ResNet34+Unet(可以直接用)

ResNet34+Unet(可以直接用)importtorchfromtorchimportnnimporttorch.nn.functionalasF#因为ResNet34包含重复的单元,故用ResidualBlock类来简化代码classResidualBlock(nn.Module):def__init__(self,inchannel,outchannel,stride,shortcut=None):super(ResidualBlock,self).__init__(

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import torch
from torch import nn
import torch.nn.functional as F
# 因为ResNet34包含重复的单元,故用ResidualBlock类来简化代码
class ResidualBlock(nn.Module):
def __init__(self, inchannel, outchannel, stride, shortcut=None):
super(ResidualBlock, self).__init__()
self.basic = nn.Sequential(
nn.Conv2d(inchannel, outchannel, 3, stride, 1,
bias=False),  # 要采样的话在这里改变stride
nn.BatchNorm2d(outchannel),  # 批处理正则化
nn.ReLU(inplace=True),  # 激活
nn.Conv2d(outchannel, outchannel, 3, 1, 1,
bias=False),  # 采样之后注意保持feature map的大小不变
nn.BatchNorm2d(outchannel),
)
self.shortcut = shortcut
def forward(self, x):
out = self.basic(x)
residual = x if self.shortcut is None else self.shortcut(x)  # 计算残差
out += residual
return nn.ReLU(inplace=True)(out)  # 注意激活
class Conv2dReLU(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
padding=0,
stride=1,
):
super(Conv2dReLU, self).__init__()
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
bias=False
)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.relu(self.bn(self.conv(x)))
return x
class DecoderBlock(nn.Module):
def __init__(
self,
in_channels,
skip_channels,
out_channels,
):
super().__init__()
self.conv1 = Conv2dReLU(
in_channels + skip_channels,
out_channels,
kernel_size=3,
padding=1,
)
self.conv2 = Conv2dReLU(
out_channels,
out_channels,
kernel_size=3,
padding=1,
)
def forward(self, x, skip=None):
x = F.interpolate(x, scale_factor=2, mode="nearest")
if skip is not None:
x = torch.cat([x, skip], dim=1)
x = self.conv1(x)
x = self.conv2(x)
return x
class SegmentationHead(nn.Sequential):
def __init__(self,
in_channels=16,
out_channels=1,
kernel_size=3,
upsampling=1):
conv2d = nn.Conv2d(in_channels,
out_channels,
kernel_size=kernel_size,
padding=kernel_size // 2)
upsampling = nn.UpsamplingBilinear2d(
scale_factor=upsampling) if upsampling > 1 else nn.Identity()
super().__init__(conv2d, upsampling)
# ResNet类
class Resnet34(nn.Module):
def __init__(self, inchannels):
super(Resnet34, self).__init__()
self.pre = nn.Sequential(
nn.Conv2d(inchannels, 64, 7, 2, 3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 1),
)  # 开始的部分
self.body = self.makelayers([3, 4, 6, 3])  # 具有重复模块的部分
in_channels = [512, 256, 128, 128, 32]
skip_channels = [256, 128, 64, 0, 0]
out_channels = [256, 128, 64, 32, 16]
blocks = [
DecoderBlock(in_ch, skip_ch,
out_ch) for in_ch, skip_ch, out_ch in zip(
in_channels, skip_channels, out_channels)
]
self.blocks = nn.ModuleList(blocks)
self.seg = SegmentationHead()
def makelayers(self, blocklist):  # 注意传入列表而不是解列表
self.layers = []
for index, blocknum in enumerate(blocklist):
if index != 0:
shortcut = nn.Sequential(
nn.Conv2d(64 * 2**(index - 1),
64 * 2**index,
1,
2,
bias=False),
nn.BatchNorm2d(64 * 2**index))  # 使得输入输出通道数调整为一致
self.layers.append(
ResidualBlock(64 * 2**(index - 1), 64 * 2**index, 2,
shortcut))  # 每次变化通道数时进行下采样
for i in range(0 if index == 0 else 1, blocknum):
self.layers.append(
ResidualBlock(64 * 2**index, 64 * 2**index, 1))
return nn.Sequential(*self.layers)
def forward(self, x):
self.features = []
# 下采样
# x = self.pre(x)
for i, l in enumerate(self.pre):
x = l(x)
if i == 2:
self.features.append(x)
print("y=", len(self.features))
for i, l in enumerate(self.body):
if i == 3 or i == 7 or i == 13:
self.features.append(x)
x = l(x)
skips = self.features[::-1]
# skips = self.features[1:]
for i, decoder_block in enumerate(self.blocks):
skip = skips[i] if i < len(skips) else None
x = decoder_block(x, skip)
x = self.seg(x)
return x



四次Skipconnect分别在:Maxpool前;另外三次在通道数变化前。
上采样combine时采用的是插值(nn.functionnal.interpolate)。

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