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MultiWOZ.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import os
import time
import json
import numpy as np
import torch
import logging
from transformer import Constants
import copy
logger = logging.getLogger(__name__)
def get_batch(data_dir, option, tokenizer, max_seq_length):
examples = []
prev_sys = None
num = 0
if option == 'train':
with open('{}/train.json'.format(data_dir)) as f:
source = json.load(f)
predicted_acts = None
elif option == 'val':
with open('{}/val.json'.format(data_dir)) as f:
source = json.load(f)
with open('{}/BERT_dev_prediction.json'.format(data_dir)) as f:
predicted_acts = json.load(f)
#predicted_acts = None
else:
with open('{}/test.json'.format(data_dir)) as f:
source = json.load(f)
with open('{}/BERT_test_prediction.json'.format(data_dir)) as f:
predicted_acts = json.load(f)
#predicted_acts = None
logger.info("Loading total {} dialogs".format(len(source)))
for num_dial, dialog_info in enumerate(source):
hist = []
hist_segment = []
dialog_file = dialog_info['file']
dialog = dialog_info['info']
for turn_num, turn in enumerate(dialog):
#user = [vocab[w] if w in vocab else vocab['<UNK>'] for w in turn['user'].split()]
tokens = tokenizer.tokenize(turn['user'])
query = copy.copy(tokens)
if len(tokens) > max_seq_length - 2:
query = query[:max_seq_length - 2]
#if 'book' in tokens or 'booked' in tokens or 'booking' in tokens:
segment_user = 1#turn_num * 2 if turn_num * 2 < Constants.MAX_SEGMENT else Constants.MAX_SEGMENT - 1
segment_sys = 2#turn_num * 2 + 1 if turn_num * 2 + 1 < Constants.MAX_SEGMENT else Constants.MAX_SEGMENT - 1
if len(hist) == 0:
if len(tokens) > max_seq_length - 2:
tokens = tokens[:max_seq_length - 2]
segment_ids = [segment_user] * len(tokens)
else:
#segment_ids = [0] * (len(hist) + 1) + [1] * len(tokens)
segment_ids = hist_segment + [Constants.PAD] + [segment_user] * len(tokens)
tokens = hist + [Constants.SEP_WORD] + tokens
if len(tokens) > max_seq_length - 2:
tokens = tokens[-(max_seq_length - 2):]
segment_ids = segment_ids[-(max_seq_length - 2):]
"""
template = tokenizer.tokenize(turn['template'])
if len(template) > Constants.TEMPLATE_MAX_LEN:
template = template[:Constants.TEMPLATE_MAX_LEN]
template_ids = tokenizer.convert_tokens_to_ids(template)
padded_template_ids = template_ids + [Constants.PAD] * (Constants.TEMPLATE_MAX_LEN - len(template_ids))
"""
resp = [Constants.SOS_WORD] + tokenizer.tokenize(turn['sys']) + [Constants.EOS_WORD]
if len(resp) > Constants.RESP_MAX_LEN:
resp = resp[:Constants.RESP_MAX_LEN-1] + [Constants.EOS_WORD]
else:
resp = resp + [Constants.PAD_WORD] * (Constants.RESP_MAX_LEN - len(resp))
resp_inp_ids = tokenizer.convert_tokens_to_ids(resp[:-1])
resp_out_ids = tokenizer.convert_tokens_to_ids(resp[1:])
bs = [0] * len(Constants.belief_state)
if turn['BS'] != "None":
for domain in turn['BS']:
for key, value in turn['BS'][domain]:
bs[Constants.belief_state.index(domain + '-' + key)] = 1
if turn['KB'] == 0:
query_results = [1, 0, 0, 0]
elif turn['KB'] == 2:
query_results = [0, 1, 0, 0]
elif turn['KB'] == 3:
query_results = [0, 0, 1, 0]
elif turn['KB'] >= 4:
query_results = [0, 0, 0, 1]
"""
if turn_num == 0:
query_results += [1, 0, 0, 0]
elif turn_num == 1:
query_results += [0, 1, 0, 0]
elif turn_num == 2:
query_results += [0, 0, 1, 0]
elif turn_num >= 3:
query_results += [0, 0, 0, 1]
"""
tokens = [Constants.CLS_WORD] + tokens + [Constants.SEP_WORD]
query = [Constants.CLS_WORD] + query + [Constants.SEP_WORD]
segment_ids = [Constants.PAD] + segment_ids + [Constants.PAD]
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_ids)
query_ids = tokenizer.convert_tokens_to_ids(query)
query_mask = [1] * len(query_ids)
query_segment_ids = [1] * len(query_mask)
query_padding = [Constants.PAD] * (max_seq_length - len(query_ids))
