def build_caption_attention(self):
"""
caption_attention
"""
num_V_ft = self.batch['num_boxes']
v_linear_v = self.mid_result['v_linear_v']
w_embed = tf.nn.embedding_lookup(self.v_word_map,
self.batch['cap_att/word_tokens'])
w_L_ft = modules.fc_layer( # [bs, #proposal, len, L_DIM]
w_embed, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='v_word_fc')
w_len = self.batch['cap_att/word_tokens_len']
mask = tf.sequence_mask( # [bs, #proposal, len]
w_len, maxlen=tf.shape(w_L_ft)[-2],
dtype=tf.float32)
pooled_w_L_ft = tf.reduce_sum(w_L_ft * tf.expand_dims(mask, axis=-1),
axis=-2)
pooled_w_L_ft = pooled_w_L_ft / \
tf.expand_dims(tf.to_float(w_len), axis=-1)
l_linear_v = modules.fc_layer(
pooled_w_L_ft, V_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='q_linear_v')
tile_v_linear_v = tf.tile(tf.expand_dims(v_linear_v, axis=1),
[1, self.data_cfg.n_cap_att, 1, 1])
flat_tile_v_linear_v = tf.reshape(tile_v_linear_v,
[-1, self.data_cfg.max_box_num, V_DIM])
tile_num_V_ft = tf.tile(tf.expand_dims(num_V_ft, axis=1),
[1, self.data_cfg.n_cap_att])
flat_tile_num_V_ft = tf.reshape(tile_num_V_ft, [-1])
flat_l_linear_v = tf.reshape(l_linear_v, [-1, V_DIM])
# flat_att_logit: [bs * #obj, num_proposal]
flat_att_logit = modules.hadamard_attention(
flat_tile_v_linear_v, flat_tile_num_V_ft, flat_l_linear_v,
use_ln=False, is_train=self.is_train, normalizer=None)
n_entry = self.data_cfg.n_cap_att
n_proposal = self.data_cfg.max_box_num
logit = tf.reshape(flat_att_logit, [-1, n_entry, n_proposal])
with tf.name_scope('loss/caption_attend'):
multilabel_gt = tf.to_float(
tf.greater(self.batch['cap_att/att_scores'], 0.5))
num_valid_entry = self.batch['cap_att/num']
valid_mask = tf.sequence_mask(
num_valid_entry, maxlen=self.data_cfg.n_cap_att,
dtype=tf.float32)
loss, acc, recall, precision, top_1_prec, top_k_recall = \
self.binary_classification_loss(logit, multilabel_gt, valid_mask,
depth=self.data_cfg.max_box_num)
self.losses['caption_att'] = loss
self.report['caption_att_loss'] = loss
self.report['caption_att_acc'] = acc
self.report['caption_att_recall'] = recall
self.report['caption_att_precision'] = precision
self.report['caption_att_top_1_prec'] = top_1_prec
self.report['caption_att_top_{}_recall'.format(TOP_K)] = top_k_recall
def build_attribute_blank_fill(self):
"""
attribute_blank_fill
"""
# [#obj, #proposal] x [#proposal x feat_dim] -> [#obj,feat_dim]
V_ft = tf.matmul(self.batch['attr_blank_fill/weights'],
self.batch['image_ft'])
v_linear_l = modules.fc_layer(V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
blank_embed = tf.nn.embedding_lookup( # [bs, #proposal, len, W_DIM]
self.l_word_map, self.batch['attr_blank_fill/blanks'])
blank_len = self.batch['attr_blank_fill/blanks_len']
blank_maxlen = tf.shape(blank_embed)[-2]
flat_blank_ft = modules.encode_L( # [bs * #proposal, L_DIM]
tf.reshape(blank_embed, [-1, blank_maxlen, W_DIM]),
tf.reshape(blank_len, [-1]),
L_DIM,
scope='encode_L_blank',
cell_type='GRU')
blank_ft = tf.reshape(flat_blank_ft,
[-1, self.data_cfg.n_obj_bf, L_DIM])
l_linear_l = modules.fc_layer(blank_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result[
'attr_blank_fill/logit'] = logit # [bs, #attr, #answer]
with tf.name_scope('loss/attr_blank_fill'):
onehot_gt = tf.one_hot(self.batch['attr_blank_fill/fills'],
depth=self.num_answer)
num_valid_entry = self.batch['attr_blank_fill/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_attr_bf,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
self.losses['attr_blank_fill'] = loss
self.report['attr_blank_fill_loss'] = loss
self.report['attr_blank_fill_acc'] = acc
self.report['attr_blank_fill_top_{}_acc'.format(TOP_K)] = top_k_acc
def build(self):
"""
build network architecture and loss
"""
"""
Visual features
"""
with tf.device('/cpu:0'):
def load_feature(image_idx):
selected_features = np.take(self.features, image_idx, axis=0)
return selected_features
V_ft = tf.py_func(
load_feature, inp=[self.batch['image_idx']], Tout=tf.float32,
name='sample_features')
# [B, # of box, dim]
V_ft.set_shape([None, self.max_box_num, self.vfeat_dim])
num_V_ft = tf.gather(self.num_boxes, self.batch['image_idx'],
name='gather_num_V_ft', axis=0)
self.mid_result['num_V_ft'] = num_V_ft
normal_boxes = tf.gather(self.normal_boxes, self.batch['image_idx'],
name='gather_normal_boxes', axis=0)
self.mid_result['normal_boxes'] = normal_boxes
log.warning('v_linear_v')
# [B, # of box, V_DIM]
v_linear_v = modules.fc_layer(
V_ft, V_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='v_linear_v')
"""
Average pooling
"""
# [B, # of box, V_DIM] -> [B, V_DIM]
avg_pooled_V_ft tf.reduce_mean(V_ft, 1, keepdims=False)
# [B, R_DIM * 2]
c_0_h_0 = modules.fc_layer(
avg_pooled_V_ft, R_DIM * 2, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='c_0_h_0')
# c_0, h_0: [B, R_DIM]
c_0, h_0 = tf.split(c_0_h_0, [R_DIM, R_DIM], 1)
state_in = tc.LSTMStateTuple(c_0, h_0)
self.att_lstm = tc.BasicLSTMCell(R_DIM)
self.lang_lstm = tc.BasicLSTMCell(R_DIM)
