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from core.leras import nn
tf = nn.tf
class InstanceNorm2D(nn.LayerBase):
def __init__(self, in_ch, dtype=None, **kwargs):
self.in_ch = in_ch
if dtype is None:
dtype = nn.floatx
self.dtype = dtype
super().__init__(**kwargs)
def build_weights(self):
kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype)
self.weight = tf.get_variable("weight", (self.in_ch,), dtype=self.dtype, initializer=kernel_initializer )
self.bias = tf.get_variable("bias", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros() )
def get_weights(self):
return [self.weight, self.bias]
def forward(self, x):
if nn.data_format == "NHWC":
shape = (1,1,1,self.in_ch)
else:
shape = (1,self.in_ch,1,1)
weight = tf.reshape ( self.weight , shape )
bias = tf.reshape ( self.bias , shape )
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 *= weight
x += bias
return x
nn.InstanceNorm2D = InstanceNorm2D |