File size: 9,087 Bytes
977b06e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | #!/usr/bin/env python3
"""Build one resumable shard of the official FrameSkip score/pruning cache."""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import sys
import types
from pathlib import Path
import numpy as np
import timm
from omegaconf import OmegaConf
def stable_shard(dataset_name: str, trajectory_id: int | str, count: int) -> int:
key = f"{dataset_name}\0{trajectory_id}".encode()
return int.from_bytes(hashlib.sha256(key).digest()[:8], "little") % count
def load_completed(path: Path) -> set[tuple[str, str]]:
completed: set[tuple[str, str]] = set()
if not path.exists():
return completed
with path.open(encoding="utf-8") as source:
for line_number, line in enumerate(source, 1):
line = line.strip()
if not line:
continue
try:
record = json.loads(line)
except json.JSONDecodeError as exc:
raise RuntimeError(f"corrupt cache shard {path}:{line_number}") from exc
completed.add((record["dataset_name"], str(record["trajectory_id"])))
return completed
def bind_upstream_helpers(dataset, frame_dataset_class, calculator) -> None:
for method_name in (
"_extract_action_from_traj",
"_get_actions_from_traj_data",
"_extract_frames_from_traj",
"_load_trajectory_for_importance",
):
method = getattr(frame_dataset_class, method_name)
setattr(dataset, method_name, types.MethodType(method, dataset))
dataset.importance_calculator = calculator
dataset.vac_video_backend = "torchvision_av"
dataset.vac_allow_backend_fallback = False
dataset.max_vac_frames = 16
dataset._frameskip_runtime_stats = {
"vac_frame_extraction_failures": 0,
"vac_trajectories_without_frames": 0,
}
def protected_indices(actions: np.ndarray) -> set[int]:
if len(actions) == 0:
return set()
protected = {0, len(actions) - 1}
if len(actions) > 1:
if actions.shape[1] >= 7:
gripper = actions[:, -1]
changes = np.abs(np.diff(gripper, prepend=gripper[0]))
protected.update(int(value) for value in np.where(changes > 0.1)[0])
action_delta = np.linalg.norm(np.diff(actions, axis=0), axis=1)
threshold = np.percentile(action_delta, 90)
protected.update(int(value) for value in np.where(action_delta >= threshold)[0] + 1)
return protected
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--project-root", required=True)
parser.add_argument("--upstream-root", required=True)
parser.add_argument("--config", required=True)
parser.add_argument("--required-draws", required=True)
parser.add_argument("--visual-checkpoint", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--shard-index", type=int, required=True)
parser.add_argument("--shard-count", type=int, required=True)
parser.add_argument("--device", default="cuda:0")
args = parser.parse_args()
project_root = Path(args.project_root).resolve()
upstream_package = Path(args.upstream_root).resolve() / "starVLA"
sys.path.insert(0, str(project_root))
import starVLA
# Resolve the official FrameSkip package while retaining the current StarVLA base classes.
starVLA.__path__.append(str(upstream_package))
from starVLA.dataloader.lerobot_datasets import get_vla_dataset
from starVLA.frameskip.dataloader.frameskip_dataset import FrameSkipSingleDataset
from starVLA.frameskip.utils.frame_pruner import FramePruner
from starVLA.frameskip.utils.importance_metrics import GripperAwareImportanceCalculator
required_payload = json.loads(Path(args.required_draws).read_text(encoding="utf-8"))
config = OmegaConf.load(args.config)
data_cfg = config.datasets.vla_data
data_cfg.traceable_sampling = False
data_cfg.valid_anchor_manifest_path = None
data_cfg.valid_anchor_manifest_migration_path = None
data_cfg.temporal_proposal_path = None
mixture = get_vla_dataset(
data_cfg=data_cfg,
balance_dataset_weights=False,
balance_trajectory_weights=False,
)
calculator = GripperAwareImportanceCalculator(
alpha=0.6,
beta=0.2,
gamma=0.2,
enable_vac=True,
visual_encoder_name=None,
device=args.device,
gripper_boost=2.0,
)
