def generator(self, inputs, reuse=False, is_training=True):
'''
inputs.shape = (?, 1, 1, 100)
convt1.shape = (?, 4, 4, 512)
convt2.shape = (?, 8, 8, 256)
convt3.shape = (?, 16, 16, 128)
convt4.shape = (?, 32, 32, 64)
convt5.shape = (?, 64, 64, 3)
'''
'''
inputs.shape = (?, 64, 64, 3)
c7s1_32.shape = (?, 64, 64, 64)
d64.shape = (?, 32, 32, 128)
d128.shape = (?, 16, 16, 256)
'''
with tf.variable_scope('gen', reuse=reuse):
# conv layers
print('inputs.shape = {}'.format(inputs.shape)) # (?, 64, 64, 3)
c7s1_32 = ops.c7s1_k(inputs,
self.ngf,
is_training=self.is_training,
norm=self.norm,
reuse=self.gen_reuse,
name='c7s1_32')
print('c7s1_32.shape = {}'.format(
c7s1_32.shape)) # (?, 64, 64, 64)
d64 = ops.dk(c7s1_32,
2 * self.ngf,
is_training=self.is_training,
norm=self.norm,
reuse=self.gen_reuse,
name='d64')
print('d64.shape = {}'.format(d64.shape)) # (?, 32, 32, 128)
d128 = ops.dk(d64,
4 * self.ngf,
is_training=self.is_training,
norm=self.norm,
reuse=self.gen_reuse,
name='d128')
print('d128.shape = {}'.format(d128.shape)) # (?, 16, 16, 256)
if self.image_size <= 128:
# use 6 residual blocks for 128x128 images
res_output = ops.n_res_blocks(d128, reuse=self.gen_reuse, n=6)
else:
# 9 blocks for higher resolution
res_output = ops.n_res_blocks(d128, reuse=self.gen_reuse, n=9)
print('res_output.shape = {}'.format(
res_output.shape)) # res_output.shape = (?, 16, 16, 256)
convt1 = convt2d(res_output, (None, self.image_size // 2,
self.image_size // 2, 2 * self.ngf),
kernal=(5, 5),
strides=(2, 2),
padding="SAME",
name="convt1")
convt1 = tf.contrib.layers.batch_norm(convt1,
is_training=is_training)
convt1 = leaky_relu(convt1)
print('convt1.shape = {}'.format(convt1.shape)
) # convt1.shape = (?, 4, 4, 512) -> (?, 32, 32, 128)
convt2 = convt2d(
convt1, (None, self.image_size, self.image_size, self.ngf),
kernal=(5, 5),
strides=(2, 2),
padding="SAME",
name="convt2")
convt2 = tf.contrib.layers.batch_norm(convt2,
is_training=is_training)
convt2 = leaky_relu(convt2)
print('convt2.shape = {}'.format(
convt2.shape)) # convt1.shape = (?, 64, 64, 64)
# conv layer
# Note: the paper said that ReLU and _norm were used
# but actually tanh was used and no _norm here
output = ops.c7s1_k(convt2,
3,
norm=None,
activation='tanh',
reuse=self.gen_reuse,
name='output')
print('output.shape = {}'.format(
output.shape)) # output.shape = (?, 64, 64, 64)
'''
def c7s1_k(input, k, reuse=False, norm='instance', activation='relu', is_training=True, name='c7s1_k'):
""" A 7x7 Convolution-BatchNorm-ReLU layer with k filters and stride 1
Args:
input: 4D tensor
k: integer, number of filters (output depth)
norm: 'instance' or 'batch' or None
activation: 'relu' or 'tanh'
name: string, e.g. 'c7sk-32'
is_training: boolean or BoolTensor
name: string
reuse: boolean
Returns:
4D tensor
conv1 = conv2d(inputs, 64, kernal=(5, 5), strides=(2, 2), padding="SAME", activate_fn=leaky_relu,
name="conv1")
"""
with tf.variable_scope(name, reuse=reuse):
