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diff --git a/config.py b/config.py
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index 0000000000000000000000000000000000000000..24ef9985271ed5b920e0f982a014c9dbefdbcb94
--- /dev/null
+++ b/config.py
@@ -0,0 +1,38 @@
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+DEVICE = "cuda"
+TRAIN_DIR = "data/train"
+VAL_DIR = "data/val"
+LEARNING_RATE = 2e-4
+BATCH_SIZE = 16
+NUM_WORKERS = 2
+IMAGE_SIZE = 256
+CHANNELS_IMG = 3
+L1_LAMBDA = 100
+LAMBDA_GP = 10
+NUM_EPOCHS = 1
+LOAD_MODEL = True
+SAVE_MODEL = False
+CHECKPOINT_DISC = "disc.pth.tar"
+CHECKPOINT_GEN = "gen.pth.tar"
+
+both_transform = A.Compose(
+    [A.Resize(width=256, height=256), ], additional_targets={"image0": "image"}, is_check_shapes=False
+)
+
+transform_only_input = A.Compose(
+    [
+        A.HorizontalFlip(p=0.5),
+        A.ColorJitter(p=0.2),
+        A.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_pixel_value=255.0, ),
+        ToTensorV2(),
+    ]
+)
+
+transform_only_mask = A.Compose(
+    [
+        A.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_pixel_value=255.0, ),
+        ToTensorV2(),
+    ]
+)
\ No newline at end of file
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diff --git a/dataset.py b/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..b61fa5524da52fef0c675376e5a7da1c7a41f9f9
--- /dev/null
+++ b/dataset.py
@@ -0,0 +1,48 @@
+import numpy as np
+import config
+import os
+from PIL import Image
+from torch.utils.data import Dataset, DataLoader
+from torchvision.utils import save_image
+
+
+class MapDataset(Dataset):
+    def __init__(self, root_dir_comics, root_dir_face):
+        self.root_dir_face = root_dir_face
+        self.list_files_face = os.listdir(self.root_dir_face)
+        self.root_dir_comics = root_dir_comics
+        self.list_files_comics = os.listdir(self.root_dir_comics)
+
+    def __len__(self):
+        return len(self.list_files_face)
+
+    def __getitem__(self, index):
+        img_file_input = self.list_files_face[index]
+        img_file_target = self.list_files_comics[index]
+        img_path_input = os.path.join(self.root_dir_face, img_file_input)
+        img_path_target = os.path.join(self.root_dir_comics, img_file_target)
+        image_input = np.array(Image.open(img_path_input))
+        image_target = np.array(Image.open(img_path_target))
+        input_image = image_input
+        target_image = image_target
+
+        augmentations = config.both_transform(image=input_image, image0=target_image)
+        input_image = augmentations["image"]
+        target_image = augmentations["image0"]
+
+        input_image = config.transform_only_input(image=input_image)["image"]
+        target_image = config.transform_only_mask(image=target_image)["image"]
+
+        return input_image, target_image
+
+
+if __name__ == "__main__":
+    dataset = MapDataset("data/train/")
+    loader = DataLoader(dataset, batch_size=5)
+    for x, y in loader:
+        print(x.shape)
+        save_image(x, "x.png")
+        save_image(y, "y.png")
+        import sys
+
+        sys.exit()
\ No newline at end of file
diff --git a/disc.pth.tar b/disc.pth.tar
new file mode 100644
index 0000000000000000000000000000000000000000..206341aa9b3bfd7145754c15c17a0aa4473e21e6
--- /dev/null
+++ b/disc.pth.tar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9eade8fe1c334aedd6df0ec1906fc98988e370ab2ba0c1e0de78e3f85bb99d9
+size 33250221
diff --git a/discriminator_model.py b/discriminator_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcae8e3d6588d0623af80889d1a78b6f8685af38
--- /dev/null
+++ b/discriminator_model.py
@@ -0,0 +1,68 @@
+import torch
+import torch.nn as nn
+
+
+class CNNBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride):
+        super(CNNBlock, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                in_channels, out_channels, 4, stride, 1, bias=False, padding_mode="reflect"
+            ),
+            nn.BatchNorm2d(out_channels),
+            nn.LeakyReLU(0.2),
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class Discriminator(nn.Module):
+    def __init__(self, in_channels=3, features=[64, 128, 256, 512]):
+        super().__init__()
+        self.initial = nn.Sequential(
+            nn.Conv2d(
+                in_channels * 2,
+                features[0],
+                kernel_size=4,
+                stride=2,
+                padding=1,
+                padding_mode="reflect",
+            ),
+            nn.LeakyReLU(0.2),
+        )
+
+        layers = []
+        in_channels = features[0]
+        for feature in features[1:]:
+            layers.append(
+                CNNBlock(in_channels, feature, stride=1 if feature == features[-1] else 2),
+            )
+            in_channels = feature
+
+        layers.append(
+            nn.Conv2d(
+                in_channels, 1, kernel_size=4, stride=1, padding=1, padding_mode="reflect"
+            ),
+        )
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x, y):
+        x = torch.cat([x, y], dim=1)
+        x = self.initial(x)
+        x = self.model(x)
+        return x
+
+
+def test():
+    x = torch.randn((1, 3, 256, 256))
+    y = torch.randn((1, 3, 256, 256))
+    model = Discriminator(in_channels=3)
+    preds = model(x, y)
+    print(model)
+    print(preds.shape)
+
+
+if __name__ == "__main__":
+    test()
\ No newline at end of file
diff --git a/evaluation/input_0.png b/evaluation/input_0.png
new file mode 100644
index 0000000000000000000000000000000000000000..ed2460dd561be7cc9acba02f5573abcbb53be2f3
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diff --git a/evaluation/y_gen_0.png b/evaluation/y_gen_0.png
new file mode 100644
index 0000000000000000000000000000000000000000..0464d2ce23b415953e9578ea676df4b9d3fd846b
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diff --git a/gen.pth.tar b/gen.pth.tar
new file mode 100644
index 0000000000000000000000000000000000000000..ac28053731ee02bad64ce7f52cf21eefc5801e9a
--- /dev/null
+++ b/gen.pth.tar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebe48025f3276240c856d829928655efd1c171945ea4782240a0c6a4051e39e1
+size 653080267
diff --git a/generator_model.py b/generator_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..826e0b09c150204cbffc863057f594d44038e2ed
--- /dev/null
+++ b/generator_model.py
@@ -0,0 +1,101 @@
+import torch
+import torch.nn as nn
+
+
+class Block(nn.Module):
+    def __init__(self, in_channels, out_channels, down=True, act="relu", use_dropout=False):
+        super(Block, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False, padding_mode="reflect")
+            if down
+            else nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU() if act == "relu" else nn.LeakyReLU(0.2),
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(0.5)
+        self.down = down
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.dropout(x) if self.use_dropout else x
+
+
+class Generator(nn.Module):
+    def __init__(self, in_channels=3, features=64):
+        super().__init__()
+        self.initial_down = nn.Sequential(
+            nn.Conv2d(in_channels, features, 4, 2, 1, padding_mode="reflect"),
+            nn.LeakyReLU(0.2),
+        )
+        self.down1 = Block(features, features * 2, down=True, act="leaky", use_dropout=False)
+        self.down2 = Block(
+            features * 2, features * 4, down=True, act="leaky", use_dropout=False
+        )
+        self.down3 = Block(
+            features * 4, features * 8, down=True, act="leaky", use_dropout=False
+        )
+        self.down4 = Block(
+            features * 8, features * 8, down=True, act="leaky", use_dropout=False