lstm_out, lstm_state = tf.nn.dynamic_rnn(
self.lang_lstm,
# [batch_size, max_time, ...]
lstm_in,
# [batch_size, cell.state_size]
initial_state=state_in,
time_major=False)
lstm_c, lstm_h = lstm_state
# TODO(taehoon): time should be shifted
# gate [batch_size, max_time, 1]
gate = modules.fc_layer(
lstm_h, 1, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.sigmoid, is_training=self.is_train,
scope='gate')
# visual sentinel [batch_size, max_time, R_DIM]
sentinel = gate * tf.nn.tanh(lstm_c)
sentinel_linear = modules.fc_layer(
sentinel, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='sentinel_linear')
h_linear = modules.fc_layer(
lstm_h, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='h_linear')
logit = tf.nn.tanh(tf.concat([state_t, lstm_h]))
"""
Answer classification
"""
# perform two layer feature encoding and predict output
with tf.variable_scope('reasoning') as scope:
log.warning(scope.name)
# [bs, L_DIM]
log.warning('pooled_linear_l')
pooled_linear_l = modules.fc_layer(
pooled_V_ft, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='pooled_linear_l')
log.warning('q_linear_l')
q_linear_l = modules.fc_layer(
q_L_ft, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(
pooled_linear_l * q_linear_l, 2048,
use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train, scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(
joint, self.num_answer,
use_bias=True, use_bn=False, use_ln=False,
activation_fn=None, is_training=self.is_train, scope='classifier')
self.output['logit'] = logit
"""
Compute loss and accuracy
"""
with tf.name_scope('loss'):
answer_target = self.batch['answer_target']
loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=answer_target, logits=logit)
loss = tf.reduce_mean(tf.reduce_sum(loss, axis=-1))
pred = tf.cast(tf.argmax(logit, axis=-1), dtype=tf.int32)
one_hot_pred = tf.one_hot(pred, depth=self.num_answer,
dtype=tf.float32)
self.output['pred'] = pred
all_score = tf.reduce_sum(one_hot_pred * answer_target, axis=-1)
max_train_score = tf.reduce_max(
answer_target * self.train_answer_mask, axis=-1)
self.output['all_score'] = all_score
self.output['max_train_score'] = max_train_score
acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target, axis=-1))
exist_acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target * self.answer_exist_mask,
axis=-1))
test_acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target * self.test_answer_mask,
axis=-1))
test_obj_acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target * self.test_answer_mask *
self.obj_answer_mask, axis=-1))
test_attr_acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target * self.test_answer_mask *
self.attr_answer_mask, axis=-1))
train_exist_acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target * self.answer_exist_mask *
self.train_answer_mask,
axis=-1))
max_exist_answer_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.answer_exist_mask, axis=-1))
max_train_exist_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.answer_exist_mask *
self.train_answer_mask, axis=-1))
test_obj_max_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.test_answer_mask *
self.obj_answer_mask, axis=-1))
test_attr_max_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.test_answer_mask *
self.attr_answer_mask, axis=-1))
test_max_answer_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.test_answer_mask, axis=-1))
test_max_exist_answer_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.answer_exist_mask *
self.test_answer_mask, axis=-1))
normal_test_obj_acc = tf.where(
tf.equal(test_obj_max_acc, 0),
test_obj_max_acc,
test_obj_acc / test_obj_max_acc)
normal_test_attr_acc = tf.where(
tf.equal(test_attr_max_acc, 0),
test_attr_max_acc,
test_attr_acc / test_attr_max_acc)
normal_train_exist_acc = tf.where(
tf.equal(max_train_exist_acc, 0),
max_train_exist_acc,
train_exist_acc / max_train_exist_acc)
normal_exist_acc = tf.where(
tf.equal(max_exist_answer_acc, 0),
max_exist_answer_acc,
exist_acc / max_exist_answer_acc)
normal_test_acc = tf.where(
tf.equal(test_max_answer_acc, 0),
test_max_answer_acc,
test_acc / test_max_answer_acc)
self.mid_result['pred'] = pred
self.losses['answer'] = loss
self.report['answer_train_loss'] = loss
self.report['answer_report_loss'] = loss
self.report['answer_acc'] = acc
self.report['exist_acc'] = exist_acc
self.report['test_acc'] = test_acc
self.report['normal_test_acc'] = normal_test_acc
self.report['normal_test_object_acc'] = normal_test_obj_acc
self.report['normal_test_attribute_acc'] = normal_test_attr_acc
self.report['normal_exist_acc'] = normal_exist_acc
self.report['normal_train_exist_acc'] = normal_train_exist_acc
self.report['max_exist_acc'] = max_exist_answer_acc
self.report['test_max_acc'] = test_max_answer_acc
