File size: 5,269 Bytes
7f173cd ba3985e 7f173cd ba3985e 7f173cd | 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 | from pathlib import Path
import argparse
import json
import sys
from typing import Dict, Optional
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from rr_label_study.oven_study import (
BimanualTakeTrayOutOfOven,
ReplayCache,
_aggregate_summary,
_annotate_phase_columns,
_analyze_episode,
_derive_templates,
_episode_metrics_from_frames,
_keyframe_subset,
_keypoint_discovery,
_launch_replay_env,
_load_demo,
_load_descriptions,
_pregrasp_progress_and_distance,
_pregrasp_score_and_success,
_frame_metrics,
)
def _recompute_columns(
episode_dir: Path,
templates,
checkpoint_stride: int,
base_df: pd.DataFrame,
) -> pd.DataFrame:
demo = _load_demo(episode_dir)
num_frames = min(len(demo), len(base_df))
frame_df = base_df.iloc[:num_frames].copy()
env = _launch_replay_env()
try:
task = env.get_task(BimanualTakeTrayOutOfOven)
cache = ReplayCache(task, demo, checkpoint_stride=checkpoint_stride)
cache.reset()
for frame_index in range(num_frames):
cache.step_to(frame_index)
state = cache.current_state()
visibility, _ = _frame_metrics(episode_dir, demo, state, templates)
pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance(
state.left_gripper_pose,
state.tray_pose,
templates,
)
p_pre, y_pre, _ = _pregrasp_score_and_success(task, templates)
frame_df.at[frame_index, "frame_index"] = frame_index
frame_df.at[frame_index, "time_norm"] = frame_index / max(1, num_frames - 1)
frame_df.at[frame_index, "door_angle"] = state.door_angle
frame_df.at[frame_index, "right_gripper_open"] = state.right_gripper_open
frame_df.at[frame_index, "left_gripper_open"] = state.left_gripper_open
frame_df.at[frame_index, "pregrasp_progress"] = pregrasp_progress
frame_df.at[frame_index, "pregrasp_distance"] = pregrasp_distance
frame_df.at[frame_index, "p_pre"] = p_pre
frame_df.at[frame_index, "y_pre_raw"] = float(bool(y_pre))
frame_df.at[frame_index, "y_pre"] = float(bool(y_pre))
for key, value in visibility.items():
frame_df.at[frame_index, key] = value
if (frame_index + 1) % 25 == 0 or (frame_index + 1) == num_frames:
print(
f"[{episode_dir.name}] recomputed {frame_index + 1}/{num_frames} dense frames",
flush=True,
)
return frame_df
finally:
env.shutdown()
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--dataset-root", required=True)
parser.add_argument("--episode-dir", required=True)
parser.add_argument("--input-dense-csv", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--checkpoint-stride", type=int, default=16)
parser.add_argument("--template-episode-dir")
args = parser.parse_args()
dataset_root = Path(args.dataset_root)
episode_dir = Path(args.episode_dir)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
base_df = pd.read_csv(args.input_dense_csv)
demo = _load_demo(episode_dir)
descriptions = _load_descriptions(episode_dir)
template_episode_dir = (
Path(args.template_episode_dir) if args.template_episode_dir else episode_dir
)
templates, template_frames = _derive_templates(dataset_root, template_episode_dir)
with output_dir.joinpath("templates.json").open("w", encoding="utf-8") as handle:
json.dump(
{
"templates": templates.to_json(),
"template_episode": template_episode_dir.name,
"template_frames": template_frames,
},
handle,
indent=2,
)
frame_df = _recompute_columns(
episode_dir=episode_dir,
templates=templates,
checkpoint_stride=args.checkpoint_stride,
base_df=base_df,
)
frame_df = _annotate_phase_columns(frame_df)
keyframes = [index for index in _keypoint_discovery(demo) if index < len(frame_df)]
key_df = _keyframe_subset(frame_df, keyframes)
metrics = _episode_metrics_from_frames(
frame_df=frame_df,
key_df=key_df,
episode_name=episode_dir.name,
description=descriptions[0],
interventions={},
)
frame_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.dense.csv"), index=False)
key_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.keyframes.csv"), index=False)
with output_dir.joinpath(f"{episode_dir.name}.metrics.json").open("w", encoding="utf-8") as handle:
json.dump(metrics, handle, indent=2)
summary = _aggregate_summary([metrics])
with output_dir.joinpath("summary.json").open("w", encoding="utf-8") as handle:
json.dump(summary, handle, indent=2)
print(json.dumps(summary, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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