fix tensor getitem in graph_dataset (#633)

Author: dario-coscia

pina/data/dataset.py

@@ -276,20 +276,6 @@ def _create_graph_batch(self, data):
         batch = LabelBatch.from_data_list(data)
         return batch
 
-    def _create_tensor_batch(self, data):
-        """
-        Reshape properly ``data`` tensor to be processed handle by the graph
-        based models.
-
-        :param data: torch.Tensor object of shape ``(N, ...)`` where ``N`` is
-            the number of data objects.
-        :type data: torch.Tensor | LabelTensor
-        :return: Reshaped tensor object.
-        :rtype: torch.Tensor | LabelTensor
-        """
-        out = data.reshape(-1, *data.shape[2:])
-        return out
-
     def create_batch(self, data):
         """
         Create a Batch object from a list of :class:`~torch_geometric.data.Data`
@@ -324,7 +310,7 @@ def _retrive_data(self, data, idx_list):
             k: (
                 self._create_graph_batch([v[i] for i in idx_list])
                 if isinstance(v, list)
-                else self._create_tensor_batch(v[idx_list])
+                else v[idx_list]
             )
             for k, v in data.items()
         }

tests/test_data/test_graph_dataset.py

@@ -101,7 +101,7 @@ def test_getitem(conditions_dict, max_conditions_lengths):
         [d["input"].x.shape == torch.Size((400, 10)) for d in data.values()]
     )
     assert all(
-        [d["target"].shape == torch.Size((400, 10)) for d in data.values()]
+        [d["target"].shape == torch.Size((20, 20, 10)) for d in data.values()]
     )
     assert all(
         [

tests/test_solver/test_ensemble_supervised_solver.py

@@ -2,6 +2,7 @@
 import pytest
 from torch._dynamo.eval_frame import OptimizedModule
 from torch_geometric.nn import GCNConv
+from torch_geometric.utils import to_dense_batch
 from pina import Condition, LabelTensor
 from pina.condition import InputTargetCondition
 from pina.problem import AbstractProblem
@@ -82,7 +83,7 @@ def forward(self, batch):
             y = self.conv(y, edge_index)
             y = self.activation(y)
             y = self.output(y)
-        return y
+        return to_dense_batch(y, batch.batch)[0]
 
 
 graph_models = [Models() for i in range(10)]

tests/test_solver/test_supervised_solver.py

@@ -2,6 +2,7 @@
 import pytest
 from torch._dynamo.eval_frame import OptimizedModule
 from torch_geometric.nn import GCNConv
+from torch_geometric.utils import to_dense_batch
 from pina import Condition, LabelTensor
 from pina.condition import InputTargetCondition
 from pina.problem import AbstractProblem
@@ -82,7 +83,7 @@ def forward(self, batch):
             y = self.conv(y, edge_index)
             y = self.activation(y)
             y = self.output(y)
-        return y
+        return to_dense_batch(y, batch.batch)[0]
 
 
 graph_model = Model()