attn = (Q * (Q.shape[1] ** -0.5)) @ K.transpose(-2, -1)

这是一个自注意力机制中计算注意力分数的公式，其中Q、K、V分别表示查询、键、值，@表示张量的矩阵乘法运算符。通过将查询和键相乘得到注意力分数矩阵，再将该矩阵与值相乘得到注意力加权值。 具体来说，在一个序列中，每个元素都会有一个对应的查询向量,这个查询向量会去和序列中的其他元素做点乘来得到注意力分数，这个查询向量就是Q；同时序列中每个元素也对应一个键和一个值，这个键就是K，值就是V。通过计算Q和K的点积，再除以$\sqrt{d}$来得到注意力分数，最后再将W和V相乘得到输出结果。

相关问题

Swin-Transform模块代码详细

以下是Swin-Transform模块的详细代码示例：

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

class SwinTransformer(nn.Module):
    def __init__(self, image_size=224, patch_size=4, embed_dim=96, depths=[2, 2, 6, 2], 
                 num_heads=[3, 6, 12, 24], num_classes=1000):
        super(SwinTransformer, self).__init__()
        assert image_size % patch_size == 0, "image size must be divisible by patch size"
        num_patches = (image_size // patch_size) ** 2
        patch_dim = 3 * patch_size ** 2

        # Patch Embedding
        self.patch_embed = nn.Conv2d(3, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))

        # Transformer Encoder
        self.transformer_encoder = TransformerEncoder(embed_dim, depths, num_heads)

        # Classification Head
        self.head = nn.Linear(embed_dim, num_classes)

    def forward(self, x):
        x = self.patch_embed(x)  # [batch_size, embed_dim, H', W']
        x = x.flatten(2).transpose(1, 2)  # [batch_size, num_patches, embed_dim]
        batch_size, num_patches, _ = x.shape

        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # [batch_size, 1, embed_dim]
        x = torch.cat((cls_tokens, x), dim=1)  # [batch_size, num_patches+1, embed_dim]
        x = x + self.pos_embed  # [batch_size, num_patches+1, embed_dim]

        x = self.transformer_encoder(x)

        x = x.mean(dim=1)  # [batch_size, embed_dim]
        x = self.head(x)  # [batch_size, num_classes]

        return x


class TransformerEncoder(nn.Module):
    def __init__(self, embed_dim, depths, num_heads):
        super(TransformerEncoder, self).__init__()
        self.layers = nn.ModuleList()
        for i in range(len(depths)):
            self.layers.append(TransformerEncoderLayer(embed_dim, depths[i], num_heads[i]))

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x


class TransformerEncoderLayer(nn.Module):
    def __init__(self, embed_dim, depth, num_heads):
        super(TransformerEncoderLayer, self).__init__()
        self.attention_norm = nn.LayerNorm(embed_dim)
        self.ffn_norm = nn.LayerNorm(embed_dim)
        self.attention = Attention(embed_dim, num_heads)
        self.ffn = FeedForwardNetwork(embed_dim)
        self.depth = depth

    def forward(self, x):
        residual = x
        for _ in range(self.depth):
            x = x + self.attention_norm(self.attention(x))
            x = x + self.ffn_norm(self.ffn(x))
        return x


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

        self.qkv = nn.Linear(embed_dim, embed_dim * 3)
        self.proj = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        qkv = self.qkv(x)
        q, k, v = torch.split(qkv, self.embed_dim, dim=-1)

        q = q.reshape(*q.shape[:-1], self.num_heads, self.head_dim).transpose(-2, -3)
        k = k.reshape(*k.shape[:-1], self.num_heads, self.head_dim).permute(0, 2, 3, 1)
        v = v.reshape(*v.shape[:-1], self.num_heads, self.head_dim).transpose(-2, -3)

        attn_scores = torch.matmul(q, k) / (self.head_dim ** 0.5)
        attn_scores = F.softmax(attn_scores, dim=-1)
        attn_output = torch.matmul(attn_scores, v)
        attn_output = attn_output.transpose(-2, -3).reshape(*attn_output.shape[:-3], self.embed_dim)

        x = self.proj(attn_output)
        return x


class FeedForwardNetwork(nn.Module):
    def __init__(self, embed_dim):
        super(FeedForwardNetwork, self).__init__()
        self.ffn = nn.Sequential(
            nn.Linear(embed_dim, 4 * embed_dim),
            nn.GELU(),
            nn.Linear(4 * embed_dim, embed_dim)
        )

    def forward(self, x):
        return self.ffn(x)

这就是Swin-Transform模块的详细代码。你可以将这段代码集成到你的FFANet模型中，并根据需要进行调整。记得根据实际情况修改输入输出尺寸、深度、头数等模块的参数。

python语言实现multi-head-self-attention示例的代码：

### 回答1：
以下是一个 Python 代码示例，用于实现 multi-head self-attention：

import torch
import torch.nn as nn

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

        self.query_linear = nn.Linear(d_model, d_model)
        self.key_linear = nn.Linear(d_model, d_model)
        self.value_linear = nn.Linear(d_model, d_model)

        self.output_linear = nn.Linear(d_model, d_model)

    def forward(self, query, key, value, mask=None):
        batch_size = query.size()

        # Linear transformations
        query = self.query_linear(query)
        key = self.key_linear(key)
        value = self.value_linear(value)

