def test_obs_is_image(self):
env = TfEnv(DummyDiscretePixelEnv(), is_image=True)
with mock.patch(('garage.tf.policies.'
'categorical_cnn_policy.CNNModel._build'),
autospec=True,
side_effect=CNNModel._build) as build:
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=(32, ),
conv_filter_sizes=(1, ),
conv_strides=(1, ),
conv_pad='VALID',
hidden_sizes=(3, ))
normalized_obs = build.call_args_list[0][0][1]
input_ph = tf.compat.v1.get_default_graph().get_tensor_by_name(
'Placeholder:0')
fake_obs = [np.full(env.spec.observation_space.shape, 255)]
assert (self.sess.run(normalized_obs,
feed_dict={input_ph: fake_obs}) == 1.).all()
obs_dim = env.spec.observation_space.shape
state_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + obs_dim)
policy.dist_info_sym(state_input, name='another')
normalized_obs = build.call_args_list[1][0][1]
input_ph = tf.compat.v1.get_default_graph().get_tensor_by_name(
'Placeholder_1:0')
fake_obs = [np.full(env.spec.observation_space.shape, 255)]
assert (self.sess.run(normalized_obs,
feed_dict={state_input:
fake_obs}) == 1.).all()
def test_dist_info_sym(self, obs_dim, action_dim, filter_dims,
filter_sizes, strides, padding, hidden_sizes):
env = TfEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'categorical_cnn_policy.MLPModel'),
new=SimpleMLPModel):
with mock.patch(('garage.tf.policies.'
'categorical_cnn_policy.CNNModel'),
new=SimpleCNNModel):
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=filter_dims,
conv_filter_sizes=filter_sizes,
conv_strides=strides,
conv_pad=padding,
hidden_sizes=hidden_sizes)
env.reset()
obs, _, _, _ = env.step(1)
expected_prob = np.full(action_dim, 0.5)
obs_dim = env.spec.observation_space.shape
state_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + obs_dim)
dist1 = policy.dist_info_sym(state_input, name='policy2')
prob = self.sess.run(dist1['prob'], feed_dict={state_input: [obs]})
assert np.array_equal(prob[0], expected_prob)
