144 lines
5.2 KiB
Python
144 lines
5.2 KiB
Python
import json
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import os
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import numpy as np
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import torch
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from torch.utils.data import DataLoader, Subset
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from tqdm import tqdm
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from config import Config, build_argparser, apply_overrides
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from model import ActionPredictor
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from dataset import ActionDataset
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def split_episodes(data_dir: str, val_ratio: float, seed: int = 42):
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n = ActionDataset.get_episode_count(data_dir)
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indices = list(range(n))
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rng = np.random.RandomState(seed)
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rng.shuffle(indices)
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val_count = max(1, int(n * val_ratio))
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return indices[val_count:], indices[:val_count]
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def train_one_epoch(model, loader, optimizer, device):
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model.train()
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total_loss = 0.0
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count = 0
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for batch in tqdm(loader, desc=" train", leave=False):
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image = batch["image"].to(device)
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history = batch["action_history"].to(device)
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target = batch["target"].to(device)
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optimizer.zero_grad()
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pred = model(image, history)
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loss = torch.nn.functional.mse_loss(pred, target)
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loss.backward()
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optimizer.step()
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total_loss += loss.item() * image.size(0)
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count += image.size(0)
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return total_loss / max(count, 1)
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@torch.no_grad()
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def validate(model, loader, device):
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model.eval()
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total_loss = 0.0
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count = 0
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for batch in tqdm(loader, desc=" val ", leave=False):
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image = batch["image"].to(device)
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history = batch["action_history"].to(device)
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target = batch["target"].to(device)
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pred = model(image, history)
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loss = torch.nn.functional.mse_loss(pred, target)
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total_loss += loss.item() * image.size(0)
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count += image.size(0)
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return total_loss / max(count, 1)
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def main():
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parser = build_argparser("actpred training")
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args = parser.parse_args()
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cfg = apply_overrides(Config(), args)
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os.makedirs(cfg.train.checkpoint_dir, exist_ok=True)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Device: {device}")
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train_indices, val_indices = split_episodes(
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cfg.data.data_dir, cfg.train.val_split)
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print(f"Episodes: {len(train_indices)} train, {len(val_indices)} val")
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train_ds = ActionDataset(
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cfg.data.data_dir, cfg.data.history_len, cfg.data.action_dim,
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cfg.data.image_size, cfg.data.normalize_actions,
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cfg.data.action_stats_path, train_indices)
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val_ds = ActionDataset(
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cfg.data.data_dir, cfg.data.history_len, cfg.data.action_dim,
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cfg.data.image_size, cfg.data.normalize_actions,
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cfg.data.action_stats_path, val_indices)
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print(f"Samples: {len(train_ds)} train, {len(val_ds)} val")
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train_loader = DataLoader(train_ds, batch_size=cfg.train.batch_size,
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shuffle=True, num_workers=cfg.train.num_workers)
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val_loader = DataLoader(val_ds, batch_size=cfg.train.batch_size,
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shuffle=False, num_workers=cfg.train.num_workers)
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model = ActionPredictor(cfg.model, cfg.data).to(device)
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n_params = sum(p.numel() for p in model.parameters())
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print(f"Model params: {n_params:,}")
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optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.train.lr,
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weight_decay=cfg.train.weight_decay)
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=cfg.train.epochs)
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best_val_loss = float("inf")
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patience_counter = 0
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best_path = os.path.join(cfg.train.checkpoint_dir, "best.pt")
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for epoch in range(1, cfg.train.epochs + 1):
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print(f"\nEpoch {epoch}/{cfg.train.epochs} lr={optimizer.param_groups[0]['lr']:.6f}")
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train_loss = train_one_epoch(model, train_loader, optimizer, device)
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val_loss = validate(model, val_loader, device)
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scheduler.step()
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print(f" train_loss={train_loss:.6f} val_loss={val_loss:.6f}")
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if val_loss < best_val_loss:
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best_val_loss = val_loss
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patience_counter = 0
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torch.save({
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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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"val_loss": val_loss,
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"config": cfg.to_dict(),
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}, best_path)
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inference_path = os.path.join(cfg.train.checkpoint_dir, "best_inference.pt")
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torch.save({
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"model_state_dict": model.state_dict(),
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"config": cfg.to_dict(),
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}, inference_path)
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print(f" -> saved best (val_loss={val_loss:.6f})")
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else:
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patience_counter += 1
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if patience_counter >= cfg.train.patience:
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print(f"\nEarly stopping at epoch {epoch} (patience={cfg.train.patience})")
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break
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print(f"\nTraining done. Best val_loss={best_val_loss:.6f}")
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print(f"Best model saved to: {best_path}")
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action_stats = train_ds.get_action_stats()
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if action_stats is not None:
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stats_path = os.path.join(cfg.train.checkpoint_dir, "action_stats.npy")
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np.save(stats_path, action_stats)
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print(f"Action stats saved to: {stats_path}")
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if args.save_config:
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with open(args.save_config, "w") as f:
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json.dump(cfg.to_dict(), f, indent=2, ensure_ascii=False)
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print(f"Config saved to: {args.save_config}")
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if __name__ == "__main__":
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main()
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