def _load_answer_type(self, quest_ids):
answer_type_file = 'data/%sanswer_type_std_mscoco_%s.data' % (
self._version_suffix, self._subset)
if not os.path.exists(answer_type_file):
_mc_ctx = MultiChoiceQuestionManger(subset='val')
answer_type_ids = [
_mc_ctx.get_answer_type_coding(quest_id)
for quest_id in quest_ids
]
answer_type_ids = np.array(answer_type_ids, dtype=np.int32)
save_hdf5(
answer_type_file, {
'answer_type': answer_type_ids,
'quest_ids': np.array(quest_ids, dtype=np.int32)
})
else:
d = load_hdf5(answer_type_file)
answer_type_ids = d['answer_type']
self._answer_type = answer_type_ids
def load_vqa_predictions(subset):
data_file = '../iccv_vaq/data/sparse_vqa_scores_%s_0.h5' % subset
data_file = '../iccv_vaq/data/sparse_vqa_scores_dev_5nogt.h5'
d = load_hdf5(data_file)
quest_ids = d['quest_ids']
pdb.set_trace()
mc_ctx = MultiChoiceQuestionManger(subset='val', load_ans=True)
type_labels = []
for qid in quest_ids:
coding = mc_ctx.get_answer_type_coding(qid)
type_labels.append(coding)
type_labels = np.array(type_labels)
unique_ids = np.unique(type_labels)
print(np.bincount(type_labels))
valid_types = [u'other', u'number', u'yes/no']
yes_no_id = mc_ctx._answer_type2id['yes/no']
# yes_no_id = mc_ctx._answer_type2id['other']
# yes_no_id = mc_ctx._answer_type2id['number']
is_yes_no = type_labels == yes_no_id
candidates = d['top_k_index'][:, :3]
num_yes_no = is_yes_no.sum()
num_tot = candidates.shape[0]
print('Yes/No: %d in %d' % (num_yes_no, num_tot))
yes_top_ans_id = 0
no_top_ans_id = 1
yes_no_cands = candidates[is_yes_no]
has_yes = (yes_no_cands == yes_top_ans_id).sum(axis=1) == 1
print(has_yes.sum())
has_no = (yes_no_cands == no_top_ans_id).sum(axis=1) == 1
print(has_no.sum())
yn_inter = np.logical_and(has_yes, has_no)
yn_union = np.logical_or(has_yes, has_no)
print('Either yes/no %d:' % yn_union.sum())
print('Both yes/no %d:' % yn_inter.sum())
