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')
self.heavy_output['condition'] = q_L_ft
# [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.output['att_score'] = att_score
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
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
##############################
# 1. Fixed vlmap classifier
##############################
"""
Answer classification
"""
log.warning('q_linear_l')
l_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')
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,
default_bias=-100.0,
scope='WordWeightAnswer')
min_logit = tf.tile(tf.reduce_min(logit, axis=1, keepdims=True),
[1, self.num_answer])
logit = logit * self.answer_exist_mask + min_logit * self.answer_non_exist_mask
##########################
# 2. Fine tuned vlmap
##########################
log.warning('tuned_q_linear_l')
tuned_l_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='tuned_q_linear_l')
self.mid_result['tuned_l_linear_l'] = tuned_l_linear_l
tuned_joint = modules.fc_layer(pooled_linear_l * tuned_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='tuned_joint_fc')
tuned_joint = tf.nn.dropout(tuned_joint, 0.5)
self.mid_result['tuned_joint'] = tuned_joint
tuned_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='TunedWordWeightAnswer')
##########################
# 3. Combine logits
##########################
self.output['logit'] = logit + tuned_logit
self.mid_result['logit'] = logit + tuned_logit
"""
Compute loss and accuracy
"""
with tf.name_scope('loss'):
answer_target = self.batch['answer_target']
untuned_loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=answer_target, logits=logit)
tuned_loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=answer_target, logits=logit + tuned_logit)
loss = untuned_loss + tuned_loss
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 + tuned_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)
test_obj_score = tf.reduce_sum(one_hot_pred * answer_target *
self.test_answer_mask *
self.obj_answer_mask,
axis=-1)
test_obj_max_score = tf.reduce_max(
answer_target * self.test_answer_mask * self.obj_answer_mask,
axis=-1)
test_attr_score = tf.reduce_sum(one_hot_pred * answer_target *
self.test_answer_mask *
self.attr_answer_mask,
axis=-1)
test_attr_max_score = tf.reduce_max(
answer_target * self.test_answer_mask * self.attr_answer_mask,
axis=-1)
self.output['test_obj_score'] = test_obj_score
self.output['test_obj_max_score'] = test_obj_max_score
self.output['test_attr_score'] = test_attr_score
self.output['test_attr_max_score'] = test_attr_max_score
self.output['all_score'] = all_score
self.output['max_train_score'] = max_train_score
acc = tf.reduce_mean(all_score)
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(test_obj_score)
test_attr_acc = tf.reduce_mean(test_attr_score)
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(test_obj_max_score)
test_attr_max_acc = tf.reduce_mean(test_attr_max_score)
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'] = train_loss
self.report['answer_train_loss'] = train_loss
self.report['answer_report_loss'] = report_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 = 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_attention(self):
"""
attribute_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['attr_att/word_tokens'])
w_L_ft = modules.fc_layer(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['attr_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_attr_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_attr_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 * #attr, 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_attr_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/attr_attend'):
multilabel_gt = tf.to_float(
tf.greater(self.batch['attr_att/att_scores'], 0.5))
num_valid_entry = self.batch['attr_att/num']
valid_mask = tf.sequence_mask(num_valid_entry,
maxlen=self.data_cfg.n_attr_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['attr_att'] = loss
self.report['attr_att_loss'] = loss
self.report['attr_att_acc'] = acc
self.report['attr_att_recall'] = recall
self.report['attr_att_precision'] = precision
self.report['attr_att_top_1_prec'] = top_1_prec
self.report['attr_att_top_{}_recall'.format(TOP_K)] = top_k_recall
def build_object_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_obj_bf, 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_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_obj_bf]) # [bs, #obj]
num_V_ft = tf.reshape(num_V_ft, [-1]) # [bs * #obj]
key_spat_ft = self.batch['obj_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_obj_bf, self.data_cfg.vfeat_dim])
self.mid_result['object_pooled_V_ft'] = pooled_V_ft
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')
self.heavy_output['condition'] = q_L_ft
# [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')
"""
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.output['att_score'] = att_score
self.mid_result['att_score'] = att_score
pooled_V_ft = modules.attention_pooling(V_ft, att_score)
"""
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)
joint2 = modules.fc_layer(joint,
300,
use_bias=True,
use_bn=False,
use_ln=False,
activation_fn=None,
is_training=self.is_train,
scope='classifier')
output_glove = modules.LearnGloVe(self.answer_dict,
learnable=False,
oov_mean_initialize=True)
logit = tf.matmul(joint2, output_glove)
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)
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)
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)
test_obj_score = tf.reduce_sum(one_hot_pred * answer_target *
self.test_answer_mask *
self.obj_answer_mask,
axis=-1)
test_obj_max_score = tf.reduce_max(
answer_target * self.test_answer_mask * self.obj_answer_mask,
axis=-1)
test_attr_score = tf.reduce_sum(one_hot_pred * answer_target *
self.test_answer_mask *
self.attr_answer_mask,
axis=-1)
test_attr_max_score = tf.reduce_max(
answer_target * self.test_answer_mask * self.attr_answer_mask,
axis=-1)
self.output['test_obj_score'] = test_obj_score
self.output['test_obj_max_score'] = test_obj_max_score
self.output['test_attr_score'] = test_attr_score
self.output['test_attr_max_score'] = test_attr_max_score
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(test_obj_score)
test_attr_acc = tf.reduce_mean(test_attr_score)
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(test_obj_max_score)
test_attr_max_acc = tf.reduce_mean(test_attr_max_score)
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'] = train_loss
self.report['answer_train_loss'] = train_loss
self.report['answer_report_loss'] = report_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
"""
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