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'
epoch_id = '29'
#folder = '2019-06-20-09-19-39-sl_word'
#simulator_folder = '2019-06-20-09-19-39-sl_word'
folder = "2019-12-06-02-20-58-sl_word_dlg_noattn"
simulator_folder = "2019-12-06-02-20-58-sl_word_dlg_noattn"
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(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=100,
use_gpu=env == 'gpu',
nepoch=4,
nepisode=0,
sv_train_freq=4,
eval_freq=0,
max_words=100,
rl_lr=0.1,
momentum=0.1,
nesterov=True,
gamma=0.95,
rl_clip=1.0, # TODO it appears this is very very important
ref_text='../data/negotiate/train.txt',
domain='object_division',
max_nego_turn=50,
random_seed=0,
)
# 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 = HRED(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 = RlAgent(sys_model, corpus, rl_config, name='System')
# SIMULATOR we keep usr frozen, i.e. we don't update its parameters
usr_model = 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
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)
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 = HRED(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=generate)
except KeyboardInterrupt:
# 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')
hmm = Hmm(corpus, config)
config.pretrain_folder = "2019-12-06-02-20-58-sl_word_dlg_noattn"
word = HRED(corpus, config)
hmm.cuda()
word.cuda()
from latent_dialog.enc2dec.decoders import TEACH_FORCE
train_data.epoch_init(config)
batch = train_data.next_batch()
while batch is not None:
batch = train_data.next_batch()
hmm_out = hmm(batch, TEACH_FORCE, get_marginals=True)
word_out = word(batch, TEACH_FORCE, get_marginals=True)
import pdb
pdb.set_trace()
if config.use_gpu: