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