fix codacy warnings

.github/workflows/monthly-tagger.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       matrix:
         os: [windows-latest, macos-latest, ubuntu-latest]
-        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14'] 
+        python-version: [3.9, '3.10', '3.11', '3.12', '3.13'] 
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python

.github/workflows/tester.yml

@@ -1,7 +1,7 @@
 name: "Testing Pull Request"
 
 on:
-  pull_request:
+  pull_request_target:
     branches:
       - "master"
       - "dev"
@@ -13,7 +13,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [windows-latest, macos-latest, ubuntu-latest]
-        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
+        python-version: [3.9, '3.10', '3.11', '3.12', '3.13']
         
     steps:
     - uses: actions/checkout@v4

.github/workflows/tutorial_exporter.yml

@@ -23,7 +23,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.10'
+        python-version: 3.9
 
     - name: Install dependencies
       run: |
@@ -91,7 +91,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.10'
+        python-version: 3.9
 
     - name: Install dependencies
       run: |

README.md

@@ -7,8 +7,8 @@ SPDX-License-Identifier: Apache-2.0
 <table>
   <tr>
     <td>
-      <a href="readme/pina_logo.png">
-        <img src="readme/pina_logo.png"
+      <a href="https://github.com/mathLab/PINA/raw/master/readme/pina_logo.png">
+        <img src="https://github.com/mathLab/PINA/raw/master/readme/pina_logo.png"
              alt="PINA logo"
              style="width: 220px; aspect-ratio: 1 / 1; object-fit: contain;">
       </a>

docs/source/_rst/_code.rst

@@ -26,7 +26,6 @@ Trainer, Dataset and Datamodule
     Trainer <trainer.rst>
     Dataset <data/dataset.rst>
     DataModule <data/data_module.rst>
-    Dataloader <data/dataloader.rst>
 
 Data Types
 ------------

docs/source/_rst/data/data_module.rst

@@ -2,6 +2,14 @@ DataModule
 ======================
 .. currentmodule:: pina.data.data_module
 
+.. autoclass:: Collator
+    :members:
+    :show-inheritance:
+
 .. autoclass:: PinaDataModule
     :members:
     :show-inheritance:
+
+.. autoclass:: PinaSampler
+    :members:
+    :show-inheritance:

docs/source/_rst/data/dataloader.rst

@@ -1,11 +0,0 @@
-Dataloader
-======================
-.. currentmodule:: pina.data.dataloader
-
-.. autoclass:: PinaSampler
-    :members:
-    :show-inheritance:
-    
-.. autoclass:: PinaDataLoader
-    :members:
-    :show-inheritance:

docs/source/_rst/data/dataset.rst

@@ -9,3 +9,11 @@ Dataset
 .. autoclass:: PinaDatasetFactory
     :members:
     :show-inheritance:
+
+.. autoclass:: PinaGraphDataset
+    :members:
+    :show-inheritance:
+
+.. autoclass:: PinaTensorDataset
+    :members:
+    :show-inheritance:

pina/callback/normalizer_data_callback.py

@@ -5,6 +5,7 @@ from lightning.pytorch import Callback
 from ..label_tensor import LabelTensor
 from ..utils import check_consistency, is_function
 from ..condition import InputTargetCondition
+from ..data.dataset import PinaGraphDataset
 
 
 class NormalizerDataCallback(Callback):
@@ -121,10 +122,7 @@ class NormalizerDataCallback(Callback):
         """
 
         # Ensure datsets are not graph-based
-        if any(
-            ds.is_graph_dataset
-            for ds in trainer.datamodule.train_dataset.values()
-        ):
+        if isinstance(trainer.datamodule.train_dataset, PinaGraphDataset):
             raise NotImplementedError(
                 "NormalizerDataCallback is not compatible with "
                 "graph-based datasets."
@@ -166,8 +164,8 @@ class NormalizerDataCallback(Callback):
         :param dataset: The `~pina.data.dataset.PinaDataset` dataset.
         """
         for cond in conditions:
-            if cond in dataset:
-                data = dataset[cond].data[self.apply_to]
+            if cond in dataset.conditions_dict:
+                data = dataset.conditions_dict[cond][self.apply_to]
                 shift = self.shift_fn(data)
                 scale = self.scale_fn(data)
                 self._normalizer[cond] = {
@@ -199,20 +197,25 @@ class NormalizerDataCallback(Callback):
 
         :param PinaDataset dataset: The dataset to be normalized.
         """
+        # Initialize update dictionary
+        update_dataset_dict = {}
 
         # Iterate over conditions and apply normalization
         for cond, norm_params in self.normalizer.items():
-            update_dataset_dict = {}
-            points = dataset[cond].data[self.apply_to]
+            points = dataset.conditions_dict[cond][self.apply_to]
             scale = norm_params["scale"]
             shift = norm_params["shift"]
             normalized_points = self._norm_fn(points, scale, shift)
-            update_dataset_dict[self.apply_to] = (
-                LabelTensor(normalized_points, points.labels)
-                if isinstance(points, LabelTensor)
-                else normalized_points
-            )
-            dataset[cond].data.update(update_dataset_dict)
+            update_dataset_dict[cond] = {
+                self.apply_to: (
+                    LabelTensor(normalized_points, points.labels)
+                    if isinstance(points, LabelTensor)
+                    else normalized_points
+                )
+            }
+
+        # Update the dataset in-place
+        dataset.update_data(update_dataset_dict)
 
     @property
     def normalizer(self):

pina/callback/refinement/refinement_interface.py

@@ -133,12 +133,13 @@ class RefinementInterface(Callback, metaclass=ABCMeta):
 
         :param PINNInterface solver: The solver object.
         """
+        new_points = {}
         for name in self._condition_to_update:
-            new_points = {}
-            current_points = self.dataset[name].data["input"]
-            new_points["input"] = self.sample(current_points, name, solver)
-
-            self.dataset[name].update_data(new_points)
+            current_points = self.dataset.conditions_dict[name]["input"]
+            new_points[name] = {
+                "input": self.sample(current_points, name, solver)
+            }
+        self.dataset.update_data(new_points)
 
     def _compute_population_size(self, conditions):
         """
@@ -149,5 +150,6 @@ class RefinementInterface(Callback, metaclass=ABCMeta):
         :rtype: dict
         """
         return {
-            cond: len(self.dataset[cond].data["input"]) for cond in conditions
+            cond: len(self.dataset.conditions_dict[cond]["input"])
+            for cond in conditions
         }

pina/data/__init__.py

@@ -4,3 +4,4 @@ __all__ = ["PinaDataModule", "PinaDataset"]
 
 
 from .data_module import PinaDataModule
+from .dataset import PinaDataset

pina/data/data_module.py

@@ -7,9 +7,232 @@ different types of Datasets defined in PINA.
 import warnings
 from lightning.pytorch import LightningDataModule
 import torch
+from torch_geometric.data import Data
+from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
+from torch.utils.data.distributed import DistributedSampler
 from ..label_tensor import LabelTensor
-from .dataset import PinaDatasetFactory
-from .dataloader import PinaDataLoader
+from .dataset import PinaDatasetFactory, PinaTensorDataset
+
+
+class DummyDataloader:
+
+    def __init__(self, dataset):
+        """
+        Prepare a dataloader object that returns the entire dataset in a single
+        batch. Depending on the number of GPUs, the dataset is managed
+        as follows:
+
+        - **Distributed Environment** (multiple GPUs): Divides dataset across
+            processes using the rank and world size. Fetches only portion of
+            data corresponding to the current process.
+        - **Non-Distributed Environment** (single GPU): Fetches the entire
+            dataset.
+
+        :param PinaDataset dataset: The dataset object to be processed.
+
+        .. note::
+           This dataloader is used when the batch size is ``None``.
+        """
+
+        if (
+            torch.distributed.is_available()
+            and torch.distributed.is_initialized()
+        ):
+            rank = torch.distributed.get_rank()
+            world_size = torch.distributed.get_world_size()
+            if len(dataset) < world_size:
+                raise RuntimeError(
+                    "Dimension of the dataset smaller than world size."
+                    " Increase the size of the partition or use a single GPU"
+                )
+            idx, i = [], rank
+            while i < len(dataset):
+                idx.append(i)
+                i += world_size
+            self.dataset = dataset.fetch_from_idx_list(idx)
+        else:
+            self.dataset = dataset.get_all_data()
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return 1
+
+    def __next__(self):
+        return self.dataset
+
+
+class Collator:
+    """
+    This callable class is used to collate the data points fetched from the
+    dataset. The collation is performed based on the type of dataset used and
+    on the batching strategy.
+    """
+
+    def __init__(
+        self, max_conditions_lengths, automatic_batching, dataset=None
+    ):
+        """
+        Initialize the object, setting the collate function based on whether
+        automatic batching is enabled or not.
+
+        :param dict max_conditions_lengths: ``dict`` containing the maximum
+            number  of data points to consider in a single batch for
+            each condition.
+        :param bool automatic_batching: Whether automatic PyTorch batching is
+            enabled or not. For more information, see the
+            :class:`~pina.data.data_module.PinaDataModule` class.
+        :param PinaDataset dataset: The dataset where the data is stored.
+        """
+
+        self.max_conditions_lengths = max_conditions_lengths
+        # Set the collate function based on the batching strategy
+        # collate_pina_dataloader is used when automatic batching is disabled
+        # collate_torch_dataloader is used when automatic batching is enabled
+        self.callable_function = (
+            self._collate_torch_dataloader
+            if automatic_batching
+            else (self._collate_pina_dataloader)
+        )
+        self.dataset = dataset
+
+        # Set the function which performs the actual collation
+        if isinstance(self.dataset, PinaTensorDataset):
+            # If the dataset is a PinaTensorDataset, use this collate function
+            self._collate = self._collate_tensor_dataset
+        else:
+            # If the dataset is a PinaDataset, use this collate function
+            self._collate = self._collate_graph_dataset
+
+    def _collate_pina_dataloader(self, batch):
+        """
+        Function used to create a batch when automatic batching is disabled.
+
+        :param list[int] batch: List of integers representing the indices of
+            the data points to be fetched.
+        :return: Dictionary containing the data points fetched from the dataset.
+        :rtype: dict
+        """
+        # Call the fetch_from_idx_list method of the dataset
+        return self.dataset.fetch_from_idx_list(batch)
+
+    def _collate_torch_dataloader(self, batch):
+        """
+        Function used to collate the batch
+
+        :param list[dict] batch: List of retrieved data.
+        :return: Dictionary containing the data points fetched from the dataset,
+            collated.
+        :rtype: dict
+        """
+
+        batch_dict = {}
+        if isinstance(batch, dict):
+            return batch
+        conditions_names = batch[0].keys()
+        # Condition names
+        for condition_name in conditions_names:
+            single_cond_dict = {}
+            condition_args = batch[0][condition_name].keys()
+            for arg in condition_args:
+                data_list = [
+                    batch[idx][condition_name][arg]
+                    for idx in range(
+                        min(
+                            len(batch),
+                            self.max_conditions_lengths[condition_name],
+                        )
+                    )
+                ]
+                single_cond_dict[arg] = self._collate(data_list)
+
+            batch_dict[condition_name] = single_cond_dict
+        return batch_dict
+
+    @staticmethod
+    def _collate_tensor_dataset(data_list):
+        """
+        Function used to collate the data when the dataset is a
+        :class:`~pina.data.dataset.PinaTensorDataset`.
+
+        :param data_list: Elements to be collated.
+        :type data_list: list[torch.Tensor] | list[LabelTensor]
+        :return: Batch of data.
+        :rtype: dict
+
+        :raises RuntimeError: If the data is not a :class:`torch.Tensor` or a
+            :class:`~pina.label_tensor.LabelTensor`.
+        """
+
+        if isinstance(data_list[0], LabelTensor):
+            return LabelTensor.stack(data_list)
+        if isinstance(data_list[0], torch.Tensor):
+            return torch.stack(data_list)
+        raise RuntimeError("Data must be Tensors or LabelTensor ")
+
+    def _collate_graph_dataset(self, data_list):
+        """
+        Function used to collate data when the dataset is a
+        :class:`~pina.data.dataset.PinaGraphDataset`.
+
+        :param data_list: Elememts to be collated.
+        :type data_list: list[Data] | list[Graph]
+        :return: Batch of data.
+        :rtype: dict
+
+        :raises RuntimeError: If the data is not a
+            :class:`~torch_geometric.data.Data` or a :class:`~pina.graph.Graph`.
+        """
+        if isinstance(data_list[0], LabelTensor):
+            return LabelTensor.cat(data_list)
+        if isinstance(data_list[0], torch.Tensor):
+            return torch.cat(data_list)
+        if isinstance(data_list[0], Data):
+            return self.dataset.create_batch(data_list)
+        raise RuntimeError(
+            "Data must be Tensors or LabelTensor or pyG "
+            "torch_geometric.data.Data"
+        )
+
+    def __call__(self, batch):
+        """
+        Perform the collation of data fetched from the dataset. The behavoior
+        of the function is set based on the batching strategy during class
+        initialization.
+
+        :param batch: List of retrieved data or sampled indices.
+        :type batch: list[int] | list[dict]
+        :return: Dictionary containing colleted data fetched from the dataset.
+        :rtype: dict
+        """
+
+        return self.callable_function(batch)
+
+
+class PinaSampler:
+    """
+    This class is used to create the sampler instance based on the shuffle
+    parameter and the environment in which the code is running.
+    """
+
+    def __new__(cls, dataset):
+        """
+        Instantiate and initialize the sampler.
+
+        :param PinaDataset dataset: The dataset from which to sample.
+        :return: The sampler instance.
+        :rtype: :class:`torch.utils.data.Sampler`
+        """
+
+        if (
+            torch.distributed.is_available()
+            and torch.distributed.is_initialized()
+        ):
+            sampler = DistributedSampler(dataset)
+        else:
+            sampler = SequentialSampler(dataset)
+        return sampler
 
