def mt50_usage():
profile = {}
for env_cls in ALL_ENVIRONMENTS.values():
target = (build_and_step, [env_cls], {})
memory_usage = memory_profiler.memory_usage(target)
profile[env_cls] = max(memory_usage)
return profile
from gym.spaces import Discrete
import pytest
import numpy as np
from tests.helpers import step_env
from metaworld.envs.mujoco.env_dict import ALL_ENVIRONMENTS, _hard_mode_args_kwargs
@pytest.fixture(scope='module', params=list(ALL_ENVIRONMENTS.keys()))
def env(request):
env_cls = ALL_ENVIRONMENTS[request.param]
env_args_kwargs = _hard_mode_args_kwargs(env_cls, request.param)
env_args = env_args_kwargs['args']
env_kwargs = env_args_kwargs['kwargs']
del env_kwargs['task_id']
env = env_cls(*env_args, **env_kwargs)
yield env
# clean-up
env.close()
def test_all_envs_step(env):
step_env(env, max_path_length=10)
def test_obs_type(env):
o = env.reset()
o_g = env._get_obs()
def test_avg_memory_usage():
# average usage no greater than 60MB/env
target = (build_and_step_all, [ALL_ENVIRONMENTS.values()], {})
usage = memory_profiler.memory_usage(target)
average = max(usage) / len(ALL_ENVIRONMENTS)
assert average < 60
envs += [env]
@pytest.fixture(scope='module')
def mt50_usage():
profile = {}
for env_cls in ALL_ENVIRONMENTS.values():
target = (build_and_step, [env_cls], {})
memory_usage = memory_profiler.memory_usage(target)
profile[env_cls] = max(memory_usage)
return profile
@pytest.mark.skip
@pytest.mark.parametrize('env_cls', ALL_ENVIRONMENTS.values())
def test_max_memory_usage(env_cls, mt50_usage):
# No env should use more than 250MB
#
# Note: this is quite a bit higher than the average usage cap, because
# loading a single environment incurs a fixed memory overhead which can't
# be shared among environment in the same process
assert mt50_usage[env_cls] < 250
@pytest.mark.skip
def test_avg_memory_usage():
# average usage no greater than 60MB/env
target = (build_and_step_all, [ALL_ENVIRONMENTS.values()], {})
usage = memory_profiler.memory_usage(target)
average = max(usage) / len(ALL_ENVIRONMENTS)
def test_static_task_ids(task_name):
env = ML45.from_task(task_name)
assert env.active_task == list(ALL_ENVIRONMENTS.keys()).index(task_name)
import pytest
import numpy as np
from metaworld.benchmarks import ML10, ML45
from metaworld.envs.mujoco.env_dict import HARD_MODE_CLS_DICT, MEDIUM_MODE_CLS_DICT, MEDIUM_MODE_ARGS_KWARGS, ALL_ENVIRONMENTS
from metaworld.envs.mujoco.multitask_env import MultiClassMultiTaskEnv
from metaworld.envs.mujoco.sawyer_xyz import SawyerReachPushPickPlaceEnv
from metaworld.envs.mujoco.sawyer_xyz import SawyerReachPushPickPlaceWallEnv
HARD_MODE_LIST = (list(HARD_MODE_CLS_DICT['train'].values()) +
list(HARD_MODE_CLS_DICT['test'].values()))
@pytest.mark.parametrize('env_cls', ALL_ENVIRONMENTS.values())
def test_single_env_multi_goals_discrete(env_cls):
env_cls_dict = {'wrapped': env_cls}
env_args_kwargs = {'wrapped': dict(args=[], kwargs={'task_id' : 1})}
multi_task_env = MultiClassMultiTaskEnv(
task_env_cls_dict=env_cls_dict,
task_args_kwargs=env_args_kwargs,
sample_goals=True,
obs_type='with_goal_id'
)
goals = multi_task_env.active_env.sample_goals_(2)
assert len(goals) == 2
goals_dict = {'wrapped': goals}
multi_task_env.discretize_goal_space(goals_dict)
assert multi_task_env._fully_discretized
tasks_with_goals = multi_task_env.sample_tasks(2)
assert multi_task_env.observation_space.shape == (9, )
elif observation_type == 'with_goal_id':
assert multi_task_env.observation_space.shape == (59, )
elif observation_type == 'with_goal_and_id':
assert multi_task_env.observation_space.shape == (62, )
elif observation_type == 'with_goal':
assert multi_task_env.observation_space.shape == (12, )
def test_action_space():
env_cls_dict = {
'pick-place-v1': MEDIUM_MODE_CLS_DICT['train']['pick-place-v1']
}
env_args_kwargs = {
key: MEDIUM_MODE_ARGS_KWARGS['train'][key]
for key in env_cls_dict.keys()
}
multi_task_env = MultiClassMultiTaskEnv(
task_env_cls_dict=env_cls_dict,
task_args_kwargs=env_args_kwargs,
sample_goals=True,
obs_type="plain",
)
assert multi_task_env.action_space.shape == (4, )
@pytest.mark.parametrize('task_name', list(ALL_ENVIRONMENTS.keys())[:10])
def test_static_task_ids(task_name):
env = ML45.from_task(task_name)
assert env.active_task == list(ALL_ENVIRONMENTS.keys()).index(task_name)