+        )
+        self.down5 = Block(
+            features * 8, features * 8, down=True, act="leaky", use_dropout=False
+        )
+        self.down6 = Block(
+            features * 8, features * 8, down=True, act="leaky", use_dropout=False
+        )
+        self.bottleneck = nn.Sequential(
+            nn.Conv2d(features * 8, features * 8, 4, 2, 1), nn.ReLU()
+        )
+
+        self.up1 = Block(features * 8, features * 8, down=False, act="relu", use_dropout=True)
+        self.up2 = Block(
+            features * 8 * 2, features * 8, down=False, act="relu", use_dropout=True
+        )
+        self.up3 = Block(
+            features * 8 * 2, features * 8, down=False, act="relu", use_dropout=True
+        )
+        self.up4 = Block(
+            features * 8 * 2, features * 8, down=False, act="relu", use_dropout=False
+        )
+        self.up5 = Block(
+            features * 8 * 2, features * 4, down=False, act="relu", use_dropout=False
+        )
+        self.up6 = Block(
+            features * 4 * 2, features * 2, down=False, act="relu", use_dropout=False
+        )
+        self.up7 = Block(features * 2 * 2, features, down=False, act="relu", use_dropout=False)
+        self.final_up = nn.Sequential(
+            nn.ConvTranspose2d(features * 2, in_channels, kernel_size=4, stride=2, padding=1),
+            nn.Tanh(),
+        )
+
+    def forward(self, x):
+        d1 = self.initial_down(x)
+        d2 = self.down1(d1)
+        d3 = self.down2(d2)
+        d4 = self.down3(d3)
+        d5 = self.down4(d4)
+        d6 = self.down5(d5)
+        d7 = self.down6(d6)
+        bottleneck = self.bottleneck(d7)
+        up1 = self.up1(bottleneck)
+        up2 = self.up2(torch.cat([up1, d7], 1))
+        up3 = self.up3(torch.cat([up2, d6], 1))
+        up4 = self.up4(torch.cat([up3, d5], 1))
+        up5 = self.up5(torch.cat([up4, d4], 1))
+        up6 = self.up6(torch.cat([up5, d3], 1))
+        up7 = self.up7(torch.cat([up6, d2], 1))
+        return self.final_up(torch.cat([up7, d1], 1))
+
+
+def test():
+    x = torch.randn((1, 3, 256, 256))
+    model = Generator(in_channels=3, features=64)
+    preds = model(x)
+    print(preds.shape)
+
+
+if __name__ == "__main__":
+    test()
\ No newline at end of file
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diff --git a/input/face/3189.jpg b/input/face/3189.jpg
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diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..d61a72b3ebde698065b4024b2036e048abbe8198
--- /dev/null
+++ b/train.py
@@ -0,0 +1,102 @@
+import torch
+from utils import save_checkpoint, load_checkpoint, save_some_examples
+import torch.nn as nn
+import torch.optim as optim
+import config
+from dataset import MapDataset
+from generator_model import Generator
+from discriminator_model import Discriminator
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from torchvision.utils import save_image
+
+torch.backends.cudnn.benchmark = True
+
+
+def train_fn(
+        disc, gen, loader, opt_disc, opt_gen, l1_loss, bce, g_scaler, d_scaler,
+):
+    loop = tqdm(loader, leave=True)
+
+    for idx, (x, y) in enumerate(loop):
+        x = x.to(config.DEVICE)
+        y = y.to(config.DEVICE)
+
+        # Train Discriminator
+        with torch.cuda.amp.autocast():
+            y_fake = gen(x)
+            D_real = disc(x, y)
+            D_real_loss = bce(D_real, torch.ones_like(D_real))
+            D_fake = disc(x, y_fake.detach())
+            D_fake_loss = bce(D_fake, torch.zeros_like(D_fake))
+            D_loss = (D_real_loss + D_fake_loss) / 2
+
+        disc.zero_grad()
+        d_scaler.scale(D_loss).backward()
+        d_scaler.step(opt_disc)
+        d_scaler.update()
+
+        # Train generator
+        with torch.cuda.amp.autocast():
+            D_fake = disc(x, y_fake)
+            G_fake_loss = bce(D_fake, torch.ones_like(D_fake))
+            L1 = l1_loss(y_fake, y) * config.L1_LAMBDA
+            G_loss = G_fake_loss + L1
+
+        opt_gen.zero_grad()
+        g_scaler.scale(G_loss).backward()
+        g_scaler.step(opt_gen)
+        g_scaler.update()
+
+        if idx % 10 == 0:
+            loop.set_postfix(
+                D_real=torch.sigmoid(D_real).mean().item(),
+                D_fake=torch.sigmoid(D_fake).mean().item(),
+            )
+
+
+def main():
+    disc = Discriminator(in_channels=3).to(config.DEVICE)
+    gen = Generator(in_channels=3, features=64).to(config.DEVICE)
+    opt_disc = optim.Adam(disc.parameters(), lr=config.LEARNING_RATE, betas=(0.5, 0.999), )
+    opt_gen = optim.Adam(gen.parameters(), lr=config.LEARNING_RATE, betas=(0.5, 0.999))
+    BCE = nn.BCEWithLogitsLoss()
+    L1_LOSS = nn.L1Loss()
+
+    if config.LOAD_MODEL:
+        load_checkpoint(
+            config.CHECKPOINT_GEN, gen, opt_gen, config.LEARNING_RATE,
+        )
+        load_checkpoint(
+            config.CHECKPOINT_DISC, disc, opt_disc, config.LEARNING_RATE,
+        )
+
+    train_dataset = MapDataset(root_dir_face=config.TRAIN_DIR + "/face", root_dir_comics=config.TRAIN_DIR + "/comics")
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=config.BATCH_SIZE,
+        shuffle=True,
+        num_workers=config.NUM_WORKERS,
+    )
+    g_scaler = torch.cuda.amp.GradScaler()
+    d_scaler = torch.cuda.amp.GradScaler()
+    val_dataset = MapDataset(root_dir_face=config.VAL_DIR + "/face", root_dir_comics=config.VAL_DIR + "/comics")
+    val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)
+
+    input_data = MapDataset(root_dir_face="input/face", root_dir_comics="input/comics")
+    input_loader = DataLoader(input_data, batch_size=1, shuffle=False)
+
+    for epoch in range(config.NUM_EPOCHS):
+        # train_fn(
+        #     disc, gen, train_loader, opt_disc, opt_gen, L1_LOSS, BCE, g_scaler, d_scaler,
+        # )
+        #
+        # if config.SAVE_MODEL and epoch % 5 == 0:
+        #     save_checkpoint(gen, opt_gen, filename=config.CHECKPOINT_GEN)
+        #     save_checkpoint(disc, opt_disc, filename=config.CHECKPOINT_DISC)
+
+        save_some_examples(gen, input_loader, epoch, folder="evaluation")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..46ecbd7096477ebad80306358e9ebc1e1234bcd9
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,38 @@
+import torch
+import config
+from torchvision.utils import save_image
+
+
+def save_some_examples(gen, val_loader, epoch, folder):
+    x, y = next(iter(val_loader))
+    x, y = x.to(config.DEVICE), y.to(config.DEVICE)
+    gen.eval()
+    with torch.no_grad():
+        y_fake = gen(x)
+        y_fake = y_fake * 0.5 + 0.5  # remove normalization#
+        save_image(y_fake, folder + f"/y_gen_{epoch}.png")
+        save_image(x * 0.5 + 0.5, folder + f"/input_{epoch}.png")
+        if epoch == 1:
+            save_image(y * 0.5 + 0.5, folder + f"/label_{epoch}.png")
+    gen.train()
+
+
+def save_checkpoint(model, optimizer, filename="my_checkpoint.pth.tar"):
+    print("=> Saving checkpoint")
+    checkpoint = {
+        "state_dict": model.state_dict(),
+        "optimizer": optimizer.state_dict(),
+    }
+    torch.save(checkpoint, filename)
+
+
+def load_checkpoint(checkpoint_file, model, optimizer, lr):
+    print("=> Loading checkpoint")
+    checkpoint = torch.load(checkpoint_file, map_location=config.DEVICE)
+    model.load_state_dict(checkpoint["state_dict"])
+    optimizer.load_state_dict(checkpoint["optimizer"])
+
+    # If we don't do this then it will just have learning rate of old checkpoint
+    # and it will lead to many hours of debugging \:
+    for param_group in optimizer.param_groups:
+        param_group["lr"] = lr