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- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- def conv3x3(in_channels, out_channels, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
- def conv1x1(in_channels, out_channels, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0, bias=False)
- class ReactionDiffusionLayer(nn.Module):
- def __init__(self, channel_size, kernel_size=3):
- super(ReactionDiffusionLayer, self).__init__()
- self.channel_size = channel_size
- self.D = nn.Parameter(torch.ones(1, channel_size, 1, 1)) # 可训练的扩散系数D
- self.conv = nn.Conv2d(channel_size, channel_size, kernel_size, padding=1, groups=channel_size, bias=False)
- self.reaction = nn.Sequential( # 定义R(C)的非线性反应函数
- nn.Conv2d(channel_size, channel_size, 1),
- nn.ReLU(),
- nn.Conv2d(channel_size, channel_size, 1)
- )
- # 初始化卷积核为拉普拉斯算子
- laplacian_kernel = torch.tensor([[[[0, 1, 0], [1, -4, 1], [0, 1, 0]]]], dtype=torch.float32)
- self.conv.weight = nn.Parameter(laplacian_kernel.repeat(channel_size, 1, 1, 1))
-
- def forward(self, x):
- diffusion = self.conv(x * self.D) # 执行扩散过程
- reaction = self.reaction(x) # 执行反应过程
- return x + diffusion + reaction # 根据反应-扩散方程更新特征图
- class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, in_channels, out_channels, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- # self.conv1 = conv3x3(in_channels, out_channels, stride)
- self.conv1 = conv1x1(in_channels, out_channels, stride)
- self.bn1 = nn.BatchNorm2d(out_channels)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(out_channels, out_channels)
- self.bn2 = nn.BatchNorm2d(out_channels)
- self.downsample = downsample
- self.stride = stride
- self.rd_layer = ReactionDiffusionLayer(in_channels)
-
- def forward(self, x):
- identity = x
-
- out=self.rd_layer(x)
- # out = self.conv1(x)
- out = self.conv1(out)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
-
- return out
-
- class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, in_channels, out_channels, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
- # self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(out_channels)
- self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(out_channels)
- self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
- self.rd_layer = ReactionDiffusionLayer(in_channels)
- def forward(self, x):
- identity = x
- out=self.rd_layer(x)
- out = self.conv1(out)
- # out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
-
- return out
-
- class ResNet(nn.Module):
- def __init__(self, block, layers, num_classes=1000):
- super(ResNet, self).__init__()
- self.in_channels = 64
-
- 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(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.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-
- def _make_layer(self, block, out_channels, blocks, stride=1):
- downsample = None
- if stride != 1 or self.in_channels != out_channels * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(out_channels * block.expansion),
- )
-
- layers = []
- layers.append(block(self.in_channels, out_channels, stride, downsample))
-
- self.in_channels = out_channels * block.expansion
- for _ in range(1, blocks):
- layers.append(block(self.in_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
-
- def resnet18():
- return ResNet(BasicBlock, [2, 2, 2, 2])
-
- def resnet34():
- return ResNet(BasicBlock, [3, 4, 6, 3])
-
- def resnet50():
- return ResNet(Bottleneck, [3, 4, 6, 3])
-
- def resnet101():
- return ResNet(Bottleneck, [3, 4, 23, 3])
-
- def resnet152():
- return ResNet(Bottleneck, [3, 8, 36, 3])
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