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import argparse
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
import hashlib
import json
from pathlib import Path
import sys
import time
import torch
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from anima_style_probe.factor_interventions import apply_intervention # noqa: E402
from anima_style_probe.feature_parts import completed_part_records, write_feature_part # noqa: E402
from anima_style_probe.image_preprocess import image_tensor # noqa: E402
from anima_style_probe.style_dataset import ( # noqa: E402
assigned_shards,
iter_prefetched_batches,
iter_style_samples,
)
from extract_style_backbone_features import ( # noqa: E402
compact_feature_map,
forward_intermediates,
load_model,
)
def read_jsonl(path: Path) -> list[dict]:
with path.open(encoding="utf-8") as handle:
return [json.loads(line) for line in handle if line.strip()]
def save_image_atomic(image, path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
if path.is_file():
return
temporary = path.with_suffix(path.suffix + ".tmp")
image.save(temporary, format="WEBP", quality=90, method=4)
temporary.replace(path)
def flush_part(
output_dir: Path,
worker_index: int,
part_index: int,
full: list[torch.Tensor],
face: list[torch.Tensor],
face_mask: list[torch.Tensor],
records: list[dict],
*,
manifest_sha256: str,
layer_indices: list[int],
) -> None:
write_feature_part(
output_dir,
"intervention",
worker_index,
part_index,
{
"full": torch.cat(full).to(torch.bfloat16),
"face": torch.cat(face).to(torch.bfloat16),
"face_mask": torch.cat(face_mask).to(torch.bool),
},
{
"kind": "factor_intervention_full_face",
"backbone": "siglip2_so400m",
"layer_indices": layer_indices,
"manifest_sha256": manifest_sha256,
"records": records,
},
)
def main() -> int:
parser = argparse.ArgumentParser(description="Extract resumable factor-intervention features.")
parser.add_argument("--dataset-root", type=Path, required=True)
parser.add_argument("--manifest", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--worker-index", type=int, required=True)
parser.add_argument("--num-workers", type=int, default=4)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--source-batch-size", type=int, default=16)
parser.add_argument("--decode-workers", type=int, default=16)
parser.add_argument("--part-size", type=int, default=4096)
parser.add_argument("--image-size", type=int, default=512)
parser.add_argument("--mode", choices=("panel", "full"), default="full")
parser.add_argument("--panel-dir", type=Path)
parser.add_argument("--anima-pilot-dir", type=Path)
args = parser.parse_args()
if min(args.batch_size, args.source_batch_size, args.decode_workers, args.part_size) < 1:
raise ValueError("batch and worker sizes must be positive")
if not torch.cuda.is_available():
raise RuntimeError("CUDA is required")
manifest_sha256 = hashlib.sha256(args.manifest.read_bytes()).hexdigest()
rows = read_jsonl(args.manifest)
if args.mode == "panel":
rows = [row for row in rows if row.get("panel")]
worker_shards = {
path.name
for path in assigned_shards(args.dataset_root, args.worker_index, args.num_workers)
}
rows = [row for row in rows if row["shard"] in worker_shards]
by_source: dict[str, list[dict]] = defaultdict(list)
for row in rows:
by_source[row["source_record_id"]].append(row)
completed, part_index = completed_part_records(
args.output_dir, "intervention", args.worker_index
)
for path in args.output_dir.glob(f"intervention-w{args.worker_index}-p*.json"):
metadata = json.loads(path.read_text(encoding="utf-8"))
if metadata.get("manifest_sha256") != manifest_sha256:
raise RuntimeError(f"manifest mismatch with completed part: {path}")
pending_by_source = {
source_id: [row for row in variants if row["record_id"] not in completed]
for source_id, variants in by_source.items()
}
pending_by_source = {key: value for key, value in pending_by_source.items() if value}
device = torch.device("cuda")
dtype = torch.bfloat16
model, depth = load_model("siglip2_so400m", device, dtype)
layer_indices = [round(0.25 * (depth - 1)), round(0.55 * (depth - 1)), depth - 1]