self.report['test_max_exist_acc'] = test_max_exist_answer_acc
"""
Prepare image summary
"""
"""
with tf.name_scope('prepare_summary'):
self.vis_image['image_attention_qa'] = self.visualize_vqa_result(
self.batch['image_id'],
self.mid_result['normal_boxes'], self.mid_result['num_V_ft'],
self.mid_result['att_score'],
self.batch['q_intseq'], self.batch['q_intseq_len'],
self.batch['answer_target'], self.mid_result['pred'],
max_batch_num=20, line_width=2)
"""
self.loss = self.losses['answer']
# scalar summary
for key, val in self.report.items():
tf.summary.scalar('train/{}'.format(key), val,
collections=['heavy_train', 'train'])
tf.summary.scalar('val/{}'.format(key), val,
collections=['heavy_val', 'val'])
tf.summary.scalar('testval/{}'.format(key), val,
collections=['heavy_testval', 'testval'])
# image summary
for key, val in self.vis_image.items():
tf.summary.image('train-{}'.format(key), val, max_outputs=10,
collections=['heavy_train'])
tf.summary.image('val-{}'.format(key), val, max_outputs=10,
collections=['heavy_val'])
tf.summary.image('testval-{}'.format(key), val, max_outputs=10,
collections=['heavy_testval'])
return self.loss
def build(self):
"""
build network architecture and loss
"""
"""
Visual features
"""
with tf.device('/cpu:0'):
def load_feature(image_idx):
selected_features = np.take(self.features, image_idx, axis=0)
return selected_features
V_ft = tf.py_func(load_feature,
inp=[self.batch['image_idx']],
Tout=tf.float32,
name='sample_features')
V_ft.set_shape([None, self.max_box_num, self.vfeat_dim])
num_V_ft = tf.gather(self.num_boxes,
self.batch['image_idx'],
name='gather_num_V_ft',
axis=0)
self.mid_result['num_V_ft'] = num_V_ft
normal_boxes = tf.gather(self.normal_boxes,
self.batch['image_idx'],
name='gather_normal_boxes',
axis=0)
self.mid_result['normal_boxes'] = normal_boxes
log.warning('v_linear_v')
v_linear_v = modules.fc_layer(V_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='v_linear_v')
"""
Encode question
"""
q_embed = tf.nn.embedding_lookup(self.glove_map,
self.batch['q_intseq'])
# [bs, L_DIM]
q_L_ft = modules.encode_L(q_embed,
self.batch['q_intseq_len'],
L_DIM,
cell_type='GRU')
q_L_mean = modules.fc_layer(q_L_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='q_L_mean')
# [bs, V_DIM}
log.warning('q_linear_v')
q_linear_v = modules.fc_layer(q_L_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_v')
self.mid_result['q_linear_v'] = q_linear_v
"""
Perform attention
"""
att_score = modules.hadamard_attention(v_linear_v,
num_V_ft,
q_linear_v,
use_ln=False,
is_train=self.is_train)
self.mid_result['att_score'] = att_score
pooled_V_ft = modules.attention_pooling(V_ft, att_score)
self.mid_result['pooled_V_ft'] = pooled_V_ft
"""
Answer classification
"""
log.warning('pooled_linear_l')
pooled_linear_l = modules.fc_layer(pooled_V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
self.mid_result['pooled_linear_l'] = pooled_linear_l
log.warning('q_linear_l')
l_linear_l = modules.fc_layer(q_L_mean,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
self.mid_result['l_linear_l'] = l_linear_l
joint = modules.fc_layer(pooled_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
self.mid_result['joint'] = joint
logit = modules.WordWeightAnswer(joint,
self.answer_dict,
self.word_weight_dir,
use_bias=True,
is_training=self.is_train,
scope='WordWeightAnswer')
self.mid_result['logit'] = logit
"""
Compute loss and accuracy
"""
with tf.name_scope('loss'):
answer_target = self.batch['answer_target']
loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=answer_target, logits=logit)
train_loss = tf.reduce_mean(
tf.reduce_sum(loss * self.train_answer_mask, axis=-1))
report_loss = tf.reduce_mean(tf.reduce_sum(loss, axis=-1))
pred = tf.cast(tf.argmax(logit, axis=-1), dtype=tf.int32)
one_hot_pred = tf.one_hot(pred,
depth=self.num_answer,
dtype=tf.float32)
acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target, axis=-1))
exist_acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target *
self.answer_exist_mask,
axis=-1))
test_acc = tf.reduce_mean(
tf.reduce_sum(one_hot_pred * answer_target *
self.test_answer_mask,
axis=-1))
max_exist_answer_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.answer_exist_mask, axis=-1))
test_max_answer_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.test_answer_mask, axis=-1))
test_max_exist_answer_acc = tf.reduce_mean(
tf.reduce_max(answer_target * self.answer_exist_mask *
self.test_answer_mask,
axis=-1))
normal_test_acc = tf.where(tf.equal(test_max_answer_acc,
0), test_max_answer_acc,
test_acc / test_max_answer_acc)
self.mid_result['pred'] = pred
self.losses['answer'] = train_loss
self.report['answer_train_loss'] = train_loss
self.report['answer_report_loss'] = report_loss
