Fix: removed input re-normalization by rin inside io_processor (uni…

…t8co#2160) * prevented input re-normalization by rin using .clone() inside `io_processor` * Update CHANGELOG.md --------- Co-authored-by: Dennis Bader <[email protected]>
spicehq · Jan 13, 2024 · cb724d1 · cb724d1
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -12,7 +12,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Improved**
 
 **Fixed**
-
+- Fixed a bug when using a `TorchForecastingModel` with `use_reversible_instance_norm=True` and predicting with `n > output_chunk_length`. The input normalized multiple times. [#2160](https://github.com/unit8co/darts/pull/2160) by [FourierMourier](https://github.com/FourierMourier).
 ### For developers of the library:
 
 ## [0.27.1](https://github.com/unit8co/darts/tree/0.27.1) (2023-12-10)

diff --git a/darts/models/forecasting/pl_forecasting_module.py b/darts/models/forecasting/pl_forecasting_module.py
@@ -50,7 +50,8 @@ def forward_wrapper(self, *args, **kwargs):
 
         # x is input batch tuple which by definition has the past features in the first element starting with the
         # first n target features
-        x: Tuple = args[0][0]
+        # assuming `args[0][0]` is torch.Tensor we could clone it to prevent target re-normalization
+        x: Tuple = args[0][0].clone()
         # apply reversible instance normalization
         x[:, :, : self.n_targets] = self.rin(x[:, :, : self.n_targets])
         # run the forward pass