# The released scorer preprocesses at 224px, while current timm metadata fixes
# DINOv2-S/14 at 518px. Dynamic positional interpolation preserves the released
# 224px preprocessing and the official DINOv2 weights.
calculator.visual_encoder = timm.create_model(
"vit_small_patch14_dinov2.lvd142m",
pretrained=False,
num_classes=0,
checkpoint_path=args.visual_checkpoint,
dynamic_img_size=True,
).to(args.device).eval()
pruner = FramePruner(
compression_ratios=[0.2],
preserve_key_stages=True,
min_frames_per_trajectory=5,
)
datasets = {dataset.dataset_name: dataset for dataset in mixture.datasets}
for dataset in datasets.values():
bind_upstream_helpers(dataset, FrameSkipSingleDataset, calculator)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
output_path = output_dir / f"shard_{args.shard_index:02d}_of_{args.shard_count:02d}.jsonl"
completed = load_completed(output_path)
assignments: list[tuple[str, int | str]] = []
for dataset_name, trajectory_ids in required_payload["datasets"].items():
for trajectory_id in trajectory_ids:
if stable_shard(dataset_name, trajectory_id, args.shard_count) == args.shard_index:
assignments.append((dataset_name, trajectory_id))
assignments.sort(key=lambda item: (item[0], type(item[1]).__name__, str(item[1])))
written = 0
with output_path.open("a", encoding="utf-8", buffering=1) as destination:
for dataset_name, trajectory_id in assignments:
key = (dataset_name, str(trajectory_id))
if key in completed:
continue
dataset = datasets[dataset_name]
trajectory = dataset._load_trajectory_for_importance(trajectory_id)
actions = np.asarray(trajectory["actions"])
if len(actions) == 0 or not trajectory["observations"]:
raise RuntimeError(
f"FrameSkip preprocessing has no usable action/video data for {key}"
)
scores = calculator.compute_importance(trajectory)
if len(scores) != len(actions) or not np.isfinite(scores).all():
raise RuntimeError(f"invalid FrameSkip scores for {key}")
result = pruner.prune_trajectory(trajectory, scores, 0.2)
keep_indices = np.asarray(result.keep_indices, dtype=np.int64)
protected = protected_indices(actions)
retained_set = set(int(value) for value in keep_indices)
protected_retained = protected.intersection(retained_set)
record = {
"dataset_name": dataset_name,
"trajectory_id": trajectory_id,
"trajectory_length": int(len(actions)),
"retention_ratio": 0.2,
"actual_ratio": float(len(keep_indices) / len(actions)),
"keep_indices": keep_indices.tolist(),
"importance_scores": np.asarray(scores, dtype=np.float64).tolist(),
"protected_indices": sorted(protected),
"protected_retained_count": len(protected_retained),
"protected_total_count": len(protected),
"protected_retention_rate": (
len(protected_retained) / len(protected) if protected else 1.0
),
"vac_frames": len(trajectory["observations"]),
}
destination.write(json.dumps(record, separators=(",", ":")) + "\n")
written += 1
if written % 25 == 0:
print(
json.dumps({
"shard": args.shard_index,
"new": written,
"completed": len(completed) + written,
"total": len(assignments),
}),
flush=True,
)
stats = {
"schema_version": 1,
"shard_index": args.shard_index,
"shard_count": args.shard_count,
"assigned": len(assignments),
"completed_before": len(completed),
"written": written,
"vac_runtime_stats": {
name: dataset._frameskip_runtime_stats for name, dataset in datasets.items()
},
}
stats_path = output_dir / f"shard_{args.shard_index:02d}_of_{args.shard_count:02d}.status.json"
stats_path.write_text(json.dumps(stats, indent=2) + "\n", encoding="utf-8")
print(json.dumps(stats, indent=2))
if __name__ == "__main__":
main()
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