self.report['answer_accuracy'] = acc
self.report['exist_answer_accuracy'] = exist_acc
self.report['test_answer_accuracy'] = test_acc
self.report['normal_test_answer_accuracy'] = normal_test_acc
self.report['max_exist_answer_accuracy'] = max_exist_answer_acc
self.report['test_max_answer_accuracy'] = test_max_answer_acc
self.report[
'test_max_exist_answer_accuracy'] = test_max_exist_answer_acc
"""
Prepare image summary
"""
"""
with tf.name_scope('prepare_summary'):
self.vis_image['image_attention_qa'] = self.visualize_vqa_result(
self.batch['image_id'],
self.mid_result['normal_boxes'], self.mid_result['num_V_ft'],
self.mid_result['att_score'],
self.batch['q_intseq'], self.batch['q_intseq_len'],
self.batch['answer_target'], self.mid_result['pred'],
max_batch_num=20, line_width=2)
"""
self.loss = 0
for key, loss in self.losses.items():
self.loss = self.loss + loss
# scalar summary
for key, val in self.report.items():
tf.summary.scalar('train/{}'.format(key),
val,
collections=['heavy_train', 'train'])
tf.summary.scalar('val/{}'.format(key),
val,
collections=['heavy_val', 'val'])
tf.summary.scalar('testval/{}'.format(key),
val,
collections=['heavy_testval', 'testval'])
# image summary
for key, val in self.vis_image.items():
tf.summary.image('train-{}'.format(key),
val,
max_outputs=10,
collections=['heavy_train'])
tf.summary.image('val-{}'.format(key),
val,
max_outputs=10,
collections=['heavy_val'])
tf.summary.image('testval-{}'.format(key),
val,
max_outputs=10,
collections=['heavy_testval'])
return self.loss
def build_attribute_predict(self):
"""
attribute_predict
"""
# [#attr, #proposal] x [#proposal x feat_dim] -> [#attr,feat_dim]
V_ft = tf.matmul(self.batch['attr_pred/weights'],
self.batch['image_ft'])
v_linear_l = modules.fc_layer(V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
L_ft = tf.nn.embedding_lookup(self.l_answer_word_map,
self.batch['attr_pred/object_labels'])
reg_l_ft = modules.fc_layer(L_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.tanh,
is_training=self.is_train,
scope='attr_pred/encode_object_labels')
self.mid_result['attr_pred/reg_l_ft'] = reg_l_ft
l_linear_l = modules.fc_layer(reg_l_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result['attr_pred/logit'] = logit # [bs, #attr, #answer]
with tf.name_scope('loss/attr_predict'):
multilabel_gt = self.batch['attr_pred/labels']
num_valid_entry = self.batch['attr_pred/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_attr_pred,
dtype=tf.float32)
loss, acc, recall, precision, top_1_prec, top_k_recall = \
self.binary_classification_loss(logit, multilabel_gt, valid_mask,
depth=self.num_answer)
self.losses['attr_pred'] = loss
self.report['attr_pred_loss'] = loss
self.report['attr_pred_acc'] = acc
self.report['attr_pred_recall'] = recall
self.report['attr_pred_precision'] = precision
self.report['attr_pred_top_1_prec'] = top_1_prec
self.report['attr_pred_top_{}_recall'.format(TOP_K)] = top_k_recall
def build_attribute_wordset(self):
"""
attribute_wordset
"""
V_ft = self.mid_result['attribute_V_ft']
num_V_ft = self.mid_result['attribute_num_V_ft']
v_linear_v = modules.fc_layer( # [bs * #attr, #proposal, V_DIM]
V_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='wordset_v_linear_v')
wordset_embed = tf.tanh(
tf.nn.embedding_lookup(self.wordset_map,
self.batch['attr_blank_fill/wordsets']))
wordset_ft = modules.fc_layer(wordset_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.tanh,
is_training=self.is_train,
scope='wordset_ft')
q_linear_v = modules.fc_layer( # [bs, #attr, V_DIM]
wordset_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='wordset_q_linear_v')
flat_q_linear_v = tf.reshape(q_linear_v,
[-1, V_DIM]) # [bs * #attr, V_DIM]
att_score = modules.hadamard_attention( # [bs * #attr, len]
v_linear_v,
num_V_ft,
flat_q_linear_v,
use_ln=False,
is_train=self.is_train,
scope='wordset_att')
flat_pooled_V_ft = modules.attention_pooling(
V_ft, att_score) # [bs * #attr, V_DIM]
pooled_V_ft = tf.reshape(
flat_pooled_V_ft,
[-1, self.data_cfg.n_attr_bf, self.data_cfg.vfeat_dim])
v_linear_l = modules.fc_layer(pooled_V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
l_linear_l = modules.fc_layer(wordset_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result[
'attr_blank_fill/logit'] = logit # [bs, #attr, #answer]
with tf.name_scope('loss/attr_wordset'):
onehot_gt = tf.one_hot(self.batch['attr_blank_fill/fills'],
depth=self.num_answer)
num_valid_entry = self.batch['attr_blank_fill/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_attr_bf,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
self.losses['attr_wordset'] = loss
self.report['attr_wordset_loss'] = loss
self.report['attr_wordset_acc'] = acc
self.report['attr_wordset_top_{}_acc'.format(TOP_K)] = top_k_acc
def build_object_blank_fill(self):
