def _build_generator(self):
layer_list = [
tf.keras.layers.Reshape([4, 4, self.latent_dim // 16]),
layers.Conv(256, ks=4, strides=2, transpose=True),
layers.Act('relu'),
layers.Conv(128, ks=4, strides=2, transpose=True),
layers.Act('relu'),
layers.Conv(FLAGS.out_dim, ks=4, strides=2, transpose=True)
]
return self.sequential(layer_list)
def _build_inference(self):
layer_list = [
layers.Conv(32, ks=3, strides=2),
layers.Act('relu'),
layers.Conv(64, ks=3, strides=2),
layers.Act('relu'),
tf.keras.layers.Flatten(),
layers.Dense(self.latent_dim + self.latent_dim)
]
return self.sequential(layer_list)
def _build_encoder(self):
layer_list = [
layers.Conv(16, ks=4, strides=2),
layers.Norm(),
layers.Conv(64, ks=4, strides=2),
layers.Norm(),
layers.Conv(256, ks=4, strides=2),
layers.Norm(),
layers.Conv(512, ks=4, strides=2)
]
return self.sequential(layer_list)
def _build_generator(self):
layer_list = [
layers.Dense(7 * 7 * 32),
layers.Act('relu'),
tf.keras.layers.Reshape([7, 7, 32]),
layers.Conv(64, ks=3, strides=2, transpose=True),
layers.Act('relu'),
layers.Conv(32, ks=3, strides=2, transpose=True),
layers.Act('relu'),
layers.Conv(FLAGS.out_dim, ks=3, strides=1, transpose=True)
]
return self.sequential(layer_list)
def __init__(self,
filters,
ks,
strides=1,
preact=False,
use_norm=True,
use_act=True,
use_bias=False,
last_norm=False,
transpose=False):
super(BasicBlock, self).__init__(filters, strides=strides)
self.preact = preact
self.use_norm = use_norm
self.use_act = use_act
self.last_norm = last_norm
if self.use_norm:
norm_scale = False if 'relu' in FLAGS.conv_act.lower() else True
self.bn = layers.Norm(scale=norm_scale)
if self.use_act:
self.act = layers.Act()
self.conv = layers.Conv(filters,
ks,
strides=strides,
use_bias=use_bias,
transpose=transpose)
if self.last_norm:
self.last_bn = layers.Norm()