Delete model

model

@@ -1,52 +0,0 @@
-# model/anime_gan.py
-import torch.nn as nn
-
-class ConvLayer(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride):
-        super(ConvLayer, self).__init__()
-        reflection_padding = kernel_size // 2
-        self.layer = nn.Sequential(
-            nn.ReflectionPad2d(reflection_padding),
-            nn.Conv2d(in_channels, out_channels, kernel_size, stride),
-            nn.InstanceNorm2d(out_channels, affine=True),
-            nn.ReLU()
-        )
-
-    def forward(self, x):
-        return self.layer(x)
-
-class ResidualBlock(nn.Module):
-    def __init__(self, channels):
-        super(ResidualBlock, self).__init__()
-        self.block = nn.Sequential(
-            ConvLayer(channels, channels, 3, 1),
-            ConvLayer(channels, channels, 3, 1)
-        )
-
-    def forward(self, x):
-        return x + self.block(x)
-
-class Generator(nn.Module):
-    def __init__(self):
-        super(Generator, self).__init__()
-        self.encoder = nn.Sequential(
-            ConvLayer(3, 32, 7, 1),
-            ConvLayer(32, 64, 3, 2),
-            ConvLayer(64, 128, 3, 2),
-        )
-        self.res_blocks = nn.Sequential(*[ResidualBlock(128) for _ in range(5)])
-        self.decoder = nn.Sequential(
-            nn.Upsample(scale_factor=2),
-            ConvLayer(128, 64, 3, 1),
-            nn.Upsample(scale_factor=2),
-            ConvLayer(64, 32, 3, 1),
-            nn.ReflectionPad2d(3),
-            nn.Conv2d(32, 3, 7, 1),
-            nn.Tanh()
-        )
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.res_blocks(x)
-        x = self.decoder(x)
-        return x