215 lines
8.4 KiB
Python
215 lines
8.4 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
class SEBlock(nn.Module):
|
|
def __init__(self, in_channels, reduced_dim):
|
|
super(SEBlock, self).__init__()
|
|
self.se = nn.Sequential(
|
|
nn.AdaptiveAvgPool2d(1), # 全局平均池化
|
|
nn.Conv2d(in_channels, reduced_dim, kernel_size=1),
|
|
nn.ReLU(),
|
|
nn.Conv2d(reduced_dim, in_channels, kernel_size=1),
|
|
nn.Sigmoid() # 使用Sigmoid是因为我们要对通道进行权重归一化
|
|
)
|
|
|
|
def forward(self, x):
|
|
return x * self.se(x)
|
|
# 定义Masked Autoencoder模型
|
|
class Conv(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):
|
|
super(Conv, self).__init__(
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
|
|
dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)
|
|
)
|
|
class ConvBNReLU(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d,
|
|
bias=False):
|
|
super(ConvBNReLU, self).__init__(
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
|
|
dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
|
|
norm_layer(out_channels),
|
|
nn.ReLU()
|
|
)
|
|
class SeparableBNReLU(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, norm_layer= nn.BatchNorm2d):
|
|
super(SeparableBNReLU, self).__init__(
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation,
|
|
padding=((stride - 1) + dilation * (kernel_size -1))//2, groups=in_channels, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
|
|
nn.ReLU6()
|
|
|
|
)
|
|
|
|
|
|
class ResidualBlock(nn.Module):
|
|
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
|
|
super(ResidualBlock, self).__init__()
|
|
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
|
|
self.bn1 = nn.BatchNorm2d(out_channels)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
|
|
self.bn2 = nn.BatchNorm2d(out_channels)
|
|
self.downsample = downsample
|
|
if in_channels != out_channels or stride != 1:
|
|
self.downsample = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
|
|
nn.BatchNorm2d(out_channels)
|
|
)
|
|
|
|
def forward(self, x):
|
|
identity = x
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv2(out)
|
|
out = self.bn2(out)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
class Mlp(nn.Module):
|
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = nn.Conv2d(in_features, hidden_features, 1, 1, 0, bias=True)
|
|
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Conv2d(hidden_features, out_features, 1, 1, 0, bias=True)
|
|
self.drop = nn.Dropout(drop, inplace=True)
|
|
|
|
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
|
|
|
|
|
|
class MultiHeadAttentionBlock(nn.Module):
|
|
def __init__(self, embed_dim, num_heads, dropout=0.1):
|
|
super(MultiHeadAttentionBlock, self).__init__()
|
|
self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)
|
|
self.norm = nn.LayerNorm(embed_dim)
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
def forward(self, x):
|
|
# (B, C, H, W) -> (HW, B, C) for MultiheadAttention compatibility
|
|
B, C, H, W = x.shape
|
|
x = x.view(B, C, H * W).permute(2, 0, 1) # (B, C, H, W) -> (HW, B, C)
|
|
|
|
# Apply multihead attention
|
|
attn_output, _ = self.attention(x, x, x)
|
|
|
|
# Apply normalization and dropout
|
|
attn_output = self.norm(attn_output)
|
|
attn_output = self.dropout(attn_output)
|
|
|
|
# Reshape back to (B, C, H, W)
|
|
attn_output = attn_output.permute(1, 2, 0).view(B, C, H, W)