query_ids += query_padding
query_mask += query_padding
padded_query_segment_ids = query_segment_ids + query_padding
padding = [Constants.PAD] * (max_seq_length - len(input_ids))
input_ids += padding
input_mask += padding
padded_segment_ids = segment_ids + padding
assert len(input_ids) == len(input_mask) == len(padded_segment_ids) == max_seq_length, \
"length {}, {}, {}".format(len(input_ids), len(input_mask), len(segment_ids))
act_vecs = [0] * len(Constants.act_ontology)
if turn['act'] != "None":
for w in turn['act']:
act_vecs[Constants.act_ontology.index(w)] = 1
if predicted_acts is not None:
hierarchical_act_vecs = np.asarray(predicted_acts[dialog_file][str(turn_num)], 'int64')
else:
hierarchical_act_vecs = np.zeros((Constants.act_len), 'int64')
if turn['act'] != "None":
for w in turn['act']:
#for _ in Constants.domain_imapping[w]:
# hierarchical_act_vecs[Constants.domains.index(_)] = 1
d, f, s = w.split('-')
hierarchical_act_vecs[Constants.domains.index(d)] = 1
#for _ in Constants.function_imapping[w]:
hierarchical_act_vecs[len(Constants.domains) + Constants.functions.index(f)] = 1
#for _ in Constants.arguments_imapping[w]:
hierarchical_act_vecs[len(Constants.domains) + len(Constants.functions) + Constants.arguments.index(s)] = 1
"""
if turn['act'] != "None":
if len(turn['act']) == 0:
frequency = 0
else:
frequency = sum([freq[w] for w in turn['act']]) / len(turn['act'])
else:
frequency = 0
"""
#examples.append([input_ids, input_mask, padded_segment_ids, act_vecs, \
# query_results, resp_inp_ids, resp_out_ids, bs, hierarchical_act_vecs, dialog_file])
examples.append([input_ids, input_mask, padded_segment_ids, act_vecs, \
query_results, resp_inp_ids, resp_out_ids, bs, hierarchical_act_vecs, dialog_file])
num += 1
if num < 5 and option == 'train':
logger.info("*** Example ***")
logger.info("guid: %s" % (str(num)))
logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info("segment_ids: %s" % " ".join([str(x) for x in padded_segment_ids]))
logger.info("action_vecs: %s" % " ".join([str(x) for x in hierarchical_act_vecs]))
logger.info("query results: %s" % " ".join([str(x) for x in query_results]))
logger.info("belief states: %s" % " ".join([str(x) for x in bs]))
logger.info("system response: %s" % " ".join([str(x) for x in resp if x != "[PAD]"]))
#logger.info("one hot dialog act: %s " % " ".join([str(x) for x in act_vecs]))
logger.info("")
sys = tokenizer.tokenize(turn['sys'])
if turn_num == 0:
hist = tokens[1:-1] + [Constants.SEP_WORD] + sys
hist_segment = segment_ids[1:-1] + [Constants.PAD] + [segment_sys] * len(sys)
else:
hist = hist + [Constants.SEP_WORD] + tokens[1:-1] + [Constants.SEP_WORD] + sys
hist_segment = hist_segment + [Constants.PAD] + segment_ids[1:-1] + [Constants.PAD] + [segment_sys] * len(sys)
all_input_ids = torch.tensor([f[0] for f in examples], dtype=torch.long)
all_input_mask = torch.tensor([f[1] for f in examples], dtype=torch.long)
all_segment_ids = torch.tensor([f[2] for f in examples], dtype=torch.long)
all_act_vecs = torch.tensor([f[3] for f in examples], dtype=torch.float32)
all_query_results = torch.tensor([f[4] for f in examples], dtype=torch.float32)
all_response_in = torch.tensor([f[5] for f in examples], dtype=torch.long)
all_response_out = torch.tensor([f[6] for f in examples], dtype=torch.long)
all_belief_state = torch.tensor([f[7] for f in examples], dtype=torch.float32)
all_hierarchical_act_vecs = torch.tensor([f[8] for f in examples], dtype=torch.float32)
all_files = [f[9] for f in examples]
#all_template_ids = torch.tensor([f[9] for f in examples], dtype=torch.long)
return all_input_ids, all_input_mask, all_segment_ids, all_act_vecs, \
all_query_results, all_response_in, all_response_out, all_belief_state, \
all_hierarchical_act_vecs, all_files