Coverage (#1058)

* docs

* docs

* docs

* docs

pytorch_lightning/trainer/distrib_parts.py

@@ -512,7 +512,7 @@ class TrainerDPMixin(ABC):
         # check for this bug (amp + dp + !01 doesn't work)
         # https://github.com/NVIDIA/apex/issues/227
         if self.use_dp and self.use_amp:
-            if self.amp_level == 'O2':
+            if self.amp_level == 'O2':  # pragma: no cover
                 m = f"""
                 Amp level {self.amp_level} with DataParallel is not supported.
                 See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.

pytorch_lightning/trainer/trainer.py

@@ -461,40 +461,6 @@ class Trainer(TrainerIOMixin,
         params = vars(args)
         return cls(**params)
 
-    def __parse_gpu_ids(self, gpus):
-        """Parse GPUs id.
-
-        :param list|str|int gpus: input GPU ids
-        :return list(int):
-        """
-        # if gpus = -1 then use all available devices
-        # otherwise, split the string using commas
-        if gpus is not None:
-            if isinstance(gpus, list):
-                gpus = gpus
-            elif isinstance(gpus, str):
-                if gpus == '-1':
-                    gpus = list(range(0, torch.cuda.device_count()))
-                else:
-                    gpus = [int(x.strip()) for x in gpus.split(',')]
-            elif isinstance(gpus, int):
-                gpus = gpus
-            else:
-                raise ValueError('`gpus` has to be a string, int or list of ints')
-
-        return gpus
-
-    def __set_root_gpu(self, gpus):
-        if gpus is None:
-            return None
-
-        # set root gpu
-        root_gpu = 0
-        if isinstance(gpus, list):
-            root_gpu = gpus[0]
-
-        return root_gpu
-
     @property
     def num_gpus(self) -> int:
         gpus = self.data_parallel_device_ids
@@ -617,7 +583,7 @@ class Trainer(TrainerIOMixin,
         elif self.single_gpu:
             self.single_gpu_train(model)
 
-        elif self.use_tpu:
+        elif self.use_tpu:  # pragma: no cover
             log.info(f'training on {self.num_tpu_cores} TPU cores')
 
             #  COLAB_GPU is an env var available by default in Colab environments.
@@ -877,7 +843,7 @@ class Trainer(TrainerIOMixin,
         if model is not None:
             self.model = model
             self.fit(model)
-        elif self.use_ddp or self.use_tpu:
+        elif self.use_ddp or self.use_tpu:  # pragma: no cover
             # attempt to load weights from a spawn
             path = os.path.join(self.default_save_path, '__temp_weight_ddp_end.ckpt')
             test_model = self.model
@@ -902,51 +868,3 @@ class _PatchDataLoader(object):
 
     def __call__(self) -> Union[List[DataLoader], DataLoader]:
         return self.dataloader
-
-
-def _set_dataloader(model, dataloader, attribute):
-    r'''
-    Check dataloaders passed to .fit() method if they are pytorch DataLoader
-    objects and whether or not we should overright the corresponding dataloader
-    in the model
-
-    Args:
-        model (LightningModule): The model to check
-
-        dataloader: If a pytorch dataloader (or a list of pytorch dataloaders)
-            is passed, it will be incorporate into the model as model.attribute.
-            If attribute alreay exist it will warn the userpass. If not a
-            dataloader will throw an error
-
-        attribute (str): The attribute to save the dataloader under
-
-    '''
-    # Check if attribute comes directly from base class or
-    # derived in user subclass
-    if LightningModule.__qualname__ in getattr(model, attribute).__qualname__:
-        # Val and test should be list of dataloaders
-        dataloader = dataloader if attribute == 'train_dataloader' or \
-            (attribute != 'train_dataloader' and isinstance(dataloader, list)) else [dataloader]
-
-        # Check we are given valid dataloaders
-        is_dataloader = isinstance(dataloader, torch.utils.data.DataLoader)
-        is_dataloader_list = isinstance(dataloader, list)
-        valid_loaders = None
-        if is_dataloader_list:
-            valid_loaders = all(isinstance(d, torch.utils.data.DataLoader) for d in dataloader)
-        if is_dataloader or is_dataloader_list and valid_loaders:
-
-            # Overwrite abstract methods
-            def dl():
-                return dataloader
-            dl.__name__ = attribute
-            setattr(model, attribute, dl)
-
-        elif dataloader and dataloader != [None]:
-            raise ValueError(f'`{attribute}` needs to be an instance of '
-                             '`torch.utils.data.DataLoader` or a list of '
-                             'DataLoaders, instead got %r`' % dataloader)
-
-    elif dataloader:  # if default (None) is passed, do not warn the user
-        warnings.warn(f'Model has predefined `{attribute}`,'
-                      f' will skip `{attribute}={dataloader}` passed to fit method.')

pytorch_lightning/trainer/training_io.py

@@ -206,7 +206,7 @@ class TrainerIOMixin(ABC):
             signal.signal(signal.SIGUSR1, self.sig_handler)
             signal.signal(signal.SIGTERM, self.term_handler)
 
-    def sig_handler(self, signum, frame):
+    def sig_handler(self, signum, frame):  # pragma: no cover
         if self.proc_rank == 0:
             # save weights
             log.info('handling SIGUSR1')