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import torch.nn as nn
# 残差块
class IRBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
super(IRBlock, self).__init__()
self.bn0 = nn.BatchNorm2d(inplanes)
self.conv1 = nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(inplanes)
self.prelu = nn.PReLU()
self.conv2 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample # downsample 对输入特征图大小进行减半处理
self.stride = stride
self.use_se = use_se
if self.use_se:
self.se = SEBlock(planes)
# 残差块里首先有 2 个相同输出通道数的 卷积层,
# 每个卷积层后接一个批量归一化层和 ReLU 激活函数*,
# 然后我们将输入跳过这 2 个卷积运算后直接加在最后的 ReLU 激活函数*前.
# *此文件中是 PReLU 激活函数
# PReLU 和 ReLU 的区别主要是前者在 输入小于 0 的部分加了一个系数 a,
# 若 a ==0, PReLU 退化为 ReLU；若 a 很小(比如0.01) ,PReLU 退化为 LReLU,
# 有实验证明，与ReLU相比，LReLU对最终的结果几乎没什么影响。
def forward(self, x):
residual = x
out = self.bn0(x)
out = self.conv1(out)
out = self.bn1(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.use_se:
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.prelu(out)
return out
class SEBlock(nn.Module):
def __init__(self, channel, reduction=16):
super(SEBlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
nn.PReLU(),
nn.Linear(channel // reduction, channel),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
class ResNet(nn.Module):
def __init__(self, block, layers, use_se=True):
self.inplanes = 64
self.use_se = use_se
super(ResNet, self).__init__()
# 所有 ResNet 网络的输入由一个大卷积核+最大池化组成，极大减少了存储所需大小
self.conv1 = nn.Conv2d(1, 64, kernel_size=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.prelu = nn.PReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=3)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.pool = nn.AdaptiveMaxPool2d((1, 1))
self.bn4 = nn.BatchNorm2d(512)
self.dropout = nn.Dropout()
self.flatten = nn.Flatten()
self.fc5 = nn.Linear(512, 512)
self.bn5 = nn.BatchNorm1d(512)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),)
layers = [block(self.inplanes, planes, stride, downsample, use_se=self.use_se)]
self.inplanes = planes
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, use_se=self.use_se))
return nn.Sequential(*layers)
# 规定了网络数据的流向
def forward(self, x):
# 输入
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.maxpool(x)
# 中间卷积
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
# 输出
x = self.pool(x)
x = self.bn4(x)
x = self.dropout(x)
x = self.flatten(x)
x = self.fc5(x)
x = self.bn5(x)
return x
# 3,4,6,3 是 ResNet34 卷积部分的配置，至于为什么要用这个配置没有解释清楚
def resnet34(use_se=True):
model = ResNet(IRBlock, [3, 4, 6, 3], use_se=use_se)
return model
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