show_z_random_switch.py

"""
Launcher for experiments with CSRO

"""
import os
import glob
from pathlib import Path
import numpy as np
import click
import json
import ast
import torch
import random
import multiprocessing as mp
from itertools import product
import sys
from tensorboardX import SummaryWriter

import numpy as np
np.int = int  # 动态修复 np.int 被废弃的问题


mujoco_version = '200'  # 默认版本
if '--mujoco_version' in sys.argv:
    idx = sys.argv.index('--mujoco_version')
    if idx + 1 < len(sys.argv):
        mujoco_version = sys.argv[idx + 1].strip()

# 设置 MuJoCo 环境变量
if mujoco_version == '131':
        os.environ['MUJOCO_PY_MJPRO_PATH'] = os.path.expanduser('~/.mujoco/mjpro131')
        os.environ['LD_LIBRARY_PATH'] = f"{os.environ.get('LD_LIBRARY_PATH', '')}:{os.path.expanduser('~/.mujoco/mjpro131/bin')}:/usr/lib/nvidia"
elif mujoco_version == '200':
    os.environ['MUJOCO_PY_MJPRO_PATH'] = '/home/autolab/.mujoco/mujoco200'
    os.environ['LD_LIBRARY_PATH'] = f"{os.environ.get('LD_LIBRARY_PATH', '')}:/home/autolab/.mujoco/mujoco200/bin:/usr/lib/nvidia"
else:
    raise ValueError(f"Unsupported MuJoCo version: {mujoco_version}. Supported versions: '131', '200'")

print(f"MuJoCo version {mujoco_version} set successfully!")


from rlkit.envs import ENVS
from rlkit.envs.wrappers import NormalizedBoxEnv
from rlkit.torch.sac.policies import TanhGaussianPolicy
from rlkit.torch.multi_task_dynamics import MultiTaskDynamics
from rlkit.torch.networks import FlattenMlp, MlpEncoder, RecurrentEncoder, MlpDecoder
from rlkit.torch.sac.sac import CERTAINSoftActorCritic
from rlkit.torch.sac.agent import PEARLAgent
from rlkit.launchers.launcher_util import setup_logger
import rlkit.torch.pytorch_util as ptu
from configs.default import default_config

def global_seed(seed=0):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)

def experiment(gpu_id, variant, seed=None):
    os.sched_setaffinity(0, [gpu_id*8+i for i in range(8)])
    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)

    # create multi-task environment and sample tasks, normalize obs if provided with 'normalizer.npz'
    if 'normalizer.npz' in os.listdir(variant['algo_params']['data_dir']):
        obs_absmax = np.load(os.path.join(variant['algo_params']['data_dir'], 'normalizer.npz'))['abs_max']
        env = NormalizedBoxEnv(ENVS[variant['env_name']](**variant['env_params']), obs_absmax=obs_absmax)
    else:
        env = NormalizedBoxEnv(ENVS[variant['env_name']](**variant['env_params']))
    
    if seed is not None:
        global_seed(seed)
        env.seed(seed)

    tasks = env.get_all_task_idx()
    obs_dim = int(np.prod(env.observation_space.shape))
    action_dim = int(np.prod(env.action_space.shape))
    reward_dim = 1

    # instantiate networks
    latent_dim = variant['latent_size']
    context_encoder_input_dim = 2 * obs_dim + action_dim + reward_dim if variant['algo_params']['use_next_obs_in_context'] else obs_dim + action_dim + reward_dim
    context_encoder_output_dim = latent_dim * 2 if variant['algo_params']['use_information_bottleneck'] else latent_dim
    net_size = variant['net_size']
    recurrent = variant['algo_params']['recurrent']
    encoder_model = RecurrentEncoder if recurrent else MlpEncoder

    if variant['algo_params']['club_use_sa']:
        club_input_dim = obs_dim + action_dim
    else:
        club_input_dim = obs_dim + action_dim + reward_dim if variant['algo_params']['use_next_obs_in_context'] else obs_dim + action_dim

    club_model = encoder_model(
        hidden_sizes=[200, 200, 200],
        input_size=club_input_dim,
        output_size=latent_dim * 2,
        output_activation=torch.tanh,
    )
    
    context_encoder = encoder_model(
        hidden_sizes=[200, 200, 200],
        input_size=context_encoder_input_dim,
        output_size=context_encoder_output_dim,
        output_activation=torch.tanh,
        layer_norm=variant['algo_params']['layer_norm'] if 'layer_norm' in variant['algo_params'].keys() else False
    )

