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):
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, image, batch_size):
o_dim = self.o_dim if (self.o_dim > 0) else 8 * self.dim
h0 = lrelu(conv2d(image, self.dim, name=self.prefix + 'h0_conv'))
h1 = lrelu(
self.d_bn1(conv2d(h0, self.dim * 2, name=self.prefix + 'h1_conv')))
h2 = lrelu(
self.d_bn2(conv2d(h1, self.dim * 4, name=self.prefix + 'h2_conv')))
h3 = lrelu(
self.d_bn3(conv2d(h2, self.dim * 8, name=self.prefix + 'h3_conv')))
hF = linear(tf.reshape(h3, [batch_size, -1]), o_dim,
self.prefix + 'h4_lin')
return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'hF': hF}
def network(self, image, batch_size):
from core.resnet import block, ops
image = tf.transpose(image, [0, 3, 1, 2]) # NHWC to NCHW
h0 = lrelu(
ops.conv2d.Conv2D(self.prefix + 'h0_conv',
3,
self.dim,
3,
image,
he_init=False))
h1 = block.ResidualBlock(self.prefix + 'res1',
self.dim,
2 * self.dim,
3,
h0,
resample='down')
h2 = block.ResidualBlock(self.prefix + 'res2',
2 * self.dim,
4 * self.dim,
3,
h1,
resample='down')
h3 = block.ResidualBlock(self.prefix + 'res3',
4 * self.dim,
8 * self.dim,
3,
h2,
resample='down')
h4 = block.ResidualBlock(self.prefix + 'res4',
8 * self.dim,
8 * self.dim,
3,
h3,
resample='down')
hF = tf.reshape(h4, [-1, 4 * 4 * 8 * self.dim])
hF = linear(hF, self.o_dim, self.prefix + 'h5_lin')
return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'h4': h4, 'hF': hF}
def network(self, seed, batch_size):
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, fused=True)
h4 = tf.nn.relu(h4)
# h5 = lib.ops.conv2d.Conv2D('g_h5', dim, 3, 3, h4)
h5 = tf.transpose(h4, [0, 2, 3, 1]) # NCHW to NHWC
h5 = deconv2d(h5, [batch_size, s1, s1, self.c_dim], name='g_h5')
return tf.nn.sigmoid(h5)
