def main(config):
prepare_dirs_loggers(config, os.path.basename(__file__))
corpus_client = ZslStanfordCorpus(config)
warmup_data = corpus_client.get_seed_responses(config.target_example_cnt)
dial_corpus = corpus_client.get_corpus()
train_dial, valid_dial, test_dial = dial_corpus['train'],\
dial_corpus['valid'],\
dial_corpus['test']
evaluator = evaluators.BleuEntEvaluator("SMD", corpus_client.ent_metas)
# create data loader that feed the deep models
train_feed = data_loaders.ZslSMDDialDataLoader("Train", train_dial, config, warmup_data)
valid_feed = data_loaders.ZslSMDDialDataLoader("Valid", valid_dial, config)
test_feed = data_loaders.ZslSMDDialDataLoader("Test", test_dial, config)
if config.action_match:
if config.use_ptr:
model = models.ZeroShotPtrHRED(corpus_client, config)
else:
model = models.ZeroShotHRED(corpus_client, config)
else:
if config.use_ptr:
model = models.PtrHRED(corpus_client, config)
else:
model = models.HRED(corpus_client, config)
if config.forward_only:
session_dir = os.path.join(config.log_dir, config.load_sess)
test_file = os.path.join(session_dir, "{}-test-{}.txt".format(get_time(),
config.gen_type))
model_file = os.path.join(config.log_dir, config.load_sess, "model")
else:
session_dir = config.session_dir
test_file = os.path.join(config.session_dir,
"{}-test-{}.txt".format(get_time(), config.gen_type))
model_file = os.path.join(config.session_dir, "model")
if config.use_gpu:
model.cuda()
if config.forward_only is False:
try:
train(model, train_feed, valid_feed, test_feed, config, evaluator, gen=hred_utils.generate)
except KeyboardInterrupt:
print("Training stopped by keyboard.")
config.batch_size = 20
model.load_state_dict(torch.load(model_file))
# hred_utils.dump_latent(model, test_feed, config, session_dir)
# run the model on the test dataset.
validate(model, test_feed, config)
with open(os.path.join(test_file), "wb") as f:
hred_utils.generate(model, test_feed, config, evaluator, num_batch=None, dest_f=f)
def main(config):
prepare_dirs_loggers(config, os.path.basename(__file__))
corpus_client_class = get_corpus_client(config)
train_client = corpus_client_class(config)
corpus = train_client.get_corpus()
train_dial, valid_dial, test_dial = corpus['train'], corpus[
'valid'], corpus['test']
evaluator = evaluators.BleuEntEvaluator("SMD", train_client.ent_metas)
# create data loader that feed the deep models
data_loader_class = get_data_loader(config)
train_feed = data_loader_class("Train", train_dial, config)
valid_feed = data_loader_class("Valid", valid_dial, config)
test_feed = data_loader_class("Test", test_dial, config)
model = get_model(config, train_client)
if config.forward_only:
session_dir = os.path.join(config.log_dir, config.load_sess)
test_file = os.path.join(
session_dir, "{}-test-{}.txt".format(get_time(), config.gen_type))
model_file = os.path.join(config.log_dir, config.load_sess, "model")
else:
session_dir = config.session_dir
test_file = os.path.join(
session_dir, "{}-test-{}.txt".format(get_time(), config.gen_type))
model_file = os.path.join(config.session_dir, "model")
if config.use_gpu:
model.cuda()
if not config.forward_only:
try:
train(model,
train_feed,
valid_feed,
test_feed,
config,
evaluator,
gen=hred_utils.generate)
except KeyboardInterrupt:
print("Training stopped by keyboard.")
