def burn_in(senv, replay_mem):
tic = time.time()
for i_idx in range(FLAGS.burn_in_length):
if i_idx % 10 == 0 and i_idx != 0:
toc = time.time()
log_string('Burning in {}/{} sequences, time taken: {}s'.format(
i_idx, FLAGS.burn_in_length, toc - tic))
tic = time.time()
state, model_id = senv.reset(True)
actions = []
RGB_temp_list = np.zeros(
(FLAGS.max_episode_length, FLAGS.resolution, FLAGS.resolution, 3),
dtype=np.float32)
R_list = np.zeros((FLAGS.max_episode_length, 3, 4), dtype=np.float32)
RGB_temp_list[0, ...], _ = replay_mem.read_png_to_uint8(
state[0][0], state[1][0], model_id)
R_list[0, ...] = replay_mem.get_R(state[0][0], state[1][0])
for e_idx in range(FLAGS.max_episode_length - 1):
actions.append(np.random.randint(FLAGS.action_num))
state, next_state, done, model_id = senv.step(actions[-1])
RGB_temp_list[e_idx + 1, ...], _ = replay_mem.read_png_to_uint8(
next_state[0], next_state[1], model_id)
R_list[e_idx + 1, ...] = replay_mem.get_R(next_state[0],
next_state[1])
if done:
traj_state = state
traj_state[0] += [next_state[0]]
traj_state[1] += [next_state[1]]
temp_traj = trajectData(traj_state, actions, model_id)
replay_mem.append(temp_traj)
break
def burn_in(senv, replay_mem):
K_single = np.asarray([[420.0, 0.0, 112.0], [0.0, 420.0, 112.0],
[0.0, 0.0, 1]])
K_list = np.tile(K_single[None, None, ...],
(1, FLAGS.max_episode_length, 1, 1))
tic = time.time()
for i_idx in xrange(FLAGS.burn_in_length):
if i_idx % 10 == 0 and i_idx != 0:
toc = time.time()
log_string('Burning in {}/{} sequences, time taken: {}s'.format(
i_idx, FLAGS.burn_in_length, toc - tic))
tic = time.time()
state, model_id = senv.reset(True)
actions = []
RGB_temp_list = np.zeros(
(FLAGS.max_episode_length, FLAGS.resolution, FLAGS.resolution, 3),
dtype=np.float32)
R_list = np.zeros((FLAGS.max_episode_length, 3, 4), dtype=np.float32)
#vox_temp = np.zeros((FLAGS.voxel_resolution, FLAGS.voxel_resolution, FLAGS.voxel_resolution),
# dtype=np.float32)
RGB_temp_list[0, ...], _ = replay_mem.read_png_to_uint8(
state[0][0], state[1][0], model_id)
R_list[0, ...] = replay_mem.get_R(state[0][0], state[1][0])
#vox_temp_list = replay_mem.get_vox_pred(RGB_temp_list, R_list, K_list, 0)
#vox_temp = np.squeeze(vox_temp_list[0, ...])
## run simulations and get memories
for e_idx in xrange(FLAGS.max_episode_length - 1):
actions.append(np.random.randint(FLAGS.action_num))
state, next_state, done, model_id = senv.step(actions[-1])
RGB_temp_list[e_idx + 1, ...], _ = replay_mem.read_png_to_uint8(
next_state[0], next_state[1], model_id)
R_list[e_idx + 1, ...] = replay_mem.get_R(next_state[0],
next_state[1])
## TODO: update vox_temp
#vox_temp_list = replay_mem.get_vox_pred(RGB_temp_list, R_list, K_list, e_idx+1)
#vox_temp = np.squeeze(vox_temp_list[e_idx+1, ...])
if done:
traj_state = state
traj_state[0] += [next_state[0]]
traj_state[1] += [next_state[1]]
#rewards = replay_mem.get_seq_rewards(RGB_temp_list, R_list, K_list, model_id)
#print 'rewards: {}'.format(rewards)
temp_traj = trajectData(traj_state, actions, model_id)
replay_mem.append(temp_traj)
break
def go(self,
i_idx,
verbose=True,
add_to_mem=True,
mode='active',
is_train=True):
''' does 1 rollout, returns mvnet_input'''
state, model_id = self.env.reset(is_train, i_idx)
actions = []
mvnet_input = MVInputs(self.FLAGS, batch_size=1)
mvnet_input.put(self.single_input_for_state(state), episode_idx=0)
for e_idx in range(1, self.FLAGS.max_episode_length):
tic = time.time()
if mode == 'active':
#if np.random.uniform(0, 1) < self.FLAGS.epsilon:
# probs = [1.0/8]*8
# agent_action = np.random.choice(self.env.action_space_n, p=probs)
#else:
agent_action = self.agent.select_action(mvnet_input,
e_idx - 1,
is_training=is_train)
elif mode == 'random':
probs = [1.0 / 8] * 8
agent_action = np.random.choice(self.env.action_space_n,
p=probs)
elif mode == 'nolimit':
agent_action = 0
elif mode == 'oneway':
if len(actions) == 0:
probs = [1.0 / 8] * 8
agent_action = np.random.choice(self.env.action_space_n,
p=probs)
agent_action = np.random.choice([0, 1, 4, 7])
else:
agent_action = actions[0]
actions.append(agent_action)
if mode is not 'nolimit':
state, next_state, done, model_id = self.env.step(actions[-1])
else:
state, next_state, done, model_id = self.env.step(actions[-1],
nolimit=True)
mvnet_input.put(self.single_input_for_state(next_state),
episode_idx=e_idx)
if verbose:
log_string(
'Iter: {}, e_idx: {}, azim: {}, elev: {}, model_id: {}, time: {}s'
.format(i_idx, e_idx, next_state[0], next_state[1],
model_id,
time.time() - tic))
if done:
traj_state = state
traj_state[0] += [next_state[0]]
traj_state[1] += [next_state[1]]
if add_to_mem:
temp_traj = trajectData(traj_state, actions, model_id)
self.mem.append(temp_traj)
self.last_trajectory = traj_state
break
return mvnet_input, actions