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Unet3D.py
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from torch.nn import Module, Sequential
from torch.nn import Conv3d, ConvTranspose3d, BatchNorm3d, MaxPool3d, AvgPool1d
from torch.nn import ReLU, Sigmoid
import torch
class UNet3D(Module):
# __ __
# 1|__ ________________ __|1
# 2|__ ____________ __|2
# 3|__ ______ __|3
# 4|__ __ __|4
# The convolution operations on either side are residual subject to 1*1 Convolution for channel homogeneity
def __init__(self,num_channels=32,feat_channels=[64, 128, 256, 512, 1024], residual='conv'):
#residual: 是否加入残差边，不加则为None
super(UNet3D, self).__init__()
# Encoder downsamplers
self.pool1 = MaxPool3d((1,2,2))
self.pool2 = MaxPool3d((1,2,2))
self.pool3 = MaxPool3d((1,2,2))
self.pool4 = MaxPool3d((1,2,2))
# Encoder convolutions
self.conv_blk1 = Conv3D_Block(num_channels, feat_channels[0], residual=residual)
self.conv_blk2 = Conv3D_Block(feat_channels[0], feat_channels[1], residual=residual)
self.conv_blk3 = Conv3D_Block(feat_channels[1], feat_channels[2], residual=residual)
self.conv_blk4 = Conv3D_Block(feat_channels[2], feat_channels[3], residual=residual)
self.conv_blk5 = Conv3D_Block(feat_channels[3], feat_channels[4], residual=residual)
# Decoder convolutions
self.dec_conv_blk4 = Conv3D_Block(2*feat_channels[3], feat_channels[3], residual=residual)
self.dec_conv_blk3 = Conv3D_Block(2*feat_channels[2], feat_channels[2], residual=residual)
self.dec_conv_blk2 = Conv3D_Block(2*feat_channels[1], feat_channels[1], residual=residual)
self.dec_conv_blk1 = Conv3D_Block(2*feat_channels[0], feat_channels[0], residual=residual)
# Decoder upsamplers
self.deconv_blk4 = Deconv3D_Block(feat_channels[4], feat_channels[3])
self.deconv_blk3 = Deconv3D_Block(feat_channels[3], feat_channels[2])
self.deconv_blk2 = Deconv3D_Block(feat_channels[2], feat_channels[1])
self.deconv_blk1 = Deconv3D_Block(feat_channels[1], feat_channels[0])
# Final 1*1 Conv Segmentation map
self.one_conv = Conv3d(feat_channels[0], num_channels, kernel_size=1, stride=1, padding=0, bias=True)
# Activation function
self.sigmoid = Sigmoid()
def forward(self, x):
# 编码器
x1 = self.conv_blk1(x)
x_low1 = self.pool1(x1)
x2 = self.conv_blk2(x_low1)
x_low2 = self.pool2(x2)
x3 = self.conv_blk3(x_low2)
x_low3 = self.pool3(x3)
x4 = self.conv_blk4(x_low3)
x_low4 = self.pool4(x4)
base = self.conv_blk5(x_low4)
# 解码器
d4 = torch.cat([self.deconv_blk4(base), x4], dim=1)
d_high4 = self.dec_conv_blk4(d4)
d3 = torch.cat([self.deconv_blk3(d_high4), x3], dim=1)
d_high3 = self.dec_conv_blk3(d3)
d2 = torch.cat([self.deconv_blk2(d_high3), x2], dim=1)
d_high2 = self.dec_conv_blk2(d2)
d1 = torch.cat([self.deconv_blk1(d_high2), x1], dim=1)
d_high1 = self.dec_conv_blk1(d1)
seg = self.sigmoid(self.one_conv(d_high1))
return seg
class Conv3D_Block(Module):
def __init__(self, inp_feat, out_feat, kernel=3, stride=1, padding=1, residual=None):
super(Conv3D_Block, self).__init__()
self.conv1 = Sequential(
Conv3d(inp_feat, out_feat, kernel_size=kernel,
stride=stride, padding=padding, bias=True),
BatchNorm3d(out_feat),
ReLU())
self.conv2 = Sequential(
Conv3d(out_feat, out_feat, kernel_size=kernel,
stride=stride, padding=padding, bias=True),
BatchNorm3d(out_feat),
ReLU())
self.residual = residual
if self.residual is not None:
self.residual_upsampler = Conv3d(inp_feat, out_feat, kernel_size=1, bias=False)
def forward(self, x):
res = x
if not self.residual:
return self.conv2(self.conv1(x))
else:
return self.conv2(self.conv1(x)) + self.residual_upsampler(res)
class Deconv3D_Block(Module):
def __init__(self, inp_feat, out_feat, kernel=4, stride=2, padding=1):
super(Deconv3D_Block, self).__init__()
self.deconv = Sequential(
#3D反卷积
ConvTranspose3d(inp_feat, out_feat, kernel_size=(1,kernel,kernel),
stride=(1,stride,stride), padding=(0, padding, padding), output_padding=0, bias=True),
ReLU())
def forward(self, x):
return self.deconv(x)
class ChannelPool3d(AvgPool1d):
def __init__(self, kernel_size, stride, padding):
super(ChannelPool3d, self).__init__(kernel_size, stride, padding)
self.pool_1d = AvgPool1d(self.kernel_size, self.stride, self.padding, self.ceil_mode)
def forward(self, inp):
n, c, d, w, h = inp.size()
inp = inp.view(n,c,d*w*h).permute(0,2,1)
pooled = self.pool_1d(inp)
c = int(c/self.kernel_size[0])
return inp.view(n,c,d,w,h)
if __name__=='__main__':
net = UNet3D(residual='pool')
import torch
x = torch.ones(4, 32, 1, 128, 128)
print (net.forward(x).size())