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milk10k_effb2_metadata/cli.py

@@ -9,8 +9,8 @@ from pathlib import Path
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="Train MILK10k dual EfficientNet-B2 with metadata fusion.")
     parser.add_argument("--data-dir", type=Path, default=None)
-    parser.add_argument("--clinical-checkpoint", type=Path, required=True)
-    parser.add_argument("--dermoscopic-checkpoint", type=Path, required=True)
+    parser.add_argument("--clinical-checkpoint", type=Path, default=None)
+    parser.add_argument("--dermoscopic-checkpoint", type=Path, default=None)
     parser.add_argument(
         "--resume-checkpoint",
         type=Path,

milk10k_effb2_metadata/training.py

@@ -31,6 +31,42 @@ from milk10k_effb2_metadata.metrics import compute_metrics, move_batch, predict,
 from milk10k_effb2_metadata.models import DualEffB2MetadataClassifier, set_encoder_trainable
 
 
+def infer_branch_backend_from_state(state: dict[str, torch.Tensor], branch_prefix: str) -> str:
+    keys = [key.removeprefix(branch_prefix) for key in state if key.startswith(branch_prefix)]
+    timm_prefixes = ("conv_stem.", "bn1.", "blocks.", "conv_head.", "bn2.", "stages.", "stem.")
+    torchvision_prefixes = ("features.", "avgpool.", "classifier.")
+    timm_hits = sum(key.startswith(timm_prefixes) for key in keys)
+    torchvision_hits = sum(key.startswith(torchvision_prefixes) for key in keys)
+    if timm_hits > torchvision_hits:
+        return "timm"
+    if torchvision_hits > timm_hits:
+        return "torchvision"
+    if any(key.startswith("layer") for key in keys):
+        return "timm"
+    raise RuntimeError(f"Cannot infer backend for resume checkpoint branch prefix {branch_prefix!r}.")
+
+
+def resolve_training_backbone_backends(args: argparse.Namespace, device: torch.device) -> tuple[str, str]:
+    if args.backbone_backend != "auto":
+        return args.backbone_backend, args.backbone_backend
+    if args.clinical_checkpoint is not None and args.dermoscopic_checkpoint is not None:
+        return resolve_backbone_backends(args, device)
+    if args.resume_checkpoint is None:
+        raise ValueError(
+            "Pass --clinical-checkpoint and --dermoscopic-checkpoint for a fresh run, "
+            "or pass --resume-checkpoint to resume without branch checkpoints."
+        )
+    checkpoint = torch.load(args.resume_checkpoint.expanduser().resolve(), map_location=device, weights_only=False)
+    state = checkpoint["model_state"]
+    clinical_backend = infer_branch_backend_from_state(state, "clinical_encoder.")
+    dermoscopic_backend = infer_branch_backend_from_state(state, "dermoscopic_encoder.")
+    checkpoint_args = checkpoint.get("args", {})
+    if checkpoint_args.get("backbone") and args.backbone == "efficientnet_b2":
+        args.backbone = checkpoint_args["backbone"]
+    print(f"Auto-detected resume backends: clinical={clinical_backend}, dermoscopic={dermoscopic_backend}")
+    return clinical_backend, dermoscopic_backend
+
+
 def build_optimizer(model: DualEffB2MetadataClassifier, args: argparse.Namespace, encoders_trainable: bool) -> torch.optim.Optimizer:
     head_params = []
     encoder_params = []
@@ -274,8 +310,11 @@ def build_model(
         backbone=args.backbone,
         disable_metadata=args.disable_metadata,
     ).to(device)
-    load_encoder_checkpoint(args.clinical_checkpoint, model.clinical_encoder, "clinical", device)
-    load_encoder_checkpoint(args.dermoscopic_checkpoint, model.dermoscopic_encoder, "dermoscopic", device)
+    if args.resume_checkpoint is None:
+        if args.clinical_checkpoint is None or args.dermoscopic_checkpoint is None:
+            raise ValueError("Fresh training requires --clinical-checkpoint and --dermoscopic-checkpoint.")
+        load_encoder_checkpoint(args.clinical_checkpoint, model.clinical_encoder, "clinical", device)
+        load_encoder_checkpoint(args.dermoscopic_checkpoint, model.dermoscopic_encoder, "dermoscopic", device)
     if args.disable_metadata or args.freeze_metadata_head:
         set_metadata_head_trainable(model, False)
     return model
@@ -549,7 +588,7 @@ def run(args: argparse.Namespace) -> None:
     class_names = sorted(df["label"].unique())
     label_to_idx = {label: idx for idx, label in enumerate(class_names)}
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    clinical_backbone_backend, dermoscopic_backbone_backend = resolve_backbone_backends(args, device)
+    clinical_backbone_backend, dermoscopic_backbone_backend = resolve_training_backbone_backends(args, device)
 
     print(f"Data dir: {data_dir}")
     if args.k_folds == 1: