def main():
parser = argparse.ArgumentParser(description='Negotiator')
parser.add_argument('--dataset', type=str, default='./data/negotiate/val.txt',
help='location of the dataset')
parser.add_argument('--model_file', type=str,
help='model file')
parser.add_argument('--smart_ai', action='store_true', default=False,
help='to use rollouts')
parser.add_argument('--seed', type=int, default=1,
help='random seed')
parser.add_argument('--temperature', type=float, default=1.0,
help='temperature')
parser.add_argument('--domain', type=str, default='object_division',
help='domain for the dialogue')
parser.add_argument('--log_file', type=str, default='',
help='log file')
args = parser.parse_args()
utils.set_seed(args.seed)
model = utils.load_model(args.model_file)
ai = LstmAgent(model, args)
logger = DialogLogger(verbose=True, log_file=args.log_file)
domain = get_domain(args.domain)
score_func = rollout if args.smart_ai else likelihood
dataset, sents = read_dataset(args.dataset)
ranks, n, k = 0, 0, 0
for ctx, dialog in dataset:
start_time = time.time()
# start new conversation
ai.feed_context(ctx)
for sent, you in dialog:
if you:
# if it is your turn to say, take the target word and compute its rank
rank = compute_rank(sent, sents, ai, domain, args.temperature, score_func)
# compute lang_h for the groundtruth sentence
enc = ai._encode(sent, ai.model.word_dict)
_, ai.lang_h, lang_hs = ai.model.score_sent(enc, ai.lang_h, ai.ctx_h, args.temperature)
# save hidden states and the utterance
ai.lang_hs.append(lang_hs)
ai.words.append(ai.model.word2var('YOU:'))
ai.words.append(Variable(enc))
ranks += rank
n += 1
else:
ai.read(sent)
k += 1
time_elapsed = time.time() - start_time
logger.dump('dialogue %d | avg rank %.3f | raw %d/%d | time %.3f' % (k, 1. * ranks / n, ranks, n, time_elapsed))
logger.dump('final avg rank %.3f' % (1. * ranks / n))
def __init__(self, dialog, ctx_gen, args, engine, corpus, logger=None):
self.dialog = dialog
self.ctx_gen = ctx_gen
self.args = args
self.engine = engine
self.corpus = corpus
self.logger = logger if logger else DialogLogger()
def __init__(self, dialog, ctx_gen, args, engine, corpus, logger=None):
"""Initialize an RL learning class
@param dialog: a dialog generator with policy update functionality
@param ctx_gen: context (i.e. token amounts and values) generator
@param engine: supervised language model training engine
@param corpus: training corpus
"""
self.dialog = dialog
self.ctx_gen = ctx_gen
self.args = args
self.engine = engine
self.corpus = corpus
self.logger = logger if logger else DialogLogger()
def main():
parser = argparse.ArgumentParser(description='Negotiator')
parser.add_argument('--dataset',
type=str,
default='./data/negotiate/val.txt',
help='location of the dataset')
parser.add_argument('--model_file', type=str, help='model file')
parser.add_argument('--smart_ai',
action='store_true',
default=False,
help='to use rollouts')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
parser.add_argument('--log_file', type=str, default='', help='log file')
args = parser.parse_args()
utils.set_seed(args.seed)
model = utils.load_model(args.model_file)
ai = LstmAgent(model, args)
logger = DialogLogger(verbose=True, log_file=args.log_file)
domain = get_domain(args.domain)
score_func = rollout if args.smart_ai else likelihood
dataset, sents = read_dataset(args.dataset)
ranks, n, k = 0, 0, 0
for ctx, dialog in dataset:
start_time = time.time()
ai.feed_context(ctx)
for sent, you in dialog:
if you:
rank = compute_rank(sent, sents, ai, domain, args.temperature,
score_func)
# Compute lang_h for the groundtruth sentence
enc = ai._encode(sent, ai.model.word_dict)
_, ai.lang_h, lang_hs = ai.model.score_sent(
enc, ai.lang_h, ai.ctx_h, args.temperature)
ai.lang_hs.append(lang_hs)
ai.words.append(ai.model.word2var('YOU:'))
ai.words.append(Variable(enc))
ranks += rank
