def main(): start_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time())) print('[START]', start_time, '=' * 30) env = 'gpu' pretrained_folder = '2018-11-13-21-27-21-sys_sl_bdu2resp' pretrained_model_id = 61 exp_dir = os.path.join('sys_config_log_model', pretrained_folder, 'rl-' + start_time) # create exp folder if not os.path.exists(exp_dir): os.mkdir(exp_dir) # RL configuration rl_config = Pack( train_path='../data/norm-multi-woz/train_dials.json', valid_path='../data/norm-multi-woz/val_dials.json', test_path='../data/norm-multi-woz/test_dials.json', sv_config_path=os.path.join('sys_config_log_model', pretrained_folder, 'config.json'), sv_model_path=os.path.join('sys_config_log_model', pretrained_folder, '{}-model'.format(pretrained_model_id)), rl_config_path=os.path.join(exp_dir, 'rl_config.json'), rl_model_path=os.path.join(exp_dir, 'rl_model'), ppl_best_model_path=os.path.join(exp_dir, 'ppl_best.model'), reward_best_model_path=os.path.join(exp_dir, 'reward_best.model'), record_path=exp_dir, record_freq=200, sv_train_freq= 1000, # TODO pay attention to main.py, cuz it is also controlled there use_gpu=env == 'gpu', nepoch=10, nepisode=0, max_words=100, episode_repeat=1.0, temperature=1.0, rl_lr=0.01, momentum=0.0, nesterov=False, gamma=0.99, rl_clip=5.0, random_seed=10, ) # save configuration with open(rl_config.rl_config_path, 'w') as f: json.dump(rl_config, f, indent=4) # set random seed set_seed(rl_config.random_seed) # load previous supervised learning configuration and corpus sv_config = Pack(json.load(open(rl_config.sv_config_path))) sv_config['use_gpu'] = rl_config.use_gpu corpus = NormMultiWozCorpus(sv_config) # TARGET AGENT sys_model = SysPerfectBD2Word(corpus, sv_config) if sv_config.use_gpu: sys_model.cuda() sys_model.load_state_dict( th.load(rl_config.sv_model_path, map_location=lambda storage, location: storage)) sys_model.eval() sys = OfflineRlAgent(sys_model, corpus, rl_config, name='System', tune_pi_only=False) # start RL reinforce = OfflineTaskReinforce(sys, corpus, sv_config, sys_model, rl_config, task_generate) reinforce.run() # save sys model th.save(sys_model.state_dict(), rl_config.rl_model_path) end_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time())) print('[END]', end_time, '=' * 30)
fix_train_batch=False, avg_type='word', print_step=300, ckpt_step=1416, improve_threshold=0.996, patient_increase=2.0, save_model=True, early_stop=False, gen_type='greedy', preview_batch_num=None, k=domain_info.input_length(), init_range=0.1, pretrain_folder='2019-06-20-21-43-06-sl_cat', forward_only=False ) set_seed(config.seed) start_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time())) stats_path = 'sys_config_log_model' if config.forward_only: saved_path = os.path.join(stats_path, config.pretrain_folder) config = Pack(json.load(open(os.path.join(saved_path, 'config.json')))) config['forward_only'] = True else: saved_path = os.path.join( stats_path, start_time + '-' + os.path.basename(__file__).split('.')[0]) if not os.path.exists(saved_path): os.makedirs(saved_path) config.saved_path = saved_path prepare_dirs_loggers(config) logger = logging.getLogger()