def eval_all():
output_model_file = "../model_dir/best_model"
output_config_file = "../model_dir/bert_config.json"
config = BertConfig(output_config_file)
model = BertForQuestionAnswering(config)
model.load_state_dict(
torch.load(output_model_file)) #, map_location='cpu'))
evaluate(model.cpu(), result_file="../metric/predicts.json")
def eval_all():
output_model_file = "../model_dir/best_model"
output_config_file = "../model_dir/bert_configbase.json"
config = BertConfig(output_config_file)
model = BertForQuestionAnswering(config)
# 针对多卡训练加载模型的方法：
state_dict = torch.load(output_model_file, map_location='cuda:0')
# 初始化一个空 dict
new_state_dict = OrderedDict()
# 修改 key，没有module字段则需要不上，如果有，则需要修改为 module.features
for k, v in state_dict.items():
if 'module' not in k:
k = k
else:
k = k.replace('module.', '')
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
# model.load_state_dict(torch.load(output_model_file)) #, map_location='cpu'))
evaluate(model.cpu(), result_file="../metric/predicts_dev.json")