def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
# self.image_module = resnet.resnet18(pretrained=True)
# constants["image_emb_dim"] = 1000
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (2 * constants["image_emb_dim"]
+ constants["action_emb_dim"])
if config["do_action_prediction"]:
self.action_prediction_module = ActionPredictionModule(
2 * constants["image_emb_dim"], constants["image_emb_dim"], config["num_actions"])
else:
self.action_prediction_module = None
if config["do_temporal_autoencoding"]:
self.temporal_autoencoder_module = TemporalAutoencoderModule(
self.action_module, constants["image_emb_dim"], constants["action_emb_dim"], constants["image_emb_dim"])
else:
self.temporal_autoencoder_module = None
if config["do_object_detection"]:
self.landmark_names = get_all_landmark_names()
self.object_detection_module = ObjectDetectionModule(
image_module=self.image_module, image_emb_size=constants["image_emb_dim"], num_objects=63)
else:
self.object_detection_module = None
final_module = IncrementalMultimodalRecurrentSimpleGoalImageModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
if self.action_prediction_module is not None:
self.action_prediction_module.cuda()
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.cuda()
if self.object_detection_module is not None:
self.object_detection_module.cuda()
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
if config["use_pointer_model"]:
raise AssertionError("Not implemented")
# self.text_module = TextPointerModule(
# emb_dim=constants["word_emb_dim"],
# hidden_dim=constants["lstm_emb_dim"],
# vocab_size=config["vocab_size"])
else:
self.text_module = TextImplicitFactorizationModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"],
num_factors=2,
factors_vocabulary_size=60,
factors_embedding_size=250)
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
if config["use_pointer_model"]:
total_emb_size = (constants["image_emb_dim"] +
4 * constants["lstm_emb_dim"] +
constants["action_emb_dim"])
else:
total_emb_size = (constants["image_emb_dim"] + 2 * 250 +
constants["action_emb_dim"])
final_module = IncrementalMultimodalRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageTextKernelResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
text_emb_size=constants["lstm_emb_dim"],
using_recurrence=True)
self.image_recurrence_module = RecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
if config["use_pointer_model"]:
self.text_module = TextPointerModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
else:
self.text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
if config["use_pointer_model"]:
total_emb_size = (constants["image_emb_dim"] +
4 * constants["lstm_emb_dim"] +
constants["action_emb_dim"])
else:
total_emb_size = (constants["image_emb_dim"] +
constants["lstm_emb_dim"] +
constants["action_emb_dim"])
final_module = MultimodalTextKernelRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.config = config
self.constants = constants
# CNN over images - using SimpleImage for testing for now!
self.image_module = ImageCnnEmnlp(
image_emb_size=config["image_emb_dim"],
input_num_channels=3 *
5, #3 channels per image - 5 images in history
image_height=config["image_height"],
image_width=config["image_width"])
# this is somewhat counter intuitivie - emb_dim is the word size
# hidden_size is the output size
self.text_module = TextSimpleModule(emb_dim=config["word_emb_dim"],
hidden_dim=config["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.previous_action_module = ActionSimpleModule(
num_actions=config["no_actions"],
action_emb_size=config["previous_action_embedding_dim"])
self.previous_block_module = ActionSimpleModule(
num_actions=config["no_blocks"],
action_emb_size=config["previous_block_embedding_dim"])
self.final_module = IncrementalMultimodalEmnlp(
image_module=self.image_module,
text_module=self.text_module,
previous_action_module=self.previous_action_module,
previous_block_module=self.previous_block_module,
input_embedding_size=config["lstm_emb_dim"] +
config["image_emb_dim"] + config["previous_action_embedding_dim"] +
config["previous_block_embedding_dim"],
output_hidden_size=config["h1_hidden_dim"],
blocks_hidden_size=config["no_blocks"],
directions_hidden_size=config["no_actions"],
max_episode_length=(constants["horizon"] + 5))
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.previous_action_module.cuda()
self.previous_block_module.cuda()
self.final_module.cuda()
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
landmark_names = get_all_landmark_names()
self.radius_module = RadiusModule(15)
self.angle_module = AngleModule(48)
self.landmark_module = LandmarkModule(63)
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.image_recurrence_module = RecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
self.text_module = SymbolicInstructionModule(
radius_embedding=self.radius_module,
theta_embedding=self.angle_module,
landmark_embedding=self.landmark_module)
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (constants["image_emb_dim"]
+ 32 * 4
+ constants["action_emb_dim"])
final_module = MultimodalRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
self.radius_module.cuda()
self.angle_module.cuda()
self.landmark_module.cuda()
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.config = config
self.constants = constants
# CNN over images - using what is essentially SimpleImage currently
self.image_module = ImageCnnEmnlp(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3 *
5, # 3 channels per image - 5 images in history
image_height=config["image_height"],
image_width=config["image_width"])
# LSTM to embed text
self.text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
# Action module to embed previous action+block
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
# Put it all together
self.final_module = IncrementalMultimodalEmnlp(
image_module=self.image_module,
text_module=self.text_module,
action_module=self.action_module,
input_embedding_size=constants["lstm_emb_dim"] +
constants["image_emb_dim"] + constants["action_emb_dim"],
output_hidden_size=config["h1_hidden_dim"],
blocks_hidden_size=config["blocks_hidden_dim"],
directions_hidden_size=config["action_hidden_dim"],
max_episode_length=(constants["horizon"] + 5))
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
landmark_names = get_all_landmark_names()
self.radius_module = RadiusModule(15)
self.angle_module = AngleModule(48)
self.landmark_module = LandmarkModule(63)
self.image_module = SymbolicImageModule(
landmark_names=landmark_names,
radius_module=self.radius_module,
angle_module=self.angle_module,
landmark_module=self.landmark_module)
if config["use_pointer_model"]:
self.text_module = TextPointerModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
else:
self.text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (32 * 3 * 63
+ constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
final_module = MultimodalSimpleModule(
image_module=self.image_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
self.radius_module.cuda()
self.angle_module.cuda()
self.landmark_module.cuda()
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
landmark_names = get_all_landmark_names()
self.radius_module = RadiusModule(15)
self.angle_module = AngleModule(48)
self.landmark_module = LandmarkModule(63)
self.image_module = SymbolicImageModule(
landmark_names=landmark_names,
radius_module=self.radius_module,
angle_module=self.angle_module,
landmark_module=self.landmark_module)
self.text_module = SymbolicInstructionModule(
radius_embedding=self.radius_module,
theta_embedding=self.angle_module,
landmark_embedding=self.landmark_module)
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (32 * 3 * 63
+ 32 * 4
+ constants["action_emb_dim"])
final_module = MultimodalSimpleModule(
image_module=self.image_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
self.radius_module.cuda()
self.angle_module.cuda()
self.landmark_module.cuda()
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3*constants["max_num_images"],
image_height=config["image_height"],
image_width=config["image_width"])
if config["use_pointer_model"]:
self.text_module = TextPointerModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
else:
self.text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (constants["image_emb_dim"]
+ constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
final_module = MultimodalSimpleWithStopModule(
image_module=self.image_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
class ModelPolicyNetworkResnetWithStop(AbstractModel):
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3*constants["max_num_images"],
image_height=config["image_height"],
image_width=config["image_width"])
if config["use_pointer_model"]:
self.text_module = TextPointerModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
else:
self.text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (constants["image_emb_dim"]
+ constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
final_module = MultimodalSimpleWithStopModule(
image_module=self.image_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True
)
images = [aos.get_image() for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(images)).float())
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
read_pointers = [aos.get_read_pointers()
for aos in agent_observed_state_list]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch = self.final_module(image_batch, instructions_batch,
prev_actions_batch, mode)
return probs_batch
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for text nn
text_module_path = os.path.join(save_dir, "text_module_state.bin")
torch.save(self.text_module.state_dict(), text_module_path)
# save state file for action emb
action_module_path = os.path.join(save_dir, "action_module_state.bin")
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
def get_parameters(self):
parameters = list(self.image_module.parameters())
parameters += list(self.text_module.parameters())
parameters += list(self.action_module.parameters())
parameters += list(self.final_module.parameters())
return parameters
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.num_cameras = 1
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=(constants["image_emb_dim"] * self.num_cameras + constants["action_emb_dim"]),
output_emb_dim=constants["image_emb_dim"])
if config["use_pointer_model"]:
self.text_module = TextPointerModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
else:
self.text_module = TextBiLSTMModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
if config["use_pointer_model"]:
total_emb_size = (constants["image_emb_dim"]
+ 4 * constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
else:
total_emb_size = ((self.num_cameras + 1) * constants["image_emb_dim"]
+ 2 * constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
if config["do_action_prediction"]:
self.action_prediction_module = ActionPredictionModule(
2 * self.num_cameras * constants["image_emb_dim"], constants["image_emb_dim"], config["num_actions"])
else:
self.action_prediction_module = None
if config["do_temporal_autoencoding"]:
self.temporal_autoencoder_module = TemporalAutoencoderModule(
self.action_module, self.num_cameras * constants["image_emb_dim"],
constants["action_emb_dim"], constants["image_emb_dim"])
else:
self.temporal_autoencoder_module = None
if config["do_object_detection"]:
self.landmark_names = get_all_landmark_names()
self.object_detection_module = ObjectDetectionModule(
image_module=self.image_module, image_emb_size=self.num_cameras * constants["image_emb_dim"], num_objects=67)
else:
self.object_detection_module = None
if config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_module = SymbolicLanguagePredictionModule(
total_emb_size=2 * constants["lstm_emb_dim"])
else:
self.symbolic_language_prediction_module = None
if config["do_goal_prediction"]:
self.goal_prediction_module = GoalPredictionModule(
total_emb_size=32)
else:
self.goal_prediction_module = None
final_module = TmpIncrementalMultimodalDenseValtsRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
if self.action_prediction_module is not None:
self.action_prediction_module.cuda()
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.cuda()
if self.object_detection_module is not None:
self.object_detection_module.cuda()
if self.symbolic_language_prediction_module is not None:
self.symbolic_language_prediction_module.cuda()
if self.goal_prediction_module is not None:
self.goal_prediction_module.cuda()
class IncrementalModelRecurrentPolicyNetworkResnet(AbstractIncrementalModel):
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.num_cameras = 1
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=(constants["image_emb_dim"] * self.num_cameras + constants["action_emb_dim"]),
output_emb_dim=constants["image_emb_dim"])
if config["use_pointer_model"]:
self.text_module = TextPointerModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
else:
self.text_module = TextBiLSTMModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
if config["use_pointer_model"]:
total_emb_size = (constants["image_emb_dim"]
+ 4 * constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
else:
total_emb_size = ((self.num_cameras + 1) * constants["image_emb_dim"]
+ 2 * constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
if config["do_action_prediction"]:
self.action_prediction_module = ActionPredictionModule(
2 * self.num_cameras * constants["image_emb_dim"], constants["image_emb_dim"], config["num_actions"])
else:
self.action_prediction_module = None
if config["do_temporal_autoencoding"]:
self.temporal_autoencoder_module = TemporalAutoencoderModule(
self.action_module, self.num_cameras * constants["image_emb_dim"],
constants["action_emb_dim"], constants["image_emb_dim"])
else:
self.temporal_autoencoder_module = None
if config["do_object_detection"]:
self.landmark_names = get_all_landmark_names()
self.object_detection_module = ObjectDetectionModule(
image_module=self.image_module, image_emb_size=self.num_cameras * constants["image_emb_dim"], num_objects=67)
else:
self.object_detection_module = None
if config["do_symbolic_language_prediction"]:
self.symbolic_language_prediction_module = SymbolicLanguagePredictionModule(
total_emb_size=2 * constants["lstm_emb_dim"])
else:
self.symbolic_language_prediction_module = None
if config["do_goal_prediction"]:
self.goal_prediction_module = GoalPredictionModule(
total_emb_size=32)
else:
self.goal_prediction_module = None
final_module = TmpIncrementalMultimodalDenseValtsRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
if self.action_prediction_module is not None:
self.action_prediction_module.cuda()
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.cuda()
if self.object_detection_module is not None:
self.object_detection_module.cuda()
if self.symbolic_language_prediction_module is not None:
self.symbolic_language_prediction_module.cuda()
if self.goal_prediction_module is not None:
self.goal_prediction_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
raise AssertionError("Buggy")
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True
)
image_seq_lens = [aos.get_num_images()
for aos in agent_observed_state_list]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
max_len = max(image_seq_lens)
image_seqs = [aos.get_image()[:max_len]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
read_pointers = [aos.get_read_pointers()
for aos in agent_observed_state_list]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, _ = self.final_module(image_batch, image_seq_lens_batch,
instructions_batch, prev_actions_batch,
mode, model_state=None)
return probs_batch
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
# max_len = max(image_seq_lens)
# image_seqs = [aos.get_image()[:max_len]
# for aos in agent_observed_state_list]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
read_pointers = [aos.get_read_pointers()
for aos in agent_observed_state_list]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)), volatile)
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, image_seq_lens_batch, instructions_batch, prev_actions_batch, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
def action_prediction_log_prob(self, batch_input):
assert self.action_prediction_module is not None, "Action prediction module not created. Check config."
return self.action_prediction_module(batch_input)
def predict_action_result(self, batch_image_feature, action_batch):
assert self.temporal_autoencoder_module is not None, "Temporal action module not created. Check config."
return self.temporal_autoencoder_module(batch_image_feature, action_batch)
def predict_goal_result(self, batch_state_feature):
assert self.goal_prediction_module is not None, "Goal Prediction module not created. Check config."
return self.goal_prediction_module(batch_state_feature)
def get_probs_and_visible_objects(self, agent_observed_state_list, batch_image_feature):
assert self.object_detection_module is not None, "Object detection module not created. Check config."
landmarks_visible = []
for aos in agent_observed_state_list:
x_pos, z_pos, y_angle = aos.get_position_orientation()
landmark_pos_dict = aos.get_landmark_pos_dict()
visible_landmarks_dict = self.object_detection_module.get_visible_landmark_r_theta(
x_pos, z_pos, y_angle, landmark_pos_dict)
landmarks_visible.append(visible_landmarks_dict)
# shape is BATCH_SIZE x num objects x 2
landmark_log_prob, distance_log_prob, theta_log_prob = self.object_detection_module(batch_image_feature)
# landmarks_visible is list of length BATCH_SIZE, each item is a set containing landmark indices
return landmark_log_prob, distance_log_prob, theta_log_prob, landmarks_visible
def get_language_prediction_probs(self, batch_input):
assert self.symbolic_language_prediction_module is not None, \
"Language prediction module not created. Check config."
