mirror of
https://github.com/iperov/DeepFaceLab.git
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61472cdaf7
removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG. fanseg model file in facelib/ is renamed
38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
from core.leras import nn
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tf = nn.tf
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class FRNorm2D(nn.LayerBase):
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"""
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Tensorflow implementation of
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Filter Response Normalization Layer: Eliminating Batch Dependence in theTraining of Deep Neural Networks
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https://arxiv.org/pdf/1911.09737.pdf
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"""
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def __init__(self, in_ch, dtype=None, **kwargs):
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self.in_ch = in_ch
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if dtype is None:
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dtype = nn.floatx
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self.dtype = dtype
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super().__init__(**kwargs)
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def build_weights(self):
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self.weight = tf.get_variable("weight", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.ones() )
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self.bias = tf.get_variable("bias", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros() )
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self.eps = tf.get_variable("eps", (1,), dtype=self.dtype, initializer=tf.initializers.constant(1e-6) )
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def get_weights(self):
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return [self.weight, self.bias, self.eps]
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def forward(self, x):
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if nn.data_format == "NHWC":
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shape = (1,1,1,self.in_ch)
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else:
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shape = (1,self.in_ch,1,1)
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weight = tf.reshape ( self.weight, shape )
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bias = tf.reshape ( self.bias , shape )
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nu2 = tf.reduce_mean(tf.square(x), axis=nn.conv2d_spatial_axes, keepdims=True)
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x = x * ( 1.0/tf.sqrt(nu2 + tf.abs(self.eps) ) )
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return x*weight + bias
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nn.FRNorm2D = FRNorm2D |