MTRF/r3l/r3l/r3l_agents/softlearning/evaluation_scripts/phased_evals_reorient.py [114:155]:
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        save_video(video_name, np.asarray(frames), fps=40)

        ckpt_numbers.append(ckpt_number)
        success_rate = np.array(successes).astype(int).mean()
        print("success % = ", success_rate)
        success_rates.append(success_rate)
        obs_dicts_per_policy.append(obs_dicts)
        rew_dicts_per_policy.append(rew_dicts)
        returns_per_policy.append(np.mean(returns))
        break

    return {
        "iters": ckpt_numbers,
        "success": success_rates,
        "obs": obs_dicts_per_policy,
        "rew": rew_dicts_per_policy,
        "returns": returns_per_policy,
    }

if __name__ == "__main__":
    # import argparse

    # parser = argparse.ArgumentParser()
    # parser.add_argument("-s", "--save_filename", help="save filename", type=str)
    # parser.add_argument("-p", "--exp_path", help="top level experiment path", type=str)

    exp_path = Path("/home/justinvyu/ray_results/gym/SawyerDhandInHandValve3/RepositionReorientPickupPerturbResetFree-v0/2020-10-28T19-43-42-4phase_fixedsawyerxzrange_newobskeys_repos_to_middle")
    # save_filename = "reorient_phased_eval_data.pkl"

    # seed_dirs = [d for d in glob.glob(str(exp_path / "*")) if os.path.isdir(d)]
    # # do_evals(seed_dirs[0])
    # from multiprocessing import Pool
    # with Pool(processes=len(seed_dirs)) as pool:
    #     eval_results = pool.map(do_evals, seed_dirs)

    # with(open(save_filename, "wb")) as f:
    #     pickle.dump(eval_results, f)

    seed_dirs = [d for d in glob.glob(str(exp_path / "*")) if os.path.isdir(d)]
    seed_dirs = [d for d in seed_dirs if "seed=470" in d]
    print(seed_dirs)
    do_evals(seed_dirs[0])
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MTRF/r3l/r3l/r3l_agents/softlearning/evaluation_scripts/phased_evals_reposition.py [115:156]:
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        save_video(video_name, np.asarray(frames), fps=40)

        ckpt_numbers.append(ckpt_number)
        success_rate = np.array(successes).astype(int).mean()
        print("success % = ", success_rate)
        success_rates.append(success_rate)
        obs_dicts_per_policy.append(obs_dicts)
        rew_dicts_per_policy.append(rew_dicts)
        returns_per_policy.append(np.mean(returns))
        break

    return {
        "iters": ckpt_numbers,
        "success": success_rates,
        "obs": obs_dicts_per_policy,
        "rew": rew_dicts_per_policy,
        "returns": returns_per_policy,
    }

if __name__ == "__main__":
    # import argparse

    # parser = argparse.ArgumentParser()
    # parser.add_argument("-s", "--save_filename", help="save filename", type=str)
    # parser.add_argument("-p", "--exp_path", help="top level experiment path", type=str)

    exp_path = Path("/home/justinvyu/ray_results/gym/SawyerDhandInHandValve3/RepositionReorientPickupPerturbResetFree-v0/2020-10-28T19-43-42-4phase_fixedsawyerxzrange_newobskeys_repos_to_middle")

    # save_filename = "repos_phased_eval_data.pkl"

    # seed_dirs = [d for d in glob.glob(str(exp_path / "*")) if os.path.isdir(d)]
    # from multiprocessing import Pool
    # with Pool(processes=len(seed_dirs)) as pool:
    #     eval_results = pool.map(do_evals, seed_dirs)

    # with(open(save_filename, "wb")) as f:
    #     pickle.dump(eval_results, f)

    seed_dirs = [d for d in glob.glob(str(exp_path / "*")) if os.path.isdir(d)]
    seed_dirs = [d for d in seed_dirs if "seed=470" in d]
    print(seed_dirs)
    do_evals(seed_dirs[0])
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