Add support for predict_dataloader in LightningNodeData (#4884)

* Update lightning_datamodule.py

* Update lightning_datamodule.py

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* update

* update

* update

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>

CHANGELOG.md

@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [2.0.5] - 2022-MM-DD
 ### Added
+- Added `predict()` support to the `LightningNodeData` module ([#4884](https://github.com/pyg-team/pytorch_geometric/pull/4884))
 - Added `time_attr` argument to `LinkNeighborLoader` ([#4877](https://github.com/pyg-team/pytorch_geometric/pull/4877))
 - Added a `filter_per_worker` argument to data loaders to allow filtering of data within sub-processes ([#4873](https://github.com/pyg-team/pytorch_geometric/pull/4873))
 - Added a `NeighborLoader` benchmark script ([#4815](https://github.com/pyg-team/pytorch_geometric/pull/4815))

torch_geometric/data/lightning_datamodule.py

@@ -191,15 +191,30 @@ class LightningNodeData(LightningDataModule):
         data (Data or HeteroData): The :class:`~torch_geometric.data.Data` or
             :class:`~torch_geometric.data.HeteroData` graph object.
         input_train_nodes (torch.Tensor or str or (str, torch.Tensor)): The
-            indices of training nodes. If not given, will try to automatically
-            infer them from the :obj:`data` object. (default: :obj:`None`)
+            indices of training nodes.
+            If not given, will try to automatically infer them from the
+            :obj:`data` object by searching for :obj:`train_mask`,
+            :obj:`train_idx`, or :obj:`train_index` attributes.
+            (default: :obj:`None`)
         input_val_nodes (torch.Tensor or str or (str, torch.Tensor)): The
-            indices of validation nodes. If not given, will try to
-            automatically infer them from the :obj:`data` object.
+            indices of validation nodes.
+            If not given, will try to automatically infer them from the
+            :obj:`data` object by searching for :obj:`val_mask`,
+            :obj:`valid_mask`, :obj:`val_idx`, :obj:`valid_idx`,
+            :obj:`val_index`, or :obj:`valid_index` attributes.
             (default: :obj:`None`)
         input_test_nodes (torch.Tensor or str or (str, torch.Tensor)): The
-            indices of test nodes. If not given, will try to automatically
-            infer them from the :obj:`data` object. (default: :obj:`None`)
+            indices of test nodes.
+            If not given, will try to automatically infer them from the
+            :obj:`data` object by searching for :obj:`test_mask`,
+            :obj:`test_idx`, or :obj:`test_index` attributes.
+            (default: :obj:`None`)
+        input_pred_nodes (torch.Tensor or str or (str, torch.Tensor)): The
+            indices of prediction nodes.
+            If not given, will try to automatically infer them from the
+            :obj:`data` object by searching for :obj:`pred_mask`,
+            :obj:`pred_idx`, or :obj:`pred_index` attributes.
+            (default: :obj:`None`)
         loader (str): The scalability technique to use (:obj:`"full"`,
             :obj:`"neighbor"`). (default: :obj:`"neighbor"`)
         batch_size (int, optional): How many samples per batch to load.
@@ -216,6 +231,7 @@ def __init__(
         input_train_nodes: InputNodes = None,
         input_val_nodes: InputNodes = None,
         input_test_nodes: InputNodes = None,
+        input_pred_nodes: InputNodes = None,
         loader: str = "neighbor",
         batch_size: int = 1,
         num_workers: int = 0,
@@ -236,6 +252,9 @@ def __init__(
         if input_test_nodes is None:
             input_test_nodes = infer_input_nodes(data, split='test')
 
+        if input_pred_nodes is None:
+            input_pred_nodes = infer_input_nodes(data, split='pred')
+
         if loader == 'full' and batch_size != 1:
             warnings.warn(f"Re-setting 'batch_size' to 1 in "
                           f"'{self.__class__.__name__}' for loader='full' "
@@ -279,6 +298,7 @@ def __init__(
         self.input_train_nodes = input_train_nodes
         self.input_val_nodes = input_val_nodes
         self.input_test_nodes = input_test_nodes
+        self.input_pred_nodes = input_pred_nodes
 
     def prepare_data(self):
         """"""
@@ -323,6 +343,10 @@ def test_dataloader(self) -> DataLoader:
         """"""
         return self.dataloader(self.input_test_nodes, shuffle=False)
 
+    def predict_dataloader(self) -> DataLoader:
+        """"""
+        return self.dataloader(self.input_pred_nodes, shuffle=False)
+
     def __repr__(self) -> str:
         kwargs = kwargs_repr(data=self.data, loader=self.loader, **self.kwargs)
         return f'{self.__class__.__name__}({kwargs})'