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.
Questo esercizio fa parte del corso
Deep Learning for Images with PyTorch
Istruzioni dell'esercizio
- Define the last decoder block, using
torch.cat()to form the skip connection.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
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)