removed checkpoint from prediction (#821)

darts/models/forecasting/torch_forecasting_model.py

@@ -1223,13 +1223,8 @@ def predict_from_dataset(
         trainer = trainer if trainer is not None else self.trainer
         self._setup_trainer(trainer=trainer, verbose=verbose, epochs=self.n_epochs)
 
-        # if model checkpoint was loaded without calling fit afterwards (when `load_ckpt_path is not None`),
-        # trainer needs to be instantiated here
-        ckpt_path = self.load_ckpt_path
-        self.load_ckpt_path = None
-
         # prediction output comes as nested list: list of predicted `TimeSeries` for each batch.
-        predictions = self.trainer.predict(self.model, pred_loader, ckpt_path=ckpt_path)
+        predictions = self.trainer.predict(self.model, pred_loader)
         # flatten and return
         return [ts for batch in predictions for ts in batch]
 
@@ -1316,6 +1311,16 @@ def save_model(self, path: str) -> None:
     def load_model(path: str) -> "TorchForecastingModel":
         """loads a model from a given file path. The file name should end with '.pth.tar'
 
+        Example for loading a :class:`RNNModel`:
+
+            .. highlight:: python
+            .. code-block:: python
+
+                from darts.models import RNNModel
+
+                model_loaded = RNNModel.load_model("my_model.pth.tar")
+            ..
+
         Parameters
         ----------
         path
@@ -1339,8 +1344,20 @@ def load_from_checkpoint(
         """
         Load the model from automatically saved checkpoints under '{work_dir}/darts_logs/{model_name}/checkpoints/'.
         This method is used for models that were created with ``save_checkpoints=True``.
+
         If you manually saved your model, consider using :meth:`load_model() <TorchForeCastingModel.load_model()>`.
 
+        Example for loading a :class:`RNNModel` from checkpoint (``model_name`` is the ``model_name`` used at model
+        creation):
+
+            .. highlight:: python
+            .. code-block:: python
+
+                from darts.models import RNNModel
+
+                model_loaded = RNNModel.load_from_checkpoint(model_name, best=True)
+            ..
+
         If ``file_name`` is given, returns the model saved under
         '{work_dir}/darts_logs/{model_name}/checkpoints/{file_name}'.
 
@@ -1387,7 +1404,7 @@ def load_from_checkpoint(
             file_name = _get_checkpoint_fname(work_dir, model_name, best=best)
 
         file_path = os.path.join(checkpoint_dir, file_name)
-        logger.info("loading {}".format(file_name))
+        logger.info(f"loading {file_name}")
 
         model.model = model.model.__class__.load_from_checkpoint(file_path)
         model.load_ckpt_path = file_path