class VanillaModel(BaseVQAModel):
def __init__(self,
ckpt_file='model/kprestval_VQA-BaseNorm/model.ckpt-26000'):
BaseVQAModel.__init__(self)
self.top_k = 2
self.g = tf.Graph()
self.ckpt_file = ckpt_file
from models.vqa_base import BaseModel
from vqa_config import ModelConfig
config = ModelConfig()
self._subset = 'test'
self._year = 2015
self.name = ' ------- DeeperLSTM ------- '
with self.g.as_default():
self.sess = tf.Session()
self.model = BaseModel(config, phase='test')
self.model.build()
vars = tf.trainable_variables()
self.saver = tf.train.Saver(var_list=vars)
self.saver.restore(self.sess, ckpt_file)
def _load_image(self, image_id):
FEAT_ROOT = '/usr/data/fl302/data/VQA/ResNet152/resnet_res5c'
filename = '%s%d/COCO_%s%d_%012d.jpg' % (
self._subset, self._year, self._subset, self._year, image_id)
f = np.load(os.path.join(FEAT_ROOT, filename + '.npz'))['x']
f1 = np.mean(np.mean(f.transpose((1, 2, 0)), axis=0), axis=0)
# f2 = f.reshape([2048, -1]).mean(axis=1)
# import pdb
# pdb.set_trace()
return f1[np.newaxis, ::]
class VQAWrapper(object):
def __init__(self, g, sess):
from models.vqa_base import BaseModel
from vqa_config import ModelConfig
config = ModelConfig()
self.sess = sess
ckpt_file = 'model/kprestval_VQA-BaseNorm/model.ckpt-26000'
with g.as_default():
self.sess = tf.Session()
self.model = BaseModel(config, phase='test')
with tf.variable_scope('VQA'):
self.model.build()
# vars = tf.trainable_variables()
vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='VQA')
vars_dict = {v.name.replace('VQA/', '').split(':')[0]: v for v in vars}
self.saver = tf.train.Saver(var_list=vars_dict)
self.saver.restore(self.sess, ckpt_file)
def get_scores(self, sampled, image, top_ans_id):
pathes = []
for p in sampled:
if p[-1] == END_TOKEN:
pathes.append(p[1:-1]) # remove start end token
else:
pathes.append(p[1:]) # remove start end token
num_unk = len(sampled)
images_aug = np.tile(image, [num_unk, 1])
# put to arrays
arr, arr_len = put_to_array(pathes)
scores = self.model.inference(self.sess, [images_aug, arr, arr_len])
vqa_scores = scores[:, top_ans_id].flatten()
return vqa_scores
class VanillaModel(BaseVQAModel):
def __init__(self,
ckpt_file='model/kprestval_VQA-BaseNorm/model.ckpt-26000'):
BaseVQAModel.__init__(self)
self.top_k = 2
self.g = tf.Graph()
self.ckpt_file = ckpt_file
from models.vqa_base import BaseModel
from vqa_config import ModelConfig
config = ModelConfig()
self.name = ' ------- DeeperLSTM ------- '
with self.g.as_default():
self.sess = tf.Session()
self.model = BaseModel(config, phase='test')
self.model.build()
vars = tf.trainable_variables()
self.saver = tf.train.Saver(var_list=vars)
self.saver.restore(self.sess, ckpt_file)
self._init_image_cache()
def _init_image_cache(self):
from util import load_hdf5
d = load_hdf5('data/res152_std_mscoco_kptest.data')
# d = load_hdf5('data/res152_std_mscoco_kpval.data')
image_ids = d['image_ids']
self.im_feats = d['features']
image_id2index = {
image_id: idx
for idx, image_id in enumerate(image_ids)
}
self.image_id2index = image_id2index
def _load_image(self, image_id):
idx = self.image_id2index[image_id]
return self.im_feats[idx][np.newaxis, :]
class VQARewards(object):
def __init__(self, ckpt_file='', use_dis_reward=False,
use_attention_model=False):
self.g = tf.Graph()
self.ckpt_file = ckpt_file
self.use_attention_model = use_attention_model
from models.vqa_base import BaseModel
from vqa_config import ModelConfig
config = ModelConfig()
self.ans2id = AnswerTokenToTopAnswer()
self.use_dis_reward = use_dis_reward
with self.g.as_default():
self.sess = tf.Session()
if self.use_attention_model:
self.model = AttentionModel(config, phase='test')
self.model.build()
else:
self.model = BaseModel(config, phase='test')
self.model.build()
vars = tf.trainable_variables()
self.saver = tf.train.Saver(var_list=vars)
self.saver.restore(self.sess, ckpt_file)
def process_answers(self, ans, ans_len):
ans_pathes = _parse_gt_questions(ans, ans_len)
return self.ans2id.get_top_answer(ans_pathes)
def get_reward(self, sampled, inputs):
if len(inputs) == 3:
images, ans, ans_len = inputs
top_ans_ids = self.process_answers(ans, ans_len)
else:
assert (len(inputs) == 4)
images, ans, ans_len, top_ans_ids = inputs
images_aug = []
top_ans_ids_aug = []
answer_aug = []
answer_len_aug = []
pathes = []
for _idx, ps in enumerate(sampled):
for p in ps:
if p[-1] == END_TOKEN:
pathes.append(p[1:-1]) # remove start end token
else:
pathes.append(p[1:]) # remove start end token
images_aug.append(images[_idx][np.newaxis, :])
answer_aug.append(ans[_idx][np.newaxis, :])
answer_len_aug.append(ans_len[_idx])
top_ans_ids_aug.append(top_ans_ids[_idx])
# put to arrays
arr, arr_len = put_to_array(pathes)
images_aug = np.concatenate(images_aug)
answer_aug = np.concatenate(answer_aug).astype(np.int32)
top_ans_ids_aug = np.array(top_ans_ids_aug)
answer_len_aug = np.array(answer_len_aug, dtype=np.int32)
# run inference in VQA
scores = self.model.inference(self.sess, [images_aug, arr, arr_len])
if self.use_dis_reward:
vqa_scores = np.require(scores.argmax(axis=1) == top_ans_ids_aug,
np.float32)
else:
_this_batch_size = scores.shape[0]
vqa_scores = scores[np.arange(_this_batch_size), top_ans_ids_aug]
is_valid = top_ans_ids_aug != 2000
return vqa_scores, [images_aug, answer_aug, answer_len_aug, is_valid]