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import json
from multiprocessing import cpu_count
from pathlib import Path
from typing import Any
import numpy as np
from datasets import Dataset, concatenate_datasets
from tqdm import tqdm
from dataset_upload.helpers import (
create_hf_trajectory,
generate_unique_id,
load_sentence_transformer_model,
)
# We do not stream; assume RLDS TFDS builders are already downloaded locally.
import tensorflow_datasets as tfds
# soar_new_success_labels_path = "dataset_upload/dataset_helpers/soar_vlm_labels_checkpoint.json"
soar_new_success_labels_path = "dataset_upload/dataset_helpers/soar_label_corrections_full.json"
def _build_video_paths(output_dir: str, dataset_label: str, episode_idx: int, view_key: str) -> tuple[str, str]:
shard_index = episode_idx // 1000
shard_dir = f"shard_{shard_index:04d}"
episode_dir = os.path.join(output_dir, dataset_label.lower(), shard_dir, f"episode_{episode_idx:06d}")
os.makedirs(episode_dir, exist_ok=True)
filename = f"clip@{view_key}.mp4"
full_path = os.path.join(episode_dir, filename)
rel_path = os.path.join(dataset_label.lower(), shard_dir, f"episode_{episode_idx:06d}", filename)
return full_path, rel_path
def _process_episode(args):
episode_steps, ep_idx, task, lang_vec, output_dir, dataset_label, max_frames, fps, img_key, quality_label = args
# Collect frames for the given image key
frames: list[np.ndarray] = []
for step in episode_steps:
obs = step.get("observation", {}) if isinstance(step, dict) else {}
if img_key not in obs:
continue
frame = obs[img_key]
if isinstance(frame, np.ndarray):
if frame.ndim == 3 and frame.shape[-1] in (1, 3, 4):
if frame.shape[-1] == 1:
frame = np.repeat(frame, 3, axis=-1)
elif frame.shape[-1] == 4:
frame = frame[..., :3]
if frame.dtype != np.uint8:
frame = frame.astype(np.uint8, copy=False)
frames.append(frame)
if not frames:
return []
full_path, rel_path = _build_video_paths(output_dir, dataset_label, ep_idx, img_key)
traj_dict = {
"id": generate_unique_id(),
"frames": frames,
"task": task,
"is_robot": True,
"quality_label": quality_label,
"preference_group_id": None,
"preference_rank": None,
}
entry = create_hf_trajectory(
traj_dict=traj_dict,
video_path=full_path,
lang_vector=lang_vec,
max_frames=max_frames,
dataset_name=dataset_label,
use_video=True,
fps=fps,
)
if entry:
entry["frames"] = rel_path
return [entry]
return []
def convert_soar_dataset_to_hf(
dataset_path: str,
dataset_name: str,
output_dir: str,
max_trajectories: int | None = None,
max_frames: int = 64,
fps: int = 10,
num_workers: int = -1,
) -> Dataset:
"""Convert SOAR RLDS (local TFDS) to HF dataset. Non-streaming, local builders only.
Expects directory structure:
<dataset_path>/rlds/<split>/<version>/ (TFDS builder dir)
"""
root = Path(os.path.expanduser(dataset_path))
if not root.exists():
raise FileNotFoundError(f"'rlds' directory not found under: {dataset_path}")
if num_workers == -1:
num_workers = min(cpu_count(), 8)
elif num_workers == 0:
num_workers = 1
lang_model = load_sentence_transformer_model()
lang_cache: dict[str, Any] = {}
label_corrections = {}
if os.path.exists(soar_new_success_labels_path):
with open(soar_new_success_labels_path) as f:
data = json.load(f)
# Keys are strings in JSON, convert to int
label_corrections = {int(k): v for k, v in data.get("label_corrections", {}).items()}
print(f"Loaded label corrections for {len(label_corrections)} trajectories")
datasets_list: list[Dataset] = []
builder = tfds.builder_from_directory(root)
success_episode_instructions = set() # to only upload failures that have a corresponding success episode
for split_name in ["success", "failure"]:
ds = builder.as_dataset(split=split_name, shuffle_files=False)
if split_name == "success":
with open(soar_new_success_labels_path, "r") as f:
# new_success_labels = json.load(f)["results"]
new_success_labels = json.load(f)["label_corrections"]
# episodes where qwen-3-vl predicted success
# new_success_labels = [
# result["predicted_label"] for result in new_success_labels if result["original_label"] == "success"
# ]
# convert to int keys
new_success_labels = {int(k): v for k, v in new_success_labels.items()}
entries: list[dict] = []
produced = 0
max_limit = float("inf") if (max_trajectories is None or max_trajectories == -1) else int(max_trajectories)
for ep_idx, episode in enumerate(tqdm(ds, desc=f"SOAR {split_name} episodes")):
if split_name == "success":
if new_success_labels[ep_idx] != "successful":
# disagree with qwen-3-vl's prediction, skip this episode
continue
if produced >= max_limit:
break
# Convert to numpy steps list
try:
steps_np = list(tfds.as_numpy(episode["steps"]))
except Exception:
continue
# Extract language instruction from first step
task_text: str | None = None
first = steps_np[0] if steps_np else None
if first is not None:
# First try step-level keys
if "language_instruction" in first:
val = first["language_instruction"]
task_text = val.decode() if isinstance(val, (bytes, bytearray)) else str(val)
if not task_text:
continue
elif split_name == "failure":
if new_success_labels[ep_idx] != "failure": # skip if the label is not correct
continue
elif task_text not in success_episode_instructions:
# no corresponding success episode, skip this failure
print(f"No corresponding success episode for failure {ep_idx}, skipping")
continue
if task_text not in lang_cache:
lang_cache[task_text] = lang_model.encode(task_text)
lang_vec = lang_cache[task_text]
# Choose a valid image key
valid_img_key: str | None = None
valid_img_key = "image_0"
# Determine quality label
quality_label = "successful" if split_name.lower().startswith("success") else "failure"
# Build entry for this view
episode_entries = _process_episode((
steps_np,
ep_idx,
task_text,
lang_vec,
output_dir,
dataset_name,
max_frames,
fps,
valid_img_key,
quality_label,
))
entries.extend(episode_entries)
produced += len(episode_entries)
if not entries:
ds_out = Dataset.from_dict({
"id": [],
"task": [],
"lang_vector": [],
"data_source": [],
"frames": [],
"is_robot": [],
"quality_label": [],
"preference_group_id": [],
"preference_rank": [],
})
else:
ds_out = Dataset.from_list(entries)
datasets_list.append(ds_out)
if not datasets_list:
return Dataset.from_dict({
"id": [],
"task": [],
"lang_vector": [],
"data_source": [],
"frames": [],
"is_robot": [],
"quality_label": [],
"preference_group_id": [],
"preference_rank": [],
})
if len(datasets_list) == 1:
return datasets_list[0]
return concatenate_datasets(datasets_list)
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