return self.symbolic_language_prediction_module(batch_input)
def init_weights(self):
self.text_module.init_weights()
self.image_recurrence_module.init_weights()
self.image_module.init_weights()
def share_memory(self):
self.image_module.share_memory()
self.image_recurrence_module.share_memory()
self.text_module.share_memory()
self.action_module.share_memory()
self.final_module.share_memory()
if self.action_prediction_module is not None:
self.action_prediction_module.share_memory()
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.share_memory()
if self.object_detection_module is not None:
self.object_detection_module.share_memory()
if self.symbolic_language_prediction_module is not None:
self.symbolic_language_prediction_module.share_memory()
if self.goal_prediction_module is not None:
self.goal_prediction_module.share_memory()
def get_state_dict(self):
nested_state_dict = dict()
nested_state_dict["image_module"] = self.image_module.state_dict()
nested_state_dict["image_recurrence_module"] = self.image_recurrence_module.state_dict()
nested_state_dict["text_module"] = self.text_module.state_dict()
nested_state_dict["action_module"] = self.action_module.state_dict()
nested_state_dict["final_module"] = self.final_module.state_dict()
if self.action_prediction_module is not None:
nested_state_dict["ap_module"] = self.action_prediction_module.state_dict()
if self.temporal_autoencoder_module is not None:
nested_state_dict["tae_module"] = self.temporal_autoencoder_module.state_dict()
if self.object_detection_module is not None:
nested_state_dict["od_module"] = self.object_detection_module.state_dict()
if self.symbolic_language_prediction_module is not None:
nested_state_dict["sym_lang_module"] = self.symbolic_language_prediction_module.state_dict()
if self.goal_prediction_module is not None:
nested_state_dict["goal_pred_module"] = self.goal_prediction_module.state_dict()
return nested_state_dict
def load_from_state_dict(self, nested_state_dict):
self.image_module.load_state_dict(nested_state_dict["image_module"])
self.image_recurrence_module.load_state_dict(nested_state_dict["image_recurrence_module"])
self.text_module.load_state_dict(nested_state_dict["text_module"])
self.action_module.load_state_dict(nested_state_dict["action_module"])
self.final_module.load_state_dict(nested_state_dict["final_module"])
if self.action_prediction_module is not None:
self.action_prediction_module.load_state_dict(nested_state_dict["ap_module"])
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.load_state_dict(nested_state_dict["tae_module"])
if self.object_detection_module is not None:
self.object_detection_module.load_state_dict(nested_state_dict["od_module"])
if self.symbolic_language_prediction_module is not None:
self.symbolic_language_prediction_module.load_state_dict(nested_state_dict["sym_lang_module"])
if self.goal_prediction_module is not None:
self.goal_prediction_module.load_state_dict(nested_state_dict["goal_pred_module"])
def load_resnet_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
def fix_resnet(self):
self.image_module.fix_resnet()
def load_lstm_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
image_recurrence_module_path = os.path.join(
load_dir, "image_recurrence_module_state.bin")
self.image_recurrence_module.load_state_dict(
torch_load(image_recurrence_module_path))
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
if self.action_prediction_module is not None:
auxiliary_action_prediction_path = os.path.join(load_dir, "auxiliary_action_prediction.bin")
self.action_prediction_module.load_state_dict(torch_load(auxiliary_action_prediction_path))
if self.temporal_autoencoder_module is not None:
auxiliary_temporal_autoencoder_path = os.path.join(load_dir, "auxiliary_temporal_autoencoder.bin")
self.temporal_autoencoder_module.load_state_dict(torch_load(auxiliary_temporal_autoencoder_path))
if self.object_detection_module is not None:
auxiliary_object_detection_path = os.path.join(load_dir, "auxiliary_object_detection.bin")
self.object_detection_module.load_state_dict(torch_load(auxiliary_object_detection_path))
if self.symbolic_language_prediction_module is not None:
auxiliary_symbolic_language_prediction_path = os.path.join(
load_dir, "auxiliary_symbolic_language_prediction.bin")
self.symbolic_language_prediction_module.load_state_dict(
torch_load(auxiliary_symbolic_language_prediction_path))
if self.goal_prediction_module is not None:
auxiliary_goal_prediction_path = os.path.join(load_dir, "auxiliary_goal_prediction.bin")
self.goal_prediction_module.load_state_dict(torch_load(auxiliary_goal_prediction_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for image recurrence nn
image_recurrence_module_path = os.path.join(
save_dir, "image_recurrence_module_state.bin")
torch.save(self.image_recurrence_module.state_dict(),
image_recurrence_module_path)
# save state file for text nn
text_module_path = os.path.join(save_dir, "text_module_state.bin")
torch.save(self.text_module.state_dict(), text_module_path)
# save state file for action emb
action_module_path = os.path.join(save_dir, "action_module_state.bin")
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
# save the auxiliary models
if self.action_prediction_module is not None:
auxiliary_action_prediction_path = os.path.join(save_dir, "auxiliary_action_prediction.bin")
torch.save(self.action_prediction_module.state_dict(), auxiliary_action_prediction_path)
if self.temporal_autoencoder_module is not None:
auxiliary_temporal_autoencoder_path = os.path.join(save_dir, "auxiliary_temporal_autoencoder.bin")
torch.save(self.temporal_autoencoder_module.state_dict(), auxiliary_temporal_autoencoder_path)
if self.object_detection_module is not None:
auxiliary_object_detection_path = os.path.join(save_dir, "auxiliary_object_detection.bin")
torch.save(self.object_detection_module.state_dict(), auxiliary_object_detection_path)
if self.symbolic_language_prediction_module is not None:
auxiliary_symbolic_language_prediction_path = os.path.join(
save_dir, "auxiliary_symbolic_language_prediction.bin")
torch.save(self.symbolic_language_prediction_module.state_dict(),
auxiliary_symbolic_language_prediction_path)
if self.goal_prediction_module is not None:
auxiliary_goal_prediction_path = os.path.join(save_dir, "auxiliary_goal_prediction.bin")
torch.save(self.goal_prediction_module.state_dict(), auxiliary_goal_prediction_path)
def get_parameters(self):
parameters = list(self.image_module.parameters())
parameters += list(self.image_recurrence_module.parameters())
parameters += list(self.text_module.parameters())
parameters += list(self.action_module.parameters())
parameters += list(self.final_module.parameters())
if self.action_prediction_module is not None:
parameters += list(self.action_prediction_module.parameters())
if self.temporal_autoencoder_module is not None:
parameters += list(self.temporal_autoencoder_module.parameters())
if self.object_detection_module is not None:
parameters += list(self.object_detection_module.parameters())
if self.symbolic_language_prediction_module is not None:
parameters += list(self.symbolic_language_prediction_module.parameters())
if self.goal_prediction_module is not None:
parameters += list(self.goal_prediction_module.parameters())
return parameters
def get_named_parameters(self):
named_parameters = list(self.image_module.named_parameters())
named_parameters += list(self.image_recurrence_module.named_parameters())
named_parameters += list(self.text_module.named_parameters())
named_parameters += list(self.action_module.named_parameters())
named_parameters += list(self.final_module.named_parameters())
if self.action_prediction_module is not None:
named_parameters += list(self.action_prediction_module.named_parameters())
if self.temporal_autoencoder_module is not None:
named_parameters += list(self.temporal_autoencoder_module.named_parameters())
if self.object_detection_module is not None:
named_parameters += list(self.object_detection_module.named_parameters())
if self.symbolic_language_prediction_module is not None:
named_parameters += list(self.symbolic_language_prediction_module.named_parameters())
if self.goal_prediction_module is not None:
named_parameters += list(self.goal_prediction_module.named_parameters())
return named_parameters
class IncrementalModelRecurrentPolicyNetworkGoalImageResnet(AbstractIncrementalModel):
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
# self.image_module = resnet.resnet18(pretrained=True)
# constants["image_emb_dim"] = 1000
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (2 * constants["image_emb_dim"]
+ constants["action_emb_dim"])
if config["do_action_prediction"]:
self.action_prediction_module = ActionPredictionModule(
2 * constants["image_emb_dim"], constants["image_emb_dim"], config["num_actions"])
else:
self.action_prediction_module = None
if config["do_temporal_autoencoding"]:
self.temporal_autoencoder_module = TemporalAutoencoderModule(
