mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2024-12-25 23:41:12 -08:00
61472cdaf7
removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG. fanseg model file in facelib/ is renamed
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
from core.leras import nn
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tf = nn.tf
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class AdaIN(nn.LayerBase):
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"""
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"""
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def __init__(self, in_ch, mlp_ch, kernel_initializer=None, dtype=None, **kwargs):
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self.in_ch = in_ch
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self.mlp_ch = mlp_ch
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self.kernel_initializer = kernel_initializer
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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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kernel_initializer = self.kernel_initializer
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if kernel_initializer is None:
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kernel_initializer = tf.initializers.he_normal()
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self.weight1 = tf.get_variable("weight1", (self.mlp_ch, self.in_ch), dtype=self.dtype, initializer=kernel_initializer)
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self.bias1 = tf.get_variable("bias1", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros())
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self.weight2 = tf.get_variable("weight2", (self.mlp_ch, self.in_ch), dtype=self.dtype, initializer=kernel_initializer)
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self.bias2 = tf.get_variable("bias2", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros())
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def get_weights(self):
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return [self.weight1, self.bias1, self.weight2, self.bias2]
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def forward(self, inputs):
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x, mlp = inputs
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gamma = tf.matmul(mlp, self.weight1)
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gamma = tf.add(gamma, tf.reshape(self.bias1, (1,self.in_ch) ) )
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beta = tf.matmul(mlp, self.weight2)
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beta = tf.add(beta, tf.reshape(self.bias2, (1,self.in_ch) ) )
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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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x_mean = tf.reduce_mean(x, axis=nn.conv2d_spatial_axes, keepdims=True )
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x_std = tf.math.reduce_std(x, axis=nn.conv2d_spatial_axes, keepdims=True ) + 1e-5
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x = (x - x_mean) / x_std
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x *= tf.reshape(gamma, shape)
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x += tf.reshape(beta, shape)
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return x
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nn.AdaIN = AdaIN |