File size: 3,746 Bytes
e1c08ca | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
from tqdm import tqdm
DEFAULT_TEST_REPLAYS = [
"241019_TESvsT1_3Set_cropped",
"241020_GENvsFLY_3Set_cropped",
"241026_WBGvsBLG_3Set_cropped",
"241027_T1vsGEN_3Set_cropped",
"241102_T1vsBLG_3Set_cropped",
]
def load_legacy_pair(path: Path) -> tuple[np.ndarray, np.ndarray]:
arr = np.load(path, allow_pickle=True)
frame = np.asarray(arr[0], dtype=np.uint8)
mask = np.asarray(arr[1], dtype=np.uint8)
if frame.shape != (2, 256, 256):
raise ValueError(f"Expected frame shape (2, 256, 256), got {frame.shape} in {path}")
if mask.shape != (1, 256, 256):
raise ValueError(f"Expected mask shape (1, 256, 256), got {mask.shape} in {path}")
return frame, mask
def export_replay(replay_dir: Path, out_dir: Path, chunk_size: int) -> list[dict]:
replay_id = replay_dir.name
files = sorted(replay_dir.glob("*.npy"), key=lambda item: int(item.stem))
shards: list[dict] = []
for chunk_start in tqdm(range(0, len(files), chunk_size), desc=replay_id):
chunk_files = files[chunk_start : chunk_start + chunk_size]
frames, masks, indices = [], [], []
for path in chunk_files:
frame, mask = load_legacy_pair(path)
frames.append(frame)
masks.append(mask)
indices.append(int(path.stem))
start_frame = indices[0]
shard_name = f"{replay_id}_{start_frame:06d}_{indices[-1]:06d}.npz"
shard_path = out_dir / shard_name
np.savez_compressed(
shard_path,
frames=np.stack(frames).astype(np.uint8),
masks=np.stack(masks).astype(np.uint8),
frame_indices=np.asarray(indices, dtype=np.int32),
)
shards.append(
{
"replay_id": replay_id,
"path": f"shards/{shard_name}",
"start_frame": int(start_frame),
"num_frames": int(len(indices)),
}
)
return shards
def main() -> None:
parser = argparse.ArgumentParser(description="Convert legacy per-frame .npy files into compressed release shards.")
parser.add_argument("--legacy-root", required=True, help="Path to data_viewport_youtube_1118")
parser.add_argument("--output-root", default="data/processed", help="Release dataset output root")
parser.add_argument("--chunk-size", type=int, default=1024)
parser.add_argument("--test-replays", nargs="+", default=DEFAULT_TEST_REPLAYS)
args = parser.parse_args()
legacy_root = Path(args.legacy_root)
output_root = Path(args.output_root)
shard_dir = output_root / "shards"
shard_dir.mkdir(parents=True, exist_ok=True)
replay_dirs = sorted([path for path in legacy_root.iterdir() if path.is_dir()])
test_replays = set(args.test_replays)
train_replays = [path.name for path in replay_dirs if path.name not in test_replays]
shards: list[dict] = []
for replay_dir in replay_dirs:
shards.extend(export_replay(replay_dir, shard_dir, args.chunk_size))
manifest = {
"format": "lol_viewport_sharded_npz_v1",
"frame_shape": [2, 256, 256],
"mask_shape": [1, 256, 256],
"role_values": {"0": "background", "1": "TOP", "2": "JUNGLE", "3": "MID", "4": "BOT", "5": "SUPPORT"},
"splits": {
"train": train_replays,
"test": list(args.test_replays),
"validation": "sampled from train during optimization",
},
"shards": shards,
}
with (output_root / "manifest.json").open("w", encoding="utf-8") as f:
json.dump(manifest, f, indent=2)
if __name__ == "__main__":
main()
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