File size: 6,573 Bytes
94f697a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | """Convert raw YOR episodes (thor2 recorder format) to patch-policy training format.
Raw (per episode):
robot_data.csv wide tick rows, incl. frame_idx (odin) + t1_ns clock
odin.mp4 head cam, one frame per unique image
wrist_left/right.mp4 + *_index.csv (own clock domain, same machine)
Output (patch-policy style, like cube/pusht):
<out-dir>/
seq_lengths.pkl list[int], subsampled lengths
actions.pth FloatTensor [N, Tmax, 58] zero-padded
obses/episode_00000.pth ... uint8 Tensor [T, V=3, H, W, C]
meta.json action column order, stride, img size, episode names
Action (58D, all commanded where available):
[left_ee_target(7), right_ee_target(7),
left_hand_qpos(20, wuji-commanded), right_hand_qpos(20),
lift(1), base_pose_target(3)]
Images: center-crop square -> resize 224 (DINO/14 native -> 256 patches).
Views: V=3 [odin, wrist_left, wrist_right], timestamp-matched per tick.
"""
import argparse
import csv
import json
import pickle
from pathlib import Path
import cv2
import numpy as np
import torch
EE_L = [f"left_ee_target_wxyz_xyz_{i}" for i in range(7)]
EE_R = [f"right_ee_target_wxyz_xyz_{i}" for i in range(7)]
HAND_L = [f"left_hand_qpos_{i}" for i in range(20)]
HAND_R = [f"right_hand_qpos_{i}" for i in range(20)]
LIFT = ["lift"]
BASE = ["base_pose_target_0", "base_pose_target_1", "base_pose_target_2"]
ACTION_COLS = EE_L + EE_R + HAND_L + HAND_R + LIFT + BASE
assert len(ACTION_COLS) == 58
def read_csv_rows(path):
with open(path, newline="") as f:
r = csv.DictReader(f)
return r.fieldnames, list(r)
def decode_resize(video_path, size=224):
"""Decode mp4 -> list of uint8 HxWxC resized frames (center-crop square first)."""
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
raise RuntimeError(f"cannot open {video_path}")
frames = []
while True:
ok, img = cap.read()
if not ok:
break
h, w = img.shape[:2]
side = min(h, w)
x0 = (w - side) // 2
y0 = (h - side) // 2
crop = img[y0:y0 + side, x0:x0 + side]
small = cv2.resize(crop, (size, size), interpolation=cv2.INTER_AREA)
small = cv2.cvtColor(small, cv2.COLOR_BGR2RGB)
frames.append(small)
cap.release()
return np.stack(frames) # [F, H, W, C] uint8
def nearest_idx(sorted_ts, queries):
"""For each query, index of closest entry in sorted_ts (both int64 ns)."""
pos = np.searchsorted(sorted_ts, queries)
pos = np.clip(pos, 0, len(sorted_ts) - 1)
prev = np.clip(pos - 1, 0, len(sorted_ts) - 1)
pick_next = np.abs(sorted_ts[pos] - queries) < np.abs(sorted_ts[prev] - queries)
return np.where(pick_next, pos, prev)
def convert_episode(epdir, size=224, stride=3):
_, ticks = read_csv_rows(epdir / "robot_data.csv")
_, wleft_idx = read_csv_rows(epdir / "wrist_left_index.csv")
_, wright_idx = read_csv_rows(epdir / "wrist_right_index.csv")
print(f" decoding {epdir.name} ...", flush=True)
odin = decode_resize(epdir / "odin.mp4", size)
wl = decode_resize(epdir / "wrist_left.mp4", size)
wr = decode_resize(epdir / "wrist_right.mp4", size)
print(f" odin={odin.shape} wl={wl.shape} wr={wr.shape}", flush=True)
wleft_ts = np.array([int(r["img_recv_ts_ns"]) for r in wleft_idx], dtype=np.int64)
wright_ts = np.array([int(r["img_recv_ts_ns"]) for r in wright_idx], dtype=np.int64)
keep = list(range(0, len(ticks), stride))
T = len(keep)
V = 3
obs = np.empty((T, V, size, size, 3), dtype=np.uint8)
act = np.zeros((T, len(ACTION_COLS)), dtype=np.float32)
tick_t1 = np.array([int(ticks[k]["t1_ns"]) for k in keep], dtype=np.int64)
wl_match = nearest_idx(wleft_ts, tick_t1)
wr_match = nearest_idx(wright_ts, tick_t1)
prev = np.zeros(len(ACTION_COLS), dtype=np.float32)
for j, k in enumerate(keep):
row = ticks[k]
fi = int(float(row["frame_idx"]))
fi = max(0, min(fi, len(odin) - 1))
obs[j, 0] = odin[fi]
obs[j, 1] = wl[wl_match[j]]
obs[j, 2] = wr[wr_match[j]]
vals = []
for c in ACTION_COLS:
v = row.get(c, "")
try:
vals.append(float(v) if v != "" else np.nan)
except ValueError:
vals.append(np.nan)
a = np.array(vals, dtype=np.float32)
nan = np.isnan(a)
a[nan] = prev[nan] # forward-fill; row0 NaN -> 0
prev = a
act[j] = a
return obs, act
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--raw-root", type=str,
default=str(Path.home() / "data/yor_episodes/5c829ca0"))
ap.add_argument("--out-dir", type=str,
default=str(Path.home() / "data/yor_dataset/yor_dataset"))
ap.add_argument("--stride", type=int, default=3)
ap.add_argument("--img-size", type=int, default=224)
args = ap.parse_args()
raw_root = Path(args.raw_root)
out = Path(args.out_dir)
(out / "obses").mkdir(parents=True, exist_ok=True)
epdirs = sorted(raw_root.glob("ep_*"))
assert epdirs, f"no episodes in {raw_root}"
print(f"found {len(epdirs)} episodes")
all_act, lengths, names = [], [], []
for i, epdir in enumerate(epdirs):
obs, act = convert_episode(epdir, args.img_size, args.stride)
torch.save(torch.from_numpy(obs),
out / "obses" / f"episode_{i:05d}.pth")
all_act.append(torch.from_numpy(act))
lengths.append(act.shape[0])
names.append(epdir.name)
print(f" [{i}] {epdir.name}: T={act.shape[0]} act_mean={act.mean():.4f}")
Tmax = max(lengths)
A = len(ACTION_COLS)
actions = torch.zeros(len(all_act), Tmax, A, dtype=torch.float32)
for i, a in enumerate(all_act):
actions[i, :a.shape[0]] = a
torch.save(actions, out / "actions.pth")
with open(out / "seq_lengths.pkl", "wb") as f:
pickle.dump(lengths, f)
with open(out / "meta.json", "w") as f:
json.dump({
"action_columns": ACTION_COLS,
"action_dim": A,
"views": 3,
"view_names": ["odin", "wrist_left", "wrist_right"],
"img_size": args.img_size,
"stride": args.stride,
"episodes": names,
"seq_lengths": lengths,
}, f, indent=2)
print(f"wrote {out}: N={len(all_act)} Tmax={Tmax} A={A}")
print(f" actions range [{actions.min():.3f}, {actions.max():.3f}]")
if __name__ == "__main__":
main()
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