2 * prec * rec / max(prec + rec, 1e-6)
return correct, f1
# ── Main ──────────────────────────────────────────────────────────────────────
def train(args):
device = (
torch.device("mps") if torch.backends.mps.is_available() else
torch.device("cuda") if torch.cuda.is_available() else
torch.device("cpu")
)
print(f"Device : {device}")
print(f"Provider: {args.provider} | effective batch: {args.batch_size * args.grad_accum}")
# ── Data ──────────────────────────────────────────────────────────────────
print("Loading dataset from HuggingFace Hub...")
ds = load_dataset("gabrielkasmi/bdappv", args.provider)
train_ds = ClassificationDataset(ds["train"], img_size=299, augment=True)
val_ds = ClassificationDataset(ds["validation"], img_size=299, augment=False)
print(f" train : {len(train_ds):,} | val : {len(val_ds):,}")
train_loader = DataLoader(
train_ds, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=(device.type != "mps"),
)
val_loader = DataLoader(
val_ds, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=(device.type != "mps"),
)
# ── Model ─────────────────────────────────────────────────────────────────
model = inception_v3(weights=Inception_V3_Weights.DEFAULT)
if args.checkpoint:
# Poids issus d'un modèle à 2 classes — on charge avec la tête originale
# puis on la remplace par une tête binaire (1 sortie, BCEWithLogitsLoss)
model.fc = nn.Linear(model.fc.in_features, 2)
model.AuxLogits.fc = nn.Linear(model.AuxLogits.fc.in_features, 2)
ckpt = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
state = ckpt["model_state_dict"] if isinstance(ckpt, dict) else ckpt
model.load_state_dict(state, strict=True)
print(f"Loaded checkpoint: {args.checkpoint}")
# Remplace la tête par 1 sortie pour BCEWithLogitsLoss
model.fc = nn.Linear(model.fc.in_features, 1)
model.AuxLogits.fc = nn.Linear(model.AuxLogits.fc.in_features, 1)
model = model.to(device)
# ── Optimizer + scheduler ─────────────────────────────────────────────────
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
criterion = nn.BCEWithLogitsLoss()
# ── Training loop ─────────────────────────────────────────────────────────
os.makedirs(args.out_dir, exist_ok=True)
best_f1 = 0.0
patience_ctr = 0
for epoch in range(1, args.epochs + 1):
model.train()
total_loss = 0.0
optimizer.zero_grad()
pbar = tqdm(train_loader, desc=f"Epoch {epoch}/{args.epochs}", leave=False)
for step, (images, labels) in enumerate(pbar):
images = images.to(device)
labels = labels.to(device).unsqueeze(1)
out = model(images)
logits = out.logits if hasattr(out, "logits") else out[0]
aux = out.aux_logits if hasattr(out, "aux_logits") else out[1]
loss = (criterion(logits, labels) + 0.4 * criterion(aux, labels)) / args.grad_accum
loss.backward()
total_loss += loss.item() * args.grad_accum
pbar.set_postfix(loss=f"{loss.item() * args.grad_accum:.4f}")
if (step + 1) % args.grad_accum == 0 or (step + 1) == len(train_loader):
optimizer.step()
optimizer.zero_grad()
# ── Validation ────────────────────────────────────────────────────────
model.eval()
all_logits, all_labels = [], []
with torch.no_grad():
for images, labels in val_loader:
images = images.to(device)
logits = model(images)
if hasattr(logits, "logits"):
logits = logits.logits
all_logits.append(logits.cpu())
all_labels.append(labels.unsqueeze(1))
all_logits = torch.cat(all_logits)
all_labels = torch.cat(all_labels)
acc, f1 = accuracy_f1(all_logits, all_labels)
avg_loss = total_loss / len(train_loader)
print(f"Epoch {epoch:3d}/{args.epochs} loss={avg_loss:.4f} val_acc={acc:.4f} val_f1={f1:.4f}")
scheduler.step()
if f1 > best_f1:
best_f1 = f1
patience_ctr = 0
ckpt_path = os.path.join(args.out_dir, f"inception_{args.provider}_best.pth")
torch.save(model.state_dict(), ckpt_path)
print(f" → checkpoint saved (F1={best_f1:.4f})")
else:
patience_ctr += 1
if patience_ctr >= args.patience:
print(f"\nEarly stopping — no improvement for {args.patience} epochs.")
break
print(f"\nDone. Best val F1: {best_f1:.4f}")
print(f"Weights: {os.path.join(args.out_dir, f'inception_{args.provider}_best.pth')}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--provider", default="google", choices=["google", "ign"])
parser.add_argument("--epochs", type=int, default=50)
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--grad_accum", type=int, default=4, help="Gradient accumulation steps")
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument("--num_workers", type=int, default=2, help="Workers pour le dataloader")
parser.add_argument("--checkpoint", default=None)
parser.add_argument("--out_dir", default="weights")
parser.add_argument("--patience", type=int, default=7, help="Early stopping patience (epochs)")
args = parser.parse_args()
train(args)
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