swin transformer补丁融合
时间: 2025-02-24 13:38:27 浏览: 66
### Swin Transformer 中的 Patch Merging 实现
在 Swin Transformer 的架构设计中,Patch Merging 层用于逐步降低空间分辨率并增加通道维度。这一操作有助于构建分层特征表示,在不同尺度上捕捉图像信息。
具体来说,对于给定的空间分辨率为 \( H \times W \) 和通道数为 C 的输入张量,Patch Merging 将相邻的 2x2 补丁合并成一个新的补丁,从而使得输出的空间分辨率变为原来的一半 (即 \( H/2 \times W/2 \)),而通道数量则加倍至 2C[^4]。
以下是 Python 代码示例展示如何实现 Patch Merging:
```python
import torch.nn as nn
import torch
class PatchMerging(nn.Module):
""" Patch Merging Layer.
Args:
input_dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
Attributes:
reduction (Linear): Linear transformation to double the channel dimension after merging patches.
norm (LayerNorm): Applies normalization over a mini-batch of inputs.
"""
def __init__(self, input_dim, norm_layer=nn.LayerNorm):
super().__init__()
self.reduction = nn.Linear(input_dim * 4, 2 * input_dim, bias=False)
self.norm = norm_layer(input_dim * 4)
def forward(self, x, size):
h, w = size
b, _, c = x.shape # batch_size, num_patches, channels
# Reshape from sequence length back into spatial dimensions
x = x.view(b, h, w, c)
# Split image into non-overlapping windows with stride equal to window size
x0 = x[:, 0::2, 0::2, :] # top-left corner
x1 = x[:, 1::2, 0::2, :] # bottom-left corner
x2 = x[:, 0::2, 1::2, :] # top-right corner
x3 = x[:, 1::2, 1::2, :] # bottom-right corner
# Concatenate all four parts along last axis
x = torch.cat([x0, x1, x2, x3], -1)
# Apply linear projection followed by layernorm
x = self.norm(x)
x = self.reduction(x)
return x, (h // 2, w // 2)
```
上述代码定义了一个 `PatchMerging` 类来执行具体的降采样逻辑,并通过线性变换调整通道数目。该类接收一个形状为 `[batch_size, height*width, channels]` 的输入张量以及对应的高宽参数 `(height, width)`,最终返回经过处理后的张量及其新的尺寸信息[(高度//2), (宽度//2)]。
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