class TmpAsynchronousContextualBandit(AbstractLearning):
""" Perform Contextual Bandit learning (Kakade and Langford (circa 2006) & Misra, Langford and Artzi EMNLP 2017) """
def __init__(self, shared_model, local_model, action_space, meta_data_util, config, constants, tensorboard):
self.max_epoch = constants["max_epochs"]
self.shared_model = shared_model
self.local_model = local_model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.entropy_coef = constants["entropy_coefficient"]
self.logger = None
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(self.local_model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(self.local_model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectDetection(self.local_model, num_objects=67)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(self.local_model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(self.local_model)
self.goal_prediction_loss = None
self.optimizer = optim.Adam(shared_model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.shared_model, self.local_model, self.calc_loss,
self.optimizer, self.config, self.constants, self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
immediate_rewards = []
action_batch = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
immediate_rewards.append(replay_item.get_reward())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
immediate_rewards = cuda_var(torch.from_numpy(np.array(immediate_rewards)).float())
# self.logger.log("Learning from Log Probabilities is %r " % log_probabilities.data.cpu().numpy())
# self.logger.log("Learning from Action Batch is %r " % action_batch.data.cpu().numpy())
# self.logger.log("Learning from Immediate Rewards is %r " % immediate_rewards.data.cpu().numpy())
# num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
chosen_log_probs = model_log_prob_batch.gather(1, action_batch.view(-1, 1))
reward_log_probs = immediate_rewards * chosen_log_probs.view(-1)
# self.logger.log("Learning from Chosen Log Probs is %r " % chosen_log_probs.data.cpu().numpy())
# self.logger.log("Learning from Reward Log Probs is %r " % reward_log_probs.data.cpu().numpy())
model_prob_batch = torch.exp(model_log_prob_batch)
# mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
# self.cross_entropy = -torch.sum(gold_distribution * torch.log(mini_batch_action_distribution))
self.entropy = -torch.sum(torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(reward_log_probs)
# self.logger.log("Objective is %r and entropy is %r and entropy coef is %r " %
# (objective, self.entropy, self.entropy_coef))
# Essentially we want the objective to increase and cross entropy to decrease
loss = -objective - self.entropy_coef * self.entropy
self.ratio = torch.abs(objective)/(self.entropy_coef * self.entropy) # we want the ratio to be high
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants["action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(batch_replay_items)
self.object_detection_loss = self.constants["object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_loss = self.goal_prediction_calculator.calc_loss(batch_replay_items)
self.goal_prediction_loss = self.constants["goal_prediction_coeff"] * self.goal_prediction_loss
loss = loss + self.goal_prediction_loss
else:
self.goal_prediction_loss = None
return loss
@staticmethod
def convert_text_to_indices(text, vocab, ignore_case=True):
# Tokenize the text
print ("instruction ", text)
token_seq = nltk.word_tokenize(text)
indices = []
for token in token_seq:
if ignore_case:
ltoken = token.lower()
else:
ltoken = token
if ltoken in vocab:
indices.append(vocab[ltoken])
else:
indices.append(vocab["$UNK$"])
return indices
@staticmethod
def convert_indices_to_text(indices, vocab):
return " ".join([vocab[index] for index in indices])
def get_goal(self, metadata):
if metadata["goal-screen"] is None:
return None, None, None, None
left, bottom, depth = metadata["goal-screen"]
if 0.01 < left < self.config["image_width"] and 0.01 < bottom < self.config["image_height"] and depth > 0.01:
scaled_left = left / float(self.config["image_width"])
scaled_top = 1.0 - bottom / float(self.config["image_height"])
row_real = self.config["num_manipulation_row"] * scaled_top
col_real = self.config["num_manipulation_col"] * scaled_left
row, col = round(row_real), round(col_real)
if row < 0:
row = 0
elif row >= self.config["num_manipulation_row"]:
row = self.config["num_manipulation_row"] - 1
if col < 0:
col = 0
elif col >= self.config["num_manipulation_col"]:
col = self.config["num_manipulation_col"] - 1
return row, col, row_real, col_real
else:
return None, None, None, None
@staticmethod
def do_train(house_id, shared_model, config, action_space, meta_data_util,
constants, train_dataset, tune_dataset, experiment,
experiment_name, rank, server, logger, model_type, vocab, use_pushover=False):
try:
TmpAsynchronousContextualBandit.do_train_(house_id, shared_model, config, action_space, meta_data_util,
constants, train_dataset, tune_dataset, experiment,
experiment_name, rank, server, logger, model_type,
vocab, use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_train_(house_id, shared_model, config, action_space, meta_data_util, constants,
train_dataset, tune_dataset, experiment, experiment_name, rank, server,
logger, model_type, vocab, use_pushover=False):
logger.log("In Training...")
launch_k_unity_builds([config["port"]], "./house_" + str(house_id) + "_elmer.x86_64",
arg_str="--config ./AssetsHouse/config" + str(house_id) + ".json",
cwd="./simulators/house/")
logger.log("Launched Builds.")
server.initialize_server()
logger.log("Server Initialized.")
# Test policy
test_policy = gp.get_argmax_action
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
logger.log('Created Tensorboard Server.')
else:
tensorboard = None
if use_pushover:
pushover_logger = None
else:
pushover_logger = None
# Create a local model for rollouts
local_model = model_type(config, constants)
# local_model.train()
# Create the Agent
tmp_agent = TmpHouseAgent(server=server,
model=local_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent.")
action_counts = [0] * action_space.num_actions()
max_epochs = 100000 # constants["max_epochs"]
dataset_size = len(train_dataset)
tune_dataset_size = len(tune_dataset)
if tune_dataset_size > 0:
# Test on tuning data
tmp_agent.test(tune_dataset, vocab, tensorboard=tensorboard,
logger=logger, pushover_logger=pushover_logger)
# Create the learner to compute the loss
learner = TmpAsynchronousContextualBandit(shared_model, local_model, action_space, meta_data_util,
config, constants, tensorboard)
# TODO change 2 --- unity launch moved up
learner.logger = logger
for epoch in range(1, max_epochs + 1):
for data_point_ix, data_point in enumerate(train_dataset):
# Sync with the shared model
# local_model.load_state_dict(shared_model.state_dict())
local_model.load_from_state_dict(shared_model.get_state_dict())
if (data_point_ix + 1) % 100 == 0:
logger.log("Done %d out of %d" %(data_point_ix, dataset_size))
logger.log("Training data action counts %r" % action_counts)
num_actions = 0
max_num_actions = constants["horizon"]
max_num_actions += constants["max_extra_horizon"]
image, metadata = tmp_agent.server.reset_receive_feedback(data_point)
instruction = data_point.get_instruction()
# instruction_str = TmpAsynchronousContextualBandit.convert_indices_to_text(instruction, vocab)
# print("Instruction str is ", instruction_str)
# Pose and Orientation gone TODO change 3
state = AgentObservedState(instruction=instruction,
config=config,
constants=constants,
start_image=image,
previous_action=None,
data_point=data_point)
state.goal = learner.get_goal(metadata)
model_state = None
batch_replay_items = []
total_reward = 0
forced_stop = True
while num_actions < max_num_actions:
# logger.log("Training: Meta Data %r " % metadata)
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, state_feature = \
local_model.get_probs(state, model_state)
probabilities = list(torch.exp(log_probabilities.data))[0]
# Sample action from the probability
action = gp.sample_action_from_prob(probabilities)
action_counts[action] += 1
if action == action_space.get_stop_action_index():
forced_stop = False
break
# Send the action and get feedback
image, reward, metadata = tmp_agent.server.send_action_receive_feedback(action)
# logger.log("Action is %r, Reward is %r Probability is %r " % (action, reward, probabilities))
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state, action, reward, log_prob=log_probabilities)
batch_replay_items.append(replay_item)
# Update the agent state
# Pose and orientation gone, TODO change 4
state = state.update(image, action, data_point=data_point)
state.goal = learner.get_goal(metadata)
num_actions += 1
total_reward += reward
# Send final STOP action and get feedback
image, reward, metadata = tmp_agent.server.halt_and_receive_feedback()
total_reward += reward
# Store it in the replay memory list
if not forced_stop:
# logger.log("Action is Stop, Reward is %r Probability is %r " % (reward, probabilities))
replay_item = ReplayMemoryItem(state, action_space.get_stop_action_index(),
reward, log_prob=log_probabilities)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
if len(batch_replay_items) > 0: # 32
loss_val = learner.do_update(batch_replay_items)
if tensorboard is not None:
# cross_entropy = float(learner.cross_entropy.data[0])
# tensorboard.log(cross_entropy, loss_val, 0)
tensorboard.log_scalar("loss", loss_val)
entropy = float(learner.entropy.data[0])/float(num_actions + 1)
tensorboard.log_scalar("entropy", entropy)
ratio = float(learner.ratio.data[0])
tensorboard.log_scalar("Abs_objective_to_entropy_ratio", ratio)
tensorboard.log_scalar("total_reward", total_reward)
tensorboard.log_scalar("mean navigation error", metadata['mean-navigation-error'])
if learner.action_prediction_loss is not None:
action_prediction_loss = float(learner.action_prediction_loss.data[0])
learner.tensorboard.log_action_prediction_loss(action_prediction_loss)
if learner.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(learner.temporal_autoencoder_loss.data[0])
tensorboard.log_temporal_autoencoder_loss(temporal_autoencoder_loss)
if learner.object_detection_loss is not None:
object_detection_loss = float(learner.object_detection_loss.data[0])
tensorboard.log_object_detection_loss(object_detection_loss)
if learner.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(learner.symbolic_language_prediction_loss.data[0])
tensorboard.log_scalar("sym_language_prediction_loss", symbolic_language_prediction_loss)
if learner.goal_prediction_loss is not None:
goal_prediction_loss = float(learner.goal_prediction_loss.data[0])
tensorboard.log_scalar("goal_prediction_loss", goal_prediction_loss)
# Save the model
local_model.save_model(experiment + "/contextual_bandit_" + str(rank) + "_epoch_" + str(epoch))
logger.log("Training data action counts %r" % action_counts)
if tune_dataset_size > 0:
# Test on tuning data
tmp_agent.test(tune_dataset, vocab, tensorboard=tensorboard,
logger=logger, pushover_logger=pushover_logger)
class MultiClientIncrementalDAGGER(AbstractLearning):
""" Perform DAGGER algorithm of Ross et al. """
def __init__(self, model, action_space, meta_data_util, config, constants,
tensorboard):
self.max_epoch = constants["max_epochs"]
self.model = model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.num_client = config["num_client"]
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.entropy_coef = constants["entropy_coefficient"]
self.beta = 0.9
self.beta_exp_decay = 0.9
logging.info(
"DAGGER: using starting beta of %r and beta exp decay of %r",
self.beta, self.beta_exp_decay)
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectDetection(
self.model, num_objects=67)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.model)
self.symbolic_language_prediction_loss = None
self.optimizer = optim.Adam(model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.model, self.calc_loss,
self.optimizer, self.config, self.constants,
self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
action_batch = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action()) # expert action
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
# model_log_prob_batch = self.model.get_probs_batch(agent_observation_state_ls)
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
self.cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
self.entropy = -torch.mean(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(chosen_log_probs) / num_states
# Essentially we want the objective to increase and cross entropy to decrease
loss = -objective - self.entropy_coef * self.entropy
# loss = -objective + self.entropy_coef * self.cross_entropy
# Minimize the Factor Entropy if the model is implicit factorization model
if isinstance(self.model,
IncrementalModelRecurrentImplicitFactorizationResnet):
self.mean_factor_entropy = torch.mean(torch.cat(factor_entropy))
loss = loss + self.mean_factor_entropy
else:
self.mean_factor_entropy = None
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
return loss
@staticmethod
def get_oracle_length(data_point):
start_x, start_z, start_angle = data_point.get_start_pos()
dummy_metadata = {
"x_pos": start_x,
"z_pos": start_z,
"y_angle": start_angle
}
goal_x, goal_z = data_point.get_destination_list()[-1]
oracle_trajectory = get_oracle_trajectory(dummy_metadata, goal_x,
goal_z, data_point)
return len(oracle_trajectory)
def do_train(self, agent, train_dataset, tune_dataset, experiment_name):
""" Perform training """
clients = []
batch_replay_items = []
for client_ix in range(0, self.num_client):
client = Client(agent, self.config, self.constants,
self.action_space, self.tensorboard, client_ix,
batch_replay_items, self.beta)
clients.append(client)
dataset_iterator = DatasetIterator(train_dataset)
epoch = 1
action_counts = [0] * self.action_space.num_actions()
if epoch <= self.max_epoch:
logging.info("Starting epoch %d", epoch)
# Test on tuning data
# agent.test(tune_dataset, tensorboard=self.tensorboard)
probabilities_batch = [None] * self.num_client
client_state = [None] * self.num_client
while True:
for client_ix in range(0, self.num_client):
client = clients[client_ix]
# See if the client can progress
client_status = client.try_to_progress()
if client_status == Client.WAITING_FOR_EXAMPLE:
# Provide the next example
data_point = dataset_iterator.get_next()
if data_point is None:
continue
# max_num_actions = len(data_point.get_trajectory())
max_num_actions = self.get_oracle_length(data_point)
max_num_actions += self.constants["max_extra_horizon"]
# max_num_actions = self.constants["horizon"]
if self.tensorboard is not None:
self.tensorboard.log_scalar("total_reward",
client.total_reward)
client.accept_new_example(data_point, max_num_actions)
elif client_status == Client.WAITING_FOR_ACTION:
# Generate probabilities over actions and take action
log_probabilities, new_model_state, image_emb_seq = self.model.get_probs(
client.get_state(), client.get_model_state())
if isinstance(
self.model,
IncrementalModelRecurrentImplicitFactorizationResnet
):
factor_entropy = self.model.get_recent_factorization_entropy(
)
else:
factor_entropy = None
client.take_action(log_probabilities, new_model_state,
image_emb_seq, factor_entropy)
