存一串代码（简化的动态稀疏视觉Transformer的PyTorch代码）

import torch 
import torch.nn  as nn 
import torch.nn.functional  as F class DynamicSparseAttention(nn.Module): def __init__(self, dim, num_heads=8, dropout=0.1): super().__init__() self.num_heads  = num_heads self.head_dim  = dim // num_heads self.scale  = self.head_dim  ** -0.5 self.qkv  = nn.Linear(dim, dim * 3, bias=False) self.attn_drop  = nn.Dropout(dropout) self.proj  = nn.Linear(dim, dim) self.proj_drop  = nn.Dropout(dropout) def forward(self, x): B, N, C = x.shape  qkv = self.qkv(x).reshape(B,  N, 3, self.num_heads,  self.head_dim).permute(2,  0, 3, 1, 4) q, k, v = qkv.unbind(0)  attn = (q @ k.transpose(-2,  -1)) * self.scale  attn = attn.softmax(dim=-1)  attn = self.attn_drop(attn)  x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x)  x = self.proj_drop(x)  return x class HierarchicalRoutingBlock(nn.Module): def __init__(self, dim, num_heads=8, mlp_ratio=4., dropout=0.1): super().__init__() self.norm1  = nn.LayerNorm(dim) self.attn  = DynamicSparseAttention(dim, num_heads, dropout) self.norm2  = nn.LayerNorm(dim) self.mlp  = nn.Sequential( nn.Linear(dim, int(dim * mlp_ratio)), nn.GELU(), nn.Dropout(dropout), nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(dropout) ) def forward(self, x): x = x + self.attn(self.norm1(x))  x = x + self.mlp(self.norm2(x))  return x class DynamicSparseVisionTransformer(nn.Module): def __init__(self, img_size=224, patch_size=16, num_classes=1000, dim=768, num_heads=8, depth=12, mlp_ratio=4., dropout=0.1): super().__init__() self.patch_embed  = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size) self.pos_embed  = nn.Parameter(torch.zeros(1,  (img_size // patch_size) ** 2, dim)) self.dropout  = nn.Dropout(dropout) self.blocks  = nn.ModuleList([HierarchicalRoutingBlock(dim, num_heads, mlp_ratio, dropout) for _ in range(depth)]) self.norm  = nn.LayerNorm(dim) self.head  = nn.Linear(dim, num_classes) if num_classes > 0 else nn.Identity() def forward(self, x): x = self.patch_embed(x).flatten(2).transpose(1,  2) x = x + self.pos_embed  x = self.dropout(x)  for blk in self.blocks:  x = blk(x) x = self.norm(x)  x = x[:, 0] x = self.head(x)  return x # 使用 
model = DynamicSparseVisionTransformer() 
x = torch.randn(1,  3, 224, 224) 
output = model(x) 
print(output.shape)  

代码解释
DynamicSparseAttention：实现动态稀疏注意力模块。
HierarchicalRoutingBlock：实现层次化路由块，包含注意力模块和多层感知机。
DynamicSparseVisionTransformer：实现完整的动态稀疏视觉Transformer模型，包括补丁嵌入、位置嵌入、层次化路由块和分类头。