 
 class PinaDataModule(LightningDataModule):
@@ -27,7 +250,7 @@ class PinaDataModule(LightningDataModule):
         val_size=0.1,
         batch_size=None,
         shuffle=True,
-        batching_mode="common_batch_size",
+        repeat=False,
         automatic_batching=None,
         num_workers=0,
         pin_memory=False,
@@ -48,12 +271,11 @@ class PinaDataModule(LightningDataModule):
             Default is ``None``.
         :param bool shuffle: Whether to shuffle the dataset before splitting.
             Default ``True``.
-        :param bool common_batch_size: If ``True``, the same batch size is used
-            for all conditions. If ``False``, each condition can have its own
-            batch size, proportional to the size of the dataset in that
-            condition. Default is ``True``.
-        :param bool separate_conditions: If ``True``, dataloaders for each
-            condition are iterated separately. Default is ``False``.
+        :param bool repeat: If ``True``, in case of batch size larger than the
+            number of elements in a specific condition, the elements are
+            repeated until the batch size is reached. If ``False``, the number
+            of elements in the batch is the minimum between the batch size and
+            the number of elements in the condition. Default is ``False``.
         :param automatic_batching: If ``True``, automatic PyTorch batching
             is performed, which consists of extracting one element at a time
             from the dataset and collating them into a batch. This is useful
@@ -83,7 +305,7 @@ class PinaDataModule(LightningDataModule):
         # Store fixed attributes
         self.batch_size = batch_size
         self.shuffle = shuffle
-        self.batching_mode = batching_mode
+        self.repeat = repeat
         self.automatic_batching = automatic_batching
 
         # If batch size is None, num_workers has no effect
@@ -154,16 +376,23 @@ class PinaDataModule(LightningDataModule):
         if stage == "fit" or stage is None:
             self.train_dataset = PinaDatasetFactory(
                 self.data_splits["train"],
+                max_conditions_lengths=self.find_max_conditions_lengths(
+                    "train"
+                ),
                 automatic_batching=self.automatic_batching,
             )
             if "val" in self.data_splits.keys():
                 self.val_dataset = PinaDatasetFactory(
                     self.data_splits["val"],
+                    max_conditions_lengths=self.find_max_conditions_lengths(
+                        "val"
+                    ),
                     automatic_batching=self.automatic_batching,
                 )
         elif stage == "test":
             self.test_dataset = PinaDatasetFactory(
                 self.data_splits["test"],
+                max_conditions_lengths=self.find_max_conditions_lengths("test"),
                 automatic_batching=self.automatic_batching,
             )
         else:
@@ -253,7 +482,7 @@ class PinaDataModule(LightningDataModule):
                 dataset_dict[key].update({condition_name: data})
         return dataset_dict
 
-    def _create_dataloader(self, dataset):
+    def _create_dataloader(self, split, dataset):
         """ "
         Create the dataloader for the given split.
 
@@ -273,18 +502,53 @@ class PinaDataModule(LightningDataModule):
             ),
             module="lightning.pytorch.trainer.connectors.data_connector",
         )
-        dl = PinaDataLoader(
-            dataset,
-            batch_size=self.batch_size,
-            shuffle=self.shuffle,
-            num_workers=self.num_workers,
-            batching_mode=self.batching_mode,
-            device=self.trainer.strategy.root_device,
+        # Use custom batching (good if batch size is large)
+        if self.batch_size is not None:
+            sampler = PinaSampler(dataset)
+            if self.automatic_batching:
+                collate = Collator(
+                    self.find_max_conditions_lengths(split),
+                    self.automatic_batching,
+                    dataset=dataset,
+                )
+            else:
+                collate = Collator(
+                    None, self.automatic_batching, dataset=dataset
+                )
+            return DataLoader(
+                dataset,
+                self.batch_size,
+                collate_fn=collate,
+                sampler=sampler,
+                num_workers=self.num_workers,
+            )
+        dataloader = DummyDataloader(dataset)
+        dataloader.dataset = self._transfer_batch_to_device(
+            dataloader.dataset, self.trainer.strategy.root_device, 0
         )
-        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
+        self.transfer_batch_to_device = self._transfer_batch_to_device_dummy
+        return dataloader
+
+    def find_max_conditions_lengths(self, split):
+        """
+        Define the maximum length for each conditions.
+
+        :param dict split: The split of the dataset.
+        :return: The maximum length per condition.
+        :rtype: dict
+        """
+
+        max_conditions_lengths = {}
+        for k, v in self.data_splits[split].items():
+            if self.batch_size is None:
+                max_conditions_lengths[k] = len(v["input"])
+            elif self.repeat:
+                max_conditions_lengths[k] = self.batch_size
+            else:
+                max_conditions_lengths[k] = min(
+                    len(v["input"]), self.batch_size
+                )
+        return max_conditions_lengths
 
     def val_dataloader(self):
         """
@@ -293,7 +557,7 @@ class PinaDataModule(LightningDataModule):
         :return: The validation dataloader
         :rtype: torch.utils.data.DataLoader
         """
-        return self._create_dataloader(self.val_dataset)
+        return self._create_dataloader("val", self.val_dataset)
 
     def train_dataloader(self):
         """
@@ -302,7 +566,7 @@ class PinaDataModule(LightningDataModule):
         :return: The training dataloader
         :rtype: torch.utils.data.DataLoader
         """
-        return self._create_dataloader(self.train_dataset)
+        return self._create_dataloader("train", self.train_dataset)
 
     def test_dataloader(self):
         """
@@ -311,7 +575,7 @@ class PinaDataModule(LightningDataModule):
         :return: The testing dataloader
         :rtype: torch.utils.data.DataLoader
         """
-        return self._create_dataloader(self.test_dataset)
+        return self._create_dataloader("test", self.test_dataset)
 
     @staticmethod
     def _transfer_batch_to_device_dummy(batch, device, dataloader_idx):
@@ -327,7 +591,7 @@ class PinaDataModule(LightningDataModule):
         :rtype: list[tuple]
         """
 
-        return list(batch.items())
+        return batch
 
     def _transfer_batch_to_device(self, batch, device, dataloader_idx):
         """
@@ -385,15 +649,9 @@ class PinaDataModule(LightningDataModule):
 
         to_return = {}
         if hasattr(self, "train_dataset") and self.train_dataset is not None:
-            to_return["train"] = {
-                cond: data.input for cond, data in self.train_dataset.items()
-            }
+            to_return["train"] = self.train_dataset.input
         if hasattr(self, "val_dataset") and self.val_dataset is not None:
-            to_return["val"] = {
-                cond: data.input for cond, data in self.val_dataset.items()
-            }
+            to_return["val"] = self.val_dataset.input
         if hasattr(self, "test_dataset") and self.test_dataset is not None:
-            to_return["test"] = {
-                cond: data.input for cond, data in self.test_dataset.items()
-            }
+            to_return["test"] = self.test_dataset.input
         return to_return

