File size: 3,474 Bytes
70cba97 | 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 | """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()
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