# try:
# engine.vae_train(model, train_feed, valid_feed, test_feed, config)
# except KeyboardInterrupt:
# print("Training stopped by keyboard.")
# print("AutoEncoder Training Done ! ")
# load_model_vae(model, config)
# this is a pretrained vae model, you can load it to the current model. TODO: move path todata_args
path='./logs/2019-09-06T10:50:18.034181-mwoz_gan_vae.py'
load_model_vae(model, path)
print("Start GAN training")
if config.forward_only is False:
try:
engine.gan_train(model, machine_data, train_feed, valid_feed, test_feed, config, evaluator, pred_list, generator_samples)
except KeyboardInterrupt:
print("Training stopped by keyboard.")
print("Reward Model Training Done ! ")
print("Saved path: {}".format(model_file))
if __name__ == "__main__":
config, unparsed = get_config()
config = process_config(config)
main(config)
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)