|
|
|
|
return attn_output
|
|
|
|
|
|
class SpatialAttentionBlock(nn.Module):
|
|
def __init__(self):
|
|
super(SpatialAttentionBlock, self).__init__()
|
|
self.conv = nn.Conv2d(2, 1, kernel_size=7, padding=3, bias=False)
|
|
|
|
def forward(self, x):
|
|
avg_out = torch.mean(x, dim=1, keepdim=True)
|
|
max_out, _ = torch.max(x, dim=1, keepdim=True)
|
|
out = torch.cat([avg_out, max_out], dim=1)
|
|
out = torch.sigmoid(self.conv(out))
|
|
return x * out
|
|
|
|
class DecoderAttentionBlock(nn.Module):
|
|
def __init__(self, in_channels):
|
|
super(DecoderAttentionBlock, self).__init__()
|
|
self.conv1 = nn.Conv2d(in_channels, in_channels // 2, kernel_size=1)
|
|
self.conv2 = nn.Conv2d(in_channels // 2, in_channels, kernel_size=1)
|
|
self.spatial_attention = SpatialAttentionBlock()
|
|
|
|
def forward(self, x):
|
|
# 通道注意力
|
|
b, c, h, w = x.size()
|
|
avg_pool = F.adaptive_avg_pool2d(x, 1)
|
|
max_pool = F.adaptive_max_pool2d(x, 1)
|
|
|
|
avg_out = self.conv1(avg_pool)
|
|
max_out = self.conv1(max_pool)
|
|
|
|
out = avg_out + max_out
|
|
out = torch.sigmoid(self.conv2(out))
|
|
|
|
# 添加空间注意力
|
|
out = x * out
|
|
out = self.spatial_attention(out)
|
|
return out
|
|
|
|
class MaskedAutoencoder(nn.Module):
|
|
def __init__(self):
|
|
super(MaskedAutoencoder, self).__init__()
|
|
self.encoder = nn.Sequential(
|
|
Conv(1, 32, kernel_size=3, stride=2),
|
|
nn.ReLU(),
|
|
SEBlock(32,32),
|
|
ConvBNReLU(32, 64, kernel_size=3, stride=2),
|
|
ResidualBlock(64,64),
|
|
SeparableBNReLU(64, 128, kernel_size=3, stride=2),
|
|
MultiHeadAttentionBlock(embed_dim=128, num_heads=4),
|
|
SEBlock(128, 128)
|
|
)
|
|
self.mlp = Mlp(in_features=128, hidden_features=256, out_features=128, act_layer=nn.ReLU6, drop=0.1)
|
|
self.decoder = nn.Sequential(
|
|
nn.ConvTranspose2d(128, 128, kernel_size=3, stride=2, padding=1, output_padding=1),
|
|
nn.ReLU(),
|
|
DecoderAttentionBlock(128), # 在第一层后添加注意力模块
|
|
nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
|
|
nn.ReLU(),
|
|
nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
|
|
DecoderAttentionBlock(32), # 在最后一层前添加注意力模块
|
|
nn.ReLU(),
|
|
nn.ConvTranspose2d(32, 1, kernel_size=3, stride=2, padding=1, output_padding=1),
|
|
nn.Sigmoid() # Sigmoid输出
|
|
)
|
|
|
|
|
|
# class MaskedAutoencoder(nn.Module):
|
|
# def __init__(self):
|
|
# super(MaskedAutoencoder, self).__init__()
|
|
# self.encoder = nn.Sequential(
|
|
# nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1),
|
|
# nn.ReLU(),
|
|
# nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
|
|
# nn.ReLU(),
|
|
# nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
|
|
# nn.ReLU(),
|
|
# SEBlock(128, 128)
|
|
# )
|
|
# self.decoder = nn.Sequential(
|
|
# nn.ConvTranspose2d(128, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
|
|
# nn.ReLU(),
|
|
# nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1),
|
|
# nn.ReLU(),
|
|
# nn.ConvTranspose2d(16, 1, kernel_size=3, stride=2, padding=1, output_padding=1),
|
|
# nn.Sigmoid() # 使用Sigmoid是因为输入数据是0-1之间的
|
|
# )
|
|
#
|
|
# def forward(self, x):
|
|
# encoded = self.encoder(x)
|
|
# decoded = self.decoder(encoded)
|
|
# return decoded
|
|
|
|
# 实例化模型、损失函数和优化器
|
|
model = MaskedAutoencoder()
|
|
criterion = nn.MSELoss()
|
|
optimizer = torch.optim.Adam(model.parameters(), lr=0.001) |