### 关于Shunted Transformer的Python实现与解析
#### Python代码实现
Shunted Transformer是一种改进型视觉变换器架构，在计算机视觉任务中表现出色。下面展示了一个简化版的Shunted Self-Attention机制的核心部分，该机制允许不同尺度下的特征聚合。
```python
import torch
from torch import nn
class ShuntedSelfAttention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads."
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.num_heads = num_heads
self.q_proj = nn.Linear(dim, dim, bias=qkv_bias)
self.kv_proj = nn.Conv2d(dim, dim * 2, kernel_size=(1, 1), stride=(1, 1))
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, H, W, C = x.shape
N = H * W
# Query projection with shape transformation from (B,H,W,C) to (B,N,num_heads,C//num_heads)
q = self.q_proj(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# Key and Value projections using convolution; reshaping into multi-head format.
kv = self.kv_proj(x.permute(0, 3, 1, 2)).reshape(B, 2*C, N).transpose(-2, -1)
k, v = kv.chunk(2, dim=-1)
k = k.reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
v = v.reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# Compute attention scores between queries and keys across all heads.
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
# Apply the weighted sum of values according to computed attentions.
out = (attn @ v).transpose(1, 2).reshape(B, N, C)
# Final linear layer followed by dropout as per original paper design.
out = self.proj(out)
out = self.proj_drop(out)
return out.reshape(B, H, W, C)
# Example usage:
model = ShuntedSelfAttention(dim=768, num_heads=12)
input_tensor = torch.randn((1, 14, 14, 768)) # Batch size 1, image patch grid 14x14, embedding dimension 768
output = model(input_tensor)
print(output.shape) # Should output: torch.Size([1, 14, 14, 768])
```
此段代码定义了`ShuntedSelfAttention`类，实现了论文中的关键组件之一——多尺度token聚集[^4]。通过这种方式，模型能够在保持较低参数量的同时提高性能表现。
对于MATLAB仿真方面，由于Shunted Transformer主要基于PyTorch框架开发并优化用于深度学习应用，因此官方并没有提供相应的MATLAB版本。不过有兴趣的研究者可以根据上述Python代码逻辑自行转换到MATLAB环境中去实现相似功能[^2]。
该博客详细介绍了OliverRensu在Shunted Transformer上的研究，这是一种针对大型预训练模型的压缩技术。作者讨论了如何通过结构化剪枝和量化等方法有效减小模型大小，同时保持高性能。博客还涵盖了在实际应用中实施这些优化策略的步骤和经验。
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