apply PEP8

pytorch_lightning/root_module/grads.py

@@ -4,6 +4,7 @@ Module to describe gradients
 
 from torch import nn
 
+
 class GradInformation(nn.Module):
 
     def grad_norm(self, norm_type):
@@ -17,11 +18,10 @@ class GradInformation(nn.Module):
                     norm = param_norm ** (1 / norm_type)
 
                     results['grad_{}_norm_{}'.format(norm_type, i)] = round(norm.data.cpu().numpy().flatten()[0], 3)
-                except Exception as e:
+                except Exception:
                     # this param had no grad
                     pass
 
         total_norm = total_norm ** (1. / norm_type)
         results['grad_{}_norm_total'.format(norm_type)] = round(total_norm.data.cpu().numpy().flatten()[0], 3)
         return results
-

pytorch_lightning/root_module/hooks.py

@@ -43,4 +43,3 @@ class ModelHooks(torch.nn.Module):
         :return:
         """
         pass
-

pytorch_lightning/root_module/memory.py

@@ -94,7 +94,7 @@ class ModelSummary(object):
         mods = list(self.model.modules())
         sizes = []
 
-        for i in range(1,len(mods)):
+        for i in range(1, len(mods)):
             m = mods[i]
             p = list(m.parameters())
             modsz = []
@@ -127,7 +127,7 @@ class ModelSummary(object):
         if self.model.example_input_array is not None:
             cols.extend(['In_sizes', 'Out_sizes'])
 
-        df = pd.DataFrame(np.zeros( (len(self.layer_names), len(cols))))
+        df = pd.DataFrame(np.zeros((len(self.layer_names), len(cols))))
         df.columns = cols
 
         df['Name'] = self.layer_names
@@ -152,16 +152,16 @@ class ModelSummary(object):
         self.make_summary()
 
 
-def print_mem_stack(): # pragma: no cover
+def print_mem_stack():  # pragma: no cover
     for obj in gc.get_objects():
         try:
             if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
                 print(type(obj), obj.size())
-        except Exception as e:
+        except Exception:
             pass
 
 
-def count_mem_items(): # pragma: no cover
+def count_mem_items():  # pragma: no cover
     nb_params = 0
     nb_tensors = 0
     for obj in gc.get_objects():
@@ -172,7 +172,7 @@ def count_mem_items(): # pragma: no cover
                     nb_params += 1
                 else:
                     nb_tensors += 1
-        except Exception as e:
+        except Exception:
             pass
 
     return nb_params, nb_tensors

pytorch_lightning/root_module/root_module.py

@@ -129,6 +129,3 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
     def unfreeze(self):
         for param in self.parameters():
             param.requires_grad = True
-
-
-