fix: revert some changes

unit8co · Dec 17, 2024 · f376f09 · f376f09
1 parent c2c00f3
commit f376f09
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diff --git a/darts/models/forecasting/rnn_model.py b/darts/models/forecasting/rnn_model.py
@@ -587,64 +587,6 @@ def _verify_train_dataset_type(self, train_dataset: TrainingDataset):
             "RNNModel requires a shifted training dataset with shift=1.",
         )
 
-    def to_onnx(self, path: Optional[str] = None, **kwargs):
-        """Export model to ONNX format for optimized inference, wrapping around PyTorch Lightning's
-        :func:`torch.onnx.export` method ()`official documentation <https://lightning.ai/docs/pytorch/
-        stable/common/lightning_module.html#to-onnx>`_).
-
-        Note: onnx library (optionnal dependency) must be installed in order to call this method
-
-        Parameters
-        ----------
-        path
-            Path under which to save the model at its current state. Please avoid path starting with "last-" or
-            "best-" to avoid collision with Pytorch-Ligthning checkpoints. If no path is specified, the model
-            is automatically saved under ``"{ModelClass}_{YYYY-mm-dd_HH_MM_SS}.pt"``.
-        **kwargs
-            Additional kwargs for PyTorch's :func:`torch.onnx.export` method, such as ``verbose`` prints a
-            description of the model being exported to stdout.
-            For more information, read the `official documentation <https://pytorch.org/docs/master/
-            onnx.html#torch.onnx.export>`_.
-        """
-        raise_if_not(
-            self._fit_called, "`fit()` needs to be called before `to_onnx()`.", logger
-        )
-
-        if path is None:
-            # default path
-            path = self._default_save_path() + ".onnx"
-
-        # mimic preprocessing performed by RNNModule_._get_batch_prediction()
-        (
-            past_target,
-            past_covariates,
-            historic_future_covariates,
-            future_covariates,
-            future_past_covariates,
-        ) = (
-            torch.Tensor(x).unsqueeze(0) if x is not None else None
-            for x in self.train_sample
-        )
-
-        if historic_future_covariates is not None:
-            # RNNs need as inputs (target[t] and covariates[t+1]) so here we shift the covariates
-            all_covariates = torch.cat(
-                [historic_future_covariates[:, 1:, :], future_covariates], dim=1
-            )
-            cov_past, _ = (
-                all_covariates[:, : past_target.shape[1], :],
-                all_covariates[:, past_target.shape[1] :, :],
-            )
-            input_past = torch.cat([past_target, cov_past], dim=2)
-        else:
-            input_past = past_target
-
-        input_sample = [
-            input_past.double(),
-            future_covariates.double() if future_covariates is not None else None,
-        ]
-        self.model.to_onnx(file_path=path, input_sample=input_sample, **kwargs)
-
     @property
     def supports_multivariate(self) -> bool:
         return True

diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py
@@ -2621,13 +2621,6 @@ def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset):
     def _verify_predict_sample(self, predict_sample: tuple):
         _basic_compare_sample(self.train_sample, predict_sample)
 
-    def _verify_past_future_covariates(self, past_covariates, future_covariates):
-        raise_if_not(
-            future_covariates is None,
-            "Some future_covariates have been provided to a PastCovariates model. These models "
-            "support only past_covariates.",
-        )
-
     @property
     def _model_encoder_settings(
         self,
@@ -2721,13 +2714,6 @@ def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset):
     def _verify_predict_sample(self, predict_sample: tuple):
         _basic_compare_sample(self.train_sample, predict_sample)
 
-    def _verify_past_future_covariates(self, past_covariates, future_covariates):
-        raise_if_not(
-            past_covariates is None,
-            "Some past_covariates have been provided to a PastCovariates model. These models "
-            "support only future_covariates.",
-        )
-
     @property
     def _model_encoder_settings(
         self,
@@ -2822,13 +2808,6 @@ def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset):
     def _verify_predict_sample(self, predict_sample: tuple):
         _basic_compare_sample(self.train_sample, predict_sample)
 
-    def _verify_past_future_covariates(self, past_covariates, future_covariates):
-        raise_if_not(
-            past_covariates is None,
-            "Some past_covariates have been provided to a DualCovariates Torch model. These models "
-            "support only future_covariates.",
-        )
-
     @property
     def _model_encoder_settings(
         self,
@@ -2923,10 +2902,6 @@ def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset):
     def _verify_predict_sample(self, predict_sample: tuple):
         _mixed_compare_sample(self.train_sample, predict_sample)
 
-    def _verify_past_future_covariates(self, past_covariates, future_covariates):
-        # both covariates are supported; do nothing
-        pass
-
     @property
     def _model_encoder_settings(
         self,