def eval_policy(eval_positions):
    """Evaluate all positions with all models save the policy heatmaps as CSVs

    CSV name is "heatmap-<position_name>-<model-index>.csv"
    CSV format is: model number, value network output, policy network outputs

    position_name is taken from the SGF file
    Policy network outputs (19x19) are saved in flat order (see coord.from_flat)
    """

    model_paths = oneoff_utils.get_model_paths(fsdb.models_dir())

    idx_start = FLAGS.idx_start
    eval_every = FLAGS.eval_every

    print("Evaluating models {}-{}, eval_every={}".format(
          idx_start, len(model_paths), eval_every))

    player = None
    for i, idx in enumerate(tqdm(range(idx_start, len(model_paths), eval_every))):
        if player and i % 20 == 0:
            player.network.sess.close()
            tf.reset_default_graph()
            player = None

        if not player:
            player = oneoff_utils.load_player(model_paths[idx])
        else:
            oneoff_utils.restore_params(model_paths[idx], player)

        pos_names, positions = zip(*eval_positions)
        # This should be batched at somepoint.
        eval_probs, eval_values = player.network.run_many(positions)

        for pos_name, probs, value in zip(pos_names, eval_probs, eval_values):
            save_file = os.path.join(
                FLAGS.data_dir, "heatmap-{}-{}.csv".format(pos_name, idx))

            with open(save_file, "w") as data:
                data.write("{},  {},  {}\n".format(
                    idx, value, ",".join(map(str, probs))))