autoencoder-anomaly/model.py at main · super-zyw/autoencoder-anomaly · GitHub

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import torch
from torch import nn


class ae(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = torch.nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
        )

        self.decoder = torch.nn.Sequential(

            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1),
            nn.Sigmoid(),
        )

    def forward(self, x):
        x1 = self.encoder(x)
        x2 = self.decoder(x1)
        return x1, x2