n += 1
else:
ai.read(sent)
k += 1
time_elapsed = time.time() - start_time
logger.dump('dialogue %d | avg rank %.3f | raw %d/%d | time %.3f' %
(k, 1. * ranks / n, ranks, n, time_elapsed))
logger.dump('final avg rank %.3f' % (1. * ranks / n))
def main():
parser = argparse.ArgumentParser(description='chat utility')
parser.add_argument('--model_file', type=str,
help='model file')
parser.add_argument('--domain', type=str, default='object_division',
help='domain for the dialogue')
parser.add_argument('--context_file', type=str, default='',
help='context file')
parser.add_argument('--temperature', type=float, default=1.0,
help='temperature')
parser.add_argument('--num_types', type=int, default=3,
help='number of object types')
parser.add_argument('--num_objects', type=int, default=6,
help='total number of objects')
parser.add_argument('--max_score', type=int, default=10,
help='max score per object')
parser.add_argument('--score_threshold', type=int, default=6,
help='successful dialog should have more than score_threshold in score')
parser.add_argument('--seed', type=int, default=1,
help='random seed')
parser.add_argument('--smart_ai', action='store_true', default=False,
help='make AI smart again')
parser.add_argument('--ai_starts', action='store_true', default=False,
help='allow AI to start the dialog')
parser.add_argument('--ref_text', type=str,
help='file with the reference text')
args = parser.parse_args()
utils.set_seed(args.seed)
human = HumanAgent(domain.get_domain(args.domain))
alice_ty = LstmRolloutAgent if args.smart_ai else LstmAgent
ai = alice_ty(utils.load_model(args.model_file), args)
agents = [ai, human] if args.ai_starts else [human, ai]
dialog = Dialog(agents, args)
logger = DialogLogger(verbose=True)
if args.context_file == '':
ctx_gen = ManualContextGenerator(args.num_types, args.num_objects, args.max_score)
else:
ctx_gen = ContextGenerator(args.context_file)
chat = Chat(dialog, ctx_gen, logger)
chat.run()
def main():
parser = argparse.ArgumentParser(description='Reinforce')
parser.add_argument('--data',
type=str,
default='./data/negotiate',
help='location of the data corpus')
parser.add_argument('--unk_threshold',
type=int,
default=20,
help='minimum word frequency to be in dictionary')
parser.add_argument('--alice_model_file',
type=str,
help='Alice model file')
parser.add_argument('--bob_model_file', type=str, help='Bob model file')
parser.add_argument('--output_model_file',
type=str,
help='output model file')
parser.add_argument('--context_file', type=str, help='context file')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='print out converations')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument(
'--score_threshold',
type=int,
default=6,
help='successful dialog should have more than score_threshold in score'
)
parser.add_argument('--log_file',
type=str,
default='',
help='log successful dialogs to file for training')
parser.add_argument('--smart_bob',
action='store_true',
default=False,
help='make Bob smart again')
parser.add_argument('--gamma',
type=float,
default=0.99,
help='discount factor')
parser.add_argument('--eps', type=float, default=0.5, help='eps greedy')
parser.add_argument('--nesterov',
action='store_true',
default=False,
help='enable nesterov momentum')
parser.add_argument('--momentum',
type=float,
default=0.0,
help='momentum for sgd')
parser.add_argument('--lr', type=float, default=0.1, help='learning rate')
parser.add_argument('--clip',
type=float,
default=0.1,
help='gradient clip')
parser.add_argument('--rl_lr',
type=float,
default=0.1,
help='RL learning rate')
parser.add_argument('--rl_clip',
type=float,
default=0.1,
help='RL gradient clip')
parser.add_argument('--ref_text',
type=str,
help='file with the reference text')
parser.add_argument('--bsz', type=int, default=8, help='batch size')
parser.add_argument('--sv_train_freq',
