设计神经网络的一般步骤
- 设计框架
- 设计骨干网络
Unet网络设计的步骤
- 设计Unet网络工厂模式
- 设计编解码结构
- 设计卷积模块
- unet实例模块
Unet网络最重要的特征
编解码结构。
2. 解码结构,比FCN更加完善,采用连接方式。
3. 本质是一个框架,编码部分可以使用很多图像分类网络。
示例代码
import torch
import torch.nn as nn
class Unet(nn.Module):
#初始化参数:Encoder,Decoder,bridge
#bridge默认值为无,如果有参数传入,则用该参数替换None
def __init__(self,Encoder,Decoder,bridge = None):
super(Unet,self).__init__()
self.encoder = Encoder(encoder_blocks)
self.decoder = Decoder(decoder_blocks)
self.bridge = bridge
def forward(self,x):
res = self.encoder(x)
out,skip = res[0],res[1,:]
if bridge is not None:
out = bridge(out)
out = self.decoder(out,skip)
return out
#设计编码模块
class Encoder(nn.Module):
def __init__(self,blocks):
super(Encoder,self).__init__()
#assert:断言函数,避免出现参数错误
assert len(blocks) > 0
#nn.Modulelist():模型列表,所有的参数可以纳入网络,但是没有forward函数
self.blocks = nn.Modulelist(blocks)
def forward(self,x):
skip = []
for i in range(len(self.blocks) - 1):
x = self.blocks[i](x)
skip.append(x)
res = [self.block[i+1](x)]
#列表之间可以通过+号拼接
res += skip
return res
#设计Decoder模块
class Decoder(nn.Module):
def __init__(self,blocks):
super(Decoder, self).__init__()
assert len(blocks) > 0
self.blocks = nn.Modulelist(blocks)
def ceter_crop(self,skips,x):
_,_,height1,width1 = skips.shape()
_,_,height2,width2 = x.shape()
#对图像进行剪切处理,拼接的时候保持对应size参数一致
ht,wt = min(height1,height2),min(width1,width2)
dh1 = (height1 - height2)//2 if height1 > height2 else 0
dw1 = (width1 - width2)//2 if width1 > width2 else 0
dh2 = (height2 - height1)//2 if height2 > height1 else 0
dw2 = (width2 - width1)//2 if width2 > width1 else 0
return skips[:,:,dh1:(dh1 + ht),dw1:(dw1 + wt)],\
x[:,:,dh2:(dh2 + ht),dw2 : (dw2 + wt)]
def forward(self, skips,x,reverse_skips = True):
assert len(skips) == len(blocks) - 1
if reverse_skips is True:
skips = skips[: : -1]
x = self.blocks[0](x)
for i in range(1, len(self.blocks)):
skip = skips[i-1]
x = torch.cat(skip,x,1)
x = self.blocks[i](x)
return x
#定义了一个卷积block
def unet_convs(in_channels,out_channels,padding = 0):
#nn.Sequential:与Modulelist相比,包含了forward函数
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernal_size = 3, padding = padding, bias = False),
nn.BatchNorm2d(outchannels),
nn.ReLU(inplace = True),
nn.Conv2d(in_channels, out_channels, kernal_size=3, padding=padding, bias=False),
nn.BatchNorm2d(outchannels),
nn.ReLU(inplace=True),
)
#实例化Unet模型
def unet(in_channels,out_channels):
encoder_blocks = [unet_convs(in_channels, 64),\
nn.Sequential(nn.Maxpool2d(kernal_size = 2, stride = 2, ceil_mode = True),\
unet_convs(64,128)), \
nn.Sequential(nn.Maxpool2d(kernal_size=2, stride=2, ceil_mode=True), \
unet_convs(128, 256)),
nn.Sequential(nn.Maxpool2d(kernal_size=2, stride=2, ceil_mode=True), \
unet_convs(256, 512)),
]
bridge = nn.Sequential(unet_convs(512, 1024))
decoder_blocks = [nn.conTranpose2d(1024, 512), \
nn.Sequential(unet_convs(1024, 512),
nn.conTranpose2d(512, 256)),\
nn.Sequential(unet_convs(512, 256),
nn.conTranpose2d(256, 128)), \
nn.Sequential(unet_convs(512, 256),
nn.conTranpose2d(256, 128)), \
nn.Sequential(unet_convs(256, 128),
nn.conTranpose2d(128, 64))
]
return Unet(encoder_blocks,decoder_blocks,bridge)