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from 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, pretrain=False):
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)
if pretrain:
x5 = tf.zeros_like(x5)
x = self.uconv53(tf.concat([x,x5],axis=nn.conv2d_ch_axis))
x = self.uconv52(x)
x = self.uconv51(x)
x = self.up4(x)
if pretrain:
x4 = tf.zeros_like(x4)
x = self.uconv43(tf.concat([x,x4],axis=nn.conv2d_ch_axis))
x = self.uconv42(x)
x = self.uconv41(x)
x = self.up3(x)
if pretrain:
x3 = tf.zeros_like(x3)
x = self.uconv33(tf.concat([x,x3],axis=nn.conv2d_ch_axis))
x = self.uconv32(x)
x = self.uconv31(x)
x = self.up2(x)
if pretrain:
x2 = tf.zeros_like(x2)
x = self.uconv22(tf.concat([x,x2],axis=nn.conv2d_ch_axis))
x = self.uconv21(x)
x = self.up1(x)
if pretrain:
x1 = tf.zeros_like(x1)
x = self.uconv12(tf.concat([x,x1],axis=nn.conv2d_ch_axis))
x = self.uconv11(x)
x = self.up0(x)
if pretrain:
x0 = tf.zeros_like(x0)
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 |