type=int,
default=-1,
help='supervision train frequency')
parser.add_argument('--nepoch',
type=int,
default=1,
help='number of epochs')
parser.add_argument('--visual',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
args = parser.parse_args()
device_id = utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
alice_model = utils.load_model(args.alice_model_file)
# We don't want to use Dropout during RL
alice_model.eval()
alice = RlAgent(alice_model, args, name='Alice')
bob_ty = LstmRolloutAgent if args.smart_bob else LstmAgent
bob_model = utils.load_model(args.bob_model_file)
bob_model.eval()
bob = bob_ty(bob_model, args, name='Bob')
dialog = Dialog([alice, bob], args)
logger = DialogLogger(verbose=args.verbose, log_file=args.log_file)
ctx_gen = ContextGenerator(args.context_file)
corpus = data.WordCorpus(args.data, freq_cutoff=args.unk_threshold)
engine = Engine(alice_model, args, device_id, verbose=False)
reinforce = Reinforce(dialog, ctx_gen, args, engine, corpus, logger)
reinforce.run()
utils.save_model(alice.model, args.output_model_file)
def __init__(self, dialog, ctx_gen, logger=None):
self.dialog = dialog
self.ctx_gen = ctx_gen
self.logger = logger if logger else DialogLogger()
def main():
parser = argparse.ArgumentParser(description='Reinforce')
parser.add_argument('--alice_model_file',
type=str,
help='Alice model file')
parser.add_argument('--bob_model_file', type=str, help='Bob model file')
parser.add_argument('--output_model_file',
type=str,
help='output model file')
parser.add_argument('--context_file', type=str, help='context file')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--pred_temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='print out converations')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument(
'--score_threshold',
type=int,
default=6,
help='successful dialog should have more than score_threshold in score'
)
parser.add_argument('--log_file',
type=str,
default='',
help='log successful dialogs to file for training')
parser.add_argument('--smart_bob',
action='store_true',
default=False,
help='make Bob smart again')
parser.add_argument('--gamma',
type=float,
default=0.99,
help='discount factor')
parser.add_argument('--eps', type=float, default=0.5, help='eps greedy')
parser.add_argument('--momentum',
type=float,
default=0.1,
help='momentum for sgd')
parser.add_argument('--lr', type=float, default=0.1, help='learning rate')
parser.add_argument('--clip',
type=float,
default=0.1,
help='gradient clip')
parser.add_argument('--rl_lr',
type=float,
default=0.002,
help='RL learning rate')
parser.add_argument('--rl_clip',
type=float,
default=2.0,
help='RL gradient clip')
parser.add_argument('--ref_text',
type=str,
help='file with the reference text')
parser.add_argument('--sv_train_freq',
type=int,
default=-1,
help='supervision train frequency')
parser.add_argument('--nepoch',
type=int,
default=1,
help='number of epochs')
parser.add_argument('--hierarchical',
action='store_true',
default=False,
help='use hierarchical training')
parser.add_argument('--visual',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
parser.add_argument('--selection_model_file',
type=str,
default='',
help='path to save the final model')
parser.add_argument('--data',
type=str,
default='data/negotiate',
help='location of the data corpus')
parser.add_argument('--unk_threshold',
type=int,
default=20,
help='minimum word frequency to be in dictionary')
parser.add_argument('--bsz', type=int, default=16, help='batch size')
parser.add_argument('--validate',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--scratch',
action='store_true',
default=False,
help='erase prediciton weights')
parser.add_argument('--sep_sel',
action='store_true',
default=False,
help='use separate classifiers for selection')
args = parser.parse_args()
utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
