def network(self, seed, batch_size):
s1, s2, s4, s8, s16 = conv_sizes(self.output_size, layers=4, stride=2)
# 64, 32, 16, 8, 4 - for self.output_size = 64
# default architecture
# For Cramer: self.gf_dim = 64
z_ = linear(seed, self.dim * 8 * s16 * s16, self.prefix +
'h0_lin') # project random noise seed and reshape
h0 = tf.reshape(z_, [batch_size, s16, s16, self.dim * 8])
h0 = tf.nn.relu(self.g_bn0(h0))
h1 = deconv2d(h0, [batch_size, s8, s8, self.dim * 4],
name=self.prefix + 'h1')
h1 = tf.nn.relu(self.g_bn1(h1))
h2 = deconv2d(h1, [batch_size, s4, s4, self.dim * 2],
name=self.prefix + 'h2')
h2 = tf.nn.relu(self.g_bn2(h2))
h3 = deconv2d(h2, [batch_size, s2, s2, self.dim * 1],
name=self.prefix + 'h3')
h3 = tf.nn.relu(self.g_bn3(h3))
h4 = deconv2d(h3, [batch_size, s1, s1, self.c_dim],
name=self.prefix + 'h4')
return tf.nn.sigmoid(h4)
def network(self, seed, batch_size):
s1, s2, s4, s8, s16, s32 = conv_sizes(self.output_size,
layers=5,
stride=2)
# project `z` and reshape
z_ = linear(seed, self.dim * 16 * s32 * s32, self.prefix + 'h0_lin')
h0 = tf.reshape(z_, [-1, s32, s32, self.dim * 16])
h0 = tf.nn.relu(self.g_bn0(h0))
h1 = deconv2d(h0, [batch_size, s16, s16, self.dim * 8],
name=self.prefix + 'h1')
h1 = tf.nn.relu(self.g_bn1(h1))
h2 = deconv2d(h1, [batch_size, s8, s8, self.dim * 4],
name=self.prefix + 'h2')
h2 = tf.nn.relu(self.g_bn2(h2))
h3 = deconv2d(h2, [batch_size, s4, s4, self.dim * 2],
name=self.prefix + 'h3')
h3 = tf.nn.relu(self.g_bn3(h3))
h4 = deconv2d(h3, [batch_size, s2, s2, self.dim],
name=self.prefix + 'h4')
h4 = tf.nn.relu(self.g_bn4(h4))
h5 = deconv2d(h4, [batch_size, s1, s1, self.c_dim],
name=self.prefix + 'h5')
return tf.nn.sigmoid(h5)
def network(self, seed, batch_size, update_collection):
s1, s2, s4, s8, s16 = conv_sizes(self.output_size, layers=4, stride=2)
z_ = linear(seed,
self.dim * 8 * s8 * s8,
self.prefix + 'h0_lin',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale
) # project random noise seed and reshape
h0 = tf.reshape(z_, self.data_format(batch_size, s8, s8, self.dim * 8))
h0 = tf.nn.relu(self.g_bn0(h0))
h1 = deconv2d(h0,
self.data_format(batch_size, s4, s4, self.dim * 4),
name=self.prefix + 'h1',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h1 = tf.nn.relu(self.g_bn1(h1))
h2 = deconv2d(h1,
self.data_format(batch_size, s2, s2, self.dim * 2),
name=self.prefix + 'h2',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h2 = tf.nn.relu(self.g_bn2(h2))
h3 = deconv2d(h2,
self.data_format(batch_size, s1, s1, self.dim * 1),
name=self.prefix + 'h3',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h3 = tf.nn.relu(self.g_bn3(h3))
# SN dcgan generator implementation has smaller convolutional field and stride=1
h4 = deconv2d(h3,
self.data_format(batch_size, s1, s1, self.c_dim),
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name=self.prefix + 'h4',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
return tf.nn.sigmoid(h4)
def network(self, seed, batch_size, update_collection):
s1, s2, s4, s8, s16 = conv_sizes(self.output_size, layers=4, stride=2)
# 64, 32, 16, 8, 4 - for self.output_size = 64
# default architecture
# For Cramer: self.gf_dim = 64
z_ = linear(seed,
self.dim * 8 * s16 * s16,
self.prefix + 'h0_lin',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale
) # project random noise seed and reshape
h0 = tf.reshape(z_, self.data_format(batch_size, s16, s16,
self.dim * 8))
h0 = tf.nn.relu(self.g_bn0(h0))
h1 = deconv2d(h0,
self.data_format(batch_size, s8, s8, self.dim * 4),
name=self.prefix + 'h1',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h1 = tf.nn.relu(self.g_bn1(h1))
h2 = deconv2d(h1,
self.data_format(batch_size, s4, s4, self.dim * 2),
name=self.prefix + 'h2',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h2 = tf.nn.relu(self.g_bn2(h2))
h3 = deconv2d(h2,
self.data_format(batch_size, s2, s2, self.dim * 1),
name=self.prefix + 'h3',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h3 = tf.nn.relu(self.g_bn3(h3))
h4 = deconv2d(h3,