self.action_module, constants["image_emb_dim"], constants["action_emb_dim"], constants["image_emb_dim"])
else:
self.temporal_autoencoder_module = None
if config["do_object_detection"]:
self.landmark_names = get_all_landmark_names()
self.object_detection_module = ObjectDetectionModule(
image_module=self.image_module, image_emb_size=constants["image_emb_dim"], num_objects=63)
else:
self.object_detection_module = None
final_module = IncrementalMultimodalRecurrentSimpleGoalImageModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
if self.action_prediction_module is not None:
self.action_prediction_module.cuda()
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.cuda()
if self.object_detection_module is not None:
self.object_detection_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
raise NotImplementedError()
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True
)
image_seq_lens = [aos.get_num_images()
for aos in agent_observed_state_list]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
max_len = max(image_seq_lens)
image_seqs = [aos.get_image()[:max_len]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
read_pointers = [aos.get_read_pointers()
for aos in agent_observed_state_list]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, _ = self.final_module(image_batch, image_seq_lens_batch,
instructions_batch, prev_actions_batch,
mode, model_state=None)
return probs_batch
# def resize(self, img):
# img = img.swapaxes(0, 1).swapaxes(1, 2)
# resized_img = scipy.misc.imresize(img, (224, 224))
# return resized_img.swapaxes(1, 2).swapaxes(0, 1)
#
# def get_probs(self, agent_observed_state, model_state, mode=None):
#
# assert isinstance(agent_observed_state, AgentObservedState)
# agent_observed_state_list = [agent_observed_state]
#
# image_seq_lens = [1]
# image_seq_lens_batch = cuda_tensor(
# torch.from_numpy(np.array(image_seq_lens)))
# image_seqs = [self.resize(aos.get_last_image())
# for aos in agent_observed_state_list]
# image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
#
# goal_image_seqs = [self.resize(aos.get_goal_image()) for aos in agent_observed_state_list]
# goal_image_batch = cuda_var(torch.from_numpy(np.array(goal_image_seqs)).float())
#
# prev_actions_raw = [aos.get_previous_action()
# for aos in agent_observed_state_list]
# prev_actions = [self.none_action if a is None else a
# for a in prev_actions_raw]
# prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
#
# probs_batch, new_model_state, image_emb_seq = self.final_module(image_batch, image_seq_lens_batch,
# goal_image_batch, prev_actions_batch,
# mode, model_state)
# return probs_batch, new_model_state, image_emb_seq
def get_probs(self, agent_observed_state, model_state, mode=None):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
goal_image_seqs = [[aos.get_goal_image()] for aos in agent_observed_state_list]
goal_image_batch = cuda_var(torch.from_numpy(np.array(goal_image_seqs)).float())
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, new_model_state, image_emb_seq = self.final_module(image_batch, image_seq_lens_batch,
goal_image_batch, prev_actions_batch,
mode, model_state)
return probs_batch, new_model_state, image_emb_seq
def action_prediction_log_prob(self, batch_input):
assert self.action_prediction_module is not None, "Action prediction module not created. Check config."
return self.action_prediction_module(batch_input)
def predict_action_result(self, batch_image_feature, action_batch):
assert self.temporal_autoencoder_module is not None, "Temporal action module not created. Check config."
return self.temporal_autoencoder_module(batch_image_feature, action_batch)
def get_probs_and_visible_objects(self, agent_observed_state_list, batch_image_feature):
assert self.object_detection_module is not None, "Object detection module not created. Check config."
landmarks_visible = []
for aos in agent_observed_state_list:
x_pos, z_pos, y_angle = aos.get_position_orientation()
landmark_pos_dict = aos.get_landmark_pos_dict()
visible_landmarks = self.object_detection_module.get_visible_landmark_r_theta(
x_pos, z_pos, y_angle, landmark_pos_dict, self.landmark_names)
landmarks_visible.append(visible_landmarks)
# shape is BATCH_SIZE x 63 x 2
probs_batch = self.object_detection_module(batch_image_feature)
# landmarks_visible is list of length BATCH_SIZE, each item is a set containing landmark indices
return probs_batch, landmarks_visible
def load_resnet_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
image_recurrence_module_path = os.path.join(
load_dir, "image_recurrence_module_state.bin")
self.image_recurrence_module.load_state_dict(
torch_load(image_recurrence_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
if self.action_prediction_module is not None:
auxiliary_action_prediction_path = os.path.join(load_dir, "auxiliary_action_prediction.bin")
self.action_prediction_module.load_state_dict(torch_load(auxiliary_action_prediction_path))
if self.temporal_autoencoder_module is not None:
auxiliary_temporal_autoencoder_path = os.path.join(load_dir, "auxiliary_temporal_autoencoder.bin")
self.temporal_autoencoder_module.load_state_dict(torch_load(auxiliary_temporal_autoencoder_path))
if self.object_detection_module is not None:
auxiliary_object_detection_path = os.path.join(load_dir, "auxiliary_object_detection.bin")
self.object_detection_module.load_state_dict(torch_load(auxiliary_object_detection_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for image recurrence nn
image_recurrence_module_path = os.path.join(
save_dir, "image_recurrence_module_state.bin")
torch.save(self.image_recurrence_module.state_dict(),
image_recurrence_module_path)
# save state file for action emb
action_module_path = os.path.join(save_dir, "action_module_state.bin")
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
# save the auxiliary models
if self.action_prediction_module is not None:
auxiliary_action_prediction_path = os.path.join(save_dir, "auxiliary_action_prediction.bin")
torch.save(self.action_prediction_module.state_dict(), auxiliary_action_prediction_path)
if self.temporal_autoencoder_module is not None:
auxiliary_temporal_autoencoder_path = os.path.join(save_dir, "auxiliary_temporal_autoencoder.bin")
torch.save(self.temporal_autoencoder_module.state_dict(), auxiliary_temporal_autoencoder_path)
if self.object_detection_module is not None:
auxiliary_object_detection_path = os.path.join(save_dir, "auxiliary_object_detection.bin")
torch.save(self.object_detection_module.state_dict(), auxiliary_object_detection_path)
def get_parameters(self):
parameters = list(self.image_module.parameters())
parameters += list(self.image_recurrence_module.parameters())
parameters += list(self.action_module.parameters())
parameters += list(self.final_module.parameters())
if self.action_prediction_module is not None:
parameters += list(self.action_prediction_module.parameters())
if self.temporal_autoencoder_module is not None:
parameters += list(self.temporal_autoencoder_module.parameters())
if self.object_detection_module is not None:
parameters += list(self.object_detection_module.parameters())
return parameters
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
landmark_names = get_all_landmark_names()
self.radius_module = RadiusModule(15)
self.angle_module = AngleModule(12) # (48)
self.landmark_module = LandmarkModule(67)
self.num_cameras = 1
self.image_module = ImageRyanResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"] * self.num_cameras, # + constants["action_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
self.text_module = SymbolicInstructionModule(
radius_embedding=self.radius_module,
theta_embedding=self.angle_module,
landmark_embedding=self.landmark_module)
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = ((self.num_cameras) * constants["image_emb_dim"]
+ 32 * 2
+ constants["action_emb_dim"])
if config["do_action_prediction"]:
self.action_prediction_module = ActionPredictionModule(
2 * self.num_cameras * constants["image_emb_dim"], constants["image_emb_dim"], config["num_actions"])
else:
self.action_prediction_module = None
if config["do_temporal_autoencoding"]:
self.temporal_autoencoder_module = TemporalAutoencoderModule(
self.action_module, self.num_cameras * constants["image_emb_dim"],
constants["action_emb_dim"], constants["image_emb_dim"])
else:
self.temporal_autoencoder_module = None
if config["do_object_detection"]:
self.landmark_names = get_all_landmark_names()
self.object_detection_module = ObjectDetectionModule(
image_module=self.image_module,
image_emb_size=self.num_cameras * constants["image_emb_dim"], num_objects=67)
else:
self.object_detection_module = None
final_module = IncrementalMultimodalRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
if self.action_prediction_module is not None:
self.action_prediction_module.cuda()
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.cuda()
if self.object_detection_module is not None:
self.object_detection_module.cuda()
class IncrementalModelEmnlp(AbstractIncrementalModel):