# if client_state[client_ix] is None:
# # This client has not waited so make it wait for 1 iteration
# # Take its state and compute the probabiltiy at the end.
# client_state[client_ix] = client.get_state()
# else:
# # This client has waited so its probability must be ready.
# probabilities = probabilities_batch[client_ix]
# # Generate probabilities over actions and take action
# # probabilities = list(torch.exp(self.model.get_probs(client.get_state()).data))
# client.take_action(probabilities)
# probabilities_batch[client_ix] = None
# client_state[client_ix] = None
elif client_status == Client.WAITING_TO_RECEIVE:
pass
else:
raise AssertionError("Unknown status. Found " +
str(client_status))
# states = [state for state in client_state if state is not None]
# if len(states) > 0:
# probabilities = list(torch.exp(self.model.get_probs_batch(states).data))
# assert len(states) == len(probabilities)
# ctr = 0
# for i in range(0, self.num_client):
# if client_state[i] is not None:
# probabilities_batch[i] = probabilities[ctr]
# ctr += 1
# else:
# probabilities_batch[i] = None
# Perform update
if len(batch_replay_items) > 32:
loss_val = self.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
del batch_replay_items[:] # in place list clear
cross_entropy = float(self.cross_entropy.data[0])
self.tensorboard.log(cross_entropy, loss_val, 0)
entropy = float(self.entropy.data[0])
self.tensorboard.log_scalar("entropy", entropy)
if self.action_prediction_loss is not None:
action_prediction_loss = float(
self.action_prediction_loss.data[0])
self.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if self.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
self.temporal_autoencoder_loss.data[0])
self.tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if self.object_detection_loss is not None:
object_detection_loss = float(
self.object_detection_loss.data[0])
self.tensorboard.log_object_detection_loss(
object_detection_loss)
if self.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
self.symbolic_language_prediction_loss.data[0])
self.tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if self.mean_factor_entropy is not None:
mean_factor_entropy = float(
self.mean_factor_entropy.data[0])
self.tensorboard.log_factor_entropy_loss(
mean_factor_entropy)
# Check if an epoch is finished. An epoch is over if all clients are waiting
# for an example (at which point the iterator also returns none)
epoch_completed = all([
client.get_status() == Client.WAITING_FOR_EXAMPLE
for client in clients
])
if epoch_completed:
assert dataset_iterator.get_next() is None
# Reset the iterator
dataset_iterator.reset()
# Attenuate the dagger beta value
self.beta = math.pow(self.beta_exp_decay, epoch)
for client in clients:
client.update_dagger_beta(self.beta)
logging.info("Attenuated the beta to %r after epoch %r",
self.beta, epoch)
# Save the model
self.model.save_model(experiment_name + "/dagger_epoch_" +
str(epoch))
if epoch >= self.max_epoch:
break
epoch += 1
logging.info("Starting epoch %d", epoch)
# Test on tuning data
agent.test(tune_dataset, tensorboard=self.tensorboard)
class TmpSupervisedLearning(AbstractLearning):
""" Perform Supervised Learning """
def __init__(self, shared_model, local_model, action_space, meta_data_util,
config, constants, tensorboard):
self.max_epoch = constants["max_epochs"]
self.shared_model = shared_model
self.local_model = local_model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.entropy_coef = constants["entropy_coefficient"]
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.local_model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.local_model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectDetection(
self.local_model, num_objects=67)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.local_model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(self.local_model)
self.goal_prediction_loss = None
self.optimizer = optim.Adam(shared_model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.shared_model, self.local_model,
self.calc_loss, self.optimizer, self.config,
self.constants, self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
action_batch = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
# model_log_prob_batch = self.model.get_probs_batch(agent_observation_state_ls)
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
# gold_distribution = cuda_var(torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
# self.cross_entropy = -torch.sum(gold_distribution * torch.log(mini_batch_action_distribution))
self.entropy = -torch.mean(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(chosen_log_probs) / num_states
# Essentially we want the objective to increase and cross entropy to decrease
loss = -objective - self.entropy_coef * self.entropy
self.ratio = torch.abs(objective) / (self.entropy_coef * self.entropy
) # we want the ratio to be high
# loss = -objective + self.entropy_coef * self.cross_entropy
# Minimize the Factor Entropy if the model is implicit factorization model
if isinstance(self.local_model,
IncrementalModelRecurrentImplicitFactorizationResnet):
self.mean_factor_entropy = torch.mean(torch.cat(factor_entropy))
loss = loss + self.mean_factor_entropy
else:
self.mean_factor_entropy = None
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_loss = self.goal_prediction_calculator.calc_loss(
batch_replay_items)
self.goal_prediction_loss = self.constants["goal_prediction_coeff"] * \
self.goal_prediction_loss
loss = loss + self.goal_prediction_loss
else:
self.goal_prediction_loss = None
return loss
@staticmethod
def convert_text_to_indices(text, vocab, ignore_case=True):
# Tokenize the text
token_seq = nltk.word_tokenize(text)
indices = []
for token in token_seq:
if ignore_case:
ltoken = token.lower()
else:
ltoken = token
if ltoken in vocab:
indices.append(vocab[ltoken])
else:
indices.append(vocab["$UNK$"])
return indices
@staticmethod
def do_train(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
vocab,
use_pushover=False):
try:
TmpSupervisedLearning.do_train_(shared_model, config, action_space,
meta_data_util, constants,
train_dataset, tune_dataset,
experiment, experiment_name, rank,
server, logger, model_type, vocab,
use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_train_(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
vocab,
use_pushover=False):
print("In training...")
launch_k_unity_builds([config["port"]],
"./simulators/house_3_elmer.x86_64")
server.initialize_server()
print("launched builds")
# Test policy
test_policy = gp.get_argmax_action
# torch.manual_seed(args.seed + rank)
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
else:
tensorboard = None
if use_pushover:
# pushover_logger = PushoverLogger(experiment_name)
pushover_logger = None
else:
pushover_logger = None
# Create a local model for rollouts
local_model = model_type(config, constants)
# local_model.train()
# Create the Agent
logger.log("STARTING AGENT")
tmp_agent = TmpHouseAgent(server=server,
model=local_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent...")
action_counts = [0] * action_space.num_actions()
max_epochs = constants["max_epochs"]
dataset_size = len(train_dataset)
tune_dataset_size = len(tune_dataset)
# Create the learner to compute the loss
learner = TmpSupervisedLearning(shared_model, local_model,
action_space, meta_data_util, config,
constants, tensorboard)
# TODO change 2 --- unity launch moved up
for epoch in range(1, max_epochs + 1):
for data_point_ix, data_point in enumerate(train_dataset):
# Sync with the shared model
# local_model.load_state_dict(shared_model.state_dict())
local_model.load_from_state_dict(shared_model.get_state_dict())
if (data_point_ix + 1) % 100 == 0:
logger.log("Done %d out of %d" %
(data_point_ix, dataset_size))
logger.log("Training data action counts %r" %
action_counts)
image, metadata = tmp_agent.server.reset_receive_feedback(
data_point)
# instruction = TmpSupervisedLearning.convert_text_to_indices(metadata["instruction"], vocab)
instruction = data_point.get_instruction()
# Pose and Orientation gone TODO change 3
state = AgentObservedState(instruction=instruction,
config=config,
constants=constants,
start_image=image,
previous_action=None,
data_point=data_point)
model_state = None
batch_replay_items = []
total_reward = 0
# trajectory = metadata["trajectory"]
trajectory = data_point.get_trajectory()[0:300]
for action in trajectory:
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, state_feature = \
local_model.get_probs(state, model_state)
# Sample action from the probability
action_counts[action] += 1
# Send the action and get feedback
image, reward, metadata = tmp_agent.server.send_action_receive_feedback(
action)
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state,
action,
reward,
log_prob=log_probabilities)
batch_replay_items.append(replay_item)
# Update the agent state
# Pose and orientation gone, TODO change 4
state = state.update(image, action, data_point=data_point)
total_reward += reward
# Send final STOP action and get feedback
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, state_feature = \
local_model.get_probs(state, model_state)
image, reward, metadata = tmp_agent.server.halt_and_receive_feedback(
)
total_reward += reward
# if tensorboard is not None:
# tensorboard.log_all_train_errors(
# metadata["edit_dist_error"], metadata["closest_dist_error"], metadata["stop_dist_error"])
# Store it in the replay memory list
replay_item = ReplayMemoryItem(
state,
action_space.get_stop_action_index(),
reward,
log_prob=log_probabilities)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
if len(batch_replay_items) > 0: # 32
loss_val = learner.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
# del batch_replay_items[:] # in place list clear
if tensorboard is not None:
# cross_entropy = float(learner.cross_entropy.data[0])
# tensorboard.log(cross_entropy, loss_val, 0)
num_actions = len(trajectory) + 1
tensorboard.log_scalar(
"loss_val", loss_val) # /float(num_actions))
entropy = float(
learner.entropy.data[0]) # /float(num_actions)
tensorboard.log_scalar("entropy", entropy)
ratio = float(learner.ratio.data[0])
tensorboard.log_scalar(
"Abs_objective_to_entropy_ratio", ratio)
if learner.action_prediction_loss is not None:
action_prediction_loss = float(
learner.action_prediction_loss.data[0])
learner.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if learner.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
learner.temporal_autoencoder_loss.data[0])
tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if learner.object_detection_loss is not None:
object_detection_loss = float(
learner.object_detection_loss.data[0])
tensorboard.log_object_detection_loss(
object_detection_loss)
if learner.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
learner.symbolic_language_prediction_loss.
