def __init__(self):
import os
#实体追踪
et = EntityTracker()
#词袋 word2vec
self.bow_enc = BoW_encoder()
#加载word2vec embedding
self.emb = UtteranceEmbed()
#将实体追踪器添加到动作追踪器中
at = ActionTracker(et)
#得到数据集和对话开始 结束行数
self.dataset, dialog_indices = Data(et, at).trainset
#划分数据集：200对做训练 50对做测试
self.dialog_indices_tr = dialog_indices
self.dialog_indices_dev = dialog_indices
#obs_size 300维的词向量 + 85个袋中的词 + 4个槽位
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
#话术模板
self.action_templates = at.get_action_templates()
#动作个数
action_size = at.action_size
#隐藏层神经元个数
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
self.train_dataset, train_dialog_indices = Data(et, at).train_set
self.test_dataset, test_dialog_indices = Data(et, at).test_set
print('=========================\n')
print('length of Train dialog indices : ', len(train_dialog_indices))
print('=========================\n')
print('=========================\n')
print('length of Test dialog indices : ', len(test_dialog_indices))
print('=========================\n')
# Shuffle Training Dataset
random.shuffle(train_dialog_indices)
self.dialog_indices_tr = train_dialog_indices
self.dialog_indices_dev = test_dialog_indices
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
# nb_hidden = 128
nb_hidden = 150
print('=========================\n')
print('Action_templates: ', action_size)
print('=========================\n')
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
self.et = et
self.at = at
action_projection = []
for action in self.action_templates:
action_projection.append(self.emb.encode(action))
self.action_projection = np.transpose(action_projection)
self.action_size = action_size
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size, action_size=action_size, nb_hidden=nb_hidden)
# restore checkpoint
self.net.restore()
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
self.dataset, dialog_indices = Data(et, at).trainset
self.dialog_indices_tr = dialog_indices[:200]
self.dialog_indices_dev = dialog_indices[200:250]
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
'''
['any preference on a type of cuisine', 'api_call <cuisine> <location> <party_size> <rest_type>', 'great let me do the reservation', 'hello what can i help you with today', 'here it is <info_address>', 'here it is <info_phone>', 'how many people would be in your party', "i'm on it", 'is there anything i can help you with', 'ok let me look into some options for you', 'sure is there anything else to update', 'sure let me find an other option for you', 'what do you think of this option: <restaurant>', 'where should it be', 'which price range are looking for', "you're welcome"]
'''
self.dataset, dialog_indices = Data(et, at).trainset
self.dialog_indices_tr = dialog_indices[:200]
self.dialog_indices_dev = dialog_indices[200:250]
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
def __init__(self):
et = EntityTracker()
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed()
at = ActionTracker(et)
self.dataset, dialog_indices = Data(et, at).trainset
train_indices = joblib.load('data/train_test_list/train_indices_759')
test_indices = joblib.load('data/train_test_list/test_indices_759_949')
self.dialog_indices_tr = train_indices
self.dialog_indices_dev = test_indices
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
self.net = LSTM_net(obs_size=obs_size,
action_size=action_size,
nb_hidden=nb_hidden)
def __init__(self):
# stor whole dialogues
self.story = []
self.sp_confidecne = []
self.file_path = os.path.join(
rospkg.RosPack().get_path('dialogue_system'), 'log',
'dialogue.txt')
# count turn taking
self.usr_count = 0
self.sys_count = 0
# paramaters
self.network_type = rospy.get_param('~network_model', 'stacked_lstm')
self.lang_type = rospy.get_param('~lang', 'eng')
self.is_emb = rospy.get_param('~embedding', 'false')
self.is_am = rospy.get_param('~action_mask', "true")
self.user_num = rospy.get_param('~user_number', '0')
# call rest of modules
self.et = EntityTracker()
self.at = ActionTracker(self.et)
self.bow_enc = BoW_encoder()
self.emb = UtteranceEmbed(lang=self.lang_type)
# select observation size for RNN
if self.is_am and self.is_emb:
obs_size = self.emb.dim + self.bow_enc.vocab_size + self.et.num_features + self.at.action_size
elif self.is_am and not (self.is_emb):
obs_size = self.bow_enc.vocab_size + self.et.num_features + self.at.action_size
elif not (self.is_am) and self.is_emb:
obs_size = self.emb.dim + self.bow_enc.vocab_size + self.et.num_features
elif not (self.is_am) and not (self.is_emb):
obs_size = self.bow_enc.vocab_size + self.et.num_features
self.action_template = self.at.get_action_templates()
self.at.do_display_template()
# must clear entities space
self.et.do_clear_entities()
action_size = self.at.action_size
nb_hidden = 128
if self.network_type == 'gru':
self.net = GRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
elif self.network_type == 'reversed_lstm':
self.net = ReversingLSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
elif self.network_type == 'reversed_gru':
self.net = ReversingGRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
elif self.network_type == 'stacked_gru':
self.net = StackedGRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
elif self.network_type == 'stacked_lstm':
self.net = StackedLSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
elif self.network_type == 'lstm':
self.net = LSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
elif self.network_type == 'bidirectional_lstm':
self.net = BidirectionalLSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
elif self.network_type == 'bidirectional_gru':
self.net = BidirectionalGRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_am)
# restore trained model
self.net.restore()
# rostopics
self.pub_reply = rospy.Publisher('reply', Reply, queue_size=10)
self.pub_complete = rospy.Publisher('complete_execute_scenario',
Empty,
queue_size=10)
rospy.Subscriber('raising_events', RaisingEvents,
self.handle_raise_events)
try:
rospy.wait_for_service('reception_db/query_data')
self.get_response_db = rospy.ServiceProxy(
'reception_db/query_data', DBQuery)
rospy.logwarn("waiting for reception DB module...")