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.config = config
self.constants = constants
# CNN over images - using SimpleImage for testing for now!
self.image_module = ImageCnnEmnlp(
image_emb_size=config["image_emb_dim"],
input_num_channels=3 *
5, #3 channels per image - 5 images in history
image_height=config["image_height"],
image_width=config["image_width"])
# this is somewhat counter intuitivie - emb_dim is the word size
# hidden_size is the output size
self.text_module = TextSimpleModule(emb_dim=config["word_emb_dim"],
hidden_dim=config["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.previous_action_module = ActionSimpleModule(
num_actions=config["no_actions"],
action_emb_size=config["previous_action_embedding_dim"])
self.previous_block_module = ActionSimpleModule(
num_actions=config["no_blocks"],
action_emb_size=config["previous_block_embedding_dim"])
self.final_module = IncrementalMultimodalEmnlp(
image_module=self.image_module,
text_module=self.text_module,
previous_action_module=self.previous_action_module,
previous_block_module=self.previous_block_module,
input_embedding_size=config["lstm_emb_dim"] +
config["image_emb_dim"] + config["previous_action_embedding_dim"] +
config["previous_block_embedding_dim"],
output_hidden_size=config["h1_hidden_dim"],
blocks_hidden_size=config["no_blocks"],
directions_hidden_size=config["no_actions"],
max_episode_length=(constants["horizon"] + 5))
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.previous_action_module.cuda()
self.previous_block_module.cuda()
self.final_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
raise NotImplementedError()
def get_probs(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
#supposedly this is already padded with zeros, but i need to double check that code
images = agent_observed_state.get_image()[-5:]
# image_seqs = [[aos.get_last_image()]
# for aos in agent_observed_state_list]
image_batch = cuda_var(
torch.from_numpy(np.array(images)).float(), volatile)
#flatten them? TODO: maybe don't hardcode this later on? batch size is 1 ;)
image_batch = image_batch.view(1, 15, 128, 128)
# list of list :)
instructions_batch = ([agent_observed_state.get_instruction()], False)
#instructions_batch = (cuda_var(torch.from_numpy(np.array(instructions)).long()), False)
#print("instructions", instructions)
#print("instructins_batch", instructions_batch)
prev_actions_raw = agent_observed_state.get_previous_action()
prev_actions_raw = self.none_action if prev_actions_raw is None else prev_actions_raw
if prev_actions_raw == 81:
previous_direction_id = [4]
else:
previous_direction_id = [prev_actions_raw % 4]
#this input is is over the space 81 things :)
previous_block_id = [int(prev_actions_raw / 4)]
prev_block_id_batch = cuda_var(
torch.from_numpy(np.array(previous_block_id)))
prev_direction_id_batch = cuda_var(
torch.from_numpy(np.array(previous_direction_id)))
# prev_actions = [self.none_action if a is None else a
# for a in prev_actions_raw]
#prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, new_model_state = self.final_module(
image_batch, instructions_batch, prev_block_id_batch,
prev_direction_id_batch, model_state)
# last two we don't really need...
return probs_batch, new_model_state, None, None
def init_weights(self):
self.text_module.init_weights()
self.image_module.init_weights()
self.previous_action_module.init_weights()
self.previous_block_module.init_weights()
self.final_module.init_weights()
def share_memory(self):
self.image_module.share_memory()
self.text_module.share_memory()
self.previous_action_module.share_memory()
self.previous_block_module.share_memory()
self.final_module.share_memory()
def get_state_dict(self):
nested_state_dict = dict()
nested_state_dict["image_module"] = self.image_module.state_dict()
nested_state_dict["text_module"] = self.text_module.state_dict()
nested_state_dict[
"previous_action_module"] = self.previous_action_module.state_dict(
)
nested_state_dict[
"previous_block_module"] = self.previous_block_module.state_dict()
nested_state_dict["final_module"] = self.final_module.state_dict()
return nested_state_dict
def load_from_state_dict(self, nested_state_dict):
self.image_module.load_state_dict(nested_state_dict["image_module"])
self.text_module.load_state_dict(nested_state_dict["text_module"])
self.previous_action_module.load_state_dict(
nested_state_dict["previous_action_module"])
self.previous_block_module.load_state_dict(
nested_state_dict["previous_block_module"])
self.final_module.load_state_dict(nested_state_dict["final_module"])
def load_resnet_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
def load_lstm_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
previous_action_module_path = os.path.join(
load_dir, "previous_action_module_state.bin")
self.previous_action_module.load_state_dict(
torch_load(previous_action_module_path))
previous_block_module_path = os.path.join(
load_dir, "previous_block_module_state.bin")
self.previous_block_module.load_state_dict(
torch_load(previous_block_module_path))
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
# action_module_path = os.path.join(load_dir, "action_module_state.bin")
# self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for image recurrence nn
previous_action_module_path = os.path.join(
save_dir, "previous_action_module_state.bin")
torch.save(self.previous_action_module.state_dict(),
previous_action_module_path)
previous_block_module_path = os.path.join(
save_dir, "previous_block_module_state.bin")
torch.save(self.previous_block_module.state_dict(),
previous_block_module_path)
# save state file for text nn
text_module_path = os.path.join(save_dir, "text_module_state.bin")
torch.save(self.text_module.state_dict(), text_module_path)
# save state file for action emb
# action_module_path = os.path.join(save_dir, "action_module_state.bin")
# torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
def get_parameters(self):
# parameters = list(self.image_module.parameters())
# parameters += list(self.action_module.parameters())
# parameters += list(self.text_module.parameters())
parameters = list(self.final_module.parameters())
return parameters
def get_named_parameters(self):
# named_parameters = list(self.image_module.named_parameters())
# named_parameters += list(self.action_module.named_parameters())
# named_parameters += list(self.text_module.named_parameters())
named_parameters = list(self.final_module.named_parameters())
return named_parameters
class IncrementalModelEmnlp(AbstractIncrementalModel):
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.config = config
self.constants = constants
# CNN over images - using what is essentially SimpleImage currently
self.image_module = ImageCnnEmnlp(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3 *
5, # 3 channels per image - 5 images in history
image_height=config["image_height"],
image_width=config["image_width"])
# LSTM to embed text
self.text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
# Action module to embed previous action+block
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
# Put it all together
self.final_module = IncrementalMultimodalEmnlp(
image_module=self.image_module,
text_module=self.text_module,
action_module=self.action_module,
input_embedding_size=constants["lstm_emb_dim"] +
constants["image_emb_dim"] + constants["action_emb_dim"],
output_hidden_size=config["h1_hidden_dim"],
blocks_hidden_size=config["blocks_hidden_dim"],
directions_hidden_size=config["action_hidden_dim"],
max_episode_length=(constants["horizon"] + 5))
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
raise NotImplementedError()
def get_probs(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
# Image list is already padded with zero-images if <5 images are available
images = agent_observed_state.get_image()[-5:]
image_batch = cuda_var(
torch.from_numpy(np.array(images)).float(), volatile)
# Flatten them? TODO: maybe don't hardcode this later on? batch size is 1 ;)
image_batch = image_batch.view(1, 15, self.config["image_height"],
self.config["image_width"])
# List of instructions. False is there because it expects a second argument. TODO: figure out what this is
instructions_batch = ([agent_observed_state.get_instruction()], False)
# Previous action
prev_actions_raw = [agent_observed_state.get_previous_action()]
