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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
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