data[0])
tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if learner.goal_prediction_loss is not None:
goal_prediction_loss = float(
learner.goal_prediction_loss.data[0])
tensorboard.log_scalar("goal_prediction_loss",
goal_prediction_loss)
if learner.mean_factor_entropy is not None:
mean_factor_entropy = float(
learner.mean_factor_entropy.data[0])
tensorboard.log_factor_entropy_loss(
mean_factor_entropy)
# Save the model
local_model.save_model(experiment + "/contextual_bandit_" +
str(rank) + "_epoch_" + str(epoch))
logger.log("Training data action counts %r" % action_counts)
if tune_dataset_size > 0:
# Test on tuning data
print("Going for testing")
tmp_agent.test(tune_dataset,
vocab,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)
print("Done testing")
class AsynchronousAdvantageActorGAECritic(AbstractLearning):
""" Perform Asynchronous Advantage Actor Critic with Generalized Advantage Estimate """
def __init__(self, shared_model, local_model, action_space, meta_data_util,
config, constants, tensorboard):
self.max_epoch = constants["max_epochs"]
self.gamma = constants["gamma"]
self.tau = 1.0
self.shared_model = shared_model
self.local_model = local_model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.ratio = None
self.value_loss = None
self.epoch = 0
self.entropy_coef = constants["entropy_coefficient"]
self.image_channels, self.image_height, self.image_width = shared_model.image_module.get_final_dimension(
)
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.local_model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.local_model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectPixelIdentification(
self.local_model,
num_objects=67,
camera_angle=60,
image_height=self.image_height,
image_width=self.image_width,
object_height=0) # -2.5)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.local_model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(
self.local_model, self.image_height, self.image_width)
self.goal_prediction_loss = None
self.optimizer = optim.Adam(shared_model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.shared_model, self.local_model,
self.calc_loss, self.optimizer, self.config,
self.constants, self.tensorboard)
def calc_loss(self, batch_replay_items):
""" Assumes that the batch replay items contains items ordered temporarily """
agent_observation_state_ls = []
action_batch = []
log_probabilities = []
factor_entropy = []
v_values = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
v_values.append(replay_item.get_volatile_features()["state_value"])
# Compute the generalized advantage.
generalized_advantages = []
total_reward = []
last_v_value = None
sum_reward = 0
generalized_advantage = cuda_var(torch.zeros(1))
for replay_item in reversed(batch_replay_items):
v_value = replay_item.get_volatile_features()["state_value"]
if last_v_value is None:
reward = replay_item.get_reward()
q_val = reward
advantange = q_val - v_value
generalized_advantage = advantange
else:
reward = replay_item.get_reward()
q_val = reward + self.gamma * last_v_value
advantange = q_val - v_value
generalized_advantage = self.tau * self.gamma * generalized_advantage + advantange
sum_reward += reward
last_v_value = v_value
generalized_advantages.append(generalized_advantage)
total_reward.append(sum_reward)
# Reverse the advantages and total reward to temporal order
generalized_advantages.reverse()
total_reward.reverse()
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
generalized_advantages = torch.cat(generalized_advantages).view(-1)
total_reward = cuda_var(
torch.from_numpy(np.array(total_reward)).float()).view(-1)
v_values = torch.cat(v_values).view(-1)
model_log_prob_batch = log_probabilities
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
advantage_log_prob = generalized_advantages * chosen_log_probs.view(-1)
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
self.value_loss = torch.sum((v_values - total_reward)**2)
self.cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
# self.entropy = -torch.mean(torch.sum(model_log_prob_batch * model_prob_batch, 1))
self.entropy = -torch.sum(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(advantage_log_prob) # / num_states
# Essentially we want the objective to increase and cross entropy to decrease
entropy_coef = max(0, self.entropy_coef - self.epoch * 0.01)
loss = -objective - entropy_coef * self.entropy + 0.25 * self.value_loss
self.ratio = torch.abs(objective) / (entropy_coef * self.entropy
) # we want the ratio to be high
# loss = -objective + self.entropy_coef * self.cross_entropy
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
if self.object_detection_loss is not None:
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_loss, _, _ = self.goal_prediction_calculator.calc_loss(
batch_replay_items)
if self.goal_prediction_loss is not None:
self.goal_prediction_loss = self.constants["goal_prediction_coeff"] * \
self.goal_prediction_loss
loss = loss + self.goal_prediction_loss # * len(batch_replay_items) # scale the loss
else:
self.goal_prediction_loss = None
return loss
@staticmethod
def do_train(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
try:
AsynchronousAdvantageActorGAECritic.do_train_(
shared_model, config, action_space, meta_data_util, constants,
train_dataset, tune_dataset, experiment, experiment_name, rank,
server, logger, model_type, use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_train_(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
server.initialize_server()
# Test policy
test_policy = gp.get_argmax_action
# torch.manual_seed(args.seed + rank)
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
else:
tensorboard = None
if use_pushover:
pushover_logger = PushoverLogger(experiment_name)
else:
pushover_logger = None
# Create a local model for rollouts
local_model = model_type(config, constants)
# local_model.train()
# Create the Agent
logger.log("STARTING AGENT")
agent = Agent(server=server,
model=local_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent...")
action_counts = [0] * action_space.num_actions()
max_epochs = constants["max_epochs"]
dataset_size = len(train_dataset)
tune_dataset_size = len(tune_dataset)
# Create the learner to compute the loss
learner = AsynchronousAdvantageActorGAECritic(shared_model,
local_model,
action_space,
meta_data_util, config,
constants, tensorboard)
# Launch unity
launch_k_unity_builds([config["port"]],
"./simulators/NavDroneLinuxBuild.x86_64")
for epoch in range(1, max_epochs + 1):
learner.epoch = epoch
task_completion_accuracy = 0
mean_stop_dist_error = 0
stop_dist_errors = []
for data_point_ix, data_point in enumerate(train_dataset):
# Sync with the shared model
# local_model.load_state_dict(shared_model.state_dict())
local_model.load_from_state_dict(shared_model.get_state_dict())
if (data_point_ix + 1) % 100 == 0:
logger.log("Done %d out of %d" %
(data_point_ix, dataset_size))
logger.log("Training data action counts %r" %
action_counts)
num_actions = 0
max_num_actions = constants["horizon"] + constants[
"max_extra_horizon"]
image, metadata = agent.server.reset_receive_feedback(
data_point)
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"], metadata["z_pos"],
metadata["y_angle"])
state = AgentObservedState(
instruction=data_point.instruction,
config=config,
constants=constants,
start_image=image,
previous_action=None,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
state.goal = GoalPrediction.get_goal_location(
metadata, data_point, learner.image_height,
learner.image_width)
model_state = None
batch_replay_items = []
total_reward = 0
forced_stop = True
while num_actions < max_num_actions:
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, volatile = \
local_model.get_probs(state, model_state)
probabilities = list(torch.exp(log_probabilities.data))[0]
# Sample action from the probability
action = gp.sample_action_from_prob(probabilities)
action_counts[action] += 1
# Generate goal
if config["do_goal_prediction"]:
goal = learner.goal_prediction_calculator.get_goal_location(
metadata, data_point, learner.image_height,
learner.image_width)
else:
goal = None
if action == action_space.get_stop_action_index():
forced_stop = False
break
# Send the action and get feedback
image, reward, metadata = agent.server.send_action_receive_feedback(
action)
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state,
action,
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=goal)
batch_replay_items.append(replay_item)
# Update the agent state
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"],
metadata["z_pos"],
metadata["y_angle"])
state = state.update(
image,
action,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
state.goal = GoalPrediction.get_goal_location(
metadata, data_point, learner.image_height,
learner.image_width)
num_actions += 1
total_reward += reward
# Send final STOP action and get feedback
image, reward, metadata = agent.server.halt_and_receive_feedback(
)
total_reward += reward
if metadata["stop_dist_error"] < 5.0:
task_completion_accuracy += 1
mean_stop_dist_error += metadata["stop_dist_error"]
stop_dist_errors.append(metadata["stop_dist_error"])
if tensorboard is not None:
tensorboard.log_all_train_errors(
metadata["edit_dist_error"],
metadata["closest_dist_error"],
metadata["stop_dist_error"])
# Store it in the replay memory list
if not forced_stop:
replay_item = ReplayMemoryItem(
state,
action_space.get_stop_action_index(),
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=goal)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
if len(batch_replay_items) > 0: # 32:
loss_val = learner.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
# del batch_replay_items[:] # in place list clear
if tensorboard is not None:
cross_entropy = float(learner.cross_entropy.data[0])
tensorboard.log(cross_entropy, loss_val, 0)
entropy = float(
learner.entropy.data[0]) / float(num_actions + 1)
v_value_loss_per_step = float(
learner.value_loss.data[0]) / float(num_actions +
1)
tensorboard.log_scalar("entropy", entropy)
tensorboard.log_scalar("total_reward", total_reward)
tensorboard.log_scalar("v_value_loss_per_step",
v_value_loss_per_step)
ratio = float(learner.ratio.data[0])
tensorboard.log_scalar(
"Abs_objective_to_entropy_ratio", ratio)
if learner.action_prediction_loss is not None:
action_prediction_loss = float(
learner.action_prediction_loss.data[0])
learner.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if learner.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
learner.temporal_autoencoder_loss.data[0])
tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if learner.object_detection_loss is not None:
object_detection_loss = float(
learner.object_detection_loss.data[0])
tensorboard.log_object_detection_loss(
object_detection_loss)
if learner.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
learner.symbolic_language_prediction_loss.
