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Running on Zero
multimodalart HF Staff
Learn2Splat ZeroGPU demo: app, vendored optgs, prebuilt sm_120 wheels
9196862 verified | """Export the trained ReSplat initializer for public release. | |
| Takes the in-repo training run (Lightning checkpoint + its v0 config.yaml) and | |
| produces a clean, release-ready pair under a new directory: | |
| <out_dir>/config.yaml migrated to the current config version | |
| <out_dir>/checkpoints/<ckpt_name> slimmed to just the initializer weights | |
| The config is run through ``config_migrate.migrate`` (v0 -> current) and its | |
| ``output_dir`` is repointed at the release directory. The checkpoint keeps only | |
| ``state_dict`` (the ``initializer.*`` weights), dropping the Lightning training | |
| state (optimizer moments, loops, callbacks, lr schedulers) that inference never | |
| reads. The resulting layout matches what ``_find_config_for_checkpoint`` expects, | |
| so it loads directly as ``pretrained_initializer`` (locally or via ``hf://``). | |
| Usage: | |
| python -m optgs.scripts.dev.export_resplat_init \ | |
| --src checkpoints/optgs/unified-dl3dv-8views/init \ | |
| --out checkpoints/learn2splat/resplat_init | |
| """ | |
| import argparse | |
| from pathlib import Path | |
| import torch | |
| from omegaconf import OmegaConf | |
| from optgs.config_migrate import migrate | |
| from optgs.misc.checkpointing import _rename_optimizer_attrs, find_latest_ckpt | |
| from optgs.misc.io import cyan, read_omega_cfg | |
| def export_config(src_cfg: Path, out_cfg: Path, out_dir: Path) -> None: | |
| cfg = read_omega_cfg(src_cfg) | |
| cfg = migrate(cfg) | |
| # Repoint output_dir at the release directory as a plain string (the source | |
| # carried a pickled PosixPath, which serializes as an ugly !!python tag). | |
| cfg.output_dir = str(out_dir) | |
| out_cfg.parent.mkdir(parents=True, exist_ok=True) | |
| OmegaConf.save(cfg, out_cfg) | |
| print(cyan(f"Wrote migrated config -> {out_cfg}")) | |
| def export_checkpoint(src_ckpt: Path, out_ckpt: Path) -> None: | |
| ckpt = torch.load(src_ckpt, map_location="cpu") | |
| state_dict = ckpt["state_dict"] if "state_dict" in ckpt else ckpt | |
| # Keep only the initializer weights and apply the optimizer-attr renames | |
| # (a no-op here, since an init-only checkpoint has no optimizer keys). | |
| state_dict = {k: v for k, v in state_dict.items() if k.startswith("initializer.")} | |
| state_dict = _rename_optimizer_attrs(state_dict) | |
| out_ckpt.parent.mkdir(parents=True, exist_ok=True) | |
| torch.save({"state_dict": state_dict}, out_ckpt) | |
| src_mb = src_ckpt.stat().st_size / 1e6 | |
| out_mb = out_ckpt.stat().st_size / 1e6 | |
| print(cyan(f"Wrote slimmed checkpoint -> {out_ckpt} " | |
| f"({len(state_dict)} keys, {src_mb:.0f}MB -> {out_mb:.0f}MB)")) | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument("--src", type=Path, required=True, | |
| help="Source run dir containing config.yaml and checkpoints/") | |
| parser.add_argument("--out", type=Path, required=True, | |
| help="Release dir to write config.yaml and checkpoints/ into") | |
| parser.add_argument("--ckpt-name", type=str, default=None, | |
| help="Checkpoint filename under src/checkpoints (default: latest by step)") | |
| args = parser.parse_args() | |
| src_ckpt = (args.src / "checkpoints" / args.ckpt_name) if args.ckpt_name \ | |
| else find_latest_ckpt(args.src / "checkpoints") | |
| out_ckpt = args.out / "checkpoints" / src_ckpt.name | |
| export_config(args.src / "config.yaml", args.out / "config.yaml", args.out) | |
| export_checkpoint(src_ckpt, out_ckpt) | |
| if __name__ == "__main__": | |
| main() | |