| """Load one saved checkpoint and run a single inference. The real test of a checkpoint. |
| |
| Run from inside the openpi checkout: |
| |
| # while training still owns the A100 -- CPU, slow (~1-2 min), proves loadability |
| JAX_PLATFORMS=cpu uv run python <this file> /data/checkpoints/.../pickplace_full_v1/7500 |
| |
| # after training exits -- GPU, fast |
| uv run python <this file> /data/checkpoints/.../pickplace_full_v1/29999 |
| |
| `check_checkpoints.py` verifies the files are structurally complete. This verifies they actually |
| deserialise into the model, that norm stats resolve, and that the policy emits a finite (50, 28) |
| action chunk for a plausible observation. A checkpoint can pass the structural check and still be |
| unusable -- wrong config, mismatched action_dim, missing norm stats for this exact config name. |
| |
| Nothing here touches the robot. The action values are meaningless (the images are synthetic); only |
| their shape, dtype and finiteness are being checked. |
| """ |
| import argparse |
| import pathlib |
| import sys |
| import time |
|
|
| TASK = "pick octopus and place inside brown basket" |
|
|
|
|
| def main() -> int: |
| ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) |
| ap.add_argument("checkpoint", type=pathlib.Path, help="a step directory containing params/ and assets/") |
| ap.add_argument("--config", default="pi05_g1_pickplace") |
| ap.add_argument("--prompt", default=TASK) |
| ap.add_argument("--state-dim", type=int, default=43, choices=(28, 43)) |
| ap.add_argument("--repeat", type=int, default=2, help="inferences to run; the 2nd shows warm latency") |
| a = ap.parse_args() |
|
|
| if not (a.checkpoint / "params").is_dir(): |
| print(f"FAIL: {a.checkpoint}/params does not exist", file=sys.stderr) |
| return 2 |
|
|
| import numpy as np |
| import jax |
|
|
| from openpi.policies import policy_config |
| from openpi.training import config as _config |
|
|
| print(f"jax backend : {jax.default_backend()} devices={jax.devices()}") |
| print(f"config : {a.config}") |
| print(f"checkpoint : {a.checkpoint}") |
|
|
| cfg = _config.get_config(a.config) |
|
|
| t0 = time.time() |
| policy = policy_config.create_trained_policy(cfg, a.checkpoint) |
| print(f"loaded : {time.time() - t0:.1f}s") |
|
|
| |
| |
| rng = np.random.default_rng(0) |
| obs = { |
| "observation.images.ego_view": rng.integers(0, 256, (480, 640, 3), dtype=np.uint8), |
| "observation.images.ego_left": rng.integers(0, 256, (480, 640, 3), dtype=np.uint8), |
| "observation.images.ego_right": rng.integers(0, 256, (480, 640, 3), dtype=np.uint8), |
| "observation.state": np.zeros(a.state_dim, dtype=np.float32), |
| "prompt": a.prompt, |
| } |
|
|
| for i in range(a.repeat): |
| t0 = time.time() |
| out = policy.infer(obs) |
| dt = time.time() - t0 |
| act = np.asarray(out["actions"]) |
| print(f"infer #{i + 1} : {dt:.2f}s shape={act.shape} dtype={act.dtype}") |
|
|
| problems = [] |
| if act.shape != (50, 28): |
| problems.append(f"expected action chunk (50, 28), got {act.shape}") |
| if not np.all(np.isfinite(act)): |
| n = int((~np.isfinite(act)).sum()) |
| problems.append(f"{n} non-finite value(s) in the action chunk") |
| if np.allclose(act, 0.0): |
| problems.append("every action is exactly 0.0 -- norm stats or params likely not loaded") |
|
|
| print(f"\nfirst step : {np.array2string(act[0], precision=3, max_line_width=200)}") |
| print(f"per-dim |max|: {np.abs(act).max(axis=0).round(3)}") |
| print(f"range : [{act.min():.3f}, {act.max():.3f}]") |
|
|
| if problems: |
| print("\nFAIL") |
| for p in problems: |
| print(f" {p}") |
| return 1 |
|
|
| print("\nPASS -- checkpoint loads and produces a finite (50, 28) chunk.") |
| print("Action VALUES are not validated here; the images were noise. Only §14 rollouts tell you") |
| print("whether this checkpoint is any good.") |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| sys.exit(main()) |
|
|
