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| from typing import Union, Optional, Tuple | |
| import os | |
| from functools import partial | |
| from copy import deepcopy | |
| import easydict | |
| import torch | |
| import numpy as np | |
| from tensorboardX import SummaryWriter | |
| from torch.utils.data import DataLoader, Dataset | |
| 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 dizoo.maze.envs.maze_env import Maze | |
| # BFS algorithm | |
| def get_vi_sequence(env, observation): | |
| """Returns [L, W, W] optimal actions.""" | |
| xy = np.where(observation[Ellipsis, -1] == 1) | |
| start_x, start_y = xy[0][0], xy[1][0] | |
| target_location = env.target_location | |
| nav_map = env.nav_map | |
| current_points = [target_location] | |
| chosen_actions = {target_location: 0} | |
| visited_points = {target_location: True} | |
| vi_sequence = [] | |
| vi_map = np.full((env.size, env.size), fill_value=env.n_action, dtype=np.int32) | |
| found_start = False | |
| while current_points and not found_start: | |
| next_points = [] | |
| for point_x, point_y in current_points: | |
| for (action, (next_point_x, next_point_y)) in [(0, (point_x - 1, point_y)), (1, (point_x, point_y - 1)), | |
| (2, (point_x + 1, point_y)), (3, (point_x, point_y + 1))]: | |
| if (next_point_x, next_point_y) in visited_points: | |
| continue | |
| if not (0 <= next_point_x < len(nav_map) and 0 <= next_point_y < len(nav_map[next_point_x])): | |
| continue | |
| if nav_map[next_point_x][next_point_y] == 'x': | |
| continue | |
| next_points.append((next_point_x, next_point_y)) | |
| visited_points[(next_point_x, next_point_y)] = True | |
| chosen_actions[(next_point_x, next_point_y)] = action | |
| vi_map[next_point_x, next_point_y] = action | |
| if next_point_x == start_x and next_point_y == start_y: | |
| found_start = True | |
| vi_sequence.append(vi_map.copy()) | |
| current_points = next_points | |
| track_back = [] | |
| if found_start: | |
| cur_x, cur_y = start_x, start_y | |
| while cur_x != target_location[0] or cur_y != target_location[1]: | |
| act = vi_sequence[-1][cur_x, cur_y] | |
| track_back.append((torch.FloatTensor(env.process_states([cur_x, cur_y], env.get_maze_map())), act)) | |
| if act == 0: | |
| cur_x += 1 | |
| elif act == 1: | |
| cur_y += 1 | |
| elif act == 2: | |
| cur_x -= 1 | |
| elif act == 3: | |
| cur_y -= 1 | |
| return np.array(vi_sequence), track_back | |
| class BCDataset(Dataset): | |
| def __init__(self, all_data): | |
| self._data = all_data | |
| def __getitem__(self, item): | |
| return {'obs': self._data[item][0], 'action': self._data[item][1]} | |
| def __len__(self): | |
| return len(self._data) | |
| def load_bc_dataset(train_seeds=1, test_seeds=1, batch_size=32): | |
| def load_env(seed): | |
| ccc = easydict.EasyDict({'size': 16}) | |
| e = Maze(ccc) | |
| e.seed(seed) | |
| e.reset() | |
| return e | |
| envs = [load_env(i) for i in range(train_seeds + test_seeds)] | |
| data_train = [] | |
| data_test = [] | |
| for idx, env in enumerate(envs): | |
| if idx < train_seeds: | |
| data = data_train | |
| else: | |
| data = data_test | |
| start_obs = env.process_states(env._get_obs(), env.get_maze_map()) | |
| _, track_back = get_vi_sequence(env, start_obs) | |
| data += track_back | |
| train_data = BCDataset(data_train) | |
| test_data = BCDataset(data_test) | |
| train_dataset = DataLoader(train_data, batch_size=batch_size, shuffle=True) | |
| test_dataset = DataLoader(test_data, batch_size=batch_size, shuffle=True) | |
| return train_dataset, test_dataset | |
| def serial_pipeline_bc( | |
| 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 imitation learning. | |
| 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. | |
| - data_path (:obj:`str`): Path of training data. | |
| - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module. | |
| Returns: | |
| - policy (:obj:`Policy`): Converged policy. | |
| - convergence (:obj:`bool`): whether il 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')) | |
| dataloader, test_dataloader = load_bc_dataset() | |
| 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): | |
| # Evaluate policy performance | |
| loss_list = [] | |
| for _, bat in enumerate(test_dataloader): | |
| bat['action'] = bat['action'].long() | |
| res = policy._forward_eval(bat['obs']) | |
| res = torch.argmax(res['logit'], dim=1) | |
| loss_list.append(torch.sum(res == bat['action'].squeeze(-1)).item() / bat['action'].shape[0]) | |
| label = 'validation_acc' | |
| tb_logger.add_scalar(label, sum(loss_list) / len(loss_list), iter_cnt) | |
| for i, train_data in enumerate(dataloader): | |
| if evaluator.should_eval(learner.train_iter): | |
| stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter) | |
| if stop: | |
| break | |
| train_data['action'] = train_data['action'].long() | |
| learner.train(train_data) | |
| iter_cnt += 1 | |
| if iter_cnt >= max_iter: | |
| stop = True | |
| break | |
| if stop: | |
| break | |
| learner.call_hook('after_run') | |
| print('final reward is: {}'.format(reward)) | |
| return policy, stop | |
| if __name__ == '__main__': | |
| from dizoo.maze.config.maze_bc_config import main_config, create_config | |
| serial_pipeline_bc([main_config, create_config], seed=0) | |