Optional val

README.md

@@ -81,36 +81,40 @@ class CoolModel(pl.LightningModule):
     def forward(self, x):
         return torch.relu(self.l1(x.view(x.size(0), -1)))
 
-    def my_loss(self, y_hat, y):
-        return F.cross_entropy(y_hat, y)
-
     def training_step(self, batch, batch_nb):
+        # REQUIRED
         x, y = batch
         y_hat = self.forward(x)
-        return {'loss': self.my_loss(y_hat, y)}
+        return {'loss': F.cross_entropy(y_hat, y)}
 
     def validation_step(self, batch, batch_nb):
+        # OPTIONAL
         x, y = batch
         y_hat = self.forward(x)
         return {'val_loss': self.my_loss(y_hat, y)}
 
     def validation_end(self, outputs):
+        # OPTIONAL
         avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
         return {'avg_val_loss': avg_loss}
 
     def configure_optimizers(self):
+        # REQUIRED
         return [torch.optim.Adam(self.parameters(), lr=0.02)]
 
     @pl.data_loader
     def tng_dataloader(self):
+        # REQUIRED
         return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 
     @pl.data_loader
     def val_dataloader(self):
+        # OPTIONAL
         return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 
     @pl.data_loader
     def test_dataloader(self):
+        # OPTIONAL
         return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 ```
 

docs/LightningModule/RequiredTrainerInterface.md

@@ -10,16 +10,14 @@ Otherwise, to Define a Lightning Module, implement the following methods:
 **Required**:  
 
 - [training_step](RequiredTrainerInterface.md#training_step)   
-- [validation_step](RequiredTrainerInterface.md#validation_step)
-- [validation_end](RequiredTrainerInterface.md#validation_end)
-
-- [configure_optimizers](RequiredTrainerInterface.md#configure_optimizers)
-
-- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
 - [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
-- [test_dataloader](RequiredTrainerInterface.md#test_dataloader)
+- [configure_optimizers](RequiredTrainerInterface.md#configure_optimizers)
 
 **Optional**:   
+- [validation_step](RequiredTrainerInterface.md#validation_step)
+- [validation_end](RequiredTrainerInterface.md#validation_end)
+- [val_dataloader](RequiredTrainerInterface.md#val_dataloader)
+- [test_dataloader](RequiredTrainerInterface.md#test_dataloader)
 
 - [on_save_checkpoint](RequiredTrainerInterface.md#on_save_checkpoint)
 - [on_load_checkpoint](RequiredTrainerInterface.md#on_load_checkpoint)

pytorch_lightning/models/trainer.py

@@ -13,6 +13,7 @@
 import torch.multiprocessing as mp
 import torch.distributed as dist
 
+from pytorch_lightning.root_module.root_module import LightningModule
 from pytorch_lightning.root_module.memory import get_gpu_memory_map
 from pytorch_lightning.root_module.model_saving import TrainerIO
 from pytorch_lightning.pt_overrides.override_data_parallel import (
@@ -312,6 +313,14 @@ def __is_function_implemented(self, f_name):
         f_op = getattr(model, f_name, None)
         return callable(f_op)
 
+    def __is_overriden(self, f_name):
+        model = self.__get_model()
+        super_object = super(model.__class__, model)
+
+        # when code pointers are different, it was overriden
+        is_overriden = getattr(model, f_name).__code__ is not getattr(super_object, f_name).__code__
+        return is_overriden
+
     @property
     def __tng_tqdm_dic(self):
         tqdm_dic = {
@@ -345,13 +354,13 @@ def __layout_bookeeping(self):
         self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)
 
         # determine number of validation batches
-        self.nb_val_batches = len(self.val_dataloader)
+        self.nb_val_batches = len(self.val_dataloader) if self.val_dataloader is not None else 0
         self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)
         self.nb_val_batches = max(1, self.nb_val_batches)
         self.nb_val_batches = self.nb_val_batches
 
         # determine number of test batches
-        self.nb_test_batches = len(self.test_dataloader)
+        self.nb_test_batches = len(self.test_dataloader) if self.test_dataloader is not None else 0
         self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)
 
         # determine when to check validation
@@ -372,6 +381,10 @@ def validate(self, model, dataloader, max_batches):
         :param max_batches: Scalar
         :return:
         """
+        # skip validation if model has no validation_step defined
+        if not self.__is_overriden('validation_step'):
+            return {}
+
         # enable eval mode
         model.zero_grad()
         model.eval()
@@ -418,11 +431,13 @@ def validate(self, model, dataloader, max_batches):
             if self.progress_bar and self.prog_bar is not None:
                 self.prog_bar.update(1)
 
-        # give model a chance to do something with the outputs
-        if self.data_parallel:
-            val_results = model.module.validation_end(outputs)
-        else:
-            val_results = model.validation_end(outputs)
+        # give model a chance to do something with the outputs (and method defined)
+        val_results = {}
+        if self.__is_overriden('validation_end'):
+            if self.data_parallel:
+                val_results = model.module.validation_end(outputs)
+            else:
+                val_results = model.validation_end(outputs)
 
         # enable train mode again
         model.train()
@@ -439,6 +454,7 @@ def get_dataloaders(self, model):
         :return:
         """
         self.tng_dataloader = model.tng_dataloader
+
         self.test_dataloader = model.test_dataloader
         self.val_dataloader = model.val_dataloader
 

pytorch_lightning/root_module/root_module.py

@@ -36,18 +36,20 @@ def forward(self, *args, **kwargs):
     def validation_step(self, data_batch, batch_nb):
         """
         return whatever outputs will need to be aggregated in validation_end
+        OPTIONAL
         :param data_batch:
         :return:
         """
-        raise NotImplementedError
+        pass
 
     def validation_end(self, outputs):
         """
         Outputs has the appended output after each validation step
+        OPTIONAL
         :param outputs:
         :return: dic_with_metrics for tqdm
         """
-        raise NotImplementedError
+        pass
 
     def training_step(self, data_batch, batch_nb):
         """
@@ -67,26 +69,26 @@ def configure_optimizers(self):
     @data_loader
     def tng_dataloader(self):
         """
-        Implement a function to load an h5py of this data
+        Implement a PyTorch DataLoader
         :return:
         """
         raise NotImplementedError
 
     @data_loader
     def test_dataloader(self):
         """
-        Implement a function to load an h5py of this data
+        Implement a PyTorch DataLoader
         :return:
         """
-        raise NotImplementedError
+        return None
 
     @data_loader
     def val_dataloader(self):
         """
-        Implement a function to load an h5py of this data
+        Implement a PyTorch DataLoader
         :return:
         """
-        raise NotImplementedError
+        return None
 
     @classmethod
     def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None):

pytorch_lightning/testing/__init__.py

@@ -0,0 +1,3 @@
+from .lm_test_module import LightningTestModel
+from .no_val_end_module import NoValEndTestModel
+from .no_val_module import NoValModel