def infer(
self,
images: List[OrthographicImage],
method: SelectionMethod,
verbose=1,
uncertainty_images: List[OrthographicImage] = None,
) -> Action:
start = time.time()
if method == SelectionMethod.Random:
action = Action()
action.index = np.random.choice(range(3))
action.pose = RobotPose(affine=Affine(
x=np.random.uniform(self.lower_random_pose[0],
self.upper_random_pose[0]), # [m]
y=np.random.uniform(self.lower_random_pose[1],
self.upper_random_pose[1]), # [m]
a=np.random.uniform(self.lower_random_pose[3],
self.upper_random_pose[3]), # [rad]
))
action.estimated_reward = -1
action.estimated_reward_std = 0.0
action.method = method
action.step = 0
return action
input_images = [self.get_images(i) for i in images]
result = self.model.predict(input_images)
if self.with_types:
estimated_reward = result[0]
types = result[1]
else:
estimated_reward = result
estimated_reward_std = np.zeros(estimated_reward.shape)
filter_method = method
filter_measure = estimated_reward
# Calculate model uncertainty
if self.monte_carlo:
rewards_sampling = [
self.model.predict(input_images)
for i in range(self.monte_carlo)
]
estimated_reward = np.mean(rewards_sampling, axis=0)
estimated_reward_std += self.mutual_information(rewards_sampling)
if verbose:
logger.info(f'Current monte carlo s: {self.current_s}')
# Calculate input uncertainty
if self.input_uncertainty:
input_uncertainty_images = [
self.get_images(i) for i in uncertainty_images
]
array_estimated_unc = tkb.get_session().run(
self.propagation_input_uncertainty,
feed_dict={
self.model.input: input_images[0],
self.uncertainty_placeholder: input_uncertainty_images[0]
})
estimated_input_uncertainty = np.concatenate(array_estimated_unc,
axis=3)
estimated_reward_std += 0.0025 * estimated_input_uncertainty
# Adapt filter measure for more fancy SelectionMethods
if method == SelectionMethod.Top5LowerBound:
filter_measure = estimated_reward - estimated_reward_std
filter_method = SelectionMethod.Top5
elif method == SelectionMethod.BayesTop:
filter_measure = self.probability_in_policy(
estimated_reward, s=self.current_s) * estimated_reward_std
filter_method = SelectionMethod.Top5
elif method == SelectionMethod.BayesProb:
filter_measure = self.probability_in_policy(
estimated_reward, s=self.current_s) * estimated_reward_std
filter_method = SelectionMethod.Prob
filter_lambda = self.get_filter(filter_method)
# Set some action (indices) to zero
if self.keep_indixes:
self.keep_array_at_last_indixes(filter_measure, self.keep_indixes)
# Grasp specific types
if self.with_types and self.current_type > -1:
alpha = 0.7
factor_current_type = np.tile(
np.expand_dims(types[:, :, :, self.current_type], axis=-1),
reps=(1, 1, 1, estimated_reward.shape[-1]))
factor_alt_type = np.tile(np.expand_dims(types[:, :, :, 1],
axis=-1),
reps=(1, 1, 1,
estimated_reward.shape[-1]))
filter_measure = estimated_reward * (alpha * factor_current_type +
(1 - alpha) * factor_alt_type)
# Find the index and corresponding action using the selection method
index_raveled = filter_lambda(filter_measure)
index = np.unravel_index(index_raveled, filter_measure.shape)
action = Action()
action.index = index[3]
action.pose = self.pose_from_index(index, filter_measure.shape,
images[0])
action.estimated_reward = estimated_reward[index]
action.estimated_reward_std = estimated_reward_std[index]
action.method = method
action.step = 0 # Default value
if verbose:
logger.info(f'NN inference time [s]: {time.time() - start:.3}')
return action
def infer(
self,
images: List[OrthographicImage],
goal_images: List[OrthographicImage],
method: SelectionMethod,
verbose=1,
place_images: List[OrthographicImage] = None,
) -> List[Action]:
start = time.time()
if method == SelectionMethod.Random:
grasp_action = Action(action_type='grasp')
grasp_action.index = np.random.choice(range(3))
grasp_action.pose = RobotPose(affine=Affine(
x=np.random.uniform(self.lower_random_pose[0],
self.upper_random_pose[0]), # [m]
y=np.random.uniform(self.lower_random_pose[1],
self.upper_random_pose[1]), # [m]
a=np.random.uniform(self.lower_random_pose[3],
self.upper_random_pose[3]), # [rad]
))
