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| from typing import Union, Optional, Tuple | |
| import os | |
| from functools import partial | |
| from copy import deepcopy | |
| import torch | |
| from tensorboardX import SummaryWriter | |
| from torch.utils.data import DataLoader | |
| from ding.envs import get_vec_env_setting, create_env_manager | |
| from ding.worker import BaseLearner, InteractionSerialEvaluator | |
| from ding.config import read_config, compile_config | |
| from ding.policy import create_policy | |
| from ding.utils import set_pkg_seed | |
| from ding.utils.data.dataset import load_bfs_datasets | |
| def serial_pipeline_pc( | |
| input_cfg: Union[str, Tuple[dict, dict]], | |
| seed: int = 0, | |
| model: Optional[torch.nn.Module] = None, | |
| max_iter=int(1e6), | |
| ) -> Union['Policy', bool]: # noqa | |
| r""" | |
| Overview: | |
| Serial pipeline entry of procedure cloning using BFS as expert policy. | |
| Arguments: | |
| - input_cfg (:obj:`Union[str, Tuple[dict, dict]]`): Config in dict type. \ | |
| ``str`` type means config file path. \ | |
| ``Tuple[dict, dict]`` type means [user_config, create_cfg]. | |
| - seed (:obj:`int`): Random seed. | |
| - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module. | |
| - max_iter (:obj:`Optional[int]`): Max iteration for executing PC training. | |
| Returns: | |
| - policy (:obj:`Policy`): Converged policy. | |
| - convergence (:obj:`bool`): whether the training is converged | |
| """ | |
| if isinstance(input_cfg, str): | |
| cfg, create_cfg = read_config(input_cfg) | |
| else: | |
| cfg, create_cfg = deepcopy(input_cfg) | |
| cfg = compile_config(cfg, seed=seed, auto=True, create_cfg=create_cfg) | |
| # Env, Policy | |
| env_fn, _, evaluator_env_cfg = get_vec_env_setting(cfg.env) | |
| evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg]) | |
| # Random seed | |
| evaluator_env.seed(cfg.seed, dynamic_seed=False) | |
| set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) | |
| policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'eval']) | |
| # Main components | |
| tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) | |
| train_data, test_data = load_bfs_datasets(train_seeds=cfg.train_seeds) | |
| dataloader = DataLoader(train_data, batch_size=cfg.policy.learn.batch_size, shuffle=True) | |
| test_dataloader = DataLoader(test_data, batch_size=cfg.policy.learn.batch_size, shuffle=True) | |
| learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) | |
| evaluator = InteractionSerialEvaluator( | |
| cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name | |
| ) | |
| # ========== | |
| # Main loop | |
| # ========== | |
| learner.call_hook('before_run') | |
| stop = False | |
| iter_cnt = 0 | |
| for epoch in range(cfg.policy.learn.train_epoch): | |
| # train | |
| criterion = torch.nn.CrossEntropyLoss() | |
| for i, train_data in enumerate(dataloader): | |
| learner.train(train_data) | |
| iter_cnt += 1 | |
| if iter_cnt >= max_iter: | |
| stop = True | |
| break | |
| if epoch % 69 == 0: | |
| policy._optimizer.param_groups[0]['lr'] /= 10 | |
| if stop: | |
| break | |
| losses = [] | |
| acces = [] | |
| # Evaluation | |
| for _, test_data in enumerate(test_dataloader): | |
| observations, bfs_input_maps, bfs_output_maps = test_data['obs'], test_data['bfs_in'].long(), \ | |
| test_data['bfs_out'].long() | |
| states = observations | |
| bfs_input_onehot = torch.nn.functional.one_hot(bfs_input_maps, 5).float() | |
| bfs_states = torch.cat([ | |
| states, | |
| bfs_input_onehot, | |
| ], dim=-1).cuda() | |
| logits = policy._model(bfs_states)['logit'] | |
| logits = logits.flatten(0, -2) | |
| labels = bfs_output_maps.flatten(0, -1).cuda() | |
| loss = criterion(logits, labels).item() | |
| preds = torch.argmax(logits, dim=-1) | |
| acc = torch.sum((preds == labels)) / preds.shape[0] | |
| losses.append(loss) | |
| acces.append(acc) | |
| print('Test Finished! Loss: {} acc: {}'.format(sum(losses) / len(losses), sum(acces) / len(acces))) | |
| stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter) | |
| learner.call_hook('after_run') | |
| print('final reward is: {}'.format(reward)) | |
| return policy, stop | |