data[0])
tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if learner.goal_prediction_loss is not None:
goal_prediction_loss = float(
learner.goal_prediction_loss.data[0])
tensorboard.log_scalar("goal_prediction_loss",
goal_prediction_loss)
# Save the model
local_model.save_model(experiment + "/contextual_bandit_" +
str(rank) + "_epoch_" + str(epoch))
logger.log("Training data action counts %r" % action_counts)
mean_stop_dist_error = mean_stop_dist_error / float(
len(train_dataset))
task_completion_accuracy = (task_completion_accuracy *
100.0) / float(len(train_dataset))
logger.log("Training: Mean stop distance error %r" %
mean_stop_dist_error)
logger.log("Training: Task completion accuracy %r " %
task_completion_accuracy)
bins = range(0, 80, 3) # range of distance
histogram, _ = np.histogram(stop_dist_errors, bins)
logger.log("Histogram of train errors %r " % histogram)
if tune_dataset_size > 0:
# Test on tuning data
agent.test(tune_dataset,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)
class MultiClientIncrementalContextualBanditGoalImage(AbstractLearning):
""" Perform Contextual Bandit learning (Kakade and Langford (circa 2006) & Misra, Langford and Artzi EMNLP 2017) """
def __init__(self, model, action_space, meta_data_util, config, constants,
tensorboard):
self.max_epoch = constants["max_epochs"]
self.model = model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.num_client = config["num_client"]
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.entropy_coef = constants["entropy_coefficient"]
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectDetection(self.model)
self.object_detection_loss = None
self.optimizer = optim.Adam(model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.model, self.calc_loss,
self.optimizer, self.config, self.constants)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
immediate_rewards = []
action_batch = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
immediate_rewards.append(replay_item.get_reward())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
immediate_rewards = cuda_var(
torch.from_numpy(np.array(immediate_rewards)).float())
num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
# model_log_prob_batch = self.model.get_probs_batch(agent_observation_state_ls)
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
reward_log_probs = immediate_rewards * chosen_log_probs.view(-1)
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
objective = torch.sum(reward_log_probs) / num_states
# Essentially we want the objective to increase and cross entropy to decrease
loss = -objective + self.entropy_coef * cross_entropy
self.cross_entropy = cross_entropy
# Minimize the Factor Entropy if the model is implicit factorization model
if isinstance(self.model,
IncrementalModelRecurrentImplicitFactorizationResnet):
self.mean_factor_entropy = torch.mean(torch.cat(factor_entropy))
loss = loss + self.mean_factor_entropy
else:
self.mean_factor_entropy = None
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
return loss
@staticmethod
def read_goal_images(dataset, tag):
dataset_size = len(dataset)
images = []
for i in range(0, dataset_size):
img = scipy.misc.imread("goal_images/" + str(tag) +
"_images/final_image_" + str(i) + ".png")
images.append(img.swapaxes(1, 2).swapaxes(0, 1))
return images
def do_train(self, agent, train_dataset, tune_dataset, experiment_name):
""" Perform training """
clients = []
batch_replay_items = []
for client_ix in range(0, self.num_client):
client = Client(agent, self.config, self.constants,
self.tensorboard, client_ix, batch_replay_items)
clients.append(client)
dataset_iterator = DatasetIterator(train_dataset)
epoch = 1
action_counts = [0] * self.action_space.num_actions()
print("Reading images")
start = time.time()
train_images = self.read_goal_images(train_dataset, "train")
tune_images = self.read_goal_images(tune_dataset, "tune")
end = time.time()
print("Read all images. Time taken " + str(end - start) + " seconds. ")
if epoch <= self.max_epoch:
logging.info("Starting epoch %d", epoch)
# Test on tuning data
agent.test(tune_dataset, tune_images, tensorboard=self.tensorboard)
probabilities_batch = [None] * self.num_client
client_state = [None] * self.num_client
while True:
for client_ix in range(0, self.num_client):
client = clients[client_ix]
# See if the client can progress
client_status = client.try_to_progress()
if client_status == Client.WAITING_FOR_EXAMPLE:
# Provide the next example
data_point = dataset_iterator.get_next()
if data_point is None:
continue
max_num_actions = len(data_point.get_trajectory())
max_num_actions += self.constants["max_extra_horizon"]
# max_num_actions = self.constants["horizon"]
goal_image = train_images[dataset_iterator.datapoint_ix -
1]
client.accept_new_example(data_point, max_num_actions,
goal_image)
elif client_status == Client.WAITING_FOR_ACTION:
# Generate probabilities over actions and take action
log_probabilities, new_model_state, image_emb_seq = self.model.get_probs(
client.get_state(), client.get_model_state())
if isinstance(
self.model,
IncrementalModelRecurrentImplicitFactorizationResnet
):
factor_entropy = self.model.get_recent_factorization_entropy(
)
else:
factor_entropy = None
client.take_action(log_probabilities, new_model_state,
image_emb_seq, factor_entropy)
# if client_state[client_ix] is None:
# # This client has not waited so make it wait for 1 iteration
# # Take its state and compute the probabiltiy at the end.
# client_state[client_ix] = client.get_state()
# else:
# # This client has waited so its probability must be ready.
# probabilities = probabilities_batch[client_ix]
# # Generate probabilities over actions and take action
# # probabilities = list(torch.exp(self.model.get_probs(client.get_state()).data))
# client.take_action(probabilities)
# probabilities_batch[client_ix] = None
# client_state[client_ix] = None
elif client_status == Client.WAITING_TO_RECEIVE:
pass
else:
raise AssertionError("Unknown status. Found " +
str(client_status))
# states = [state for state in client_state if state is not None]
# if len(states) > 0:
# probabilities = list(torch.exp(self.model.get_probs_batch(states).data))
# assert len(states) == len(probabilities)
# ctr = 0
# for i in range(0, self.num_client):
# if client_state[i] is not None:
# probabilities_batch[i] = probabilities[ctr]
# ctr += 1
# else:
# probabilities_batch[i] = None
# Perform update
if len(batch_replay_items) > 32:
loss_val = self.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
del batch_replay_items[:] # in place list clear
# entropy_val = float(self.entropy.data[0])
# self.tensorboard.log(entropy_val, loss_val, total_reward)
cross_entropy = float(self.cross_entropy.data[0])
self.tensorboard.log(cross_entropy, loss_val, 0)
if self.action_prediction_loss is not None:
action_prediction_loss = float(
self.action_prediction_loss.data[0])
self.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if self.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
self.temporal_autoencoder_loss.data[0])
self.tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if self.object_detection_loss is not None:
object_detection_loss = float(
self.object_detection_loss.data[0])
self.tensorboard.log_object_detection_loss(
object_detection_loss)
if self.mean_factor_entropy is not None:
mean_factor_entropy = float(
self.mean_factor_entropy.data[0])
self.tensorboard.log_factor_entropy_loss(
mean_factor_entropy)
# Check if an epoch is finished. An epoch is over if all clients are waiting
# for an example (at which point the iterator also returns none)
epoch_completed = all([
client.get_status() == Client.WAITING_FOR_EXAMPLE
for client in clients
])
if epoch_completed:
assert dataset_iterator.get_next() is None
# Reset the iterator
dataset_iterator.reset()
# Save the model
self.model.save_model(experiment_name +
"/contextual_bandit_resnet_epoch_" +
str(epoch))
if epoch >= self.max_epoch:
break
epoch += 1
logging.info("Starting epoch %d", epoch)
# Test on tuning data
agent.test(tune_dataset,
tune_images,
tensorboard=self.tensorboard)
class AsynchronousSupervisedLearning(AbstractLearning):
""" Perform supervised learning """
def __init__(self, shared_model, local_model, action_space, meta_data_util,
config, constants, tensorboard):
self.max_epoch = constants["max_epochs"]
self.shared_model = shared_model
self.local_model = local_model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.epoch = 0
self.entropy_coef = constants["entropy_coefficient"]
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.local_model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.local_model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectPixelIdentification(
self.local_model,
num_objects=67,
camera_angle=60,
image_height=8,
image_width=8,
object_height=0) #-2.5)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.local_model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(self.local_model)
self.goal_prediction_loss = None
self.optimizer = optim.Adam(shared_model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.shared_model, self.local_model,
self.calc_loss, self.optimizer, self.config,
self.constants, self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
action_batch = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
self.cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
self.entropy = -torch.mean(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(chosen_log_probs) / num_states
# Essentially we want the objective to increase and cross entropy to decrease
loss = -objective - self.entropy_coef * self.entropy
self.ratio = torch.abs(objective) / (self.entropy_coef * self.entropy
) # we want the ratio to be high
# loss = -objective + self.entropy_coef * self.cross_entropy
# Minimize the Factor Entropy if the model is implicit factorization model
if isinstance(self.local_model,
IncrementalModelRecurrentImplicitFactorizationResnet):
self.mean_factor_entropy = torch.mean(torch.cat(factor_entropy))
loss = loss + self.mean_factor_entropy
else:
self.mean_factor_entropy = None
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
if self.object_detection_loss is not None:
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_loss, self.goal_prob = self.goal_prediction_calculator.calc_loss(
batch_replay_items)
if self.goal_prediction_loss is not None:
self.goal_prediction_loss = self.constants["goal_prediction_coeff"] * \
self.goal_prediction_loss
# loss = loss + self.goal_prediction_loss # * len(batch_replay_items) # scale the loss
loss = self.goal_prediction_loss
else:
loss = None
else:
self.goal_prediction_loss = None
return loss
@staticmethod
def save_goal(batch_replay_items, data_point_ix, trajectory):
assert len(batch_replay_items) == len(trajectory) + 1
f = open(
"../logs/oracle_images/tune_images/example_" + str(data_point_ix) +
"/goal.txt", "w")
for item in batch_replay_items:
row, col, row_real, col_real = item.goal
f.write("%r %r %r %r\n" % (row, col, row_real, col_real))
f.flush()
f.close()
@staticmethod
def do_train(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
try:
AsynchronousSupervisedLearning.do_train_(
shared_model, config, action_space, meta_data_util, constants,
train_dataset, tune_dataset, experiment, experiment_name, rank,
server, logger, model_type, use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_train_(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
server.initialize_server()
# Test policy
test_policy = gp.get_argmax_action
# torch.manual_seed(args.seed + rank)
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
else:
tensorboard = None
if use_pushover:
pushover_logger = PushoverLogger(experiment_name)
else:
pushover_logger = None
# Create a local model for rollouts
local_model = model_type(config, constants)
# Create the Agent
logger.log("STARTING AGENT")
agent = Agent(server=server,
model=local_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent...")
