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from core.leras import nn
tf = nn.tf
class AdaIN(nn.LayerBase):
"""
"""
def __init__(self, in_ch, mlp_ch, kernel_initializer=None, dtype=None, **kwargs):
self.in_ch = in_ch
self.mlp_ch = mlp_ch
self.kernel_initializer = kernel_initializer
if dtype is None:
dtype = nn.floatx
self.dtype = dtype
super().__init__(**kwargs)
def build_weights(self):
kernel_initializer = self.kernel_initializer
if kernel_initializer is None:
kernel_initializer = tf.initializers.he_normal()
self.weight1 = tf.get_variable("weight1", (self.mlp_ch, self.in_ch), dtype=self.dtype, initializer=kernel_initializer)
self.bias1 = tf.get_variable("bias1", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros())
self.weight2 = tf.get_variable("weight2", (self.mlp_ch, self.in_ch), dtype=self.dtype, initializer=kernel_initializer)
self.bias2 = tf.get_variable("bias2", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros())
def get_weights(self):
return [self.weight1, self.bias1, self.weight2, self.bias2]
def forward(self, inputs):
x, mlp = inputs
gamma = tf.matmul(mlp, self.weight1)
gamma = tf.add(gamma, tf.reshape(self.bias1, (1,self.in_ch) ) )
beta = tf.matmul(mlp, self.weight2)
beta = tf.add(beta, tf.reshape(self.bias2, (1,self.in_ch) ) )
if nn.data_format == "NHWC":
shape = (-1,1,1,self.in_ch)
else:
shape = (-1,self.in_ch,1,1)
x_mean = tf.reduce_mean(x, axis=nn.conv2d_spatial_axes, keepdims=True )
x_std = tf.math.reduce_std(x, axis=nn.conv2d_spatial_axes, keepdims=True ) + 1e-5
x = (x - x_mean) / x_std
x *= tf.reshape(gamma, shape)
x += tf.reshape(beta, shape)
return x
nn.AdaIN = AdaIN |