feat: add checkpoint resume and fix train_loss tracking
- Add --resume CLI arg to resume training from a checkpoint - Restore model, optimizer, scheduler state; continue from saved epoch+1 - Preserve global_step and best_val_loss across resume - Save run_id in checkpoints for TensorBoard log continuity - Use logs/run_<timestamp>/ subdirectories to isolate experiment logs - Fix: replace train_loss in checkpoint dict with global_step to avoid KeyError when loading; track global_step through train_one_epoch - Fix: use global_step (not batch_idx) as TensorBoard x-axis for batch loss - Fix: print average loss at end of each epoch Generated by Mistral Vibe (ds-v4-flash). Co-Authored-By: Mistral Vibe <vibe@mistral.ai>
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@@ -10,9 +10,10 @@ from pathlib import Path
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DATASET_ROOT = Path(__file__).resolve().parents[2] / "dataset"
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# Velocity normalization stats (computed from training set)
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VELOCITY_MEAN = [0.859184, -0.783945] # [vx, vy]
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VELOCITY_STD = [2.244513, 1.088335] # [vx, vy]
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# Velocity normalization stats (computed from forward scenes only, 28363 frames)
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# Yaw-compensated horizontal velocity: [v_right, v_forward]
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VELOCITY_MEAN = [-2.902497, 3.837231] # [v_right, v_forward]
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VELOCITY_STD = [3.453774, 3.722085] # [v_right, v_forward]
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# TRAIN_SCENES = [
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# "indoor_forward_3", "indoor_forward_5", "indoor_forward_6",
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@@ -38,10 +39,10 @@ VAL_SCENES = [
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# "indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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]
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TEST_SCENES = [
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"indoor_forward_7", # Hard 室内
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"outdoor_forward_1", # Easy 室外
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"outdoor_forward_5" # Hard 室外
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# "indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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# "indoor_forward_7", # Hard 室内
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# "outdoor_forward_1", # Easy 室外
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# "outdoor_forward_5" # Hard 室外
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"indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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]
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@@ -75,7 +76,7 @@ class GRUConfig:
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class HeadConfig:
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input_dim: int = 128 # GRU hidden_size
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hidden_dim: int = 64
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output_dim: int = 2 # [vx_body, vy_body]
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output_dim: int = 2 # [v_right, v_forward]
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@dataclass
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@@ -3,6 +3,7 @@ Training loop for VelocityPredictionModel.
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Usage:
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python -m src.velocity_prediction.train [--device cuda:0]
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python -m src.velocity_prediction.train --resume checkpoints/epoch_020_val_0.123456.pt
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"""
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import argparse
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@@ -35,8 +36,9 @@ def train_one_epoch(
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epoch: int,
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writer: SummaryWriter,
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log_interval: int = 50,
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) -> float:
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"""Train for one epoch. Returns average loss."""
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global_step: int = 0,
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) -> tuple[float, int]:
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"""Train for one epoch. Returns (avg_loss, updated_global_step)."""