# If previous action is non-existant then encode that as a stop?
prev_actions = [
self.none_action if a is None else a for a in prev_actions_raw
]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
# Get probabilities
probs_batch, new_model_state = self.final_module(
image_batch, instructions_batch, prev_actions_batch, model_state)
# last two we don't really need...
return probs_batch, new_model_state, None, None
def init_weights(self):
self.text_module.init_weights()
self.image_module.init_weights()
self.action_module.init_weights()
self.final_module.init_weights()
def share_memory(self):
self.image_module.share_memory()
self.text_module.share_memory()
self.action_module.share_memory()
self.final_module.share_memory()
def get_state_dict(self):
nested_state_dict = dict()
nested_state_dict["image_module"] = self.image_module.state_dict()
nested_state_dict["text_module"] = self.text_module.state_dict()
nested_state_dict["action_module"] = self.action_module.state_dict()
nested_state_dict["final_module"] = self.final_module.state_dict()
return nested_state_dict
def load_from_state_dict(self, nested_state_dict):
self.image_module.load_state_dict(nested_state_dict["image_module"])
self.text_module.load_state_dict(nested_state_dict["text_module"])
self.action_module.load_state_dict(nested_state_dict["action_module"])
self.final_module.load_state_dict(nested_state_dict["final_module"])
def load_resnet_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
def load_lstm_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
# action_module_path = os.path.join(load_dir, "action_module_state.bin")
# self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for image recurrence nn
action_module_path = os.path.join(save_dir, "action_module_state.bin")
#torch.save(self.action_module.state_dict(),
# action_module_path)
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for text nn
text_module_path = os.path.join(save_dir, "text_module_state.bin")
torch.save(self.text_module.state_dict(), text_module_path)
# save state file for action emb
# action_module_path = os.path.join(save_dir, "action_module_state.bin")
# torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
def get_parameters(self):
# parameters = list(self.image_module.parameters())
# parameters += list(self.action_module.parameters())
# parameters += list(self.text_module.parameters())
parameters = list(self.final_module.parameters())
return parameters
def get_named_parameters(self):
# named_parameters = list(self.image_module.named_parameters())
# named_parameters += list(self.action_module.named_parameters())
# named_parameters += list(self.text_module.named_parameters())
named_parameters = list(self.final_module.named_parameters())
return named_parameters
class IncrementalModelRecurrentPolicyNetworkSymbolicTextWithLSTMResnet(
AbstractIncrementalModel):
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
landmark_names = get_all_landmark_names()
self.radius_module = RadiusModule(15)
self.angle_module = AngleModule(12) # (48)
self.landmark_module = LandmarkModule(67)
self.num_cameras = 1
self.image_module = ImageRyanResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"] *
self.num_cameras, # + constants["action_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
self.symbolic_text_module = SymbolicInstructionModule(
radius_embedding=self.radius_module,
theta_embedding=self.angle_module,
landmark_embedding=self.landmark_module)
self.lstm_text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = ((self.num_cameras) * constants["image_emb_dim"] +
32 * 2 + constants["lstm_emb_dim"] +
+constants["action_emb_dim"])
if config["do_action_prediction"]:
self.action_prediction_module = ActionPredictionModule(
2 * self.num_cameras * constants["image_emb_dim"],
constants["image_emb_dim"], config["num_actions"])
else:
self.action_prediction_module = None
if config["do_temporal_autoencoding"]:
self.temporal_autoencoder_module = TemporalAutoencoderModule(
self.action_module,
self.num_cameras * constants["image_emb_dim"],
constants["action_emb_dim"], constants["image_emb_dim"])
else:
self.temporal_autoencoder_module = None
if config["do_object_detection"]:
self.landmark_names = get_all_landmark_names()
self.object_detection_module = ObjectDetectionModule(
image_module=self.image_module,
image_emb_size=self.num_cameras * constants["image_emb_dim"],
num_objects=67)
else:
self.object_detection_module = None
final_module = IncrementalMultimodalMixedTextRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
symbolic_text_module=self.symbolic_text_module,
lstm_text_module=self.lstm_text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.symbolic_text_module.cuda()
self.lstm_text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
if self.action_prediction_module is not None:
self.action_prediction_module.cuda()
if self.temporal_autoencoder_module is not None:
self.temporal_autoencoder_module.cuda()
if self.object_detection_module is not None:
self.object_detection_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
raise NotImplementedError()
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True)
image_seq_lens = [
aos.get_num_images() for aos in agent_observed_state_list
]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
max_len = max(image_seq_lens)
image_seqs = [
aos.get_image()[:max_len] for aos in agent_observed_state_list
]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
read_pointers = [
aos.get_read_pointers() for aos in agent_observed_state_list
]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [
aos.get_previous_action() for aos in agent_observed_state_list
]
prev_actions = [
self.none_action if a is None else a for a in prev_actions_raw
]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, _ = self.final_module(image_batch,
image_seq_lens_batch,
instructions_batch,
prev_actions_batch,
mode,
model_state=None)
return probs_batch
def get_probs(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(
torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions_batch = [
aos.get_symbolic_instruction() for aos in agent_observed_state_list
]
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
read_pointers = [
aos.get_read_pointers() for aos in agent_observed_state_list
]
lstm_instructions_batch = (instructions, read_pointers)
prev_actions_raw = [
aos.get_previous_action() for aos in agent_observed_state_list
]
prev_actions = [
self.none_action if a is None else a for a in prev_actions_raw
]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)),
volatile)
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, image_seq_lens_batch, instructions_batch,
lstm_instructions_batch, prev_actions_batch, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
def get_probs_symbolic_text(self,
agent_observed_state,
symbolic_text,
model_state,
mode=None,
volatile=False):
""" Same as get_probs instead forces the model to use the given symbolic text """
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(
torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions_batch = [symbolic_text]
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
read_pointers = [
aos.get_read_pointers() for aos in agent_observed_state_list
]
real_instructions_batch = (instructions, read_pointers)
prev_actions_raw = [
aos.get_previous_action() for aos in agent_observed_state_list
]
prev_actions = [
self.none_action if a is None else a for a in prev_actions_raw
]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)),
volatile)
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, image_seq_lens_batch, instructions_batch,
real_instructions_batch, prev_actions_batch, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
def action_prediction_log_prob(self, batch_input):
assert self.action_prediction_module is not None, "Action prediction module not created. Check config."
return self.action_prediction_module(batch_input)
def predict_action_result(self, batch_image_feature, action_batch):
assert self.temporal_autoencoder_module is not None, "Temporal action module not created. Check config."
return self.temporal_autoencoder_module(batch_image_feature,
action_batch)
def get_probs_and_visible_objects(self, agent_observed_state_list,
batch_image_feature):
assert self.object_detection_module is not None, "Object detection module not created. Check config."