decode_pool = ThreadPoolExecutor(
max_workers=args.decode_workers, thread_name_prefix="intervention"
)
started = time.perf_counter()
processed = 0
full_parts: list[torch.Tensor] = []
face_parts: list[torch.Tensor] = []
mask_parts: list[torch.Tensor] = []
part_records: list[dict] = []
feature_buffer: list[tuple[torch.Tensor, torch.Tensor, bool, dict]] = []
def decode_source(sample):
decoded = []
for spec in pending_by_source[sample.metadata["record_id"]]:
full_image, face_image = apply_intervention(
sample.full_bytes,
sample.face_bytes,
sample.metadata,
spec,
size=args.image_size,
)
if args.panel_dir is not None and spec.get("panel"):
save_image_atomic(
full_image,
args.panel_dir / spec["source"] / spec["factor"] / f"{spec['record_id']}.webp",
)
if args.anima_pilot_dir is not None and spec.get("anima_pilot"):
save_image_atomic(full_image, args.anima_pilot_dir / f"{spec['record_id']}.webp")
full_tensor = image_tensor(full_image)
face_tensor = torch.zeros_like(full_tensor) if face_image is None else image_tensor(face_image)
record = {
**spec,
"source_shard": sample.shard,
"content_id": sample.metadata.get("content_id") or sample.metadata.get("cell_id"),
"seed": sample.metadata.get("seed"),
"face_present": face_image is not None,
}
decoded.append((full_tensor, face_tensor, face_image is not None, record))
return decoded
def process_features(batch) -> None:
nonlocal processed, part_index, full_parts, face_parts, mask_parts, part_records
full_pixels, face_pixels, masks, records = map(list, zip(*batch, strict=True))
pixels = torch.cat((torch.stack(full_pixels), torch.stack(face_pixels))).to(
device=device, dtype=dtype
)
with torch.inference_mode(), torch.autocast("cuda", dtype=dtype):
maps = forward_intermediates(model, "siglip2_so400m", pixels, layer_indices)
compact = torch.cat(
[
compact_feature_map(value, pool)
for value, pool in zip(maps, (2, 4, 2), strict=True)
],
dim=1,
).to(device="cpu", dtype=torch.bfloat16)
size = len(batch)
full_parts.append(compact[:size])
face_parts.append(compact[size:])
mask_parts.append(torch.tensor(masks, dtype=torch.bool))
part_records.extend(records)
processed += size
if len(part_records) >= args.part_size:
flush_part(
args.output_dir,
args.worker_index,
part_index,
full_parts,
face_parts,
mask_parts,
part_records,
manifest_sha256=manifest_sha256,
layer_indices=layer_indices,
)
part_index += 1
full_parts, face_parts, mask_parts, part_records = [], [], [], []
print(
json.dumps(
{
"worker": args.worker_index,
"new_records": processed,
"expected": len(rows),
"already_complete": len(completed),
"elapsed_seconds": round(time.perf_counter() - started, 1),
}
),
flush=True,
)
try:
samples = iter_style_samples(
args.dataset_root,
worker_index=args.worker_index,
num_workers=args.num_workers,
include_record_ids=set(pending_by_source),
)
for source_batch in iter_prefetched_batches(samples, args.source_batch_size):
for group in decode_pool.map(decode_source, source_batch):
feature_buffer.extend(group)
while len(feature_buffer) >= args.batch_size:
process_features(feature_buffer[: args.batch_size])
del feature_buffer[: args.batch_size]
if feature_buffer:
process_features(feature_buffer)
finally:
decode_pool.shutdown()
if part_records:
flush_part(
args.output_dir,
args.worker_index,
part_index,
full_parts,
face_parts,
mask_parts,
part_records,
manifest_sha256=manifest_sha256,
layer_indices=layer_indices,
)
if processed + len(completed) != len(rows):
raise RuntimeError(
f"worker coverage mismatch: {processed} + {len(completed)} != {len(rows)}"
)
summary = {
"status": "complete",
"mode": args.mode,
"worker_index": args.worker_index,
"manifest_sha256": manifest_sha256,
"records": len(rows),
"already_complete": len(completed),
"new_records": processed,
"elapsed_seconds": time.perf_counter() - started,
"peak_vram_bytes": torch.cuda.max_memory_allocated(),
}
path = args.output_dir / f"intervention-worker-{args.worker_index}.json"
path.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
print(json.dumps(summary, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
|