resnet18加多头注意力机制代码

以下是使用PyTorch实现的ResNet18加多头注意力机制的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiheadAttention(nn.Module):
    def __init__(self, d_model, num_heads, dropout=0.1):
        super().__init__()
        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
        self.d_k = d_model // num_heads
        self.num_heads = num_heads
        self.dropout = dropout
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.out_linear = nn.Linear(d_model, d_model)
        
    def forward(self, q, k, v, mask=None):
        bs = q.size(0)
        # perform linear operation and split into h heads
        k = self.k_linear(k).view(bs, -1, self.num_heads, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.num_heads, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.num_heads, self.d_k)
        
        # transpose to get dimensions bs * h * sl * d_model
        k = k.transpose(1,2)
        q = q.transpose(1,2)
        v = v.transpose(1,2)
        
        # calculate attention scores
        scores = torch.matmul(q, k.transpose(-2, -1)) /  math.sqrt(self.d_k)
        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = scores.masked_fill(mask == 0, -1e9)
        
        # apply softmax to get attention probabilities
        attention = F.softmax(scores, dim=-1)
        attention = self.dropout(attention)
        
        # apply attention to v
        context = torch.matmul(attention, v)
        
        # transpose to get dimensions bs * sl * h * d_model
        context = context.transpose(1,2).contiguous().view(bs, -1, self.num_heads * self.d_k)
        
        # apply output linear layer
        output = self.out_linear(context)
        return output

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        self.multihead_attn = MultiheadAttention(d_model=out_channels, num_heads=8)
        
    def forward(self, x):
        shortcut = self.shortcut(x)
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.bn2(self.conv2(x))
        x = F.relu(torch.add(x, shortcut))
        x = self.multihead_attn(x, x, x)
        return x

class ResNet18(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNet18, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = nn.Sequential(
            ResidualBlock(64, 64),
            ResidualBlock(64, 64)
        )
        self.layer2 = nn.Sequential(
            ResidualBlock(64, 128, stride=2),
            ResidualBlock(128, 128)
        )
        self.layer3 = nn.Sequential(
            ResidualBlock(128, 256, stride=2),
            ResidualBlock(256, 256)
        )
        self.layer4 = nn.Sequential(
            ResidualBlock(256, 512, stride=2),
            ResidualBlock(512, 512)
        )
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
        
    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

在这个代码中，我们定义了一个 `MultiheadAttention` 类，它接受三个输入张量 `q`、`k`、`v`，并对它们进行多头注意力计算。我们还定义了一个 `ResidualBlock` 类，它是ResNet18中的基本块，其中包含一个卷积层、一个BatchNorm层、一个ReLU层、一个多头注意力层和一个跳跃连接。最后我们定义了一个 `ResNet18` 类，它由多个 `ResidualBlock` 组成，用于对图像进行分类。