def _concat(z, z_, _a):
feats = [z]
if z_ is not None:
feats.append(z_)
if _a is not None:
_a = tf.reshape(_a, [-1, 1, 1, tl.shape(_a)[-1]])
_a = tf.tile(_a, [1, tl.shape(z)[1], tl.shape(z)[2], 1])
feats.append(_a)
return tf.concat(feats, axis=3)
def _concat(x, a):
images = x
atts = a
feats = [images]
atts = tf.reshape(atts, [tl.shape(atts)[-1], 1, 1, -1])
atts = tf.tile(atts, [1, tl.shape(images)[1], tl.shape(images)[2], 1])
feats.append(atts)
return tf.concat(feats, axis=3)
def _concat(encoded_input, next_input, labels):
features = [encoded_input]
if next_input is not None:
features.append(next_input)
if labels is not None:
labels = tf.reshape(labels, [-1, 1, 1, tl.shape(labels)[-1]])
labels = tf.tile(
labels,
[1,
tl.shape(encoded_input)[1],
tl.shape(encoded_input)[2], 1])
# append to list
features.append(labels)
# concat the feature on the last channel
return tf.concat(features, axis=3)
