/
discriminator.py
33 lines (27 loc) · 1.22 KB
/
discriminator.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
import tensorflow as tf
import ops
class Discriminator:
def __init__(self, name, is_training, norm='instance', use_sigmoid=False):
self.name = name
self.is_training = is_training
self.norm = norm
self.reuse = False
self.use_sigmoid = use_sigmoid
def __call__(self, input):
with tf.variable_scope(self.name):
# convolution layers
C64 = ops.Ck(input, 64, reuse=self.reuse, norm=None,
is_training=self.is_training, name='C64')
C128 = ops.Ck(C64, 128, reuse=self.reuse, norm=self.norm,
is_training=self.is_training, name='C128')
C256 = ops.Ck(C128, 256, reuse=self.reuse, norm=self.norm,
is_training=self.is_training, name='C256')
C512 = ops.Ck(C256, 512,reuse=self.reuse, norm=self.norm,
is_training=self.is_training, name='C512')
# apply a convolution to produce a 1 dimensional output (1 channel?)
# use_sigmoid = False if use_lsgan = True
output = ops.last_conv(C512, reuse=self.reuse,
use_sigmoid=self.use_sigmoid, name='output')
self.reuse = True
self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
return output