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
