mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-14 04:41:12 +08:00
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
import torch
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import torch.nn as nn
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class RandomInputHead(nn.Module):
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def __init__(self, in_size=(32, 32), out_size=(32, 32)):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(1, 32, 3, padding=1, padding_mode='replicate'),
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nn.InstanceNorm2d(32),
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nn.ELU(),
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nn.Conv2d(32, 64, 3, padding=1, padding_mode='replicate'),
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nn.InstanceNorm2d(64),
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nn.ELU(),
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nn.Conv2d(64, 128, 3, padding=1, padding_mode='replicate'),
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nn.InstanceNorm2d(128),
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nn.ELU(),
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)
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self.out_conv = nn.Sequential(
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nn.Conv2d(128, 32, 1),
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)
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def forward(self, x):
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x = self.conv(x)
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x = self.out_conv(x)
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return x
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class GinkaInput(nn.Module):
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def __init__(self, in_ch=32, out_ch=64, in_size=(13, 13), out_size=(32, 32)):
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super().__init__()
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self.out_size = out_size
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self.fc = nn.Sequential(
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nn.Linear(in_size[0] * in_size[1], out_size[0] * out_size[1]),
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nn.LayerNorm(out_size[0] * out_size[1]),
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nn.ELU()
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)
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self.conv = nn.Sequential(
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nn.Conv2d(in_ch, out_ch, 3, padding=1, padding_mode='replicate'),
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nn.InstanceNorm2d(out_ch),
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nn.ELU()
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)
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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)
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x = self.fc(x)
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x = x.view(B, C, self.out_size[0], self.out_size[1])
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x = self.conv(x)
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return x
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