Building a U-Net: forward method

With the encoder and decoder layers defied, you can now implement the forward() method of the U-net. The inputs have already been passed through the encoder for you. However, you need to define the last decoder block.

The goal of the decoder is to upsample the feature maps so that its output is of the same height and width as the U-Net's input image. This will allow you to obtain pixel-level semantic masks.

Bu egzersiz

Deep Learning for Images with PyTorch

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Define the last decoder block, using torch.cat() to form the skip connection.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

def forward(self, x):
    x1 = self.enc1(x)
    x2 = self.enc2(self.pool(x1))
    x3 = self.enc3(self.pool(x2))
    x4 = self.enc4(self.pool(x3))

    x = self.upconv3(x4)
    x = torch.cat([x, x3], dim=1)
    x = self.dec1(x)

    x = self.upconv2(x)
    x = torch.cat([x, x2], dim=1)
    x = self.dec2(x)

    # Define the last decoder block with skip connections
    x = ____
    x = ____
    x = ____

    return self.out(x)

Kodu Düzenle ve Çalıştır