def __init__(self, mode='winner_take_all'):
self.scorer = ciderEval('vqa_%s_idxs_end' % 'kptrain')
self.pred_has_start_end_token = True
self.use_end_token = True
self.thresh = 9.0
self.verbose = False
self.mode = mode
self.diversity_scorer = UniqueReward()
assert (self.mode in ['winner_take_all', 'kill_all'])
def compute_cider_token(quest_ids, gts, res):
# warp gts
w_gts = {str(quest_id): [capt] for quest_id, capt in zip(quest_ids, gts)}
w_res = [{
'image_id': str(quest_id),
'caption': [capt]
} for quest_id, capt in zip(quest_ids, res)]
from pyciderevalcap.fast_eval import CIDErEvalCap as ciderEval
scorer = ciderEval(w_gts, w_res, 'ivqa_train_idxs')
scores = scorer.evaluate()
return scores
def __init__(self, metric='cider', gt_has_start_end_token=False,
pred_has_start_end_token=True, use_end_token=True,
subset='kptrain'):
self.gt_has_start_end_token = gt_has_start_end_token
self.pred_has_start_end_token = pred_has_start_end_token
self.use_end_token = use_end_token
if metric == 'cider':
self.scorer = ciderEval('vqa_%s_idxs_end' % subset)
elif metric == 'bleu':
self.scorer = Bleu(n=4)
assert (metric == 'cider')
self.to_sentence = SentenceGenerator(trainset='trainval')
self._num_call = long(0)
self.print_iterval = 100
def compute_cider_token_1vsall(quest_ids, res):
# warp gts
w_gts = {str(quest_id): res for quest_id in quest_ids}
w_res = [{
'image_id': str(quest_id),
'caption': [capt]
} for quest_id, capt in zip(quest_ids, res)]
from pyciderevalcap.fast_eval import CIDErEvalCap as ciderEval
from time import time
# t = time()
scorer = ciderEval(w_gts, w_res, 'ivqa_train_idxs')
scores = scorer.evaluate()
# print(time()-t)
return scores
def __init__(self):
print('Creating NN model')
subset = 'kptrain'
meta_file = 'data/vqa_std_mscoco_%s.meta' % subset
data_file = 'data/vqa_std_mscoco_%s.data' % subset
d = load_json(meta_file)
quest_ids = d['quest_id']
self.quest_id2index = {
quest_id: i
for (i, quest_id) in enumerate(quest_ids)
}
d = load_hdf5(data_file)
self._quest = d['quest_arr'].astype(np.int32)
self._quest_len = d['quest_len'].astype(np.int32)
self.cider_scorer = ciderEval('ivqa_train_idxs')
print('Done')
def __init__(self,
graph=None,
sess=None,
use_vqa_reward=False,
metric='cider'):
self.graph = graph
self.sess = sess
self.gamma = 0.0
self.use_vqa_reward = use_vqa_reward and self.gamma > 0
# self.cider_scorer = ciderEval('ivqa_train_idxs')
if metric == 'cider':
self.scorer = ciderEval('v2_ivqa_train_idxs')
elif metric == 'bleu':
self.scorer = Bleu(n=4)
# self.cider_scorer = CiderD(df='v2_ivqa_train_idxs')
if self.use_vqa_reward:
with graph.as_default():
self._build_vqa_agent()
