191 lines
7.2 KiB
Python
191 lines
7.2 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class SEBlock(nn.Module):
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def __init__(self, in_channels, reduced_dim):
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super(SEBlock, self).__init__()
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self.se = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Conv2d(in_channels, reduced_dim, kernel_size=1),
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nn.ReLU(),
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nn.Conv2d(reduced_dim, in_channels, kernel_size=1),
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nn.Sigmoid()
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)
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def forward(self, x):
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return x * self.se(x)
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class Conv(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):
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super(Conv, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)
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)
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class ConvBNReLU(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d, bias=False):
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super(ConvBNReLU, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
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norm_layer(out_channels),
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nn.ReLU()
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)
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class SeparableBNReLU(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, norm_layer=nn.BatchNorm2d):
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super(SeparableBNReLU, self).__init__(
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nn.Conv2d(in_channels, in_channels, kernel_size=kernel_size, stride=stride, dilation=dilation,
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padding=((stride - 1) + dilation * (kernel_size - 1)) // 2, groups=in_channels, bias=False),
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# 分离卷积，仅调整空间信息
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norm_layer(in_channels), # 对输入通道进行归一化
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), # 这里进行升维操作
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nn.ReLU6()
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)
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class ResidualBlock(nn.Module):
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def __init__(self, in_channels, out_channels, stride=1, downsample=None):
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super(ResidualBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(out_channels)
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# 如果输入和输出通道不一致，进行降采样操作
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self.downsample = downsample
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if in_channels != out_channels or stride != 1:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(out_channels)
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)
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def forward(self, x):
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identity = x
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if self.downsample is not None:
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identity = self.downsample(x)
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out += identity
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out = self.relu(out)
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return out
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Conv2d(in_features, hidden_features, 1, 1, 0, bias=True)
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self.act = act_layer()
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self.fc2 = nn.Conv2d(hidden_features, out_features, 1, 1, 0, bias=True)
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self.drop = nn.Dropout(drop, inplace=True)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class MultiHeadAttentionBlock(nn.Module):
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def __init__(self, embed_dim, num_heads, dropout=0.1):
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super(MultiHeadAttentionBlock, self).__init__()
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self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)
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self.norm = nn.LayerNorm(embed_dim)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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B, C, H, W = x.shape
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x = x.view(B, C, H * W).permute(2, 0, 1) # (B, C, H, W) -> (HW, B, C)
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attn_output, _ = self.attention(x, x, x)
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attn_output = self.norm(attn_output)
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attn_output = self.dropout(attn_output)
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attn_output = attn_output.permute(1, 2, 0).view(B, C, H, W)
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return attn_output
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class SpatialAttentionBlock(nn.Module):
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def __init__(self):
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super(SpatialAttentionBlock, self).__init__()
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self.conv = nn.Conv2d(2, 1, kernel_size=7, padding=3, bias=False)
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def forward(self, x): #(B, 64, H, W)
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avg_out = torch.mean(x, dim=1, keepdim=True) #(B, 1, H, W)
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max_out, _ = torch.max(x, dim=1, keepdim=True)#(B, 1, H, W)
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out = torch.cat([avg_out, max_out], dim=1)#(B, 2, H, W)
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out = torch.sigmoid(self.conv(out))#(B, 1, H, W)
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return x * out #(B, C, H, W)
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class DecoderAttentionBlock(nn.Module):
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def __init__(self, in_channels):
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super(DecoderAttentionBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, in_channels // 2, kernel_size=1)
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self.conv2 = nn.Conv2d(in_channels // 2, in_channels, kernel_size=1)
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self.spatial_attention = SpatialAttentionBlock()
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def forward(self, x):
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# 通道注意力
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b, c, h, w = x.size()
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avg_pool = F.adaptive_avg_pool2d(x, 1)
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max_pool = F.adaptive_max_pool2d(x, 1)
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avg_out = self.conv1(avg_pool)
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max_out = self.conv1(max_pool)
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out = avg_out + max_out
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out = torch.sigmoid(self.conv2(out))
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# 添加空间注意力
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out = x * out
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out = self.spatial_attention(out)
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return out
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class MaskedAutoencoder(nn.Module):
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def __init__(self):
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super(MaskedAutoencoder, self).__init__()
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self.encoder = nn.Sequential(
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Conv(1, 32, kernel_size=3, stride=2),
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nn.ReLU(),
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SEBlock(32,32),
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ConvBNReLU(32, 64, kernel_size=3, stride=2),
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ResidualBlock(64,64),
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SeparableBNReLU(64, 128, kernel_size=3, stride=2),
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MultiHeadAttentionBlock(embed_dim=128, num_heads=4),
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SEBlock(128, 128)
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)
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self.mlp = Mlp(in_features=128, hidden_features=256, out_features=128, act_layer=nn.ReLU6, drop=0.1)
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self.decoder = nn.Sequential(
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nn.ConvTranspose2d(128, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
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nn.ReLU(),
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DecoderAttentionBlock(32),
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nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1),
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nn.ReLU(),
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DecoderAttentionBlock(16),
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nn.ReLU(),
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nn.ConvTranspose2d(16, 1, kernel_size=3, stride=2, padding=1, output_padding=1), # 修改为 output_padding=1
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nn.Sigmoid()
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)
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def forward(self, x):
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encoded = self.encoder(x)
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print("Encoded size:", encoded.size())
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decoded = self.decoder(encoded)
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print("Encoded size:", decoded.size())
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return decoded
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model = MaskedAutoencoder()
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x = torch.randn(1, 1, 256, 256)
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output = model(x)
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print(output.shape)
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