| |
| """Decode each frozen 81-frame MP4 once and write a resumable triple bank.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import hashlib |
| import json |
| import os |
| from pathlib import Path |
| import tempfile |
| import time |
|
|
| import numpy as np |
| import torch |
| import torch.distributed as dist |
| from safetensors import safe_open |
| from safetensors.torch import save_file |
|
|
| from utils.triple_video_feature_bank import ( |
| BANK_KIND, |
| BANK_SCHEMA_VERSION, |
| canonical_sha256, |
| file_sha256, |
| ) |
| from utils.matched_video_manifest import ( |
| load_matched_video_manifest, |
| sha256_file, |
| ) |
| from video_fd.encoders.dinov3_temporal_pool_adapter import ( |
| DINOv3TemporalPoolAdapter, |
| ) |
| from video_fd.encoders.segmented_videomae_v2 import ( |
| SegmentedVideoMAEv2Ensemble, |
| ) |
| from video_fd.encoders.videomae_v2_dense_metadata import ( |
| canonical_videomae_v2_dense_feature_metadata, |
| uniform_segment_token_indices, |
| ) |
| from video_fd.encoders.vjepa2_adapter import VJEPA2Adapter |
| from video_fd.encoders.vjepa2_dense_metadata import ( |
| canonical_vjepa2_dense_feature_metadata, |
| uniform_dense_token_indices, |
| ) |
|
|
|
|
| VIDEOMAE_REVISION = "9981a9c8f77118c421e5228e1b219468a4b0238d" |
|
|
|
|
| def atomic_json(path: Path, value) -> None: |
| path.parent.mkdir(parents=True, exist_ok=True) |
| descriptor, name = tempfile.mkstemp( |
| dir=path.parent, prefix=f".{path.name}.", suffix=".tmp") |
| try: |
| with os.fdopen(descriptor, "w", encoding="utf-8") as handle: |
| json.dump(value, handle, sort_keys=True, indent=2, ensure_ascii=False) |
| handle.write("\n") |
| handle.flush() |
| os.fsync(handle.fileno()) |
| os.replace(name, path) |
| finally: |
| Path(name).unlink(missing_ok=True) |
|
|
|
|
| def tensor_sha256(tensor: torch.Tensor) -> str: |
| value = tensor.detach().cpu().contiguous() |
| return hashlib.sha256(value.numpy().tobytes()).hexdigest() |
|
|
|
|
| def decode_81(path: Path, *, size: int, mtime_ns: int) -> torch.Tensor: |
| stat = path.stat() |
| if stat.st_size != size or stat.st_mtime_ns != mtime_ns: |
| raise ValueError(f"frozen video stat changed: {path}") |
| import av |
|
|
| frames = [] |
| with av.open(str(path), mode="r") as container: |
| if len(container.streams.video) != 1: |
| raise ValueError(f"expected one video stream: {path}") |
| stream = container.streams.video[0] |
| for frame in container.decode(stream): |
| array = frame.to_ndarray(format="rgb24") |
| if array.dtype != np.uint8 or array.ndim != 3 or array.shape[2] != 3: |
| raise ValueError(f"invalid RGB frame in {path}") |
| frames.append(torch.from_numpy( |
| np.ascontiguousarray(array)).permute(2, 0, 1)) |
| if len(frames) != 81: |
| raise ValueError(f"{path.name} has {len(frames)} frames, expected 81") |
| shape = tuple(frames[0].shape) |
| if any(tuple(frame.shape) != shape for frame in frames): |
| raise ValueError(f"resolution changes within {path}") |
| return torch.stack(frames).float().div_(255.0).unsqueeze(0) |
|
|
|
|
| def load_download_manifest(model_dir: Path) -> dict: |
| path = model_dir / "download_manifest.json" |
| with path.open("r", encoding="utf-8") as handle: |
| manifest = json.load(handle) |
| payload = dict(manifest) |
| saved = payload.pop("content_hash") |
| if ( |
| manifest.get("revision") != VIDEOMAE_REVISION |
| or canonical_sha256(payload) != saved |
| ): |
| raise ValueError("invalid pinned VideoMAE download manifest") |
| for item in manifest["files"]: |
| path = (model_dir / item["path"]).resolve() |
| if ( |
| model_dir not in path.parents |
| or path.stat().st_size != int(item["size_bytes"]) |
| or file_sha256(path) != item["sha256"] |
| ): |
| raise ValueError(f"VideoMAE file identity mismatch: {path}") |
| return manifest |
|
|
|
|
| def build_encoders(args, device): |
| dino = DINOv3TemporalPoolAdapter({ |
| "repo_path": str(args.dinov3_repo), |
| "weight_path": str(args.dinov3_weights), |
| "source_commit": str(args.dinov3_source_commit), |
