stdout, stderr = pro.communicate()
returncode = pro.returncode
# 根据执行结果判断是否通过
return judge_pass(returncode, stdout, stderr)
if __name__ == '__main__':
# 环境检查
from Fuzzer.environment_check import check
check()
# 读取工作路径和训练词汇表
print("正在恢复词汇表和模型，请稍等...")
workspace_path = os.path.dirname(hparams.gen_model)
token_to_idx = load_json(os.path.join(workspace_path, 'token_to_idx.json'))
idx_to_token = load_json(os.path.join(workspace_path, 'idx_to_token.json'),
transfer=True)
# 恢复模型（注意load方法没有device参数）
model = torch.load(hparams.gen_model,
map_location=f'cuda:{hparams.gpu}').to(device)
model.device = device
# 设置计数器
total_count = 0
pass_count = 0
no_pass_count = 0
engine = '/root/.jsvu/ChakraCoreFiles/bin/ch'
pass_testcases = []
no_pass_testcases = []
from CodeGenerator.model import LSTM
device = torch.device(
f"cuda:{hparams.gpu}" if torch.cuda.is_available() else "cpu")
def write_file(file_path, content: str):
with open(file_path, 'a+', encoding='utf-8') as f:
f.write(content)
if __name__ == '__main__':
# 读取工作路径和训练词汇表
print("正在恢复词汇表和模型，请稍等...")
workspace_path = os.path.dirname(hparams.gen_model)
char_to_idx = load_json(os.path.join(workspace_path, 'char_to_idx.json'))
idx_to_char = load_json(os.path.join(workspace_path, 'idx_to_char.json'),
transfer=True)
# 恢复模型（注意load方法没有device参数）
model = torch.load(hparams.gen_model).to(device)
model.device = device
# 设置计数器
seed_count = 0
total_count = 0
new_seed_count = 0
syntax_correct_count = 0
semantic_correct_count = 0
filtered_case_count = 0