action_counts = [0] * action_space.num_actions()
max_epochs = constants["max_epochs"]
dataset_size = len(train_dataset)
tune_dataset_size = len(tune_dataset)
# Create the learner to compute the loss
learner = AsynchronousSupervisedLearning(shared_model, local_model,
action_space, meta_data_util,
config, constants,
tensorboard)
# Launch unity
launch_k_unity_builds([config["port"]],
"./simulators/NavDroneLinuxBuild.x86_64")
for epoch in range(1, max_epochs + 1):
learner.epoch = epoch
for data_point_ix, data_point in enumerate(train_dataset):
# Sync with the shared model
# local_model.load_state_dict(shared_model.state_dict())
local_model.load_from_state_dict(shared_model.get_state_dict())
if (data_point_ix + 1) % 100 == 0:
logger.log("Done %d out of %d" %
(data_point_ix, dataset_size))
logger.log("Training data action counts %r" %
action_counts)
num_actions = 0
trajectory = data_point.get_trajectory()
image, metadata = agent.server.reset_receive_feedback(
data_point)
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"], metadata["z_pos"],
metadata["y_angle"])
state = AgentObservedState(
instruction=data_point.instruction,
config=config,
constants=constants,
start_image=image,
previous_action=None,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
model_state = None
batch_replay_items = []
total_reward = 0
for action in trajectory:
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, volatile = \
local_model.get_probs(state, model_state)
action_counts[action] += 1
# Generate goal
if config["do_goal_prediction"]:
goal = learner.goal_prediction_calculator.get_goal_location(
metadata, data_point, 8, 8)
# learner.goal_prediction_calculator.save_attention_prob(image, volatile)
# time.sleep(5)
else:
goal = None
# Send the action and get feedback
image, reward, metadata = agent.server.send_action_receive_feedback(
action)
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state,
action,
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=goal)
batch_replay_items.append(replay_item)
# Update the agent state
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"],
metadata["z_pos"],
metadata["y_angle"])
state = state.update(
image,
action,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
num_actions += 1
total_reward += reward
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, volatile = \
local_model.get_probs(state, model_state)
# Generate goal
if config["do_goal_prediction"]:
goal = learner.goal_prediction_calculator.get_goal_location(
metadata, data_point, 8, 8)
# learner.goal_prediction_calculator.save_attention_prob(image, volatile)
# time.sleep(5)
else:
goal = None
# Send final STOP action and get feedback
image, reward, metadata = agent.server.halt_and_receive_feedback(
)
total_reward += reward
if tensorboard is not None:
tensorboard.log_all_train_errors(
metadata["edit_dist_error"],
metadata["closest_dist_error"],
metadata["stop_dist_error"])
# Store it in the replay memory list
replay_item = ReplayMemoryItem(
state,
action_space.get_stop_action_index(),
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=goal)
batch_replay_items.append(replay_item)
###########################################3
AsynchronousSupervisedLearning.save_goal(
batch_replay_items, data_point_ix, trajectory)
###########################################3
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
if len(batch_replay_items) > 0: # 32:
loss_val = learner.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
# del batch_replay_items[:] # in place list clear
if tensorboard is not None:
cross_entropy = float(learner.cross_entropy.data[0])
tensorboard.log(cross_entropy, loss_val, 0)
entropy = float(
learner.entropy.data[0]) / float(num_actions + 1)
tensorboard.log_scalar("entropy", entropy)
tensorboard.log_scalar("total_reward", total_reward)
ratio = float(learner.ratio.data[0])
tensorboard.log_scalar(
"Abs_objective_to_entropy_ratio", ratio)
if learner.action_prediction_loss is not None:
action_prediction_loss = float(
learner.action_prediction_loss.data[0])
learner.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if learner.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
learner.temporal_autoencoder_loss.data[0])
tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if learner.object_detection_loss is not None:
object_detection_loss = float(
learner.object_detection_loss.data[0])
tensorboard.log_object_detection_loss(
object_detection_loss)
if learner.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
learner.symbolic_language_prediction_loss.
data[0])
tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if learner.goal_prediction_loss is not None:
goal_prediction_loss = float(
learner.goal_prediction_loss.data[0])
tensorboard.log_scalar("goal_prediction_loss",
goal_prediction_loss)
if learner.goal_prob is not None:
goal_prob = float(learner.goal_prob.data[0])
tensorboard.log_scalar("goal_prob", goal_prob)
if learner.mean_factor_entropy is not None:
mean_factor_entropy = float(
learner.mean_factor_entropy.data[0])
tensorboard.log_factor_entropy_loss(
mean_factor_entropy)
# Save the model
local_model.save_model(experiment + "/supervised_learning_" +
str(rank) + "_epoch_" + str(epoch))
logger.log("Training data action counts %r" % action_counts)
if tune_dataset_size > 0:
# Test on tuning data
agent.test_goal_prediction(tune_dataset,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)
class AsynchronousTwoStageContextualBandit(AbstractLearning):
""" Perform Contextual Bandit learning (Kakade and Langford (circa 2006) & Misra, Langford and Artzi EMNLP 2017)
on the two stage model. """
def __init__(self, shared_navigator_model, local_navigator_model,
shared_predictor_model, local_predictor_model, action_space,
meta_data_util, config, constants, tensorboard):
self.max_epoch = constants["max_epochs"]
self.shared_navigator_model = shared_navigator_model
self.local_navigator_model = local_navigator_model
self.shared_predictor_model = shared_predictor_model
self.local_predictor_model = local_predictor_model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.ratio = None
self.epoch = 0
self.entropy_coef = constants["entropy_coefficient"]
self.image_channels, self.image_height, self.image_width = shared_navigator_model.image_module.get_final_dimension(
)
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.local_navigator_model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.local_navigator_model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectPixelIdentification(
self.local_navigator_model,
num_objects=67,
camera_angle=60,
image_height=self.image_height,
image_width=self.image_width,
object_height=0) # -2.5)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.local_navigator_model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(
self.local_navigator_model, self.image_height,
self.image_width)
self.goal_prediction_loss = None
parameters = self.shared_navigator_model.get_parameters()
parameters.extend(self.shared_predictor_model.get_parameters())
self.optimizer = optim.Adam(parameters, lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.shared_navigator_model,
self.local_navigator_model, self.calc_loss,
self.optimizer, self.config, self.constants,
self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
immediate_rewards = []
action_batch = []
log_probabilities = []
factor_entropy = []
chosen_log_goal_prob = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
immediate_rewards.append(replay_item.get_reward())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
chosen_log_goal_prob.append(
replay_item.get_volatile_features()["goal_sample_prob"])
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
immediate_rewards = cuda_var(
torch.from_numpy(np.array(immediate_rewards)).float())
num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
# model_log_prob_batch = self.model.get_probs_batch(agent_observation_state_ls)
chosen_log_action_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
# Take the probability of goal generation into account
chosen_log_goal_prob = torch.cat(chosen_log_goal_prob)
chosen_log_probs = chosen_log_action_probs.view(
-1) + chosen_log_goal_prob.view(-1)
reward_log_probs = immediate_rewards * chosen_log_probs
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
self.cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
# self.entropy = -torch.mean(torch.sum(model_log_prob_batch * model_prob_batch, 1))
self.entropy = -torch.sum(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(reward_log_probs) # / num_states
# Essentially we want the objective to increase and cross entropy to decrease
entropy_coef = max(0, self.entropy_coef - self.epoch * 0.01)
loss = -objective - entropy_coef * self.entropy
self.ratio = torch.abs(objective) / (entropy_coef * self.entropy
) # we want the ratio to be high
# loss = -objective + self.entropy_coef * self.cross_entropy
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
if self.object_detection_loss is not None:
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_loss, _, _ = self.goal_prediction_calculator.calc_loss(
batch_replay_items)
if self.goal_prediction_loss is not None:
self.goal_prediction_loss = self.constants["goal_prediction_coeff"] * \
self.goal_prediction_loss
loss = loss + self.goal_prediction_loss # * len(batch_replay_items) # scale the loss
else:
self.goal_prediction_loss = None
return loss
def _sample_goal(self, exploration_image, data_point, panaroma=True):
state = AgentObservedState(
instruction=data_point.instruction,
config=self.config,
constants=self.constants,
start_image=exploration_image,
previous_action=None,
pose=None,
position_orientation=data_point.get_start_pos(),
data_point=data_point)
volatile = self.local_predictor_model.get_attention_prob(
state, model_state=None)
attention_prob = list(
volatile["attention_probs"].view(-1)[:-1].data.cpu().numpy())
sampled_ix = gp.sample_action_from_prob(attention_prob)
sampled_prob = volatile["attention_probs"][sampled_ix]
#################################################
# Max pointed about that when inferred ix above is the last value then calculations are buggy. He is right.
predicted_row = int(sampled_ix / float(192))
predicted_col = sampled_ix % 192
screen_pos = (predicted_row, predicted_col)
if panaroma:
# Index of the 6 image where the goal is
region_index = int(predicted_col / 32)
predicted_col = predicted_col % 32 # Column within that image where the goal is
pos = data_point.get_start_pos()
new_pos_angle = GoalPredictionSingle360ImageSupervisedLearningFromDisk.\
get_new_pos_angle_from_region_index(region_index, pos)
metadata = {
"x_pos": pos[0],
"z_pos": pos[1],
"y_angle": new_pos_angle
}
else:
pos = data_point.get_start_pos()
metadata = {"x_pos": pos[0], "z_pos": pos[1], "y_angle": pos[2]}
row, col = predicted_row + 0.5, predicted_col + 0.5
start_pos = current_pos_from_metadata(metadata)
start_pose = current_pose_from_metadata(metadata)
goal_pos = data_point.get_destination_list()[-1]
height_drone = 2.5
x_gen, z_gen = get_inverse_object_position(
row, col, height_drone, 30, 32, 32,
(start_pos[0], start_pos[1], start_pose))
predicted_goal_pos = (x_gen, z_gen)
x_goal, z_goal = goal_pos
x_diff = x_gen - x_goal
z_diff = z_gen - z_goal
dist = math.sqrt(x_diff * x_diff + z_diff * z_diff)
return predicted_goal_pos, dist, screen_pos, sampled_prob
@staticmethod
def do_train(shared_navigator_model,
shared_predictor_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
navigator_model_type,
predictor_model_type,
use_pushover=False):
try:
AsynchronousTwoStageContextualBandit.do_train_(
shared_navigator_model, shared_predictor_model, config,
action_space, meta_data_util, constants, train_dataset,
tune_dataset, experiment, experiment_name, rank, server,
logger, navigator_model_type, predictor_model_type,
use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_train_(shared_navigator_model,
shared_predictor_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
navigator_model_type,
predictor_model_type,
use_pushover=False):
server.initialize_server()
# Test policy
test_policy = gp.get_argmax_action
# torch.manual_seed(args.seed + rank)
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
else:
tensorboard = None
if use_pushover:
pushover_logger = PushoverLogger(experiment_name)
else:
pushover_logger = None
# Create a local model for rollouts
local_predictor_model = predictor_model_type(
config,
constants,
final_model_type="unet-positional-encoding",
final_dimension=(64, 32, 32 * 6))
local_navigator_model = navigator_model_type(config, constants)
# local_model.train()
# Create the Agent
logger.log("STARTING AGENT")
agent = PredictorPlannerAgent(server=server,
predictor_model=local_predictor_model,
model=local_navigator_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent...")
