def evaluate(args):
"""
:param args:
:return:
"""
grammar = semQL.Grammar()
sql_data, table_data, val_sql_data,\
val_table_data= utils.load_dataset(args.dataset, use_small=args.toy)
model = IRNet(args, grammar)
if args.cuda: model.cuda()
print('load pretrained model from %s'% (args.load_model))
pretrained_model = torch.load(args.load_model,
map_location=lambda storage, loc: storage)
import copy
pretrained_modeled = copy.deepcopy(pretrained_model)
for k in pretrained_model.keys():
if k not in model.state_dict().keys():
del pretrained_modeled[k]
model.load_state_dict(pretrained_modeled)
model.word_emb = utils.load_word_emb(args.glove_embed_path)
json_datas, sketch_acc, acc = utils.epoch_acc(model, args.batch_size, val_sql_data, val_table_data,
beam_size=args.beam_size)
print('Sketch Acc: %f, Acc: %f' % (sketch_acc, acc))
# utils.eval_acc(json_datas, val_sql_data)
import json
with open('./predict_lf.json', 'w') as f:
json.dump(json_datas, f)
def model_fn(model_dir):
arg_parser = arg.init_arg_parser()
args = arg.init_config(arg_parser)
args.conceptNet = 'preprocess/conceptNet'
grammar = semQL.Grammar()
model = IRNet(args, grammar)
if args.cuda: model.cuda()
print('load pretrained model from %s' % (model_dir))
pretrained_model = torch.load(model_dir,
map_location=lambda storage, loc: storage)
import copy
pretrained_modeled = copy.deepcopy(pretrained_model)
for k in pretrained_model.keys():
if k not in model.state_dict().keys():
del pretrained_modeled[k]
model.load_state_dict(pretrained_modeled)
model.word_emb = utils.load_word_emb(args.glove_embed_path)
#with open(args.table_path, 'r', encoding='utf8') as f:
# table_datas = json.load(f)
#tables = load_tables(table_datas)
return Model(args, model)
def train(args):
"""
:param args:
:return:
"""
grammar = semQL.Grammar()
sql_data, table_data, val_sql_data, val_table_data = utils.load_dataset(
args.dataset, use_small=args.toy)
model = IRNet(args, grammar)
if args.cuda: model.cuda()
# now get the optimizer
optimizer_cls = eval('torch.optim.%s' % args.optimizer)
optimizer = optimizer_cls(model.parameters(), lr=args.lr)
print('Enable Learning Rate Scheduler: ', args.lr_scheduler)
if args.lr_scheduler:
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[21, 41], gamma=args.lr_scheduler_gammar)
else:
scheduler = None
print('Loss epoch threshold: %d' % args.loss_epoch_threshold)
print('Sketch loss coefficient: %f' % args.sketch_loss_coefficient)
if args.load_model:
print('load pretrained model from %s' % (args.load_model))
pretrained_model = torch.load(
args.load_model, map_location=lambda storage, loc: storage)
pretrained_modeled = copy.deepcopy(pretrained_model)
for k in pretrained_model.keys():
if k not in model.state_dict().keys():
del pretrained_modeled[k]
model.load_state_dict(pretrained_modeled)
model.word_emb = utils.load_word_emb(args.glove_embed_path)
# begin train
model_save_path = utils.init_log_checkpoint_path(args)
utils.save_args(args, os.path.join(model_save_path, 'config.json'))
best_dev_acc = .0
try:
with open(os.path.join(model_save_path, 'epoch.log'), 'w') as epoch_fd:
for epoch in tqdm.tqdm(range(args.epoch)):
if args.lr_scheduler:
scheduler.step()
epoch_begin = time.time()
loss = utils.epoch_train(
model,
optimizer,
args.batch_size,
sql_data,
table_data,
args,
loss_epoch_threshold=args.loss_epoch_threshold,
sketch_loss_coefficient=args.sketch_loss_coefficient)
epoch_end = time.time()
json_datas, sketch_acc, acc, counts, corrects = utils.epoch_acc(
model,
args.batch_size,
val_sql_data,
val_table_data,
beam_size=args.beam_size)
# acc = utils.eval_acc(json_datas, val_sql_data)
if acc > best_dev_acc:
utils.save_checkpoint(
model, os.path.join(model_save_path,
'best_model.model'))
best_dev_acc = acc
utils.save_checkpoint(
model,
os.path.join(model_save_path, '{%s}_{%s}.model') %
(epoch, acc))
log_str = 'Epoch: %d, Loss: %f, Sketch Acc: %f, Acc: %f, time: %f\n' % (
epoch + 1, loss, sketch_acc, acc, epoch_end - epoch_begin)
tqdm.tqdm.write(log_str)
epoch_fd.write(log_str)
epoch_fd.flush()
except Exception as e:
# Save model
utils.save_checkpoint(model,
os.path.join(model_save_path, 'end_model.model'))
print(e)
tb = traceback.format_exc()
print(tb)
else:
utils.save_checkpoint(model,
os.path.join(model_save_path, 'end_model.model'))
json_datas, sketch_acc, acc, counts, corrects = utils.epoch_acc(
model,
args.batch_size,
val_sql_data,
val_table_data,
beam_size=args.beam_size)
# acc = utils.eval_acc(json_datas, val_sql_data)
print("Sketch Acc: %f, Acc: %f, Beam Acc: %f" % (
sketch_acc,
acc,
acc,
))
