updates and debugs

utils/data.py

@@ -14,9 +14,8 @@ def prepare_data(inputs):
     return np.stack([pad_data(x, max_len) for x in inputs])
 
 
-def pad_per_step(inputs, outputs_per_step):
-    """zero pad inputs if it is not divisible with outputs_per_step (r)"""
+def pad_per_step(inputs, pad_len):
     timesteps = inputs.shape[-1]
     return np.pad(inputs, [[0, 0], [0, 0],
-                           [0, outputs_per_step - (timesteps % outputs_per_step)]],
+                           [0, pad_len]],
                   mode='constant', constant_values=0.0)

utils/generic_utils.py

@@ -48,7 +48,8 @@ def copy_config_file(config_file, path):
     shutil.copyfile(config_file, out_path)
 
 
-def save_checkpoint(model, model_loss, best_loss, out_path):
+def save_checkpoint(model, optimizer, model_loss, best_loss, out_path,
+                    current_step, epoch):
     checkpoint_path = 'checkpoint_{}.pth.tar'.format(current_step)
     checkpoint_path = os.path.join(out_path, checkpoint_path)
     print("\n | > Checkpoint saving : {}".format(checkpoint_path))
@@ -56,16 +57,24 @@ def save_checkpoint(model, model_loss, best_loss, out_path):
              'optimizer': optimizer.state_dict(),
              'step': current_step,
              'epoch': epoch,
-             'total_loss': loss.data[0],
-             'linear_loss': linear_loss.data[0],
-             'mel_loss': mel_loss.data[0],
+             'linear_loss': model_loss,
              'date': datetime.date.today().strftime("%B %d, %Y")}
     torch.save(state, checkpoint_path)
+
+
+def save_best_model(model, optimizer, model_loss, best_loss, out_path,
+                    current_step, epoch):
     if model_loss < best_loss:
+        state = {'model': model.state_dict(),
+                 'optimizer': optimizer.state_dict(),
+                 'step': current_step,
+                 'epoch': epoch,
+                 'linear_loss': model_loss,
+                 'date': datetime.date.today().strftime("%B %d, %Y")}
         best_loss = model_loss
-        bestmodel_path = 'best_model.pth.tar'.format(current_step)
+        bestmodel_path = 'best_model.pth.tar'
         bestmodel_path = os.path.join(out_path, bestmodel_path)
-        print("\n | > Best model saving with loss {} : {}".format(model_loss, bestmodel_path))
+        print("\n | > Best model saving with loss {0:.2f} : {1:}".format(model_loss, bestmodel_path))
         torch.save(state, bestmodel_path)
     return best_loss