def _closest_vehicle_distance_pp(x):
    """Predict the distance to the closest vehicle."""
    # Label distribution:

    # Location feature contains (x, y, z) in meters w.r.t. the camera.
    vehicles = tf.where(x['objects']['type'] < 3)  # Car, Van, Truck.
    vehicle_z = tf.gather(params=x['objects']['location'][:, 2], indices=vehicles)
    vehicle_z = tf.concat([vehicle_z, tf.constant([[1000.0]])], axis=0)
    dist = tf.reduce_min(vehicle_z)
    # Results in a uniform distribution over three distances, plus one class for
    # "no vehicle".
    thrs = np.array([-100.0, 8.0, 20.0, 999.0])
    label = tf.reduce_max(tf.where((thrs - dist) < 0))
    return {'image': x['image'], 'label': label}
Predict the distance to the closest vehicle.
clip_benchmark/clip_benchmark/datasets/kitti.py