def setup_method(self):
self.width = 16
self.height = 16
self.env = DummyDiscrete2DEnv()
self.env_r = Resize(DummyDiscrete2DEnv(),
width=self.width,
height=self.height)
def test_resize_invalid_environment_shape(self):
with self.assertRaises(ValueError):
self.env.observation_space = Box(low=0,
high=255,
shape=(4, ),
dtype=np.uint8)
Resize(self.env, width=self.width, height=self.height)
def setUp(self):
self.shape = (50, 50)
self.env = mock.Mock()
self.env.observation_space = Box(low=0,
high=255,
shape=self.shape,
dtype=np.uint8)
self.env.reset.return_value = np.zeros(self.shape)
self.env.step.side_effect = self._step
self._width = 16
self._height = 16
self.env_r = Resize(self.env, width=self._width, height=self._height)
self.obs = self.env.reset()
self.obs_r = self.env_r.reset()
class TestResize(unittest.TestCase):
@overrides
def setUp(self):
self.shape = (50, 50)
self.env = mock.Mock()
self.env.observation_space = Box(low=0,
high=255,
shape=self.shape,
dtype=np.uint8)
self.env.reset.return_value = np.zeros(self.shape)
self.env.step.side_effect = self._step
self._width = 16
self._height = 16
self.env_r = Resize(self.env, width=self._width, height=self._height)
self.obs = self.env.reset()
self.obs_r = self.env_r.reset()
def _step(self, action):
return np.full(self.shape, 125), 0, False, dict()
def test_resize_invalid_environment_type(self):
with self.assertRaises(ValueError):
self.env.observation_space = Discrete(64)
Resize(self.env, width=self._width, height=self._height)
def test_resize_invalid_environment_shape(self):
with self.assertRaises(ValueError):
self.env.observation_space = Box(low=0,
high=255,
shape=(4, ),
dtype=np.uint8)
Resize(self.env, width=self._width, height=self._height)
def test_resize_output_observation_space(self):
assert self.env_r.observation_space.shape == (self._width,
self._height)
def test_resize_output_reset(self):
assert self.obs_r.shape == (self._width, self._height)
def test_resize_output_step(self):
obs_r, _, _, _ = self.env_r.step(0)
assert obs_r.shape == (self._width, self._height)
class TestResize:
def setup_method(self):
self.width = 16
self.height = 16
self.env = DummyDiscrete2DEnv()
self.env_r = Resize(
DummyDiscrete2DEnv(), width=self.width, height=self.height)
def teardown_method(self):
self.env.close()
self.env_r.close()
def test_resize_invalid_environment_type(self):
with pytest.raises(ValueError):
self.env.observation_space = Discrete(64)
Resize(self.env, width=self.width, height=self.height)
def test_resize_invalid_environment_shape(self):
with pytest.raises(ValueError):
self.env.observation_space = Box(
low=0, high=255, shape=(4, ), dtype=np.uint8)
Resize(self.env, width=self.width, height=self.height)
def test_resize_output_observation_space(self):
assert self.env_r.observation_space.shape == (self.width, self.height)
def test_resize_output_reset(self):
assert self.env_r.reset().shape == (self.width, self.height)
def test_resize_output_step(self):
self.env_r.reset()
obs_r, _, _, _ = self.env_r.step(1)
assert obs_r.shape == (self.width, self.height)
def test_baseline(self):
"""Test the baseline initialization."""
box_env = TfEnv(DummyBoxEnv())
deterministic_mlp_baseline = DeterministicMLPBaseline(env_spec=box_env)
gaussian_mlp_baseline = GaussianMLPBaseline(env_spec=box_env)
discrete_env = TfEnv(Resize(DummyDiscrete2DEnv(), width=64, height=64))
gaussian_conv_baseline = GaussianConvBaseline(
env_spec=discrete_env,
regressor_args=dict(
conv_filters=[32, 32],
conv_filter_sizes=[1, 1],
conv_strides=[1, 1],
conv_pads=["VALID", "VALID"],
hidden_sizes=(32, 32)))
self.sess.run(tf.global_variables_initializer())
deterministic_mlp_baseline.get_param_values(trainable=True)
gaussian_mlp_baseline.get_param_values(trainable=True)
gaussian_conv_baseline.get_param_values(trainable=True)
def test_resize_invalid_environment_type(self):
with self.assertRaises(ValueError):
self.env.observation_space = Discrete(64)
Resize(self.env, width=self.width, height=self.height)
def setUp(self):
self.width = 16
self.height = 16
self.env = TfEnv(DummyDiscrete2DEnv())
self.env_r = TfEnv(
Resize(DummyDiscrete2DEnv(), width=self.width, height=self.height))