action_counts = [0] * action_space.num_actions()
max_epochs = constants["max_epochs"]
dataset_size = len(train_dataset)
tune_dataset_size = len(tune_dataset)
# Create the learner to compute the loss
learner = AsynchronousTwoStageContextualBandit(
shared_navigator_model, local_navigator_model,
shared_predictor_model, local_predictor_model, action_space,
meta_data_util, config, constants, tensorboard)
# Launch unity
launch_k_unity_builds([config["port"]],
"./simulators/NavDroneLinuxBuild.x86_64")
for epoch in range(1, max_epochs + 1):
learner.epoch = epoch
task_completion_accuracy = 0
mean_stop_dist_error = 0
stop_dist_errors = []
for data_point_ix, data_point in enumerate(train_dataset):
# Sync with the shared model
# local_model.load_state_dict(shared_model.state_dict())
local_navigator_model.load_from_state_dict(
shared_navigator_model.get_state_dict())
local_predictor_model.load_from_state_dict(
shared_predictor_model.get_state_dict())
if (data_point_ix + 1) % 100 == 0:
logger.log("Done %d out of %d" %
(data_point_ix, dataset_size))
logger.log("Training data action counts %r" %
action_counts)
num_actions = 0
max_num_actions = constants["horizon"] + constants[
"max_extra_horizon"]
image, metadata = agent.server.reset_receive_feedback(
data_point)
# Generate goal probability
# Test image
panorama = agent.get_exploration_image()
# Sample a goal location and compute 3D mapping
predicted_goal, predictor_error, predicted_pixel, sample_prob = learner._sample_goal(
panorama, data_point, panaroma=True)
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"], metadata["z_pos"],
metadata["y_angle"])
state = AgentObservedState(
instruction=data_point.instruction,
config=config,
constants=constants,
start_image=image,
previous_action=None,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
current_bot_location = metadata["x_pos"], metadata["z_pos"]
current_bot_pose = metadata["y_angle"]
state.goal = PredictorPlannerAgent.get_goal_location(
current_bot_location, current_bot_pose, predicted_goal, 32,
32)
model_state = None
batch_replay_items = []
total_reward = 0
forced_stop = True
while num_actions < max_num_actions:
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, volatile = \
local_navigator_model.get_probs(state, model_state)
probabilities = list(torch.exp(log_probabilities.data))[0]
# Sample action from the probability
action = gp.sample_action_from_prob(probabilities)
action_counts[action] += 1
if action == action_space.get_stop_action_index():
forced_stop = False
break
# Send the action and get feedback
image, reward, metadata = agent.server.send_action_receive_feedback(
action)
# Store it in the replay memory list
volatile["goal_sample_prob"] = sample_prob
replay_item = ReplayMemoryItem(state,
action,
reward,
log_prob=log_probabilities,
volatile=volatile)
batch_replay_items.append(replay_item)
# Update the agent state
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"],
metadata["z_pos"],
metadata["y_angle"])
state = state.update(
image,
action,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
current_bot_location = metadata["x_pos"], metadata["z_pos"]
current_bot_pose = metadata["y_angle"]
state.goal = PredictorPlannerAgent.get_goal_location(
current_bot_location, current_bot_pose, predicted_goal,
32, 32)
num_actions += 1
total_reward += reward
# Send final STOP action and get feedback
image, reward, metadata = agent.server.halt_and_receive_feedback(
)
total_reward += reward
if metadata["stop_dist_error"] < 5.0:
task_completion_accuracy += 1
mean_stop_dist_error += metadata["stop_dist_error"]
stop_dist_errors.append(metadata["stop_dist_error"])
if tensorboard is not None:
tensorboard.log_all_train_errors(
metadata["edit_dist_error"],
metadata["closest_dist_error"],
metadata["stop_dist_error"])
# Store it in the replay memory list
if not forced_stop:
volatile["goal_sample_prob"] = sample_prob
replay_item = ReplayMemoryItem(
state,
action_space.get_stop_action_index(),
reward,
log_prob=log_probabilities,
volatile=volatile)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
if len(batch_replay_items) > 0: # 32:
loss_val = learner.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
# del batch_replay_items[:] # in place list clear
if tensorboard is not None:
tensorboard.log_scalar("gold_sample_prob",
float(sample_prob.data[0]))
tensorboard.log_scalar("predicted_error",
predictor_error)
cross_entropy = float(learner.cross_entropy.data[0])
tensorboard.log(cross_entropy, loss_val, 0)
entropy = float(
learner.entropy.data[0]) / float(num_actions + 1)
tensorboard.log_scalar("entropy", entropy)
tensorboard.log_scalar("total_reward", total_reward)
ratio = float(learner.ratio.data[0])
tensorboard.log_scalar(
"Abs_objective_to_entropy_ratio", ratio)
if learner.action_prediction_loss is not None:
action_prediction_loss = float(
learner.action_prediction_loss.data[0])
learner.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if learner.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
learner.temporal_autoencoder_loss.data[0])
tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if learner.object_detection_loss is not None:
object_detection_loss = float(
learner.object_detection_loss.data[0])
tensorboard.log_object_detection_loss(
object_detection_loss)
if learner.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
learner.symbolic_language_prediction_loss.
data[0])
tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if learner.goal_prediction_loss is not None:
goal_prediction_loss = float(
learner.goal_prediction_loss.data[0])
tensorboard.log_scalar("goal_prediction_loss",
goal_prediction_loss)
# Save the model
local_navigator_model.save_model(experiment +
"/navigator_contextual_bandit_" +
str(rank) + "_epoch_" +
str(epoch))
local_predictor_model.save_model(experiment +
"/predictor_contextual_bandit_" +
str(rank) + "_epoch_" +
str(epoch))
logger.log("Training data action counts %r" % action_counts)
mean_stop_dist_error = mean_stop_dist_error / float(
len(train_dataset))
task_completion_accuracy = (task_completion_accuracy *
100.0) / float(len(train_dataset))
logger.log("Training: Mean stop distance error %r" %
mean_stop_dist_error)
logger.log("Training: Task completion accuracy %r " %
task_completion_accuracy)
bins = range(0, 80, 3) # range of distance
histogram, _ = np.histogram(stop_dist_errors, bins)
logger.log("Histogram of train errors %r " % histogram)
if tune_dataset_size > 0:
# Test on tuning data
agent.test(tune_dataset,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)
class AsynchronousContextualBandit(AbstractLearning):
""" Perform expected reward maximization """
def __init__(self, shared_model, local_model, action_space, meta_data_util,
config, constants, tensorboard):
self.max_epoch = constants["max_epochs"]
self.shared_model = shared_model
self.local_model = local_model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.num_client = config["num_client"]
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.entropy_coef = constants["entropy_coefficient"]
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.local_model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.local_model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectDetection(
self.local_model, num_objects=67)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.local_model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(self.local_model)
self.goal_prediction_loss = None
self.optimizer = optim.Adam(shared_model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.shared_model, self.local_model,
self.calc_loss, self.optimizer, self.config,
self.constants, self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
all_rewards = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
all_rewards.append(replay_item.get_all_rewards())
all_rewards = cuda_var(
torch.from_numpy(np.array(all_rewards)).float()) # batch x action
log_probabilities = torch.cat(log_probabilities)
num_states = int(all_rewards.size()[0])
model_log_prob_batch = log_probabilities
model_prob_batch = torch.exp(model_log_prob_batch)
reward_probs = all_rewards * model_prob_batch
objective = torch.sum(reward_probs) / num_states
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
self.cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
self.entropy = -torch.mean(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
# Essentially we want the objective to increase and cross entropy to decrease
loss = -objective + self.entropy_coef * self.cross_entropy
# Minimize the Factor Entropy if the model is implicit factorization model
if isinstance(self.local_model,
IncrementalModelRecurrentImplicitFactorizationResnet):
self.mean_factor_entropy = torch.mean(torch.cat(factor_entropy))
loss = loss + self.mean_factor_entropy
else:
self.mean_factor_entropy = None
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
return loss
def get_all_rewards(self, metadata):
rewards = []
for i in range(0, self.config["num_actions"]):
reward = metadata["reward_dict"][self.action_space.get_action_name(
i)]
rewards.append(reward)
return rewards
@staticmethod
def do_train(shared_model,
config,
action_space,
meta_data_util,
args,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
try:
AsynchronousContextualBandit.do_train_(
shared_model, config, action_space, meta_data_util, args,
constants, train_dataset, tune_dataset, experiment,
experiment_name, rank, server, logger, model_type,
use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_train_(shared_model,
config,
action_space,
meta_data_util,
args,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
server.initialize_server()
# Test policy
test_policy = gp.get_argmax_action
# torch.manual_seed(args.seed + rank)
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
else:
tensorboard = None
if use_pushover:
pushover_logger = PushoverLogger(experiment_name)
else:
pushover_logger = None
# Create a local model for rollouts
local_model = model_type(args, config=config)
if torch.cuda.is_available():
local_model.cuda()
local_model.train()
# Create the Agent
logger.log("STARTING AGENT")
agent = Agent(server=server,
model=local_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent...")
action_counts = [0] * action_space.num_actions()
max_epochs = constants["max_epochs"]
dataset_size = len(train_dataset)
tune_dataset_size = len(tune_dataset)
# Create the learner to compute the loss
learner = AsynchronousContextualBandit(shared_model, local_model,
action_space, meta_data_util,
config, constants, tensorboard)
# Launch unity
launch_k_unity_builds([
config["port"]
], "/home/dipendra/Downloads/NavDroneLinuxBuild/NavDroneLinuxBuild.x86_64"
)
for epoch in range(1, max_epochs + 1):
if tune_dataset_size > 0:
# Test on tuning data
agent.test(tune_dataset,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)
for data_point_ix, data_point in enumerate(train_dataset):
# Sync with the shared model
# local_model.load_state_dict(shared_model.state_dict())
local_model.load_from_state_dict(shared_model.get_state_dict())
if (data_point_ix + 1) % 100 == 0:
logging.info("Done %d out of %d", data_point_ix,
dataset_size)
logging.info("Training data action counts %r",
action_counts)
num_actions = 0
# max_num_actions = len(data_point.get_trajectory())
# max_num_actions += self.constants["max_extra_horizon"]
max_num_actions = constants["horizon"]
image, metadata = agent.server.reset_receive_feedback(
data_point)
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"], metadata["z_pos"],
metadata["y_angle"])
state = AgentObservedState(
instruction=data_point.instruction,
config=config,
constants=constants,
start_image=image,
previous_action=None,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
model_state = None
batch_replay_items = []
total_reward = 0
forced_stop = True
while num_actions < max_num_actions:
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, state_feature = \
local_model.get_probs(state, model_state)
probabilities = list(torch.exp(log_probabilities.data))[0]
# Sample action from the probability
action = gp.sample_action_from_prob(probabilities)
action_counts[action] += 1
if action == action_space.get_stop_action_index():
forced_stop = False
break
# Send the action and get feedback
image, reward, metadata = agent.server.send_action_receive_feedback(
action)
# Store it in the replay memory list
rewards = learner.get_all_rewards(metadata)
replay_item = ReplayMemoryItem(state,
action,
reward,
log_prob=log_probabilities,
all_rewards=rewards)
batch_replay_items.append(replay_item)
# Update the agent state
pose = int(metadata["y_angle"] / 15.0)
position_orientation = (metadata["x_pos"],
metadata["z_pos"],
metadata["y_angle"])
state = state.update(
image,
action,
pose=pose,
position_orientation=position_orientation,
data_point=data_point)
num_actions += 1
total_reward += reward
# Send final STOP action and get feedback
image, reward, metadata = agent.server.halt_and_receive_feedback(
)
rewards = learner.get_all_rewards(metadata)
total_reward += reward
if tensorboard is not None:
tensorboard.log_all_train_errors(
metadata["edit_dist_error"],
metadata["closest_dist_error"],
metadata["stop_dist_error"])
# Store it in the replay memory list
if not forced_stop:
replay_item = ReplayMemoryItem(
state,
action_space.get_stop_action_index(),
reward,
log_prob=log_probabilities,
all_rewards=rewards)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
if len(batch_replay_items) > 0:
loss_val = learner.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
del batch_replay_items[:] # in place list clear
if tensorboard is not None:
cross_entropy = float(learner.cross_entropy.data[0])
tensorboard.log(cross_entropy, loss_val, 0)
entropy = float(learner.entropy.data[0])
tensorboard.log_scalar("entropy", entropy)
ratio = float(learner.ratio.data[0])
tensorboard.log_scalar(
"Abs_objective_to_entropy_ratio", ratio)
if learner.action_prediction_loss is not None:
action_prediction_loss = float(
learner.action_prediction_loss.data[0])
learner.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if learner.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
learner.temporal_autoencoder_loss.data[0])
tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if learner.object_detection_loss is not None:
object_detection_loss = float(
learner.object_detection_loss.data[0])
tensorboard.log_object_detection_loss(
object_detection_loss)
if learner.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
learner.symbolic_language_prediction_loss.
