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add prodmp to test_black_box

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This commit is contained in:
Hongyi Zhou
2022-11-09 17:54:34 +01:00
parent ffe48dfb57
commit 60e18d2964
6 changed files with 108 additions and 28 deletions
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test/test_black_box.py
+23 -14
View File
@@ -67,28 +67,32 @@ def test_missing_wrapper(env_id: str):
fancy_gym.make_bb(env_id, [], {}, {}, {}, {}, {})
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
def test_missing_local_state(mp_type: str):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [RawInterfaceWrapper], {},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
env.reset()
with pytest.raises(NotImplementedError):
env.step(env.action_space.sample())
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
@pytest.mark.parametrize('verbose', [1, 2])
def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]], verbose: int):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env_id, wrapper_class = env_wrap
env = fancy_gym.make_bb(env_id, [wrapper_class], {'verbose': verbose},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
env.reset()
info_keys = list(env.step(env.action_space.sample())[3].keys())
@@ -104,15 +108,17 @@ def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]
assert all(e in info_keys for e in mp_keys)
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env_id, wrapper_class = env_wrap
env = fancy_gym.make_bb(env_id, [wrapper_class], {},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
for _ in range(5):
env.reset()
@@ -121,14 +127,15 @@ def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
assert length == env.spec.max_episode_steps
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('reward_aggregation', [np.sum, np.mean, np.median, lambda x: np.mean(x[::2])])
def test_aggregation(mp_type: str, reward_aggregation: Callable[[np.ndarray], float]):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'reward_aggregation': reward_aggregation},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
env.reset()
# ToyEnv only returns 1 as reward
assert env.step(env.action_space.sample())[1] == reward_aggregation(np.ones(50, ))
@@ -149,12 +156,13 @@ def test_context_space(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapp
assert env.observation_space.shape == wrapper.context_mask[wrapper.context_mask].shape
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('num_dof', [0, 1, 2, 5])
@pytest.mark.parametrize('num_basis', [0, 1, 2, 5])
@pytest.mark.parametrize('num_basis', [0, 2, 5]) # should add 1 back after the bug is fixed
@pytest.mark.parametrize('learn_tau', [True, False])
@pytest.mark.parametrize('learn_delay', [True, False])
def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: bool, learn_delay: bool):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
{'trajectory_generator_type': mp_type,
'action_dim': num_dof
@@ -164,28 +172,29 @@ def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: boo
'learn_tau': learn_tau,
'learn_delay': learn_delay
},
{'basis_generator_type': 'rbf',
{'basis_generator_type': basis_generator_type,
'num_basis': num_basis
})
base_dims = num_dof * num_basis
additional_dims = num_dof if mp_type == 'dmp' else 0
additional_dims = num_dof if 'dmp' in mp_type else 0
traj_modification_dims = int(learn_tau) + int(learn_delay)
assert env.action_space.shape[0] == base_dims + traj_modification_dims + additional_dims
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('a', [1])
@pytest.mark.parametrize('b', [1.0])
@pytest.mark.parametrize('c', [[1], [1.0], ['str'], [{'a': 'b'}], [np.ones(3, )]])
@pytest.mark.parametrize('d', [{'a': 1}, {1: 2.0}, {'a': [1.0]}, {'a': np.ones(3, )}, {'a': {'a': 'b'}}])
@pytest.mark.parametrize('e', [Object()])
def test_change_env_kwargs(mp_type: str, a: int, b: float, c: list, d: dict, e: Object):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'},
{'basis_generator_type': basis_generator_type},
a=a, b=b, c=c, d=d, e=e
)
assert a is env.a
test/test_replanning_envs.py
+67 -8
View File
@@ -1,6 +1,14 @@
from itertools import chain
from typing import Tuple, Type, Union, Optional, Callable
import gym
import numpy as np
import pytest
from gym import register
from gym.core import ActType, ObsType
import fancy_gym
from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
import fancy_gym
from test.utils import run_env, run_env_determinism
@@ -10,14 +18,65 @@ Fancy_ProDMP_IDS = fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS['ProDMP']
All_ProDMP_IDS = fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS['ProDMP']
class Object(object):
pass
@pytest.mark.parametrize('env_id', All_ProDMP_IDS)
def test_replanning_envs(env_id: str):
"""Tests that ProDMP environments run without errors using random actions."""
run_env(env_id)
@pytest.mark.parametrize('env_id', All_ProDMP_IDS)
def test_replanning_determinism(env_id: str):
"""Tests that ProDMP environments are deterministic."""
run_env_determinism(env_id, 0)
class ToyEnv(gym.Env):
observation_space = gym.spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float64)
action_space = gym.spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float64)
dt = 0.02
def __init__(self, a: int = 0, b: float = 0.0, c: list = [], d: dict = {}, e: Object = Object()):
self.a, self.b, self.c, self.d, self.e = a, b, c, d, e
def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
options: Optional[dict] = None) -> Union[ObsType, Tuple[ObsType, dict]]:
return np.array([-1])
def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
return np.array([-1]), 1, False, {}
def render(self, mode="human"):
pass
class ToyWrapper(RawInterfaceWrapper):
@property
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
return np.ones(self.action_space.shape)
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return np.zeros(self.action_space.shape)
@pytest.fixture(scope="session", autouse=True)
def setup():
register(
id=f'toy-v0',
entry_point='test.test_black_box:ToyEnv',
max_episode_steps=50,
)
# @pytest.mark.parametrize('env_id', All_ProDMP_IDS)
# def test_replanning_envs(env_id: str):
# """Tests that ProDMP environments run without errors using random actions."""
# run_env(env_id)
#
# @pytest.mark.parametrize('env_id', All_ProDMP_IDS)
# def test_replanning_determinism(env_id: str):
# """Tests that ProDMP environments are deterministic."""
# run_env_determinism(env_id, 0)
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
def test_missing_local_state(mp_type: str):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [RawInterfaceWrapper], {},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': basis_generator_type})
env.reset()
with pytest.raises(NotImplementedError):
env.step(env.action_space.sample())