except rospy.exceptions.ROSInterruptException as e:
rospy.logerr(e)
quit()
rospy.logwarn(
"network: {}, lang: {}, action_mask: {}, embedding: {}, user_number: {}"
.format(self.network_type, self.lang_type, self.is_am, self.is_emb,
self.user_num))
self.story.append('user number: %s' % self.user_num)
rospy.loginfo('\033[94m[%s]\033[0m initialized.' % rospy.get_name())
def __init__(self, args):
self.response_accuracy = []
self.dialog_accuracy = []
try:
###################### selective import #############################
if args[0] == 'am':
self.is_action_mask = True
else:
self.is_action_mask = False
if args[1] == 'emb':
self.is_emb = True
else:
self.is_emb = False
self.network_type = args[2]
self.lang_type = args[3]
if self.lang_type == 'eng':
from modules.entities import EntityTracker
from modules.data_utils import Data
from modules.actions import ActionTracker
from modules.bow import BoW_encoder
elif self.lang_type == 'kor':
from modules.entities_kor import EntityTracker
from modules.data_utils_kor import Data
from modules.actions_kor import ActionTracker
from modules.bow_kor import BoW_encoder
###################################################################
except:
rospy.logwarn(
"please try again. i.e. ... train.py <am> <emb> <bidirectional_lstm> <eng>"
)
if self.is_emb:
if self.lang_type == 'eng':
self.emb = UtteranceEmbed(lang=self.lang_type)
elif self.lang_type == 'kor':
self.emb = UtteranceEmbed(lang=self.lang_type)
et = EntityTracker()
self.bow_enc = BoW_encoder()
at = ActionTracker(et)
at.do_display_template()
self.dataset, dialog_indices = Data(et, at).trainset
# self.dialog_indices_tr = dialog_indices[:200]
self.dialog_indices_tr = random.sample(dialog_indices, 200)
# self.dialog_indices_dev = dialog_indices[200:250]
self.dialog_indices_dev = random.sample(dialog_indices, 50)
self.action_templates = at.get_action_templates()
action_size = at.action_size
nb_hidden = 128
# set feature input
if self.is_action_mask and self.is_emb:
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features + at.action_size
elif self.is_action_mask and not (self.is_emb):
obs_size = self.bow_enc.vocab_size + et.num_features + at.action_size
elif not (self.is_action_mask) and self.is_emb:
obs_size = self.emb.dim + self.bow_enc.vocab_size + et.num_features
elif not (self.is_action_mask) and not (self.is_emb):
obs_size = self.bow_enc.vocab_size + et.num_features
# set network_type type
if self.network_type == 'gru':
self.net = GRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
elif self.network_type == 'reversed_lstm':
self.net = ReversingLSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
elif self.network_type == 'reversed_gru':
self.net = ReversingGRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
elif self.network_type == 'stacked_gru':
self.net = StackedGRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
elif self.network_type == 'stacked_lstm':
self.net = StackedLSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
elif self.network_type == 'lstm':
self.net = LSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
elif self.network_type == 'bidirectional_lstm':
self.net = BidirectionalLSTM(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
elif self.network_type == 'bidirectional_gru':
self.net = BidirectionalGRU(obs_size=obs_size,
nb_hidden=nb_hidden,
action_size=action_size,
lang=self.lang_type,
is_action_mask=self.is_action_mask)
file_path = os.path.join(rospkg.RosPack().get_path('dialogue_system'),
'log', self.network_type)
# init logging
self.logger = self.get_logger(file_path)
msg = "'\033[94m[%s trainer]\033[0m initialized - %s (action_mask: %s, embedding: %s, lang: %s, obs_size: %s, bow: %s, context_feature: %s, action_size: %s)" % (
rospy.get_name(), self.net.__class__.__name__, self.is_action_mask,
self.is_emb, self.lang_type, obs_size, self.bow_enc.vocab_size,
et.num_features, action_size)
rospy.loginfo(msg)