feat(feature-compressor): add DINOv2 feature extraction and compression pipeline

mini-nav/feature_compressor/utils/feature_utils.py

@@ -0,0 +1,83 @@
+"""Feature processing utilities."""
+
+from pathlib import Path
+from typing import Dict
+
+import numpy as np
+import torch
+import yaml
+
+
+def normalize_features(features: torch.Tensor) -> torch.Tensor:
+    """L2-normalize features.
+
+    Args:
+        features: Tensor of shape [batch, dim] or [batch, seq, dim]
+
+    Returns:
+        L2-normalized features
+    """
+    norm = torch.norm(features, p=2, dim=-1, keepdim=True)
+    return features / (norm + 1e-8)
+
+
+def compute_feature_stats(features: torch.Tensor) -> Dict[str, float]:
+    """Compute basic statistics for features.
+
+    Args:
+        features: Tensor of shape [batch, dim] or [batch, seq, dim]
+
+    Returns:
+        Dictionary with mean, std, min, max
+    """
+    with torch.no_grad():
+        return {
+            "mean": float(features.mean().item()),
+            "std": float(features.std().item()),
+            "min": float(features.min().item()),
+            "max": float(features.max().item()),
+        }
+
+
+def save_features_to_json(
+    features: torch.Tensor, path: Path, metadata: Dict = None
+) -> None:
+    """Save features to JSON file.
+
+    Args:
+        features: Tensor to save
+        path: Output file path
+        metadata: Optional metadata dictionary
+    """
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    features_np = features.cpu().numpy()
+
+    data = {
+        "features": features_np.tolist(),
+        "shape": list(features.shape),
+    }
+
+    if metadata:
+        data["metadata"] = metadata
+
+    with open(path, "w") as f:
+        import json
+
+        json.dump(data, f, indent=2)
+
+
+def save_features_to_csv(features: torch.Tensor, path: Path) -> None:
+    """Save features to CSV file.
+
+    Args:
+        features: Tensor to save
+        path: Output file path
+    """
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    features_np = features.cpu().numpy()
+
+    np.savetxt(path, features_np, delimiter=",", fmt="%.6f")