def evaluate()

in train.py [0:0]

• 50 lines of code
• 8 McCabe index (conditional complexity)

def evaluate(env, agent, video, num_episodes, L, step, device=None, embed_viz_dir=None, do_carla_metrics=None):
    # carla metrics:
    reason_each_episode_ended = []
    distance_driven_each_episode = []
    crash_intensity = 0.
    steer = 0.
    brake = 0.
    count = 0

    # embedding visualization
    obses = []
    values = []
    embeddings = []

    for i in range(num_episodes):
        # carla metrics:
        dist_driven_this_episode = 0.

        obs = env.reset()
        video.init(enabled=(i == 0))
        done = False
        episode_reward = 0
        while not done:
            with utils.eval_mode(agent):
                action = agent.select_action(obs)

            if embed_viz_dir:
                obses.append(obs)
                with torch.no_grad():
                    values.append(min(agent.critic(torch.Tensor(obs).to(device).unsqueeze(0), torch.Tensor(action).to(device).unsqueeze(0))).item())
                    embeddings.append(agent.critic.encoder(torch.Tensor(obs).unsqueeze(0).to(device)).cpu().detach().numpy())

            obs, reward, done, info = env.step(action)

            # metrics:
            if do_carla_metrics:
                dist_driven_this_episode += info['distance']
                crash_intensity += info['crash_intensity']
                steer += abs(info['steer'])
                brake += info['brake']
                count += 1

            video.record(env)
            episode_reward += reward

        # metrics:
        if do_carla_metrics:
            reason_each_episode_ended.append(info['reason_episode_ended'])
            distance_driven_each_episode.append(dist_driven_this_episode)

        video.save('%d.mp4' % step)
        L.log('eval/episode_reward', episode_reward, step)

    if embed_viz_dir:
        dataset = {'obs': obses, 'values': values, 'embeddings': embeddings}
        torch.save(dataset, os.path.join(embed_viz_dir, 'train_dataset_{}.pt'.format(step)))

    L.dump(step)

    if do_carla_metrics:
        print('METRICS--------------------------')
        print("reason_each_episode_ended: {}".format(reason_each_episode_ended))
        print("distance_driven_each_episode: {}".format(distance_driven_each_episode))
        print('crash_intensity: {}'.format(crash_intensity / num_episodes))
        print('steer: {}'.format(steer / count))
        print('brake: {}'.format(brake / count))
        print('---------------------------------')