| "checkpoint_sha256": str(args.dinov3_checkpoint_sha256), |
| "source_num_frames": 81, |
| "frame_indices": ( |
| 0, 4, 8, 13, 17, 21, 25, 29, 34, 38, |
| 42, 46, 51, 55, 59, 63, 67, 72, 76, 80), |
| "input_size": 256, |
| "patch_size": 16, |
| "spatial_axis_indices": tuple(range(16)), |
| "forward_chunk_frames": 4, |
| "geometry": "temporal_mean5_full256", |
| "temporal_groups": 4, |
| "temporal_pool_frames": 5, |
| }).to(device=device, dtype=torch.float32).eval().requires_grad_(False) |
| vj = VJEPA2Adapter({ |
| "name": "vjepa2_dense", |
| "model_id": str(args.vjepa_model), |
| "input_frames": 64, |
| "dense_num_frames": 64, |
| "input_size": 256, |
| "resize_short_side": 292, |
| "crop_mode": "center", |
| "dense_only": True, |
| "dense_layer_index": 15, |
| }).to(device=device, dtype=torch.float32).eval().requires_grad_(False) |
| vm = SegmentedVideoMAEv2Ensemble({ |
| "name": "videomae_v2_five_segment_dense", |
| "model_id": str(args.videomae_model), |
| "revision": VIDEOMAE_REVISION, |
| "local_files_only": True, |
| "encoder_count": 5, |
| "skip_leading_frames": 1, |
| "segment_num_frames": 16, |
| "source_num_frames": 81, |
| "input_frames": 16, |
| "dense_num_frames": 16, |
| "input_size": 224, |
| "resize_short_side": 224, |
| "crop_mode": "center", |
| "dense_only": True, |
| "dense_layer_index": 15, |
| }).to(device=device, dtype=torch.float32).eval().requires_grad_(False) |
| if (dino.dense_feature_dim, vj.dense_feature_dim, vm.dense_feature_dim) != (1024, 1024, 1024): |
| raise RuntimeError("encoder feature dimensions differ from contract") |
| return dino, vj, vm |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--manifest", required=True) |
| parser.add_argument("--manifest-sha256", required=True) |
| parser.add_argument("--source-csv-sha256", required=True) |
| parser.add_argument("--video-root", required=True) |
| parser.add_argument("--dinov3-repo", required=True) |
| parser.add_argument("--dinov3-weights", required=True) |
| parser.add_argument("--dinov3-source-commit", required=True) |
| parser.add_argument("--dinov3-checkpoint-sha256", required=True) |
| parser.add_argument("--vjepa-model", required=True) |
| parser.add_argument("--vjepa-reference-model-id", required=True) |
| parser.add_argument("--vjepa-model-sha256", required=True) |
| parser.add_argument("--videomae-model", required=True) |
| parser.add_argument("--output-dir", required=True) |
| parser.add_argument("--shard-rows", type=int, default=32) |
| parser.add_argument("--device", default="cuda:0") |
| parser.add_argument("--expected-count", type=int, default=5000) |
| args = parser.parse_args() |
| if args.shard_rows != 32: |
| raise ValueError("triple teacher bank requires shard-rows=32") |
| manifest_path = Path(args.manifest).expanduser().resolve() |
| if sha256_file(manifest_path) != args.manifest_sha256: |
| raise ValueError("source manifest SHA-256 mismatch") |
| entries, source_metadata = load_matched_video_manifest( |
| manifest_path, |
| expected_source_csv_sha256=args.source_csv_sha256, |
| expected_count=args.expected_count, |
| ) |
| video_root = Path(args.video_root).expanduser().resolve() |
| output = Path(args.output_dir).expanduser().resolve() |
| shard_dir = output / "shards" |
| shard_dir.mkdir(parents=True, exist_ok=True) |
| progress_path = output / "progress.json" |
| final_path = output / "manifest.json" |
| vm_download = load_download_manifest( |
| Path(args.videomae_model).expanduser().resolve()) |
| world_size = int(os.environ.get("WORLD_SIZE", "1")) |
| distributed = world_size > 1 |
| if distributed: |
| local_rank = int(os.environ["LOCAL_RANK"]) |
| torch.cuda.set_device(local_rank) |
| dist.init_process_group(backend="nccl") |
| rank = int(dist.get_rank()) |
| world_size = int(dist.get_world_size()) |
| device = torch.device("cuda", local_rank) |
| else: |
| rank = 0 |
| device = torch.device(args.device) |
| dino, vj, vm = build_encoders(args, device) |
| vj_indices = uniform_dense_token_indices( |
| vj.dense_raw_tokens, 1024, device=device) |
