mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-14 04:41:12 +08:00
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
import torch
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import torch.nn as nn
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from shared.attention import CBAM
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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):
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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.BatchNorm2d(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.BatchNorm2d(out_channels),
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# CBAM(out_channels),
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nn.GELU()
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)
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self.pool = nn.MaxPool2d(2)
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def forward(self, x):
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x_res = self.conv(x)
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x_down = self.pool(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):
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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.BatchNorm2d(out_channels),
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# CBAM(out_channels),
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nn.GELU()
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)
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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):
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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.BatchNorm2d(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.BatchNorm2d(out_channels),
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nn.GELU(),
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)
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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)
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self.down2 = GinkaEncoder(in_ch*2, in_ch*4)
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self.down3 = GinkaEncoder(in_ch*4, in_ch*8)
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self.down4 = GinkaEncoder(in_ch*8, in_ch*16)
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self.bottleneck = GinkaBottleneck(in_ch*16, in_ch*16)
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self.up1 = GinkaDecoder(in_ch*16, in_ch*8)
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self.up2 = GinkaDecoder(in_ch*8, in_ch*4)
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self.up3 = GinkaDecoder(in_ch*4, in_ch*2)
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self.up4 = GinkaDecoder(in_ch*2, in_ch)
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self.final = nn.Sequential(
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nn.Conv2d(in_ch, out_ch, 1),
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# nn.Softmax(dim=1) # 适用于分类任务
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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) # 用 down2 的 skip
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x = self.up2(x, skip3) # 用 down2 的 skip
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x = self.up3(x, skip2) # 用 down1 的 skip
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x = self.up4(x, skip1) # 用 down1 的 skip
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return self.final(x)
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