landmarks_visible = []
for aos in agent_observed_state_list:
x_pos, z_pos, y_angle = aos.get_position_orientation()
landmark_pos_dict = aos.get_landmark_pos_dict()
visible_landmarks_dict = self.object_detection_module.get_visible_landmark_r_theta(
x_pos, z_pos, y_angle, landmark_pos_dict)
landmarks_visible.append(visible_landmarks_dict)
# shape is BATCH_SIZE x num objects x 2
landmark_log_prob, distance_log_prob, theta_log_prob = self.object_detection_module(
batch_image_feature)
# landmarks_visible is list of length BATCH_SIZE, each item is a set containing landmark indices
return landmark_log_prob, distance_log_prob, theta_log_prob, landmarks_visible
def init_weights(self):
self.image_module.init_weights()
self.image_recurrence_module.init_weights()
def load_resnet_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
image_recurrence_module_path = os.path.join(
load_dir, "image_recurrence_module_state.bin")
self.image_recurrence_module.load_state_dict(
torch_load(image_recurrence_module_path))
symbolic_text_module_path = os.path.join(
load_dir, "symbolic_text_module_state.bin")
self.symbolic_text_module.load_state_dict(
torch_load(symbolic_text_module_path))
lstm_text_module_path = os.path.join(load_dir,
"lstm_text_module_state.bin")
self.lstm_text_module.load_state_dict(
torch_load(lstm_text_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
if self.action_prediction_module is not None:
auxiliary_action_prediction_path = os.path.join(
load_dir, "auxiliary_action_prediction.bin")
self.action_prediction_module.load_state_dict(
torch_load(auxiliary_action_prediction_path))
if self.temporal_autoencoder_module is not None:
auxiliary_temporal_autoencoder_path = os.path.join(
load_dir, "auxiliary_temporal_autoencoder.bin")
self.temporal_autoencoder_module.load_state_dict(
torch_load(auxiliary_temporal_autoencoder_path))
if self.object_detection_module is not None:
auxiliary_object_detection_path = os.path.join(
load_dir, "auxiliary_object_detection.bin")
self.object_detection_module.load_state_dict(
torch_load(auxiliary_object_detection_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for image recurrence nn
image_recurrence_module_path = os.path.join(
save_dir, "image_recurrence_module_state.bin")
torch.save(self.image_recurrence_module.state_dict(),
image_recurrence_module_path)
# save state file for text nn
symbolic_text_module_path = os.path.join(
save_dir, "symbolic_text_module_state.bin")
torch.save(self.symbolic_text_module.state_dict(),
symbolic_text_module_path)
lstm_text_module_path = os.path.join(save_dir,
"lstm_text_module_state.bin")
torch.save(self.lstm_text_module.state_dict(), lstm_text_module_path)
# save state file for action emb
action_module_path = os.path.join(save_dir, "action_module_state.bin")
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
# save the auxiliary models
if self.action_prediction_module is not None:
auxiliary_action_prediction_path = os.path.join(
save_dir, "auxiliary_action_prediction.bin")
torch.save(self.action_prediction_module.state_dict(),
auxiliary_action_prediction_path)
if self.temporal_autoencoder_module is not None:
auxiliary_temporal_autoencoder_path = os.path.join(
save_dir, "auxiliary_temporal_autoencoder.bin")
torch.save(self.temporal_autoencoder_module.state_dict(),
auxiliary_temporal_autoencoder_path)
if self.object_detection_module is not None:
auxiliary_object_detection_path = os.path.join(
save_dir, "auxiliary_object_detection.bin")
torch.save(self.object_detection_module.state_dict(),
auxiliary_object_detection_path)
def get_parameters(self):
# parameters = list(self.image_module.parameters())
parameters = list(self.image_recurrence_module.parameters())
parameters += list(self.symbolic_text_module.parameters())
parameters += list(self.lstm_text_module.parameters())
parameters += list(self.action_module.parameters())
parameters += list(self.final_module.parameters())
if self.action_prediction_module is not None:
parameters += list(self.action_prediction_module.parameters())
if self.temporal_autoencoder_module is not None:
parameters += list(self.temporal_autoencoder_module.parameters())
if self.object_detection_module is not None:
parameters += list(self.object_detection_module.parameters())
return parameters
def get_named_parameters(self):
# named_parameters = list(self.image_module.named_parameters())
named_parameters = list(
self.image_recurrence_module.named_parameters())
named_parameters += list(self.symbolic_text_module.named_parameters())
named_parameters += list(self.lstm_text_module.named_parameters())
named_parameters += list(self.action_module.named_parameters())
named_parameters += list(self.final_module.named_parameters())
if self.action_prediction_module is not None:
named_parameters += list(
self.action_prediction_module.named_parameters())
if self.temporal_autoencoder_module is not None:
named_parameters += list(
self.temporal_autoencoder_module.named_parameters())
if self.object_detection_module is not None:
named_parameters += list(
self.object_detection_module.named_parameters())
'''if self.symbolic_language_prediction_module is not None:
named_parameters += list(self.symbolic_language_prediction_module.named_parameters())'''
return named_parameters
class ModelPolicyNetworkSymbolicText(AbstractModel):
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
landmark_names = get_all_landmark_names()
self.radius_module = RadiusModule(15)
self.angle_module = AngleModule(48)
self.landmark_module = LandmarkModule(63)
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.image_recurrence_module = RecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
self.text_module = SymbolicInstructionModule(
radius_embedding=self.radius_module,
theta_embedding=self.angle_module,
landmark_embedding=self.landmark_module)
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (constants["image_emb_dim"]
+ 32 * 4
+ constants["action_emb_dim"])
final_module = MultimodalRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
self.radius_module.cuda()
self.angle_module.cuda()
self.landmark_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True
)
image_seq_lens = [aos.get_num_images()
for aos in agent_observed_state_list]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
max_len = max(image_seq_lens)
image_seqs = [aos.get_image()[:max_len]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
instructions_batch = [aos.get_symbolic_instruction()
for aos in agent_observed_state_list]
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch = self.final_module(image_batch, image_seq_lens_batch,
instructions_batch, prev_actions_batch,
mode)
return probs_batch
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
radius_module_path = os.path.join(load_dir, "radius_module_state.bin")
self.radius_module.load_state_dict(torch_load(radius_module_path))
angle_module_path = os.path.join(load_dir, "angle_module_state.bin")
self.angle_module.load_state_dict(torch_load(angle_module_path))
landmark_module_path = os.path.join(load_dir, "landmark_module_state.bin")
self.landmark_module.load_state_dict(torch_load(landmark_module_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for text nn
text_module_path = os.path.join(save_dir, "text_module_state.bin")
torch.save(self.text_module.state_dict(), text_module_path)
# save state file for action emb
action_module_path = os.path.join(save_dir, "action_module_state.bin")
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
# save state file for radius nn
radius_module_path = os.path.join(save_dir, "radius_module_state.bin")
torch.save(self.radius_module.state_dict(), radius_module_path)
# save state file for angle nn
angle_module_path = os.path.join(save_dir, "angle_module_state.bin")
torch.save(self.angle_module.state_dict(), angle_module_path)
# save state file for landmark nn
landmark_module_path = os.path.join(save_dir, "landmark_module_state.bin")
torch.save(self.landmark_module.state_dict(), landmark_module_path)
def get_parameters(self):
parameters = list(self.image_module.parameters())
parameters += list(self.text_module.parameters())
parameters += list(self.action_module.parameters())
parameters += list(self.final_module.parameters())
parameters += list(self.radius_module.parameters())
parameters += list(self.angle_module.parameters())
parameters += list(self.landmark_module.parameters())
return parameters
class ModelPolicyNetworkSymbolic(AbstractModel):
def __init__(self, config, constants):
AbstractModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
