Bug fix and add additional tests for Dataset and DataModule (#517)

pina/data/data_module.py

@@ -217,12 +217,11 @@ class PinaSampler:
     parameter and the environment in which the code is running.
     """
 
-    def __new__(cls, dataset, shuffle):
+    def __new__(cls, dataset):
         """
         Instantiate and initialize the sampler.
 
         :param PinaDataset dataset: The dataset from which to sample.
-        :param bool shuffle: Whether to shuffle the dataset.
         :return: The sampler instance.
         :rtype: :class:`torch.utils.data.Sampler`
         """
@@ -231,12 +230,9 @@ class PinaSampler:
             torch.distributed.is_available()
             and torch.distributed.is_initialized()
         ):
-            sampler = DistributedSampler(dataset, shuffle=shuffle)
+            sampler = DistributedSampler(dataset)
         else:
-            if shuffle:
+            sampler = SequentialSampler(dataset)
-                sampler = RandomSampler(dataset)
-            else:
-                sampler = SequentialSampler(dataset)
         return sampler
 
 
@@ -496,8 +492,6 @@ class PinaDataModule(LightningDataModule):
         :return: The dataloader for the given split.
         :rtype: torch.utils.data.DataLoader
         """
-
-        shuffle = self.shuffle if split == "train" else False
         # Suppress the warning about num_workers.
         # In many cases, especially for PINNs,
         # serial data loading can outperform parallel data loading.
@@ -511,7 +505,7 @@ class PinaDataModule(LightningDataModule):
         )
         # Use custom batching (good if batch size is large)
         if self.batch_size is not None:
-            sampler = PinaSampler(dataset, shuffle)
+            sampler = PinaSampler(dataset)
             if self.automatic_batching:
                 collate = Collator(
                     self.find_max_conditions_lengths(split),

pina/data/dataset.py

@@ -167,9 +167,15 @@ class PinaDataset(Dataset, ABC):
         :return: A dictionary containing all the data in the dataset.
         :rtype: dict
         """
-
+        to_return_dict = {}
-        index = list(range(len(self)))
+        for condition, data in self.conditions_dict.items():
-        return self.fetch_from_idx_list(index)
+            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):
         """
@@ -306,3 +312,13 @@ class PinaGraphDataset(PinaDataset):
             )
             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()}

tests/test_data/test_data_module.py

@@ -238,3 +238,94 @@ def test_dataloader_labels(input_, output_, automatic_batching):
         assert data["data"]["input"].labels == ["u", "v", "w"]
     assert isinstance(data["data"]["target"], torch.Tensor)
     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
+
+    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")
+    input_ = datamodule.input
+    assert isinstance(input_, dict)
+    assert isinstance(input_["train"], dict)
+    assert isinstance(input_["val"], dict)
+    assert isinstance(input_["test"], dict)
+    assert torch.isclose(input_["train"]["data"], input[:700]).all()
+    assert torch.isclose(input_["val"]["data"], input[900:]).all()
+    assert torch.isclose(input_["test"]["data"], input[700:900]).all()
+
+
+def test_input_propery_graph():
+    problem = SupervisedProblem(input_graph, output_graph)
+    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")
+    input_ = datamodule.input
+    assert isinstance(input_, dict)
+    assert isinstance(input_["train"], dict)
+    assert isinstance(input_["val"], dict)
+    assert isinstance(input_["test"], dict)
+    assert isinstance(input_["train"]["data"], list)
+    assert isinstance(input_["val"]["data"], list)
+    assert isinstance(input_["test"]["data"], list)
+    assert len(input_["train"]["data"]) == 70
+    assert len(input_["val"]["data"]) == 10
+    assert len(input_["test"]["data"]) == 20

tests/test_data/test_graph_dataset.py

@@ -31,7 +31,7 @@ conditions_dict_single = {
 max_conditions_lengths_single = {"data": 100}
 
 # Problem with multiple conditions
-conditions_dict_single_multi = {
+conditions_dict_multi = {
     "data_1": {
         "input": input_,
         "target": output_,
@@ -49,7 +49,7 @@ max_conditions_lengths_multi = {"data_1": 100, "data_2": 50}
     "conditions_dict, max_conditions_lengths",
     [
         (conditions_dict_single, max_conditions_lengths_single),
-        (conditions_dict_single_multi, max_conditions_lengths_multi),
+        (conditions_dict_multi, max_conditions_lengths_multi),
     ],
 )
 def test_constructor(conditions_dict, max_conditions_lengths):
@@ -66,7 +66,7 @@ def test_constructor(conditions_dict, max_conditions_lengths):
     "conditions_dict, max_conditions_lengths",
     [
         (conditions_dict_single, max_conditions_lengths_single),
-        (conditions_dict_single_multi, max_conditions_lengths_multi),
+        (conditions_dict_multi, max_conditions_lengths_multi),
     ],
 )
 def test_getitem(conditions_dict, max_conditions_lengths):
@@ -110,3 +110,29 @@ def test_getitem(conditions_dict, max_conditions_lengths):
         ]
     )
     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_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"]])