flake8 & isort (#5647)

pytorch_lightning/core/memory.py

@@ -182,7 +182,7 @@ class ModelSummary(object):
         self._model = model
         self._mode = mode
         self._layer_summary = self.summarize()
-        self._precision_megabytes = (self._model.precision / 8.0) * 1e-6 # 1 byte -> 8 bits
+        self._precision_megabytes = (self._model.precision / 8.0) * 1e-6  # 1 byte -> 8 bits
 
     @property
     def named_modules(self) -> List[Tuple[str, nn.Module]]:
@@ -389,9 +389,11 @@ def get_gpu_memory_map() -> Dict[str, int]:
     }
     return gpu_memory_map
 
+
 def get_formatted_model_size(total_model_size: float) -> float:
     return f"{total_model_size:,.3f}"
 
+
 def get_human_readable_count(number: int) -> str:
     """
     Abbreviates an integer number with K, M, B, T for thousands, millions,

pytorch_lightning/loggers/wandb.py

@@ -29,9 +29,8 @@ from pytorch_lightning.utilities.warning_utils import WarningCache
 _WANDB_AVAILABLE = _module_available("wandb")
 
 try:
-    from wandb.wandb_run import Run
-
     import wandb
+    from wandb.wandb_run import Run
 except ImportError:
     # needed for test mocks, these tests shall be updated
     wandb, Run = None, None

tests/core/test_memory.py

@@ -40,8 +40,8 @@ class PreCalculatedModel(BoringModel):
 
     def __init__(self, precision: int = 32):
         super().__init__()
-        self.layer = nn.Linear(32, 1000, bias=False) # 32K params
-        self.layer1 = nn.Linear(1000, 218, bias=False) # 218K params 
+        self.layer = nn.Linear(32, 1000, bias=False)  # 32K params
+        self.layer1 = nn.Linear(1000, 218, bias=False)  # 218K params
 
         # calculate model size based on precision.
         self.pre_calculated_model_size = 1.0 / (32 / precision)
@@ -50,6 +50,7 @@ class PreCalculatedModel(BoringModel):
         x = self.layer(x)
         return self.layer1(x)
 
+
 class UnorderedModel(LightningModule):
     """ A model in which the layers not defined in order of execution """