config.batch_size = 10
model.load_state_dict(torch.load(model_file))
# run the model on the test dataset.
validate(model, test_feed, config)
with open(os.path.join(test_file), "wb") as f:
hred_utils.generate(model,
test_feed,
config,
evaluator,
num_batch=None,
dest_f=f)
def main(config):
prepare_dirs_loggers(config, os.path.basename(__file__))
corpus_client = getattr(corpora, config.corpus_client)(config)
corpus_client.vocab, corpus_client.rev_vocab, corpus_client.unk_id = load_vocab(
config.vocab)
# warmup_data = maluuba_client.get_seed_responses(len(maluuba_client.domain_descriptions))
# maluuba_corpus = maluuba_client.get_corpus()
# train_dial, valid_dial = maluuba_corpus['train'], maluuba_corpus['valid']
corpus = corpus_client.get_corpus()
train_dial, valid_dial, test_dial = (corpus['train'], corpus['valid'],
corpus['test'])
evaluator = evaluators.BleuEntEvaluator("SMD", corpus_client.ent_metas)
laed_z = load_laed_features(config.laed_z_folder)
config.laed_z_size = laed_z['dialog'][0].shape[-1]
laed_z_test = laed_z['dialog'][len(train_dial) + len(valid_dial):]
test_feed = data_loaders.ZslLASMDDialDataLoader("Test", test_dial,
laed_z_test, [], config)
if config.action_match:
if config.use_ptr:
model = ZeroShotLAPtrHRED(corpus_client, config)
else:
raise NotImplementedError()
else:
raise NotImplementedError()
session_dir = os.path.join(config.log_dir, config.load_sess)
test_file = os.path.join(
session_dir, "{}-test-{}.txt".format(get_time(), config.gen_type))
model_file = os.path.join(config.log_dir, config.load_sess, "model")
if config.use_gpu:
model.cuda()
config.batch_size = 20
model.load_state_dict(torch.load(model_file))
# run the model on the test dataset.
validate(model, test_feed, config)
with open(os.path.join(test_file), "wb") as f:
hred_utils.generate(model,
test_feed,
config,
evaluator,
num_batch=None,
dest_f=f)
def main(config):
prepare_dirs_loggers(config, os.path.basename(__file__))
train_client = getattr(corpora, config.corpus_client)(config)
utt_cnt_map = defaultdict(lambda: config.source_example_cnt)
for black_domain in config.black_domains:
utt_cnt_map[black_domain] = config.target_example_cnt
warmup_data = train_client.get_seed_responses(utt_cnt_map)
train_corpus = train_client.get_corpus()
train_dial, valid_dial, test_dial = train_corpus['train'], train_corpus[
'valid'], train_corpus['test']
evaluator = evaluators.BleuEntEvaluator("SMD", train_client.ent_metas)
data_loader_class = data_loaders.ZslLASMDDialDataLoader \
if len(config.laed_z_folders) \
else data_loaders.ZslSMDDialDataLoader
# create data loader that feed the deep models
train_feed = data_loader_class("Train", train_dial, config, warmup_data)
valid_feed = data_loader_class("Valid", valid_dial, config)
test_feed = data_loader_class("Test", test_dial, config)
model = get_model(train_client, config)
if config.forward_only:
session_dir = os.path.join(config.log_dir, config.load_sess)
test_file = os.path.join(
session_dir, "{}-test-{}.txt".format(get_time(), config.gen_type))
model_file = os.path.join(config.log_dir, config.load_sess, "model")
else:
session_dir = config.session_dir
test_file = os.path.join(
session_dir, "{}-test-{}.txt".format(get_time(), config.gen_type))
model_file = os.path.join(config.session_dir, "model")
if config.use_gpu:
model.cuda()
if not config.forward_only:
try:
train(model,
train_feed,
valid_feed,
test_feed,
config,
evaluator,
gen=hred_utils.generate)
except KeyboardInterrupt:
print("Training stopped by keyboard.")
config.batch_size = 10
model.load_state_dict(torch.load(model_file))
# run the model on the test dataset.
validate(model, test_feed, config)
with open(os.path.join(test_file), "wb") as f:
hred_utils.generate(model,
test_feed,
config,
evaluator,
num_batch=None,
dest_f=f)