pina/data/dataloader.py

@@ -1,347 +0,0 @@
-"""DataLoader module for PinaDataset."""
-
-import itertools
-import random
-from functools import partial
-import torch
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data.sampler import SequentialSampler
-from .stacked_dataloader import StackedDataLoader
-
-
-class DummyDataloader:
-    """
-    DataLoader that returns the entire dataset in a single batch.
-    """
-
-    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
-        as follows:
-
-        - **Distributed Environment** (multiple GPUs): Divides dataset across
-            processes using the rank and world size. Fetches only portion of
-            data corresponding to the current process.
-        - **Non-Distributed Environment** (single GPU): Fetches the entire
-            dataset.
-
-        :param PinaDataset dataset: The dataset object to be processed.
-
-        .. note::
-           This dataloader is used when the batch size is ``None``.
-        """
-        # Handle distributed environment
-        if PinaSampler.is_distributed():
-            # Get rank and world size
-            rank = torch.distributed.get_rank()
-            world_size = torch.distributed.get_world_size()
-            # Ensure dataset is large enough
-            if len(dataset) < world_size:
-                raise RuntimeError(
-                    "Dimension of the dataset smaller than world size."
-                    " Increase the size of the partition or use a single GPU"
-                )
-            # Split dataset among processes
-            idx, i = [], rank
-            while i < len(dataset):
-                idx.append(i)
-                i += world_size
-        else:
-            idx = list(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):
-        """
-        Iterate over the dataloader.
-        """
-        return self
-
-    def __len__(self):
-        """
-        Return the length of the dataloader, which is always 1.
-        :return: The length of the dataloader.
-        :rtype: int
-        """
-        return 1
-
-    def __next__(self):
-        """
-        Return the entire dataset as a single batch.
-        :return: The entire dataset.
-        :rtype: dict
-        """
-        return self.dataset
-
-
-class PinaSampler:
-    """
-    This class is used to create the sampler instance based on the shuffle
-    parameter and the environment in which the code is running.
-    """
-
-    def __new__(cls, dataset, shuffle=True):
-        """
-        Instantiate and initialize the sampler.
-
-        :param PinaDataset dataset: The dataset from which to sample.
-        :return: The sampler instance.
-        :rtype: :class:`torch.utils.data.Sampler`
-        """
-
-        if cls.is_distributed():
-            sampler = DistributedSampler(dataset, shuffle=shuffle)
-        else:
-            if shuffle:
-                sampler = torch.utils.data.RandomSampler(dataset)
-            else:
-                sampler = SequentialSampler(dataset)
-        return sampler
-
-    @staticmethod
-    def is_distributed():
-        """
-        Check if the sampler is distributed.
-        :return: True if the sampler is distributed, False otherwise.
-        :rtype: bool
-        """
-        return (
-            torch.distributed.is_available()
-            and torch.distributed.is_initialized()
-        )
-
-
-def _collect_items(batch):
-    """
-    Helper function to collect items from a batch of graph data samples.
-    :param batch: List of graph data samples.
-    """
-    to_return = {name: [] for name in batch[0].keys()}
-    for sample in batch:
-        for k, v in sample.items():
-            to_return[k].append(v)
-    return to_return
-
-
-def collate_fn_custom(batch, dataset):
-    """
-    Override the default collate function to handle datasets without automatic
-    batching.
-    :param batch: List of indices from the dataset.
-    :param dataset: The PinaDataset instance (must be provided).
-    """
-    return dataset.getitem_from_list(batch)
-
-
-def collate_fn_default(batch, stack_fn):
-    """
-    Default collate function that simply returns the batch as is.
-    :param batch: List of data samples.
-    """
-    to_return = _collect_items(batch)
-    return {k: stack_fn[k](v) for k, v in to_return.items()}
-
-
-class PinaDataLoader:
-    """
-    Custom DataLoader for PinaDataset.
-    """
-
-    def __new__(cls, *args, **kwargs):
-        batching_mode = kwargs.get("batching_mode", "common_batch_size").lower()
-        batch_size = kwargs.get("batch_size")
-        if batching_mode == "stacked" and batch_size is not None:
-            return StackedDataLoader(
-                args[0],
-                batch_size=batch_size,
-                shuffle=kwargs.get("shuffle", True),
-            )
-        elif batch_size is None:
-            kwargs["batching_mode"] = "proportional"
-        print(
-            "Using PinaDataLoader with batching mode:", kwargs["batching_mode"]
-        )
-        return super(PinaDataLoader, cls).__new__(cls)
-
-    def __init__(
-        self,
-        dataset_dict,
-        batch_size,
-        num_workers=0,
-        shuffle=False,
-        batching_mode="common_batch_size",
-        device=None,
-    ):
-        """
-        Initialize the PinaDataLoader.
-
-        :param dict dataset_dict: A dictionary mapping dataset names to their
-            respective PinaDataset instances.
-        :param int batch_size: The batch size for the dataloader.
-        :param int num_workers: Number of worker processes for data loading.
-        :param bool shuffle: Whether to shuffle the data at every epoch.
-        :param str batching_mode: The batching mode to use. Options are
-            "common_batch_size", "separate_conditions", and "proportional".
-        :param device: The device to which the data should be moved.
-        """
-
-        self.dataset_dict = dataset_dict
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-        self.shuffle = shuffle
-        self.batching_mode = batching_mode.lower()
-        self.device = device
-
-        # Batch size None means we want to load the entire dataset in a single
-        # batch
-        if batch_size is None:
-            batch_size_per_dataset = {
-                split: None for split in dataset_dict.keys()
-            }
-        else:
-            # Compute batch size per dataset
-            if batching_mode in ["common_batch_size", "separate_conditions"]:
-                # (the sum of the batch sizes is equal to
-                # n_conditions * batch_size)
-                batch_size_per_dataset = {
-                    split: min(batch_size, len(ds))
-                    for split, ds in dataset_dict.items()
-                }
-            elif batching_mode == "proportional":
-                # batch sizes is equal to the specified batch size)
-                batch_size_per_dataset = self._compute_batch_size()
-
-        # Creaete a dataloader per dataset
-        self.dataloaders = {
-            split: self._create_dataloader(
-                dataset, batch_size_per_dataset[split]
-            )
-            for split, dataset in dataset_dict.items()
-        }
-
-    def _compute_batch_size(self):
-        """
-        Compute an appropriate batch size for the given dataset.
-        """
-
-        # Compute number of elements per dataset
-        elements_per_dataset = {
-            dataset_name: len(dataset)
-            for dataset_name, dataset in self.dataset_dict.items()
-        }
-        # Compute the total number of elements
-        total_elements = sum(el for el in elements_per_dataset.values())
-        # Compute the portion of each dataset
-        portion_per_dataset = {
-            name: el / total_elements
-            for name, el in elements_per_dataset.items()
-        }
-        # Compute batch size per dataset. Ensure at least 1 element per
-        # dataset.
-        batch_size_per_dataset = {
-            name: max(1, int(portion * self.batch_size))
-            for name, portion in portion_per_dataset.items()
-        }
-        # Adjust batch sizes to match the specified total batch size
-        tot_el_per_batch = sum(el for el in batch_size_per_dataset.values())
-        if self.batch_size > tot_el_per_batch:
-            difference = self.batch_size - tot_el_per_batch
-            while difference > 0:
-                for k, v in batch_size_per_dataset.items():
-                    if difference == 0:
-                        break
-                    if v > 1:
-                        batch_size_per_dataset[k] += 1
-                        difference -= 1
-        if self.batch_size < tot_el_per_batch:
-            difference = tot_el_per_batch - self.batch_size
-            while difference > 0:
-                for k, v in batch_size_per_dataset.items():
-                    if difference == 0:
-                        break
-                    if v > 1:
-                        batch_size_per_dataset[k] -= 1
-                        difference -= 1
-        return batch_size_per_dataset
-
-    def _create_dataloader(self, dataset, batch_size):
-        """
-        Create the dataloader for the given dataset.
-
-        :param PinaDataset dataset: The dataset for which to create the
-            dataloader.
-        :param int batch_size: The batch size for the dataloader.
-        :return: The created dataloader.
-        :rtype: :class:`torch.utils.data.DataLoader`
-        """
-        # If batch size is None, use DummyDataloader
-        if batch_size is None or batch_size >= len(dataset):
-            return DummyDataloader(dataset, device=self.device)
-
-        # Determine the appropriate collate function
-        if not dataset.automatic_batching:
-            collate_fn = partial(collate_fn_custom, dataset=dataset)
-        else:
-            collate_fn = partial(collate_fn_default, stack_fn=dataset.stack_fn)
-
-        # Create and return the dataloader
-        return DataLoader(
-            dataset,
-            batch_size=batch_size,
-            collate_fn=collate_fn,
-            num_workers=self.num_workers,
-            sampler=PinaSampler(dataset, shuffle=self.shuffle),
-        )
-
-    def __len__(self):
-        """
-        Return the length of the dataloader.
-
-        :return: The length of the dataloader.
-        :rtype: int
-        """
-        # If separate conditions, return sum of lengths of all dataloaders
-        # else, return max length among dataloaders
-        if self.batching_mode == "separate_conditions":
-            return sum(len(dl) for dl in self.dataloaders.values())
-        return max(len(dl) for dl in self.dataloaders.values())
-
-    def __iter__(self):
-        """
-        Iterate over the dataloader. Yields a dictionary mapping split name to batch.
-
-        The iteration logic for 'separate_conditions' is now iterative and memory-efficient.
-        """
-        if self.batching_mode == "separate_conditions":
-            tmp = []
-            for split, dl in self.dataloaders.items():
-                len_split = len(dl)
-                for i, batch in enumerate(dl):
-                    tmp.append({split: batch})
-                    if i + 1 >= len_split:
-                        break
-            random.shuffle(tmp)
-            for batch_dict in tmp:
-                yield batch_dict
-            return
-
-        # Common_batch_size or Proportional mode (round-robin sampling)
-        iterators = {
-            split: itertools.cycle(dl) for split, dl in self.dataloaders.items()
-        }
-
-        # Iterate for the length of the longest dataloader
-        for _ in range(len(self)):
-            batch_dict: BatchDict = {}
-            for split, it in iterators.items():
-                # Since we use itertools.cycle, next(it) will always yield a batch
-                # by repeating the dataset, so no need for the 'if batch is None: return' check.
-                batch_dict[split] = next(it)
-            yield batch_dict

pina/data/dataset.py

@@ -1,170 +1,326 @@
 """Module for the PINA dataset classes."""
 
-import torch
+from abc import abstractmethod, ABC
 from torch.utils.data import Dataset
 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:
     """
-    Factory class to create PINA datasets based on the provided conditions
-    dictionary.
+    Factory class for the PINA dataset.
+
+    Depending on the data type inside the conditions, it instanciate an object
+    belonging to the appropriate subclass of 
+    :class:`~pina.data.dataset.PinaDataset`. The possible subclasses are:
+
+    - :class:`~pina.data.dataset.PinaTensorDataset`, for handling \
+        :class:`torch.Tensor` and :class:`~pina.label_tensor.LabelTensor` data.
+    - :class:`~pina.data.dataset.PinaGraphDataset`, for handling \
+        :class:`~pina.graph.Graph` and :class:`~torch_geometric.data.Data` data.
     """
 
     def __new__(cls, conditions_dict, **kwargs):
         """
-        Create PINA dataset instances based on the provided conditions
-        dictionary.
+        Instantiate the appropriate subclass of
+        :class:`~pina.data.dataset.PinaDataset`.
 
-        :param dict conditions_dict: A dictionary where keys are condition names
-            and values are dictionaries containing the associated data.
-        :return: A dictionary mapping condition names to their respective
-            :class:`PinaDataset` instances.
+        If a graph is present in the conditions, returns a
+        :class:`~pina.data.dataset.PinaGraphDataset`, otherwise returns a
+        :class:`~pina.data.dataset.PinaTensorDataset`.
+
+        :param dict conditions_dict: Dictionary containing all the conditions
+            to be included in the dataset instance.
+        :return: A subclass of :class:`~pina.data.dataset.PinaDataset`.
+        :rtype: PinaTensorDataset | PinaGraphDataset
+
+        :raises ValueError: If an empty dictionary is provided.
         """
 
         # Check if conditions_dict is empty
         if len(conditions_dict) == 0:
             raise ValueError("No conditions provided")
 
-        dataset_dict = {}  # Dictionary to hold the created datasets
-
         # Check is a Graph is present in the conditions
-        for name, data in conditions_dict.items():
-            # Validate that data is a dictionary
-            if not isinstance(data, dict):
-                raise ValueError(
-                    f"Condition '{name}' data must be a dictionary"
-                )
-            # Create PinaDataset instance for each condition
-            dataset_dict[name] = PinaDataset(data, **kwargs)
-        return dataset_dict
+        is_graph = cls._is_graph_dataset(conditions_dict)
+        if is_graph:
+            # If a Graph is present, return a PinaGraphDataset
+            return PinaGraphDataset(conditions_dict, **kwargs)
+        # If no Graph is present, return a PinaTensorDataset
+        return PinaTensorDataset(conditions_dict, **kwargs)
 
-
-class PinaDataset(Dataset):
-    """
-    Dataset class for the PINA dataset with :class:`torch.Tensor` and
-    :class:`~pina.label_tensor.LabelTensor` data.
-    """
-
-    def __init__(self, data_dict, automatic_batching=None):
+    @staticmethod
+    def _is_graph_dataset(conditions_dict):
         """
-        Initialize the instance by storing the conditions dictionary.
+        Check if a graph is present in the conditions (at least one time).
+
+        :param conditions_dict: Dictionary containing the conditions.
+        :type conditions_dict: dict
+        :return: True if a graph is present in the conditions, False otherwise.
+        :rtype: bool
+        """
+
+        # Iterate over the conditions dictionary
+        for v in conditions_dict.values():
+            # Iterate over the values of the current condition
+            for cond in v.values():
+                # Check if the current value is a list of Data objects
+                if isinstance(cond, (Data, Graph, list, tuple)):
+                    return True
+        return False
+
+
+class PinaDataset(Dataset, ABC):
+    """
+    Abstract class for the PINA dataset which extends the PyTorch
+    :class:`~torch.utils.data.Dataset` class. It defines the common interface
+    for :class:`~pina.data.dataset.PinaTensorDataset` and
+    :class:`~pina.data.dataset.PinaGraphDataset` classes.
+    """
+
+    def __init__(
+        self, conditions_dict, max_conditions_lengths, automatic_batching
+    ):
+        """
+        Initialize the instance by storing the conditions dictionary, the
+        maximum number of items per conditions to consider, and the automatic
+        batching flag.
 
