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):
corpus_client = getattr(corpora, config.corpus_client)(config)
corpus_client.vocab, corpus_client.rev_vocab, corpus_client.unk_id = load_vocab(
config.vocab)
prepare_dirs_loggers(config, os.path.basename(__file__))
dial_corpus = corpus_client.get_corpus()
train_dial, valid_dial, test_dial = (dial_corpus['train'],
dial_corpus['valid'],
dial_corpus['test'])
evaluator = evaluators.BleuEvaluator("CornellMovie")
# create data loader that feed the deep models
train_feed = data_loaders.SMDDialogSkipLoader("Train", train_dial, config)
valid_feed = data_loaders.SMDDialogSkipLoader("Valid", valid_dial, config)
test_feed = data_loaders.SMDDialogSkipLoader("Test", test_dial, config)
model = dialog_models.VAE(corpus_client, config)
if config.forward_only:
test_file = os.path.join(
config.log_dir, config.load_sess,
"{}-test-{}.txt".format(get_time(), config.gen_type))
dump_file = os.path.join(config.log_dir, config.load_sess,
"{}-z.pkl".format(get_time()))
model_file = os.path.join(config.log_dir, config.load_sess, "model")
else:
test_file = os.path.join(
config.session_dir,
"{}-test-{}.txt".format(get_time(), config.gen_type))
dump_file = os.path.join(config.session_dir,
"{}-z.pkl".format(get_time()))
model_file = os.path.join(config.session_dir, "model")
if config.use_gpu:
model.cuda()
if config.forward_only is False:
try:
engine.train(model,
train_feed,
valid_feed,
test_feed,
config,
evaluator,
gen=dialog_utils.generate_vae)
except KeyboardInterrupt:
print("Training stopped by keyboard.")
def main(config):
corpus_client = getattr(corpora, config.corpus_client)(config)
corpus_client.vocab, corpus_client.rev_vocab, corpus_client.unk_id = load_vocab(
config.vocab)
prepare_dirs_loggers(config, os.path.basename(__file__))
dial_corpus = corpus_client.get_corpus()
train_dial, valid_dial, test_dial = (dial_corpus['train'],
dial_corpus['valid'],
dial_corpus['test'])
evaluator = evaluators.BleuEvaluator("CornellMovie")
# create data loader that feed the deep models
train_feed = data_loaders.SMDDialogSkipLoader("Train", train_dial, config)
valid_feed = data_loaders.SMDDialogSkipLoader("Valid", valid_dial, config)
test_feed = data_loaders.SMDDialogSkipLoader("Test", test_dial, config)
model = dialog_models.StED(corpus_client, config)
if config.forward_only:
test_file = os.path.join(
config.log_dir, config.load_sess,
"{}-test-{}.txt".format(get_time(), config.gen_type))
dump_file = os.path.join(config.log_dir, config.load_sess,
"{}-z.pkl".format(get_time()))
model_file = os.path.join(config.log_dir, config.load_sess, "model")
else:
test_file = os.path.join(
config.session_dir,
"{}-test-{}.txt".format(get_time(), config.gen_type))
dump_file = os.path.join(config.session_dir,
"{}-z.pkl".format(get_time()))
model_file = os.path.join(config.session_dir, "model")
if config.use_gpu:
model.cuda()
if not config.forward_only:
try:
engine.train(model,
train_feed,
valid_feed,
test_feed,
config,
evaluator,
gen=dialog_utils.generate_with_adv)
except KeyboardInterrupt:
print("Training stopped by keyboard.")
