深度残差收缩网络的pytorch代码
时间: 2023-09-23 13:11:35 浏览: 120
以下是深度残差收缩网络的 PyTorch 代码:
```python
import torch
import torch.nn as nn
class ResidualShrinkageBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, padding=1, dilation=1,
reduction_ratio=16, last=False):
super(ResidualShrinkageBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,
padding=padding, dilation=dilation, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1,
padding=padding, dilation=dilation, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
self.padding = padding
self.dilation = dilation
self.last = last
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Linear(out_channels, out_channels // reduction_ratio)
self.fc2 = nn.Linear(out_channels // reduction_ratio, out_channels)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.last:
out = out
else:
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.fc1(out)
out = self.relu(out)
out = self.fc2(out)
out = self.sigmoid(out)
out = out.view(out.size(0), out.size(1), 1, 1)
out = out * identity
out += identity
out = self.relu(out)
return out
class ResidualShrinkageNet(nn.Module):
def __init__(self, num_classes=1000):
super(ResidualShrinkageNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(64, 256, 3)
self.layer2 = self._make_layer(256, 512, 4, stride=2)
self.layer3 = self._make_layer(512, 1024, 6, stride=2)
self.layer4 = self._make_layer(1024, 2048, 3, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(2048, num_classes)
def _make_layer(self, in_channels, out_channels, blocks, stride=1):
layers = []
layers.append(ResidualShrinkageBlock(in_channels, out_channels,
stride=stride, last=True))
for i in range(1, blocks):
layers.append(ResidualShrinkageBlock(out_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
这是一个四层残差结构的 Residual Shrinkage Net,其中每个残差块有一个特殊的缩减层,用于减少冗余特征。可以根据需要调整层数和通道数。
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