| vm_indices = uniform_segment_token_indices( |
| vm.dense_raw_tokens, 256, device=device) |
| vj_contract = canonical_vjepa2_dense_feature_metadata( |
| model_id=args.vjepa_reference_model_id, |
| layer_index=15, |
| num_frames=64, |
| resize_short_side=292, |
| crop_size=256, |
| raw_tokens=vj.dense_raw_tokens, |
| num_tokens=1024, |
| feature_dim=1024, |
| token_indices=vj_indices.cpu(), |
| frame_sampling="last_contiguous", |
| source_num_frames=81, |
| ) |
| vm_contract = canonical_videomae_v2_dense_feature_metadata( |
| model_id="VideoMAEv2-Large", |
| layer_index=15, |
| num_tokens=256, |
| token_indices=vm_indices.cpu(), |
| ) |
| fixed = { |
| "schema_version": BANK_SCHEMA_VERSION, |
| "kind": BANK_KIND, |
| "sample_count": len(entries), |
| "source_manifest": { |
| "path": str(manifest_path), |
| "sha256": args.manifest_sha256, |
| "source_csv_sha256": args.source_csv_sha256, |
| "sample_identity_sha256": source_metadata.get( |
| "sample_identity_sha256"), |
| }, |
| "branches": { |
| "dinov3": { |
| "sample_shape": [1024, 1024], |
| "dtype": "float16", |
| "feature_contract": dino.feature_metadata(), |
| "model_identity": { |
| "model_id": "dinov3_vitl16", |
| "repo_path": str(Path(args.dinov3_repo).resolve()), |
| "weight_path": str(Path(args.dinov3_weights).resolve()), |
| "source_commit": args.dinov3_source_commit, |
| "checkpoint_sha256": args.dinov3_checkpoint_sha256, |
| }, |
| }, |
| "vjepa2": { |
| "sample_shape": [1024, 1024], |
| "dtype": "float16", |
| "feature_contract": vj_contract, |
| "model_identity": { |
| "model_id": args.vjepa_reference_model_id, |
| "load_path": str(Path(args.vjepa_model).resolve()), |
| "sha256": args.vjepa_model_sha256, |
| }, |
| }, |
| "videomae_v2": { |
| "sample_shape": [1280, 1024], |
| "dtype": "float16", |
| "feature_contract": vm_contract, |
| "model_identity": { |
| "model_id": "VideoMAEv2-Large", |
| "revision": VIDEOMAE_REVISION, |
| "download_manifest_content_hash": vm_download[ |
| "content_hash"], |
| }, |
| }, |
| }, |
| "samples": [{ |
| "index": entry.manifest_index, |
| "sample_id": entry.sample_id, |
| "prompt": entry.caption, |
| } for entry in entries], |
| } |
| progress_identity = canonical_sha256(fixed) |
| receipt_dir = output / "receipts" |
| receipt_dir.mkdir(parents=True, exist_ok=True) |
| shard_count = (len(entries) + args.shard_rows - 1) // args.shard_rows |
| started = time.time() |
| for shard_index in range(rank, shard_count, world_size): |
| start = shard_index * args.shard_rows |
| end = min(start + args.shard_rows, len(entries)) |
| relative = f"shards/shard-{shard_index:05d}.safetensors" |
| path = output / relative |
| receipt_path = receipt_dir / f"shard-{shard_index:05d}.json" |
| if receipt_path.is_file(): |
| receipt = json.loads(receipt_path.read_text(encoding="utf-8")) |
| if ( |
| receipt.get("progress_identity") != progress_identity |
| or int(receipt.get("index", -1)) != shard_index |
| or int(receipt.get("sample_start", -1)) != start |
| or int(receipt.get("sample_end", -1)) != end |
| or not path.is_file() |
| or file_sha256(path) != receipt.get("sha256") |
| ): |
| raise ValueError( |
| f"resumable triple shard identity mismatch: {path}") |
| print(json.dumps({ |
| "rank": rank, |
| "resumed_shard": shard_index, |
| "completed": end, |
| }), flush=True) |
| continue |
|
|
| branch_rows = { |
| "dinov3": [], |
| "vjepa2": [], |
| "videomae_v2": [], |
| } |
| for entry in entries[start:end]: |
| video = decode_81( |
| video_root / entry.video_name, |
| size=entry.video_size_bytes, |
| mtime_ns=entry.video_mtime_ns, |
| ).to(device=device, dtype=torch.float32) |
| temporal = torch.arange( |
| 17, 81, device=device, dtype=torch.long).unsqueeze(0) |
| with torch.inference_mode(): |
| dino_feature = dino.forward_dense_no_grad( |
| video, gradient_checkpointing=False) |
| vj_feature = vj.forward_dense( |
| video, |
| layer_index=15, |
| token_indices=vj_indices, |
| gradient_checkpointing=False, |
| temporal_window_indices=temporal, |
| ) |
| vm_feature = vm.forward_dense( |
