def __init__(self,
dictionary,
corpus,
np2var,
bad_tokens=['<disconnect>', '<disagree>']):
super(CombinedNLLEntropy4CLF, self).__init__()
self.dictionary = dictionary
self.domain = domain.get_domain('object_division')
self.corpus = corpus
self.np2var = np2var
self.bad_tokens = bad_tokens
def __init__(self,
archive_file=DEFAULT_ARCHIVE_FILE,
cuda_device=DEFAULT_CUDA_DEVICE,
model_file=None):
SysPolicy.__init__(self)
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for LaRL is specified!")
archive_file = cached_path(model_file)
temp_path = tempfile.mkdtemp()
zip_ref = zipfile.ZipFile(archive_file, 'r')
zip_ref.extractall(temp_path)
zip_ref.close()
self.prev_state = init_state()
self.prev_active_domain = None
domain_name = 'object_division'
domain_info = domain.get_domain(domain_name)
data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data')
train_data_path = os.path.join(data_path, 'norm-multi-woz', 'train_dials.json')
if not os.path.exists(train_data_path):
zipped_file = os.path.join(data_path, 'norm-multi-woz.zip')
archive = zipfile.ZipFile(zipped_file, 'r')
archive.extractall(data_path)
norm_multiwoz_path = os.path.join(data_path, 'norm-multi-woz')
with open(os.path.join(norm_multiwoz_path, 'input_lang.index2word.json')) as f:
self.input_lang_index2word = json.load(f)
with open(os.path.join(norm_multiwoz_path, 'input_lang.word2index.json')) as f:
self.input_lang_word2index = json.load(f)
with open(os.path.join(norm_multiwoz_path, 'output_lang.index2word.json')) as f:
self.output_lang_index2word = json.load(f)
with open(os.path.join(norm_multiwoz_path, 'output_lang.word2index.json')) as f:
self.output_lang_word2index = json.load(f)
config = Pack(
seed=10,
train_path=train_data_path,
max_vocab_size=1000,
last_n_model=5,
max_utt_len=50,
max_dec_len=50,
backward_size=2,
batch_size=1,
use_gpu=True,
op='adam',
init_lr=0.001,
l2_norm=1e-05,
momentum=0.0,
grad_clip=5.0,
dropout=0.5,
max_epoch=100,
embed_size=100,
num_layers=1,
utt_rnn_cell='gru',
utt_cell_size=300,
bi_utt_cell=True,
enc_use_attn=True,
dec_use_attn=True,
dec_rnn_cell='lstm',
dec_cell_size=300,
dec_attn_mode='cat',
y_size=10,
k_size=20,
beta=0.001,
simple_posterior=True,
contextual_posterior=True,
use_mi=False,
use_pr=True,
use_diversity=False,
#
beam_size=20,
fix_batch=True,
fix_train_batch=False,
avg_type='word',
print_step=300,
ckpt_step=1416,
improve_threshold=0.996,
patient_increase=2.0,
save_model=True,
early_stop=False,
gen_type='greedy',
preview_batch_num=None,
k=domain_info.input_length(),
init_range=0.1,
pretrain_folder='2019-09-20-21-43-06-sl_cat',
forward_only=False
)
config.use_gpu = config.use_gpu and torch.cuda.is_available()
self.corpus = corpora_inference.NormMultiWozCorpus(config)
self.model = SysPerfectBD2Cat(self.corpus, config)
self.config = config
if config.use_gpu:
self.model.load_state_dict(torch.load(
os.path.join(temp_path, 'larl_model/best-model')))
self.model.cuda()
else:
self.model.load_state_dict(torch.load(os.path.join(
temp_path, 'larl_model/best-model'), map_location=lambda storage, loc: storage))
self.model.eval()
self.dic = pickle.load(
open(os.path.join(temp_path, 'larl_model/svdic.pkl'), 'rb'))
vec_out_db = np.array(out_db) # (batch_size, 30)
for b_id in range(batch_size):
vec_ctx_utts[b_id, :vec_ctx_lens[b_id], :] = ctx_utts[b_id]
return Pack(context_lens=vec_ctx_lens, # (batch_size, )
# (batch_size, max_ctx_len, max_utt_len)
contexts=vec_ctx_utts,
bs=vec_out_bs, # (batch_size, 94)
db=vec_out_db # (batch_size, 30)
)
if __name__ == '__main__':
domain_name = 'object_division'
domain_info = domain.get_domain(domain_name)
train_data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data/norm-multi-woz/train_dials.json')
config = Pack(
seed=10,
train_path=train_data_path,
max_vocab_size=1000,
last_n_model=5,
max_utt_len=50,
max_dec_len=50,
backward_size=2,
batch_size=1,
use_gpu=True,
op='adam',
init_lr=0.001,
def __init__(self, model, device_id):
self.model = model
self.domain = domain.get_domain('object_division')
self.device_id = device_id
