vit block

### 关于Vision Transformer (ViT) Block 实现及其相关问题 #### ViT Block 的基本结构 Vision Transformer 中的核心组件是 Transformer Block，它主要由多头自注意力机制（Multi-head Self-Attention, MSA）和前馈网络（Feed Forward Network, FFN）组成。每个 Transformer Block 都会通过残差连接来增强模型的学习能力[^1]。 ```python class TransformerBlock(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.): super().__init__() self.norm1 = nn.LayerNorm(dim) self.attn = MultiHeadSelfAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop_rate, proj_drop=drop_rate) self.drop_path = DropPath(drop_rate) if drop_rate > 0. else nn.Identity() self.norm2 = nn.LayerNorm(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, drop=drop_rate) def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x ``` 上述代码展示了如何实现一个标准的 Transformer Block，其中包含了 Layer Normalization 和 Dropout 层以提高模型稳定性。 #### 可能存在的问题及解决方案 ##### 1. **有限的感受野** 基于窗口的自注意力机制可能会导致感受野受限的问题，尤其是在高分辨率输入的情况下。Swin Transformer 提出了移位窗口技术来缓解这一问题，通过引入空间混洗操作扩展局部特征的信息交互范围[^3]。 ##### 2. **计算复杂度** 全图尺度上的自注意力计算量较大，因此 Swin Transformer 将图像划分为不重叠的小窗口，在每个窗口内部执行自注意力运算，从而显著降低计算开销。 ##### 3. **跨阶段信息融合** 为了更好地捕捉全局上下文信息，Swin Transformer 设计了逐级下采样的策略，使得每一阶段都能获得不同层次的空间表示[^2]。 #### 示例：Swin Transformer中的Transformer Block调整 以下是针对 Swin Transformer 特定需求修改后的 Transformer Block： ```python class SwinTransformerBlock(TransformerBlock): def __init__(self, dim, input_resolution, num_heads, window_size, shift_size=0, mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., norm_layer=nn.LayerNorm): super(SwinTransformerBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, drop, attn_drop) self.input_resolution = input_resolution self.window_size = window_size self.shift_size = shift_size assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" # Window partition and merge functions self.attn_mask = None def forward(self, x): H, W = self.input_resolution B, L, C = x.shape assert L == H * W, f"Input feature has wrong size {L}, expected {H*W}" shortcut = x x = self.norm1(x) # Partition windows x_windows = window_partition(x.view(B, H, W, C), self.window_size).view(-1, self.window_size*self.window_size, C) # Apply shifted attention mask during training phase only when necessary. if self.shift_size > 0: attn_mask = generate_attn_mask(self.input_resolution, self.window_size, self.shift_size, device=x.device) else: attn_mask = None # Perform multi-head self-attention with optional masking attn_windows = self.attn(x_windows, mask=attn_mask)[0].view(-1, self.window_size, self.window_size, C) # Merge windows back into original shape shifted_x = window_reverse(attn_windows, self.window_size, H, W).view(B, H * W, C) # Reverse cyclic shift before residual connection if self.shift_size > 0: x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) else: x = shifted_x # Residual connection followed by normalization & feedforward network layers as usual x = shortcut + self.drop_path(x) x = x + self.drop_path(self.mlp(self.norm2(x))) return x ``` 此版本增加了对窗口划分、移位以及反向移位的支持，适用于更复杂的视觉任务场景。 ---