data[0])
tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if learner.goal_prediction_loss is not None:
goal_prediction_loss = float(
learner.goal_prediction_loss.data[0])
tensorboard.log_scalar("goal_prediction_loss",
goal_prediction_loss)
if learner.mean_factor_entropy is not None:
mean_factor_entropy = float(
learner.mean_factor_entropy.data[0])
tensorboard.log_factor_entropy_loss(
mean_factor_entropy)
# Save the model
local_model.save_model(experiment + "/contextual_bandit_" +
str(rank) + "_epoch_" + str(epoch))
logging.info("Training data action counts %r", action_counts)
class TmpStreetViewAsynchronousContextualBandit(AbstractLearning):
""" Temp file with modification for streetview corpus.
Perform Contextual Bandit learning (Kakade and Langford (circa 2006) & Misra, Langford and Artzi EMNLP 2017) """
def __init__(self, shared_model, local_model, action_space, meta_data_util,
config, constants, tensorboard):
self.max_epoch = constants["max_epochs"]
self.shared_model = shared_model
self.local_model = local_model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.ratio = None
self.epoch = 0
self.entropy_coef = constants["entropy_coefficient"]
# self.image_channels, self.image_height, self.image_width = shared_model.image_module.get_final_dimension()
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.local_model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.local_model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectPixelIdentification(
self.local_model,
num_objects=67,
camera_angle=60,
image_height=self.image_height,
image_width=self.image_width,
object_height=0) # -2.5)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.local_model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(
self.local_model, self.image_height, self.image_width)
self.goal_prediction_loss = None
self.optimizer = optim.Adam(shared_model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.shared_model, self.local_model,
self.calc_loss, self.optimizer, self.config,
self.constants, self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
immediate_rewards = []
action_batch = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
immediate_rewards.append(replay_item.get_reward())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
immediate_rewards = cuda_var(
torch.from_numpy(np.array(immediate_rewards)).float())
num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
# model_log_prob_batch = self.model.get_probs_batch(agent_observation_state_ls)
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
reward_log_probs = immediate_rewards * chosen_log_probs.view(-1)
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
self.cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
# self.entropy = -torch.mean(torch.sum(model_log_prob_batch * model_prob_batch, 1))
self.entropy = -torch.sum(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(reward_log_probs) # / num_states
# Essentially we want the objective to increase and cross entropy to decrease
entropy_coef = max(0, self.entropy_coef - self.epoch * 0.01)
loss = -objective - entropy_coef * self.entropy
self.ratio = torch.abs(objective) / (entropy_coef * self.entropy
) # we want the ratio to be high
# loss = -objective + self.entropy_coef * self.cross_entropy
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
if self.object_detection_loss is not None:
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_loss, _, _ = self.goal_prediction_calculator.calc_loss(
batch_replay_items)
if self.goal_prediction_loss is not None:
self.goal_prediction_loss = self.constants["goal_prediction_coeff"] * \
self.goal_prediction_loss
loss = loss + self.goal_prediction_loss # * len(batch_replay_items) # scale the loss
else:
self.goal_prediction_loss = None
return loss
@staticmethod
def do_train(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
try:
TmpStreetViewAsynchronousContextualBandit.do_train_(
shared_model, config, action_space, meta_data_util, constants,
train_dataset, tune_dataset, experiment, experiment_name, rank,
server, logger, model_type, use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_train_(shared_model,
config,
action_space,
meta_data_util,
constants,
train_dataset,
tune_dataset,
experiment,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
server.initialize_server()
# Test policy
test_policy = gp.get_argmax_action
# torch.manual_seed(args.seed + rank)
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
else:
tensorboard = None
if use_pushover:
pushover_logger = PushoverLogger(experiment_name)
else:
pushover_logger = None
# Create a local model for rollouts
local_model = model_type(config, constants)
# local_model.train()
# Create the Agent
logger.log("STARTING AGENT")
agent = Agent(server=server,
model=local_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent...")
action_counts = [0] * action_space.num_actions()
action_rewards = [0.0] * action_space.num_actions()
max_epochs = constants["max_epochs"]
dataset_size = len(train_dataset)
tune_dataset_size = len(tune_dataset)
# Create the learner to compute the loss
learner = TmpStreetViewAsynchronousContextualBandit(
shared_model, local_model, action_space, meta_data_util, config,
constants, tensorboard)
for epoch in range(1, max_epochs + 1):
learner.epoch = epoch
task_completion_accuracy = 0
mean_stop_dist_error = 0
for data_point_ix, data_point in enumerate(train_dataset):
# Sync with the shared model
# local_model.load_state_dict(shared_model.state_dict())
local_model.load_from_state_dict(shared_model.get_state_dict())
if (data_point_ix + 1) % 100 == 0:
logger.log("Done %d out of %d" %
(data_point_ix, dataset_size))
logger.log("Training data action counts %r" %
action_counts)
mean_action_reward = [
action_sum / max(1.0, action_count) for (
action_sum,
action_count) in zip(action_rewards, action_counts)
]
logger.log("Training data action rewards %r" %
mean_action_reward)
num_actions = 0
max_num_actions = constants["horizon"] + constants[
"max_extra_horizon"]
image, metadata = agent.server.reset_receive_feedback(
data_point)
state = AgentObservedState(instruction=data_point.instruction,
config=config,
constants=constants,
start_image=image,
previous_action=None,
data_point=data_point)
# state.goal = GoalPrediction.get_goal_location(metadata, data_point,
# learner.image_height, learner.image_width)
model_state = None
batch_replay_items = []
total_reward = 0
forced_stop = True
while num_actions < max_num_actions:
# Sample action using the policy
log_probabilities, model_state, image_emb_seq, volatile = \
local_model.get_probs(state, model_state)
probabilities = list(torch.exp(log_probabilities.data))[0]
# Sample action from the probability
action = gp.sample_action_from_prob(probabilities)
action_counts[action] += 1
# Generate goal
if config["do_goal_prediction"]:
goal = learner.goal_prediction_calculator.get_goal_location(
metadata, data_point, learner.image_height,
learner.image_width)
else:
goal = None
if action == action_space.get_stop_action_index():
forced_stop = False
break
# Send the action and get feedback
image, reward, metadata = agent.server.send_action_receive_feedback(
action)
action_rewards[action] += reward
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state,
action,
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=goal)
batch_replay_items.append(replay_item)
# Update the agent state
state = state.update(image, action, data_point=data_point)
# state.goal = GoalPrediction.get_goal_location(metadata, data_point,
# learner.image_height, learner.image_width)
num_actions += 1
total_reward += reward
# Send final STOP action and get feedback
image, reward, metadata = agent.server.halt_and_receive_feedback(
)
total_reward += reward
if metadata["navigation_error"] <= 5.0:
task_completion_accuracy += 1
mean_stop_dist_error += metadata["navigation_error"]
if tensorboard is not None:
tensorboard.log_scalar("navigation_error",
metadata["navigation_error"])
# Store it in the replay memory list
if not forced_stop:
replay_item = ReplayMemoryItem(
state,
action_space.get_stop_action_index(),
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=goal)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
if len(batch_replay_items) > 0: # 32:
loss_val = learner.do_update(batch_replay_items)
# self.action_prediction_loss_calculator.predict_action(batch_replay_items)
# del batch_replay_items[:] # in place list clear
if tensorboard is not None:
cross_entropy = float(learner.cross_entropy.data[0])
tensorboard.log(cross_entropy, loss_val, 0)
entropy = float(
learner.entropy.data[0]) / float(num_actions + 1)
tensorboard.log_scalar("entropy", entropy)
tensorboard.log_scalar("total_reward", total_reward)
ratio = float(learner.ratio.data[0])
tensorboard.log_scalar(
"Abs_objective_to_entropy_ratio", ratio)
logger.log(
"Avg. Entropy %r, Total Reward %r, Rollout Length %r, stop-error %r, ratio %r "
% (entropy, total_reward, num_actions + 1,
metadata["navigation_error"], ratio))
if learner.action_prediction_loss is not None:
action_prediction_loss = float(
learner.action_prediction_loss.data[0])
learner.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if learner.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
learner.temporal_autoencoder_loss.data[0])
tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if learner.object_detection_loss is not None:
object_detection_loss = float(
learner.object_detection_loss.data[0])
tensorboard.log_object_detection_loss(
object_detection_loss)
if learner.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
learner.symbolic_language_prediction_loss.
