fix

pina/data/data_module.py

@@ -275,14 +275,19 @@ class PinaDataModule(LightningDataModule):
             ),
             module="lightning.pytorch.trainer.connectors.data_connector",
         )
-        return PinaDataLoader(
+        dl = PinaDataLoader(
             dataset,
             batch_size=self.batch_size,
             shuffle=self.shuffle,
             num_workers=self.num_workers,
             common_batch_size=self.common_batch_size,
             separate_conditions=self.separate_conditions,
+            device=self.trainer.strategy.root_device,
         )
+        if self.batch_size is None:
+            # Override the method to transfer the batch to the device
+            self.transfer_batch_to_device = self._transfer_batch_to_device_dummy
+        return dl
 
     def val_dataloader(self):
         """
@@ -325,7 +330,7 @@ class PinaDataModule(LightningDataModule):
         :rtype: list[tuple]
         """
 
-        return batch
+        return [(k, v) for k, v in batch.items()]
 
     def _transfer_batch_to_device(self, batch, device, dataloader_idx):
         """
@@ -383,9 +388,15 @@ class PinaDataModule(LightningDataModule):
 
         to_return = {}
         if hasattr(self, "train_dataset") and self.train_dataset is not None:
-            to_return["train"] = self.train_dataset.input
+            to_return["train"] = {
+                cond: data.input for cond, data in self.train_dataset.items()
+            }
         if hasattr(self, "val_dataset") and self.val_dataset is not None:
-            to_return["val"] = self.val_dataset.input
+            to_return["val"] = {
+                cond: data.input for cond, data in self.val_dataset.items()
+            }
         if hasattr(self, "test_dataset") and self.test_dataset is not None:
-            to_return["test"] = self.test_dataset.input
+            to_return["test"] = {
+                cond: data.input for cond, data in self.test_dataset.items()
+            }
         return to_return

pina/data/dataloader.py

@@ -13,7 +13,7 @@ class DummyDataloader:
     DataLoader that returns the entire dataset in a single batch.
     """
 
-    def __init__(self, dataset):
+    def __init__(self, dataset, device=None):
         """
         Prepare a dataloader object that returns the entire dataset in a single
         batch. Depending on the number of GPUs, the dataset is managed
@@ -47,9 +47,14 @@ class DummyDataloader:
                 idx.append(i)
                 i += world_size
         else:
-            idx = list(range(len(dataset)))
+            idx = [i for i in range(len(dataset))]
-
         self.dataset = dataset.getitem_from_list(idx)
+        self.device = device
+        self.dataset = (
+            {k: v.to(self.device) for k, v in self.dataset.items()}
+            if self.device
+            else self.dataset
+        )
 
     def __iter__(self):
         """
@@ -155,12 +160,14 @@ class PinaDataLoader:
         shuffle=False,
         common_batch_size=True,
         separate_conditions=False,
+        device=None,
     ):
         self.dataset_dict = dataset_dict
         self.batch_size = batch_size
         self.num_workers = num_workers
         self.shuffle = shuffle
         self.separate_conditions = separate_conditions
+        self.device = device
 
         # Batch size None means we want to load the entire dataset in a single
         # batch
@@ -238,7 +245,7 @@ class PinaDataLoader:
         """
         # If batch size is None, use DummyDataloader
         if batch_size is None or batch_size >= len(dataset):
-            return DummyDataloader(dataset)
+            return DummyDataloader(dataset, device=self.device)
 
         # Determine the appropriate collate function
         if not dataset.automatic_batching:

pina/data/dataset.py

@@ -6,6 +6,12 @@ from torch_geometric.data import Data
 from ..graph import Graph, LabelBatch
 from ..label_tensor import LabelTensor
 
+STACK_FN_MAP = {
+    "label_tensor": LabelTensor.stack,
+    "tensor": torch.stack,
+    "data": LabelBatch.from_data_list,
+}
+
 
 class PinaDatasetFactory:
     """
@@ -65,18 +71,18 @@ class PinaDataset(Dataset):
         self.automatic_batching = (
             automatic_batching if automatic_batching is not None else True
         )
-        self.stack_fn = {}
+        self._stack_fn = {}
         self.is_graph_dataset = False
         # Determine stacking functions for each data type (used in collate_fn)
         for k, v in data_dict.items():
             if isinstance(v, LabelTensor):
-                self.stack_fn[k] = LabelTensor.stack
+                self._stack_fn[k] = "label_tensor"
             elif isinstance(v, torch.Tensor):
-                self.stack_fn[k] = torch.stack
+                self._stack_fn[k] = "tensor"
             elif isinstance(v, list) and all(
                 isinstance(item, (Data, Graph)) for item in v
             ):
-                self.stack_fn[k] = LabelBatch.from_data_list
+                self._stack_fn[k] = "data"
                 self.is_graph_dataset = True
             else:
                 raise ValueError(
@@ -84,6 +90,11 @@ class PinaDataset(Dataset):
                 )
 
     def __len__(self):
+        """
+        Return the length of the dataset.
+        :return: The length of the dataset.
+        :rtype: int
+        """
         return len(next(iter(self.data.values())))
 
     def __getitem__(self, idx):
@@ -113,10 +124,9 @@ class PinaDataset(Dataset):
 
         to_return = {}
         for field_name, data in self.data.items():
-            if self.stack_fn[field_name] is LabelBatch.from_data_list:
+            if self._stack_fn[field_name] == "data":
-                to_return[field_name] = self.stack_fn[field_name](
+                fn = STACK_FN_MAP[self._stack_fn[field_name]]
-                    [data[i] for i in idx_list]
+                to_return[field_name] = fn([data[i] for i in idx_list])
-                )
             else:
                 to_return[field_name] = data[idx_list]
         return to_return
@@ -148,3 +158,14 @@ class PinaDataset(Dataset):
         :rtype: torch.Tensor | LabelTensor | Data | Graph
         """
         return self.data["input"]
+
+    @property
+    def stack_fn(self):
+        """
+        Get the mapping of stacking functions for each data type in the dataset.
+
+        :return: A dictionary mapping condition names to their respective
+            stacking function identifiers.
+        :rtype: dict
+        """
+        return {k: STACK_FN_MAP[v] for k, v in self._stack_fn.items()}