diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index 53b4920bd85ef..226e77f3a58c2 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -320,6 +320,10 @@ def __init__(
         self.predict_loop = PredictLoop(self)
 
         # training state
+        if weights_summary is not None and weights_summary not in ModelSummary.MODES:
+            raise MisconfigurationException(
+                f"`weights_summary` can be None, {', '.join(ModelSummary.MODES)}, but got {weights_summary}"
+            )
         self.weights_summary = weights_summary
         self.shown_warnings = set()
 
@@ -351,7 +355,6 @@ def __init__(
             max_steps,
             min_steps,
             num_sanity_val_steps,
-            weights_summary,
         )
         self.evaluation_loop.on_trainer_init()
 
@@ -544,10 +547,7 @@ def _pre_training_routine(self):
 
         # print model summary
         if self.is_global_zero and self.weights_summary is not None and not self.testing:
-            if self.weights_summary in ModelSummary.MODES:
-                ref_model.summarize(mode=self.weights_summary)
-            else:
-                raise MisconfigurationException("weights_summary can be None, " + ", ".join(ModelSummary.MODES))
+            ref_model.summarize(mode=self.weights_summary)
 
         # restore training and model before hpc is called
         self.checkpoint_connector.restore_weights()
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index 7e737c424ff26..26a178c4fd77b 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -14,12 +14,12 @@
 
 from contextlib import contextmanager, suppress
 from copy import copy, deepcopy
+from typing import Optional
 
 import numpy as np
 import torch
 
 from pytorch_lightning.callbacks import EarlyStopping
-from pytorch_lightning.core.memory import ModelSummary
 from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.core.step_result import Result
 from pytorch_lightning.plugins import ParallelPlugin
@@ -36,7 +36,7 @@
 
 class TrainLoop:
 
-    def __init__(self, trainer, multiple_trainloader_mode):
+    def __init__(self, trainer, multiple_trainloader_mode: str):
         self.trainer = trainer
         self.early_stopping_accumulator = None
         self.checkpoint_accumulator = None
@@ -53,13 +53,12 @@ def __init__(self, trainer, multiple_trainloader_mode):
 
     def on_trainer_init(
         self,
-        max_epochs,
-        min_epochs,
-        max_steps,
-        min_steps,
-        num_sanity_val_steps,
-        weights_summary,
-    ):
+        max_epochs: Optional[int],
+        min_epochs: Optional[int],
+        max_steps: Optional[int],
+        min_steps: Optional[int],
+        num_sanity_val_steps: int,
+    ) -> None:
         self.trainer.global_step = 0
         self.trainer.current_epoch = 0
         self.trainer.should_stop = False
@@ -82,12 +81,6 @@ def on_trainer_init(
         else:
             self.trainer.num_sanity_val_steps = num_sanity_val_steps
 
-        self.trainer.weights_summary = weights_summary
-        if weights_summary is not None and weights_summary not in ModelSummary.MODES:
-            raise MisconfigurationException(
-                f"`weights_summary` can be None, {', '.join(ModelSummary.MODES)}, got {weights_summary}"
-            )
-
     @property
     def num_optimizers(self):
         num_optimizers = len(self.get_optimizers_iterable())