"""
object_blank_fill
"""
V_ft = self.mid_result['object_V_ft']
num_V_ft = self.mid_result['object_num_V_ft']
v_linear_v = modules.fc_layer( # [bs * #obj, #proposal, V_DIM]
V_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='bf_v_linear_v')
blank_embed = tf.nn.embedding_lookup( # [bs, #proposal, len, W_DIM]
self.l_word_map, self.batch['obj_blank_fill/blanks'])
blank_len = self.batch['obj_blank_fill/blanks_len']
blank_maxlen = tf.shape(blank_embed)[-2]
flat_blank_ft = modules.encode_L( # [bs * #proposal, L_DIM]
tf.reshape(blank_embed, [-1, blank_maxlen, W_DIM]),
tf.reshape(blank_len, [-1]),
L_DIM,
scope='encode_L_blank',
cell_type='GRU')
blank_ft = tf.reshape(flat_blank_ft,
[-1, self.data_cfg.n_obj_bf, L_DIM])
q_linear_v = modules.fc_layer( # [bs, #obj, V_DIM]
blank_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='bf_q_linear_v')
flat_q_linear_v = tf.reshape(q_linear_v,
[-1, V_DIM]) # [bs * #obj, V_DIM]
att_score = modules.hadamard_attention( # [bs * #obj, len]
v_linear_v,
num_V_ft,
flat_q_linear_v,
use_ln=False,
is_train=self.is_train,
scope='bf_att')
flat_pooled_V_ft = modules.attention_pooling(
V_ft, att_score) # [bs * #obj, vfeat_dim]
pooled_V_ft = tf.reshape(
flat_pooled_V_ft,
[-1, self.data_cfg.n_obj_bf, self.data_cfg.vfeat_dim])
v_linear_l = modules.fc_layer(pooled_V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
l_linear_l = modules.fc_layer(blank_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result['obj_blank_fill/logit'] = logit # [bs, #obj, #answer]
with tf.name_scope('loss/obj_blank_fill'):
onehot_gt = tf.one_hot(self.batch['obj_blank_fill/fills'],
depth=self.num_answer)
num_valid_entry = self.batch['obj_blank_fill/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_obj_bf,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
self.losses['obj_blank_fill'] = loss
self.report['obj_blank_fill_loss'] = loss
self.report['obj_blank_fill_acc'] = acc
self.report['obj_blank_fill_top_{}_acc'.format(TOP_K)] = top_k_acc
def build_object_enwiki(self):
"""
object_enwiki
"""
pooled_V_ft = self.mid_result['object_pooled_V_ft']
enwiki_embed = tf.nn.embedding_lookup( # [bs, #proposal, len, W_DIM]
self.enwiki_map, self.batch['obj_blank_fill/enwiki_context'])
enwiki_len = self.batch['obj_blank_fill/enwiki_context_len']
enwiki_maxlen = tf.shape(enwiki_embed)[-2]
flat_enwiki_ft = modules.encode_L( # [bs * #proposal, L_DIM]
tf.reshape(enwiki_embed, [-1, enwiki_maxlen, W_DIM]),
tf.reshape(enwiki_len, [-1]),
L_DIM,
scope='encode_L_enwiki',
cell_type='GRU')
enwiki_ft = tf.reshape(flat_enwiki_ft,
[-1, self.data_cfg.n_obj_bf, L_DIM])
v_linear_l = modules.fc_layer(pooled_V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
l_linear_l = modules.fc_layer(enwiki_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
v_joint = modules.fc_layer(v_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_v')
v_joint = tf.nn.dropout(v_joint, 0.5)
l_joint = modules.fc_layer(l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_l')
l_joint = tf.nn.dropout(l_joint, 0.5)
v_logit = modules.fc_layer(v_joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier_v')
l_logit = modules.fc_layer(l_joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier_l')
with tf.name_scope('loss/obj_enwiki'):
onehot_gt = tf.one_hot(self.batch['obj_blank_fill/fills'],
depth=self.num_answer)
num_valid_entry = self.batch['obj_blank_fill/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_obj_bf,
dtype=tf.float32)
v_loss, v_acc, v_top_k_acc = \
self.n_way_classification_loss(v_logit, onehot_gt, valid_mask)
l_loss, l_acc, l_top_k_acc = \
self.n_way_classification_loss(l_logit, onehot_gt, valid_mask)
self.losses['obj_enwiki_v'] = v_loss
self.losses['obj_enwiki_l'] = l_loss
self.report['obj_enwiki_v_loss'] = v_loss
self.report['obj_enwiki_l_loss'] = l_loss
self.report['obj_enwiki_v_acc'] = v_acc
self.report['obj_enwiki_l_acc'] = l_acc
self.report['obj_enwiki_v_top_{}_acc'.format(TOP_K)] = v_top_k_acc
self.report['obj_enwiki_l_top_{}_acc'.format(TOP_K)] = l_top_k_acc
def build_attribute_wordset(self):
"""
attribute_wordset
"""
pooled_V_ft = self.mid_result['attribute_pooled_V_ft']
wordset_embed = tf.tanh(tf.nn.embedding_lookup(
self.wordset_map, self.batch['attr_blank_fill/wordsets']))
wordset_ft = modules.fc_layer(
wordset_embed, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.tanh, is_training=self.is_train, scope='wordset_ft')
v_linear_l = modules.fc_layer(
pooled_V_ft, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='pooled_linear_l')
l_linear_l = modules.fc_layer(
wordset_ft, L_DIM, use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train,
scope='q_linear_l')
v_joint = modules.fc_layer(
v_linear_l, L_DIM * 2,
use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train, scope='joint_v')
