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2026-09-10 12:21:57 +08:00 · 2020-03-03 10:52:16 -05:00
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+Child Modules
+-------------
+Research projects tend to test different approaches to the same dataset.
+This is very easy to do in Lightning with inheritance.
+
+For example, imaging we now want to train an Autoencoder to use as a feature extractor for MNIST images.
+Recall that `CoolMNIST` already defines all the dataloading etc... The only things
+that change in the `Autoencoder` model are the init, forward, training, validation and test step.
+
+.. code-block::
+
+    class Encoder(torch.nn.Module):
+        ...
+
+    class AutoEncoder(CoolMNIST):
+        def __init__(self):
+            self.encoder = Encoder()
+            self.decoder = Decoder()
+
+        def forward(self, x):
+            generated = self.decoder(x)
+
+        def training_step(self, batch, batch_idx):
+            x, _ = batch
+
+            representation = self.encoder(x)
+            x_hat = self.forward(representation)
+
+            loss = MSE(x, x_hat)
+            return loss
+
+        def validation_step(self, batch, batch_idx):
+            return self._shared_eval(batch, batch_idx, 'val'):
+
+        def test_step(self, batch, batch_idx):
+            return self._shared_eval(batch, batch_idx, 'test'):
+
+        def _shared_eval(self, batch, batch_idx, prefix):
+            x, y = batch
+            representation = self.encoder(x)
+            x_hat = self.forward(representation)
+
+            loss = F.nll_loss(logits, y)
+            return {f'{prefix}_loss': loss}
+
+and we can train this using the same trainer
+
+.. code-block:: python
+
+    autoencoder = AutoEncoder()
+    trainer = Trainer()
+    trainer.fit(autoencoder)
+
+And remember that the forward method is to define the practical use of a LightningModule.
+In this case, we want to use the `AutoEncoder` to extract image representations
+
+.. code-block:: python
+
+    some_images = torch.Tensor(32, 1, 28, 28)
+    representations = autoencoder(some_images)
+
+..