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 __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 __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 __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.epoch = 0
self.global_id = 0
self.entropy_coef = constants["entropy_coefficient"]
self.final_num_channels, self.final_height, self.final_width = model.image_module.get_final_dimension(
)
self.ignore_none = False
self.inference_procedure = GoalPredictionHouseSingle360ImageSupervisedLearningFromDisk.MODE
# 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 = None
# self.object_detection_loss_calculator = ObjectPixelIdentification(
# self.model, num_objects=67, camera_angle=60, image_height=self.final_height,
# image_width=self.final_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.model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(
self.model, self.final_height, self.final_width)
self.goal_prediction_loss = None
self.cross_entropy_loss = None
self.dist_loss = None
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,
self.tensorboard)
logging.info("Created Single Image goal predictor with ignore_none %r",
self.ignore_none)
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 __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.epoch = 0
self.entropy_coef = constants["entropy_coefficient"]
self.final_height, self.final_width = 32, 32
self.ignore_none = True
self.only_first = True
self.vocab = {}
vocab_path = config["vocab_file"]
word_index = 0
with open(vocab_path) as f:
for line in f.readlines():
token = line.strip()
self.vocab[token] = word_index
word_index += 1
# 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 = ObjectPixelIdentification(
self.model,
num_objects=67,
camera_angle=60,
image_height=self.final_height,
image_width=self.final_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.model)
self.symbolic_language_prediction_loss = None
if self.config["do_goal_prediction"]:
self.goal_prediction_calculator = GoalPrediction(
self.model, self.final_height, self.final_width)
self.goal_prediction_loss = None
self.cross_entropy_loss = None
self.dist_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)
logging.info(
"Created Goal predictor with ignore_none %r and only_first %r",
self.ignore_none, self.only_first)