grasp_action.estimated_reward = -1
grasp_action.estimated_reward_std = 0.0
grasp_action.method = method
grasp_action.step = 0
place_action = Action(action_type='place')
place_action.index = np.random.choice(range(3))
place_action.pose = RobotPose(affine=Affine(
x=np.random.uniform(self.lower_random_pose[0],
self.upper_random_pose[0]), # [m]
y=np.random.uniform(self.lower_random_pose[1],
self.upper_random_pose[1]), # [m]
a=np.random.uniform(self.lower_random_pose[3],
self.upper_random_pose[3]), # [rad]
))
place_action.estimated_reward = -1
place_action.estimated_reward_std = 0.0
place_action.method = method
place_action.step = 0
return [grasp_action, place_action]
input_images = [self.get_images(i) for i in images]
goal_input_images = [self.get_images(i) for i in goal_images]
if self.network_type == '2' and not place_images:
raise Exception(
f'Place images are missing for network type {self.network_type}'
)
elif place_images:
place_input_images = [self.get_images(i) for i in place_images]
grasp_input = input_images + goal_input_images if self.network_type == '1' else input_images
place_input = input_images + goal_input_images if self.network_type == '1' else place_input_images + goal_input_images
m_reward, m_z = self.grasp_model.predict(grasp_input, batch_size=128)
# m_reward, *m_z_list = self.grasp_model.predict(grasp_input, batch_size=128)
# m_z_list = tuple(np.expand_dims(m_zi, axis=3) for m_zi in m_z_list)
# m_z = np.concatenate(m_z_list, axis=3)
p_reward, p_z = self.place_model.predict(place_input, batch_size=128)
if self.keep_indixes:
self.keep_array_at_last_indixes(m_reward, self.keep_indixes)
first_method = SelectionMethod.PowerProb if method in [
SelectionMethod.Top5, SelectionMethod.Max
] else method
# first_method = SelectionMethod.Top5
filter_lambda_n_grasp = self.get_filter_n(first_method,
self.number_top_grasp)
filter_lambda_n_place = self.get_filter_n(first_method,
self.number_top_place)
m_top_index = filter_lambda_n_grasp(m_reward)
p_top_index = filter_lambda_n_place(p_reward)
m_top_index_unraveled = np.transpose(
np.asarray(np.unravel_index(m_top_index, m_reward.shape)))
p_top_index_unraveled = np.transpose(
np.asarray(np.unravel_index(p_top_index, p_reward.shape)))
# print(m_top_index_unraveled.tolist())
# print(p_top_index_unraveled.tolist())
m_top_z = m_z[m_top_index_unraveled[:, 0], m_top_index_unraveled[:, 1],
m_top_index_unraveled[:, 2]]
# m_top_z = m_z[m_top_index_unraveled[:, 0], m_top_index_unraveled[:, 1], m_top_index_unraveled[:, 2], m_top_index_unraveled[:, 3]]
p_top_z = p_z[p_top_index_unraveled[:, 0], p_top_index_unraveled[:, 1],
p_top_index_unraveled[:, 2]]
reward = self.merge_model.predict([m_top_z, p_top_z], batch_size=2**12)
m_top_reward = m_reward[m_top_index_unraveled[:, 0],
m_top_index_unraveled[:, 1],
m_top_index_unraveled[:, 2],
m_top_index_unraveled[:, 3]]
# p_top_reward = p_reward[p_top_index_unraveled[:, 0], p_top_index_unraveled[:, 1], p_top_index_unraveled[:, 2]]
m_top_reward_repeated = np.repeat(np.expand_dims(np.expand_dims(
m_top_reward, axis=1),
axis=1),
self.number_top_place,
axis=1)
filter_measure = reward * m_top_reward_repeated
filter_lambda = self.get_filter(method)
index_raveled = filter_lambda(filter_measure)
index_unraveled = np.unravel_index(index_raveled, reward.shape)
m_index = m_top_index_unraveled[index_unraveled[0]]
p_index = p_top_index_unraveled[index_unraveled[1]]
grasp_action = Action(action_type='grasp')
grasp_action.index = m_index[3]
grasp_action.pose = self.pose_from_index(m_index, m_reward.shape,
images[0])
grasp_action.estimated_reward = m_reward[tuple(m_index)]
grasp_action.method = method
grasp_action.step = 0
place_action = Action(action_type='place')
place_action.index = p_index[3]
place_action.pose = self.pose_from_index(p_index,
p_reward.shape,
images[0],
resolution_factor=1.0)
place_action.estimated_reward = reward[
index_unraveled] # reward[index_raveled, 0] # p_reward[tuple(p_index)]
place_action.method = method
place_action.step = 0
if verbose:
logger.info(f'NN inference time [s]: {time.time() - start:.3}')
return [grasp_action, place_action]