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)