landmark_names = get_all_landmark_names()
self.radius_module = RadiusModule(15)
self.angle_module = AngleModule(48)
self.landmark_module = LandmarkModule(63)
self.image_module = SymbolicImageModule(
landmark_names=landmark_names,
radius_module=self.radius_module,
angle_module=self.angle_module,
landmark_module=self.landmark_module)
if config["use_pointer_model"]:
self.text_module = TextPointerModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
else:
self.text_module = TextSimpleModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"])
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
total_emb_size = (32 * 3 * 63
+ constants["lstm_emb_dim"]
+ constants["action_emb_dim"])
final_module = MultimodalSimpleModule(
image_module=self.image_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
self.radius_module.cuda()
self.angle_module.cuda()
self.landmark_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True
)
symbolic_image_list = []
for aos in agent_observed_state_list:
x_pos, z_pos, y_angle = aos.get_position_orientation()
landmark_pos_dict = aos.get_landmark_pos_dict()
symbolic_image = get_visible_landmark_r_theta(
x_pos, z_pos, y_angle, landmark_pos_dict)
symbolic_image_list.append(symbolic_image)
image_batch = symbolic_image_list
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
read_pointers = [aos.get_read_pointers()
for aos in agent_observed_state_list]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch = self.final_module(image_batch, instructions_batch,
prev_actions_batch, mode)
return probs_batch
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_, map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
self.final_module.load_state_dict(torch_load(final_module_path))
radius_module_path = os.path.join(load_dir, "radius_module_state.bin")
self.radius_module.load_state_dict(torch_load(radius_module_path))
angle_module_path = os.path.join(load_dir, "angle_module_state.bin")
self.angle_module.load_state_dict(torch_load(angle_module_path))
landmark_module_path = os.path.join(load_dir, "landmark_module_state.bin")
self.landmark_module.load_state_dict(torch_load(landmark_module_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for text nn
text_module_path = os.path.join(save_dir, "text_module_state.bin")
torch.save(self.text_module.state_dict(), text_module_path)
# save state file for action emb
action_module_path = os.path.join(save_dir, "action_module_state.bin")
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
def get_parameters(self):
parameters = list(self.image_module.parameters())
parameters += list(self.text_module.parameters())
parameters += list(self.action_module.parameters())
parameters += list(self.final_module.parameters())
return parameters
class IncrementalModelRecurrentImplicitFactorizationResnet(
AbstractIncrementalModel):
def __init__(self, config, constants):
AbstractIncrementalModel.__init__(self, config, constants)
self.none_action = config["num_actions"]
self.image_module = ImageResnetModule(
image_emb_size=constants["image_emb_dim"],
input_num_channels=3,
image_height=config["image_height"],
image_width=config["image_width"],
using_recurrence=True)
self.image_recurrence_module = IncrementalRecurrenceSimpleModule(
input_emb_dim=constants["image_emb_dim"],
output_emb_dim=constants["image_emb_dim"])
if config["use_pointer_model"]:
raise AssertionError("Not implemented")
# self.text_module = TextPointerModule(
# emb_dim=constants["word_emb_dim"],
# hidden_dim=constants["lstm_emb_dim"],
# vocab_size=config["vocab_size"])
else:
self.text_module = TextImplicitFactorizationModule(
emb_dim=constants["word_emb_dim"],
hidden_dim=constants["lstm_emb_dim"],
vocab_size=config["vocab_size"],
num_factors=2,
factors_vocabulary_size=60,
factors_embedding_size=250)
self.action_module = ActionSimpleModule(
num_actions=config["num_actions"],
action_emb_size=constants["action_emb_dim"])
if config["use_pointer_model"]:
total_emb_size = (constants["image_emb_dim"] +
4 * constants["lstm_emb_dim"] +
constants["action_emb_dim"])
else:
total_emb_size = (constants["image_emb_dim"] + 2 * 250 +
constants["action_emb_dim"])
final_module = IncrementalMultimodalRecurrentSimpleModule(
image_module=self.image_module,
image_recurrence_module=self.image_recurrence_module,
text_module=self.text_module,
action_module=self.action_module,
total_emb_size=total_emb_size,
num_actions=config["num_actions"])
self.final_module = final_module
if torch.cuda.is_available():
self.image_module.cuda()
self.image_recurrence_module.cuda()
self.text_module.cuda()
self.action_module.cuda()
self.final_module.cuda()
def get_probs_batch(self, agent_observed_state_list, mode=None):
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True)
image_seq_lens = [
aos.get_num_images() for aos in agent_observed_state_list
]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
max_len = max(image_seq_lens)
image_seqs = [
aos.get_image()[:max_len] for aos in agent_observed_state_list
]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
read_pointers = [
aos.get_read_pointers() for aos in agent_observed_state_list
]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [
aos.get_previous_action() for aos in agent_observed_state_list
]
prev_actions = [
self.none_action if a is None else a for a in prev_actions_raw
]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, _ = self.final_module(image_batch,
image_seq_lens_batch,
instructions_batch,
prev_actions_batch,
mode,
model_state=None)
return probs_batch
def get_probs(self, agent_observed_state, model_state, mode=None):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
# max_len = max(image_seq_lens)
# image_seqs = [aos.get_image()[:max_len]
# for aos in agent_observed_state_list]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
read_pointers = [
aos.get_read_pointers() for aos in agent_observed_state_list
]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [
aos.get_previous_action() for aos in agent_observed_state_list
]
prev_actions = [
self.none_action if a is None else a for a in prev_actions_raw
]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, new_model_state, image_emb_seq = self.final_module(
image_batch, image_seq_lens_batch, instructions_batch,
prev_actions_batch, mode, model_state)
return probs_batch, new_model_state, image_emb_seq
def get_recent_factorization_entropy(self):
return self.text_module.mean_factory_entropy
def load_saved_model(self, load_dir):
if torch.cuda.is_available():
torch_load = torch.load
else:
torch_load = lambda f_: torch.load(f_,
map_location=lambda s_, l_: s_)
image_module_path = os.path.join(load_dir, "image_module_state.bin")
self.image_module.load_state_dict(torch_load(image_module_path))
image_recurrence_module_path = os.path.join(
load_dir, "image_recurrence_module_state.bin")
self.image_recurrence_module.load_state_dict(
torch_load(image_recurrence_module_path))
text_module_path = os.path.join(load_dir, "text_module_state.bin")
self.text_module.load_state_dict(torch_load(text_module_path))
action_module_path = os.path.join(load_dir, "action_module_state.bin")
self.action_module.load_state_dict(torch_load(action_module_path))
final_module_path = os.path.join(load_dir, "final_module_state.bin")
self.final_module.load_state_dict(torch_load(final_module_path))
def save_model(self, save_dir):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save state file for image nn
image_module_path = os.path.join(save_dir, "image_module_state.bin")
torch.save(self.image_module.state_dict(), image_module_path)
# save state file for image recurrence nn
image_recurrence_module_path = os.path.join(
save_dir, "image_recurrence_module_state.bin")
torch.save(self.image_recurrence_module.state_dict(),
image_recurrence_module_path)
# save state file for text nn
text_module_path = os.path.join(save_dir, "text_module_state.bin")
torch.save(self.text_module.state_dict(), text_module_path)
# save state file for action emb
action_module_path = os.path.join(save_dir, "action_module_state.bin")
torch.save(self.action_module.state_dict(), action_module_path)
# save state file for final nn
final_module_path = os.path.join(save_dir, "final_module_state.bin")
torch.save(self.final_module.state_dict(), final_module_path)
def get_parameters(self):
parameters = list(self.image_module.parameters())
parameters += list(self.image_recurrence_module.parameters())
parameters += list(self.text_module.parameters())
parameters += list(self.action_module.parameters())
parameters += list(self.final_module.parameters())
return parameters
