calling self.forward() -> self() (#1211)

* self.forward() -> self()

* update changelog

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

Author: jeremyjordan

CHANGELOG.md

@@ -14,6 +14,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for `IterableDataset` in validation and testing ([#1104](https://github.com/PyTorchLightning/pytorch-lightning/pull/1104))
 - Added support for non-primitive types in `hparams` for `TensorboardLogger` ([#1130](https://github.com/PyTorchLightning/pytorch-lightning/pull/1130))
 - Added a check that stops the training when loss or weights contain `NaN` or `inf` values. ([#1097](https://github.com/PyTorchLightning/pytorch-lightning/pull/1097))
+- Updated references to self.forward() to instead use the `__call__` interface. ([#1211](https://github.com/PyTorchLightning/pytorch-lightning/pull/1211))
 
 ### Changed
 

README.md

@@ -200,7 +200,7 @@ def validation_step(self, batch, batch_idx):
     x, y = batch
 
     # or as basic as a CNN classification
-    out = self.forward(x)
+    out = self(x)
     loss = my_loss(out, y)
     return {'loss': loss} 
 ```

docs/source/child_modules.rst

@@ -24,7 +24,7 @@ that change in the `Autoencoder` model are the init, forward, training, validati
             x, _ = batch
 
             representation = self.encoder(x)
-            x_hat = self.forward(representation)
+            x_hat = self(representation)
 
             loss = MSE(x, x_hat)
             return loss
@@ -38,7 +38,7 @@ that change in the `Autoencoder` model are the init, forward, training, validati
         def _shared_eval(self, batch, batch_idx, prefix):
             x, y = batch
             representation = self.encoder(x)
-            x_hat = self.forward(representation)
+            x_hat = self(representation)
 
             loss = F.nll_loss(logits, y)
             return {f'{prefix}_loss': loss}

docs/source/introduction_guide.rst

@@ -319,7 +319,7 @@ in the LightningModule
 
       def training_step(self, batch, batch_idx):
         x, y = batch
-        logits = self.forward(x)
+        logits = self(x)
         loss = F.nll_loss(logits, y)
         return {'loss': loss}
         # return loss (also works)
@@ -371,7 +371,7 @@ For clarity, we'll recall that the full LightningModule now looks like this.
 
       def training_step(self, batch, batch_idx):
         x, y = batch
-        logits = self.forward(x)
+        logits = self(x)
         loss = F.nll_loss(logits, y)
 
         # add logging
@@ -684,7 +684,7 @@ sample split in the `train_dataloader` method.
     class LitMNIST(pl.LightningModule):
       def validation_step(self, batch, batch_idx):
         x, y = batch
-        logits = self.forward(x)
+        logits = self(x)
         loss = F.nll_loss(logits, y)
         return {'val_loss': loss}
 
@@ -740,7 +740,7 @@ Just like the validation loop, we define exactly the same steps for testing:
     class LitMNIST(pl.LightningModule):
       def test_step(self, batch, batch_idx):
         x, y = batch
-        logits = self.forward(x)
+        logits = self(x)
         loss = F.nll_loss(logits, y)
         return {'val_loss': loss}
 
@@ -827,7 +827,7 @@ within it.
 
       def training_step(self, batch, batch_idx):
         x, y = batch
-        logits = self.forward(x)
+        logits = self(x)
         loss = F.nll_loss(logits, y)
         return loss
 
@@ -855,7 +855,7 @@ In this case, we've set this LightningModel to predict logits. But we could also
 
       def training_step(self, batch, batch_idx):
         x, y = batch
-        out, l1_feats, l2_feats, l3_feats = self.forward(x)
+        out, l1_feats, l2_feats, l3_feats = self(x)
         logits = torch.log_softmax(out, dim=1)
         ce_loss = F.nll_loss(logits, y)
         loss = perceptual_loss(l1_feats, l2_feats, l3_feats) + ce_loss
@@ -880,7 +880,7 @@ Or maybe we have a model that we use to do generation
       def training_step(self, batch, batch_idx):
         x, y = batch
