def infer(nlu1,
table_name,
data_table,
path_db,
db_name,
model,
model_bert,
bert_config,
max_seq_length,
num_target_layers,
beam_size=4,
show_table=False,
show_answer_only=False):
# I know it is of against the DRY principle but to minimize the risk of introducing bug w, the infer function introuced.
model.eval()
model_bert.eval()
engine = DBEngine(os.path.join(path_db, f"{db_name}.db"))
# Get inputs
nlu = [nlu1]
# nlu_t1 = tokenize_corenlp(client, nlu1)
nlu_t1 = tokenize_corenlp_direct_version(client, nlu1)
nlu_t = [nlu_t1]
tb1 = data_table[0]
hds1 = tb1['header']
tb = [tb1]
hds = [hds1]
hs_t = [[]]
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
prob_sca, prob_w, prob_wn_w, pr_sc, pr_sa, pr_wn, pr_sql_i = model.beam_forward(
wemb_n,
l_n,
wemb_h,
l_hpu,
l_hs,
engine,
tb,
nlu_t,
nlu_tt,
tt_to_t_idx,
nlu,
beam_size=beam_size)
# sort and generate
pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i)
if len(pr_sql_i) != 1:
raise EnvironmentError
pr_sql_q1 = generate_sql_q(pr_sql_i, [tb1])
pr_sql_q = [pr_sql_q1]
try:
pr_ans, _ = engine.execute_return_query(tb[0]['id'], pr_sc[0],
pr_sa[0], pr_sql_i[0]['conds'])
except:
pr_ans = ['Answer not found.']
pr_sql_q = ['Answer not found.']
if show_answer_only:
print(f'Q: {nlu[0]}')
print(f'A: {pr_ans[0]}')
print(f'SQL: {pr_sql_q}')
else:
print(
f'START ============================================================= '
)
print(f'{hds}')
if show_table:
print(engine.show_table(table_name))
print(f'nlu: {nlu}')
print(f'pr_sql_i : {pr_sql_i}')
print(f'pr_sql_q : {pr_sql_q}')
print(f'pr_ans: {pr_ans}')
print(
f'---------------------------------------------------------------------'
)
return pr_sql_i, pr_ans
def infernew(dev_loader,
data_table,
model,
model_bert,
bert_config,
tokenizer,
max_seq_length,
num_target_layers,
detail=False,
path_db=None,
st_pos=0,
dset_name='train',
EG=False,
beam_size=4):
model.eval()
model_bert.eval()
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
results = []
count = 0
for iB, t in enumerate(dev_loader):
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(
t, data_table, no_hs_t=True, no_sql_t=True)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx,
g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(
g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
for b in range(len(nlu)):
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[b]
results1["table_id"] = tb[b]["id"]
results.append(results1)
continue
knowledge = []
for k in t:
if "bertindex_knowledge" in k:
knowledge.append(k["bertindex_knowledge"])
else:
knowledge.append(max(l_n) * [0])
knowledge_header = []
for k in t:
if "header_knowledge" in k:
knowledge_header.append(k["header_knowledge"])
else:
knowledge_header.append(max(l_hs) * [0])
if not EG:
# No Execution guided decoding
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(
wemb_n,
l_n,
wemb_h,
l_hpu,
l_hs,
knowledge=knowledge,
knowledge_header=knowledge_header)
# get loss & step
loss = Loss_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, g_sc, g_sa,
g_wn, g_wc, g_wo, g_wvi)
# prediction
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(
s_sc,
s_sa,
s_wn,
s_wc,
s_wo,
s_wv,
)
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(
pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
# g_sql_i = generate_sql_i(g_sc, g_sa, g_wn, g_wc, g_wo, g_wv_str, nlu)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc, pr_wo,
pr_wv_str, nlu)
else:
# Execution guided decoding
prob_sca, prob_w, prob_wn_w, pr_sc, pr_sa, pr_wn, pr_sql_i = model.beam_forward(
wemb_n,
l_n,
wemb_h,
l_hpu,
l_hs,
engine,
tb,
nlu_t,
nlu_tt,
tt_to_t_idx,
nlu,
beam_size=beam_size,
knowledge=knowledge,
knowledge_header=knowledge_header)
# sort and generate
pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i)
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
loss = torch.tensor([0])
pr_sql_q1 = generate_sql_q(pr_sql_i, tb)
pr_sql_q = [pr_sql_q1]
try:
pr_ans, _ = engine.execute_return_query(tb[0]['id'], pr_sc[0],
pr_sa[0],
pr_sql_i[0]['conds'])
except:
pr_ans = ['Answer not found.']
