def configure_net(abs_dir, ext_dir, cuda):
""" load pretrained sub-modules and build the actor-critic network"""
# load pretrained abstractor model
if abs_dir is not None:
abstractor_sent = Abstractor(abs_dir, MAX_ABS_LEN, cuda)
# abs_dir = abs_dir.split(';')
# abstractor_sent = Abstractor(abs_dir[0], MAX_ABS_LEN, cuda)
# abstractor_doc = Abstractor(abs_dir[1], MAX_ABS_LEN, cuda)
else:
abstractor = identity
# load ML trained extractor net and buiild RL agent
extractor, agent_vocab = load_ext_net(ext_dir)
agent = ActorCritic(extractor._sent_enc,
extractor._art_enc, extractor._extractor,
ArticleBatcher(agent_vocab, cuda))
target_agent = ActorCritic(extractor._sent_enc, extractor._art_enc,
extractor._extractor,
ArticleBatcher(agent_vocab, cuda))
target_agent.load_state_dict(agent.state_dict())
if cuda:
agent = agent.cuda()
target_agent = target_agent.cuda()
net_args = {}
net_args['abstractor'] = (None if abs_dir is None else json.load(
open(join(abs_dir, 'meta.json'))))
net_args['extractor'] = json.load(open(join(ext_dir, 'meta.json')))
return agent, target_agent, agent_vocab, abstractor_sent, net_args
def configure_net(abs_dir, ext_dir, cuda, net_type='ml_rnn_extractor'):
""" load pretrained sub-modules and build the actor-critic network"""
# load pretrained abstractor model
if abs_dir is not None:
abstractor = Abstractor(abs_dir, MAX_ABS_LEN, cuda)
else:
abstractor = identity
# load ML trained extractor net and buiild RL agent
extractor, agent_vocab = load_ext_net(ext_dir)
agent = ActorCritic(extractor._sent_enc,
extractor._art_enc,
extractor._extractor,
ArticleBatcher(agent_vocab, cuda, net_type=net_type))
# agent = ActorCriticPreSumm(extractor._sent_enc,
# extractor._art_enc,
# extractor._extractor,
# ArticleBatcher(agent_vocab, cuda))
if cuda:
agent = agent.cuda()
net_args = {}
net_args['abstractor'] = (None if abs_dir is None
else json.load(open(join(abs_dir, 'meta.json'))))
net_args['extractor'] = json.load(open(join(ext_dir, 'meta.json')))
return agent, agent_vocab, abstractor, net_args
def configure_net(abs_dir, ext_dir, cuda):
""" load pretrained sub-modules and build the actor-critic network"""
# load pretrained abstractor model
if abs_dir is not None:
abstractor = Abstractor(abs_dir, MAX_ABS_LEN, cuda)
else:
abstractor = lambda x, y: x
#print(abstractor([[], [0,0,0,0,0,0]], [[],[1,1,1,1,1,1]]))
#exit(0)
# load ML trained extractor net and buiild RL agent
extractor, agent_vocab = load_ext_net(ext_dir)
agent = ActorCritic(extractor, ArticleBatcher(agent_vocab, cuda))
if cuda:
agent = agent.cuda()
net_args = {}
net_args['abstractor'] = (None if abs_dir is None else json.load(
open(join(abs_dir, 'meta.json'))))
net_args['extractor'] = json.load(open(join(ext_dir, 'meta.json')))
return agent, agent_vocab, abstractor, net_args
