mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-05 20:42:11 -07:00
61472cdaf7
removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG. fanseg model file in facelib/ is renamed
69 lines
No EOL
2.7 KiB
Python
69 lines
No EOL
2.7 KiB
Python
from core.leras import nn
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tf = nn.tf
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patch_discriminator_kernels = \
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{ 1 : (512, [ [1,1] ]),
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2 : (512, [ [2,1] ]),
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3 : (512, [ [2,1], [2,1] ]),
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4 : (512, [ [2,2], [2,2] ]),
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5 : (512, [ [3,2], [2,2] ]),
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6 : (512, [ [4,2], [2,2] ]),
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7 : (512, [ [3,2], [3,2] ]),
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8 : (512, [ [4,2], [3,2] ]),
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9 : (512, [ [3,2], [4,2] ]),
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10 : (512, [ [4,2], [4,2] ]),
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11 : (512, [ [3,2], [3,2], [2,1] ]),
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12 : (512, [ [4,2], [3,2], [2,1] ]),
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13 : (512, [ [3,2], [4,2], [2,1] ]),
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14 : (512, [ [4,2], [4,2], [2,1] ]),
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15 : (512, [ [3,2], [3,2], [3,1] ]),
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16 : (512, [ [4,2], [3,2], [3,1] ]),
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17 : (512, [ [3,2], [4,2], [3,1] ]),
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18 : (512, [ [4,2], [4,2], [3,1] ]),
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19 : (512, [ [3,2], [3,2], [4,1] ]),
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20 : (512, [ [4,2], [3,2], [4,1] ]),
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21 : (512, [ [3,2], [4,2], [4,1] ]),
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22 : (512, [ [4,2], [4,2], [4,1] ]),
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23 : (256, [ [3,2], [3,2], [3,2], [2,1] ]),
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24 : (256, [ [4,2], [3,2], [3,2], [2,1] ]),
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25 : (256, [ [3,2], [4,2], [3,2], [2,1] ]),
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26 : (256, [ [4,2], [4,2], [3,2], [2,1] ]),
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27 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
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28 : (256, [ [4,2], [3,2], [4,2], [2,1] ]),
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29 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
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30 : (256, [ [4,2], [4,2], [4,2], [2,1] ]),
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31 : (256, [ [3,2], [3,2], [3,2], [3,1] ]),
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32 : (256, [ [4,2], [3,2], [3,2], [3,1] ]),
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33 : (256, [ [3,2], [4,2], [3,2], [3,1] ]),
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34 : (256, [ [4,2], [4,2], [3,2], [3,1] ]),
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35 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
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36 : (256, [ [4,2], [3,2], [4,2], [3,1] ]),
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37 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
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38 : (256, [ [4,2], [4,2], [4,2], [3,1] ]),
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}
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class PatchDiscriminator(nn.ModelBase):
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def on_build(self, patch_size, in_ch, base_ch=None, conv_kernel_initializer=None):
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suggested_base_ch, kernels_strides = patch_discriminator_kernels[patch_size]
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if base_ch is None:
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base_ch = suggested_base_ch
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prev_ch = in_ch
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self.convs = []
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for i, (kernel_size, strides) in enumerate(kernels_strides):
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cur_ch = base_ch * min( (2**i), 8 )
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self.convs.append ( nn.Conv2D( prev_ch, cur_ch, kernel_size=kernel_size, strides=strides, padding='SAME', kernel_initializer=conv_kernel_initializer) )
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prev_ch = cur_ch
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self.out_conv = nn.Conv2D( prev_ch, 1, kernel_size=1, padding='VALID', kernel_initializer=conv_kernel_initializer)
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def forward(self, x):
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for conv in self.convs:
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x = tf.nn.leaky_relu( conv(x), 0.1 )
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return self.out_conv(x)
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nn.PatchDiscriminator = PatchDiscriminator |