def main():
start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
print('[START]', start_time, '=' * 30)
# RL configuration
env = 'gpu'
pretrained_folder = '2019-06-20-22-49-55-sl_cat'
pretrained_model_id = 41
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_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=
0, # TODO pay attention to main.py, cuz it is also controlled there
use_gpu=env == 'gpu',
nepoch=10,
nepisode=0,
tune_pi_only=False,
max_words=100,
temperature=1.0,
episode_repeat=1.0,
rl_lr=0.01,
momentum=0.0,
nesterov=False,
gamma=0.99,
rl_clip=5.0,
random_seed=100,
)
# 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['dropout'] = 0.0
sv_config['use_gpu'] = rl_config.use_gpu
corpus = NormMultiWozCorpus(sv_config)
# TARGET AGENT
sys_model = SysPerfectBD2Cat(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 = OfflineLatentRlAgent(sys_model,
corpus,
rl_config,
name='System',
tune_pi_only=rl_config.tune_pi_only)
# start RL
reinforce = OfflineTaskReinforce(sys, corpus, sv_config, sys_model,
rl_config, task_generate)
reinforce.run()
end_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
print('[END]', end_time, '=' * 30)
config.saved_path = saved_path
# save configuration
with open(os.path.join(saved_path, 'config.json'), 'w') as f:
json.dump(config, f, indent=4) # sort_keys=True
corpus = corpora.NormMultiWozCorpus(config)
train_dial, val_dial, test_dial = corpus.get_corpus()
train_data = BeliefDbDataLoaders('Train', train_dial, config)
val_data = BeliefDbDataLoaders('Val', val_dial, config)
test_data = BeliefDbDataLoaders('Test', test_dial, config)
evaluator = MultiWozEvaluator('SysWoz')
model = SysPerfectBD2Cat(corpus, config)
if config.use_gpu:
model.cuda()
best_epoch = None
if not config.forward_only:
try:
best_epoch = train(model,
train_data,
val_data,
test_data,
config,
evaluator,
gen=task_generate)
except KeyboardInterrupt: