replace add_scalar to add_scalars

examples/transformer_tts/train_transformer.py

@@ -29,41 +29,6 @@ from parakeet.models.transformer_tts import TransformerTTS
 from parakeet.utils import io
 
 
-def add_scalars(self, main_tag, tag_scalar_dict, step, walltime=None):
-    """Add scalars to vdl record file.
-    Args:
-        main_tag (string): The parent name for the tags
-        tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values
-        step (int): Step of scalars
-        walltime (float): Wall time of scalars.
-    Example:
-        for index in range(1, 101):
-            writer.add_scalar(tag="train/loss", value=index*0.2, step=index)
-            writer.add_scalar(tag="train/lr", value=index*0.5, step=index)
-    """
-    import time
-    from visualdl.writer.record_writer import RecordFileWriter
-    from visualdl.component.base_component import scalar
-
-    fw_logdir = self.logdir
-    walltime = round(time.time()) if walltime is None else walltime
-    for tag, value in tag_scalar_dict.items():
-        tag = os.path.join(fw_logdir, main_tag, tag)
-        if '%' in tag:
-            raise RuntimeError("% can't appear in tag!")
-        if tag in self._all_writers:
-            fw = self._all_writers[tag]
-        else:
-            fw = RecordFileWriter(
-                logdir=tag,
-                max_queue_size=self._max_queue,
-                flush_secs=self._flush_secs,
-                filename_suffix=self._filename_suffix)
-            self._all_writers.update({tag: fw})
-        fw.add_record(
-            scalar(tag=main_tag, value=value, step=step, walltime=walltime))
-
-
 def add_config_options_to_parser(parser):
     parser.add_argument("--config", type=str, help="path of the config file")
     parser.add_argument("--use_gpu", type=int, default=0, help="device to use")
@@ -99,7 +64,6 @@ def main(args):
 
     writer = LogWriter(os.path.join(args.output,
                                     'log')) if local_rank == 0 else None
-    writer.add_scalars = add_scalars
 
     fluid.enable_dygraph(place)
     network_cfg = cfg['network']
@@ -167,23 +131,28 @@ def main(args):
         loss = loss + stop_loss
 
         if local_rank == 0:
-            writer.add_scalars('training_loss', {
-                'mel_loss': mel_loss.numpy(),
-                'post_mel_loss': post_mel_loss.numpy()
-            }, global_step)
-
+            writer.add_scalar('training_loss/mel_loss',
+                              mel_loss.numpy(),
+                              global_step)
+            writer.add_scalar('training_loss/post_mel_loss',
+                              post_mel_loss.numpy(),
+                              global_step)
             writer.add_scalar('stop_loss', stop_loss.numpy(), global_step)
 
             if parallel:
-                writer.add_scalars('alphas', {
-                    'encoder_alpha': model._layers.encoder.alpha.numpy(),
-                    'decoder_alpha': model._layers.decoder.alpha.numpy(),
-                }, global_step)
+                writer.add_scalar('alphas/encoder_alpha',
+                                   model._layers.encoder.alpha.numpy(),
+                                   global_step)
+                writer.add_scalar('alphas/decoder_alpha',
+                                   model._layers.decoder.alpha.numpy(),
+                                   global_step)
             else:
-                writer.add_scalars('alphas', {
-                    'encoder_alpha': model.encoder.alpha.numpy(),
-                    'decoder_alpha': model.decoder.alpha.numpy(),
-                }, global_step)
+                writer.add_scalar('alphas/encoder_alpha',
+                                   model.encoder.alpha.numpy(),
+                                   global_step)
+                writer.add_scalar('alphas/decoder_alpha',
+                                   model.decoder.alpha.numpy(),
+                                   global_step)
 
             writer.add_scalar('learning_rate',
                               optimizer._learning_rate.step().numpy(),

examples/transformer_tts/train_vocoder.py

@@ -121,7 +121,7 @@ def main(args):
             model.clear_gradients()
 
             if local_rank == 0:
-                writer.add_scalars('training_loss', {'loss': loss.numpy(), },
+                writer.add_scalar('training_loss/loss', loss.numpy(),
                                    global_step)
 
             # save checkpoint