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_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 __init__(self, ext_dir, cuda=True):
ext_meta = json.load(open(join(ext_dir, 'meta.json')))
assert ext_meta['net'] == 'rnn-ext_abs_rl'
ext_args = ext_meta['net_args']['extractor']['net_args']
word2id = pkl.load(open(join(ext_dir, 'agent_vocab.pkl'), 'rb'))
extractor = PtrExtractSumm(**ext_args)
agent = ActorCritic(extractor, ArticleBatcher(word2id, cuda))
ext_ckpt = load_best_ckpt(ext_dir, reverse=True)
agent.load_state_dict(ext_ckpt)
self._device = torch.device('cuda' if cuda else 'cpu')
self._net = agent.to(self._device)
self._word2id = word2id
self._id2word = {i: w for w, i in word2id.items()}
def configure_net(abs_dir, ext_dir, cuda, sc, tv, rl_dir=''):
""" 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)
if sc:
agent = SelfCritic(extractor,
ArticleBatcher(agent_vocab, cuda),
time_variant=tv)
else:
agent = ActorCritic(extractor._sent_enc, extractor._art_enc,
extractor._extractor,
ArticleBatcher(agent_vocab, cuda))
if rl_dir != '':
ckpt = load_best_ckpt(rl_dir, reverse=True)
agent.load_state_dict(ckpt)
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')))
print('agent:', agent)
return agent, agent_vocab, abstractor, net_args
def __init__(self, ext_dir, cuda=True):
ext_meta = json.load(open(join(ext_dir, 'meta.json')))
assert ext_meta['net'] == 'rnn-ext_abs_rl'
ext_args = ext_meta['net_args']['extractor']['net_args']
word2id = pkl.load(open(join(ext_dir, 'agent_vocab.pkl'), 'rb'))
extractor = PtrExtractSumm(**ext_args)
agent = ActorCritic(extractor._sent_enc,
extractor._art_enc,
extractor._extractor,
ArticleBatcher(word2id, cuda))
ext_ckpt = load_best_ckpt(ext_dir, reverse=True)
agent.load_state_dict(ext_ckpt)
self._device = torch.device('cuda' if cuda else 'cpu')
self._net = agent.to(self._device)
self._word2id = word2id
self._id2word = {i: w for w, i in word2id.items()}
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
def load_rl_ckpt(abs_dir, ext_dir, cuda):
ext_meta = json.load(open(join(ext_dir, 'meta.json')))
assert ext_meta['net'] == 'rnn-ext_abs_rl'
ext_args = ext_meta['net_args']['extractor']['net_args']
vocab = pkl.load(open(join(ext_dir, 'agent_vocab.pkl'), 'rb'))
extractor = PtrExtractSumm(**ext_args)
abstractor_sent = Abstractor(abs_dir, MAX_ABS_LEN, cuda)
agent = ActorCritic(extractor._sent_enc, extractor._art_enc,
extractor._extractor, ArticleBatcher(vocab, cuda))
target_agent = ActorCritic(extractor._sent_enc,
extractor._art_enc, extractor._extractor,
ArticleBatcher(vocab, cuda))
ext_ckpt = load_best_ckpt(ext_dir, reverse=True)
agent.load_state_dict(ext_ckpt)
device = torch.device('cuda' if cuda else 'cpu')
agent = agent.to(device)
target_agent = target_agent.to(device)
return agent, target_agent, vocab, abstractor_sent, ext_meta
def configure_net(abs_dir, ext_dir, cuda, aux_device, bert_max_len, args):
""" 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 build RL agent
extractor, agent_vocab, use_bert, bert_config = load_ext_net(
ext_dir, aux_device, args)
if use_bert:
bert_type, tokenizer_cache = bert_config
bert_tokenizer = BertTokenizer.from_pretrained(
bert_type, cache_dir=tokenizer_cache)
agent = BertActorCritic(extractor._sent_enc,
extractor._art_enc,
extractor._extractor,
BertArticleBatcher(bert_tokenizer,
bert_max_len,
args.bert_max_sent, cuda),
cuda=cuda,
aux_device=aux_device)
else:
agent = ActorCritic(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