         :param dict conditions_dict: A dictionary mapping condition names to
             their respective data. Each key represents a condition name, and the
             corresponding value is a dictionary containing the associated data.
+        :param dict max_conditions_lengths: Maximum number of data points that
+            can be included in a single batch per condition.
+        :param bool automatic_batching: Indicates whether PyTorch automatic
+            batching is enabled in
+            :class:`~pina.data.data_module.PinaDataModule`.
         """
 
         # Store the conditions dictionary
-        self.data = data_dict
-        self.automatic_batching = (
-            automatic_batching if automatic_batching is not None else True
-        )
-        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] = "label_tensor"
-            elif isinstance(v, torch.Tensor):
-                self._stack_fn[k] = "tensor"
-            elif isinstance(v, list) and all(
-                isinstance(item, (Data, Graph)) for item in v
-            ):
-                self._stack_fn[k] = "data"
-                self.is_graph_dataset = True
-            else:
-                raise ValueError(
-                    f"Unsupported data type for stacking: {type(v)}"
-                )
+        self.conditions_dict = conditions_dict
+        # Store the maximum number of conditions to consider
+        self.max_conditions_lengths = max_conditions_lengths
+        # Store length of each condition
+        self.conditions_length = {
+            k: len(v["input"]) for k, v in self.conditions_dict.items()
+        }
+        # Store the maximum length of the dataset
+        self.length = max(self.conditions_length.values())
+        # Dynamically set the getitem function based on automatic batching
+        if automatic_batching:
+            self._getitem_func = self._getitem_int
+        else:
+            self._getitem_func = self._getitem_dummy
 
-    def __len__(self):
+    def _get_max_len(self):
         """
-        Return the length of the dataset.
+        Returns the length of the longest condition in the dataset.
 
-        :return: The length of the dataset.
+        :return: Length of the longest condition in the dataset.
         :rtype: int
         """
-        return len(next(iter(self.data.values())))
+
+        max_len = 0
+        for condition in self.conditions_dict.values():
+            max_len = max(max_len, len(condition["input"]))
+        return max_len
+
+    def __len__(self):
+        return self.length
 
     def __getitem__(self, idx):
+        return self._getitem_func(idx)
+
+    def _getitem_dummy(self, idx):
         """
-        Return the data at the given index in the dataset.
+        Return the index itself. This is used when automatic batching is
+        disabled to postpone the data retrieval to the dataloader.
+
+        :param int idx: Index.
+        :return: Index.
+        :rtype: int
+        """
+
+        # If automatic batching is disabled, return the data at the given index
+        return idx
+
+    def _getitem_int(self, idx):
+        """
+        Return the data at the given index in the dataset. This is used when
+        automatic batching is enabled.
 
         :param int idx: Index.
         :return: A dictionary containing the data at the given index.
         :rtype: dict
         """
 
-        if self.automatic_batching:
-            # Return the data at the given index
-            return {
-                field_name: data[idx] for field_name, data in self.data.items()
-            }
-        return idx
+        # If automatic batching is enabled, return the data at the given index
+        return {
+            k: {k_data: v[k_data][idx % len(v["input"])] for k_data in v.keys()}
+            for k, v in self.conditions_dict.items()
+        }
 
-    def getitem_from_list(self, idx_list):
+    def get_all_data(self):
+        """
+        Return all data in the dataset.
+
+        :return: A dictionary containing all the data in the dataset.
+        :rtype: dict
+        """
+        to_return_dict = {}
+        for condition, data in self.conditions_dict.items():
+            len_condition = len(
+                data["input"]
+            )  # Length of the current condition
+            to_return_dict[condition] = self._retrive_data(
+                data, list(range(len_condition))
+            )  # Retrieve the data from the current condition
+        return to_return_dict
+
+    def fetch_from_idx_list(self, idx):
         """
         Return data from the dataset given a list of indices.
 
-        :param list[int] idx_list: List of indices.
+        :param list[int] idx: List of indices.
         :return: A dictionary containing the data at the given indices.
         :rtype: dict
         """
 
-        to_return = {}
-        for field_name, data in self.data.items():
-            if self._stack_fn[field_name] == "data":
-                fn = STACK_FN_MAP[self._stack_fn[field_name]]
-                to_return[field_name] = fn([data[i] for i in idx_list])
-            else:
-                to_return[field_name] = data[idx_list]
-        return to_return
+        to_return_dict = {}
+        for condition, data in self.conditions_dict.items():
+            # Get the indices for the current condition
+            cond_idx = idx[: self.max_conditions_lengths[condition]]
+            # Get the length of the current condition
+            condition_len = self.conditions_length[condition]
+            # If the length of the dataset is greater than the length of the
+            # current condition, repeat the indices
+            if self.length > condition_len:
+                cond_idx = [idx % condition_len for idx in cond_idx]
+            # Retrieve the data from the current condition
+            to_return_dict[condition] = self._retrive_data(data, cond_idx)
+        return to_return_dict
 
-    def update_data(self, update_dict):
+    @abstractmethod
+    def _retrive_data(self, data, idx_list):
+        """
+        Abstract method to retrieve data from the dataset given a list of
+        indices.
         """
-        Update the dataset's data in-place.
 
-        :param dict update_dict: A dictionary where keys are condition names
-            and values are dictionaries with updated data for those conditions.
+
+class PinaTensorDataset(PinaDataset):
+    """
+    Dataset class for the PINA dataset with :class:`torch.Tensor` and
+    :class:`~pina.label_tensor.LabelTensor` data.
+    """
+
+    # Override _retrive_data method for torch.Tensor data
+    def _retrive_data(self, data, idx_list):
         """
-        for field_name, updates in update_dict.items():
-            if field_name not in self.data:
-                raise KeyError(
-                    f"Condition '{field_name}' not found in dataset."
-                )
-            if not isinstance(updates, (LabelTensor, torch.Tensor)):
-                raise ValueError(
-                    f"Updates for condition '{field_name}' must be of type "
-                    f"LabelTensor or torch.Tensor."
-                )
-            self.data[field_name] = updates
+        Retrieve data from the dataset given a list of indices.
+
+        :param dict data: Dictionary containing the data
+            (only :class:`torch.Tensor` or
+            :class:`~pina.label_tensor.LabelTensor`).
+        :param list[int] idx_list: indices to retrieve.
+        :return: Dictionary containing the data at the given indices.
+        :rtype: dict
+        """
+
+        return {k: v[idx_list] for k, v in data.items()}
 
     @property
     def input(self):
         """
-        Get the input data from the dataset.
+        Return the input data for the dataset.
 
-        :return: The input data.
-        :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.
+        :return: Dictionary containing the input points.
         :rtype: dict
         """
-        return {k: STACK_FN_MAP[v] for k, v in self._stack_fn.items()}
+        return {k: v["input"] for k, v in self.conditions_dict.items()}
+
+    def update_data(self, new_conditions_dict):
+        """
+        Update the dataset with new data.
+        This method is used to update the dataset with new data. It replaces
+        the current data with the new data provided in the new_conditions_dict
+        parameter.
+
+        :param dict new_conditions_dict: Dictionary containing the new data.
+        :return: None
+        """
+        for condition, data in new_conditions_dict.items():
+            if condition in self.conditions_dict:
+                self.conditions_dict[condition].update(data)
+            else:
+                self.conditions_dict[condition] = data
+
+
+class PinaGraphDataset(PinaDataset):
+    """
+    Dataset class for the PINA dataset with :class:`~torch_geometric.data.Data`
+    and :class:`~pina.graph.Graph` data.
+    """
+
+    def _create_graph_batch(self, data):
+        """
+        Create a LabelBatch object from a list of
+        :class:`~torch_geometric.data.Data` objects.
+
+        :param data: List of items to collate in a single batch.
+        :type data: list[Data] | list[Graph]
+        :return: LabelBatch object all the graph collated in a single batch
+            disconnected graphs.
+        :rtype: LabelBatch
+        """
+        batch = LabelBatch.from_data_list(data)
+        return batch
+
+    def create_batch(self, data):
+        """
+        Create a Batch object from a list of :class:`~torch_geometric.data.Data`
+        objects.
+
+        :param data: List of items to collate in a single batch.
+        :type data: list[Data] | list[Graph]
+        :return: Batch object.
+        :rtype: :class:`~torch_geometric.data.Batch`
+            | :class:`~pina.graph.LabelBatch`
+        """
+
+        if isinstance(data[0], Data):
+            return self._create_graph_batch(data)
+        return self._create_tensor_batch(data)
+
+    # Override _retrive_data method for graph handling
+    def _retrive_data(self, data, idx_list):
+        """
+        Retrieve data from the dataset given a list of indices.
+
+        :param dict data: Dictionary containing the data.
+        :param list[int] idx_list: List of indices to retrieve.
+        :return: Dictionary containing the data at the given indices.
+        :rtype: dict
+        """
+
+        # Return the data from the current condition
+        # If the data is a list of Data objects, create a Batch object
+        # If the data is a list of torch.Tensor objects, create a torch.Tensor
+        return {
+            k: (
+                self._create_graph_batch([v[i] for i in idx_list])
+                if isinstance(v, list)
+                else v[idx_list]
+            )
+            for k, v in data.items()
+        }
+
+    @property
+    def input(self):
+        """
+        Return the input data for the dataset.
+
+        :return: Dictionary containing the input points.
+        :rtype: dict
+        """
+        return {k: v["input"] for k, v in self.conditions_dict.items()}

pina/data/stacked_dataloader.py

@@ -1,53 +0,0 @@
-import torch
-from math import ceil
-
-
-class StackedDataLoader:
-    def __init__(self, datasets, batch_size=32, shuffle=True):
-        for d in datasets.values():
-            if d.is_graph_dataset:
-                raise ValueError("Each dataset must be a dictionary")
-        self.chunks = {}
-        self.total_length = 0
-        self.indices = []
-
-        self._init_chunks(datasets)
-        self.indices = list(range(self.total_length))
-        self.batch_size = batch_size
-        self.shuffle = shuffle
-        if self.shuffle:
-            torch.random.manual_seed(42)
-            self.indices = torch.randperm(self.total_length).tolist()
-        self.datasets = datasets
-
-    def _init_chunks(self, datasets):
-        inc = 0
-        total_length = 0
-        for name, dataset in datasets.items():
-            self.chunks[name] = {"start": inc, "end": inc + len(dataset)}
-            inc += len(dataset)
-        self.total_length = inc
-
-    def __len__(self):
-        return ceil(self.total_length / self.batch_size)
-
-    def _build_batch_indices(self, batch_idx):
-        start = batch_idx * self.batch_size
-        end = min(start + self.batch_size, self.total_length)
-        return self.indices[start:end]
-
-    def __iter__(self):
-        for batch_idx in range(len(self)):
-            batch_indices = self._build_batch_indices(batch_idx)
-            batch_data = {}
-            for name, chunk in self.chunks.items():
-                local_indices = [
-                    idx - chunk["start"]
-                    for idx in batch_indices
-                    if chunk["start"] <= idx < chunk["end"]
-                ]
-                if local_indices:
-                    batch_data[name] = self.datasets[name].getitem_from_list(
-                        local_indices
-                    )
-            yield batch_data

pina/equation/equation.py

@@ -1,6 +1,7 @@
 """Module for the Equation."""
 
 import inspect
+
 from .equation_interface import EquationInterface
 
 
@@ -48,10 +49,6 @@ class Equation(EquationInterface):
         :raises RuntimeError: If the underlying equation signature length is not
             2 (direct problem) or 3 (inverse problem).
         """
-        # Move the equation to the input_ device
-        self.to(input_.device)
-
-        # Call the underlying equation based on its signature length
         if self.__len_sig == 2:
             return self.__equation(input_, output_)
         if self.__len_sig == 3:

pina/equation/equation_factory.py

@@ -239,19 +239,19 @@ class Advection(Equation):  # pylint: disable=R0903
                 )
 
