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import random
import time
from collections import namedtuple
import pytest
import torch
import numpy as np
from easydict import EasyDict
from functools import partial
import gym
from ding.envs.env.base_env import BaseEnvTimestep
from ding.envs.env_manager.base_env_manager import EnvState
from ding.envs.env_manager import BaseEnvManager, SyncSubprocessEnvManager, AsyncSubprocessEnvManager
from ding.torch_utils import to_tensor, to_ndarray, to_list
from ding.utils import deep_merge_dicts
class FakeEnv(object):
def __init__(self, cfg):
self._scale = cfg.scale
self._target_time = random.randint(3, 6) * self._scale
self._current_time = 0
self._name = cfg['name']
self._id = time.time()
self._stat = None
self._seed = 0
self._data_count = 0
self.timeout_flag = False
self._launched = False
self._state = EnvState.INIT
self._dead_once = False
self.observation_space = gym.spaces.Box(
low=np.array([-1.0, -1.0, -8.0]), high=np.array([1.0, 1.0, 8.0]), shape=(3, ), dtype=np.float32
)
self.action_space = gym.spaces.Box(low=-2.0, high=2.0, shape=(1, ), dtype=np.float32)
self.reward_space = gym.spaces.Box(
low=-1 * (3.14 * 3.14 + 0.1 * 8 * 8 + 0.001 * 2 * 2), high=0.0, shape=(1, ), dtype=np.float32
)
def reset(self, stat=None):
if isinstance(stat, str) and stat == 'error':
self.dead()
if isinstance(stat, str) and stat == 'error_once':
# Die on every two reset with error_once stat.
if self._dead_once:
self._dead_once = False
self.dead()
else:
self._dead_once = True
if isinstance(stat, str) and stat == "wait":
if self.timeout_flag: # after step(), the reset can hall with status of timeout
time.sleep(5)
if isinstance(stat, str) and stat == "block":
self.block()
self._launched = True
self._current_time = 0
self._stat = stat
self._state = EnvState.RUN
return to_ndarray(torch.randn(3))
def step(self, action):
assert self._launched
assert not self._state == EnvState.ERROR
self.timeout_flag = True # after one step, enable timeout flag
if isinstance(action, str) and action == 'error':
self.dead()
if isinstance(action, str) and action == 'catched_error':
return BaseEnvTimestep(None, None, True, {'abnormal': True})
if isinstance(action, str) and action == "wait":
if self.timeout_flag: # after step(), the reset can hall with status of timeout
time.sleep(3)
if isinstance(action, str) and action == 'block':
self.block()
obs = to_ndarray(torch.randn(3))
reward = to_ndarray(torch.randint(0, 2, size=[1]).numpy())
done = self._current_time >= self._target_time
if done:
self._state = EnvState.DONE
simulation_time = random.uniform(0.5, 1) * self._scale
info = {'name': self._name, 'time': simulation_time, 'tgt': self._target_time, 'cur': self._current_time}
time.sleep(simulation_time)
self._current_time += simulation_time
self._data_count += 1
return BaseEnvTimestep(obs, reward, done, info)
def dead(self):
self._state = EnvState.ERROR
raise RuntimeError("env error, current time {}".format(self._current_time))
def block(self):
self._state = EnvState.ERROR
time.sleep(1000)
def close(self):
self._launched = False
self._state = EnvState.INIT
def seed(self, seed):
self._seed = seed
@property
def name(self):
return self._name
@property
def time_id(self):
return self._id
def user_defined(self):
pass
def __repr__(self):
return self._name
class FakeAsyncEnv(FakeEnv):
def reset(self, stat=None):
super().reset(stat)
time.sleep(random.randint(1, 3) * self._scale)
return to_ndarray(torch.randn(3))