v_joint = tf.nn.dropout(v_joint, 0.5)
l_joint = modules.fc_layer(
l_linear_l, L_DIM * 2,
use_bias=True, use_bn=False, use_ln=True,
activation_fn=tf.nn.relu, is_training=self.is_train, scope='joint_l')
l_joint = tf.nn.dropout(l_joint, 0.5)
v_logit = modules.fc_layer(
v_joint, self.num_answer,
use_bias=True, use_bn=False, use_ln=False,
activation_fn=None, is_training=self.is_train, scope='classifier_v')
l_logit = modules.fc_layer(
l_joint, self.num_answer,
use_bias=True, use_bn=False, use_ln=False,
activation_fn=None, is_training=self.is_train, scope='classifier_l')
with tf.name_scope('loss/attr_wordset'):
onehot_gt = tf.one_hot(self.batch['attr_blank_fill/fills'],
depth=self.num_answer)
num_valid_entry = self.batch['attr_blank_fill/num']
valid_mask = tf.sequence_mask(
num_valid_entry, maxlen=self.data_cfg.n_attr_bf,
dtype=tf.float32)
v_loss, v_acc, v_top_k_acc = \
self.n_way_classification_loss(v_logit, onehot_gt, valid_mask)
l_loss, l_acc, l_top_k_acc = \
self.n_way_classification_loss(l_logit, onehot_gt, valid_mask)
self.losses['attr_wordset_v'] = v_loss
self.losses['attr_wordset_l'] = l_loss
self.report['attr_wordset_v_loss'] = v_loss
self.report['attr_wordset_l_loss'] = l_loss
self.report['attr_wordset_v_acc'] = v_acc
self.report['attr_wordset_l_acc'] = l_acc
self.report['attr_wordset_v_top_{}_acc'.format(TOP_K)] = v_top_k_acc
self.report['attr_wordset_l_top_{}_acc'.format(TOP_K)] = l_top_k_acc
def build_object_wordset(self):
"""
object_wordset
"""
pooled_V_ft = self.mid_result['object_pooled_V_ft']
wordset_embed = tf.tanh(
tf.nn.embedding_lookup( # [bs, #obj, W_DIM]
self.wordset_map, self.batch['obj_blank_fill/wordsets']))
wordset_ft = modules.fc_layer( # [bs, #obj, L_DIM]
wordset_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.tanh,
is_training=self.is_train,
scope='wordset_ft')
v_linear_l = modules.fc_layer(pooled_V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
l_linear_l = modules.fc_layer(wordset_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result['obj_blank_fill/logit'] = logit # [bs, #obj, #answer]
with tf.name_scope('loss/obj_wordset'):
onehot_gt = tf.one_hot(self.batch['obj_blank_fill/fills'],
depth=self.num_answer)
num_valid_entry = self.batch['obj_blank_fill/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_obj_bf,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
self.losses['obj_wordset'] = loss
self.report['obj_wordset_loss'] = loss
self.report['obj_wordset_acc'] = acc
self.report['obj_wordset_top_{}_acc'.format(TOP_K)] = top_k_acc
def build_attribute_V_ft(self):
V_ft = self.batch['image_ft'] # [bs, #proposal, #feat_dim]
V_ft = tf.expand_dims(V_ft, axis=1) # [bs, 1, #proposal, #feat_dim]
V_ft = tf.tile(V_ft, [1, self.data_cfg.n_attr_bf, 1, 1
]) # [bs, #attr, #proposal, #feat_dim]
V_ft = tf.reshape(
V_ft, [-1, self.data_cfg.max_box_num, self.data_cfg.vfeat_dim
]) # [bs * #attr, #proposal, #feat_dim]
spat_ft = self.batch['spatial_ft']
spat_ft = tf.expand_dims(spat_ft, axis=1)
spat_ft = tf.tile(spat_ft, [1, self.data_cfg.n_attr_bf, 1, 1])
spat_ft = tf.reshape(spat_ft, [-1, self.data_cfg.max_box_num, 6])
num_V_ft = self.batch['num_boxes'] # [bs]
num_V_ft = tf.expand_dims(num_V_ft, axis=1) # [bs, 1]
num_V_ft = tf.tile(num_V_ft,
[1, self.data_cfg.n_attr_bf]) # [bs, #attr]
num_V_ft = tf.reshape(num_V_ft, [-1]) # [bs * #attr]
key_spat_ft = self.batch['attr_blank_fill/normal_boxes']
key_spat_ft = tf.concat([
key_spat_ft,
tf.expand_dims(key_spat_ft[:, :, 2] - key_spat_ft[:, :, 0],
axis=-1),
tf.expand_dims(key_spat_ft[:, :, 3] - key_spat_ft[:, :, 1],
axis=-1)
],
axis=-1)
v_linear_v = modules.fc_layer( # [bs * #obj, #proposal, V_DIM]
spat_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='spat_v_linear_v')
q_linear_v = modules.fc_layer( # [bs, #obj, V_DIM]
key_spat_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='spat_q_linear_v')
flat_q_linear_v = tf.reshape(q_linear_v,
[-1, V_DIM]) # [bs * #obj, V_DIM]
att_score = modules.hadamard_attention( # [bs * #obj, len]
v_linear_v,
num_V_ft,
flat_q_linear_v,
use_ln=False,
is_train=self.is_train,
scope='spat_att')
flat_pooled_V_ft = modules.attention_pooling(
V_ft, att_score) # [bs * #obj, vfeat_dim]
pooled_V_ft = tf.reshape(
flat_pooled_V_ft,
[-1, self.data_cfg.n_attr_bf, self.data_cfg.vfeat_dim])
self.mid_result['attribute_pooled_V_ft'] = pooled_V_ft
def build_object_predict(self):
"""
object_predict
"""
V_ft = self.batch['image_ft'] # [bs, #proposal, #feat_dim]
V_ft = tf.expand_dims(V_ft, axis=1) # [bs, 1, #proposal, #feat_dim]
V_ft = tf.tile(V_ft, [1, self.data_cfg.n_obj_pred, 1, 1
]) # [bs, #obj, #proposal, #feat_dim]
V_ft = tf.reshape(
V_ft, [-1, self.data_cfg.max_box_num, self.data_cfg.vfeat_dim
]) # [bs * #obj, #proposal, #feat_dim]
