增加脚本

scripts/convert_hf_checkpoint.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+"""Convert LeWM HuggingFace weights into eval-compatible object checkpoints."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import stable_pretraining as spt
+import torch
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from jepa import JEPA
+from module import ARPredictor, Embedder, MLP
+
+
+def _load_json(path: Path) -> dict:
+    with path.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def _strip_target(config: dict) -> dict:
+    return {key: value for key, value in config.items() if key != "_target_"}
+
+
+def infer_config_from_state_dict(state_dict: dict) -> dict:
+    action_dim = state_dict["action_encoder.patch_embed.weight"].shape[1]
+    return {
+        "encoder": {
+            "size": "tiny",
+            "patch_size": 14,
+            "image_size": 224,
+            "pretrained": False,
+            "use_mask_token": False,
+        },
+        "predictor": {
+            "num_frames": 3,
+            "input_dim": 192,
+            "hidden_dim": 192,
+            "output_dim": 192,
+            "depth": 6,
+            "heads": 16,
+            "mlp_dim": 2048,
+            "dim_head": 64,
+            "dropout": 0.1,
+            "emb_dropout": 0.0,
+        },
+        "action_encoder": {
+            "input_dim": action_dim,
+            "emb_dim": 192,
+        },
+        "projector": {
+            "input_dim": 192,
+            "output_dim": 192,
+            "hidden_dim": 2048,
+        },
+        "pred_proj": {
+            "input_dim": 192,
+            "output_dim": 192,
+            "hidden_dim": 2048,
+        },
+    }
+
+
+def build_model(config: dict) -> JEPA:
+    encoder = spt.backbone.utils.vit_hf(**_strip_target(config["encoder"]))
+    predictor = ARPredictor(**_strip_target(config["predictor"]))
+    action_encoder = Embedder(**_strip_target(config["action_encoder"]))
+
+    projector_cfg = _strip_target(config["projector"])
+    projector_cfg["norm_fn"] = torch.nn.BatchNorm1d
+    projector = MLP(**projector_cfg)
+
+    pred_proj_cfg = _strip_target(config["pred_proj"])
+    pred_proj_cfg["norm_fn"] = torch.nn.BatchNorm1d
+    pred_proj = MLP(**pred_proj_cfg)
+
+    return JEPA(
+        encoder=encoder,
+        predictor=predictor,
+        action_encoder=action_encoder,
+        projector=projector,
+        pred_proj=pred_proj,
+    )
+
+
+def convert_checkpoint(input_dir: Path, output_name: str) -> tuple[Path, Path]:
+    config_path = input_dir / "config.json"
+    weights_path = input_dir / "weights.pt"
+    if not weights_path.exists():
+        raise FileNotFoundError(f"Missing weights file: {weights_path}")
+
+    state_dict = torch.load(weights_path, map_location="cpu")
+    config = _load_json(config_path) if config_path.exists() else infer_config_from_state_dict(state_dict)
+    model = build_model(config)
+    missing, unexpected = model.load_state_dict(state_dict, strict=True)
+    if missing or unexpected:
+        raise RuntimeError(
+            f"State dict mismatch: missing={missing}, unexpected={unexpected}"
+        )
+    model.eval()
+
+    object_path = input_dir / f"{output_name}_object.ckpt"
+    weight_path = input_dir / f"{output_name}_weight.ckpt"
+    torch.save(model, object_path)
+    torch.save(model.state_dict(), weight_path)
+    return object_path, weight_path
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "input_dir",
+        type=Path,
+        help="Directory containing weights.pt and optionally config.json.",
+    )
+    parser.add_argument("--output-name", default="lewm")
+    args = parser.parse_args()
+
+    object_path, weight_path = convert_checkpoint(args.input_dir, args.output_name)
+    print(f"wrote {object_path}")
+    print(f"wrote {weight_path}")
+
+
+if __name__ == "__main__":
+    main()