[BUG] fix `AttributeError: 'ExperimentWriter' object has no attribute…

… 'add_figure'` (#1694)

### Description

This PR is a bugfix for #1256.

At present, the `AttributeError` is raised (and the script exits) if the
logger used does not have the `add_figure` method.

This fix adds a simple check prior to calling `add_figure`.

pytorch_forecasting/models/base_model.py

@@ -955,6 +955,18 @@ def log_interval(self) -> float:
         else:
             return self.hparams.log_val_interval
 
+    def _logger_supports(self, method: str) -> bool:
+        """Whether logger supports method.
+
+        Returns
+        -------
+        supports_method : bool
+            True if attribute self.logger.experiment.method exists, False otherwise.
+        """
+        if not hasattr(self, "logger") or not hasattr(self.logger, "experiment"):
+            return False
+        return hasattr(self.logger.experiment, method)
+
     def log_prediction(
         self, x: Dict[str, torch.Tensor], out: Dict[str, torch.Tensor], batch_idx: int, **kwargs
     ) -> None:
@@ -981,6 +993,10 @@ def log_prediction(
             if not mpl_available:
                 return None  # don't log matplotlib plots if not available
 
+            # Don't log figures if add_figure is not available
+            if not self._logger_supports("add_figure"):
+                return None
+
             for idx in log_indices:
                 fig = self.plot_prediction(x, out, idx=idx, add_loss_to_title=True, **kwargs)
                 tag = f"{self.current_stage} prediction"
@@ -1156,7 +1172,8 @@ def log_gradient_flow(self, named_parameters: Dict[str, torch.Tensor]) -> None:
 
         mpl_available = _check_matplotlib("log_gradient_flow", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         import matplotlib.pyplot as plt

pytorch_forecasting/models/nbeats/__init__.py

@@ -277,7 +277,8 @@ def log_interpretation(self, x, out, batch_idx):
         """
         mpl_available = _check_matplotlib("log_interpretation", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         label = ["val", "train"][self.training]

pytorch_forecasting/models/nhits/__init__.py

@@ -567,7 +567,8 @@ def log_interpretation(self, x, out, batch_idx):
         """
         mpl_available = _check_matplotlib("log_interpretation", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         label = ["val", "train"][self.training]

pytorch_forecasting/models/temporal_fusion_transformer/__init__.py

@@ -846,7 +846,8 @@ def log_interpretation(self, outputs):
 
         mpl_available = _check_matplotlib("log_interpretation", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         import matplotlib.pyplot as plt
@@ -885,6 +886,11 @@ def log_embeddings(self):
         """
         Log embeddings to tensorboard
         """
+
+        # Don't log embeddings if add_embedding is not available
+        if not self._logger_supports("add_embedding"):
+            return None
+
         for name, emb in self.input_embeddings.items():
             labels = self.hparams.embedding_labels[name]
             self.logger.experiment.add_embedding(