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import torch
import torch.nn as nn
class Autoencoder(nn.Module):
def __init__(self):
super(Autoencoder, self).__init__()
self.encoder = nn.Sequential(
nn.Conv2d(3, 64, 4, 2, 1),
nn.ReLU(),
nn.Conv2d(64, 128, 4, 2, 1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 256, 4, 2, 1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 512, 4, 2, 1),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(512, 256, 4, 2, 1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.ConvTranspose2d(256, 128, 4, 2, 1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.ConvTranspose2d(128, 64, 4, 2, 1),
nn.ReLU(),
nn.ConvTranspose2d(64, 3, 4, 2, 1),
nn.Sigmoid()
)
def forward(self, x):
return self.decoder(self.encoder(x))
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