def test(checkpoint_path=None):
subsets = ['kpval', 'kptest', 'kprestval']
quest_ids = []
result = []
config = ModelConfig()
config.sample_negative = FLAGS.sample_negative
config.use_fb_bn = FLAGS.use_fb_bn
# Get model function
model_fn = get_model_creation_fn(FLAGS.model_type)
# build and restore model
model = model_fn(config, phase='test')
model.build()
sess = tf.Session(graph=tf.get_default_graph())
tf.logging.info('Restore from model %s' %
os.path.basename(checkpoint_path))
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
for subset in subsets:
_quest_ids, _result = test_worker(model, sess, subset)
quest_ids += _quest_ids
result += _result
quest_ids = np.concatenate(quest_ids)
# save results
tf.logging.info('Saving results')
res_file = FLAGS.result_format % (FLAGS.version, 'val')
json.dump(result, open(res_file, 'w'))
tf.logging.info('Done!')
tf.logging.info('#Num eval samples %d' % len(result))
return res_file, quest_ids
def test(checkpoint_path=None):
batch_size = 64
config = ModelConfig()
config.sample_negative = FLAGS.sample_negative
config.use_fb_bn = FLAGS.use_fb_bn
# Get model function
model_fn = get_model_creation_fn(FLAGS.model_type)
# build data reader
reader = TestReader(batch_size=batch_size,
subset=TEST_SET,
use_fb_data=FLAGS.use_fb_data)
if checkpoint_path is None:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir %
(FLAGS.version, FLAGS.model_type))
checkpoint_path = ckpt.model_checkpoint_path
print(checkpoint_path)
# build and restore model
model = model_fn(config, phase='test')
model.build()
prob = model.prob
sess = tf.Session(graph=tf.get_default_graph())
tf.logging.info('Restore from model %s' %
os.path.basename(checkpoint_path))
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
quest_ids = []
result = []
print('Running inference on split %s...' % TEST_SET)
for i in range(reader.num_batches):
if i % 10 == 0:
update_progress(i / float(reader.num_batches))
outputs = reader.get_test_batch()
mc_scores = sess.run(model._logits,
feed_dict=model.fill_feed_dict(outputs[:-3]))
choice_idx = np.argmax(mc_scores, axis=1)
cands, _qids, image_ids = outputs[-3:]
for qid, cid, mcs in zip(_qids, choice_idx, cands):
answer = mcs['cands'][cid]
assert (mcs['quest_id'] == qid)
result.append({u'answer': answer, u'question_id': qid})
quest_ids.append(_qids)
quest_ids = np.concatenate(quest_ids)
# save results
tf.logging.info('Saving results')
res_file = FLAGS.result_format % (FLAGS.version, TEST_SET)
json.dump(result, open(res_file, 'w'))
tf.logging.info('Done!')
tf.logging.info('#Num eval samples %d' % len(result))
return res_file, quest_ids