1、基础理论
深度残差收缩网络是建立在三个部分的基础之上的，包括残差网络、注意力机制和软阈值化。
（面向强噪、高冗余数据的）残差收缩网络其功能特色包括：
1）由于软阈值化是信号降噪算法的常用步骤，所以深度残差收缩网络比较适合强噪、高冗余数据。同时，软阈值化的梯度要么为0，要么为1，这与ReLU激活函数是相似/一致的。
软阈值化2）由于软阈值化的阈值是通过类似于SENet的注意力机制自适应地进行设置的，深度残差收缩网络能够根据每个样本的情况，为每个样本单独地设置阈值，因此适用于每个样本内噪声含量不同的情况。
3）当数据噪声很弱、没有噪声时，深度残差收缩网络可能也是适用的。其前提是阈值可以被训练成非常接近于0的值，从而软阈值化就相当于不存在了。
4）值得注意的是，软阈值函数的阈值不能太大，否则会导致所有的输出都是0。所以深度残差收缩网络的注意力模块是经过专门设计的，与一般的SENet是存在明显区别的。
该方法的文献来源：
M. Zhao, S. Zhong, X. Fu, B. Tang, M. Pecht, Deep residual shrinkage networks for fault diagnosis, IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4681-4690, 2020. （https://ieeexplore.ieee.org/document/8850096/）
2、PyTorch代码
本文的PyTorch代码是在这份代码（https://github.com/weiaicunzai/pytorch-cifar100）的基础上修改得到的，所以要下载这份代码到本地。主要是修改了models/resnet.py（https://github.com/weiaicunzai/pytorch-cifar100/blob/master/models/resnet.py）和utils.py（https://github.com/weiaicunzai/pytorch-cifar100/blob/master/utils.py）的代码。
另一方面，残差收缩网络的核心代码，则是来源于知乎上最前线创作的一篇文章《用于故障诊断的残差收缩网络》（https://zhuanlan.zhihu.com/p/337346575）。
具体地，将resnet.py文件的名称，改为了rsnet.py，意思是residual shrinkage network。修改后的rsnet.py代码如下：
代码语言：python代码运行次数：0复制import torch
import torch.nn as nn
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1):
super().__init__()
self.shrinkage = Shrinkage(out_channels, gap_size=(1, 1))
#residual function
self.residual_function = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels * BasicBlock.expansion),
self.shrinkage
)
#shortcut
self.shortcut = nn.Sequential()
#the shortcut output dimension is not the same with residual function
#use 1*1 convolution to match the dimension
if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels * BasicBlock.expansion)
)
def forward(self, x):
return nn.ReLU(inplace=True)(self.residual_function(x) self.shortcut(x))
class Shrinkage(nn.Module):
def __init__(self, channel, gap_size):
super(Shrinkage, self).__init__()
self.gap = nn.AdaptiveAvgPool2d(gap_size)
self.fc = nn.Sequential(
nn.Linear(channel, channel),
nn.BatchNorm1d(channel),
nn.ReLU(inplace=True),
nn.Linear(channel, channel),
nn.Sigmoid(),
)
def forward(self, x):
x_raw = x
x = torch.abs(x)
x_abs = x
x = self.gap(x)
x = torch.flatten(x, 1)
# average = torch.mean(x, dim=1, keepdim=True)
average = x
x = self.fc(x)
x = torch.mul(average, x)
x = x.unsqueeze(2).unsqueeze(2)
# soft thresholding
sub = x_abs - x
zeros = sub - sub
n_sub = torch.max(sub, zeros)
x = torch.mul(torch.sign(x_raw), n_sub)
return x
class RSNet(nn.Module):
def __init__(self, block, num_block, num_classes=100):
super().__init__()
self.in_channels = 64
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True))
#we use a different inputsize than the original paper
#so conv2_x's stride is 1
self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, out_channels, num_blocks, stride):
"""make rsnet layers(by layer i didnt mean this 'layer' was the
same as a neuron netowork layer, ex. conv layer), one layer may
contain more than one residual shrinkage block
Args:
block: block type, basic block or bottle neck block
out_channels: output depth channel number of this layer
num_blocks: how many blocks per layer
stride: the stride of the first block of this layer
Return:
return a rsnet layer
"""
# we have num_block blocks per layer, the first block
# could be 1 or 2, other blocks would always be 1
strides = [stride] [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
output = self.conv1(x)
output = self.conv2_x(output)
output = self.conv3_x(output)
output = self.conv4_x(output)
output = self.conv5_x(output)
output = self.avg_pool(output)
output = output.view(output.size(0), -1)
output = self.fc(output)
return output
def rsnet18():
""" return a RSNet 18 object
"""
return RSNet(BasicBlock, [2, 2, 2, 2])
def rsnet34():
""" return a RSNet 34 object
"""
return RSNet(BasicBlock, [3, 4, 6, 3])然后，将utils.py文件中的第62-64行：
代码语言：python代码运行次数：0复制 elif args.net == 'resnet18':
from models.resnet import resnet18
net = resnet18()修改为：
代码语言：python代码运行次数：0复制 elif args.net == 'rsnet18':
from models.rsnet import rsnet18
net = rsnet18()然后在运行窗口输入：
代码语言：python代码运行次数：0复制python train.py -net rsnet18 -gpu就可以运行程序了。
3、其他代码
论文原作者在GitHub上提供了TFLearn和Keras代码，见链接：https://github.com/zhao62/Deep-Residual-Shrinkage-Networks
也有网友编写了TensorFlow 2.0的代码：
https://blog.csdn.net/qq_36758914/article/details/109452735
