fixup! Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization

examples/rtc/eval_with_real_robot.py

@@ -75,68 +75,6 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
-def tensor_stats_str(tensor: Tensor | None, name: str = "tensor") -> str:
-    """Generate readable statistics string for a tensor."""
-    if tensor is None:
-        return f"{name}: None"
-
-    stats = (
-        f"{name}:\n"
-        f"  shape={tuple(tensor.shape)}, dtype={tensor.dtype}, device={tensor.device}\n"
-        f"  min={tensor.min().item():.6f}, max={tensor.max().item():.6f}\n"
-        f"  mean={tensor.mean().item():.6f}, std={tensor.std().item():.6f}"
-    )
-    return stats
-
-
-def compare_tensors(tensor1: Tensor, tensor2: Tensor, name1: str = "tensor1", name2: str = "tensor2") -> str:
-    """Compare two tensors and return detailed difference statistics."""
-    if tensor1 is None or tensor2 is None:
-        return f"Cannot compare: {name1}={tensor1 is not None}, {name2}={tensor2 is not None}"
-
-    # Ensure same shape for comparison
-    if tensor1.shape != tensor2.shape:
-        return f"Shape mismatch: {name1}={tuple(tensor1.shape)} vs {name2}={tuple(tensor2.shape)}"
-
-    diff = tensor1 - tensor2
-    abs_diff = torch.abs(diff)
-
-    # Per-timestep statistics
-    if len(diff.shape) >= 2:
-        # Shape is (batch, time, action_dim) or (time, action_dim)
-        per_timestep_mean = abs_diff.mean(dim=-1)  # Average across action dimensions
-
-        timestep_stats = "\n  Per-timestep abs diff (averaged across action dims):\n"
-        if len(per_timestep_mean.shape) > 1:
-            # Has batch dimension
-            for batch_idx in range(per_timestep_mean.shape[0]):
-                timestep_stats += f"    Batch {batch_idx}: ["
-                for t in range(min(10, per_timestep_mean.shape[1])):  # Show first 10 timesteps
-                    timestep_stats += f"{per_timestep_mean[batch_idx, t].item():.6f}, "
-                if per_timestep_mean.shape[1] > 10:
-                    timestep_stats += "..."
-                timestep_stats += "]\n"
-        else:
-            timestep_stats += "    ["
-            for t in range(min(10, len(per_timestep_mean))):
-                timestep_stats += f"{per_timestep_mean[t].item():.6f}, "
-            if len(per_timestep_mean) > 10:
-                timestep_stats += "..."
-            timestep_stats += "]\n"
-    else:
-        timestep_stats = ""
-
-    result = (
-        f"\nDifference: {name1} - {name2}:\n"
-        f"  abs_diff: min={abs_diff.min().item():.6f}, max={abs_diff.max().item():.6f}\n"
-        f"  abs_diff: mean={abs_diff.mean().item():.6f}, std={abs_diff.std().item():.6f}\n"
-        f"  relative_diff: mean={abs_diff.mean().item() / (torch.abs(tensor2).mean().item() + 1e-8) * 100:.2f}%"
-        f"{timestep_stats}"
-    )
-
-    return result
-
-
 class RobotWrapper:
     def __init__(self, robot: Robot):
         self.robot = robot