spat_ft = self.batch['spatial_ft']
spat_ft = tf.expand_dims(spat_ft, axis=1)
spat_ft = tf.tile(spat_ft, [1, self.data_cfg.n_obj_pred, 1, 1])
spat_ft = tf.reshape(spat_ft, [-1, self.data_cfg.max_box_num, 6])
num_V_ft = self.batch['num_boxes'] # [bs]
num_V_ft = tf.expand_dims(num_V_ft, axis=1) # [bs, 1]
num_V_ft = tf.tile(num_V_ft,
[1, self.data_cfg.n_obj_pred]) # [bs, #obj]
num_V_ft = tf.reshape(num_V_ft, [-1]) # [bs * #obj]
key_spat_ft = self.batch['obj_pred/normal_boxes']
key_spat_ft = tf.concat([
key_spat_ft,
tf.expand_dims(key_spat_ft[:, :, 2] - key_spat_ft[:, :, 0],
axis=-1),
tf.expand_dims(key_spat_ft[:, :, 3] - key_spat_ft[:, :, 1],
axis=-1)
],
axis=-1)
v_linear_v = modules.fc_layer( # [bs * #obj, #proposal, V_DIM]
spat_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='spat_v_linear_v')
q_linear_v = modules.fc_layer( # [bs, #obj, V_DIM]
key_spat_ft,
V_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='spat_q_linear_v')
flat_q_linear_v = tf.reshape(q_linear_v,
[-1, V_DIM]) # [bs * #obj, V_DIM]
att_score = modules.hadamard_attention( # [bs * #obj, len]
v_linear_v,
num_V_ft,
flat_q_linear_v,
use_ln=False,
is_train=self.is_train,
scope='spat_att')
flat_pooled_V_ft = modules.attention_pooling(
V_ft, att_score) # [bs * #obj, vfeat_dim]
pooled_V_ft = tf.reshape(
flat_pooled_V_ft,
[-1, self.data_cfg.n_obj_pred, self.data_cfg.vfeat_dim])
wordset_embed = tf.tanh(
tf.nn.embedding_lookup( # [bs, #obj, W_DIM]
self.wordset_map, self.batch['obj_pred/wordsets']))
wordset_ft = modules.fc_layer( # [bs, #obj, L_DIM]
wordset_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.tanh,
is_training=self.is_train,
scope='wordset_ft')
v_linear_l = modules.fc_layer(pooled_V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
l_linear_l = modules.fc_layer(wordset_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result['obj_pred/logit'] = logit # [bs, #obj, #answer]
with tf.name_scope('loss/object_predict'):
onehot_gt = tf.one_hot(self.batch['obj_pred/labels'],
depth=self.num_answer)
num_valid_entry = self.batch['obj_pred/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_obj_pred,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
self.losses['object_pred'] = loss
self.report['object_pred_loss'] = loss
self.report['object_pred_acc'] = acc
self.report['object_pred_top_{}_acc'.format(TOP_K)] = top_k_acc
ws_dict_path = os.path.join(
config.data_dir,
'wordset_dict5_depth{}.pkl'.format(int(config.expand_depth)))
ws_dict = cPickle.load(open(ws_dict_path, 'rb'))
num_ws = len(ws_dict['vocab'])
wordset_map = modules.learn_embedding_map(ws_dict, scope='wordset_map')
L_DIM = 1024
wordset_embed = tf.tanh(wordset_map)
wordset_ft = modules.fc_layer(wordset_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.tanh,
is_training=False,
scope='wordset_ft')
session_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True),
device_count={'GPU': 1})
sess = tf.Session(config=session_config)
all_vars = tf.global_variables()
checkpoint_loader = tf.train.Saver(var_list=all_vars, max_to_keep=1)
log.info('Checkpoint path: {}'.format(config.checkpoint))
checkpoint_loader.restore(sess, config.checkpoint)
log.info('Loaded the checkpoint')
def build_object_blank_fill(self):
"""
object_blank_fill
"""
# [#obj, #proposal] x [#proposal x feat_dim] -> [#obj,feat_dim]
V_ft = tf.matmul(self.batch['obj_blank_fill/weights'],
self.batch['image_ft'])
v_linear_l = modules.fc_layer(V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
blank_embed = tf.nn.embedding_lookup( # [bs, #proposal, len, W_DIM]
self.l_word_map, self.batch['obj_blank_fill/blanks'])
blank_len = self.batch['obj_blank_fill/blanks_len']
blank_maxlen = tf.shape(blank_embed)[-2]
flat_blank_ft = modules.encode_L( # [bs * #proposal, L_DIM]
tf.reshape(blank_embed, [-1, blank_maxlen, W_DIM]),
tf.reshape(blank_len, [-1]),
L_DIM,
scope='encode_L_blank',
cell_type='GRU')
blank_ft = tf.reshape(flat_blank_ft,
[-1, self.data_cfg.n_obj_bf, L_DIM])
fill_embed = tf.nn.embedding_lookup(self.l_answer_word_map,
self.batch['obj_blank_fill/fills'])
fill_embed2 = modules.fc_layer(fill_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.tanh,
is_training=self.is_train,
scope='obj_blank_fill/fill_embed2')
blank_fill_ft = blank_ft * fill_embed2
fill_vec = modules.fc_layer(blank_fill_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='obj_blank_fill/fill_vec')
fill_log_sigma_sq = modules.fc_layer(
blank_fill_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='obj_blank_fill/fill_log_sigma_sq')
fill_sigma = tf.sqrt(tf.exp(fill_log_sigma_sq))
noise = tf.random_normal(tf.shape(fill_vec),
mean=0,
stddev=1,
seed=123)
fill_vec_noise = fill_vec + noise * fill_sigma
self.vis_hist['obj_blank_fill/fill_vec'] = fill_vec
self.vis_hist['obj_blank_fill/fill_sigma'] = fill_sigma
