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)
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 ReinforceLearning(AbstractLearning):
""" Perform Reinforce Learning (Williams 1992) """
def __init__(self, model, action_space, meta_data_util, config, constants):
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_coef = constants["entropy_coefficient"]
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 = []
q_values = []
action_batch = []
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())
q_values.append(replay_item.get_q_val())
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
q_values = cuda_var(torch.from_numpy(np.array(q_values)))
num_states = int(action_batch.size()[0])
model_prob_batch = self.model.get_probs_batch(agent_observation_state_ls)
chosen_log_probs = model_prob_batch.gather(1, action_batch.view(-1, 1))
reward_log_probs = q_values * chosen_log_probs.view(-1)
entropy = -torch.mean(torch.sum(model_prob_batch * torch.exp(model_prob_batch), 1))
objective = torch.sum(reward_log_probs) / num_states
loss = -(objective + self.entropy_coef * entropy)
self.entropy = entropy
return loss
@staticmethod
def _set_q_val(batch_replay_items):
rewards = [replay_item.get_reward() for replay_item in batch_replay_items]
for ix, replay_item in enumerate(batch_replay_items):
q_val = sum(rewards[ix:])
replay_item.set_q_val(q_val)
def do_train(self, agent, train_dataset, tune_dataset, experiment_name):
""" Perform training """
for epoch in range(1, self.max_epoch + 1):
# Test on tuning data
agent.test(tune_dataset, tensorboard=self.tensorboard)
for data_point in train_dataset:
batch_replay_items = []
num_actions = 0
total_reward = 0
max_num_actions = len(data_point.get_trajectory())
max_num_actions += self.constants["max_extra_horizon"]
image, metadata = agent.server.reset_receive_feedback(data_point)
state = AgentObservedState(instruction=data_point.instruction,
config=self.config,
constants=self.constants,
start_image=image,
previous_action=None)
forced_stop = True
instruction = instruction_to_string(
data_point.get_instruction(), self.config)
print "TRAIN INSTRUCTION: %r" % instruction
print ""
while num_actions < max_num_actions:
# Sample action using the policy
# Generate probabilities over actions
probabilities = list(torch.exp(self.model.get_probs(state).data))
# Use test policy to get the action
action = gp.sample_action_from_prob(probabilities)
if action == agent.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)
total_reward += reward
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state, action, reward)
batch_replay_items.append(replay_item)
# Update the agent state
state = state.update(image, action)
num_actions += 1
# Send final STOP action and get feedback
image, reward, metadata = agent.server.halt_and_receive_feedback()
total_reward += reward
# Store it in the replay memory list
if not forced_stop:
replay_item = ReplayMemoryItem(state, agent.action_space.get_stop_action_index(), reward)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Compute Q-values using sampled rollout
ReinforceLearning._set_q_val(batch_replay_items)
# Perform update
loss_val = self.do_update(batch_replay_items)
entropy_val = float(self.entropy.data[0])
self.tensorboard.log(entropy_val, loss_val, total_reward)
self.tensorboard.log_train_error(metadata["error"])
# Save the model
self.model.save_model(experiment_name + "/reinforce_epoch_" + str(epoch))
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)
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 = TmpStreetViewAsynchronousSupervisedLearning(
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
time_taken = dict()
time_taken["prob_time"] = 0.0
time_taken["update_time"] = 0.0
time_taken["server_time"] = 0.0
time_taken["total_time"] = 0.0
for data_point_ix, data_point in enumerate(train_dataset):
start = time.time()
# 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)
logger.log(
"Total Time %f, Server Time %f, Update Time %f, Prob Time %f "
% (time_taken["total_time"], time_taken["server_time"],
time_taken["update_time"], time_taken["prob_time"]))
num_actions = 0
time_start = time.time()
image, metadata = agent.server.reset_receive_feedback(
data_point)
time_taken["server_time"] += time.time() - time_start
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
trajectory = agent.server.get_trajectory_exact(
data_point.trajectory)
trajectory = trajectory[:min(len(trajectory
), constants["horizon"])]
for action in trajectory:
# Sample action using the policy
time_start = time.time()
log_probabilities, model_state, image_emb_seq, volatile = \
local_model.get_probs(state, model_state)
time_taken["prob_time"] += time.time() - time_start
# Sample action from the probability
action_counts[action] += 1
# Send the action and get feedback
time_start = time.time()
image, reward, metadata = agent.server.send_action_receive_feedback(
action)
time_taken["server_time"] += time.time() - time_start
# Store it in the replay memory list
replay_item = ReplayMemoryItem(state,
action,
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=None)
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
time_start = time.time()
log_probabilities, model_state, image_emb_seq, volatile = \
local_model.get_probs(state, model_state)
time_taken["prob_time"] += time.time() - time_start
# Send final STOP action and get feedback
time_start = time.time()
image, reward, metadata = agent.server.halt_and_receive_feedback(
)
time_taken["server_time"] += time.time() - time_start
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
replay_item = ReplayMemoryItem(
state,
action_space.get_stop_action_index(),
reward,
log_prob=log_probabilities,
volatile=volatile,
goal=None)
batch_replay_items.append(replay_item)
# Update the scores based on meta_data
# self.meta_data_util.log_results(metadata)
# Perform update
time_start = time.time()
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 = 0.0 # float(learner.cross_entropy.data[0])
tensorboard.log(cross_entropy, loss_val, 0)
entropy = float(
learner.entropy.data[0]) / float(num_actions + 1)
logger.log(
"Entropy %r, Total Reward %r, Loss %r, Num Actions %d, stop-error %r "
% (entropy, total_reward, loss_val,
num_actions + 1, metadata["navigation_error"]))
tensorboard.log_scalar("entropy", entropy)
tensorboard.log_scalar("total_reward", total_reward)
time_taken["update_time"] += time.time() - time_start
time_taken["total_time"] += time.time() - start
# Save the model
local_model.save_model(experiment + "/supervised_learning" +
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)
if tune_dataset_size > 0:
# Test on tuning data
agent.test(tune_dataset,
tensorboard=tensorboard,
logger=logger,
pushover_logger=pushover_logger)