Swin-Unet模型代码详解及改进思路-Toy模板网

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Swim-unet是针对水下图像分割任务提出的一种模型结构，其基于U-Net模型并加入了Swin Transformer模块，可以有效地解决水下图像分割中的光照不均匀、噪声干扰等问题。

Swim-unet模型代码详解

首先，在导入必要的库后，我们需要定义Swin Transformer模块中的一些函数和类：

import torch
from torch import nn
from einops.layers.torch import Rearrange

def window_partition(x, window_size):
"""
划分块函数

Args:
x: 输入张量
window_size: 划分窗口大小

Returns:
划分好的块
"""
# 根据窗口大小进行分组，同时保留原有维度信息
B, H, W, C = x.shape
# 取整, 获得行数和列数
# 对于不够整除的数据, 直接抛弃
col_windows = W // window_size
row_windows = H // window_size
# 分组
partitions = torch.zeros([B, row_windows*col_windows, window_size, window_size, C], dtype=x.dtype, device=x.device)
for i in range(row_windows):
for j in range(col_windows):
row_start, col_start = i * window_size, j * window_size
partition = x[:, row_start:row_start + window_size, col_start:col_start + window_size, :]
partitions[:, i*col_windows+j, :, :, :] = partition

return partitions

def window_reverse(partitions, window_size, H, W):
"""
恢复块函数

Args:
partitions: 经过划分的块
window_size: 划分窗口大小
H: 恢复后的高度
W: 恢复后的宽度

Returns:
恢复后的张量
"""
# 将每个块填充到完整图像大小
B, N, window_size, window_size, C = partitions.shape
col_windows = W // window_size
row_windows = H // window_size
x = torch.zeros([B, H, W, C], dtype=partitions.dtype, device=partitions.device)
count = 0
for i in range(row_windows):
for j in range(col_windows):
row_start, col_start = i * window_size, j * window_size
partition = partitions[:, count, :, :, :]
x[:, row_start:row_start + window_size, col_start:col_start + window_size, :] = partition
count += 1

return x

# 定义Transformer中的MLP（多层感知机）模块
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)

def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x

# 定义Transformer中的MHSA（多头注意力）模块
class WindowAttention(nn.Module):
"""
具有窗口形式的注意力机制

Args:
dim: 输入维度
window_size: 窗口大小
num_heads: 多头注意力头数
qkv_bias: 是否使用偏置项
qk_scale: 使每个维度的QK矩阵乘积具有更好的数值稳定性
attn_drop: 注意力矩阵dropout率
proj_drop: 输出结果dropout率

Returns:
经过窗口注意力后的张量
"""
def __init__(self, dim, window_size,接下来定义Swim-unet模型，包括Encoder和Decoder两部分。其中，Encoder部分采用Swin Transformer模块进行特征提取和上采样，并输出多尺度的特征图；Decoder部分则采用U-Net结构进行特征融合和下采样，并输出最终的分割结果。

```python
# 定义Swim-unet模型
class SwinUnet(nn.Module):
def __init__(self, in_channels=3, out_channels=1, init_features=32, window_size=4, img_size=256):
super().__init__()

# Encoder部分
self.encoder = nn.Sequential(
# 输入层
nn.Conv2d(in_channels, init_features, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(init_features),
nn.ReLU(inplace=True),

# 第一级Swin Transformer
SwinBlock(dim=init_features, num_heads=4, window_size=window_size),
SwinBlock(dim=init_features*2, num_heads=4, window_size=window_size),
SwinBlock(dim=init_features*4, num_heads=4, window_size=window_size),

# 第二级Swin Transformer
SwinBlock(dim=init_features*8, num_heads=4, window_size=window_size//2),
SwinBlock(dim=init_features*16, num_heads=4, window_size=window_size//2),
SwinBlock(dim=init_features*32, num_heads=4, window_size=window_size//2),

# 第三级Swin Transformer
SwinBlock(dim=init_features*64, num_heads=4, window_size=window_size//4),
SwinBlock(dim=init_features*128, num_heads=4, window_size=window_size//4),
SwinBlock(dim=init_features*256, num_heads=4, window_size=window_size//4),
)

# Decoder部分
self.decoder = nn.Sequential(
# 第一级上采样
nn.ConvTranspose2d(init_features*512, init_features*256, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
nn.BatchNorm2d(init_features*256),
nn.ReLU(inplace=True),

# 第二级上采样
nn.ConvTranspose2d(init_features*256, init_features*128, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
nn.BatchNorm2d(init_features*128),
nn.ReLU(inplace=True),

# 第三级上采样
nn.ConvTranspose2d(init_features*128, init_features*64, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
nn.BatchNorm2d(init_features*64),
nn.ReLU(inplace=True),

# 第四级上采样
nn.ConvTranspose2d(init_features*64, init_features*32, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
nn.BatchNorm2d(init_features*32),
nn.ReLU(inplace=True),

# 输出层
nn.Conv2d(init_features*32, out_channels, kernel_size=3, stride=1, padding=1)
)

def forward(self, x):
# Encoder部分
x = self.encoder(x)

# Decoder部分
x = self.decoder(x)
return x

以上是Swim-unet模型的代码详解。其中，Swin Transformer模块和U-Net结构的具体实现可以参考论文或其他开源资料。