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shuffle false
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@@ -303,29 +303,25 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
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device=device,
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)
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# Compute per-timestep normalizer for relative actions (main process computes, others load)
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# Compute per-timestep normalizer for relative actions
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# Each process computes stats independently to avoid distributed sync issues
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relative_normalizer = None
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if cfg.use_relative_actions:
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stats_path = cfg.output_dir / "relative_stats.pt"
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mode = "actions + state" if cfg.use_relative_state else "actions only"
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if is_main_process:
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logging.info(colored(f"Relative mode: {mode}", "cyan", attrs=["bold"]))
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logging.info("Computing per-timestep stats from dataset (first 1000 batches)...")
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temp_loader = torch.utils.data.DataLoader(
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dataset, batch_size=cfg.batch_size, shuffle=False, num_workers=0
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)
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mean, std = compute_relative_action_stats(temp_loader, num_batches=1000)
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relative_normalizer = PerTimestepNormalizer(mean, std)
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if is_main_process:
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cfg.output_dir.mkdir(parents=True, exist_ok=True)
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temp_loader = torch.utils.data.DataLoader(
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dataset, batch_size=cfg.batch_size, shuffle=True, num_workers=0
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)
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mean, std = compute_relative_action_stats(temp_loader, num_batches=1000)
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relative_normalizer = PerTimestepNormalizer(mean, std)
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relative_normalizer.save(stats_path)
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logging.info(f"Saved stats to: {stats_path}")
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# Barrier: wait for main process to finish computing and saving stats
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accelerator.wait_for_everyone()
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if not is_main_process:
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relative_normalizer = PerTimestepNormalizer.load(stats_path)
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relative_normalizer.save(cfg.output_dir / "relative_stats.pt")
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logging.info(f"Saved stats to: {cfg.output_dir / 'relative_stats.pt'}")
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step = 0 # number of policy updates (forward + backward + optim)
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