mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-17 06:51:11 +08:00
107 lines
3.9 KiB
Python
107 lines
3.9 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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from shared.attention import CBAM, SEBlock
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class GinkaEncoder(nn.Module):
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"""编码器（下采样）部分"""
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def __init__(self, in_channels, out_channels, attention=False, block='CBAM'):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
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nn.InstanceNorm2d(out_channels),
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nn.GELU(),
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nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
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nn.InstanceNorm2d(out_channels),
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)
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# 注意力
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if attention:
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if block == 'CBAM':
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self.conv.append(CBAM(out_channels))
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elif block == 'SEBlock':
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self.conv.append(SEBlock(out_channels))
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self.conv.append(nn.GELU())
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self.down = nn.Conv2d(out_channels, out_channels, 3, stride=2, padding=1)
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def forward(self, x):
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x_res = self.conv(x)
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x_down = self.down(x_res)
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return x_down, x_res
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class GinkaDecoder(nn.Module):
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"""解码器（上采样）部分"""
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def __init__(self, in_channels, out_channels, attention=False, block='CBAM'):
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super().__init__()
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self.upsample = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)
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self.conv = nn.Sequential(
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nn.Conv2d(in_channels + out_channels, out_channels, kernel_size=3, padding=1),
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nn.InstanceNorm2d(out_channels),
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)
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# 注意力
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if attention:
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if block == 'CBAM':
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self.conv.append(CBAM(out_channels))
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elif block == 'SEBlock':
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self.conv.append(SEBlock(out_channels))
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self.conv.append(nn.GELU())
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def forward(self, x, skip):
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x = self.upsample(x)
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x = torch.cat([x, skip], dim=1)
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x = self.conv(x)
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return x
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class GinkaBottleneck(nn.Module):
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def __init__(self, in_channels, out_channels, attention=False):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
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nn.InstanceNorm2d(out_channels),
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nn.GELU(),
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nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
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nn.InstanceNorm2d(out_channels),
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)
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if attention:
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self.conv.append(SEBlock(out_channels))
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self.conv.append(nn.GELU())
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def forward(self, x):
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return self.conv(x)
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class GinkaUNet(nn.Module):
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def __init__(self, in_ch=64, out_ch=32):
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"""Ginka Model UNet 部分
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"""
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super().__init__()
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self.down1 = GinkaEncoder(in_ch, in_ch*2, attention=True)
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self.down2 = GinkaEncoder(in_ch*2, in_ch*4, attention=True)
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self.down3 = GinkaEncoder(in_ch*4, in_ch*8, attention=True, block='SEBlock')
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self.down4 = GinkaEncoder(in_ch*8, in_ch*16, attention=True, block='SEBlock')
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self.bottleneck = GinkaBottleneck(in_ch*16, in_ch*16, attention=True)
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self.up1 = GinkaDecoder(in_ch*16, in_ch*8, attention=True, block='SEBlock')
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self.up2 = GinkaDecoder(in_ch*8, in_ch*4, attention=True, block='SEBlock')
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self.up3 = GinkaDecoder(in_ch*4, in_ch*2, attention=True)
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self.up4 = GinkaDecoder(in_ch*2, in_ch, attention=True)
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self.final = nn.Sequential(
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nn.Conv2d(in_ch, out_ch, 1),
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)
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def forward(self, x):
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x_down1, skip1 = self.down1(x)
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x_down2, skip2 = self.down2(x_down1)
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x_down3, skip3 = self.down3(x_down2)
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x_down4, skip4 = self.down4(x_down3)
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x = self.bottleneck(x_down4)
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x = self.up1(x, skip4)
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x = self.up2(x, skip3)
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x = self.up3(x, skip2)
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x = self.up4(x, skip1)
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return self.final(x)
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