    context_decoder = MlpDecoder(
        hidden_sizes=[200, 200, 200],
        input_size=latent_dim+obs_dim+action_dim,
        output_size=2*(reward_dim+obs_dim) if variant['algo_params']['use_next_obs_in_context'] else 2*reward_dim,
        layer_norm=variant['algo_params']['layer_norm'] if 'layer_norm' in variant['algo_params'].keys() else False
    )
    
    classifier = MlpDecoder(
        hidden_sizes=[net_size],
        input_size=context_encoder_output_dim,
        output_size=variant['n_train_tasks'],
        layer_norm=variant['algo_params']['layer_norm'] if 'layer_norm' in variant['algo_params'].keys() else False
    )

    uncertainty_mlp = MlpDecoder(
        hidden_sizes=[net_size],
        input_size=latent_dim,
        output_size=1,
    )

    reward_models = torch.nn.ModuleList()
    dynamic_models = torch.nn.ModuleList()
    for _ in range(variant['algo_params']['num_ensemble']):
        reward_models.append(
            FlattenMlp(hidden_sizes=[net_size, net_size, net_size],
                       input_size=latent_dim + obs_dim + action_dim,
                       output_size=1, )
        )
        dynamic_models.append(
            FlattenMlp(hidden_sizes=[net_size, net_size, net_size],
                       input_size=latent_dim + obs_dim + action_dim,
                       output_size=obs_dim, )
        )

    qf1 = FlattenMlp(
        hidden_sizes=[net_size, net_size, net_size],
        input_size=obs_dim + action_dim + latent_dim,
        output_size=1,
    )
    qf2 = FlattenMlp(
        hidden_sizes=[net_size, net_size, net_size],
        input_size=obs_dim + action_dim + latent_dim,
        output_size=1,
    )
    vf = FlattenMlp(
        hidden_sizes=[net_size, net_size, net_size],
        input_size=obs_dim + latent_dim,
        output_size=1,
    )

    c = FlattenMlp(
        hidden_sizes=[net_size, net_size, net_size],
        input_size=obs_dim + action_dim + latent_dim,
        output_size=1
    )

    policy = TanhGaussianPolicy(
        hidden_sizes=[net_size, net_size, net_size],
        obs_dim=obs_dim + latent_dim,
        latent_dim=latent_dim,
        action_dim=action_dim,
    )

    agent = PEARLAgent(
        latent_dim,
        context_encoder,
        uncertainty_mlp,
        policy,
        **variant['algo_params']
    )

    # Setting up tasks
    if 'randomize_tasks' in variant.keys() and variant['randomize_tasks']:
        train_tasks = np.random.choice(len(tasks), size=variant['n_train_tasks'], replace=False)
    elif 'interpolation' in variant.keys() and variant['interpolation']:
        step = len(tasks)/variant['n_train_tasks']
        train_tasks = np.array([tasks[int(i*step)] for i in range(variant['n_train_tasks'])])
    eval_tasks = np.array(list(set(range(len(tasks))).difference(train_tasks)))
    goal_radius = variant['env_params']['goal_radius'] if 'goal_radius' in variant['env_params'] else 1
    
    # Choose algorithm
    algo_type = variant['algo_type']
    algorithm = CERTAINSoftActorCritic(
        env=env,
        train_tasks=train_tasks,
        eval_tasks=eval_tasks,
        nets=[agent, qf1, qf2, vf, c, club_model, context_decoder, classifier, reward_models, dynamic_models],
        latent_dim=latent_dim,
        goal_radius=goal_radius,
        seed=seed,
        algo_type=algo_type,
        env_name = variant['env_name'],
        **variant['algo_params'],
    )

    ptu.set_gpu_mode(variant['util_params']['use_gpu'], variant['util_params']['gpu_id'])
    DEBUG = False
    os.environ['DEBUG'] = str(int(DEBUG))
    
    exp_id = 'debug' if DEBUG else variant['util_params']['exp_name']
    experiment_log_dir = setup_logger(
        variant['env_name'],
        variant=variant,
        exp_id=exp_id,
        base_log_dir=variant['util_params']['base_log_dir'],
        seed=seed,
        snapshot_mode="gap_and_last",
        snapshot_gap=5
    )
    
    tensorboard_log_dir = experiment_log_dir + '/tensorboard_z_random_switch'
    Path(tensorboard_log_dir).mkdir(parents=True, exist_ok=True)
    tb_writer = SummaryWriter(log_dir=tensorboard_log_dir)

