def __init__(self, num=1):
parser = argparse.ArgumentParser(description='S2S')
parser.add_argument('--no_cuda',
type=util.str2bool,
nargs='?',
const=True,
default=True,
help='enables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument('--no_models',
type=int,
default=20,
help='how many models to evaluate')
parser.add_argument('--original',
type=str,
default='model/model/',
help='Original path.')
parser.add_argument('--dropout', type=float, default=0.0)
parser.add_argument('--use_emb', type=str, default='False')
parser.add_argument('--beam_width',
type=int,
default=10,
help='Beam width used in beamsearch')
parser.add_argument('--write_n_best',
type=util.str2bool,
nargs='?',
const=True,
default=False,
help='Write n-best list (n=beam_width)')
parser.add_argument('--model_path',
type=str,
default='model/model/translate.ckpt',
help='Path to a specific model checkpoint.')
parser.add_argument('--model_dir',
type=str,
default='data/multi-woz/model/model/')
parser.add_argument('--model_name', type=str, default='translate.ckpt')
parser.add_argument('--valid_output',
type=str,
default='model/data/val_dials/',
help='Validation Decoding output dir path')
parser.add_argument('--decode_output',
type=str,
default='model/data/test_dials/',
help='Decoding output dir path')
args = parser.parse_args([])
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
self.device = torch.device("cuda" if args.cuda else "cpu")
with open(
os.path.join(os.path.dirname(__file__),
args.model_path + '.config'), 'r') as f:
add_args = json.load(f)
# print(add_args)
for k, v in add_args.items():
setattr(args, k, v)
# print(args)
args.mode = 'test'
args.load_param = True
args.dropout = 0.0
assert args.dropout == 0.0
# Start going through models
args.original = args.model_path
args.model_path = args.original
self.model = loadModel(num, args)
self.dial = {"cur": {"log": []}}
self.prev_state = default_state()
self.prev_active_domain = None
self.dic = delexicalize.prepareSlotValuesIndependent()
self.db = Database()
def createDelexData(dialogue):
"""Main function of the script - loads delexical dictionary,
goes through each dialogue and does:
1) data normalization
2) delexicalization
3) addition of database pointer
4) saves the delexicalized data
"""
# create dictionary of delexicalied values that then we will search against, order matters here!
dic = delexicalize.prepareSlotValuesIndependent()
delex_data = {}
# fin1 = open('data/multi-woz/data.json', 'r')
# data = json.load(fin1)
# dialogue = data[dialogue_name]
dial = dialogue['cur']
idx_acts = 1
for idx, turn in enumerate(dial['log']):
# print(idx)
# print(turn)
# normalization, split and delexicalization of the sentence
sent = normalize(turn['text'])
words = sent.split()
sent = delexicalize.delexicalise(' '.join(words), dic)
# parsing reference number GIVEN belief state
sent = delexicaliseReferenceNumber(sent, turn)
# changes to numbers only here
digitpat = re.compile('\d+')
sent = re.sub(digitpat, '[value_count]', sent)
# print(sent)
# delexicalized sentence added to the dialogue
dial['log'][idx]['text'] = sent
if idx % 2 == 1: # if it's a system turn
# add database pointer
pointer_vector, db_results, num_entities = addDBPointer(turn)
# add booking pointer
pointer_vector = addBookingPointer(dial, turn, pointer_vector)
# print pointer_vector
dial['log'][idx - 1]['db_pointer'] = pointer_vector.tolist()
idx_acts += 1
dial = get_dial(dial)
if dial:
dialogue = {}
dialogue['usr'] = []
dialogue['sys'] = []
dialogue['db'] = []
dialogue['bs'] = []
for turn in dial:
# print(turn)
dialogue['usr'].append(turn[0])
dialogue['sys'].append(turn[1])
dialogue['db'].append(turn[2])
dialogue['bs'].append(turn[3])
delex_data['cur'] = dialogue
return delex_data
def createDelexData():
"""Main function of the script - loads delexical dictionary,
goes through each dialogue and does:
1) data normalization
2) delexicalization
3) addition of database pointer
4) saves the delexicalized data
"""
# download the data
loadData()
# create dictionary of delexicalied values that then we will search against, order matters here!
dic = delexicalize.prepareSlotValuesIndependent()
delex_data = {}
fin1 = open('data/multi-woz/data.json', 'r')
data = json.load(fin1)
fin2 = open('data/multi-woz/dialogue_acts.json', 'r')
data2 = json.load(fin2)
for dialogue_name in tqdm(data):
dialogue = data[dialogue_name]
# print dialogue_name
idx_acts = 1
for idx, turn in enumerate(dialogue['log']):
# normalization, split and delexicalization of the sentence
sent = normalize(turn['text'])
words = sent.split()
sent = delexicalize.delexicalise(' '.join(words), dic)
# parsing reference number GIVEN belief state
sent = delexicaliseReferenceNumber(sent, turn)
# changes to numbers only here
digitpat = re.compile('\d+')
sent = re.sub(digitpat, '[value_count]', sent)
# delexicalized sentence added to the dialogue
dialogue['log'][idx]['text'] = sent
if idx % 2 == 1: # if it's a system turn
# add database pointer
pointer_vector = addDBPointer(turn)
# add booking pointer
pointer_vector = addBookingPointer(dialogue, turn,
pointer_vector)
# print pointer_vector
dialogue['log'][idx -
1]['db_pointer'] = pointer_vector.tolist()
# FIXING delexicalization:
dialogue = fixDelex(dialogue_name, dialogue, data2, idx, idx_acts)
idx_acts += 1
delex_data[dialogue_name] = dialogue
with open('data/multi-woz/delex.json', 'w') as outfile:
json.dump(delex_data, outfile)
return delex_data