import torch.nn as nn

class Decoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer = nn.Sequential(
            nn.ReflectionPad2d(1),
            nn.Conv2d(512, 256, kernel_size=3),
            nn.ReLU(inplace=True),
            
            nn.Upsample(scale_factor=2, mode='nearest'),

            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 256, kernel_size=3),
            nn.ReLU(inplace=True),

            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 256, kernel_size=3),
            nn.ReLU(inplace=True),

            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 256, kernel_size=3),
            nn.ReLU(inplace=True),

            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 128, kernel_size=3),
            nn.ReLU(inplace=True),
            
            nn.Upsample(scale_factor=2, mode='nearest'),

            nn.ReflectionPad2d(1),
            nn.Conv2d(128, 128, kernel_size=3),
            nn.ReLU(inplace=True),

            nn.ReflectionPad2d(1),
            nn.Conv2d(128, 64, kernel_size=3),
            nn.ReLU(inplace=True),
            
            nn.Upsample(scale_factor=2, mode='nearest'),
            
            nn.ReflectionPad2d(1),
            nn.Conv2d(64, 64, kernel_size=3),
            nn.ReLU(inplace=True),

            nn.ReflectionPad2d(1),
            nn.Conv2d(64, 3, kernel_size=3)
        )

    def forward(self, x):
        return self.layer(x)