data[0])
tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if learner.goal_prediction_loss is not None:
goal_prediction_loss = float(
learner.goal_prediction_loss.data[0])
tensorboard.log_scalar("goal_prediction_loss",
goal_prediction_loss)
# Save the model
local_model.save_model(experiment + "/contextual_bandit_" +
str(rank) + "_epoch_" + str(epoch))
logger.log("Training data action counts %r" % action_counts)
mean_action_reward = [
action_sum / max(1.0, action_count)
for (action_sum,
action_count) in zip(action_rewards, action_counts)
]
logger.log("Training data action rewards %r" % mean_action_reward)
mean_stop_dist_error = mean_stop_dist_error / float(
len(train_dataset))
task_completion_accuracy = (task_completion_accuracy *
100.0) / float(len(train_dataset))
logger.log("Training: Mean stop distance error %r" %
mean_stop_dist_error)
logger.log("Training: Task completion accuracy %r " %
task_completion_accuracy)
if tune_dataset_size > 0:
logger.log("Evaluating on the tune split")
# Test on tuning data
agent.test(tune_dataset,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)
# Test on as elected train set
# logger.log("Evaluating on first 50 examples in the train split.")
# agent.test(train_dataset[0:50], tensorboard=tensorboard,
# logger=logger, pushover_logger=pushover_logger)
@staticmethod
def do_test(shared_model,
config,
action_space,
meta_data_util,
constants,
test_dataset,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
try:
TmpStreetViewAsynchronousContextualBandit.do_test_(
shared_model, config, action_space, meta_data_util, constants,
test_dataset, experiment_name, rank, server, logger,
model_type, use_pushover)
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
@staticmethod
def do_test_(shared_model,
config,
action_space,
meta_data_util,
constants,
test_dataset,
experiment_name,
rank,
server,
logger,
model_type,
use_pushover=False):
server.initialize_server()
# Test policy
test_policy = gp.get_argmax_action
# torch.manual_seed(args.seed + rank)
if rank == 0: # client 0 creates a tensorboard server
tensorboard = Tensorboard(experiment_name)
else:
tensorboard = None
if use_pushover:
pushover_logger = PushoverLogger(experiment_name)
else:
pushover_logger = None
# Create a local model for rollouts
local_model = model_type(config, constants)
# local_model.train()
# Create the Agent
logger.log("STARTING AGENT")
agent = Agent(server=server,
model=local_model,
test_policy=test_policy,
action_space=action_space,
meta_data_util=meta_data_util,
config=config,
constants=constants)
logger.log("Created Agent...")
tune_dataset_size = len(test_dataset)
local_model.load_from_state_dict(shared_model.get_state_dict())
if tune_dataset_size > 0:
# Test on tuning data
agent.test(test_dataset,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)
class SupervisedLearningFromDisk(AbstractLearning):
""" Perform maximum likelihood on oracle trajectories using images stored on disk
and hence does not need client or server. """
def __init__(self, model, action_space, meta_data_util, config, constants,
tensorboard):
self.max_epoch = constants["max_epochs"]
self.model = model
self.action_space = action_space
self.meta_data_util = meta_data_util
self.config = config
self.constants = constants
self.tensorboard = tensorboard
self.entropy = None
self.cross_entropy = None
self.entropy_coef = constants["entropy_coefficient"]
# Auxiliary Objectives
if self.config["do_action_prediction"]:
self.action_prediction_loss_calculator = ActionPrediction(
self.model)
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss_calculator = TemporalAutoEncoder(
self.model)
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss_calculator = ObjectDetection(self.model)
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss_calculator = SymbolicLanguagePrediction(
self.model)
self.symbolic_language_prediction_loss = None
self.optimizer = optim.Adam(model.get_parameters(),
lr=constants["learning_rate"])
AbstractLearning.__init__(self, self.model, self.calc_loss,
self.optimizer, self.config, self.constants,
self.tensorboard)
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
action_batch = []
log_probabilities = []
factor_entropy = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
log_probabilities.append(replay_item.get_log_prob())
factor_entropy.append(replay_item.get_factor_entropy())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
num_states = int(action_batch.size()[0])
model_log_prob_batch = log_probabilities
# model_log_prob_batch = self.model.get_probs_batch(agent_observation_state_ls)
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
model_prob_batch = torch.exp(model_log_prob_batch)
mini_batch_action_distribution = torch.mean(model_prob_batch, 0)
self.cross_entropy = -torch.sum(
gold_distribution * torch.log(mini_batch_action_distribution))
self.entropy = -torch.mean(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(chosen_log_probs) / num_states
# Essentially we want the objective to increase and cross entropy to decrease
loss = -objective # - self.entropy_coef * self.entropy
# loss = -objective + self.entropy_coef * self.cross_entropy
# Minimize the Factor Entropy if the model is implicit factorization model
if isinstance(self.model,
IncrementalModelRecurrentImplicitFactorizationResnet):
self.mean_factor_entropy = torch.mean(torch.cat(factor_entropy))
loss = loss + self.mean_factor_entropy
else:
self.mean_factor_entropy = None
if self.config["do_action_prediction"]:
self.action_prediction_loss = self.action_prediction_loss_calculator.calc_loss(
batch_replay_items)
if self.action_prediction_loss is not None:
self.action_prediction_loss = self.constants[
"action_prediction_coeff"] * self.action_prediction_loss
loss = loss + self.action_prediction_loss
else:
self.action_prediction_loss = None
if self.config["do_temporal_autoencoding"]:
self.temporal_autoencoder_loss = self.temporal_autoencoder_loss_calculator.calc_loss(
batch_replay_items)
if self.temporal_autoencoder_loss is not None:
self.temporal_autoencoder_loss = \
self.constants["temporal_autoencoder_coeff"] * self.temporal_autoencoder_loss
loss = loss + self.temporal_autoencoder_loss
else:
self.temporal_autoencoder_loss = None
if self.config["do_object_detection"]:
self.object_detection_loss = self.object_detection_loss_calculator.calc_loss(
batch_replay_items)
self.object_detection_loss = self.constants[
"object_detection_coeff"] * self.object_detection_loss
loss = loss + self.object_detection_loss
else:
self.object_detection_loss = None
if self.config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_loss = \
self.symbolic_language_prediction_loss_calculator.calc_loss(batch_replay_items)
self.symbolic_language_prediction_loss = self.constants["symbolic_language_prediction_coeff"] * \
self.symbolic_language_prediction_loss
loss = loss + self.symbolic_language_prediction_loss
else:
self.symbolic_language_prediction_loss = None
return loss
@staticmethod
def parse(folder_name):
start = time.time()
dataset = []
num_examples = len(os.listdir(folder_name))
for i in range(0, num_examples):
example_folder_name = folder_name + "/example_" + str(i)
image_names = [
file for file in os.listdir(example_folder_name)
if file.endswith('.png')
]
num_actions = len(image_names)
images = []
for j in range(0, num_actions):
img = scipy.misc.imread(example_folder_name +
"/image_" + str(j) + ".png").swapaxes(
1, 2).swapaxes(0, 1)
images.append(img)
dataset.append(images)
end = time.time()
logging.info("Parsed dataset of size %r in time % seconds",
len(dataset), (end - start))
return dataset
def calc_log_prob(self, tune_dataset, tune_image, tensorboard):
total_validation_log_probability = 0
for data_point_ix, data_point in enumerate(tune_dataset):
tune_image_example = tune_image[data_point_ix]
image = tune_image_example[0]
model_state = None
state = AgentObservedState(instruction=data_point.instruction,
config=self.config,
constants=self.constants,
start_image=image,
previous_action=None,
pose=None,
position_orientation=None,
data_point=data_point)
trajectory = data_point.get_trajectory()
validation_log_probability = 0
for action_ix, action in enumerate(trajectory):
log_probabilities, model_state, image_emb_seq = self.model.get_probs(
state, model_state)
validation_log_probability += float(
log_probabilities.data[0][action])
image = tune_image_example[action_ix + 1]
state = state.update(image,
action,
pose=None,
position_orientation=None,
data_point=data_point)
log_probabilities, model_state, image_emb_seq = self.model.get_probs(
state, model_state)
validation_log_probability += float(log_probabilities.data[0][
self.action_space.get_stop_action_index()])
mean_validation_log_probability = validation_log_probability / float(
len(trajectory) + 1)
tensorboard.log_scalar("Validation_Log_Prob",
mean_validation_log_probability)
total_validation_log_probability += mean_validation_log_probability
total_validation_log_probability /= float(max(len(tune_dataset), 1))
logging.info("Mean Validation Log Prob is %r",
total_validation_log_probability)
def do_train(self, train_dataset, train_images, tune_dataset, tune_images,
experiment_name):
""" Perform training """
dataset_size = len(train_dataset)
for epoch in range(1, self.max_epoch + 1):
logging.info("Starting epoch %d", epoch)
# Test on tuning data
self.calc_log_prob(tune_dataset,
tune_images,
tensorboard=self.tensorboard)
batch_replay_items = []
episodes_in_batch = 0
for data_point_ix, data_point in enumerate(train_dataset):
if (data_point_ix + 1) % 100 == 0:
logging.info("Done %d out of %d", data_point_ix,
dataset_size)
train_images_example = train_images[data_point_ix]
image = train_images_example[0]
symbolic_form = nav_drone_symbolic_instructions.get_nav_drone_symbolic_instruction_segment(
data_point)
model_state = None
state = AgentObservedState(instruction=data_point.instruction,
config=self.config,
constants=self.constants,
start_image=image,
previous_action=None,
pose=None,
position_orientation=None,
data_point=data_point)
trajectory = data_point.get_trajectory()
for action_ix, action in enumerate(trajectory):
# Sample action using the policy
# Generate probabilities over actions
log_probabilities, model_state, image_emb_seq = self.model.get_probs(
state, model_state)
# Send the action and get feedback
image = train_images_example[action_ix + 1]
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state,
action,
0,
log_prob=log_probabilities,
symbolic_text=symbolic_form,
image_emb_seq=image_emb_seq,
text_emb=model_state[0])
batch_replay_items.append(replay_item)
# Update the agent state
state = state.update(image,
action,
pose=None,
position_orientation=None,
data_point=data_point)
log_probabilities, model_state, image_emb_seq = self.model.get_probs(
state, model_state)
# Store it in the replay memory list
replay_item = ReplayMemoryItem(
state,
self.action_space.get_stop_action_index(),
0,
log_prob=log_probabilities,
symbolic_text=symbolic_form,
image_emb_seq=image_emb_seq,
text_emb=model_state[0])
batch_replay_items.append(replay_item)
# Perform update
episodes_in_batch += 1
if episodes_in_batch == 1:
episodes_in_batch = 0
loss_val = self.do_update(batch_replay_items)
del batch_replay_items[:] # in place list clear
self.tensorboard.log_scalar("loss", loss_val)
cross_entropy = float(self.cross_entropy.data[0])
self.tensorboard.log_scalar("cross_entropy", cross_entropy)
entropy = float(self.entropy.data[0])
self.tensorboard.log_scalar("entropy", entropy)
if self.action_prediction_loss is not None:
action_prediction_loss = float(
self.action_prediction_loss.data[0])
self.tensorboard.log_action_prediction_loss(
action_prediction_loss)
if self.temporal_autoencoder_loss is not None:
temporal_autoencoder_loss = float(
self.temporal_autoencoder_loss.data[0])
self.tensorboard.log_temporal_autoencoder_loss(
temporal_autoencoder_loss)
if self.object_detection_loss is not None:
object_detection_loss = float(
self.object_detection_loss.data[0])
self.tensorboard.log_object_detection_loss(
object_detection_loss)
if self.symbolic_language_prediction_loss is not None:
symbolic_language_prediction_loss = float(
self.symbolic_language_prediction_loss.data[0])
self.tensorboard.log_scalar(
"sym_language_prediction_loss",
symbolic_language_prediction_loss)
if self.mean_factor_entropy is not None:
mean_factor_entropy = float(
self.mean_factor_entropy.data[0])
self.tensorboard.log_factor_entropy_loss(
mean_factor_entropy)
# Save the model
self.model.save_model(experiment_name +
"/contextual_bandit_resnet_epoch_" +
str(epoch))