def main():
args = parse_args()
state = prototype_state()
state_path = args.model_prefix + "_state.pkl"
model_path = args.model_prefix + "_model.npz"
with open(state_path) as src:
state.update(cPickle.load(src))
logging.basicConfig(level=getattr(logging, state['level']), format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
state['lr'] = 0.01
state['bs'] = 2
state['compute_training_updates'] = False
state['apply_meanfield_inference'] = True
model = DialogEncoderDecoder(state)
if os.path.isfile(model_path):
logger.debug("Loading previous model")
model.load(model_path)
else:
raise Exception("Must specify a valid model path")
mf_update_batch = model.build_mf_update_function()
mf_reset_batch = model.build_mf_reset_function()
saliency_batch = model.build_saliency_eval_function()
test_dialogues = open(args.test_dialogues, 'r').readlines()
for test_dialogue_idx, test_dialogue in enumerate(test_dialogues):
#print 'Visualizing dialogue: ', test_dialogue
test_dialogue_split = test_dialogue.split()
# Convert dialogue into list of ids
dialogue = []
if len(test_dialogue) == 0:
dialogue = [model.eos_sym]
else:
sentence_ids = model.words_to_indices(test_dialogue_split)
# Add eos tokens
if len(sentence_ids) > 0:
if not sentence_ids[0] == model.eos_sym:
sentence_ids = [model.eos_sym] + sentence_ids
if not sentence_ids[-1] == model.eos_sym:
sentence_ids += [model.eos_sym]
else:
sentence_ids = [model.eos_sym]
dialogue += sentence_ids
if len(dialogue) > 3:
if ((dialogue[-1] == model.eos_sym)
and (dialogue[-2] == model.eod_sym)
and (dialogue[-3] == model.eos_sym)):
del dialogue[-1]
del dialogue[-1]
dialogue = numpy.asarray(dialogue, dtype='int32').reshape((len(dialogue), 1))
#print 'dialogue', dialogue
dialogue_reversed = model.reverse_utterances(dialogue)
max_batch_sequence_length = len(dialogue)
bs = state['bs']
# Initialize batch with zeros
batch_dialogues = numpy.zeros((max_batch_sequence_length, bs), dtype='int32')
batch_dialogues_reversed = numpy.zeros((max_batch_sequence_length, bs), dtype='int32')
batch_dialogues_mask = numpy.zeros((max_batch_sequence_length, bs), dtype='float32')
batch_dialogues_reset_mask = numpy.zeros((bs), dtype='float32')
batch_dialogues_drop_mask = numpy.ones((max_batch_sequence_length, bs), dtype='float32')
# Fill in batch with values
batch_dialogues[:,0] = dialogue[:, 0]
batch_dialogues_reversed[:,0] = dialogue_reversed[:, 0]
#batch_dialogues = dialogue
#batch_dialogues_reversed = dialogue_reversed
batch_dialogues_ran_gaussian_vectors = numpy.zeros((max_batch_sequence_length, bs, model.latent_gaussian_per_utterance_dim), dtype='float32')
batch_dialogues_ran_uniform_vectors = numpy.zeros((max_batch_sequence_length, bs, model.latent_piecewise_per_utterance_dim), dtype='float32')
eos_sym_list = numpy.where(batch_dialogues[:, 0] == state['eos_sym'])[0]
if len(eos_sym_list) > 1:
second_last_eos_sym = eos_sym_list[-2]
else:
print 'WARNING: dialogue does not have at least two EOS tokens!'
