def __call__(self, image):
with tf.variable_scope('discriminator_net' + self.name,
reuse=self.reuse):
result = net.convLayer(image,
64,
4,
strides=2,
name='4x4_1',
relu=.2,
Norm='NOT',
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
128,
4,
strides=2,
name='4x4_2',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
256,
4,
strides=2,
name='4x4_3',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
512,
4,
strides=2,
name='4x4_4',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
1,
4,
strides=1,
name='4x4_5',
relu=False,
Norm='NOT',
training=self.is_training,
bias=True,
pad='SAME')
self.reuse = True
return result
def __call__(self, image):
with tf.variable_scope('generator_net' + self.name, reuse=self.reuse):
result = net.transposeConv(image,
self.ngf // 2,
4,
pad='VALID',
strides=1,
Norm='NOT,SPECTRAL',
training=self.is_training,
name='deconv',
relu=False,
bias=False)
result = net.residualBlockUp(result,
self.ngf // 2,
3,
'resU1',
pad='SAME',
training=self.is_training)
result = net.residualBlockUp(result,
self.ngf // 4,
3,
'resU2',
pad='SAME',
training=self.is_training)
result = net.residualBlockUp(result,
self.ngf // 8,
3,
'resU3',
pad='SAME',
training=self.is_training)
result = net.attention(result,
self.ngf // 8,
name='attentionG',
is_training=self.is_training)
result = net.residualBlockUp(result,
self.ngf // 16,
3,
'resU4',
pad='SAME',
training=self.is_training)
result = net.residualBlockUp(result,
self.ngf // 32,
3,
'resU5',
pad='SAME',
training=self.is_training)
result = net.batchNorm(result, self.is_training, name='bn1')
result = tf.nn.leaky_relu(result)
result = net.convLayer(result,
3,
3,
Norm='NOT,SPECTRAL',
training=self.is_training,
relu=False,
pad='SAME',
name='G_Logit')
result = tf.tanh(result)
self.reuse = True
return result
def __call__(self, image):
with tf.variable_scope('edge_net' + self.name, reuse=self.reuse):
result = tf.pad(image, [[0, 0], [3, 3], [3, 3], [0, 0]],
mode="REFLECT")
pre = net.convLayer(result,
3,
3,
strides=1,
Norm=self.Norm,
training=self.is_training,
name='Edge_P',
pad='VALID',
relu=False)
result1 = net.convLayer(pre,
3,
3,
strides=1,
Norm=self.Norm,
training=self.is_training,
name='Edge_1',
pad='VALID',
relu='RELU')
result2 = net.convLayer(pre,
3,
3,
strides=1,
Norm=self.Norm,
training=self.is_training,
name='Edge_2',
pad='VALID',
relu='RELU')
result = net.convLayer(result1 + result2,
3,
3,
strides=1,
Norm=self.Norm,
training=self.is_training,
relu=False,
name='Edge_3',
pad='VALID')
result = tanh(3 * result)
self.reuse = True
return result
def __call__(self, image):
with tf.variable_scope('discriminator_net' + self.name,
reuse=self.reuse):
result = net.residualBlockDown(image,
self.ngf * 2,
3,
'resD1',
pad='SAME',
training=self.is_training)
result = net.residualBlockDown(result,
self.ngf * 4,
3,
'resD2',
pad='SAME',
training=self.is_training)
result = net.attention(result,
self.ngf * 4,
name='attentionD',
is_training=self.is_training)
result = net.residualBlockDown(result,
self.ngf * 8,
3,
'resD3',
pad='SAME',
training=self.is_training)
result = net.residualBlockDown(result,
self.ngf * 16,
3,
'resD4',
pad='SAME',
training=self.is_training)
result = net.residualBlockDown(result,
self.ngf * 16,
3,
'resD5',
pad='SAME',
training=self.is_training)
result = net.residualBlockDown(result,
self.ngf * 32,
3,
'resD6',
pad='SAME',
training=self.is_training)
