Raise TypeError instead of using asserting with condition of types. (#5536)

Note that assert are being removed in optimized with compiling to
optimised byte code (python -o producing *.pyo files).

pytorch_lightning/core/saving.py

@@ -372,7 +372,9 @@ def save_hparams_to_yaml(config_yaml, hparams: Union[dict, Namespace]) -> None:
                     OmegaConf.save(OmegaConf.create(hparams), fp, resolve=True)
                 return
 
-    assert isinstance(hparams, dict)
+    if not isinstance(hparams, dict):
+        raise TypeError("hparams must be dictionary")
+
     hparams_allowed = {}
     # drop paramaters which contain some strange datatypes as fsspec
     for k, v in hparams.items():

pytorch_lightning/core/step_result.py

@@ -100,11 +100,14 @@ class Result(Dict):
 
     def _assert_tensor_metric(self, name: str, potential_metric: Union[bool, Tensor, None, Any]):
         if potential_metric is not None and not isinstance(potential_metric, bool):
-            assert isinstance(potential_metric, Tensor), f'{name} must be a torch.Tensor'
+            if not isinstance(potential_metric, Tensor):
+                raise TypeError(f'{name} must be a torch.Tensor')
 
     def _assert_grad_tensor_metric(self, name: str, x: Union[torch.Tensor, Any], additional_err: str = ''):
         if x is not None:
-            assert isinstance(x, Tensor), f'{name} must be a torch.Tensor'
+            if not isinstance(x, Tensor):
+                raise TypeError(f'{name} must be a torch.Tensor')
+
             m = f'{name} must have a computational graph.'
 
             if additional_err:

pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py

@@ -155,7 +155,9 @@ class HookResultStore:
         primary_dict[opt_idx][batch_idx].append(result)
 
     def append(self, result, dataloader_idx: Optional[int] = None, extra_info: Optional[dict] = None) -> None:
-        assert isinstance(result, Result)
+        if not isinstance(result, Result):
+            raise TypeError(f'{result} must be Result')
+
         if dataloader_idx is None:
             dataloader_idx = 0
 

pytorch_lightning/utilities/device_parser.py

@@ -30,7 +30,9 @@ def determine_root_gpu_device(gpus: List[int]) -> Optional[int]:
     if gpus is None:
         return None
 
-    assert isinstance(gpus, list), "gpus should be a list"
+    if not isinstance(gpus, list):
+        raise TypeError("gpus should be a list")
+
     assert len(gpus) > 0, "gpus should be a non empty list"
 
     # set root gpu