else:
saved_path = os.path.join(stats_path, start_time+'-'+os.path.basename(__file__).split('.')[0])
if not os.path.exists(saved_path):
os.mkdir(saved_path)
config.saved_path = saved_path
prepare_dirs_loggers(config)
logger = logging.getLogger()
logger.info('[START]\n{}\n{}'.format(start_time, '='*30))
# save configuration
with open(os.path.join(saved_path, 'config.json'), 'w') as f:
json.dump(config, f, indent=4) # sort_keys=True
corpus = DealCorpus(config)
train_dial, val_dial, test_dial = corpus.get_corpus()
train_data = DealDataLoaders('Train', train_dial, config)
val_data = DealDataLoaders('Val', val_dial, config)
test_data = DealDataLoaders('Test', test_dial, config)
evaluator = BleuEvaluator('Deal')
model = GaussHRED(corpus, config)
if config.use_gpu:
model.cuda()
best_epoch = None
if not config.forward_only:
def main():
start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
print('[START]', start_time, '=' * 30)
# RL configuration
folder = '2019-06-20-10-24-23-sl_gauss'
epoch_id = '28'
env = 'gpu'
sim_epoch_id = '23'
simulator_folder = '2019-06-20-09-19-39-sl_word'
exp_dir = os.path.join('config_log_model', folder, 'rl-' + start_time)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
rl_config = Pack(
train_path='../data/negotiate/train.txt',
val_path='../data/negotiate/val.txt',
test_path='../data/negotiate/test.txt',
selfplay_path='../data/negotiate/selfplay.txt',
selfplay_eval_path='../data/negotiate/selfplay_eval.txt',
sim_config_path=os.path.join('config_log_model', simulator_folder,
'config.json'),
sim_model_path=os.path.join('config_log_model', simulator_folder,
'{}-model'.format(sim_epoch_id)),
sv_config_path=os.path.join('config_log_model', folder, 'config.json'),
sv_model_path=os.path.join('config_log_model', folder,
'{}-model'.format(epoch_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'),
judger_model_path=os.path.join('../FB', 'sv_model.th'),
judger_config_path=os.path.join('../FB', 'judger_config.json'),
record_path=exp_dir,
record_freq=50,
use_gpu=env == 'gpu',
nepoch=4,
nepisode=0,
sv_train_freq=
0, # TODO pay attention to main.py, cuz it is also controlled there
eval_freq=0,
max_words=100,
rl_lr=0.2,
momentum=0.1,
nesterov=True,
gamma=0.95,
rl_clip=1.0,
ref_text='../data/negotiate/train.txt',
domain='object_division',
max_nego_turn=50,
random_seed=0,
use_latent_rl=True)
# 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)))
sim_config = Pack(json.load(open(rl_config.sim_config_path)))
# TODO revise the use_gpu in the config
sv_config['use_gpu'] = rl_config.use_gpu
sim_config['use_gpu'] = rl_config.use_gpu
corpus = DealCorpus(sv_config)
# load models for two agents
# TARGET AGENT
sys_model = models_deal.GaussHRED(corpus, sv_config)
if sv_config.use_gpu: # TODO gpu -> cpu transfer
sys_model.cuda()
sys_model.load_state_dict(
th.load(rl_config.sv_model_path,
map_location=lambda storage, location: storage))
# we don't want to use Dropout during RL
sys_model.eval()
sys = LatentRlAgent(sys_model,
corpus,
rl_config,
name='System',
use_latent_rl=rl_config.use_latent_rl)
# SIMULATOR we keep usr frozen, i.e. we don't update its parameters
usr_model = models_deal.HRED(corpus, sim_config)
if sim_config.use_gpu: # TODO gpu -> cpu transfer
usr_model.cuda()
usr_model.load_state_dict(
th.load(rl_config.sim_model_path,
map_location=lambda storage, location: storage))
usr_model.eval()
usr_type = LstmAgent
usr = usr_type(usr_model, corpus, rl_config, name='User')
# load FB judger model
# load FB judger model
judger_config = Pack(json.load(open(rl_config.judger_config_path)))
judger_config['cuda'] = rl_config.use_gpu
judger_config['data'] = '../data/negotiate'
judger_device_id = FB_use_cuda(judger_config.cuda)
judger_word_corpus = FbWordCorpus(judger_config.data,
freq_cutoff=judger_config.unk_threshold,
verbose=True)
judger_model = FbDialogModel(judger_word_corpus.word_dict,
judger_word_corpus.item_dict,
judger_word_corpus.context_dict,
judger_word_corpus.output_length,
judger_config, judger_device_id)
if judger_device_id is not None:
judger_model.cuda(judger_device_id)
judger_model.load_state_dict(
th.load(rl_config.judger_model_path,
map_location=lambda storage, location: storage))
judger_model.eval()
judger = Judger(judger_model, judger_device_id)
# initialize communication dialogue between two agents
dialog = Dialog([sys, usr], judger, rl_config)
ctx_gen = ContextGenerator(rl_config.selfplay_path)
# simulation module
dialog_eval = DialogEval([sys, usr], judger, rl_config)
ctx_gen_eval = ContextGeneratorEval(rl_config.selfplay_eval_path)
# start RL
reinforce = Reinforce(dialog, ctx_gen, corpus, sv_config, sys_model,
usr_model, rl_config, dialog_eval, ctx_gen_eval)
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)