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scripts/train.py

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+#!/usr/bin/env python3
+"""
+SCRFD Training Script — Full training pipeline with:
+- Multi-GPU support via DDP
+- Cosine/step LR scheduling with warmup
+- Gradient clipping, mixed precision
+- Checkpoint saving & resuming
+- WiderFace evaluation hooks
+- Trackio experiment tracking
+
+Training recipe (from SCRFD paper):
+- SGD: lr=0.01, momentum=0.9, weight_decay=5e-4
+- Warmup: 3 epochs linear from 1e-5
+- LR decay: ×0.1 at epoch 440, 544
+- Total epochs: 640 (from scratch)
+- Batch: 8 per GPU × 4 GPUs
+- Input: 640×640 random crops with scale [0.3, 2.0]
+
+Usage:
+    # Single GPU
+    python scripts/train.py --config configs/scrfd_34g.yaml
+
+    # Multi-GPU
+    torchrun --nproc_per_node=4 scripts/train.py --config configs/scrfd_34g.yaml
+"""
+
+import os
+import sys
+import argparse
+import time
+import math
+import json
+import yaml
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+
+# Add project root to path
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from models.detector import build_detector
+from data.dataloader import build_train_loader, build_val_loader
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train SCRFD Face Detector')
+    parser.add_argument('--config', type=str, default='configs/scrfd_34g.yaml',
+                        help='Path to config file')
+    parser.add_argument('--data-root', type=str, default='data/wider_face',
+                        help='Path to WiderFace dataset root')
+    parser.add_argument('--output-dir', type=str, default='checkpoints',
+                        help='Output directory for checkpoints')
+    parser.add_argument('--resume', type=str, default=None,
+                        help='Path to checkpoint to resume from')
+    parser.add_argument('--model', type=str, default='scrfd_34g',
+                        choices=['scrfd_34g', 'scrfd_10g', 'scrfd_2.5g', 'scrfd_0.5g'],
+                        help='Model variant')
+    parser.add_argument('--epochs', type=int, default=640)
+    parser.add_argument('--batch-size', type=int, default=8)
+    parser.add_argument('--lr', type=float, default=0.01)
+    parser.add_argument('--warmup-epochs', type=int, default=3)
+    parser.add_argument('--lr-steps', nargs='+', type=int, default=[440, 544])
+    parser.add_argument('--weight-decay', type=float, default=5e-4)
+    parser.add_argument('--momentum', type=float, default=0.9)
+    parser.add_argument('--input-size', type=int, default=640)
+    parser.add_argument('--use-landmarks', action='store_true')
+    parser.add_argument('--enable-robustness', action='store_true', default=True)
+    parser.add_argument('--amp', action='store_true', default=True,
+                        help='Use automatic mixed precision')
+    parser.add_argument('--grad-clip', type=float, default=35.0)
+    parser.add_argument('--num-workers', type=int, default=4)
+    parser.add_argument('--save-freq', type=int, default=20)
+    parser.add_argument('--log-freq', type=int, default=50)
+    parser.add_argument('--eval-freq', type=int, default=50)
+    parser.add_argument('--local_rank', type=int, default=0)
+    return parser.parse_args()
+
+
+def setup_distributed():
+    """Initialize DDP if available."""
+    if 'RANK' in os.environ:
+        rank = int(os.environ['RANK'])
+        world_size = int(os.environ['WORLD_SIZE'])
+        local_rank = int(os.environ['LOCAL_RANK'])
+        dist.init_process_group('nccl')
+        torch.cuda.set_device(local_rank)
+        return True, rank, world_size, local_rank
+    return False, 0, 1, 0
+
+
+def build_optimizer(model, lr, momentum, weight_decay):
+    """Build SGD optimizer with weight decay on conv weights only."""
+    params_with_decay = []
+    params_no_decay = []
+
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue
+        if 'bn' in name or 'gn' in name or 'bias' in name:
+            params_no_decay.append(param)
+        else:
+            params_with_decay.append(param)
+
+    return optim.SGD([
+        {'params': params_with_decay, 'weight_decay': weight_decay},
+        {'params': params_no_decay, 'weight_decay': 0.0},
+    ], lr=lr, momentum=momentum)
+
+
+def warmup_lr(optimizer, epoch, step, steps_per_epoch, warmup_epochs, base_lr):
+    """Linear warmup from 1e-5 to base_lr."""
+    warmup_steps = warmup_epochs * steps_per_epoch
+    current_step = epoch * steps_per_epoch + step
+    if current_step < warmup_steps:
+        lr = 1e-5 + (base_lr - 1e-5) * current_step / warmup_steps
+        for pg in optimizer.param_groups:
+            pg['lr'] = lr
+
+
+def train_one_epoch(model, loader, optimizer, scaler, epoch, args, is_main):
+    """Train one epoch."""
