linter fix

train.py

@@ -408,7 +408,8 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
                 if num_iter % c.print_step == 0:
                     print(
                         "   | > TotalLoss: {:.5f}   PostnetLoss: {:.5f} - {:.5f}  DecoderLoss:{:.5f} - {:.5f} "
-                        "StopLoss: {:.5f} - {:.5f}  AlignScore: {:.4f} : {:.4f}".format(loss.item(),
+                        "StopLoss: {:.5f} - {:.5f}  AlignScore: {:.4f} : {:.4f}".format(
+                            loss.item(),
                             postnet_loss.item(), keep_avg['avg_postnet_loss'],
                             decoder_loss.item(), keep_avg['avg_decoder_loss'],
                             stop_loss.item(), keep_avg['avg_stop_loss'],

utils/generic_utils.py

@@ -31,7 +31,8 @@ def load_config(config_path):
 def get_git_branch():
     try:
         out = subprocess.check_output(["git", "branch"]).decode("utf8")
-        current = next(line for line in out.split("\n") if line.startswith("*"))
+        current = next(line for line in out.split(
+            "\n") if line.startswith("*"))
         current.replace("* ", "")
     except subprocess.CalledProcessError:
         current = "inside_docker"
@@ -333,7 +334,8 @@ class KeepAverage():
             self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
             self.iters[name] += 1
         else:
-            self.avg_values[name] = self.avg_values[name] * self.iters[name] + value 
+            self.avg_values[name] = self.avg_values[name] * \
+                self.iters[name] + value
             self.iters[name] += 1
             self.avg_values[name] /= self.iters[name]
 
@@ -344,4 +346,3 @@ class KeepAverage():
     def update_values(self, value_dict):
         for key, value in value_dict.items():
             self.update_value(key, value)
-    

utils/measures.py

@@ -1,6 +1,3 @@
-import torch
-import numpy as np
-
 
 def alignment_diagonal_score(alignments):
     """
@@ -12,8 +9,3 @@ def alignment_diagonal_score(alignments):
         alignments : batch x decoder_steps x encoder_steps
     """
     return alignments.max(dim=1)[0].mean(dim=1).mean(dim=0)
-
-
-
-
-