def encode(self, reflectance_map, background):
rm_encodings = self.singlet(reflectance_map)
bg_encodings = self.singlet(background)
fully_encoded = tf.concat([rm_encodings[-1], bg_encodings[-1]],
axis=-1)
#fully_encoded = tf.nn.dropout(fully_encoded, 0.5)
fully_encoded = layers.encode_layer(fully_encoded,
1024, (3, 3), (1, 1),
1,
maxpool=False)
decode_1 = layers.decode_layer(fully_encoded, 512, (3, 3), (2, 2), 3)
decode_2 = layers.decode_layer(
tf.concat([decode_1, rm_encodings[3]], axis=-1), 512, (3, 3),
(2, 2), 3)
decode_3 = layers.decode_layer(
tf.concat([decode_2, rm_encodings[2]], axis=-1), 256, (3, 3),
(2, 2), 3)
decode_4 = layers.decode_layer(
tf.concat([decode_3, rm_encodings[1]], axis=-1), 128, (3, 3),
(2, 2), 1)
return layers.encode_layer(decode_4,
3, (1, 1), (1, 1),
1,
activation=None,
norm=False,
maxpool=False)
def decode(self, encoded):
decode_1 = layers.decode_layer(encoded, 512, (3, 3), (2, 2), FLAGS.depth)
decode_2 = layers.decode_layer(decode_1, 512, (3, 3), (2, 2), FLAGS.depth)
decode_3 = layers.decode_layer(decode_2, 256, (3, 3), (2, 2), FLAGS.depth)
decode_4 = layers.decode_layer(decode_3, 128, (3, 3), (2, 2), FLAGS.depth)
decode_5 = layers.decode_layer(decode_4, 64, (3, 3), (2, 2), FLAGS.depth)
return layers.encode_layer(decode_5, 3, (1, 1), (1, 1), 1, activation=None, norm=False, maxpool=False)
def decode(self, full, feature_maps, batch_size):
full_2 = tf.concat([full, feature_maps[0]], axis=-1)
if feature_maps:
decode_1 = ed.decode_layer(full_2, 512, (3, 3), (8, 8)) # 2,8,8,512
fm_1 = tf.reshape(feature_maps[0], [batch_size, 8, 8, -1])
decode_2 = ed.decode_layer(tf.concat([decode_1, fm_1], axis=-1), 256, (3, 3), (2, 2))
decode_3 = ed.decode_layer(tf.concat([decode_2, feature_maps[1]], axis=-1), 128, (3, 3), (2, 2))
decode_4 = ed.decode_layer(tf.concat([decode_3, feature_maps[2]], axis=-1), 3, (3, 3), (1, 1))
return decode_4
def decode(self, encoded, multiscale):
if FLAGS.multiscale:
encoded = tf.concat([encoded, multiscale[-1]], axis=-1)
decode_1 = layers.decode_layer(encoded, 512, (3, 3), (2, 2),
FLAGS.depth)
decode_1 = tf.concat([decode_1, multiscale[-2]], axis=-1)
decode_2 = layers.decode_layer(decode_1, 512, (3, 3), (2, 2),
FLAGS.depth)
decode_2 = tf.concat([decode_2, multiscale[-3]], axis=-1)
decode_3 = layers.decode_layer(decode_2, 256, (3, 3), (2, 2),
FLAGS.depth)
else:
decode_1 = layers.decode_layer(encoded, 512, (3, 3), (2, 2),
FLAGS.depth)
decode_2 = layers.decode_layer(decode_1, 512, (3, 3), (2, 2),
FLAGS.depth)
decode_3 = layers.decode_layer(decode_2, 256, (3, 3), (2, 2),
FLAGS.depth)
decode_4 = layers.decode_layer(decode_3, 128, (3, 3), (2, 2),
FLAGS.depth)
return layers.encode_layer(decode_4,
3, (1, 1), (1, 1),
1,
activation=None,
norm=False,
maxpool=False)
def decode_norms(self, encodings):
norms = layers.decode_layer(encodings, 512, (3, 3), (1, 1),
FLAGS.depth)
norms = layers.decode_layer(norms, 256, (3, 3), (2, 2), FLAGS.depth)
norms = layers.decode_layer(norms, 3, (3, 3), (1, 1), FLAGS.depth)
return norms