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model.py
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import sys
from torch.distributions.normal import Normal
import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True,
bn=False,
num_groups=8):
super(Encoder, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.relu = nn.ReLU()
self.conv1 = nn.Conv3d(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
bias=bias)
self.gn1 = nn.GroupNorm(num_groups, out_channels)
self.conv2 = nn.Conv3d(out_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
bias=bias)
self.gn2 = nn.GroupNorm(num_groups, out_channels)
def forward(self, x):
identity = x
res = self.relu(x)
res = self.conv1(res)
res = self.gn1(res)
res = self.relu(res)
res = self.conv2(res)
res = self.gn2(res)
res = self.relu(res)
if self.in_channels != self.out_channels:
pad = [0] * (2 * len(identity.size()))
pad[6] = (self.out_channels - self.in_channels)
identity = F.pad(input=identity, pad=pad, mode='constant', value=0)
return res + identity
class UNet3D(nn.Module):
def __init__(self,
in_channel,
n_classes,
use_bias=True,
inplanes=32,
num_groups=8):
self.in_channel = in_channel
self.n_classes = n_classes
self.inplanes = inplanes
self.num_groups = num_groups
planes = [inplanes * int(pow(2, i)) for i in range(0, 5)]
super(UNet3D, self).__init__()
self.ec0 = Encoder(in_channel,
planes[1],
bias=use_bias,
num_groups=num_groups)
self.ec1 = Encoder(planes[1],
planes[2],
bias=use_bias,
num_groups=num_groups)
self.ec1_2 = Encoder(planes[2],
planes[2],
bias=use_bias,
num_groups=num_groups)
self.ec2 = Encoder(planes[2],
planes[3],
bias=use_bias,
num_groups=num_groups)
self.ec2_2 = Encoder(planes[3],
planes[3],
bias=use_bias,
num_groups=num_groups)
self.ec3 = Encoder(planes[3],
planes[4],
bias=use_bias,
num_groups=num_groups)
self.ec3_2 = Encoder(planes[4],
planes[4],
bias=use_bias,
num_groups=num_groups)
self.maxpool = nn.MaxPool3d(2)
self.dc3 = Encoder(planes[4],
planes[4],
bias=use_bias,
num_groups=num_groups)
self.dc3_2 = Encoder(planes[4],
planes[4],
bias=use_bias,
num_groups=num_groups)
self.up3 = self.decoder(planes[4],
planes[3],
kernel_size=2,
stride=2,
bias=use_bias)
self.dc2 = Encoder(planes[4],
planes[3],
bias=use_bias,
num_groups=num_groups)
self.dc2_2 = Encoder(planes[3],
planes[3],
bias=use_bias,
num_groups=num_groups)
self.up2 = self.decoder(planes[3],
planes[2],
kernel_size=2,
stride=2,
bias=use_bias)
self.dc1 = Encoder(planes[3],
planes[2],
bias=use_bias,
num_groups=num_groups)
self.dc1_2 = Encoder(planes[2],
planes[2],
bias=use_bias,
num_groups=num_groups)
self.up1 = self.decoder(planes[2],
planes[1],
kernel_size=2,
stride=2,
bias=use_bias)
self.dc0a = Encoder(planes[2],
planes[1],
bias=use_bias,
num_groups=num_groups)
self.dc0b = self.decoder(planes[1],
n_classes,
kernel_size=1,
stride=1,
bias=use_bias,
relu=False)
for m in self.modules():
if isinstance(m, nn.Conv3d) or isinstance(m, nn.ConvTranspose3d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, nn.GroupNorm):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def decoder(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
output_padding=0,
bias=True,
relu=True):
layer = [
nn.ConvTranspose3d(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
output_padding=output_padding,
bias=bias),
]
if relu:
layer.append(nn.GroupNorm(self.num_groups, out_channels))
layer.append(nn.ReLU())
layer = nn.Sequential(*layer)
return layer
def forward(self, x):
e0 = self.ec0(x)
e1 = self.ec1_2(self.ec1(self.maxpool(e0)))
e2 = self.ec2_2(self.ec2(self.maxpool(e1)))
e3 = self.ec3_2(self.ec3(self.maxpool(e2)))
d3 = self.up3(self.dc3_2(self.dc3(e3)))
if d3.size()[2:] != e2.size()[2:]:
d3 = F.interpolate(d3,
e2.size()[2:],
mode='trilinear',
align_corners=False)
d3 = torch.cat((d3, e2), 1)
d2 = self.up2(self.dc2_2(self.dc2(d3)))
if d2.size()[2:] != e1.size()[2:]:
d2 = F.interpolate(d2,
e1.size()[2:],
mode='trilinear',
align_corners=False)
d2 = torch.cat((d2, e1), 1)
d1 = self.up1(self.dc1_2(self.dc1(d2)))
if d1.size()[2:] != e0.size()[2:]:
d1 = F.interpolate(d1,
e0.size()[2:],
mode='trilinear',
align_corners=False)
d1 = torch.cat((d1, e0), 1)
d0 = self.dc0b(self.dc0a(d1))
return d0