resnet18+多头注意力机制代码

以下是使用ResNet18和多头注意力机制的代码示例：

import torch
import torch.nn as nn

class MultiHeadAttention(nn.Module):
    def __init__(self, embed_dim, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads

        self.q_linear = nn.Linear(embed_dim, embed_dim)
        self.k_linear = nn.Linear(embed_dim, embed_dim)
        self.v_linear = nn.Linear(embed_dim, embed_dim)

        self.fc = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        batch_size = x.size(0)

        # Project inputs to query, key, and value tensors
        q = self.q_linear(x).view(batch_size, self.num_heads, self.head_dim)
        k = self.k_linear(x).view(batch_size, self.num_heads, self.head_dim)
        v = self.v_linear(x).view(batch_size, self.num_heads, self.head_dim)

        # Compute self-attention scores
        scores = torch.matmul(q, k.transpose(-2, -1)) / self.embed_dim**0.5

        # Apply softmax activation function
        scores = nn.functional.softmax(scores, dim=-1)

        # Compute weighted sum of values using attention scores
        weighted_values = torch.matmul(scores, v)

        # Concatenate multi-head attention outputs
        concat_heads = weighted_values.transpose(1, 2).contiguous().view(batch_size, -1, self.embed_dim)

        # Apply fully connected layer to concatenated outputs
        output = self.fc(concat_heads)

        return output

class ResNet18(nn.Module):
    def __init__(self, num_classes, embed_dim, num_heads):
        super(ResNet18, self).__init__()
        self.num_classes = num_classes
        self.embed_dim = embed_dim
        self.num_heads = num_heads

        # Define ResNet18 layers
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * self.embed_dim, self.num_classes)

        # Define multi-head attention layer
        self.multi_head_attention = MultiHeadAttention(self.embed_dim, self.num_heads)

    def forward(self, x):
        # ResNet18 layers
        x = self.conv1(x)
        x = self.bn1(x)
        x = nn.functional.relu(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)

        # Multi-head attention layer
        x = self.multi_head_attention(x)

        # Fully connected layer
        x = self.fc(x)

        return x

在此代码中，我们首先定义了一个名为`MultiHeadAttention`的多头注意力机制模块。该模块接受一个张量作为输入，并计算该张量的自注意力表示。我们还定义了一个名为`ResNet18`的ResNet18模型，该模型使用多头注意力机制以更好地捕获输入数据中的关键信息。该模型在ResNet18的基础上添加了一个多头注意力层，该层将ResNet18的输出作为输入，并在完全连接层之前对其进行处理。