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| import argparse import torch from torch.nn import Module from common_layers import Conv2, Conv1
class Autoencoder(Module): encoder: Module decoder: Module
def __init__(self): super(Autoencoder, self).__init__() self.encoder = torch.nn.Sequential( Conv2(3, 32), torch.nn.MaxPool2d(2), Conv2(32, 64), torch.nn.MaxPool2d(2), Conv2(64, 128), torch.nn.MaxPool2d(2), Conv2(128, 256), torch.nn.MaxPool2d(2), Conv2(256, 256) )
self.decoder = torch.nn.Sequential( torch.nn.ConvTranspose2d(256, 256, 4, stride=2, padding=1), Conv2(256, 256), torch.nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1), Conv2(128, 128), torch.nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1), Conv2(64, 64), torch.nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1), Conv2(32, 32), torch.nn.Conv2d(32, 3, 1) )
def forward(self, x: torch.Tensor): encoded = self.encoder(x) decoded = self.decoder(encoded) return decoded
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