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ec0a9aa | 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 | """Extract action JSONs for the action-conditioned video2world model.
Auto-detects the dataset's action layout from `meta/modality.json` and writes
JSONs in the schema expected by `video2world_action.get_action_sequence`:
{
"action": (T, 6) -- per-frame 6D delta, pre-divided by 20.0
(inference re-multiplies by 20)
"continuous_gripper_state": (T+1,) -- gripper open/close ratio in [0, 1]
}
Two source formats are supported:
(a) GR00T 1027-D layout (e.g. In-lab_Eval) -- has `delta_right_wrist` block
of shape (T, 144) = 24 future-frames * 6D. We take the first 6D as the
next-frame delta. This matches training distribution.
(b) GR00T 52-D layout (e.g. EgoDex_Eval, DreamDojo-HV_Eval) -- only has
absolute joint angles. We compute joint-space delta via `np.diff` on
right_arm[:, :6]. This is best-effort: the model was trained on
EE-space deltas, so generation quality will be lower than (a).
"""
import argparse
import glob
import json
import os
import re
import numpy as np
import pandas as pd
from tqdm import tqdm
INFERENCE_SCALE = 20.0
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_path", type=str, required=True,
help="Path to a leaf LeRobot dataset (containing meta/, data/, videos/)")
parser.add_argument("--save_path", type=str, required=True,
help="Where to save extracted action JSONs")
parser.add_argument("--output_json", type=str, required=True,
help="Path to the batch_input.json")
parser.add_argument("--output_video_dir", type=str,
default="./output/action_gen",
help="Directory used in the batch_input.json's output_video field")
parser.add_argument("--video_subdir", type=str,
default="observation.images.ego_view_freq20",
help="Video subdirectory under videos/chunk-*/")
parser.add_argument("--stride", type=int, default=5,
help="Subsample stride to convert source FPS -> model FPS "
"(default 5 = 20Hz dataset -> 4Hz model). Each output "
"delta then represents motion over 1/4 s, matching training.")
return parser.parse_args()
def load_modality(dataset_path):
path = os.path.join(dataset_path, "meta", "modality.json")
with open(path) as f:
return json.load(f)
def extract_action_ee(raw_actions, modality, stride):
"""Return (T_out, 6) array of per-frame deltas, pre-divided by INFERENCE_SCALE.
`stride` subsamples the source-FPS actions to the model FPS so each delta
represents motion over one model-frame (e.g. 20Hz -> 4Hz with stride=5).
"""
action_def = modality["action"]
if "delta_right_wrist" in action_def:
s = action_def["delta_right_wrist"]["start"]
# The 144-D block is 24 future-frame deltas at source FPS.
# First 6D = +1-frame delta. To get a +stride-frame delta at model FPS,
# use the slot at index (stride-1)*6.
slot = (stride - 1) * 6
delta = raw_actions[::stride, s + slot:s + slot + 6]
return delta / INFERENCE_SCALE, f"delta_right_wrist[slot={slot}], stride={stride}"
if "right_arm" in action_def:
s = action_def["right_arm"]["start"]
arm = raw_actions[:, s:s + 6]
# Stride-spaced finite difference: delta[i] = arm[(i+1)*stride] - arm[i*stride]
arm_ds = arm[::stride] # (T_out+1, 6) ideally
if arm_ds.shape[0] < 2:
raise ValueError("Episode too short for stride")
delta = np.diff(arm_ds, axis=0, prepend=arm_ds[:1]) # (T_out, 6)
return delta / INFERENCE_SCALE, f"right_arm_diff, stride={stride} (best-effort)"
raise ValueError("No usable action channel found in modality.json")
def extract_gripper(raw_actions, modality, stride, length):
"""Return (length,) gripper state in [0, 1] aligned to the strided actions."""
action_def = modality["action"]
if "right_hand" not in action_def:
return np.zeros(length, dtype=np.float32)
s = action_def["right_hand"]["start"]
g = raw_actions[::stride, s].astype(np.float32)[:length]
if g.shape[0] < length:
g = np.pad(g, (0, length - g.shape[0]), mode="edge")
g_min, g_max = float(g.min()), float(g.max())
if g_max - g_min > 1e-6:
g = (g - g_min) / (g_max - g_min)
else:
g = np.zeros_like(g)
return g
def main():
args = parse_args()
modality = load_modality(args.dataset_path)
video_files = sorted(glob.glob(
os.path.join(args.dataset_path, "videos", "**", "episode_*.mp4"),
recursive=True,
))
# Optionally filter by camera subdir.
if args.video_subdir:
video_files = [v for v in video_files if args.video_subdir in v]
os.makedirs(args.save_path, exist_ok=True)
batch_input = []
print(f"[extract] dataset={args.dataset_path}")
print(f"[extract] found {len(video_files)} videos")
fmt_used = None
for vid_path in tqdm(video_files):
match = re.search(r"episode_(\d+)", os.path.basename(vid_path))
if not match:
continue
ep_idx = int(match.group(1))
parquets = glob.glob(
os.path.join(args.dataset_path, "data", "**", f"episode_{ep_idx:06d}.parquet"),
recursive=True,
)
if not parquets:
print(f"[warn] parquet for episode {ep_idx} not found")
continue
df = pd.read_parquet(parquets[0])
raw_actions = np.stack(df["action"].values) # (T, D)
action_ee, fmt = extract_action_ee(raw_actions, modality, args.stride)
gripper = extract_gripper(raw_actions, modality, args.stride, action_ee.shape[0])
fmt_used = fmt
# video2world_action.get_action_sequence does gripper[1:], so we pad to T+1.
gripper_extended = np.append(gripper, gripper[-1])
anno = {
"action": action_ee.tolist(),
"continuous_gripper_state": gripper_extended.tolist(),
}
anno_path = os.path.join(args.save_path, f"episode_{ep_idx:06d}_action.json")
with open(anno_path, "w") as f:
json.dump(anno, f)
batch_input.append({
"input_video": vid_path,
"input_annotation": anno_path,
"output_video": os.path.join(args.output_video_dir, f"gen_{os.path.basename(vid_path)}"),
})
os.makedirs(os.path.dirname(args.output_json), exist_ok=True)
with open(args.output_json, "w") as f:
json.dump(batch_input, f, indent=4)
print(f"[extract] action format: {fmt_used}")
print(f"[extract] wrote {len(batch_input)} entries -> {args.output_json}")
if __name__ == "__main__":
main()
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