self.data_format(batch_size, s1, s1, self.c_dim),
name=self.prefix + 'h4',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
return tf.nn.sigmoid(h4)
def network(self, seed, batch_size, update_collection):
from core.resnet import block, ops
s1, s2, s4, s8, s16, s32 = conv_sizes(self.output_size,
layers=5,
stride=2)
# project `z` and reshape
z_ = linear(seed, self.dim * 16 * s32 * s32, self.prefix + 'h0_lin')
h0 = tf.reshape(z_, [-1, self.dim * 16, s32, s32]) # NCHW format
h1 = block.ResidualBlock(self.prefix + 'res1',
16 * self.dim,
8 * self.dim,
3,
h0,
resample='up')
h2 = block.ResidualBlock(self.prefix + 'res2',
8 * self.dim,
4 * self.dim,
3,
h1,
resample='up')
h3 = block.ResidualBlock(self.prefix + 'res3',
4 * self.dim,
2 * self.dim,
3,
h2,
resample='up')
h4 = block.ResidualBlock(self.prefix + 'res4',
2 * self.dim,
self.dim,
3,
h3,
resample='up')
h4 = ops.batchnorm.Batchnorm('g_h4', [0, 2, 3], h4)
h4 = tf.nn.relu(h4)
# h5 = lib.ops.conv2d.Conv2D('g_h5', dim, 3, 3, h4)
if self.format == 'NHWC':
h4 = tf.transpose(h4, [0, 2, 3, 1]) # NCHW to NHWC
h5 = deconv2d(h4,
self.data_format(batch_size, s1, s1, self.c_dim),
name=self.prefix + 'g_h5')
return tf.nn.sigmoid(h5)
def network(self, seed, y, batch_size, update_collection):
from core.resnet import block, ops
s1, s2, s4, s8, s16, s32 = conv_sizes(self.output_size,
layers=5,
stride=2)
# project `z` and reshape
if self.output_size == 64:
s32 = 4
z_ = linear(seed, self.dim * 16 * s32 * s32, self.prefix + 'h0_lin')
h0 = tf.reshape(z_, [-1, self.dim * 16, s32, s32]) # NCHW format
if self.output_size == 64:
h0_bis = h0
else:
h0_bis = block.ResidualBlock(self.prefix + 'res0_bis',
16 * self.dim,
16 * self.dim,
3,
h0,
y=y,
num_classes=self.num_classes,
resample='up',
mode='cond_batchnorm')
h1 = block.ResidualBlock(self.prefix + 'res1',
16 * self.dim,
8 * self.dim,
3,
h0_bis,
y=y,
num_classes=self.num_classes,
resample='up',
mode='cond_batchnorm')
h2 = block.ResidualBlock(self.prefix + 'res2',
8 * self.dim,
4 * self.dim,
3,
h1,
y=y,
num_classes=self.num_classes,
resample='up',
mode='cond_batchnorm')
h3 = block.ResidualBlock(self.prefix + 'res3',
4 * self.dim,
2 * self.dim,
3,
h2,
y=y,
num_classes=self.num_classes,
resample='up',
mode='cond_batchnorm')
h4 = block.ResidualBlock(self.prefix + 'res4',
2 * self.dim,
self.dim,
3,
h3,
y=y,
num_classes=self.num_classes,
resample='up',
mode='cond_batchnorm')
h4 = ops.batchnorm.Batchnorm('g_h4', [0, 2, 3], h4)
h4 = tf.nn.relu(h4)
if self.format == 'NHWC':
h4 = tf.transpose(h4, [0, 2, 3, 1]) # NCHW to NHWC
h5 = deconv2d(h4,
self.data_format(batch_size, s1, s1, self.c_dim),
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name=self.prefix + 'g_h5')
return tf.nn.sigmoid(h5)
def network(self, seed, batch_size, update_collection):
s1, s2, s4, s8, s16, s32 = conv_sizes(self.output_size,
layers=5,
stride=2)
# project `z` and reshape
z_ = linear(seed,
self.dim * 16 * s32 * s32,
self.prefix + 'h0_lin',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale)
h0 = tf.reshape(z_, self.data_format(-1, s32, s32, self.dim * 16))
h0 = tf.nn.relu(self.g_bn0(h0))
h1 = deconv2d(h0,
self.data_format(batch_size, s16, s16, self.dim * 8),
name=self.prefix + 'h1',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h1 = tf.nn.relu(self.g_bn1(h1))
h2 = deconv2d(h1,
self.data_format(batch_size, s8, s8, self.dim * 4),
name=self.prefix + 'h2',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h2 = tf.nn.relu(self.g_bn2(h2))
h3 = deconv2d(h2,
self.data_format(batch_size, s4, s4, self.dim * 2),
name=self.prefix + 'h3',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h3 = tf.nn.relu(self.g_bn3(h3))
h4 = deconv2d(h3,
self.data_format(batch_size, s2, s2, self.dim),
name=self.prefix + 'h4',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
h4 = tf.nn.relu(self.g_bn4(h4))
h5 = deconv2d(h4,
self.data_format(batch_size, s1, s1, self.c_dim),
name=self.prefix + 'h5',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format)
return tf.nn.sigmoid(h5)