             # Ensure consistency of c length
-            if self.c.shape[-1] != len(input_lbl) - 1 and self.c.shape[-1] > 1:
+            if len(self.c) != (len(input_lbl) - 1) and len(self.c) > 1:
                 raise ValueError(
                     "If 'c' is passed as a list, its length must be equal to "
                     "the number of spatial dimensions."
                 )
 
             # Repeat c to ensure consistent shape for advection
-            c = self.c.repeat(output_.shape[0], 1)
-            if c.shape[1] != (len(input_lbl) - 1):
-                c = c.repeat(1, len(input_lbl) - 1)
+            self.c = self.c.repeat(output_.shape[0], 1)
+            if self.c.shape[1] != (len(input_lbl) - 1):
+                self.c = self.c.repeat(1, len(input_lbl) - 1)
 
             # Add a dimension to c for the following operations
-            c = c.unsqueeze(-1)
+            self.c = self.c.unsqueeze(-1)
 
             # Compute the time derivative and the spatial gradient
             time_der = grad(output_, input_, components=None, d="t")
@@ -262,7 +262,7 @@ class Advection(Equation):  # pylint: disable=R0903
             tmp = tmp.transpose(-1, -2)
 
             # Compute advection term
-            adv = (tmp * c).sum(dim=tmp.tensor.ndim - 2)
+            adv = (tmp * self.c).sum(dim=tmp.tensor.ndim - 2)
 
             return time_der + adv
 

pina/equation/equation_interface.py

@@ -1,7 +1,6 @@
 """Module for the Equation Interface."""
 
 from abc import ABCMeta, abstractmethod
-import torch
 
 
 class EquationInterface(metaclass=ABCMeta):
@@ -34,33 +33,3 @@ class EquationInterface(metaclass=ABCMeta):
         :return: The computed residual of the equation.
         :rtype: LabelTensor
         """
-
-    def to(self, device):
-        """
-        Move all tensor attributes to the specified device.
-
-        :param torch.device device: The target device to move the tensors to.
-        :return: The instance moved to the specified device.
-        :rtype: EquationInterface
-        """
-        # Iterate over all attributes of the Equation
-        for key, val in self.__dict__.items():
-
-            # Move tensors in dictionaries to the specified device
-            if isinstance(val, dict):
-                self.__dict__[key] = {
-                    k: v.to(device) if torch.is_tensor(v) else v
-                    for k, v in val.items()
-                }
-
-            # Move tensors in lists to the specified device
-            elif isinstance(val, list):
-                self.__dict__[key] = [
-                    v.to(device) if torch.is_tensor(v) else v for v in val
-                ]
-
-            # Move tensor attributes to the specified device
-            elif torch.is_tensor(val):
-                self.__dict__[key] = val.to(device)
-
-        return self

pina/equation/system_equation.py

@@ -101,10 +101,6 @@ class SystemEquation(EquationInterface):
         :return: The aggregated residuals of the system of equations.
         :rtype: LabelTensor
         """
-        # Move the equation to the input_ device
-        self.to(input_.device)
-
-        # Compute the residual for each equation
         residual = torch.hstack(
             [
                 equation.residual(input_, output_, params_)
@@ -112,7 +108,6 @@ class SystemEquation(EquationInterface):
             ]
         )
 
-        # Skip reduction if not specified
         if self.reduction is None:
             return residual
 

pina/model/block/message_passing/en_equivariant_network_block.py

@@ -41,7 +41,7 @@ class EnEquivariantNetworkBlock(MessagePassing):
         DOI: `<https://doi.org/10.48550/arXiv.2102.09844>`_.
     """
 
-    def __init__(
+    def __init__(  # pylint: disable=R0913, R0917
         self,
         node_feature_dim,
         edge_feature_dim,
@@ -143,7 +143,9 @@ class EnEquivariantNetworkBlock(MessagePassing):
                 func=activation,
             )
 
-    def forward(self, x, pos, edge_index, edge_attr=None, vel=None):
+    def forward(
+        self, x, pos, edge_index, edge_attr=None, vel=None
+    ):  # pylint: disable=R0917
         """
         Forward pass of the block, triggering the message-passing routine.
 
@@ -169,7 +171,9 @@ class EnEquivariantNetworkBlock(MessagePassing):
             edge_index=edge_index, x=x, pos=pos, edge_attr=edge_attr, vel=vel
         )
 
-    def message(self, x_i, x_j, pos_i, pos_j, edge_attr):
+    def message(
+        self, x_i, x_j, pos_i, pos_j, edge_attr
+    ):  # pylint: disable=R0917
         """
         Compute the message to be passed between nodes and edges.
 
@@ -234,7 +238,9 @@ class EnEquivariantNetworkBlock(MessagePassing):
 
         return agg_message, agg_m_ij
 
-    def update(self, aggregated_inputs, x, pos, edge_index, vel):
+    def update(
+        self, aggregated_inputs, x, pos, edge_index, vel
+    ):  # pylint: disable=R0917
         """
         Update node features, positions, and optionally velocities.
 

pina/model/block/message_passing/equivariant_graph_neural_operator_block.py

@@ -23,7 +23,7 @@ class EquivariantGraphNeuralOperatorBlock(torch.nn.Module):
         <https://arxiv.org/abs/2401.11037>`_
     """
 
-    def __init__(
+    def __init__(  # pylint: disable=R0913, R0917
         self,
         node_feature_dim,
         edge_feature_dim,
@@ -101,7 +101,9 @@ class EquivariantGraphNeuralOperatorBlock(torch.nn.Module):
             flow=flow,
         )
 
-    def forward(self, x, pos, vel, edge_index, edge_attr=None):
+    def forward(  # pylint: disable=R0917
+        self, x, pos, vel, edge_index, edge_attr=None
+    ):
         """
         Forward pass of the Equivariant Graph Neural Operator block.
 
@@ -182,7 +184,11 @@ class EquivariantGraphNeuralOperatorBlock(torch.nn.Module):
         weights = torch.complex(real[..., :modes], img[..., :modes])
 
         # Convolution in Fourier space
-        fourier = torch.fft.rfftn(x, dim=[0])[:modes]
+        # torch.fft.rfftn and irfftn are callable functions, but pylint
+        # incorrectly flags them as E1102 (not callable).
+        fourier = torch.fft.rfftn(x, dim=[0])[:modes]  # pylint: disable=E1102
         out = torch.einsum(einsum_idx, fourier, weights)
 
-        return torch.fft.irfftn(out, s=x.shape[0], dim=0)
+        return torch.fft.irfftn(  # pylint: disable=E1102
+            out, s=x.shape[0], dim=0
+        )

pina/model/equivariant_graph_neural_operator.py

@@ -5,7 +5,9 @@ from ..utils import check_positive_integer
 from .block.message_passing import EquivariantGraphNeuralOperatorBlock
 
 
-class EquivariantGraphNeuralOperator(torch.nn.Module):
+# Disable pylint warnings for too few public methods (since this is a simple
+# model class in a standard PyTorch style)
+class EquivariantGraphNeuralOperator(torch.nn.Module):  # pylint: disable=R0903
     """
     Equivariant Graph Neural Operator (EGNO) for modeling 3D dynamics.
 
@@ -32,7 +34,9 @@ class EquivariantGraphNeuralOperator(torch.nn.Module):
         <https://arxiv.org/abs/2401.11037>`_
     """
 
-    def __init__(
+    # Disable pylint warnings for too many arguments in init (since this is a
+    # model class with many configurable parameters)
+    def __init__(  # pylint: disable=R0913, R0917, R0914
         self,
         n_egno_layers,
         node_feature_dim,

pina/problem/zoo/helmholtz.py

@@ -48,10 +48,11 @@ class HelmholtzProblem(SpatialProblem):
         :type alpha: float | int
         """
         super().__init__()
-        check_consistency(alpha, (int, float))
-        self.alpha = alpha
 
-        def forcing_term(input_):
+        self.alpha = alpha
+        check_consistency(alpha, (int, float))
+
+        def forcing_term(self, input_):
             """
             Implementation of the forcing term.
             """

pina/solver/physics_informed_solver/pinn_interface.py

@@ -71,7 +71,9 @@ class PINNInterface(SupervisedSolverInterface, metaclass=ABCMeta):
         """
         # Override the compilation, compiling only for torch < 2.8, see
         # related issue at https://github.com/mathLab/PINA/issues/621
-        if torch.__version__ >= "2.8":
+        if torch.__version__ < "2.8":
+            self.trainer.compile = True
+        else:
             self.trainer.compile = False
             warnings.warn(
                 "Compilation is disabled for torch >= 2.8. "

pina/solver/solver.py

@@ -174,7 +174,11 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
         :return: The result of the parent class ``setup`` method.
         :rtype: Any
         """
-        if self.trainer.compile and not self._is_compiled():
+        if stage == "fit" and self.trainer.compile:
+            self._setup_compile()
+        if stage == "test" and (
+            self.trainer.compile and not self._is_compiled()
+        ):
             self._setup_compile()
         return super().setup(stage)
 

pina/trainer.py

@@ -1,17 +1,12 @@
 """Module for the Trainer."""
 
 import sys
-import warnings
 import torch
 import lightning
-from .utils import check_consistency, custom_warning_format
+from .utils import check_consistency
 from .data import PinaDataModule
 from .solver import SolverInterface, PINNInterface
 
-# set the warning for compile options
-warnings.formatwarning = custom_warning_format
-warnings.filterwarnings("always", category=UserWarning)
-
 
 class Trainer(lightning.pytorch.Trainer):
     """
@@ -31,7 +26,7 @@ class Trainer(lightning.pytorch.Trainer):
         test_size=0.0,
         val_size=0.0,
         compile=None,
-        batching_mode="common_batch_size",
+        repeat=None,
         automatic_batching=None,
         num_workers=None,
         pin_memory=None,
@@ -54,14 +49,11 @@ class Trainer(lightning.pytorch.Trainer):
         :param float val_size: The percentage of elements to include in the
             validation dataset. Default is ``0.0``.
         :param bool compile: If ``True``, the model is compiled before training.
-            Default is ``False``. For Windows users, it is always disabled. Not
-            supported for python version greater or equal than 3.14.
-        :param bool common_batch_size: If ``True``, the same batch size is used
-            for all conditions. If ``False``, each condition can have its own
-            batch size, proportional to the size of the dataset in that
-            condition. Default is ``True``.
-        :param bool separate_conditions: If ``True``, dataloaders for each
-            condition are iterated separately. Default is ``False``.
+            Default is ``False``. For Windows users, it is always disabled.
+        :param bool repeat: Whether to repeat the dataset data in each
+            condition during training. For further details, see the
+            :class:`~pina.data.data_module.PinaDataModule` class. Default is
+            ``False``.
         :param bool automatic_batching: If ``True``, automatic PyTorch batching
             is performed, otherwise the items are retrieved from the dataset
             all at once. For further details, see the
@@ -84,7 +76,7 @@ class Trainer(lightning.pytorch.Trainer):
             train_size=train_size,
             test_size=test_size,
             val_size=val_size,
-            batching_mode=batching_mode,
+            repeat=repeat,
             automatic_batching=automatic_batching,
             compile=compile,
         )
@@ -112,17 +104,10 @@ class Trainer(lightning.pytorch.Trainer):
         super().__init__(**kwargs)
 
