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)
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)
if __name__ == '__main__':
arg_parser = arg.init_arg_parser()
args = arg.init_config(arg_parser)
print(args)
evaluate(args)