alice_model = utils.load_model(args.alice_model_file) # RnnModel
alice_ty = get_agent_type(alice_model) # RnnRolloutAgent
alice = alice_ty(alice_model, args, name='Alice', train=True)
alice.vis = args.visual
bob_model = utils.load_model(args.bob_model_file) # RnnModel
bob_ty = get_agent_type(bob_model) # RnnAgent
bob = bob_ty(bob_model, args, name='Bob', train=False)
dialog = Dialog([alice, bob], args)
logger = DialogLogger(verbose=args.verbose, log_file=args.log_file)
ctx_gen = ContextGenerator(args.context_file)
domain = get_domain(args.domain)
corpus = alice_model.corpus_ty(domain,
args.data,
freq_cutoff=args.unk_threshold,
verbose=True,
sep_sel=args.sep_sel)
engine = alice_model.engine_ty(alice_model, args)
reinforce = Reinforce(dialog, ctx_gen, args, engine, corpus, logger)
reinforce.run()
utils.save_model(alice.model, args.output_model_file)
def main():
parser = argparse.ArgumentParser(description='Reinforce')
parser.add_argument('--data',
type=str,
default=config.data_dir,
help='location of the data corpus')
parser.add_argument('--unk_threshold',
type=int,
default=config.unk_threshold,
help='minimum word frequency to be in dictionary')
parser.add_argument('--alice_model_file',
type=str,
help='Alice model file')
parser.add_argument('--bob_model_file', type=str, help='Bob model file')
parser.add_argument('--output_model_file',
type=str,
help='output model file')
parser.add_argument('--context_file', type=str, help='context file')
parser.add_argument('--temperature',
type=float,
default=config.rl_temperature,
help='temperature')
parser.add_argument('--cuda',
action='store_true',
default=config.cuda,
help='use CUDA')
parser.add_argument('--verbose',
action='store_true',
default=config.verbose,
help='print out converations')
parser.add_argument('--seed',
type=int,
default=config.seed,
help='random seed')
parser.add_argument(
'--score_threshold',
type=int,
default=config.rl_score_threshold,
help='successful dialog should have more than score_threshold in score'
)
parser.add_argument('--log_file',
type=str,
default='',
help='log successful dialogs to file for training')
parser.add_argument('--smart_bob',
action='store_true',
default=False,
help='make Bob smart again')
parser.add_argument('--gamma',
type=float,
default=config.rl_gamma,
help='discount factor')
parser.add_argument('--eps',
type=float,
default=config.rl_eps,
help='eps greedy')
parser.add_argument('--nesterov',
action='store_true',
default=config.nesterov,
help='enable nesterov momentum')
parser.add_argument('--momentum',
type=float,
default=config.rl_momentum,
help='momentum for sgd')
parser.add_argument('--lr',
type=float,
default=config.rl_lr,
help='learning rate')
parser.add_argument('--clip',
type=float,
default=config.rl_clip,
help='gradient clip')
parser.add_argument('--rl_lr',
type=float,
default=config.rl_reinforcement_lr,
help='RL learning rate')
parser.add_argument('--rl_clip',
type=float,
default=config.rl_reinforcement_clip,
help='RL gradient clip')
parser.add_argument('--ref_text',
type=str,
help='file with the reference text')
parser.add_argument('--bsz',
type=int,
default=config.rl_bsz,
help='batch size')
parser.add_argument('--sv_train_freq',
type=int,
default=config.rl_sv_train_freq,
help='supervision train frequency')
parser.add_argument('--nepoch',
type=int,
default=config.rl_nepoch,
help='number of epochs')
parser.add_argument('--visual',
action='store_true',
default=config.plot_graphs,
help='plot graphs')
parser.add_argument('--domain',
type=str,
default=config.domain,
help='domain for the dialogue')
parser.add_argument('--reward',
type=str,
choices=['margin', 'fair', 'length'],
default='margin',
help='reward function')
args = parser.parse_args()
device_id = utils.use_cuda(args.cuda)
logging.info("Starting training using pytorch version:%s" %
(str(torch.__version__)))