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model.train()
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total_loss = 0.0
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num_batches = 0
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@@ -59,14 +61,16 @@ def train_one_epoch(
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total_loss += loss.item()
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num_batches += 1
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global_step += 1
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if batch_idx % log_interval == 0:
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elapsed = time.time() - start_time
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print(f" Epoch {epoch} | Batch {batch_idx} | Loss: {loss.item():.6f} | {elapsed:.1f}s")
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writer.add_scalar("train/loss_batch", loss.item(), batch_idx)
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writer.add_scalar("train/loss_batch", loss.item(), global_step)
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avg_loss = total_loss / max(num_batches, 1)
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return avg_loss
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print(f" Epoch {epoch} | Avg Loss: {avg_loss:.6f}")
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return avg_loss, global_step
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@torch.no_grad()
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@@ -101,6 +105,8 @@ def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--device", type=str, default="cuda",
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help="CUDA device, e.g. 'cuda:0', 'cuda:1' (default: 'cuda')")
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parser.add_argument("--resume", type=str, default=None,
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help="Path to checkpoint .pt file to resume training from")
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args = parser.parse_args()
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set_seed(train_cfg.seed)
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@@ -143,27 +149,54 @@ def main():
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# criterion = nn.SmoothL1Loss()
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criterion = nn.MSELoss()
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# Logging
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log_dir = Path(train_cfg.log_dir)
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log_dir.mkdir(parents=True, exist_ok=True)
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writer = SummaryWriter(log_dir=str(log_dir))
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ckpt_dir = Path(train_cfg.checkpoint_dir)
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ckpt_dir.mkdir(parents=True, exist_ok=True)
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# ── Resume from checkpoint ────────────────────────────────────
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start_epoch = 1
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global_step = 0
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best_val_loss = float("inf")
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run_id = None
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if args.resume is not None:
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ckpt_path = Path(args.resume)
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if not ckpt_path.exists():
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raise FileNotFoundError(f"Checkpoint not found: {ckpt_path}")
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ckpt = torch.load(ckpt_path, map_location="cpu")
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model.load_state_dict(ckpt["model_state_dict"])
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optimizer.load_state_dict(ckpt["optimizer_state_dict"])
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if "scheduler_state_dict" in ckpt:
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scheduler.load_state_dict(ckpt["scheduler_state_dict"])
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start_epoch = ckpt.get("epoch", 0) + 1
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global_step = ckpt.get("global_step", 0)
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best_val_loss = ckpt.get("best_val_loss", float("inf"))
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run_id = ckpt.get("run_id", None)
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print(f"Resumed from checkpoint: {ckpt_path}")
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print(f" Resumed epoch={ckpt.get('epoch', '?')}, global_step={global_step}, "
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f"best_val_loss={best_val_loss:.6f}")
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else:
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print(f"\nStarting training for {train_cfg.epochs} epochs...")
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# Logging — run-specific subdirectory for isolation + resume continuity
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if run_id is None:
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run_id = time.strftime("run_%Y%m%d_%H%M%S")
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log_dir = Path(train_cfg.log_dir) / run_id
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log_dir.mkdir(parents=True, exist_ok=True)
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writer = SummaryWriter(log_dir=str(log_dir))
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print(f"\nStarting training for {train_cfg.epochs} epochs...")
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print(f" seq_len={train_cfg.seq_len}, batch_size={train_cfg.batch_size}")
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print(f" lr={train_cfg.lr}, weight_decay={train_cfg.weight_decay}")
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print(f" log_dir={log_dir}, checkpoint_dir={ckpt_dir}\n")
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for epoch in range(1, train_cfg.epochs + 1):
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for epoch in range(start_epoch, train_cfg.epochs + 1):
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epoch_start = time.time()
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train_loss = train_one_epoch(
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train_loss, global_step = train_one_epoch(
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model, train_loader, optimizer, criterion, device, epoch, writer,
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log_interval=train_cfg.log_interval,
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global_step=global_step,
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)
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val_loss = validate(model, val_loader, criterion, device)
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scheduler.step()
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@@ -187,7 +220,9 @@ def main():
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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"scheduler_state_dict": scheduler.state_dict(),
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"train_loss": train_loss,
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"global_step": global_step,
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"best_val_loss": best_val_loss,
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"run_id": run_id,
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"val_loss": val_loss,
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}, ckpt_path)
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print(f" Checkpoint saved: {ckpt_path}")
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@@ -200,6 +235,10 @@ def main():
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"epoch": epoch,
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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"scheduler_state_dict": scheduler.state_dict(),
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"global_step": global_step,
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"best_val_loss": best_val_loss,
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"run_id": run_id,
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"val_loss": val_loss,
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}, best_path)
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print(f" Best model updated: {best_path} (val_loss={val_loss:.6f})")
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