def __init__(self, label, image_size, channel_num, kernel_num, z_size,
device):
super().__init__()
self.model_name = "ae_vine"
self.label = label
self.image_size = image_size
self.channel_num = channel_num
self.kernel_num = kernel_num
self.z_size = z_size
self.device = device
self.vine = None
# encoder
self.encoder = nn.Sequential(
_conv(channel_num, kernel_num // 4),
_conv(kernel_num // 4, kernel_num // 2),
_conv(kernel_num // 2, kernel_num),
)
# encoded feature's size and volume
self.feature_size = image_size // 8
self.feature_volume = kernel_num * (self.feature_size**2)
# decoder
self.decoder = nn.Sequential(_deconv(kernel_num, kernel_num // 2),
_deconv(kernel_num // 2, kernel_num // 4),
_deconv(kernel_num // 4, channel_num),
nn.Sigmoid())
# projection
self.project = _linear(z_size, self.feature_volume, relu=False)
self.q_layer = _linear(self.feature_volume, z_size, relu=False)
def BasicBlock(self,
inputs,
filters,
strides=1,
is_training=False,
use_bn=True):
expansion = 1
conv1_bn_relu = _conv(inputs,
filters, [3, 3],
strides,
'same',
activation=tf.nn.relu,
is_training=self.is_training,
use_bn=self.use_bn)
conv2_bn = _conv(conv1_bn_relu,
filters, [3, 3],
1,
'same',
activation=None,
is_training=self.is_training,
use_bn=self.use_bn)
if strides != 1 or self.inplanes != filters * expansion:
inputs = _conv(inputs,
filters, [1, 1],
strides,
'valid',
activation=None,
is_training=self.is_training,
use_bn=self.use_bn)
self.inplanes = filters * expansion
out = tf.nn.relu(conv2_bn + inputs)
return out
def _layer0(self, inputs, filters, kernel_size=(7, 7)):
outputs = _conv(inputs,
filters, [7, 7],
2,
'same',
activation=tf.nn.relu,
is_training=self.is_training,
use_bn=self.use_bn)
outputs = tf.layers.max_pooling2d(outputs,
pool_size=3,
strides=2,
padding='same')
return outputs
def __init__(self, label, image_size, channel_num, kernel_num, z_size, device):
# configurations
super().__init__()
self.model_name = "cvae"
self.label = label
self.image_size = image_size
self.channel_num = channel_num
self.kernel_num = kernel_num
self.z_size = z_size
self.device = device
# encoder
self.encoder = nn.Sequential(
_conv(channel_num, kernel_num // 4),
_conv(kernel_num // 4, kernel_num // 2),
_conv(kernel_num // 2, kernel_num),
)
# encoded feature's size and volume
self.feature_size = image_size // 8
self.feature_volume = kernel_num * (self.feature_size ** 2)
# q
self.q_mean = _linear(self.feature_volume, z_size, relu=False)
self.q_logvar = _linear(self.feature_volume, z_size, relu=False)
n = int(self.z_size * (self.z_size - 1) / 2)
self.q_atanhcor = _linear(self.feature_volume, n, relu=False)
# projection
self.project = _linear(z_size, self.feature_volume, relu=False)
# decoder
self.decoder = nn.Sequential(
_deconv(kernel_num, kernel_num // 2),
_deconv(kernel_num // 2, kernel_num // 4),
_deconv(kernel_num // 4, channel_num),
nn.Sigmoid()
)