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Copy pathtest.py
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83 lines (75 loc) · 3.32 KB
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import json
import torch
from sqlnet.utils import *
from sqlnet.model.seq2sql import Seq2SQL
from sqlnet.model.sqlnet import SQLNet
import numpy as np
import datetime
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--toy', action='store_true',
help='If set, use small data; used for fast debugging.')
parser.add_argument('--ca', action='store_true',
help='Use conditional attention.')
parser.add_argument('--dataset', type=int, default=0,
help='0: original dataset, 1: re-split dataset')
parser.add_argument('--rl', action='store_true',
help='Use RL for Seq2SQL.')
parser.add_argument('--baseline', action='store_true',
help='If set, then test Seq2SQL model; default is SQLNet model.')
parser.add_argument('--train_emb', action='store_true',
help='Use trained word embedding for SQLNet.')
args = parser.parse_args()
N_word=300
B_word=42
if args.toy:
USE_SMALL=True
GPU=True
BATCH_SIZE=15
else:
USE_SMALL=False
GPU=True
BATCH_SIZE=64
TEST_ENTRY=(True, True, True) # (AGG, SEL, COND)
sql_data, table_data, val_sql_data, val_table_data, \
test_sql_data, test_table_data, \
TRAIN_DB, DEV_DB, TEST_DB = load_dataset(
args.dataset, use_small=USE_SMALL)
word_emb = load_word_emb('glove/glove.%dB.%dd.txt'%(B_word,N_word), \
load_used=True, use_small=USE_SMALL) # load_used can speed up loading
if args.baseline:
model = Seq2SQL(word_emb, N_word=N_word, gpu=GPU, trainable_emb = True)
else:
model = SQLNet(word_emb, N_word=N_word, use_ca=args.ca, gpu=GPU,
trainable_emb = True)
if args.train_emb:
agg_m, sel_m, cond_m, agg_e, sel_e, cond_e = best_model_name(args)
print "Loading from %s"%agg_m
model.agg_pred.load_state_dict(torch.load(agg_m))
print "Loading from %s"%sel_m
model.sel_pred.load_state_dict(torch.load(sel_m))
print "Loading from %s"%cond_m
model.cond_pred.load_state_dict(torch.load(cond_m))
print "Loading from %s"%agg_e
model.agg_embed_layer.load_state_dict(torch.load(agg_e))
print "Loading from %s"%sel_e
model.sel_embed_layer.load_state_dict(torch.load(sel_e))
print "Loading from %s"%cond_e
model.cond_embed_layer.load_state_dict(torch.load(cond_e))
else:
agg_m, sel_m, cond_m = best_model_name(args)
print "Loading from %s"%agg_m
model.agg_pred.load_state_dict(torch.load(agg_m))
print "Loading from %s"%sel_m
model.sel_pred.load_state_dict(torch.load(sel_m))
print "Loading from %s"%cond_m
model.cond_pred.load_state_dict(torch.load(cond_m))
print "Dev acc_qm: %s;\n breakdown on (agg, sel, where): %s"%epoch_acc(
model, BATCH_SIZE, val_sql_data, val_table_data, TEST_ENTRY)
print "Dev execution acc: %s"%epoch_exec_acc(
model, BATCH_SIZE, val_sql_data, val_table_data, DEV_DB)
print "Test acc_qm: %s;\n breakdown on (agg, sel, where): %s"%epoch_acc(
model, BATCH_SIZE, test_sql_data, test_table_data, TEST_ENTRY)
print "Test execution acc: %s"%epoch_exec_acc(
model, BATCH_SIZE, test_sql_data, test_table_data, TEST_DB)