weights = _weights("weights",
shape=[7, 7, input.get_shape()[3], k])
padded = tf.pad(input, [[0,0],[3,3],[3,3],[0,0]], 'REFLECT')
conv = tf.nn.conv2d(padded, weights, strides=[1, 1, 1, 1], padding='VALID')
normalized = _norm(conv, is_training, norm)
if activation == 'relu':
output = tf.nn.relu(normalized)
if activation == 'tanh':
output = tf.nn.tanh(normalized)
return output
'''
# set reuse=True for next call
self.gen_reuse = True
# self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
return output
def generator(self, inputs, reuse=False, is_training=True):
'''
inputs.shape = (?, 1, 1, 100)
convt1.shape = (?, 4, 4, 512)
convt2.shape = (?, 8, 8, 256)
convt3.shape = (?, 16, 16, 128)
convt4.shape = (?, 32, 32, 64)
convt5.shape = (?, 64, 64, 3)
'''
'''
inputs.shape = (?, 64, 64, 3)
c7s1_32.shape = (?, 64, 64, 64)
d64.shape = (?, 32, 32, 128)
d128.shape = (?, 16, 16, 256)
'''
with tf.variable_scope('gen', reuse=reuse):
# conv layers
print('inputs.shape = {}'.format(inputs.shape)) # (?, 64, 64, 3)
c7s1_32 = ops.c7s1_k(inputs,
self.ngf,
is_training=self.is_training,
norm=self.norm,
reuse=self.reuse,
name='c7s1_32')
print('c7s1_32.shape = {}'.format(
c7s1_32.shape)) # (?, 64, 64, 64)
d64 = ops.dk(c7s1_32,
2 * self.ngf,
is_training=self.is_training,
norm=self.norm,
reuse=self.reuse,
name='d64')
print('d64.shape = {}'.format(d64.shape)) # (?, 32, 32, 128)
d128 = ops.dk(d64,
4 * self.ngf,
is_training=self.is_training,
norm=self.norm,
reuse=self.reuse,
name='d128')
print('d128.shape = {}'.format(d128.shape)) # (?, 16, 16, 256)
if self.image_size <= 128:
# use 6 residual blocks for 128x128 images
res_output = ops.n_res_blocks(d128, reuse=self.reuse, n=6)
else:
# 9 blocks for higher resolution
res_output = ops.n_res_blocks(d128, reuse=self.reuse, n=9)
print('res_output.shape = {}'.format(
res_output.shape)) # res_output.shape = (?, 16, 16, 256)
convt1 = convt2d(res_output, (None, self.image_size // 2,
self.image_size // 2, 2 * self.ngf),
kernal=(5, 5),
strides=(2, 2),
padding="SAME",
name="convt1")
convt1 = tf.contrib.layers.batch_norm(convt1,
is_training=is_training)
convt1 = leaky_relu(convt1)
print('convt1.shape = {}'.format(convt1.shape)
) # convt1.shape = (?, 4, 4, 512) -> (?, 32, 32, 128)
convt2 = convt2d(
convt1, (None, self.image_size, self.image_size, self.ngf),
kernal=(5, 5),
strides=(2, 2),
padding="SAME",
name="convt2")
convt2 = tf.contrib.layers.batch_norm(convt2,
is_training=is_training)
convt2 = leaky_relu(convt2)
print('convt2.shape = {}'.format(
convt2.shape)) # convt1.shape = (?, 64, 64, 64)
# conv layer
# Note: the paper said that ReLU and _norm were used
# but actually tanh was used and no _norm here
output = ops.c7s1_k(convt2,
3,
norm=None,
activation='tanh',
reuse=self.reuse,
name='output')
print('output.shape = {}'.format(
output.shape)) # output.shape = (?, 64, 64, 64)
# set reuse=True for next call
self.reuse = True
# self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
return output