         representation = self.encoder(x)
-        imgs = self.forward(representation)
+        imgs = self(representation)
 
         loss = perceptual_loss(imgs, x)
         return loss

docs/source/multi_gpu.rst

@@ -207,7 +207,7 @@ to illustrate why this is needed, let's look at dataparallel
 
     def training_step(self, batch, batch_idx):
         x, y = batch
-        y_hat = self.forward(batch)
+        y_hat = self(batch)
 
         # on dp or ddp2 if we did softmax now it would be wrong
         # because batch is actually a piece of the full batch

pl_examples/basic_examples/lightning_module_template.py

@@ -106,7 +106,7 @@ def training_step(self, batch, batch_idx):
         x, y = batch
         x = x.view(x.size(0), -1)
 
-        y_hat = self.forward(x)
+        y_hat = self(x)
 
         # calculate loss
         loss_val = self.loss(y, y_hat)
@@ -133,7 +133,7 @@ def validation_step(self, batch, batch_idx):
         """
         x, y = batch
         x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
+        y_hat = self(x)
 
         loss_val = self.loss(y, y_hat)
 

pl_examples/domain_templates/gan.py

@@ -105,7 +105,7 @@ def training_step(self, batch, batch_idx, optimizer_idx):
                 z = z.cuda(imgs.device.index)
 
             # generate images
-            self.generated_imgs = self.forward(z)
+            self.generated_imgs = self(z)
 
             # log sampled images
             # sample_imgs = self.generated_imgs[:6]
@@ -179,7 +179,7 @@ def on_epoch_end(self):
             z = z.cuda(self.last_imgs.device.index)
 
         # log sampled images
-        sample_imgs = self.forward(z)
+        sample_imgs = self(z)
         grid = torchvision.utils.make_grid(sample_imgs)
         self.logger.experiment.add_image(f'generated_images', grid, self.current_epoch)
 

pl_examples/full_examples/imagenet/imagenet_example.py

@@ -42,7 +42,7 @@ def forward(self, x):
 
     def training_step(self, batch, batch_idx):
         images, target = batch
-        output = self.forward(images)
+        output = self(images)
         loss_val = F.cross_entropy(output, target)
         acc1, acc5 = self.__accuracy(output, target, topk=(1, 5))
 
@@ -65,7 +65,7 @@ def training_step(self, batch, batch_idx):
 
     def validation_step(self, batch, batch_idx):
         images, target = batch
-        output = self.forward(images)
+        output = self(images)
         loss_val = F.cross_entropy(output, target)
         acc1, acc5 = self.__accuracy(output, target, topk=(1, 5))
 

pl_examples/full_examples/semantic_segmentation/semseg.py

@@ -143,7 +143,7 @@ def training_step(self, batch, batch_nb):
         img, mask = batch
         img = img.float()
         mask = mask.long()
-        out = self.forward(img)
+        out = self(img)
         loss_val = F.cross_entropy(out, mask, ignore_index=250)
         return {'loss': loss_val}
 

pytorch_lightning/core/__init__.py

@@ -82,7 +82,7 @@ def forward(self, x):
 
         def training_step(self, batch, batch_idx):
             x, y = batch
-            y_hat = self.forward(x)
+            y_hat = self(x)
             return {'loss': F.cross_entropy(y_hat, y)}
 
         def train_dataloader(self):
@@ -159,7 +159,7 @@ def configure_optimizers(self):
         class LitModel(pl.LightningModule):
             def validation_step(self, batch, batch_idx):
                 x, y = batch
-                y_hat = self.forward(x)
+                y_hat = self(x)
                 return {'val_loss': F.cross_entropy(y_hat, y)}
 
             def validation_epoch_end(self, outputs):
@@ -178,7 +178,7 @@ def val_dataloader(self):
         class LitModel(pl.LightningModule):
             def test_step(self, batch, batch_idx):
                 x, y = batch
-                y_hat = self.forward(x)
+                y_hat = self(x)
                 return {'test_loss': F.cross_entropy(y_hat, y)}
 
             def test_epoch_end(self, outputs):