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gridnet3d.py
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import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
class lateral_block(nn.Module):
def __init__(self, cin , cout, kernel_size = (3,3,3), stride = (1,1,1), padding = (1,1,1), res_conn = True):
super(lateral_block, self).__init__()
self.block = nn.Sequential(
nn.PReLU(),
nn.Conv3d(cin, cout , kernel_size, stride, padding),
nn.PReLU(),
nn.Conv3d(cout, cout, kernel_size, stride, padding)
)
self.res_conn = res_conn
def forward(self, x):
x1 = self.block(x)
if (self.res_conn):
x1 = x+x1
return x1
class down_block(nn.Module):
def __init__(self, cin , cout, kernel_size = (3,3,3), padding = (1,1,1)):
super(down_block, self).__init__()
self.block = nn.Sequential(
nn.PReLU(),
nn.Conv3d(cin, cout , kernel_size, (1,2,2), padding),
nn.PReLU(),
nn.Conv3d(cout, cout , kernel_size, (1,1,1), padding)
)
def forward(self, x):
x1 = self.block(x)
return x1
class up_block(nn.Module):
def __init__(self, cin, cout, kernel_size = (3,3,3), stride = (1,1,1), padding = (1,1,1) ):
super(up_block, self).__init__()
self.block = nn.Sequential(
# nn.Upsample(scale_factor=[1,2,2], mode= 'bilinear'),
nn.PReLU(),
nn.Conv3d(cin, cout , kernel_size, stride , padding ),
nn.PReLU(),
nn.Conv3d(cout, cout, kernel_size, stride , padding)
)
def forward(self, x):
x1 = F.upsample(x, scale_factor=(1,2,2), mode='trilinear')
x1 = self.block(x1)
return x1
class Grid3DNet(nn.Module):
def __init__(self, inChannels, outChannels, channel_list=[32,64,96]):
super(Grid3DNet, self).__init__()
c1,c2,c3 = channel_list
self.Lin = lateral_block(inChannels,c1 , res_conn=False)
#row0
self.L00 = lateral_block(c1 ,c1 )
self.L01 = lateral_block(c1 ,c1 )
self.L02 = lateral_block(c1 ,c1 )
self.L03 = lateral_block(c1 ,c1 )
self.L04 = lateral_block(c1 ,c1 )
#row1
self.L10 = lateral_block(c2,c2)
self.L11 = lateral_block(c2,c2)
self.L12 = lateral_block(c2,c2)
self.L13 = lateral_block(c2,c2)
self.L14 = lateral_block(c2,c2)
#row2
self.L20 = lateral_block(c3,c3)
self.L21 = lateral_block(c3,c3)
self.L22 = lateral_block(c3,c3)
self.L23 = lateral_block(c3,c3)
self.L24 = lateral_block(c3,c3)
self.Lout = lateral_block(c1 ,outChannels, res_conn=False)
self.d00 = down_block(c1 ,c2)
self.d01 = down_block(c1 ,c2)
self.d02 = down_block(c1 ,c2)
self.d10 = down_block(c2,c3)
self.d11 = down_block(c2,c3)
self.d12 = down_block(c2,c3)
self.u00 = up_block(c2,c1 )
self.u01 = up_block(c2,c1 )
self.u02 = up_block(c2,c1 )
self.u10 = up_block(c3,c2)
self.u11 = up_block(c3,c2)
self.u12 = up_block(c3,c2)
def forward(self,x):
out_Lin = self.Lin(x)
out_L00 = self.L00(out_Lin)
out_L01 = self.L01(out_L00)
out_L02 = self.L02(out_L01)
out_d00 = self.d00(out_Lin)
out_d01 = self.d01(out_L00)
out_d02 = self.d02(out_L01)
out_L10 = self.L10(out_d00)
out_L11 = self.L11(out_d01 + out_L10)
out_L12 = self.L12(out_d02 + out_L11)
out_d10 = self.d10(out_d00)
out_d11 = self.d11(out_L10 + out_d01)
out_d12 = self.d12(out_L11 + out_d02)
out_L20 = self.L20(out_d10)
out_L21 = self.L21(out_d11 + out_L20)
out_L22 = self.L22(out_d12 + out_L21)
out_u10 = self.u10(out_L22)
out_L23 = self.L23(out_L22)
out_u11 = self.u11(out_L23)
out_L24 = self.L24(out_L23)
out_u12 = self.u12(out_L24)
out_L13 = self.L13(out_u10 + out_L12)
out_L14 = self.L14(out_u11 + out_L13)
out_u00 = self.u00(out_u10 + out_L12)
out_u01 = self.u01(out_u11 + out_L13)
out_u02 = self.u02(out_u12 + out_L14)
out_L03 = self.L03(out_u00 + out_L02)
out_L04 = self.L04(out_u01 + out_L03)
out_final = self.Lout(out_L04 + out_u02)
return out_final