config.batch_size = 10
model.load_state_dict(torch.load(model_file))
engine.validate(model, valid_feed, config)
engine.validate(model, test_feed, config)
dialog_utils.generate_with_adv(model, test_feed, config, None, num_batch=0)
selected_clusters = utt_utils.latent_cluster(model,
train_feed,
config,
num_batch=None)
selected_outs = dialog_utils.selective_generate(model, test_feed, config,
selected_clusters)
print(len(selected_outs))
with open(os.path.join(dump_file + '.json'), 'wb') as f:
json.dump(selected_clusters, f, indent=2)
with open(os.path.join(dump_file + '.out.json'), 'wb') as f:
json.dump(selected_outs, f, indent=2)
with open(os.path.join(dump_file), "wb") as f:
print("Dumping test to {}".format(dump_file))
dialog_utils.dump_latent(model, test_feed, config, f, num_batch=None)
with open(os.path.join(test_file), "wb") as f:
print("Saving test to {}".format(test_file))
dialog_utils.gen_with_cond(model,
test_feed,
config,
num_batch=None,
dest_f=f)
with open(os.path.join(test_file + '.txt'), "wb") as f:
print("Saving test to {}".format(test_file))
dialog_utils.generate(model,
test_feed,
config,
evaluator,
num_batch=None,
dest_f=f)
def main(config):
laed_config = load_config(config.model)
laed_config.use_gpu = config.use_gpu
laed_config = process_config(laed_config)
setattr(laed_config, 'black_domains', config.black_domains)
setattr(laed_config, 'black_ratio', config.black_ratio)
setattr(laed_config, 'include_domain', True)
setattr(laed_config, 'include_example', False)
setattr(laed_config, 'include_state', True)
setattr(laed_config, 'entities_file', 'NeuralDialog-ZSDG/data/stanford/kvret_entities.json')
setattr(laed_config, 'action_match', True)
setattr(laed_config, 'batch_size', config.batch_size)
setattr(laed_config, 'data_dir', config.data_dir)
setattr(laed_config, 'include_eod', False) # for StED model
setattr(laed_config, 'domain_description', config.domain_description)
if config.process_seed_data:
assert config.corpus_client[:3] == 'Zsl', 'Incompatible coprus_client for --process_seed_data flag'
corpus_client = getattr(corpora, config.corpus_client)(laed_config)
if config.vocab:
corpus_client.vocab, corpus_client.rev_vocab, corpus_client.unk_token = load_vocab(config.vocab)
prepare_dirs_loggers(config, os.path.basename(__file__))
dial_corpus = corpus_client.get_corpus()
# train_dial, valid_dial, test_dial = dial_corpus['train'], dial_corpus['valid'], dial_corpus['test']
# all_dial = train_dial + valid_dial + test_dial
# all_utts = reduce(lambda x, y: x + y, all_dial, [])
model = load_model(config.model, config.model_name, config.model_type, corpus_client=corpus_client)
if config.use_gpu:
model.cuda()
for dataset_name in ['train', 'valid', 'test']:
dataset = dial_corpus[dataset_name]
feed_data = dataset if config.model_type == 'dialog' else reduce(lambda x, y: x + y, dataset, [])
# create data loader that feed the deep models
if config.process_seed_data:
seed_utts = corpus_client.get_seed_responses(utt_cnt=len(corpus_client.domain_descriptions))
main_feed = getattr(data_loaders, config.data_loader)("Test", feed_data, laed_config)
features = process_data_feed(model, main_feed, laed_config)
if config.data_loader == 'SMDDialogSkipLoader':
pad_mode = 'start_end'
elif config.data_loader == 'SMDDataLoader':
pad_mode = 'start'
else:
pad_mode = None
features = deflatten_laed_features(features, dataset, pad_mode=pad_mode)
assert sum(map(len, dataset)) == sum(map(lambda x: x.shape[0], features))
if not os.path.exists(config.out_folder):
os.makedirs(config.out_folder)
with open(os.path.join(config.out_folder, 'dialogs_{}.pkl'.format(dataset_name)), 'w') as result_out:
pickle.dump(features, result_out)
if config.process_seed_data:
seed_utts = corpus_client.get_seed_responses(utt_cnt=len(corpus_client.domain_descriptions))
seed_feed = data_loaders.PTBDataLoader("Seed", seed_utts, laed_config)
seed_features = process_data_feed(model, seed_feed, laed_config)
with open(os.path.join(config.out_folder, 'seed_utts.pkl'), 'w') as result_out:
pickle.dump(seed_features, result_out)