result = net.convLayer(result,
1,
4,
Norm='NOT,SPECTRAL',
training=self.is_training,
relu=False,
name='D_Logit',
pad='VALID')
result = tf.squeeze(result, axis=[1, 2])
self.reuse = True
return result
def __call__(self, image):
with tf.variable_scope('edge_net' + self.name, reuse=self.reuse):
pre = net.convLayer(image,
3,
1,
strides=1,
Norm='NOT',
training=self.is_training,
name='Edge_P',
relu=False)
result1 = net.convLayer(pre,
1,
3,
strides=1,
Norm=self.Norm,
training=self.is_training,
name='Edge_1',
pad='REFLECT',
relu='RELU')
result2 = net.convLayer(pre,
1,
3,
strides=1,
Norm=self.Norm,
training=self.is_training,
name='Edge_2',
pad='REFLECT',
relu='RELU')
result = net.convLayer(result1 + result2,
3,
3,
strides=1,
Norm='NOT',
training=self.is_training,
relu=False,
name='Edge_3')
result = tf.tanh(result)
self.reuse = True
return result
def __call__(self, image):
with tf.variable_scope('generator_net' + self.name, reuse=self.reuse):
num = image.get_shape().as_list()[-1]
result = image
for i in range(9):
result = net.residualBlock(result,
num,
3,
'res' + '{:d}'.format(i),
relu='RELU',
pad='REFLECT',
training=self.is_training,
Norm=self.Norm)
result = net.resizeConv2D(result,
8 * self.ngf,
3,
name='r3x3_1',
strides=2,
pad='SAME',
relu='RELU',
training=self.is_training,
Norm=self.Norm)
result = net.resizeConv2D(result,
4 * self.ngf,
3,
name='r3x3_2',
strides=2,
pad='SAME',
relu='RELU',
training=self.is_training,
Norm=self.Norm)
result = net.resizeConv2D(result,
2 * self.ngf,
3,
name='r3x3_3',
strides=2,
pad='SAME',
relu='RELU',
training=self.is_training,
Norm=self.Norm)
result = net.resizeConv2D(result,
self.ngf,
3,
name='r3x3_4',
strides=2,
pad='SAME',
relu='RELU',
training=self.is_training,
Norm=self.Norm)
result = tf.nn.sigmoid(
net.convLayer(result,
3,
7,
name='7x7_1',
strides=1,
pad='REFLECT',
relu=False,
training=self.is_training,
Norm='NOT')) * 2. - 1.
# result = tf.nn.tanh(net.convLayer(result, 3, 7, name='7x7_1', strides=1, pad='REFLECT', relu=False,
# training=self.is_training, Norm='NOT'))
self.reuse = True
return result
def __call__(self, image):
with tf.variable_scope('discriminator_net' + self.name,
reuse=self.reuse):
result = net.convLayer(image,
self.ndf * 2,
5,
strides=2,
name='5x5_1',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result_1 = net.convLayer(result,
1,
5,
strides=1,
name='r1',
relu=False,
Norm='NOT',
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
self.ndf * 2,
5,
strides=2,
name='5x5_2',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result_2 = net.convLayer(result,
1,
10,
strides=1,
name='r2',
relu=False,
Norm='NOT',
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
self.ndf * 4,
5,
strides=2,
name='5x5_3',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
self.ndf * 8,
5,
strides=2,
name='5x5_4',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result_4 = net.convLayer(result,
1,
10,
strides=1,
name='r4',
relu=False,
Norm='NOT',
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
self.ndf * 8,
5,
strides=2,
name='5x5_5',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
self.ndf * 16,
5,
strides=2,
name='5x5_6',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result_6 = net.convLayer(result,
1,
6,
strides=1,
name='r6',
relu=False,
Norm='NOT',
training=self.is_training,
pad='SAME')
result = net.convLayer(result,
self.ndf * 16,
5,
strides=2,
