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c3b5390 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | 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
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