Update tensorboardlogger's hparam function.

Readme.md

@@ -52,7 +52,7 @@ The Logging API supports
 
 And offers different Backends, e.g. :
 * Visdom ([visdom-loggers](https://trixi.readthedocs.io/en/latest/_api/trixi.logger.html#module-trixi.logger.visdom))
-* TensorboardX ([tensorboard-loggers](https://trixi.readthedocs.io/en/latest/_api/trixi.logger.html#module-trixi.logger.tensorboard))
+* Tensorboard ([tensorboard-loggers](https://trixi.readthedocs.io/en/latest/_api/trixi.logger.html#module-trixi.logger.tensorboard))
 * Matplotlib / Seaborn ([plt-loggers](https://trixi.readthedocs.io/en/latest/_api/trixi.logger.html#module-trixi.logger.plt))
 * Local Disk ([file-loggers](https://trixi.readthedocs.io/en/latest/_api/trixi.logger.html#module-trixi.logger.file))
 * Telegram & Slack ([message-loggers](https://trixi.readthedocs.io/en/latest/_api/trixi.logger.html#module-trixi.logger.message))

trixi/logger/tensorboard/tensorboardlogger.py

@@ -233,7 +233,7 @@ def show_piechart(self, array, name="piechart", counter=None, *args, **kwargs):
 
     def show_embedding(self, tensor, labels=None, name="default", label_img=None, counter=None, *args, **kwargs):
         """
-        Displays an embedding of a tensor (for more details see tensorboardX)
+        Displays an embedding of a tensor (for more details see tensorboard)
 
         Args:
             tensor (torch.tensor/np.array): Tensor to be embedded and then displayed
@@ -295,21 +295,37 @@ def show_pr_curve(self, tensor, labels, name="pr-curve", counter=None, *args, **
             tag=name, labels=labels, predictions=tensor, global_step=self.val_dict["{}-pr-curve".format(name)]
         )
 
+    # adapted from torch.utils.tensorboard.add_hparams
     def show_hparams(self, hparam_dict=None, metric_dict=None, counter=None, *args, **kwargs):
         """
-
+        When using tensorboard's hparam-view, it can be filled with this function.
+        It is usually called once per experiment. The values can later be updated by calling show_value with the same
+        name (and WITHOUT tag) as used in metric_dict. It is recommended to choose that name such that it does not
+        coincide with other quantities your logging, because it will also appear in the scalars view
+        (e.g. `hp/loss' instead of `loss`)
         Args:
             hparam_dict: Each key-value pair in the dictionary is the name of the hyper parameter and it’s corresponding value.
             metric_dict: Each key-value pair in the dictionary is the name of the metric and it’s corresponding value.
              Note that the key used here should be unique in the tensorboard record. Otherwise the value you added by
              show_value will be displayed in the hparam plugin. In most cases, this is unwanted.
+             counter (int): Global step value
         """
         if counter is not None:
-            self.val_dict["{}-hparams"] = counter
+            self.val_dict["hparams"] = counter
         else:
-            self.val_dict["{}-hparams"] += 1
+            self.val_dict["hparams"] += 1
+
+        if type(hparam_dict) is not dict or type(metric_dict) is not dict:
+            raise TypeError('hparam_dict and metric_dict should be dictionary.')
+
+        from torch.utils.tensorboard.summary import hparams
+        exp, ssi, sei = hparams(hparam_dict, metric_dict)
 
-        self.writer.add_hparams(hparam_dict=hparam_dict, metric_dict=metric_dict)
+        self.writer.file_writer.add_summary(exp)
+        self.writer.file_writer.add_summary(ssi)
+        self.writer.file_writer.add_summary(sei)
+        for k, v in metric_dict.items():
+            self.writer.add_scalar(k, v, counter)
 
     def close(self):
         self.writer.close()