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global refactoring and fixes,

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removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG.

fanseg model file in facelib/ is renamed
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Colombo 2020-03-13 08:09:00 +04:00
commit 61472cdaf7
82 changed files with 3838 additions and 3812 deletions
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69
core/leras/models/PatchDiscriminator.py Normal file
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from core.leras import nn
tf = nn.tf
patch_discriminator_kernels = \
{ 1 : (512, [ [1,1] ]),
2 : (512, [ [2,1] ]),
3 : (512, [ [2,1], [2,1] ]),
4 : (512, [ [2,2], [2,2] ]),
5 : (512, [ [3,2], [2,2] ]),
6 : (512, [ [4,2], [2,2] ]),
7 : (512, [ [3,2], [3,2] ]),
8 : (512, [ [4,2], [3,2] ]),
9 : (512, [ [3,2], [4,2] ]),
10 : (512, [ [4,2], [4,2] ]),
11 : (512, [ [3,2], [3,2], [2,1] ]),
12 : (512, [ [4,2], [3,2], [2,1] ]),
13 : (512, [ [3,2], [4,2], [2,1] ]),
14 : (512, [ [4,2], [4,2], [2,1] ]),
15 : (512, [ [3,2], [3,2], [3,1] ]),
16 : (512, [ [4,2], [3,2], [3,1] ]),
17 : (512, [ [3,2], [4,2], [3,1] ]),
18 : (512, [ [4,2], [4,2], [3,1] ]),
19 : (512, [ [3,2], [3,2], [4,1] ]),
20 : (512, [ [4,2], [3,2], [4,1] ]),
21 : (512, [ [3,2], [4,2], [4,1] ]),
22 : (512, [ [4,2], [4,2], [4,1] ]),
23 : (256, [ [3,2], [3,2], [3,2], [2,1] ]),
24 : (256, [ [4,2], [3,2], [3,2], [2,1] ]),
25 : (256, [ [3,2], [4,2], [3,2], [2,1] ]),
26 : (256, [ [4,2], [4,2], [3,2], [2,1] ]),
27 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
28 : (256, [ [4,2], [3,2], [4,2], [2,1] ]),
29 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
30 : (256, [ [4,2], [4,2], [4,2], [2,1] ]),
31 : (256, [ [3,2], [3,2], [3,2], [3,1] ]),
32 : (256, [ [4,2], [3,2], [3,2], [3,1] ]),
33 : (256, [ [3,2], [4,2], [3,2], [3,1] ]),
34 : (256, [ [4,2], [4,2], [3,2], [3,1] ]),
35 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
36 : (256, [ [4,2], [3,2], [4,2], [3,1] ]),
37 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
38 : (256, [ [4,2], [4,2], [4,2], [3,1] ]),
}
class PatchDiscriminator(nn.ModelBase):
def on_build(self, patch_size, in_ch, base_ch=None, conv_kernel_initializer=None):
suggested_base_ch, kernels_strides = patch_discriminator_kernels[patch_size]
if base_ch is None:
base_ch = suggested_base_ch
prev_ch = in_ch
self.convs = []
for i, (kernel_size, strides) in enumerate(kernels_strides):
cur_ch = base_ch * min( (2**i), 8 )
self.convs.append ( nn.Conv2D( prev_ch, cur_ch, kernel_size=kernel_size, strides=strides, padding='SAME', kernel_initializer=conv_kernel_initializer) )
prev_ch = cur_ch
self.out_conv = nn.Conv2D( prev_ch, 1, kernel_size=1, padding='VALID', kernel_initializer=conv_kernel_initializer)
def forward(self, x):
for conv in self.convs:
x = tf.nn.leaky_relu( conv(x), 0.1 )
return self.out_conv(x)
nn.PatchDiscriminator = PatchDiscriminator