File size: 5,450 Bytes
aceb411 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | #!/usr/bin/env python3
import argparse
import json
import random
import sys
from pathlib import Path
import numpy as np
import pyarrow.parquet as pq
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
STATE_COLS = [
"observation.arm_joints",
"observation.hand_joints",
"observation.prev_height",
"observation.prev_rpy",
"observation.prev_vx",
"observation.prev_vy",
"observation.prev_vyaw",
"observation.prev_dyaw",
]
def sample_rows(parquet_path: Path, max_rows: int, rng: random.Random):
table = pq.read_table(parquet_path, columns=["action", *STATE_COLS])
total = table.num_rows
if total == 0:
return None
if max_rows >= total:
indices = list(range(total))
else:
indices = rng.sample(range(total), max_rows)
sub = table.take(indices)
actions = np.array(sub.column("action").to_pylist(), dtype=np.float32)
arms = np.array(sub.column("observation.arm_joints").to_pylist(), dtype=np.float32)
hands = np.array(sub.column("observation.hand_joints").to_pylist(), dtype=np.float32)
prev_h = np.array(sub.column("observation.prev_height").to_pylist(), dtype=np.float32).reshape(-1, 1)
prev_rpy = np.array(sub.column("observation.prev_rpy").to_pylist(), dtype=np.float32)
prev_vx = np.array(sub.column("observation.prev_vx").to_pylist(), dtype=np.float32).reshape(-1, 1)
prev_vy = np.array(sub.column("observation.prev_vy").to_pylist(), dtype=np.float32).reshape(-1, 1)
prev_vyaw = np.array(sub.column("observation.prev_vyaw").to_pylist(), dtype=np.float32).reshape(-1, 1)
prev_dyaw = np.array(sub.column("observation.prev_dyaw").to_pylist(), dtype=np.float32).reshape(-1, 1)
state = np.concatenate(
[arms, hands, prev_h, prev_rpy, prev_vx, prev_vy, prev_vyaw, prev_dyaw], axis=1
)
return actions, state
def main() -> None:
parser = argparse.ArgumentParser(description="Quick sanity checks for pick_box dataset.")
parser.add_argument("--data_root", default="/hfm/data/pick_box")
parser.add_argument("--max_episodes", type=int, default=10)
parser.add_argument("--rows_per_episode", type=int, default=200)
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
rng = random.Random(args.seed)
data_root = Path(args.data_root)
meta = data_root / "meta"
info_path = meta / "info.json"
episodes_path = meta / "episodes.jsonl"
if not info_path.exists() or not episodes_path.exists():
raise FileNotFoundError("Missing meta/info.json or meta/episodes.jsonl.")
info = json.loads(info_path.read_text())
episodes = [json.loads(line) for line in episodes_path.read_text().splitlines() if line.strip()]
print("Dataset root:", data_root)
print("Total episodes (meta):", info.get("total_episodes"))
print("Episodes listed:", len(episodes))
lengths = [int(e.get("length", 0)) for e in episodes]
print("Episode length: min", min(lengths), "max", max(lengths), "avg", np.mean(lengths))
instructions = [str(e.get("instruction", "") or "").strip() for e in episodes]
empty_instr = sum(1 for i in instructions if not i)
print("Unique instructions:", len(set(instructions)))
print("Empty instructions:", empty_instr)
data_path_tpl = info["data_path"]
chunk_size = int(info["chunks_size"])
actions_all = []
states_all = []
for ep in episodes[: args.max_episodes]:
ep_index = int(ep["episode_index"])
chunk = ep_index // chunk_size
parquet_path = data_root / data_path_tpl.format(
episode_chunk=chunk, episode_index=ep_index
)
if not parquet_path.exists():
print("Missing parquet:", parquet_path)
continue
sample = sample_rows(parquet_path, args.rows_per_episode, rng)
if sample is None:
continue
actions, states = sample
actions_all.append(actions)
states_all.append(states)
if not actions_all:
print("No action samples collected. Check dataset paths.")
return
actions = np.concatenate(actions_all, axis=0)
states = np.concatenate(states_all, axis=0)
print("Sampled actions shape:", actions.shape)
print("Sampled states shape:", states.shape)
print("Action dim:", actions.shape[1])
print("State dim:", states.shape[1])
print("Action NaN:", np.isnan(actions).any(), "Inf:", np.isinf(actions).any())
print("State NaN:", np.isnan(states).any(), "Inf:", np.isinf(states).any())
action_std = actions.std(axis=0)
state_std = states.std(axis=0)
print("Action std (min/median/max):", action_std.min(), np.median(action_std), action_std.max())
print("State std (min/median/max):", state_std.min(), np.median(state_std), state_std.max())
near_zero = np.mean(np.abs(actions) < 1e-4, axis=0)
print("Action near-zero ratio (min/median/max):", near_zero.min(), np.median(near_zero), near_zero.max())
stats_path = meta / "action_stats.json"
if stats_path.exists():
stats = json.loads(stats_path.read_text())
min_stat = np.array(stats["min"], dtype=np.float32)
max_stat = np.array(stats["max"], dtype=np.float32)
print("Stats file min/max (first 8 dims):")
print(" min:", min_stat[:8])
print(" max:", max_stat[:8])
if __name__ == "__main__":
main()
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