        # Split into heads
        query = query.view(batch_size * self.num_heads, -1, self.depth)
        key = key.view(batch_size * self.num_heads, -1, self.depth)
        value = value.view(batch_size * self.num_heads, -1, self.depth)

        # Transpose for matrix multiplication
        query = query.transpose(1, 2)
        key = key.transpose(1, 2)
        value = value.transpose(1, 2)

        # Calculate scores
        scores = torch.matmul(query, key.transpose(-2, -1))
        scores = scores / torch.sqrt(torch.tensor(self.depth).float())

        # Apply mask (if provided)
        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = scores.masked_fill(mask == , -1e9)

        # Softmax
        attention_weights = nn.Softmax(dim=-1)(scores)

        # Dropout
        attention_weights = nn.Dropout(p=.1)(attention_weights)

        # Multiply by values
        context = torch.matmul(attention_weights, value)

        # Reshape and concatenate
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.depth)

        # Linear transformation
        output = self.output_linear(context)

        return output

希望对你有所帮助！

### 回答2：
下面是使用Python语言实现multi-head self-attention的一个示例代码：

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

class MultiHeadSelfAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadSelfAttention, self).__init__()
        self.num_heads = num_heads
        self.d_head = d_model // num_heads

        self.fc_query = nn.Linear(d_model, d_model)
        self.fc_key = nn.Linear(d_model, d_model)
        self.fc_value = nn.Linear(d_model, d_model)
        self.fc_concat = nn.Linear(d_model, d_model)

    def forward(self, x):
        batch_size, seq_len, d_model = x.size()
        h = self.num_heads
        
        # Split input into multiple heads
        query = self.fc_query(x).view(batch_size, seq_len, h, self.d_head)
        key = self.fc_key(x).view(batch_size, seq_len, h, self.d_head)
        value = self.fc_value(x).view(batch_size, seq_len, h, self.d_head)
        
        # Compute attention scores
        scores = torch.matmul(query, key.transpose(-2, -1)) / (self.d_head ** 0.5)
        attn_weights = F.softmax(scores, dim=-1)
        
        # Apply attention weights to value vectors
        attended_values = torch.matmul(attn_weights, value)
        attended_values = attended_values.transpose(1, 2).contiguous().view(batch_size, seq_len, -1)
        
        # Concatenate and linearly transform attended values
        output = self.fc_concat(attended_values)
        return output

# 使用示例
d_model = 128
num_heads = 8
seq_len = 10
batch_size = 4

input_tensor = torch.randn(batch_size, seq_len, d_model)
attention = MultiHeadSelfAttention(d_model, num_heads)
output = attention(input_tensor)

print("Input Shape: ", input_tensor.shape)
print("Output Shape: ", output.shape)

上述代码定义了一个`MultiHeadSelfAttention`的类，其中`forward`函数实现了multi-head self-attention的计算过程。在使用示例中，我们输入一个大小为`(batch_size, seq_len, d_model)`的张量，经过multi-head self-attention计算后输出一个大小为`(batch_size, seq_len, d_model)`的张量。其中`d_model`表示输入的特征维度，`num_heads`表示attention头的数量。

### 回答3：
下面是使用Python实现multi-head self-attention示例的代码：

import torch
import torch.nn as nn

class MultiHeadSelfAttention(nn.Module):
    def __init__(self, embed_size, num_heads):
        super(MultiHeadSelfAttention, self).__init__()
        self.embed_size = embed_size
        self.num_heads = num_heads
        self.head_size = embed_size // num_heads
        
        self.query = nn.Linear(embed_size, embed_size)
        self.key = nn.Linear(embed_size, embed_size)
        self.value = nn.Linear(embed_size, embed_size)
        self.out = nn.Linear(embed_size, embed_size)
        
    def forward(self, x):
        batch_size, seq_len, embed_size = x.size()
        
        # Split the embedding into num_heads and reshape
        x = x.view(batch_size, seq_len, self.num_heads, self.head_size)
        x = x.permute(0, 2, 1, 3)
        
        # Apply linear transformations to obtain query, key, and value
        query = self.query(x)
        key = self.key(x)
        value = self.value(x)
        
        # Compute scaled dot product attention scores
        scores = torch.matmul(query, key.permute(0, 1, 3, 2))
        scores = scores / self.head_size**0.5
        
        # Apply softmax to obtain attention probabilities
        attn_probs = nn.Softmax(dim=-1)(scores)
        
        # Apply attention weights to value and sum across heads
        attended = torch.matmul(attn_probs, value)
        attended = attended.permute(0, 2, 1, 3)
        attended = attended.contiguous().view(batch_size, seq_len, self.embed_size)
        
        # Apply output linear transformation
        output = self.out(attended)
        
        return output

上述代码中定义了一个名为MultiHeadSelfAttention的类，继承自nn.Module，可以通过指定嵌入大小（embed_size）和头部数量（num_heads）来创建多头自注意力层。在前向传播方法forward中，先通过线性变换将输入张量分别变换为查询（query）、键（key）和值（value）张量。然后计算缩放点积注意力得分，将其作为注意力概率经过softmax函数进行归一化。通过注意力概率权重对值进行加权求和，并应用线性变换得到最终的输出张量。最后返回输出张量。