         # checking compilation and automatic batching
-        # compilation disabled for Windows and for Python 3.14+
-        if (
-            compile is None
-            or sys.platform == "win32"
-            or sys.version_info >= (3, 14)
-        ):
+        if compile is None or sys.platform == "win32":
             compile = False
-            warnings.warn(
-                "Compilation is disabled for Python 3.14+ and for Windows.",
-                UserWarning,
-            )
+
+        repeat = repeat if repeat is not None else False
 
         automatic_batching = (
             automatic_batching if automatic_batching is not None else False
@@ -139,7 +124,7 @@ class Trainer(lightning.pytorch.Trainer):
             test_size=test_size,
             val_size=val_size,
             batch_size=batch_size,
-            batching_mode=batching_mode,
+            repeat=repeat,
             automatic_batching=automatic_batching,
             pin_memory=pin_memory,
             num_workers=num_workers,
@@ -177,7 +162,7 @@ class Trainer(lightning.pytorch.Trainer):
         test_size,
         val_size,
         batch_size,
-        batching_mode,
+        repeat,
         automatic_batching,
         pin_memory,
         num_workers,
@@ -196,10 +181,8 @@ class Trainer(lightning.pytorch.Trainer):
         :param float val_size: The percentage of elements to include in the
             validation dataset.
         :param int batch_size: The number of samples per batch to load.
-        :param bool common_batch_size: Whether to use the same batch size for
-            all conditions.
-        :param bool seperate_conditions: Whether to iterate dataloaders for
-            each condition separately.
+        :param bool repeat: Whether to repeat the dataset data in each
+            condition during training.
         :param bool automatic_batching: Whether to perform automatic batching
             with PyTorch.
         :param bool pin_memory: Whether to use pinned memory for faster data
@@ -229,7 +212,7 @@ class Trainer(lightning.pytorch.Trainer):
             test_size=test_size,
             val_size=val_size,
             batch_size=batch_size,
-            batching_mode=batching_mode,
+            repeat=repeat,
             automatic_batching=automatic_batching,
             num_workers=num_workers,
             pin_memory=pin_memory,
@@ -281,7 +264,7 @@ class Trainer(lightning.pytorch.Trainer):
         train_size,
         test_size,
         val_size,
-        batching_mode,
+        repeat,
         automatic_batching,
         compile,
     ):
@@ -295,10 +278,8 @@ class Trainer(lightning.pytorch.Trainer):
             test dataset.
         :param float val_size: The percentage of elements to include in the
             validation dataset.
-        :param bool common_batch_size: Whether to use the same batch size for
-            all conditions.
-        :param bool seperate_conditions: Whether to iterate dataloaders for
-            each condition separately.
+        :param bool repeat: Whether to repeat the dataset data in each
+            condition during training.
         :param bool automatic_batching: Whether to perform automatic batching
             with PyTorch.
         :param bool compile: If ``True``, the model is compiled before training.
@@ -308,7 +289,8 @@ class Trainer(lightning.pytorch.Trainer):
         check_consistency(train_size, float)
         check_consistency(test_size, float)
         check_consistency(val_size, float)
-        check_consistency(batching_mode, str)
+        if repeat is not None:
+            check_consistency(repeat, bool)
         if automatic_batching is not None:
             check_consistency(automatic_batching, bool)
         if compile is not None:
@@ -343,23 +325,3 @@ class Trainer(lightning.pytorch.Trainer):
         if batch_size is not None:
             check_consistency(batch_size, int)
         return pin_memory, num_workers, shuffle, batch_size
-
-    @property
-    def compile(self):
-        """
-        Whether compilation is required or not.
-
-        :return: ``True`` if compilation is required, ``False`` otherwise.
-        :rtype: bool
-        """
-        return self._compile
-
-    @compile.setter
-    def compile(self, value):
-        """
-        Setting the value of compile.
-
-        :param bool value: Whether compilation is required or not.
-        """
-        check_consistency(value, bool)
-        self._compile = value

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "pina-mathlab"
-version = "0.2.5"
+version = "0.2.3"
 description = "Physic Informed Neural networks for Advance modeling."
 readme = "README.md"
 authors = [
@@ -19,7 +19,7 @@ dependencies = [
     "torch_geometric",
     "matplotlib",
 ]
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 
 [project.optional-dependencies]
 doc = [

tests/test_callback/test_adaptive_refinement_callback.py

@@ -51,7 +51,7 @@ def test_sample(condition_to_update):
     }
     trainer.train()
     after_n_points = {
-        loc: len(trainer.data_module.train_dataset[loc].input)
+        loc: len(trainer.data_module.train_dataset.input[loc])
         for loc in condition_to_update
     }
     assert before_n_points == trainer.callbacks[0].initial_population_size

tests/test_callback/test_normalizer_data_callback.py

@@ -142,10 +142,14 @@ def test_setup(solver, fn, stage, apply_to):
 
     for cond in ["data1", "data2"]:
         scale = scale_fn(
-            trainer_copy.data_module.train_dataset[cond].data[apply_to]
+            trainer_copy.data_module.train_dataset.conditions_dict[cond][
+                apply_to
+            ]
         )
         shift = shift_fn(
-            trainer_copy.data_module.train_dataset[cond].data[apply_to]
+            trainer_copy.data_module.train_dataset.conditions_dict[cond][
+                apply_to
+            ]
         )
         assert "scale" in normalizer[cond]
         assert "shift" in normalizer[cond]
@@ -154,8 +158,8 @@ def test_setup(solver, fn, stage, apply_to):
         for ds_name in stage_map[stage]:
             dataset = getattr(trainer.data_module, ds_name, None)
             old_dataset = getattr(trainer_copy.data_module, ds_name, None)
-            current_points = dataset[cond].data[apply_to]
-            old_points = old_dataset[cond].data[apply_to]
+            current_points = dataset.conditions_dict[cond][apply_to]
+            old_points = old_dataset.conditions_dict[cond][apply_to]
             expected = (old_points - shift) / scale
             assert torch.allclose(current_points, expected)
 
@@ -200,10 +204,10 @@ def test_setup_pinn(fn, stage, apply_to):
     cond = "data"
 
     scale = scale_fn(
-        trainer_copy.data_module.train_dataset[cond].data[apply_to]
+        trainer_copy.data_module.train_dataset.conditions_dict[cond][apply_to]
     )
     shift = shift_fn(
-        trainer_copy.data_module.train_dataset[cond].data[apply_to]
+        trainer_copy.data_module.train_dataset.conditions_dict[cond][apply_to]
     )
     assert "scale" in normalizer[cond]
     assert "shift" in normalizer[cond]
@@ -212,8 +216,8 @@ def test_setup_pinn(fn, stage, apply_to):
     for ds_name in stage_map[stage]:
         dataset = getattr(trainer.data_module, ds_name, None)
         old_dataset = getattr(trainer_copy.data_module, ds_name, None)
-        current_points = dataset[cond].data[apply_to]
-        old_points = old_dataset[cond].data[apply_to]
+        current_points = dataset.conditions_dict[cond][apply_to]
+        old_points = old_dataset.conditions_dict[cond][apply_to]
         expected = (old_points - shift) / scale
         assert torch.allclose(current_points, expected)
 
@@ -238,7 +242,3 @@ def test_setup_graph_dataset():
     )
     with pytest.raises(NotImplementedError):
         trainer.train()
-
-
-# if __name__ == "__main__":
-#     test_setup(supervised_solver_lt, [torch.std, torch.mean], "all", "input")

tests/test_data/test_data_module.py

@@ -1,11 +1,10 @@
 import torch
 import pytest
 from pina.data import PinaDataModule
-from pina.data.dataset import PinaDataset
+from pina.data.dataset import PinaTensorDataset, PinaGraphDataset
 from pina.problem.zoo import SupervisedProblem
 from pina.graph import RadiusGraph
-
-from pina.data.dataloader import DummyDataloader, PinaDataLoader
+from pina.data.data_module import DummyDataloader
 from pina import Trainer
 from pina.solver import SupervisedSolver
 from torch_geometric.data import Batch
@@ -45,33 +44,22 @@ def test_setup_train(input_, output_, train_size, val_size, test_size):
     )
     dm.setup()
     assert hasattr(dm, "train_dataset")
-    assert isinstance(dm.train_dataset, dict)
-    assert all(
-        isinstance(dm.train_dataset[cond], PinaDataset)
-        for cond in dm.train_dataset
-    )
-    assert all(
-        dm.train_dataset[cond].is_graph_dataset == isinstance(input_, list)
-        for cond in dm.train_dataset
-    )
-    assert all(
-        len(dm.train_dataset[cond]) == int(len(input_) * train_size)
-        for cond in dm.train_dataset
-    )
+    if isinstance(input_, torch.Tensor):
+        assert isinstance(dm.train_dataset, PinaTensorDataset)
+    else:
+        assert isinstance(dm.train_dataset, PinaGraphDataset)
+    # assert len(dm.train_dataset) == int(len(input_) * train_size)
     if test_size > 0:
         assert hasattr(dm, "test_dataset")
         assert dm.test_dataset is None
     else:
         assert not hasattr(dm, "test_dataset")
     assert hasattr(dm, "val_dataset")
-
-    assert isinstance(dm.val_dataset, dict)
-    assert all(
-        isinstance(dm.val_dataset[cond], PinaDataset) for cond in dm.val_dataset
-    )
-    assert all(
-        isinstance(dm.val_dataset[cond], PinaDataset) for cond in dm.val_dataset
-    )
+    if isinstance(input_, torch.Tensor):
+        assert isinstance(dm.val_dataset, PinaTensorDataset)
+    else:
+        assert isinstance(dm.val_dataset, PinaGraphDataset)
+    # assert len(dm.val_dataset) == int(len(input_) * val_size)
 
 
 @pytest.mark.parametrize(
@@ -99,59 +87,49 @@ def test_setup_test(input_, output_, train_size, val_size, test_size):
         assert not hasattr(dm, "val_dataset")
 
     assert hasattr(dm, "test_dataset")
-    assert all(
-        isinstance(dm.test_dataset[cond], PinaDataset)
-        for cond in dm.test_dataset
-    )
-    assert all(
-        dm.test_dataset[cond].is_graph_dataset == isinstance(input_, list)
-        for cond in dm.test_dataset
-    )
-    assert all(
-        len(dm.test_dataset[cond]) == int(len(input_) * test_size)
-        for cond in dm.test_dataset
+    if isinstance(input_, torch.Tensor):
+        assert isinstance(dm.test_dataset, PinaTensorDataset)
+    else:
+        assert isinstance(dm.test_dataset, PinaGraphDataset)
+    # assert len(dm.test_dataset) == int(len(input_) * test_size)
+
+
+@pytest.mark.parametrize(
+    "input_, output_",
+    [(input_tensor, output_tensor), (input_graph, output_graph)],
+)
+def test_dummy_dataloader(input_, output_):
+    problem = SupervisedProblem(input_=input_, output_=output_)
+    solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10))
+    trainer = Trainer(
+        solver, batch_size=None, train_size=0.7, val_size=0.3, test_size=0.0
     )
+    dm = trainer.data_module
+    dm.setup()
+    dm.trainer = trainer
+    dataloader = dm.train_dataloader()
+    assert isinstance(dataloader, DummyDataloader)
+    assert len(dataloader) == 1
+    data = next(dataloader)
+    assert isinstance(data, list)
+    assert isinstance(data[0], tuple)
+    if isinstance(input_, list):
+        assert isinstance(data[0][1]["input"], Batch)
+    else:
+        assert isinstance(data[0][1]["input"], torch.Tensor)
+    assert isinstance(data[0][1]["target"], torch.Tensor)
 
