Final polish

classifier/tools/artifact_chk.py

@@ -18,8 +18,9 @@ def parse_args():
 
 
 def iter_config_paths(config_root: Path):
-    for sub in ("phase1", "phase2"):
-        yield from sorted((config_root / sub).glob("*.json"))
+    for sub in sorted(config_root.iterdir()):
+        if sub.is_dir() and sub.name not in ("smoke",):
+            yield from sorted(sub.glob("*.json"))
 
 
 def main():

classifier/tools/fetch_ds.py

@@ -2,8 +2,8 @@
 Download the DeepFakeFace dataset from HuggingFace and extract it.
 
 Usage:
-    python tools/download_data.py
-    python tools/download_data.py --data-dir /mnt/data/DFF
+    python tools/fetch_ds.py
+    python tools/fetch_ds.py --data-dir /mnt/data/DFF
 """
 import argparse
 import zipfile

classifier/tools/inference.py

@@ -19,9 +19,27 @@ from PIL import Image
 
 from src.models import get_model, load_checkpoint
 from src.preprocessing import get_transforms
+from src.utils import load_config
 
 
-# Defaults checkpoint to outputs/models/{run_name}_best.pt when not supplied
+def _default_checkpoint(cfg: dict, checkpoint_path: Path | None) -> Path:
+    """Resolve checkpoint: explicit path, single-fold `*_best.pt`, or CV `*_fold{k}_best.pt` / `*_final.pt`."""
+    run_name = cfg["run_name"]
+    models_dir = ROOT / "outputs" / "models"
+    if checkpoint_path is not None:
+        return Path(checkpoint_path)
+    candidates: list[Path] = [models_dir / f"{run_name}_best.pt"]
+    for k in range(32):
+        candidates.append(models_dir / f"{run_name}_fold{k}_best.pt")
+    for k in range(32):
+        candidates.append(models_dir / f"{run_name}_fold{k}_final.pt")
+    for p in candidates:
+        if p.is_file():
+            return p
+    return models_dir / f"{run_name}_best.pt"
+
+
+# Defaults checkpoint under outputs/models/ (single-run or CV best/final).
 def predict(image_path, config_path, checkpoint_path=None):
     image_path = Path(image_path)
     config_path = Path(config_path)
@@ -35,10 +53,9 @@ def predict(image_path, config_path, checkpoint_path=None):
         sys.exit(1)
 
     try:
-        with open(config_path) as f:
-            cfg = json.load(f)
-    except json.JSONDecodeError as e:
-        print(f"Error: Invalid JSON in config: {e}")
+        cfg = load_config(str(config_path))
+    except (json.JSONDecodeError, OSError, ValueError) as e:
+        print(f"Error: Failed to load config: {e}")
         sys.exit(1)
     
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -50,10 +67,7 @@ def predict(image_path, config_path, checkpoint_path=None):
         print(f"Error: Failed to build model: {e}")
         sys.exit(1)
 
-    if checkpoint_path is None:
-        checkpoint_path = ROOT / "outputs" / "models" / f"{cfg['run_name']}_best.pt"
-    else:
-        checkpoint_path = Path(checkpoint_path)
+    checkpoint_path = _default_checkpoint(cfg, Path(checkpoint_path) if checkpoint_path else None)
     
     if not checkpoint_path.exists():
         print(f"Error: Checkpoint not found: {checkpoint_path}")