Updated extras.inv to fix issues with lightning's doc

The obects.inv file from lightning's documentation is missing a lot of
methods, raising errors when building with sphinx. Problematic methods
have been added to the extras.txt and encoded into extras.inv

doc/extras.inv

@@ -2,5 +2,5 @@
 # Project: extras
 # Version: stable
 # The remainder of this file is compressed using zlib.
-xE
 лSt
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-	P~M߃lYfh5Wٯit}No!%]B-4OΌ 
+xڭA
+1E[[R
MH2A\.{OItņTLR+.$Uns<lI	ԟ2hl1iB$`g/aG%<XU)ibłw/
\ No newline at end of file

doc/extras.txt

@@ -3,3 +3,6 @@
 # Version: stable
 # The remainder of this file is compressed using zlib.
 torchvision.transforms py:module 1 https://pytorch.org/vision/stable/transforms.html -
+lightning.pytorch.core.module.LightningModule.forward py:method 1 api/lightning.pytorch.core.LightningModule.html#$ -
+lightning.pytorch.core.module.LightningModule.predict_step py:method 1 api/lightning.pytorch.core.LightningModule.html#$ -
+optimizer_step py:method 1 api/lightning.pytorch.core.LightningModule.html#$ -

src/ptbench/models/alexnet.py

@@ -10,7 +10,7 @@ import torchvision.models as models
 from .normalizer import TorchVisionNormalizer
 
 
-class Alexnet(pl.core.LightningModule):
+class Alexnet(pl.LightningModule):
     """Alexnet module.
 
     Note: only usable with a normalized dataset
@@ -43,20 +43,6 @@ class Alexnet(pl.core.LightningModule):
         self.model_ft.classifier[6] = nn.Linear(512, 1)
 
     def forward(self, x):
-        """
-
-        Parameters
-        ----------
-
-        x : list
-            list of tensors.
-
-        Returns
-        -------
-
-        tensor : :py:class:`torch.Tensor`
-
-        """
         x = self.normalizer(x)
         x = self.model_ft(x)
 

src/ptbench/models/densenet.py

@@ -43,22 +43,7 @@ class Densenet(pl.LightningModule):
         )
 
     def forward(self, x):
-        """
-
-        Parameters
-        ----------
-
-        x : list
-            list of tensors.
-
-        Returns
-        -------
-
-        tensor : :py:class:`torch.Tensor`
-
-        """
         x = self.normalizer(x)
-
         x = self.model_ft(x)
 
         return x

src/ptbench/models/densenet_rs.py

@@ -38,21 +38,6 @@ class DensenetRS(pl.LightningModule):
         self.model_ft.classifier = nn.Linear(num_ftrs, 14)
 
     def forward(self, x):
-        """
-
-        Parameters
-        ----------
-
-        x : list
-            list of tensors.
-
-        Returns
-        -------
-
-        tensor : :py:class:`torch.Tensor`
-
-        """
-
         x = self.normalizer(x)
         x = self.model_ft(x)
         return x

src/ptbench/models/logistic_regression.py

@@ -27,20 +27,6 @@ class LogisticRegression(pl.LightningModule):
         self.linear = nn.Linear(self.hparams.input_size, 1)
 
     def forward(self, x):
-        """
-
-        Parameters
-        ----------
-
-        x : list
-            list of tensors.
-
-        Returns
-        -------
-
-        tensor : :py:class:`torch.Tensor`
-
-        """
         output = self.linear(x)
 
         return output

src/ptbench/models/pasa.py

@@ -93,20 +93,6 @@ class PASA(pl.LightningModule):
         self.dense = nn.Linear(80, 1)  # Fully connected layer
 
     def forward(self, x):
-        """
-
-        Parameters
-        ----------
-
-        x : list
-            list of tensors.
-
-        Returns
-        -------
-
-        tensor : :py:class:`torch.Tensor`
-
-        """
         x = self.normalizer(x)
 
         # First convolution block

src/ptbench/models/signs_to_tb.py

@@ -31,20 +31,6 @@ class SignsToTB(pl.LightningModule):
         self.fc2 = torch.nn.Linear(self.hparams.hidden_size, 1)
 
     def forward(self, x):
-        """
-
-        Parameters
-        ----------
-
-        x : list
-            list of tensors.
-
-        Returns
-        -------
-
-        tensor : :py:class:`torch.Tensor`
-
-        """
         hidden = self.fc1(x)
         relu = self.relu(hidden)