logging.info("CUDA is %s" % ("enabled. Using device_id:"+str(device_id) + " version:" \
+str(torch.version.cuda) + " on gpu:" + torch.cuda.get_device_name(0) if args.cuda else "disabled"))
alice_model = utils.load_model(args.alice_model_file)
# we don't want to use Dropout during RL
alice_model.eval()
# Alice is a RL based agent, meaning that she will be learning while selfplaying
logging.info("Creating RlAgent from alice_model: %s" %
(args.alice_model_file))
alice = RlAgent(alice_model, args, name='Alice')
# we keep Bob frozen, i.e. we don't update his parameters
logging.info("Creating Bob's (--smart_bob) LstmRolloutAgent" if args.smart_bob \
else "Creating Bob's (not --smart_bob) LstmAgent" )
bob_ty = LstmRolloutAgent if args.smart_bob else LstmAgent
bob_model = utils.load_model(args.bob_model_file)
bob_model.eval()
bob = bob_ty(bob_model, args, name='Bob')
logging.info("Initializing communication dialogue between Alice and Bob")
dialog = Dialog([alice, bob], args)
logger = DialogLogger(verbose=args.verbose, log_file=args.log_file)
ctx_gen = ContextGenerator(args.context_file)
logging.info(
"Building word corpus, requiring minimum word frequency of %d for dictionary"
% (args.unk_threshold))
corpus = data.WordCorpus(args.data, freq_cutoff=args.unk_threshold)
engine = Engine(alice_model, args, device_id, verbose=False)
logging.info("Starting Reinforcement Learning")
reinforce = Reinforce(dialog, ctx_gen, args, engine, corpus, logger)
reinforce.run()
logging.info("Saving updated Alice model to %s" % (args.output_model_file))
utils.save_model(alice.model, args.output_model_file)
def main():
parser = argparse.ArgumentParser(description='selfplaying script')
parser.add_argument('--alice_model_file',
type=str,
help='Alice model file')
parser.add_argument('--bob_model_file', type=str, help='Bob model file')
parser.add_argument('--context_file', type=str, help='context file')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='print out converations')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument(
'--score_threshold',
type=int,
default=6,
help='successful dialog should have more than score_threshold in score'
)
parser.add_argument('--max_turns',
type=int,
default=20,
help='maximum number of turns in a dialog')
parser.add_argument('--log_file',
type=str,
default='',
help='log successful dialogs to file for training')
parser.add_argument('--smart_alice',
action='store_true',
default=False,
help='make Alice smart again')
parser.add_argument('--fast_rollout',
action='store_true',
default=False,
help='to use faster rollouts')
parser.add_argument('--rollout_bsz',
type=int,
default=100,
help='rollout batch size')
parser.add_argument('--rollout_count_threshold',
type=int,
default=3,
help='rollout count threshold')
parser.add_argument('--smart_bob',
action='store_true',
default=False,
help='make Bob smart again')
parser.add_argument('--ref_text',
type=str,
help='file with the reference text')
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
args = parser.parse_args()
utils.set_seed(args.seed)
alice_model = utils.load_model(args.alice_model_file)
alice_ty = get_agent_type(alice_model, args.smart_alice, args.fast_rollout)
alice = alice_ty(alice_model, args, name='Alice')
bob_model = utils.load_model(args.bob_model_file)
bob_ty = get_agent_type(bob_model, args.smart_bob, args.fast_rollout)
bob = bob_ty(bob_model, args, name='Bob')
dialog = Dialog([alice, bob], args)
logger = DialogLogger(verbose=args.verbose, log_file=args.log_file)
ctx_gen = ContextGenerator(args.context_file)
selfplay = SelfPlay(dialog, ctx_gen, args, logger)
selfplay.run()
def main():
parser = argparse.ArgumentParser(description='selfplaying script')
parser.add_argument('--alice_model_file',
type=str,
help='Alice model file')
parser.add_argument('--alice_forward_model_file',
type=str,
help='Alice forward model file')
