class Transformer(nn.Module): def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.): super().__init__() self.layers = nn.ModuleList([]) for _ in range(depth): self.layers.append(nn.ModuleList([ PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)), PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)) ])) def forward(self, x): for attn, ff in self.layers: x = attn(x) + x x = ff(x) + x return x 每句语句什莫意思

Transformer 是一种神经网络模型，用于处理自然语言处理任务，它的架构可以提供有效的语言表示。它的核心是一系列的注意力机制，它们可以捕捉不同输入之间的关联，并使用多头注意力和前馈网络来更新输入的表示。

相关问题

发生错误TypeError: forward() missing 1 required positional argument: 'x_size'如何解决

在 `FeedForward` 类的 `forward` 方法中，`dwconv` 需要一个额外的参数 `x_size`。这个参数在调用 `dwconv` 时必须提供。你可以通过以下几种方式来解决这个问题：

### 方案一：修改 `FeedForward` 类的 `forward` 方法
确保在调用 `dwconv` 时传递 `x_size` 参数。例如：

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, act_layer=nn.GELU, dropout=0.):
        super().__init__()
        self.fc1 = nn.Linear(dim, hidden_dim)
        self.act = act_layer()
        self.before_add = emptyModule()
        self.after_add = emptyModule()
        self.dwconv = dwconv(hidden_dim=hidden_dim)
        self.fc2 = nn.Linear(dim, hidden_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, x_size):
        x = self.fc1(x)
        x = self.act(x)
        x = self.before_add(x)
        x = x + self.dwconv(x, x_size)  # 添加 x_size 参数
        x = self.after_add(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)
        return x

### 方案二：在调用 `FeedForward` 时传递 `x_size`
确保在调用 `FeedForward` 的 `forward` 方法时传递 `x_size` 参数。例如，在 `Transformer` 类的 `forward` 方法中：

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
        super().__init__()
        self.conv8 = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1)
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))
            ]))

    def forward(self, x):
        for attn, ff in self.layers:
            b, h, w = x.size()
            x7 = x.reshape(b, 1, 7, 64)
            x8 = self.conv8(x7)
            x = attn(x) + x
            x = ff(x, (h, w)) + x  # 传递 x_size 参数
            x8 = x8.reshape(b, 7, 64)
            x = x + x8
        return x

### 方案三：在 `ViT` 类的 `forward` 方法中传递 `x_size`
确保在调用 `Transformer` 的 `forward` 方法时传递 `x_size` 参数。例如：

class ViT(nn.Module):
    def __init__(self, *, image_height, image_width, patch_height, patch_width, num_classes, dim, depth, heads, mlp_dim, channels, pool='mean', dim_head=64, dropout=0., emb_dropout=0.):
        super().__init__()
        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
        num_patches = (image_height // patch_height) * (image_width // patch_width)
        patch_dim = channels * patch_height * patch_width
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_height, p2=patch_width),
            nn.Linear(patch_dim, dim),
        )
        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        self.dropout = nn.Dropout(emb_dropout)
        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout=0.)
        self.pool = pool
        self.to_latent = nn.Identity()
        self.mlp_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )

    def forward(self, img):
        x = self.to_patch_embedding(img)
        b, n, _ = x.shape
        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_embedding[:, :(n + 1)]
        x_0 = self.dropout(x)
        x_1 = self.transformer(x_0, (int(n**0.5), int(n**0.5)))  # 传递 x_size 参数
        x_2 = self.transformer(x_1, (int(n**0.5), int(n**0.5)))
        diff_1 = x_2 - x_1
        diff_1_1 = diff_1 + x_2
        x_3 = self.transformer(diff_1_1, (int(n**0.5), int(n**0.5)))
        diff_2 = x_3 - x_2
        diff_2_2 = diff_2 + x_3
        x_4 = self.transformer(diff_2_2, (int(n**0.5), int(n**0.5))) * 0.2
        x = x_0 + x_4
        x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]
        x = self.to_latent(x)
        return self.mlp_head(x)

以上三种方案都可以解决 `TypeError: forward() missing 1 required positional argument: 'x_size'` 错误。选择适合你代码结构的一种进行修改即可。

transformer、vision transformer、swin transformer

### Transformer架构概述

Transformer架构最初由Vaswani等人于2017年提出，旨在解决序列数据处理中的长期依赖问题。该架构完全基于自注意力机制(self-attention mechanism)，摒弃了传统的循环神经网络(RNN)和卷积神经网络(CNN)[^3]。

核心组件包括多头自注意力(multi-head self-attention, MHSA)模块、前馈神经网络(feed-forward neural network, FFN)以及残差连接(residual connections)与层归一化(layer normalization)技术。这种设计使得模型能够并行处理整个输入序列，在自然语言处理(NLP)领域取得了巨大成功，并逐渐扩展到计算机视觉等领域。

### Vision Transformer介绍

Vision Transformer(ViT)将纯Transformer应用于图像识别任务中。ViT通过将图片切分为固定大小的patch(补丁), 并将其线性嵌入(embedding)转换成token形式作为输入送入标准的Transformer编码器堆栈内进行特征提取[^1]。

具体来说,Vision Transformer主要包含以下几个部分:

- **Embedding Layer**: 将二维图像划分为多个不重叠的小方块(patch)，并将这些patch映射为向量表示；

- **Positional Encoding**: 添加位置信息给每个patch token以便让模型理解空间关系；

- **Transformer Encoders Stack**: 多个相同的transformer block串联起来形成深层网络结构用于捕捉全局上下文关联特性；

- **MLP Head (Multi-Layer Perceptron)**: 在最终输出之前附加一层或多层全连接层完成分类或其他下游任务预测工作。

class ViT(nn.Module):
    def __init__(self, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool='cls', channels=3, dim_head=64, dropout=0., emb_dropout=0.):
        super().__init__()
        
        assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
        num_patches = (image_size // patch_size) ** 2
        
        # Embedding layer and positional encoding...
    
    def forward(self, img):
        patches = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=self.patch_height, p2=self.patch_width)
        x = self.to_patch_embedding(patches)

        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_embedding[:, :(n + 1)]

        x = self.dropout(x)

        x = self.transformer(x)

        x = x.mean(dim=1) if self.pool == "mean" else x[:, 0]

        return self.mlp_head(x)

### Swin Transformer详解

Swin Transformer进一步改进了传统vision transformer的设计理念，提出了分层(hierarchical)视觉变换器的概念。其独特之处在于采用了移位窗口(shifted window-based)策略来构建局部感受野(local receptive field)，从而有效降低了计算成本的同时保持甚至增强了表达能力[^2]。

#### 关键特点：

- **Hierarchical Structure**：模仿CNNs的空间下采样过程，逐步减少分辨率而增加通道数，有助于获取更丰富的多层次表征；

- **Shifted Window-Based Self Attention(SW-MSA/ W-MSA)** ：利用非重叠窗口内的自注意机制代替全局范围内的交互操作，减少了不必要的冗余计算开销；当涉及到跨窗体的信息交流时，则采用交错排列的方式实现高效通信；

- **Relative Position Bias** :引入相对位置偏置项以增强对距离远近敏感性的学习效果；

- **Linear Computational Complexity**：得益于上述优化措施，即使面对高分辨率输入也能维持较低的时间复杂度增长趋势。

综上所述,Swin Transformer不仅继承和发展了经典Transformer的优点，而且针对特定应用场景进行了针对性调整，使其成为当前最先进的一类视觉感知算法之一。