-
-# @pytest.mark.parametrize(
-#     "input_, output_",
-#     [(input_tensor, output_tensor), (input_graph, output_graph)],
-# )
-# def test_dummy_dataloader(input_, output_):
-#     problem = SupervisedProblem(input_=input_, output_=output_)
-#     solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10))
-#     trainer = Trainer(
-#         solver, batch_size=None, train_size=0.7, val_size=0.3, test_size=0.0
-#     )
-#     dm = trainer.data_module
-#     dm.setup()
-#     dm.trainer = trainer
-#     dataloader = dm.train_dataloader()
-#     assert isinstance(dataloader, PinaDataLoader)
-#     print(dataloader.dataloaders)
-#     assert all([isinstance(ds, DummyDataloader) for ds in dataloader.dataloaders.values()])
-
-#     data = next(iter(dataloader))
-#     assert isinstance(data, list)
-#     assert isinstance(data[0], tuple)
-#     if isinstance(input_, list):
-#         assert isinstance(data[0][1]["input"], Batch)
-#     else:
-#         assert isinstance(data[0][1]["input"], torch.Tensor)
-#     assert isinstance(data[0][1]["target"], torch.Tensor)
-
-
-# dataloader = dm.val_dataloader()
-# assert isinstance(dataloader, DummyDataloader)
-# assert len(dataloader) == 1
-# data = next(dataloader)
-# assert isinstance(data, list)
-# assert isinstance(data[0], tuple)
-# if isinstance(input_, list):
-#     assert isinstance(data[0][1]["input"], Batch)
-# else:
-#     assert isinstance(data[0][1]["input"], torch.Tensor)
-# assert isinstance(data[0][1]["target"], torch.Tensor)
+    dataloader = dm.val_dataloader()
+    assert isinstance(dataloader, DummyDataloader)
+    assert len(dataloader) == 1
+    data = next(dataloader)
+    assert isinstance(data, list)
+    assert isinstance(data[0], tuple)
+    if isinstance(input_, list):
+        assert isinstance(data[0][1]["input"], Batch)
+    else:
+        assert isinstance(data[0][1]["input"], torch.Tensor)
+    assert isinstance(data[0][1]["target"], torch.Tensor)
 
 
 @pytest.mark.parametrize(
@@ -159,11 +137,7 @@ def test_setup_test(input_, output_, train_size, val_size, test_size):
     [(input_tensor, output_tensor), (input_graph, output_graph)],
 )
 @pytest.mark.parametrize("automatic_batching", [True, False])
-@pytest.mark.parametrize("batch_size", [None, 10])
-@pytest.mark.parametrize("batching_mode", ["common_batch_size", "propotional"])
-def test_dataloader(
-    input_, output_, automatic_batching, batch_size, batching_mode
-):
+def test_dataloader(input_, output_, automatic_batching):
     problem = SupervisedProblem(input_=input_, output_=output_)
     solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10))
     trainer = Trainer(
@@ -173,13 +147,12 @@ def test_dataloader(
         val_size=0.3,
         test_size=0.0,
         automatic_batching=automatic_batching,
-        batching_mode=batching_mode,
     )
     dm = trainer.data_module
     dm.setup()
     dm.trainer = trainer
     dataloader = dm.train_dataloader()
-    assert isinstance(dataloader, PinaDataLoader)
+    assert isinstance(dataloader, DataLoader)
     assert len(dataloader) == 7
     data = next(iter(dataloader))
     assert isinstance(data, dict)
@@ -190,8 +163,8 @@ def test_dataloader(
     assert isinstance(data["data"]["target"], torch.Tensor)
 
     dataloader = dm.val_dataloader()
-    assert isinstance(dataloader, PinaDataLoader)
-    assert len(dataloader) == 3 if batch_size is not None else 1
+    assert isinstance(dataloader, DataLoader)
+    assert len(dataloader) == 3
     data = next(iter(dataloader))
     assert isinstance(data, dict)
     if isinstance(input_, list):
@@ -229,13 +202,12 @@ def test_dataloader_labels(input_, output_, automatic_batching):
         val_size=0.3,
         test_size=0.0,
         automatic_batching=automatic_batching,
-        # common_batch_size=True,
     )
     dm = trainer.data_module
     dm.setup()
     dm.trainer = trainer
     dataloader = dm.train_dataloader()
-    assert isinstance(dataloader, PinaDataLoader)
+    assert isinstance(dataloader, DataLoader)
     assert len(dataloader) == 7
     data = next(iter(dataloader))
     assert isinstance(data, dict)
@@ -251,7 +223,7 @@ def test_dataloader_labels(input_, output_, automatic_batching):
     assert data["data"]["target"].labels == ["u", "v", "w"]
 
     dataloader = dm.val_dataloader()
-    assert isinstance(dataloader, PinaDataLoader)
+    assert isinstance(dataloader, DataLoader)
     assert len(dataloader) == 3
     data = next(iter(dataloader))
     assert isinstance(data, dict)
@@ -268,6 +240,39 @@ def test_dataloader_labels(input_, output_, automatic_batching):
     assert data["data"]["target"].labels == ["u", "v", "w"]
 
 
+def test_get_all_data():
+    input = torch.stack([torch.zeros((1,)) + i for i in range(1000)])
+    target = input
+
+    problem = SupervisedProblem(input, target)
+    datamodule = PinaDataModule(
+        problem,
+        train_size=0.7,
+        test_size=0.2,
+        val_size=0.1,
+        batch_size=64,
+        shuffle=False,
+        repeat=False,
+        automatic_batching=None,
+        num_workers=0,
+        pin_memory=False,
+    )
+    datamodule.setup("fit")
+    datamodule.setup("test")
+    assert len(datamodule.train_dataset.get_all_data()["data"]["input"]) == 700
+    assert torch.isclose(
+        datamodule.train_dataset.get_all_data()["data"]["input"], input[:700]
+    ).all()
+    assert len(datamodule.val_dataset.get_all_data()["data"]["input"]) == 100
+    assert torch.isclose(
+        datamodule.val_dataset.get_all_data()["data"]["input"], input[900:]
+    ).all()
+    assert len(datamodule.test_dataset.get_all_data()["data"]["input"]) == 200
+    assert torch.isclose(
+        datamodule.test_dataset.get_all_data()["data"]["input"], input[700:900]
+    ).all()
+
+
 def test_input_propery_tensor():
     input = torch.stack([torch.zeros((1,)) + i for i in range(1000)])
     target = input
@@ -280,6 +285,7 @@ def test_input_propery_tensor():
         val_size=0.1,
         batch_size=64,
         shuffle=False,
+        repeat=False,
         automatic_batching=None,
         num_workers=0,
         pin_memory=False,
@@ -305,6 +311,7 @@ def test_input_propery_graph():
         val_size=0.1,
         batch_size=64,
         shuffle=False,
+        repeat=False,
         automatic_batching=None,
         num_workers=0,
         pin_memory=False,

tests/test_data/test_graph_dataset.py

@@ -1,138 +1,138 @@
-# import torch
-# import pytest
-# from pina.data.dataset import PinaDatasetFactory, PinaGraphDataset
-# from pina.graph import KNNGraph
-# from torch_geometric.data import Data
+import torch
+import pytest
+from pina.data.dataset import PinaDatasetFactory, PinaGraphDataset
+from pina.graph import KNNGraph
+from torch_geometric.data import Data
 
-# x = torch.rand((100, 20, 10))
-# pos = torch.rand((100, 20, 2))
-# input_ = [
-#     KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True)
-#     for x_, pos_ in zip(x, pos)
-# ]
-# output_ = torch.rand((100, 20, 10))
+x = torch.rand((100, 20, 10))
+pos = torch.rand((100, 20, 2))
+input_ = [
+    KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True)
+    for x_, pos_ in zip(x, pos)
+]
+output_ = torch.rand((100, 20, 10))
 
-# x_2 = torch.rand((50, 20, 10))
-# pos_2 = torch.rand((50, 20, 2))
-# input_2_ = [
-#     KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True)
-#     for x_, pos_ in zip(x_2, pos_2)
-# ]
-# output_2_ = torch.rand((50, 20, 10))
+x_2 = torch.rand((50, 20, 10))
+pos_2 = torch.rand((50, 20, 2))
+input_2_ = [
+    KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True)
+    for x_, pos_ in zip(x_2, pos_2)
+]
+output_2_ = torch.rand((50, 20, 10))
 
 
-# # Problem with a single condition
-# conditions_dict_single = {
-#     "data": {
-#         "input": input_,
-#         "target": output_,
-#     }
-# }
-# max_conditions_lengths_single = {"data": 100}
+# Problem with a single condition
+conditions_dict_single = {
+    "data": {
+        "input": input_,
+        "target": output_,
+    }
+}
+max_conditions_lengths_single = {"data": 100}
 
-# # Problem with multiple conditions
-# conditions_dict_multi = {
-#     "data_1": {
-#         "input": input_,
-#         "target": output_,
-#     },
-#     "data_2": {
-#         "input": input_2_,
-#         "target": output_2_,
-#     },
-# }
+# Problem with multiple conditions
+conditions_dict_multi = {
+    "data_1": {
+        "input": input_,
+        "target": output_,
+    },
+    "data_2": {
+        "input": input_2_,
+        "target": output_2_,
+    },
+}
 
-# max_conditions_lengths_multi = {"data_1": 100, "data_2": 50}
+max_conditions_lengths_multi = {"data_1": 100, "data_2": 50}
 
 
-# @pytest.mark.parametrize(
-#     "conditions_dict, max_conditions_lengths",
-#     [
-#         (conditions_dict_single, max_conditions_lengths_single),
-#         (conditions_dict_multi, max_conditions_lengths_multi),
-#     ],
-# )
-# def test_constructor(conditions_dict, max_conditions_lengths):
-#     dataset = PinaDatasetFactory(
-#         conditions_dict,
-#         max_conditions_lengths=max_conditions_lengths,
-#         automatic_batching=True,
-#     )
-#     assert isinstance(dataset, PinaGraphDataset)
-#     assert len(dataset) == 100
+@pytest.mark.parametrize(
+    "conditions_dict, max_conditions_lengths",
+    [
+        (conditions_dict_single, max_conditions_lengths_single),
+        (conditions_dict_multi, max_conditions_lengths_multi),
+    ],
+)
+def test_constructor(conditions_dict, max_conditions_lengths):
+    dataset = PinaDatasetFactory(
+        conditions_dict,
+        max_conditions_lengths=max_conditions_lengths,
+        automatic_batching=True,
+    )
+    assert isinstance(dataset, PinaGraphDataset)
+    assert len(dataset) == 100
 
 
-# @pytest.mark.parametrize(
-#     "conditions_dict, max_conditions_lengths",
-#     [
-#         (conditions_dict_single, max_conditions_lengths_single),
-#         (conditions_dict_multi, max_conditions_lengths_multi),
-#     ],
-# )
-# def test_getitem(conditions_dict, max_conditions_lengths):
-#     dataset = PinaDatasetFactory(
-#         conditions_dict,
-#         max_conditions_lengths=max_conditions_lengths,
-#         automatic_batching=True,
-#     )
-#     data = dataset[50]
-#     assert isinstance(data, dict)
-#     assert all([isinstance(d["input"], Data) for d in data.values()])
-#     assert all([isinstance(d["target"], torch.Tensor) for d in data.values()])
-#     assert all(
-#         [d["input"].x.shape == torch.Size((20, 10)) for d in data.values()]
-#     )
-#     assert all(
-#         [d["target"].shape == torch.Size((20, 10)) for d in data.values()]
-#     )
-#     assert all(
-#         [
-#             d["input"].edge_index.shape == torch.Size((2, 60))
-#             for d in data.values()
-#         ]
-#     )
-#     assert all([d["input"].edge_attr.shape[0] == 60 for d in data.values()])
+@pytest.mark.parametrize(
+    "conditions_dict, max_conditions_lengths",
+    [
+        (conditions_dict_single, max_conditions_lengths_single),
+        (conditions_dict_multi, max_conditions_lengths_multi),
+    ],
+)
+def test_getitem(conditions_dict, max_conditions_lengths):
+    dataset = PinaDatasetFactory(
+        conditions_dict,
+        max_conditions_lengths=max_conditions_lengths,
+        automatic_batching=True,
+    )
+    data = dataset[50]
+    assert isinstance(data, dict)
+    assert all([isinstance(d["input"], Data) for d in data.values()])
+    assert all([isinstance(d["target"], torch.Tensor) for d in data.values()])
+    assert all(
+        [d["input"].x.shape == torch.Size((20, 10)) for d in data.values()]
+    )
+    assert all(
+        [d["target"].shape == torch.Size((20, 10)) for d in data.values()]
+    )
+    assert all(
+        [
+            d["input"].edge_index.shape == torch.Size((2, 60))
+            for d in data.values()
+        ]
+    )
+    assert all([d["input"].edge_attr.shape[0] == 60 for d in data.values()])
 
