linter fix

layers/losses.py

@@ -61,7 +61,7 @@ class AttentionEntropyLoss(nn.Module):
     def forward(self, align):
         """
         Forces attention to be more decisive by penalizing
-        soft attention weights 
+        soft attention weights
 
         TODO: arguments
         TODO: unit_test

layers/tacotron2.py

@@ -162,15 +162,16 @@ class Decoder(nn.Module):
         B = inputs.size(0)
         # T = inputs.size(1)
         if not keep_states:
-            self.query = torch.zeros(1, device=inputs.device).repeat(B, self.query_dim)
-            self.attention_rnn_cell_state = torch.zeros(1, device=inputs.device).repeat(B,
-                                                        self.query_dim)
-            self.decoder_hidden = torch.zeros(1, device=inputs.device).repeat(B,
-                                              self.decoder_rnn_dim)
-            self.decoder_cell = torch.zeros(1, device=inputs.device).repeat(B,
-                                            self.decoder_rnn_dim)
-            self.context = torch.zeros(1, device=inputs.device).repeat(B,
-                                       self.encoder_embedding_dim)
+            self.query = torch.zeros(1, device=inputs.device).repeat(
+                B, self.query_dim)
+            self.attention_rnn_cell_state = torch.zeros(
+                1, device=inputs.device).repeat(B, self.query_dim)
+            self.decoder_hidden = torch.zeros(1, device=inputs.device).repeat(
+                B, self.decoder_rnn_dim)
+            self.decoder_cell = torch.zeros(1, device=inputs.device).repeat(
+                B, self.decoder_rnn_dim)
+            self.context = torch.zeros(1, device=inputs.device).repeat(
+                B, self.encoder_embedding_dim)
         self.inputs = inputs
         self.processed_inputs = self.attention.inputs_layer(inputs)
         self.mask = mask
@@ -277,7 +278,7 @@ class Decoder(nn.Module):
             stop_flags[2] = t > inputs.shape[1] * 2
             if all(stop_flags):
                 break
-            elif len(outputs) == self.max_decoder_steps:
+            if len(outputs) == self.max_decoder_steps:
                 print("   | > Decoder stopped with 'max_decoder_steps")
                 break
 
@@ -317,7 +318,7 @@ class Decoder(nn.Module):
             stop_flags[2] = t > inputs.shape[1] * 2
             if all(stop_flags):
                 break
-            elif len(outputs) == self.max_decoder_steps:
+            if len(outputs) == self.max_decoder_steps:
                 print("   | > Decoder stopped with 'max_decoder_steps")
                 break