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.
Latihan ini adalah bagian dari kursus
Deep Learning for Images with PyTorch
Petunjuk latihan
- Define the last decoder block, using
torch.cat()to form the skip connection.
Latihan interaktif praktis
Cobalah latihan ini dengan menyelesaikan kode contoh berikut.
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)