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| from config import Config | |
| import os | |
| import torch | |
| import torch.nn as nn | |
| class ResidualBlock(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(ResidualBlock, self).__init__() | |
| self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.in1 = nn.InstanceNorm2d(out_channels) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.in2 = nn.InstanceNorm2d(out_channels) | |
| self.skip = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) | |
| self.skip_in = nn.InstanceNorm2d(out_channels) | |
| def forward(self, x): | |
| out = self.conv1(x) | |
| out = self.in1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.in2(out) | |
| skip = self.skip(x) | |
| skip = self.skip_in(skip) | |
| return out + skip | |
| class Generator(nn.Module): | |
| def __init__(self, input_channels=3, output_channels=3): | |
| super(Generator, self).__init__() | |
| self.enc1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=1, padding=3) | |
| self.enc2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) | |
| self.enc3 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) | |
| self.res1 = ResidualBlock(256, 256) | |
| self.res2 = ResidualBlock(256, 256) | |
| self.res3 = ResidualBlock(256, 256) | |
| self.res4 = ResidualBlock(256, 256) | |
| self.res5 = ResidualBlock(256, 256) | |
| self.res6 = ResidualBlock(256, 256) | |
| self.res7 = ResidualBlock(256, 256) | |
| self.res8 = ResidualBlock(256, 256) | |
| self.res9 = ResidualBlock(256, 256) | |
| self.dec1 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, output_padding=1) | |
| self.dec2 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1) | |
| self.dec3 = nn.Conv2d(64, output_channels, kernel_size=7, stride=1, padding=3) | |
| self.tanh = nn.Tanh() | |
| def forward(self, x): | |
| x = self.enc1(x) | |
| x = self.enc2(x) | |
| x = self.enc3(x) | |
| x = self.res1(x) | |
| x = self.res2(x) | |
| x = self.res3(x) | |
| x = self.res4(x) | |
| x = self.res5(x) | |
| x = self.res6(x) | |
| x = self.res7(x) | |
| x = self.res8(x) | |
| x = self.res9(x) | |
| x = self.dec1(x) | |
| x = self.dec2(x) | |
| x = self.dec3(x) | |
| return self.tanh(x) | |
| class Discriminator(nn.Module): | |
| def __init__(self, input_channels=3): | |
| super(Discriminator, self).__init__() | |
| self.model = nn.Sequential( | |
| nn.Conv2d(input_channels, 64, kernel_size=4, stride=2, padding=1), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1), | |
| nn.InstanceNorm2d(128), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1), | |
| nn.InstanceNorm2d(256), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1), | |
| nn.InstanceNorm2d(512), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=1) | |
| ) | |
| def forward(self, x): | |
| output = self.model(x) | |
| return output | |
| class CycleGAN(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.generator_A2B = Generator() | |
| self.generator_B2A = Generator() | |
| self.discriminator_A = Discriminator() | |
| self.discriminator_B = Discriminator() | |
| def get_model(name): | |
| model = CycleGAN() | |
| model.load_state_dict(torch.load(os.path.join(Config.WEIGHTS_PATH, f"{name}.pth"), map_location=Config.DEVICE)) | |
| return model | |