pr_sql_q = ['Answer not found.']
yes = True
if yes:
print(f'Q: {nlu[0]}')
print(f'A: {pr_ans[0]}')
print(f'SQL: {pr_sql_q}')
else:
print(
f'START ============================================================= '
)
print(f'{hds}')
if yes:
print(engine.show_table(table_name))
print(f'nlu: {nlu}')
print(f'pr_sql_i : {pr_sql_i}')
print(f'pr_sql_q : {pr_sql_q}')
print(f'pr_ans: {pr_ans}')
print(
f'---------------------------------------------------------------------'
)
return pr_sql_i, pr_ans
def infer(nlu1,
table_name,
data_table,
path_db,
db_name,
model,
model_bert,
bert_config,
max_seq_length,
num_target_layers,
beam_size=4,
show_table=False,
show_answer_only=False):
# I know it is of against the DRY principle but to minimize the risk of introducing bug w, the infer function introuced.
model.eval()
model_bert.eval()
engine = DBEngine(os.path.join(path_db, f"{db_name}.db"))
# 问题输入
nlu = [nlu1] #问题数组
'''
==tokenize_corenlp_direct_version函数作用：就是英文分词(可能按照stanza规则分?)==
==client:stanford的corenlp代理类==
==nlu1:刚刚定义的问题列表==
2020/12/02修改：修改infer中文分词问题
2020/12/11修改：取消使用stanza中文分词，直接完全分词
'''
# nlu_t1 = tokenize_corenlp_direct_version(client, nlu1)
nlu_t1 = list(nlu1)
nlu_t = [nlu_t1] # 把分词之后的数据也放到数组里
#tb1 = data_table[0]
'''
2020/12/01修改：tb1根据问题来选择表
循环查找即可
'''
for temple_table in data_table:
if temple_table['name'] == table_name:
tb1 = temple_table
break
hds1 = tb1['header']
tb = [tb1]
hds = [hds1]
hs_t = [[]]
# 获取bert-output
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
# 获取sqlova-output
'''2020/12/11修改：换用model预测'''
# prob_sca, prob_w, prob_wn_w, pr_sc, pr_sa, pr_wn, pr_sql_i = model.beam_forward(wemb_n, l_n, wemb_h, l_hpu,
# l_hs, engine, tb,
# nlu_t, nlu_tt,
# tt_to_t_idx, nlu,
# beam_size=beam_size)
# 获取sqlova得出的6大重要部分参数 pr_sc pr_sa
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu,
l_hs)
# 将权重参数变成值
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(
s_sc,
s_sa,
s_wn,
s_wc,
s_wo,
s_wv,
)
# 根据值得出where-value(这里是分词版，一个一个切分)
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt,
tt_to_t_idx, nlu)
# 最后在将值整合成conds(pre版本)
'''2020/12/03修改：将agg和sel变为列表形式（generate_sql_i函数内修改）'''
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str,
nlu)
# 切分出where-col/where-op/where-val
pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i)
if len(pr_sql_i) != 1: # 判断是不是生成了conds
raise EnvironmentError
pr_sql_q1 = generate_sql_q(pr_sql_i, [tb1]) # 根据上面的conds生成sql语句
pr_sql_q = [pr_sql_q1] # 将生成的sql语句放到list里，因为infer可能有多句
'''下面执行SQL语句'''
try:
pr_ans, _ = engine.execute_return_query(tb[0]['id'], pr_sc[0],
pr_sa[0], pr_sql_i[0]['conds'])
except:
pr_ans = ['Answer not found.']
pr_sql_q = ['Answer not found.']
if show_answer_only:
print(f'Q: {nlu[0]}')
print(f'A: {pr_ans[0]}')
print(f'SQL: {pr_sql_q}')
else:
print(
f'START ============================================================= '
)
print(f'{hds}')
if show_table:
print(engine.show_table(table_name))
print(f'nlu: {nlu}')
print(f'pr_sql_i : {pr_sql_i}')
print(f'pr_sql_q : {pr_sql_q}')
print(f'pr_ans: {pr_ans}')
print(
f'---------------------------------------------------------------------'
)
return pr_sql_i, pr_ans
