dataset-root/
├── t1z5b/
│   ├── images/           # RGB images (.JPG)
│   ├── masks/            # Semantic segmentation masks (.png, single-channel label)
│   ├── lidar/            # LiDAR point cloud (.las format with field "ground_truth")
│   └── pose/             # Camera poses and intrinsics (text files)
├── t1z6a/
│   ├── ...
├── ...
├── split.json            # JSON file specifying the train/test split
└── README.md

python inference.py --mode inference --split test --weights path/to/model.ckpt --root_dir /path/to/data/gridnet/raw

def create_npz(field_name:str="classification", root_dir:str="GridNet-HD"):

    # Iterate over each area directory
    for area_name in ["t1z4", "t1z5a", "t1z7", "t3z1", "t3z2", "t3z5", "t5a2", "t6z1", "t6z5"]:
        area_path = os.path.join(root_dir, area_name)
        lidar_path = os.path.join(area_path, "lidar")

        # Check if the lidar directory exists
        if not os.path.isdir(lidar_path):
            continue

        # Find the LAS file in the lidar directory
        las_files = [f for f in os.listdir(lidar_path) if f.lower().endswith(".las")]
        if not las_files:
            print(f"No LAS file found in {lidar_path}")
            continue

        las_file_path = os.path.join(lidar_path, las_files[0])
        print(las_file_path)
        # Read the LAS file
        try:
            las = laspy.read(las_file_path)
        except Exception as e:
            print(f"Error reading {las_file_path}: {e}")
            continue

        if field_name not in las.point_format.dimension_names:
            print(f"Error: field '{field_name}' not found in LAS file!")
            continue

        # Extract the ground_truth data
        classif_data = las[field_name].astype(np.uint8)
        las = None

        # Save the ground_truth data as a compressed .npz file
        npz_filename = f"{area_name}.npz"
        npz_path = os.path.join(root_dir, "npz", field_name, npz_filename)
        # ensure dir exist
        os.makedirs(os.path.dirname(npz_path), exist_ok=True)
        np.savez_compressed(npz_path, data=classif_data.astype(np.uint8))