mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2024-12-25 23:41:12 -08:00
170 lines
5.4 KiB
Python
170 lines
5.4 KiB
Python
from core.leras import nn
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tf = nn.tf
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class XSeg(nn.ModelBase):
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def on_build (self, in_ch, base_ch, out_ch):
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class ConvBlock(nn.ModelBase):
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def on_build(self, in_ch, out_ch):
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self.conv = nn.Conv2D (in_ch, out_ch, kernel_size=3, padding='SAME')
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self.frn = nn.FRNorm2D(out_ch)
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self.tlu = nn.TLU(out_ch)
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def forward(self, x):
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x = self.conv(x)
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x = self.frn(x)
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x = self.tlu(x)
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return x
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class UpConvBlock(nn.ModelBase):
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def on_build(self, in_ch, out_ch):
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self.conv = nn.Conv2DTranspose (in_ch, out_ch, kernel_size=3, padding='SAME')
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self.frn = nn.FRNorm2D(out_ch)
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self.tlu = nn.TLU(out_ch)
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def forward(self, x):
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x = self.conv(x)
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x = self.frn(x)
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x = self.tlu(x)
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return x
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self.base_ch = base_ch
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self.conv01 = ConvBlock(in_ch, base_ch)
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self.conv02 = ConvBlock(base_ch, base_ch)
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self.bp0 = nn.BlurPool (filt_size=4)
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self.conv11 = ConvBlock(base_ch, base_ch*2)
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self.conv12 = ConvBlock(base_ch*2, base_ch*2)
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self.bp1 = nn.BlurPool (filt_size=3)
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self.conv21 = ConvBlock(base_ch*2, base_ch*4)
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self.conv22 = ConvBlock(base_ch*4, base_ch*4)
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self.bp2 = nn.BlurPool (filt_size=2)
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self.conv31 = ConvBlock(base_ch*4, base_ch*8)
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self.conv32 = ConvBlock(base_ch*8, base_ch*8)
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self.conv33 = ConvBlock(base_ch*8, base_ch*8)
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self.bp3 = nn.BlurPool (filt_size=2)
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self.conv41 = ConvBlock(base_ch*8, base_ch*8)
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self.conv42 = ConvBlock(base_ch*8, base_ch*8)
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self.conv43 = ConvBlock(base_ch*8, base_ch*8)
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self.bp4 = nn.BlurPool (filt_size=2)
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self.conv51 = ConvBlock(base_ch*8, base_ch*8)
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self.conv52 = ConvBlock(base_ch*8, base_ch*8)
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self.conv53 = ConvBlock(base_ch*8, base_ch*8)
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self.bp5 = nn.BlurPool (filt_size=2)
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self.dense1 = nn.Dense ( 4*4* base_ch*8, 512)
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self.dense2 = nn.Dense ( 512, 4*4* base_ch*8)
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self.up5 = UpConvBlock (base_ch*8, base_ch*4)
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self.uconv53 = ConvBlock(base_ch*12, base_ch*8)
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self.uconv52 = ConvBlock(base_ch*8, base_ch*8)
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self.uconv51 = ConvBlock(base_ch*8, base_ch*8)
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self.up4 = UpConvBlock (base_ch*8, base_ch*4)
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self.uconv43 = ConvBlock(base_ch*12, base_ch*8)
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self.uconv42 = ConvBlock(base_ch*8, base_ch*8)
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self.uconv41 = ConvBlock(base_ch*8, base_ch*8)
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self.up3 = UpConvBlock (base_ch*8, base_ch*4)
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self.uconv33 = ConvBlock(base_ch*12, base_ch*8)
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self.uconv32 = ConvBlock(base_ch*8, base_ch*8)
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self.uconv31 = ConvBlock(base_ch*8, base_ch*8)
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self.up2 = UpConvBlock (base_ch*8, base_ch*4)
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self.uconv22 = ConvBlock(base_ch*8, base_ch*4)
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self.uconv21 = ConvBlock(base_ch*4, base_ch*4)
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self.up1 = UpConvBlock (base_ch*4, base_ch*2)
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self.uconv12 = ConvBlock(base_ch*4, base_ch*2)
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self.uconv11 = ConvBlock(base_ch*2, base_ch*2)
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self.up0 = UpConvBlock (base_ch*2, base_ch)
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self.uconv02 = ConvBlock(base_ch*2, base_ch)
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self.uconv01 = ConvBlock(base_ch, base_ch)
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self.out_conv = nn.Conv2D (base_ch, out_ch, kernel_size=3, padding='SAME')
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def forward(self, inp, pretrain=False):
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x = inp
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x = self.conv01(x)
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x = x0 = self.conv02(x)
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x = self.bp0(x)
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x = self.conv11(x)
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x = x1 = self.conv12(x)
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x = self.bp1(x)
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x = self.conv21(x)
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x = x2 = self.conv22(x)
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x = self.bp2(x)
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x = self.conv31(x)
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x = self.conv32(x)
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x = x3 = self.conv33(x)
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x = self.bp3(x)
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x = self.conv41(x)
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x = self.conv42(x)
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x = x4 = self.conv43(x)
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x = self.bp4(x)
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x = self.conv51(x)
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x = self.conv52(x)
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x = x5 = self.conv53(x)
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x = self.bp5(x)
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x = nn.flatten(x)
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x = self.dense1(x)
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x = self.dense2(x)
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x = nn.reshape_4D (x, 4, 4, self.base_ch*8 )
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x = self.up5(x)
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if pretrain:
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x5 = tf.zeros_like(x5)
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x = self.uconv53(tf.concat([x,x5],axis=nn.conv2d_ch_axis))
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x = self.uconv52(x)
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x = self.uconv51(x)
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x = self.up4(x)
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if pretrain:
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x4 = tf.zeros_like(x4)
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x = self.uconv43(tf.concat([x,x4],axis=nn.conv2d_ch_axis))
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x = self.uconv42(x)
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x = self.uconv41(x)
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x = self.up3(x)
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if pretrain:
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x3 = tf.zeros_like(x3)
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x = self.uconv33(tf.concat([x,x3],axis=nn.conv2d_ch_axis))
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x = self.uconv32(x)
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x = self.uconv31(x)
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x = self.up2(x)
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if pretrain:
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x2 = tf.zeros_like(x2)
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x = self.uconv22(tf.concat([x,x2],axis=nn.conv2d_ch_axis))
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x = self.uconv21(x)
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x = self.up1(x)
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if pretrain:
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x1 = tf.zeros_like(x1)
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x = self.uconv12(tf.concat([x,x1],axis=nn.conv2d_ch_axis))
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x = self.uconv11(x)
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x = self.up0(x)
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if pretrain:
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x0 = tf.zeros_like(x0)
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x = self.uconv02(tf.concat([x,x0],axis=nn.conv2d_ch_axis))
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x = self.uconv01(x)
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logits = self.out_conv(x)
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return logits, tf.nn.sigmoid(logits)
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nn.XSeg = XSeg |