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import torch
import torch.nn as nn
import torch.nn.functional as F
def passthrough(x, **kwargs):
return x
def ELUCons(elu, nchan):
if elu:
return nn.ELU(inplace=True)
else:
return nn.PReLU(nchan)
# normalization between sub-volumes is necessary
# for good performance
class ContBatchNorm3d(nn.modules.batchnorm._BatchNorm):
def _check_input_dim(self, input):
if input.dim() != 5:
raise ValueError('expected 5D input (got {}D input)'
.format(input.dim()))
super(ContBatchNorm3d, self)._check_input_dim(input)
def forward(self, input):
self._check_input_dim(input)
return F.batch_norm(
input, self.running_mean, self.running_var, self.weight, self.bias,
True, self.momentum, self.eps)
class LUConv(nn.Module):
def __init__(self, nchan, elu):
super(LUConv, self).__init__()
self.relu1 = ELUCons(elu, nchan)
self.conv1 = nn.Conv3d(nchan, nchan, kernel_size=5, padding=2)
self.bn1 = nn.BatchNorm3d(nchan)
def forward(self, x):
out = self.relu1(self.bn1(self.conv1(x)))
return out
def _make_nConv(nchan, depth, elu):
layers = []
for _ in range(depth):
layers.append(LUConv(nchan, elu))
return nn.Sequential(*layers)
class InputTransition(nn.Module):
def __init__(self, outChans, elu):
super(InputTransition, self).__init__()
self.conv1 = nn.Conv3d(1, 16, kernel_size=5, padding=2)
self.bn1 = nn.BatchNorm3d(16)
self.relu1 = ELUCons(elu, 16)
def forward(self, x):
# do we want a PRELU here as well?
out=self.conv1(x)
out = self.bn1(out)
# split input in to 16 channels
x16 = torch.cat((x, x, x, x, x, x, x, x,
x, x, x, x, x, x, x, x), 1)
out = self.relu1(torch.add(out, x16))
return out
class DownTransition(nn.Module):
def __init__(self, inChans, nConvs, elu, dropout=False):
super(DownTransition, self).__init__()
outChans = 2*inChans
self.down_conv = nn.Conv3d(inChans, outChans, kernel_size=2, stride=2)
self.bn1 = nn.BatchNorm3d(outChans)
self.do1 = passthrough
self.relu1 = ELUCons(elu, outChans)
self.relu2 = ELUCons(elu, outChans)
if dropout:
self.do1 = nn.Dropout3d()
self.ops = _make_nConv(outChans, nConvs, elu)
def forward(self, x):
down = self.relu1(self.bn1(self.down_conv(x)))
out = self.do1(down)
out = self.ops(out)
out = self.relu2(torch.add(out, down))
return out
class UpTransition(nn.Module):
def __init__(self, inChans, outChans, nConvs, elu, dropout=False):
super(UpTransition, self).__init__()
self.up_conv = nn.ConvTranspose3d(inChans, outChans // 2, kernel_size=2, stride=2)
self.bn1 = nn.BatchNorm3d(outChans // 2)
self.do1 = passthrough
self.do2 = nn.Dropout3d()
self.relu1 = ELUCons(elu, outChans // 2)
self.relu2 = ELUCons(elu, outChans)
if dropout:
self.do1 = nn.Dropout3d()
self.ops = _make_nConv(outChans, nConvs, elu)
def forward(self, x, skipx):
out = self.do1(x)
skipxdo = self.do2(skipx)
out = self.relu1(self.bn1(self.up_conv(out)))
xcat = torch.cat((out, skipxdo), 1)
out = self.ops(xcat)
out = self.relu2(torch.add(out, xcat))
return out
class OutputTransition(nn.Module):
def __init__(self, inChans, elu, nll):
super(OutputTransition, self).__init__()
self.conv1 = nn.Conv3d(inChans, 2, kernel_size=5, padding=2)
self.bn1 = nn.BatchNorm3d(2)
self.conv2 = nn.Conv3d(2, 2, kernel_size=1)
self.relu1 = ELUCons(elu, 2)
if nll:
self.softmax = F.log_softmax
else:
self.softmax = F.softmax
def forward(self, x):
# convolve 32 down to 2 channels
out = self.relu1(self.bn1(self.conv1(x)))
out = self.conv2(out)
print(out.shape)
# make channels the last axis
out = out.permute(0, 2, 3, 4, 1).contiguous()
# flatten
out = out.view(out.numel() // 2, 2)
out = self.softmax(out)
# treat channel 0 as the predicted output
return out
class AdaptivePoolTransition(nn.Module):
def __init__(self,in_channels,out_channels):
super().__init__()
self.pool=nn.AdaptiveAvgPool3d(1)
self.ops=nn.Sequential(
nn.Linear(in_channels,in_channels//2),
ELUCons(elu=True,nchan=0),
nn.Linear(in_channels // 2,out_channels),
nn.Sigmoid()
)
def forward(self,x):
B,C,D,H,W=x.shape
out=self.pool(x).view(B,C)
return self.ops(out)
class VNet(nn.Module):
# the number of convolutions in each layer corresponds
# to what is in the actual prototxt, not the intent
def __init__(self, elu=True, nll=False):
super(VNet, self).__init__()
self.in_tr = InputTransition(16, elu)
self.down_tr32 = DownTransition(16, 1, elu)
self.down_tr64 = DownTransition(32, 2, elu)
self.down_tr128 = DownTransition(64, 3, elu, dropout=True)
self.down_tr256 = DownTransition(128, 2, elu, dropout=True)
self.up_tr256 = UpTransition(256, 256, 2, elu, dropout=True)
self.up_tr128 = UpTransition(256, 128, 2, elu, dropout=True)
self.up_tr64 = UpTransition(128, 64, 1, elu)
self.up_tr32 = UpTransition(64, 32, 1, elu)
# self.out_tr = OutputTransition(32, elu, nll)
self.pool=AdaptivePoolTransition(32,6)
# The network topology as described in the diagram
# in the VNet paper
# def __init__(self):
# super(VNet, self).__init__()
# self.in_tr = InputTransition(16)
# # the number of convolutions in each layer corresponds
# # to what is in the actual prototxt, not the intent
# self.down_tr32 = DownTransition(16, 2)
# self.down_tr64 = DownTransition(32, 3)
# self.down_tr128 = DownTransition(64, 3)
# self.down_tr256 = DownTransition(128, 3)
# self.up_tr256 = UpTransition(256, 3)
# self.up_tr128 = UpTransition(128, 3)
# self.up_tr64 = UpTransition(64, 2)
# self.up_tr32 = UpTransition(32, 1)
# self.out_tr = OutputTransition(16)
def forward(self, x):
out16 = self.in_tr(x)
out32 = self.down_tr32(out16)
out64 = self.down_tr64(out32)
out128 = self.down_tr128(out64)
out256 = self.down_tr256(out128)
out = self.up_tr256(out256, out128)
out = self.up_tr128(out, out64)
out = self.up_tr64(out, out32)
out = self.up_tr32(out, out16)
# out = self.out_tr(out)
out=self.pool(out)
return out
if __name__ == '__main__':
device='cuda' if torch.cuda.is_available() else 'cpu'
print(device)
net=VNet().to(device)
image=torch.randn((1,1,128,160,160)).to(device)
with torch.no_grad():
out=net(image)
print(out.shape)
# m = nn.AdaptiveAvgPool3d((1))
# input = torch.randn(1, 64, 8, 9, 10)
# output = m(input)
# print(output.shape) |