parser.add_argument('--bob_model_file', type=str, help='Bob model file')
parser.add_argument('--context_file', type=str, help='context file')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--pred_temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='print out converations')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument(
'--score_threshold',
type=int,
default=6,
help='successful dialog should have more than score_threshold in score'
)
parser.add_argument('--max_turns',
type=int,
default=20,
help='maximum number of turns in a dialog')
parser.add_argument('--log_file',
type=str,
default='',
help='log successful dialogs to file for training')
parser.add_argument('--smart_alice',
action='store_true',
default=False,
help='make Alice smart again')
parser.add_argument('--diverse_alice',
action='store_true',
default=False,
help='make Alice smart again')
parser.add_argument('--rollout_bsz',
type=int,
default=3,
help='rollout batch size')
parser.add_argument('--rollout_count_threshold',
type=int,
default=3,
help='rollout count threshold')
parser.add_argument('--smart_bob',
action='store_true',
default=False,
help='make Bob smart again')
parser.add_argument('--selection_model_file',
type=str,
default='',
help='path to save the final model')
parser.add_argument('--rollout_model_file',
type=str,
default='',
help='path to save the final model')
parser.add_argument('--diverse_bob',
action='store_true',
default=False,
help='make Alice smart again')
parser.add_argument('--ref_text',
type=str,
help='file with the reference text')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--domain',
type=str,
default='object_division',
help='domain for the dialogue')
parser.add_argument('--visual',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--eps', type=float, default=0.0, help='eps greedy')
parser.add_argument('--data',
type=str,
default='data/negotiate',
help='location of the data corpus')
parser.add_argument('--unk_threshold',
type=int,
default=20,
help='minimum word frequency to be in dictionary')
parser.add_argument('--bsz', type=int, default=16, help='batch size')
parser.add_argument('--validate',
action='store_true',
default=False,
help='plot graphs')
args = parser.parse_args()
utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
alice_model = utils.load_model(args.alice_model_file)
alice_ty = get_agent_type(alice_model, args.smart_alice)
alice = alice_ty(alice_model,
args,
name='Alice',
train=False,
diverse=args.diverse_alice)
alice.vis = args.visual
bob_model = utils.load_model(args.bob_model_file)
bob_ty = get_agent_type(bob_model, args.smart_bob)
bob = bob_ty(bob_model,
args,
name='Bob',
train=False,
diverse=args.diverse_bob)
bob.vis = False
dialog = Dialog([alice, bob], args)
logger = DialogLogger(verbose=args.verbose, log_file=args.log_file)
ctx_gen = ContextGenerator(args.context_file)
selfplay = SelfPlay(dialog, ctx_gen, args, logger)
selfplay.run()
def main():
parser = argparse.ArgumentParser(description='selfplaying script')
parser.add_argument('--alice_model_file',
type=str,
help='Alice model file')
parser.add_argument('--alice_forward_model_file',
type=str,
help='Alice forward model file')
parser.add_argument('--bob_model_file', type=str, help='Bob model file')
parser.add_argument('--context_file', type=str, help='context file')
parser.add_argument('--temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--pred_temperature',
type=float,
default=1.0,
help='temperature')
parser.add_argument('--log_attention',
action='store_true',
default=False,
help='log attention')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='print out converations')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--max_turns',
type=int,
default=20,
help='maximum number of turns in a dialog')
parser.add_argument('--log_file',
type=str,
default='selfplay.log',