    # directory
    exp_name = variant['util_params']['exp_name']
    base_log_dir = variant['util_params']['base_log_dir']
    exp_prefix = variant['env_name']
    log_dir = Path(os.path.join(base_log_dir, exp_prefix.replace("_", "-"), exp_name, f"seed{seed}"))
    file_list = glob.glob(os.path.join(log_dir, 'agent_*.pth'))
    file_list.sort(key=lambda x: int(x.split('_')[-1].split('.')[0]))
    print(file_list)
    for i, file in enumerate(file_list):
        agent_ckpt = torch.load(str(file))
        if variant['algo_params']['policy_update_strategy'] == 'BRAC':
            algorithm.agent.policy.load_state_dict(agent_ckpt['policy'])
            algorithm.agent.uncertainty_mlp.load_state_dict(agent_ckpt['uncertainty_mlp'])
            algorithm.agent.context_encoder.load_state_dict(agent_ckpt['context_encoder'])
        if ptu.gpu_enabled():
            algorithm.to()
        
        algorithm.show_z_random_switch(tb_writer, 5 * i, i==(len(file_list)-1))
        print(f"{i==(len(file_list)-1)}")

    # # optionally save eval trajectories as pkl files
    # if variant['algo_params']['dump_eval_paths']:
    #     pickle_dir = experiment_log_dir + '/eval_trajectories'
    #     Path(pickle_dir).mkdir(parents=True, exist_ok=True)

def deep_update_dict(fr, to):
    ''' update dict of dicts with new values '''
    # assume dicts have same keys
    for k, v in fr.items():
        if type(v) is dict:
            deep_update_dict(v, to[k])
        else:
            to[k] = v
    return to

@click.command()
@click.argument('config', default=None)
@click.option('--mujoco_version', type=click.Choice(['131', '200'], case_sensitive=False), default='200', help='MuJoCo version, default is --mujoco_version=200')
@click.option('--gpu', default="0,1,2,3", type=str, help="Comma-separated list of gpu.")
@click.option('--seed', default="0", type=str, help="Comma-separated list of seeds.")
@click.option('--exp_name', default=None)
@click.option('--algo_type', type=click.Choice(['FOCAL', 'CSRO', 'CORRO', 'UNICORN', 'CLASSIFIER', 'IDAQ'], case_sensitive=False), default=None)
@click.option('--train_z0_policy', type=click.Choice(['true', 'false'], case_sensitive=False), default=None)
@click.option('--use_hvar', type=click.Choice(['true', 'false'], case_sensitive=False), default=None)
@click.option('--z_strategy', type=click.Choice(['mean', 'min', 'weighted', 'quantile'], case_sensitive=False), default=None)
@click.option('--r_thres', default=None)
# python show_z_random_switch.py configs/point-robot.json --exp_name CLASSIFIER034/classifier_mix_z0_hvar_p10_weighted --gpu 5,6,7 --seed 0,3,4 --algo_type CLASSIFIER --z_strategy weighted --train_z0_policy true --use_hvar true
# python show_z_random_switch.py configs/point-robot.json --exp_name FOCAL0135/focal_mix_z0_hvar_p10_weighted/ --gpu 5,6,7 --seed 0,1,3,5 --algo_type FOCAL --z_strategy weighted --train_z0_policy true --use_hvar true
# python show_z_random_switch.py configs/point-robot.json --exp_name UNICORN1237/unicorn_mix_z0_hvar_weighted/ --gpu 5,6,7 --seed 1,2,3,7 --algo_type UNICORN --z_strategy weighted --train_z0_policy true --use_hvar true
def main(config, mujoco_version, gpu, seed, exp_name=None, algo_type=None, train_z0_policy = None, use_hvar = None, z_strategy = None, r_thres=None):
    variant = default_config
    if config:
        with open(os.path.join(config)) as f:
            exp_params = json.load(f)
        variant = deep_update_dict(exp_params, variant)

    gpu = [int(g) for g in gpu.split(",")]
    print(f"Parsed gpus: {gpu}")
    variant['util_params']['gpu_id'] = gpu
    
    if not (exp_name == None):
        variant['util_params']['exp_name'] = exp_name
    variant['algo_params']['pretrain'] = True
    if not (algo_type == None):
        variant['algo_type'] = algo_type.upper()
    if not (train_z0_policy == None):
        variant['algo_params']['train_z0_policy'] = train_z0_policy.lower() == 'true'
    if not (use_hvar == None):
        variant['algo_params']['use_hvar'] = use_hvar.lower() == 'true'
    if not (z_strategy == None):
        variant['algo_params']['z_strategy'] = z_strategy
    if not (r_thres == None):
        variant['algo_params']['r_thres'] = float(r_thres)

    seed = [int(s) for s in seed.split(",")]
    if len(seed) > 1:
        p = mp.Pool(2*len(gpu))
        args = []
        for i, s in enumerate(seed):
            gpu_id = gpu[i % len(gpu)]
            args.append((gpu_id, variant, s))
        p.starmap(experiment, args)
    else:
        experiment(gpu_id=gpu[0], variant=variant, seed=seed[0])

if __name__ == "__main__":
    main()