l_linear_l = modules.fc_layer(fill_vec_noise,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result['obj_blank_fill/logit'] = logit # [bs, #obj, #answer]
with tf.name_scope('loss/obj_blank_fill'):
onehot_gt = tf.one_hot(self.batch['obj_blank_fill/fills'],
depth=self.num_answer)
num_valid_entry = self.batch['obj_blank_fill/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_obj_bf,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
latent_loss = self.latent_loss(fill_vec, fill_log_sigma_sq)
self.losses['obj_blank_fill'] = loss
self.losses[
'obj_blank_fill_latent'] = self.latent_loss_weight * latent_loss
self.report['obj_blank_fill_loss'] = loss
self.report['obj_blank_fill_latent_loss'] = latent_loss
self.report['obj_blank_fill_train_latent_loss'] = self.losses[
'obj_blank_fill_latent']
self.report['obj_blank_fill_acc'] = acc
self.report['obj_blank_fill_top_{}_acc'.format(TOP_K)] = top_k_acc
def build_attribute_predict(self):
"""
attribute_predict
"""
# [#attr, #proposal] x [#proposal x feat_dim] -> [#attr,feat_dim]
V_ft = tf.matmul(self.batch['attr_pred/weights'],
self.batch['image_ft'])
v_linear_l = modules.fc_layer(V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
obj_embed = tf.nn.embedding_lookup(
self.l_answer_word_map, self.batch['attr_pred/object_labels'])
obj_vec = modules.fc_layer(obj_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.tanh,
is_training=self.is_train,
scope='attr_pred/obj_vector')
# [bs, #attr, #answer_vocab] x [#answer_vocab, W_DIM]
# -> [bs, #attr, W_DIM]
attr_embed = tf.nn.embedding_lookup(
self.l_answer_word_map,
self.batch['attr_pred/random_attribute_labels'])
attr_embed2 = modules.fc_layer(attr_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.tanh,
is_training=self.is_train,
scope='attr_pred/attr_embed2')
obj_attr_embed = obj_vec * attr_embed2
attr_vec = modules.fc_layer(obj_attr_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='attr_pred/attr_vec')
attr_log_sigma_sq = modules.fc_layer(
obj_attr_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='attr_pred/attr_log_sigma_sq')
attr_sigma = tf.sqrt(tf.exp(attr_log_sigma_sq))
noise = tf.random_normal(tf.shape(attr_vec),
mean=0,
stddev=1,
seed=123)
attr_vec_noise = attr_vec + noise * attr_sigma
self.vis_hist['attr_pred/attr_vec'] = attr_vec
self.vis_hist['attr_pred/attr_sigma'] = attr_sigma
l_linear_l = modules.fc_layer(attr_vec_noise,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result['attr_pred/logit'] = logit # [bs, #attr, #answer]
with tf.name_scope('loss/attr_predict'):
onehot_gt = tf.one_hot(
self.batch['attr_pred/random_attribute_labels'],
depth=self.num_answer)
num_valid_entry = self.batch['attr_pred/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_attr_pred,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
latent_loss = self.latent_loss(attr_vec, attr_log_sigma_sq)
self.losses['attr_pred'] = loss
self.losses[
'attr_pred_latent'] = self.latent_loss_weight * latent_loss
self.report['attr_pred_loss'] = loss
self.report['attr_pred_latent_loss'] = latent_loss
self.report['attr_pred_train_latent_loss'] = self.losses[
'attr_pred_latent']
self.report['attr_pred_acc'] = acc
self.report['attr_pred_top_{}_acc'.format(TOP_K)] = top_k_acc
def build_object_predict(self):
"""
object_predict
"""
# [#obj, #proposal] x [#proposal x feat_dim] -> [#obj,feat_dim]
V_ft = tf.matmul(self.batch['obj_pred/weights'],
self.batch['image_ft'])
v_linear_l = modules.fc_layer(V_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='pooled_linear_l')
wordset_embed = tf.tanh(
tf.nn.embedding_lookup(self.wordset_map,
self.batch['obj_pred/wordsets']))
wordset_ft = modules.fc_layer(wordset_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.tanh,
is_training=self.is_train,
scope='wordset_ft2')
l_linear_l = modules.fc_layer(wordset_ft,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='q_linear_l')
joint = modules.fc_layer(v_linear_l * l_linear_l,
L_DIM * 2,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.nn.relu,
is_training=self.is_train,
scope='joint_fc')
joint = tf.nn.dropout(joint, 0.5)
logit = modules.fc_layer(joint,
self.num_answer,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
self.mid_result['obj_pred/logit'] = logit # [bs, #obj, #answer]
with tf.name_scope('loss/object_predict'):
onehot_gt = tf.one_hot(self.batch['obj_pred/labels'],
depth=self.num_answer)
num_valid_entry = self.batch['obj_pred/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_obj_pred,
dtype=tf.float32)
loss, acc, top_k_acc = \
self.n_way_classification_loss(logit, onehot_gt, valid_mask)
self.losses['object_pred'] = loss
self.report['object_pred_loss'] = loss
self.report['object_pred_acc'] = acc
self.report['object_pred_top_{}_acc'.format(TOP_K)] = top_k_acc