help='log dialogs to file')
parser.add_argument('--smart_alice',
action='store_true',
default=False,
help='make Alice smart again')
parser.add_argument('--rollout_bsz',
type=int,
default=3,
help='rollout batch size')
parser.add_argument('--rollout_count_threshold',
type=int,
default=3,
help='rollout count threshold')
parser.add_argument('--smart_bob',
action='store_true',
default=False,
help='make Bob smart again')
parser.add_argument('--selection_model_file',
type=str,
default='',
help='path to save the final model')
parser.add_argument('--rollout_model_file',
type=str,
default='',
help='path to save the final model')
parser.add_argument('--ref_text',
type=str,
help='file with the reference text')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='use CUDA')
parser.add_argument('--domain',
type=str,
default='one_common',
help='domain for the dialogue')
parser.add_argument('--visual',
action='store_true',
default=False,
help='plot graphs')
parser.add_argument('--eps', type=float, default=0.0, help='eps greedy')
parser.add_argument('--data',
type=str,
default='data/onecommon',
help='location of the data corpus')
parser.add_argument('--unk_threshold',
type=int,
default=10,
help='minimum word frequency to be in dictionary')
parser.add_argument('--bsz', type=int, default=16, help='batch size')
parser.add_argument('--plot_metrics',
action='store_true',
default=False,
help='plot metrics')
parser.add_argument('--markable_detector_file',
type=str,
default="markable_detector",
help='visualize referents')
parser.add_argument('--record_markables',
action='store_true',
default=False,
help='record markables and referents')
parser.add_argument('--repeat_selfplay',
action='store_true',
default=False,
help='repeat selfplay')
args = parser.parse_args()
if args.repeat_selfplay:
seeds = list(range(10))
else:
seeds = [args.seed]
repeat_results = []
for seed in seeds:
utils.use_cuda(args.cuda)
utils.set_seed(args.seed)
if args.record_markables:
if not os.path.exists(args.markable_detector_file + '_' +
str(seed) + '.th'):
assert False
markable_detector = utils.load_model(args.markable_detector_file +
'_' + str(seed) + '.th')
if args.cuda:
markable_detector.cuda()
else:
device = torch.device("cpu")
markable_detector.to(device)
markable_detector.eval()
markable_detector_corpus = markable_detector.corpus_ty(
domain,
args.data,
train='train_markable_{}.txt'.format(seed),
valid='valid_markable_{}.txt'.format(seed),
test='test_markable_{}.txt'.format(
seed), #test='selfplay_reference_{}.txt'.format(seed),
freq_cutoff=args.unk_threshold,
verbose=True)
else:
markable_detector = None
markable_detector_corpus = None
alice_model = utils.load_model(args.alice_model_file + '_' +
str(seed) + '.th')
alice_ty = get_agent_type(alice_model, args.smart_alice)
alice = alice_ty(alice_model, args, name='Alice', train=False)
bob_model = utils.load_model(args.bob_model_file + '_' + str(seed) +
'.th')
bob_ty = get_agent_type(bob_model, args.smart_bob)
bob = bob_ty(bob_model, args, name='Bob', train=False)
dialog = Dialog([alice, bob], args, markable_detector,
markable_detector_corpus)
ctx_gen = ContextGenerator(
os.path.join(args.data, args.context_file + '.txt'))
with open(os.path.join(args.data, args.context_file + '.json'),
"r") as f:
scenario_list = json.load(f)
scenarios = {scenario['uuid']: scenario for scenario in scenario_list}
logger = DialogLogger(verbose=args.verbose,
log_file=args.log_file,
scenarios=scenarios)
selfplay = SelfPlay(dialog, ctx_gen, args, logger)
result = selfplay.run()
repeat_results.append(result)
print("dump selfplay_markables.json")
dump_json(dialog.selfplay_markables, "selfplay_markables.json")
print("dump selfplay_referents.json")
dump_json(dialog.selfplay_referents, "selfplay_referents.json")
print("repeat selfplay results %.8f ( %.8f )" %
(np.mean(repeat_results), np.std(repeat_results)))
