def __call__(self, input, z):
with tf.variable_scope(self.name, reuse=self._reuse):
batch_size = int(input.get_shape()[0])
latent_dim = int(z.get_shape()[-1])
num_filters = [64, 128, 256, 512, 512, 512, 512]
if self._image_size == 256:
num_filters.append(512)
layers = []
G = input
z = tf.reshape(z, [batch_size, 1, 1, latent_dim])
z = tf.tile(z, [1, self._image_size, self._image_size, 1])
G = tf.concat([G, z], axis=3)
for i, n in enumerate(num_filters):
G = ops.conv_block(G,
n,
'C{}_{}'.format(n, i),
4,
2,
self._is_train,
self._reuse,
norm=self._norm if i else None,
activation='leaky')
layers.append(G)
layers.pop()
num_filters.pop()
num_filters.reverse()
for i, n in enumerate(num_filters):
G = ops.deconv_block(G,
n,
'CD{}_{}'.format(n, i),
4,
2,
self._is_train,
self._reuse,
norm=self._norm,
activation='relu')
G = tf.concat([G, layers.pop()], axis=3)
G = ops.deconv_block(G,
3,
'last_layer',
4,
2,
self._is_train,
self._reuse,
norm=None,
activation='tanh')
self._reuse = True
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
self.name)
return G
def image_decoder(input, is_training):
batch_size = int(input.get_shape()[0])
z = tf.layers.dense(input, 1024, activation=tf.nn.relu)
G = tf.reshape(z, [batch_size, 4, 4, 64])
filters = [32, 16,8]
for i, n in enumerate(filters):
G = ops.deconv_block(G, n, 'CD{}_{}'.format(n, i), 4, 2, is_training, reuse = True, norm = 'batch', activation = 'relu')
G = ops.deconv_block(G, 3, 'last_layer', 4, 2, is_training, reuse=True, norm=None, activation = 'tanh')
return G
def __call__(self, input_op):
# 改回了魔术方法的实现，更加简洁
with tf.variable_scope(self.name):
conv1 = ops.conv_block(input_op,
32,
'conv1',
7,
1,
self.is_train,
self.reuse,
self.norm,
self.activation,
pad='REFLECT')
conv2 = ops.conv_block(conv1, 64, 'conv2', 3, 2, self.is_train,
self.reuse, self.norm, self.activation)
res = ops.conv_block(conv2, 128, 'conv3', 3, 2, self.is_train,
self.reuse, self.norm, self.activation)
for i in range(self.block_size):
res = ops.residual_block(res, 128, 'res' + str(i),
self.is_train, self.reuse, self.norm)
deconv1 = ops.deconv_block(res, 64, 'deconv1', 3, 2, self.is_train,
self.reuse, self.norm, self.activation)
deconv2 = ops.deconv_block(deconv1, 32, 'deconv2', 3, 2,
self.is_train, self.reuse, self.norm,
self.activation)
self.gen = ops.conv_block(deconv2,
3,
'conv_end',
7,
1,
self.is_train,
self.reuse,
norm=None,
activation=tf.nn.tanh,
pad='REFLECT')
self.reuse = True
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
self.name)
def __call__(self, input):
with tf.variable_scope(self.name, reuse=self._reuse):
G = ops.conv_block(input,
32,
'c7s1-32',
7,
1,
self._is_train,
self._reuse,
self._norm,
self._activation,
pad='REFLECT')
G = ops.conv_block(G, 64, 'd64', 3, 2, self._is_train, self._reuse,
self._norm, self._activation)
G = ops.conv_block(G, 128, 'd128', 3, 2, self._is_train,
self._reuse, self._norm, self._activation)
for i in range(self._num_res_block):
G = ops.residual(G, 128, 'R128_{}'.format(i), self._is_train,
self._reuse, self._norm)
G = ops.deconv_block(G, 64, 'u64', 3, 2, self._is_train,
self._reuse, self._norm, self._activation)
G = ops.deconv_block(G, 32, 'u32', 3, 2, self._is_train,
self._reuse, self._norm, self._activation)
G = ops.conv_block(G,
3,
'c7s1-3',
7,
1,
self._is_train,
self._reuse,
norm=None,
activation='tanh',
pad='REFLECT')
self._reuse = True
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
self.name)
return G