batch_dialogues_mask[:, 0] = 1.0
batch_dialogues_mask[0:second_last_eos_sym+1, 0] = 0.0
print '###'
print '###'
print '###'
if False==True:
mf_reset_batch()
for i in range(10):
print ' SGD Update', i
batch_dialogues_ran_gaussian_vectors[second_last_eos_sym:, 0, :] = model.rng.normal(loc=0, scale=1, size=model.latent_gaussian_per_utterance_dim)
batch_dialogues_ran_uniform_vectors[second_last_eos_sym:, 0, :] = model.rng.uniform(low=0.0, high=1.0, size=model.latent_piecewise_per_utterance_dim)
training_cost, kl_divergence_cost_acc, kl_divergences_between_piecewise_prior_and_posterior, kl_divergences_between_gaussian_prior_and_posterior = mf_update_batch(batch_dialogues, batch_dialogues_reversed, max_batch_sequence_length, batch_dialogues_mask, batch_dialogues_reset_mask, batch_dialogues_ran_gaussian_vectors, batch_dialogues_ran_uniform_vectors, batch_dialogues_drop_mask)
print ' training_cost', training_cost
print ' kl_divergence_cost_acc', kl_divergence_cost_acc
print ' kl_divergences_between_gaussian_prior_and_posterior', numpy.sum(kl_divergences_between_gaussian_prior_and_posterior)
print ' kl_divergences_between_piecewise_prior_and_posterior', numpy.sum(kl_divergences_between_piecewise_prior_and_posterior)
batch_dialogues_ran_gaussian_vectors[second_last_eos_sym:, 0, :] = model.rng.normal(loc=0, scale=1, size=model.latent_gaussian_per_utterance_dim)
batch_dialogues_ran_uniform_vectors[second_last_eos_sym:, 0, :] = model.rng.uniform(low=0.0, high=1.0, size=model.latent_piecewise_per_utterance_dim)
gaussian_saliency, piecewise_saliency = saliency_batch(batch_dialogues, batch_dialogues_reversed, max_batch_sequence_length, batch_dialogues_mask, batch_dialogues_reset_mask, batch_dialogues_ran_gaussian_vectors, batch_dialogues_ran_uniform_vectors, batch_dialogues_drop_mask)
if test_dialogue_idx < 2:
print 'gaussian_saliency', gaussian_saliency.shape, gaussian_saliency
print 'piecewise_saliency', piecewise_saliency.shape, piecewise_saliency
gaussian_sum = 0.0
piecewise_sum = 0.0
for i in range(second_last_eos_sym+1, max_batch_sequence_length):
gaussian_sum += gaussian_saliency[dialogue[i, 0]]
piecewise_sum += piecewise_saliency[dialogue[i, 0]]
gaussian_sum = max(gaussian_sum, 0.0000000000001)
piecewise_sum = max(piecewise_sum, 0.0000000000001)
print '###'
print '###'
print '###'
print 'Topic: ', ' '.join(test_dialogue_split[0:second_last_eos_sym])
print ''
print 'Response', ' '.join(test_dialogue_split[second_last_eos_sym+1:max_batch_sequence_length])
gaussian_str = ''
piecewise_str = ''
for i in range(second_last_eos_sym+1, max_batch_sequence_length):
gaussian_str += str(gaussian_saliency[dialogue[i, 0]]/gaussian_sum) + ' '
piecewise_str += str(piecewise_saliency[dialogue[i, 0]]/piecewise_sum) + ' '
print 'Gaussian_saliency', gaussian_str
print 'Piecewise_saliency', piecewise_str
#print ''
#print 'HEY', gaussian_saliency[:, 0].argsort()[-3:][::-1]
print ''
print 'Gaussian Top 3 Words: ', model.indices_to_words(list(gaussian_saliency[:].argsort()[-3:][::-1]))
print 'Piecewise Top 3 Words: ', model.indices_to_words(list(piecewise_saliency[:].argsort()[-3:][::-1]))
print 'Gaussian Top 5 Words: ', model.indices_to_words(list(gaussian_saliency[:].argsort()[-5:][::-1]))
print 'Piecewise Top 5 Words: ', model.indices_to_words(list(piecewise_saliency[:].argsort()[-5:][::-1]))
print 'Gaussian Top 7 Words: ', model.indices_to_words(list(gaussian_saliency[:].argsort()[-7:][::-1]))
print 'Piecewise Top 7 Words: ', model.indices_to_words(list(piecewise_saliency[:].argsort()[-7:][::-1]))
logger.debug("All done, exiting...")