class FakeGymEnv(FakeEnv):
def __init__(self, cfg):
super().__init__(cfg)
self.metadata = "fake metadata"
self.action_space = gym.spaces.Box(low=-2.0, high=2.0, shape=(4, ), dtype=np.float32)
def random_action(self) -> np.ndarray:
random_action = self.action_space.sample()
if isinstance(random_action, np.ndarray):
pass
elif isinstance(random_action, int):
random_action = to_ndarray([random_action], dtype=np.int64)
elif isinstance(random_action, dict):
random_action = to_ndarray(random_action)
else:
raise TypeError(
'`random_action` should be either int/np.ndarray or dict of int/np.ndarray, but get {}: {}'.format(
type(random_action), random_action
)
)
return random_action
class FakeModel(object):
def forward(self, obs):
if random.random() > 0.5:
return {k: [] for k in obs}
else:
env_num = len(obs)
exec_env = random.randint(1, env_num + 1)
keys = list(obs.keys())[:exec_env]
return {k: [] for k in keys}
@pytest.fixture(scope='class')
def setup_model_type():
return FakeModel
def get_base_manager_cfg(env_num=3):
manager_cfg = {
'env_cfg': [{
'name': 'name{}'.format(i),
'scale': 1.0,
} for i in range(env_num)],
'episode_num': 2,
'reset_timeout': 10,
'step_timeout': 8,
'max_retry': 5,
}
return EasyDict(manager_cfg)
def get_subprecess_manager_cfg(env_num=3):
manager_cfg = {
'env_cfg': [{
'name': 'name{}'.format(i),
'scale': 1.0,
} for i in range(env_num)],
'episode_num': 2,
#'step_timeout': 8,
#'reset_timeout': 10,
'connect_timeout': 8,
'step_timeout': 5,
'max_retry': 2,
}
return EasyDict(manager_cfg)
def get_gym_vector_manager_cfg(env_num=3):
manager_cfg = {
'env_cfg': [{
'name': 'name{}'.format(i),
} for i in range(env_num)],
'episode_num': 2,
'connect_timeout': 8,
'step_timeout': 5,
'max_retry': 2,
'share_memory': True
}
return EasyDict(manager_cfg)
@pytest.fixture(scope='function')
def setup_base_manager_cfg():
manager_cfg = get_base_manager_cfg(3)
env_cfg = manager_cfg.pop('env_cfg')
manager_cfg['env_fn'] = [partial(FakeEnv, cfg=c) for c in env_cfg]
return deep_merge_dicts(BaseEnvManager.default_config(), EasyDict(manager_cfg))
@pytest.fixture(scope='function')
def setup_fast_base_manager_cfg():
manager_cfg = get_base_manager_cfg(3)
env_cfg = manager_cfg.pop('env_cfg')
for e in env_cfg:
e['scale'] = 0.1
manager_cfg['env_fn'] = [partial(FakeEnv, cfg=c) for c in env_cfg]
return deep_merge_dicts(BaseEnvManager.default_config(), EasyDict(manager_cfg))
@pytest.fixture(scope='function')
def setup_sync_manager_cfg():
manager_cfg = get_subprecess_manager_cfg(3)
env_cfg = manager_cfg.pop('env_cfg')
# TODO(nyz) test fail when shared_memory = True
manager_cfg['shared_memory'] = False
manager_cfg['env_fn'] = [partial(FakeEnv, cfg=c) for c in env_cfg]
return deep_merge_dicts(SyncSubprocessEnvManager.default_config(), EasyDict(manager_cfg))
@pytest.fixture(scope='function')
def setup_async_manager_cfg():
manager_cfg = get_subprecess_manager_cfg(3)
env_cfg = manager_cfg.pop('env_cfg')
manager_cfg['env_fn'] = [partial(FakeAsyncEnv, cfg=c) for c in env_cfg]
manager_cfg['shared_memory'] = False
return deep_merge_dicts(AsyncSubprocessEnvManager.default_config(), EasyDict(manager_cfg))
@pytest.fixture(scope='function')
def setup_gym_vector_manager_cfg():
manager_cfg = get_subprecess_manager_cfg(3)
env_cfg = manager_cfg.pop('env_cfg')
manager_cfg['env_fn'] = [partial(FakeGymEnv, cfg=c) for c in env_cfg]
manager_cfg['shared_memory'] = False
return EasyDict(manager_cfg)
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