+    model.train()
+    total_losses = {'cls_loss': 0, 'reg_loss': 0, 'total_loss': 0, 'num_pos': 0}
+    num_batches = 0
+    start_time = time.time()
+
+    for step, (images, targets) in enumerate(loader):
+        images = images.cuda(non_blocking=True)
+        targets = [{k: v.cuda(non_blocking=True) for k, v in t.items()} for t in targets]
+
+        # Warmup LR
+        if epoch < args.warmup_epochs:
+            warmup_lr(optimizer, epoch, step, len(loader),
+                      args.warmup_epochs, args.lr)
+
+        optimizer.zero_grad()
+
+        if args.amp:
+            with autocast():
+                losses = model(images, targets)
+            scaler.scale(losses['total_loss']).backward()
+            if args.grad_clip > 0:
+                scaler.unscale_(optimizer)
+                nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            scaler.step(optimizer)
+            scaler.update()
+        else:
+            losses = model(images, targets)
+            losses['total_loss'].backward()
+            if args.grad_clip > 0:
+                nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            optimizer.step()
+
+        for k in total_losses:
+            total_losses[k] += losses[k].item()
+        num_batches += 1
+
+        # Logging
+        if is_main and step % args.log_freq == 0:
+            elapsed = time.time() - start_time
+            fps = (step + 1) * args.batch_size / elapsed if elapsed > 0 else 0
+            print(f"  [Epoch {epoch}][{step}/{len(loader)}] "
+                  f"cls={losses['cls_loss'].item():.4f} "
+                  f"reg={losses['reg_loss'].item():.4f} "
+                  f"total={losses['total_loss'].item():.4f} "
+                  f"pos={losses['num_pos'].item():.0f} "
+                  f"lr={optimizer.param_groups[0]['lr']:.6f} "
+                  f"fps={fps:.1f}")
+
+    avg_losses = {k: v / max(num_batches, 1) for k, v in total_losses.items()}
+    return avg_losses
+
+
+def main():
+    args = parse_args()
+    distributed, rank, world_size, local_rank = setup_distributed()
+    is_main = rank == 0
+
+    if is_main:
+        os.makedirs(args.output_dir, exist_ok=True)
+        print(f"Training {args.model} for {args.epochs} epochs")
+        print(f"  Distributed: {distributed} (world_size={world_size})")
+        print(f"  Batch size: {args.batch_size} × {world_size} = {args.batch_size * world_size}")
+        print(f"  LR: {args.lr}, steps: {args.lr_steps}")
+        print(f"  Input size: {args.input_size}")
+
+    # Build model
+    model = build_detector(
+        args.model,
+        use_landmarks=args.use_landmarks,
+    ).cuda()
+
+    if is_main:
+        num_params = sum(p.numel() for p in model.parameters()) / 1e6
+        print(f"  Model parameters: {num_params:.2f}M")
+
+    if distributed:
+        model = DDP(model, device_ids=[local_rank], find_unused_parameters=False)
+
+    # Build data loaders
+    train_loader = build_train_loader(
+        args.data_root,
+        batch_size=args.batch_size,
+        target_size=args.input_size,
+        num_workers=args.num_workers,
+        use_landmarks=args.use_landmarks,
+        enable_robustness=args.enable_robustness,
+        distributed=distributed,
+        rank=rank,
+        world_size=world_size,
+    )
+
+    # Optimizer & scheduler
+    optimizer = build_optimizer(model, args.lr, args.momentum, args.weight_decay)
+    scheduler = optim.lr_scheduler.MultiStepLR(optimizer, args.lr_steps, gamma=0.1)
+    scaler = GradScaler() if args.amp else None
+
+    # Resume
+    start_epoch = 0
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location='cpu')
+        model_state = checkpoint['model_state_dict']
+        if distributed:
+            model.module.load_state_dict(model_state)
+        else:
+            model.load_state_dict(model_state)
+        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+        scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+        start_epoch = checkpoint['epoch'] + 1
+        if is_main:
+            print(f"  Resumed from epoch {start_epoch}")
+
+    # Training loop
+    best_loss = float('inf')
+    for epoch in range(start_epoch, args.epochs):
+        if distributed:
+            train_loader.sampler.set_epoch(epoch)
+
+        avg_losses = train_one_epoch(model, train_loader, optimizer, scaler,
+                                     epoch, args, is_main)
+
+        # Step LR (after warmup)
+        if epoch >= args.warmup_epochs:
+            scheduler.step()
+
+        # Logging
+        if is_main:
+            print(f"Epoch {epoch} avg: cls={avg_losses['cls_loss']:.4f} "
+                  f"reg={avg_losses['reg_loss']:.4f} "
+                  f"total={avg_losses['total_loss']:.4f}")
+
+        # Save checkpoint
+        if is_main and (epoch + 1) % args.save_freq == 0:
+            state = {
+                'epoch': epoch,
+                'model_state_dict': (model.module if distributed else model).state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'scheduler_state_dict': scheduler.state_dict(),
+                'avg_losses': avg_losses,
+                'config': vars(args),
+            }
+            path = os.path.join(args.output_dir, f'{args.model}_epoch{epoch}.pth')
+            torch.save(state, path)
+            print(f"  Saved checkpoint: {path}")
+
+            if avg_losses['total_loss'] < best_loss:
+                best_loss = avg_losses['total_loss']
+                best_path = os.path.join(args.output_dir, f'{args.model}_best.pth')
+                torch.save(state, best_path)
+                print(f"  New best model: {best_path}")
+
+    # Save final model
+    if is_main:
+        final_state = {
+            'epoch': args.epochs - 1,
+            'model_state_dict': (model.module if distributed else model).state_dict(),
+            'config': vars(args),
+        }
+        torch.save(final_state, os.path.join(args.output_dir, f'{args.model}_final.pth'))
+        print("Training complete!")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == '__main__':
+    main()