-#     data = dataset.fetch_from_idx_list([i for i in range(20)])
-#     assert isinstance(data, dict)
-#     assert all([isinstance(d["input"], Data) for d in data.values()])
-#     assert all([isinstance(d["target"], torch.Tensor) for d in data.values()])
-#     assert all(
-#         [d["input"].x.shape == torch.Size((400, 10)) for d in data.values()]
-#     )
-#     assert all(
-#         [d["target"].shape == torch.Size((20, 20, 10)) for d in data.values()]
-#     )
-#     assert all(
-#         [
-#             d["input"].edge_index.shape == torch.Size((2, 1200))
-#             for d in data.values()
-#         ]
-#     )
-#     assert all([d["input"].edge_attr.shape[0] == 1200 for d in data.values()])
+    data = dataset.fetch_from_idx_list([i for i in range(20)])
+    assert isinstance(data, dict)
+    assert all([isinstance(d["input"], Data) for d in data.values()])
+    assert all([isinstance(d["target"], torch.Tensor) for d in data.values()])
+    assert all(
+        [d["input"].x.shape == torch.Size((400, 10)) for d in data.values()]
+    )
+    assert all(
+        [d["target"].shape == torch.Size((20, 20, 10)) for d in data.values()]
+    )
+    assert all(
+        [
+            d["input"].edge_index.shape == torch.Size((2, 1200))
+            for d in data.values()
+        ]
+    )
+    assert all([d["input"].edge_attr.shape[0] == 1200 for d in data.values()])
 
 
-# def test_input_single_condition():
-#     dataset = PinaDatasetFactory(
-#         conditions_dict_single,
-#         max_conditions_lengths=max_conditions_lengths_single,
-#         automatic_batching=True,
-#     )
-#     input_ = dataset.input
-#     assert isinstance(input_, dict)
-#     assert isinstance(input_["data"], list)
-#     assert all([isinstance(d, Data) for d in input_["data"]])
+def test_input_single_condition():
+    dataset = PinaDatasetFactory(
+        conditions_dict_single,
+        max_conditions_lengths=max_conditions_lengths_single,
+        automatic_batching=True,
+    )
+    input_ = dataset.input
+    assert isinstance(input_, dict)
+    assert isinstance(input_["data"], list)
+    assert all([isinstance(d, Data) for d in input_["data"]])
 
 
-# def test_input_multi_condition():
-#     dataset = PinaDatasetFactory(
-#         conditions_dict_multi,
-#         max_conditions_lengths=max_conditions_lengths_multi,
-#         automatic_batching=True,
-#     )
-#     input_ = dataset.input
-#     assert isinstance(input_, dict)
-#     assert isinstance(input_["data_1"], list)
-#     assert all([isinstance(d, Data) for d in input_["data_1"]])
-#     assert isinstance(input_["data_2"], list)
-#     assert all([isinstance(d, Data) for d in input_["data_2"]])
+def test_input_multi_condition():
+    dataset = PinaDatasetFactory(
+        conditions_dict_multi,
+        max_conditions_lengths=max_conditions_lengths_multi,
+        automatic_batching=True,
+    )
+    input_ = dataset.input
+    assert isinstance(input_, dict)
+    assert isinstance(input_["data_1"], list)
+    assert all([isinstance(d, Data) for d in input_["data_1"]])
+    assert isinstance(input_["data_2"], list)
+    assert all([isinstance(d, Data) for d in input_["data_2"]])

tests/test_data/test_tensor_dataset.py

@@ -1,86 +1,86 @@
-# import torch
-# import pytest
-# from pina.data.dataset import PinaDatasetFactory, PinaTensorDataset
+import torch
+import pytest
+from pina.data.dataset import PinaDatasetFactory, PinaTensorDataset
 
-# input_tensor = torch.rand((100, 10))
-# output_tensor = torch.rand((100, 2))
+input_tensor = torch.rand((100, 10))
+output_tensor = torch.rand((100, 2))
 
-# input_tensor_2 = torch.rand((50, 10))
-# output_tensor_2 = torch.rand((50, 2))
+input_tensor_2 = torch.rand((50, 10))
+output_tensor_2 = torch.rand((50, 2))
 
-# conditions_dict_single = {
-#     "data": {
-#         "input": input_tensor,
-#         "target": output_tensor,
-#     }
-# }
+conditions_dict_single = {
+    "data": {
+        "input": input_tensor,
+        "target": output_tensor,
+    }
+}
 
-# conditions_dict_single_multi = {
-#     "data_1": {
-#         "input": input_tensor,
-#         "target": output_tensor,
-#     },
-#     "data_2": {
-#         "input": input_tensor_2,
-#         "target": output_tensor_2,
-#     },
-# }
+conditions_dict_single_multi = {
+    "data_1": {
+        "input": input_tensor,
+        "target": output_tensor,
+    },
+    "data_2": {
+        "input": input_tensor_2,
+        "target": output_tensor_2,
+    },
+}
 
-# max_conditions_lengths_single = {"data": 100}
+max_conditions_lengths_single = {"data": 100}
 
-# max_conditions_lengths_multi = {"data_1": 100, "data_2": 50}
+max_conditions_lengths_multi = {"data_1": 100, "data_2": 50}
 
 
-# @pytest.mark.parametrize(
-#     "conditions_dict, max_conditions_lengths",
-#     [
-#         (conditions_dict_single, max_conditions_lengths_single),
-#         (conditions_dict_single_multi, max_conditions_lengths_multi),
-#     ],
-# )
-# def test_constructor_tensor(conditions_dict, max_conditions_lengths):
-#     dataset = PinaDatasetFactory(
-#         conditions_dict,
-#         max_conditions_lengths=max_conditions_lengths,
-#         automatic_batching=True,
-#     )
-#     assert isinstance(dataset, PinaTensorDataset)
+@pytest.mark.parametrize(
+    "conditions_dict, max_conditions_lengths",
+    [
+        (conditions_dict_single, max_conditions_lengths_single),
+        (conditions_dict_single_multi, max_conditions_lengths_multi),
+    ],
+)
+def test_constructor_tensor(conditions_dict, max_conditions_lengths):
+    dataset = PinaDatasetFactory(
+        conditions_dict,
+        max_conditions_lengths=max_conditions_lengths,
+        automatic_batching=True,
+    )
+    assert isinstance(dataset, PinaTensorDataset)
 
 
-# def test_getitem_single():
-#     dataset = PinaDatasetFactory(
-#         conditions_dict_single,
-#         max_conditions_lengths=max_conditions_lengths_single,
-#         automatic_batching=False,
-#     )
+def test_getitem_single():
+    dataset = PinaDatasetFactory(
+        conditions_dict_single,
+        max_conditions_lengths=max_conditions_lengths_single,
+        automatic_batching=False,
+    )
 
-#     tensors = dataset.fetch_from_idx_list([i for i in range(70)])
-#     assert isinstance(tensors, dict)
-#     assert list(tensors.keys()) == ["data"]
-#     assert sorted(list(tensors["data"].keys())) == ["input", "target"]
-#     assert isinstance(tensors["data"]["input"], torch.Tensor)
-#     assert tensors["data"]["input"].shape == torch.Size((70, 10))
-#     assert isinstance(tensors["data"]["target"], torch.Tensor)
-#     assert tensors["data"]["target"].shape == torch.Size((70, 2))
+    tensors = dataset.fetch_from_idx_list([i for i in range(70)])
+    assert isinstance(tensors, dict)
+    assert list(tensors.keys()) == ["data"]
+    assert sorted(list(tensors["data"].keys())) == ["input", "target"]
+    assert isinstance(tensors["data"]["input"], torch.Tensor)
+    assert tensors["data"]["input"].shape == torch.Size((70, 10))
+    assert isinstance(tensors["data"]["target"], torch.Tensor)
+    assert tensors["data"]["target"].shape == torch.Size((70, 2))
 
 
-# def test_getitem_multi():
-#     dataset = PinaDatasetFactory(
-#         conditions_dict_single_multi,
-#         max_conditions_lengths=max_conditions_lengths_multi,
-#         automatic_batching=False,
-#     )
-#     tensors = dataset.fetch_from_idx_list([i for i in range(70)])
-#     assert isinstance(tensors, dict)
-#     assert list(tensors.keys()) == ["data_1", "data_2"]
-#     assert sorted(list(tensors["data_1"].keys())) == ["input", "target"]
-#     assert isinstance(tensors["data_1"]["input"], torch.Tensor)
-#     assert tensors["data_1"]["input"].shape == torch.Size((70, 10))
-#     assert isinstance(tensors["data_1"]["target"], torch.Tensor)
-#     assert tensors["data_1"]["target"].shape == torch.Size((70, 2))
+def test_getitem_multi():
+    dataset = PinaDatasetFactory(
+        conditions_dict_single_multi,
+        max_conditions_lengths=max_conditions_lengths_multi,
+        automatic_batching=False,
+    )
+    tensors = dataset.fetch_from_idx_list([i for i in range(70)])
+    assert isinstance(tensors, dict)
+    assert list(tensors.keys()) == ["data_1", "data_2"]
+    assert sorted(list(tensors["data_1"].keys())) == ["input", "target"]
+    assert isinstance(tensors["data_1"]["input"], torch.Tensor)
+    assert tensors["data_1"]["input"].shape == torch.Size((70, 10))
+    assert isinstance(tensors["data_1"]["target"], torch.Tensor)
+    assert tensors["data_1"]["target"].shape == torch.Size((70, 2))
 
-#     assert sorted(list(tensors["data_2"].keys())) == ["input", "target"]
-#     assert isinstance(tensors["data_2"]["input"], torch.Tensor)
-#     assert tensors["data_2"]["input"].shape == torch.Size((50, 10))
-#     assert isinstance(tensors["data_2"]["target"], torch.Tensor)
-#     assert tensors["data_2"]["target"].shape == torch.Size((50, 2))
+    assert sorted(list(tensors["data_2"].keys())) == ["input", "target"]
+    assert isinstance(tensors["data_2"]["input"], torch.Tensor)
+    assert tensors["data_2"]["input"].shape == torch.Size((50, 10))
+    assert isinstance(tensors["data_2"]["target"], torch.Tensor)
+    assert tensors["data_2"]["target"].shape == torch.Size((50, 2))

tests/test_equation/test_equation_factory.py

@@ -104,7 +104,7 @@ def test_advection_equation(c):
 
     # Should fail if c is a list and its length != spatial dimension
     with pytest.raises(ValueError):
-        equation = Advection([1, 2, 3])
+        Advection([1, 2, 3])
         residual = equation.residual(pts, u)
 
 

tests/test_solver/test_supervised_solver.py

@@ -117,10 +117,6 @@ def test_solver_train(use_lt, batch_size, compile):
         assert isinstance(solver.model, OptimizedModule)
 
 
-if __name__ == "__main__":
-    test_solver_train(use_lt=True, batch_size=20, compile=True)
-
-
 @pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
 @pytest.mark.parametrize("use_lt", [True, False])
 def test_solver_train_graph(batch_size, use_lt):