def G_Net_2D_v3(self, im, is_train, reuse):
with tf.variable_scope('generator_im', reuse=reuse) as scope:
layers = []
input = im
chns = [64] + [128] * 2 + [256] * 9 + [128] * 2 + [64, 32, 2]
chns = [x // 4 for x in chns[:-1]] + [chns[-1]]
etas = [1] * 6 + [2, 4, 8, 16] + [1] * 7
kszs = [5] + [3] * 11 + [4, 3, 4, 3, 3]
stps = [1, 2, 1, 2] + [1] * 8 + [2, 1, 2, 1, 1]
dcvs = [False] * 12 + [True, False, True, False, False]
usbn = [True] * 16 + [False]
usrl = usbn
for k in range(17):
if k == 0:
temp = input
elif k < 9:
temp = layers[-1]
elif k < 15:
temp = tf.concat([layers[-1], layers[15 - k]], axis=3)
else:
temp = layers[-1]
layers += [
utils.conv_layer_2d(temp, chns[k], etas[k], kszs[k],
stps[k], 'l%02d' % k, dcvs[k], usbn[k],
is_train, usrl[k])
]
layers += [
tf.slice(tf.nn.softmax(layers[-1]) * 2.0 - 1.0, [0, 0, 0, 1],
[-1, -1, -1, 1],
name='segmented')
]
return layers[-1], layers
def G_Net_2D_v0(self, im, is_train, reuse):
with tf.variable_scope('generator_im', reuse=reuse) as scope:
layers = []
input = im
chns = [64] + [128] * 2 + [256] * 9 + [128] * 2 + [64, 32, 1]
etas = [1] * 6 + [2, 4, 8, 16] + [1] * 7
kszs = [5] + [3] * 11 + [4, 3, 4, 3, 3]
stps = [1, 2, 1, 2] + [1] * 8 + [2, 1, 2, 1, 1]
dcvs = [False] * 12 + [True, False, True, False, False]
usbn = [True] * 16 + [False]
usrl = usbn
for k in range(17):
if k == 0:
temp = input
elif k < 9:
temp = layers[-1]
elif k < 15:
temp = tf.concat([layers[-1], layers[15 - k]], axis=3)
else:
temp = layers[-1]
layers += [
utils.conv_layer_2d(temp, chns[k], etas[k], kszs[k],
stps[k], 'l%02d' % k, dcvs[k], usbn[k],
is_train, usrl[k])
]
return tf.nn.tanh(layers[-1]), layers