| video, |
| layer_index=15, |
| token_indices=vm_indices, |
| gradient_checkpointing=False, |
| ) |
| observed = { |
| "dinov3": tuple(dino_feature.shape), |
| "vjepa2": tuple(vj_feature.shape), |
| "videomae_v2": tuple(vm_feature.shape), |
| } |
| expected = { |
| "dinov3": (1, 1024, 1024), |
| "vjepa2": (1, 1024, 1024), |
| "videomae_v2": (1, 1280, 1024), |
| } |
| if observed != expected: |
| raise RuntimeError( |
| f"triple bank row shape mismatch: {observed}") |
| branch_rows["dinov3"].append( |
| dino_feature.squeeze(0).half().cpu().contiguous()) |
| branch_rows["vjepa2"].append( |
| vj_feature.squeeze(0).half().cpu().contiguous()) |
| branch_rows["videomae_v2"].append( |
| vm_feature.squeeze(0).half().cpu().contiguous()) |
| del video, dino_feature, vj_feature, vm_feature |
|
|
| tensors = { |
| name: torch.stack(rows, dim=0) |
| for name, rows in branch_rows.items() |
| } |
| ids = [entry.sample_id for entry in entries[start:end]] |
| shard_payload = { |
| "index": shard_index, |
| "sample_start": start, |
| "sample_end": end, |
| "sample_ids_sha256": canonical_sha256(ids), |
| "tensor_sha256": { |
| name: tensor_sha256(value) |
| for name, value in tensors.items() |
| }, |
| "branches": fixed["branches"], |
| "source_manifest_sha256": args.manifest_sha256, |
| "progress_identity": progress_identity, |
| } |
| shard_content_hash = canonical_sha256(shard_payload) |
| temporary = path.with_name(f".{path.name}.rank{rank}.tmp") |
| save_file(tensors, str(temporary), metadata={ |
| "content_hash": shard_content_hash, |
| "sample_start": str(start), |
| "sample_end": str(end), |
| "source_manifest_sha256": args.manifest_sha256, |
| "progress_identity": progress_identity, |
| }) |
| os.replace(temporary, path) |
| shard = { |
| "index": shard_index, |
| "path": relative, |
| "sample_start": start, |
| "sample_end": end, |
| "sample_ids_sha256": canonical_sha256(ids), |
| "sha256": file_sha256(path), |
| "content_hash": shard_content_hash, |
| } |
| atomic_json(receipt_path, { |
| **shard, |
| "progress_identity": progress_identity, |
| "rank": rank, |
| }) |
| print(json.dumps({ |
| "rank": rank, |
| "completed": end, |
| "total": len(entries), |
| "elapsed_seconds": time.time() - started, |
| "shard": relative, |
| }), flush=True) |
|
|
| if distributed: |
| dist.barrier() |
| if rank == 0: |
| shards = [] |
| for shard_index in range(shard_count): |
| receipt_path = receipt_dir / f"shard-{shard_index:05d}.json" |
| if not receipt_path.is_file(): |
| raise FileNotFoundError( |
| f"missing completed shard receipt: {receipt_path}") |
| receipt = json.loads(receipt_path.read_text(encoding="utf-8")) |
| if receipt.pop("progress_identity", None) != progress_identity: |
| raise ValueError("triple shard progress identity mismatch") |
| receipt.pop("rank", None) |
| path = output / receipt["path"] |
| if file_sha256(path) != receipt["sha256"]: |
| raise ValueError(f"triple shard SHA-256 mismatch: {path}") |
| shards.append(receipt) |
|
|
| branch_hashers = { |
| name: hashlib.sha256() for name in fixed["branches"] |
| } |
| for shard in shards: |
| with safe_open( |
| str(output / shard["path"]), |
| framework="pt", |
| device="cpu", |
| ) as handle: |
| for name in branch_hashers: |
| value = handle.get_tensor(name).contiguous() |
| branch_hashers[name].update(value.numpy().tobytes()) |
| for name, hasher in branch_hashers.items(): |
| fixed["branches"][name]["feature_bank_fp16_sha256"] = ( |
| hasher.hexdigest()) |
| final_payload = {**fixed, "shards": shards} |
| final_payload["content_hash"] = canonical_sha256(final_payload) |
| atomic_json(final_path, final_payload) |
| print(json.dumps({ |
| "manifest": str(final_path), |
| "manifest_sha256": file_sha256(final_path), |
| "content_hash": final_payload["content_hash"], |
| "samples": len(entries), |
| "world_size": world_size, |
| }, sort_keys=True), flush=True) |
| if distributed: |
| dist.barrier() |
| dist.destroy_process_group() |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
