hparams as dict [blocked by 1041] (#1029)

* hparams as dict

* hparams as dict

* fixing

* fixing

* fixing

* fixing

* typing

* typing

* chnagelog

* update set hparams

* use setter

* simplify

* chnagelog

* imports

* pylint

* typing

* Update training_io.py

* Update training_io.py

* Update lightning.py

* Update test_trainer.py

* Update __init__.py

* Update base.py

* Update utils.py

* Update test_trainer.py

* Update training_io.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update callback_config.py

* Update callback_config.py

* Update test_trainer.py

Co-authored-by: William Falcon <waf2107@columbia.edu>

pytorch_lightning/loggers/tensorboard.py

@@ -1,8 +1,7 @@
-import argparse
 import csv
 import os
 from argparse import Namespace
-from typing import Optional, Dict, Union
+from typing import Optional, Dict, Union, Any
 from warnings import warn
 
 import torch
@@ -100,14 +99,8 @@ class TensorBoardLogger(LightningLoggerBase):
         return self._experiment
 
     @rank_zero_only
-    def log_hyperparams(self, params: argparse.Namespace):
-        if params is None:
-            return
-
-        # in case converting from namespace
-        if isinstance(params, Namespace):
-            params = vars(params)
-        params = dict(params)
+    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
+        params = self._convert_params(params)
 
         if parse_version(torch.__version__) < parse_version("1.3.0"):
             warn(
@@ -126,14 +119,14 @@ class TensorBoardLogger(LightningLoggerBase):
         self.tags.update(params)
 
     @rank_zero_only
-    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
+    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
         for k, v in metrics.items():
             if isinstance(v, torch.Tensor):
                 v = v.item()
             self.experiment.add_scalar(k, v, step)
 
     @rank_zero_only
-    def save(self):
+    def save(self) -> None:
         try:
             self.experiment.flush()
         except AttributeError:
@@ -156,7 +149,7 @@ class TensorBoardLogger(LightningLoggerBase):
                 writer.writerow({'key': k, 'value': v})
 
     @rank_zero_only
-    def finalize(self, status: str):
+    def finalize(self, status: str) -> None:
         self.save()
 
     @property