name='5x5_7',
relu=.2,
Norm=self.Norm,
training=self.is_training,
pad='SAME')
result_7 = net.convLayer(result,
1,
3,
strides=1,
name='r7',
relu=False,
Norm='NOT',
training=self.is_training,
pad='SAME')
self.reuse = True
return result_1, result_2, result_4, result_6, result_7
def __call__(self, image):
with tf.variable_scope('generator_net' + self.name, reuse=self.reuse):
# result = tf.pad(image, [[0, 0], [18, 18], [18, 18], [0, 0]], "REFLECT")
# pre = net.convLayer(result, 3, 1, strides=1, Norm='NOT', training=self.is_training,
# name='Edge_P', pad='VALID', relu=False)
# result1 = net.convLayer(pre, 1, 3, strides=1, Norm=self.Norm, training=self.is_training,
# name='Edge_1', pad='VALID', relu='RELU')
# result2 = net.convLayer(pre, 1, 3, strides=1, Norm=self.Norm, training=self.is_training,
# name='Edge_2', pad='VALID', relu='RELU')
# result = net.convLayer(result1 + result2, 3, 3, strides=1, Norm=self.Norm, training=self.is_training,
# name='Edge_3', pad='VALID', relu=False)
# result = tf.sigmoid(result) * 2 - 1
if 'BATCH' in self.Norm:
result = net.batchNorm(image, self.is_training, 'first_Norm')
elif 'INSTANCE' in self.Norm:
result = net.instanceNorm(
image,
'first_Norm',
self.is_training,
)
else:
result = image
result = tf.pad(result, [[0, 0], [15, 15], [15, 15], [0, 0]],
"REFLECT")
result = net.convLayer(result,
self.ngf,
3,
strides=1,
Norm=self.Norm,
training=self.is_training,
name='3x3_1',
pad='VALID',
relu='RELU')
result = net.convLayer(result,
self.ngf,
3,
strides=2,
Norm=self.Norm,
training=self.is_training,
name='3x3_2',
pad='VALID',
relu='RELU')
result = net.convLayer(result,
2 * self.ngf,
3,
strides=2,
Norm=self.Norm,
training=self.is_training,
name='3x3_3',
pad='VALID',
relu='RELU')
result = net.convLayer(result,
4 * self.ngf,
3,
strides=2,
Norm=self.Norm,
training=self.is_training,
name='3x3_4',
pad='VALID',
relu='RELU')
result = net.convLayer(result,
8 * self.ngf,
3,
strides=2,
Norm=self.Norm,
training=self.is_training,
name='3x3_5',
pad='VALID',
relu='RELU')
self.reuse = True
return result
def __call__(self, image):
with tf.variable_scope('generator_net' + self.name, reuse=self.reuse):
result = net.convLayer(image,
self.ngf,
7,
Norm=self.Norm,
training=self.is_training,
name='7x7_1',
relu='RELU')
result = net.convLayer(result,
2 * self.ngf,
3,
strides=2,
Norm=self.Norm,
training=self.is_training,
name='3x3_1',
pad='SAME',
relu='RELU')
result = net.convLayer(result,
4 * self.ngf,
3,
strides=2,
Norm=self.Norm,
training=self.is_training,
name='3x3_2',
pad='SAME',
relu='RELU')
if self.img_size == 128:
for i in range(6):
result = net.residualBlock(result,
4 * self.ngf,
3,
'res' + '{:d}'.format(i),
relu='RELU',
pad='REFLECT',
Norm=self.Norm,
training=self.is_training)
else:
for i in range(9):
result = net.residualBlock(result,
4 * self.ngf,
3,
'res' + '{:d}'.format(i),
relu='RELU',
pad='REFLECT',
Norm=self.Norm,
training=self.is_training)
result = net.transposeConv(result,
2 * self.ngf,
3,
name='3x3_3',
strides=2,
pad='SAME',
relu='RELU',
bias=False,
Norm=self.Norm,
training=self.is_training)
result = net.transposeConv(result,
self.ngf,
3,
name='3x3_4',
strides=2,
pad='SAME',
relu='RELU',
bias=False,
Norm=self.Norm,
training=self.is_training)
result = net.convLayer(result,
3,
7,
Norm='NOT',
training=self.is_training,
relu=False,
name='7x7_2')
result = tf.tanh(result)
self.reuse = True
return result