swin-transformer中的token

在SWIN Transformer中，"token"是指图像的一个特殊编码，用于表示整个图像。SWIN Transformer将输入图像划分为不同的局部区域，每个区域都会被编码为一个特定的向量表示，这些向量被称为"tokens"。这些tokens包含了图像的局部信息，可以用于后续的处理和分析。在SWIN Transformer中，图像的全局信息是由这些tokens来表示的。

相关问题

swin transformer中的token

在Swin Transformer中，token是指图像中的小块，类似于Vision Transformer中的patch。每个token代表图像中特定位置的信息，这个位置的尺寸是固定的，并且在不同层的Transformer block中保持相同的尺寸。 Swin Transformer通过这些token来进行全局的自注意力操作，并利用层次化构建方法来处理多尺寸特征信息。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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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。