Add docstring for repeat in DataModule

Author: FilippoOlivo

pina/data/data_module.py

@@ -283,10 +283,20 @@ def __init__(
             Default is ``None``.
         :param bool shuffle: Whether to shuffle the dataset before splitting.
             Default ``True``.
-        :param bool repeat: Whether to repeat the dataset indefinitely.
-            Default ``False``.
-        :param automatic_batching: Whether to enable automatic batching.
-            Default ``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
+            when the dataset is too large to fit into memory. On the other hand,
+            if ``False``, the items are retrieved from the dataset all at once
+            avoind the overhead of collating them into a batch and reducing the
+            __getitem__ calls to the dataset. This is useful when the dataset
+            fits into memory. Avoid using automatic batching when ``batch_size``
+            is large. Default is ``False``.
         :param int num_workers: Number of worker threads for data loading.
             Default ``0`` (serial loading).
         :param bool pin_memory: Whether to use pinned memory for faster data

pina/trainer.py

@@ -26,6 +26,7 @@ def __init__(
         test_size=0.0,
         val_size=0.0,
         compile=None,
+        repeat=False,
         automatic_batching=None,
         num_workers=None,
         pin_memory=None,
@@ -49,9 +50,13 @@ def __init__(
             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.
+        :param bool repeat: Whether to repeat the dataset data in each
+            condition during training. For further details, see the
+            :class:`~pina.data.PinaDataModule` class. Default is ``False``.
         :param bool automatic_batching: If ``True``, automatic PyTorch batching
-            is performed. Avoid using automatic batching when ``batch_size`` is
-            large. Default is ``False``.
+            is performed, otherwise the items are retrieved from the dataset
+            all at once. For further details, see the
+            :class:`~pina.data.PinaDataModule` class. Default is ``False``.
         :param int num_workers: The number of worker threads for data loading.
             Default is ``0`` (serial loading).
         :param bool pin_memory: Whether to use pinned memory for faster data
@@ -65,12 +70,13 @@ def __init__(
         """
         # check consistency for init types
         self._check_input_consistency(
-            solver,
-            train_size,
-            test_size,
-            val_size,
-            automatic_batching,
-            compile,
+            solver=solver,
+            train_size=train_size,
+            test_size=test_size,
+            val_size=val_size,
+            repeat=repeat,
+            automatic_batching=automatic_batching,
+            compile=compile,
         )
         pin_memory, num_workers, shuffle, batch_size = (
             self._check_consistency_and_set_defaults(
@@ -110,14 +116,15 @@ def __init__(
         self._move_to_device()
         self.data_module = None
         self._create_datamodule(
-            train_size,
-            test_size,
-            val_size,
-            batch_size,
-            automatic_batching,
-            pin_memory,
-            num_workers,
-            shuffle,
+            train_size=train_size,
+            test_size=test_size,
+            val_size=val_size,
+            batch_size=batch_size,
+            repeat=repeat,
+            automatic_batching=automatic_batching,
+            pin_memory=pin_memory,
+            num_workers=num_workers,
+            shuffle=shuffle,
         )
 
         # logging
@@ -151,6 +158,7 @@ def _create_datamodule(
         test_size,
         val_size,
         batch_size,
+        repeat,
         automatic_batching,
         pin_memory,
         num_workers,
@@ -169,6 +177,8 @@ def _create_datamodule(
         :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 repeat: Whether to repeat the dataset data in each
+            condition during training.
         :param bool automatic_batching: Whether to perform automatic batching
             with PyTorch. If ``True``, automatic PyTorch batching
             is performed, which consists of extracting one element at a time
@@ -206,6 +216,7 @@ def _create_datamodule(
             test_size=test_size,
             val_size=val_size,
             batch_size=batch_size,
+            repeat=repeat,
             automatic_batching=automatic_batching,
             num_workers=num_workers,
             pin_memory=pin_memory,
@@ -253,7 +264,13 @@ def solver(self, solver):
 
     @staticmethod
     def _check_input_consistency(
-        solver, train_size, test_size, val_size, automatic_batching, compile
+        solver,
+        train_size,
+        test_size,
+        val_size,
+        repeat,
+        automatic_batching,
+        compile,
     ):
         """
         Verifies the consistency of the parameters for the solver configuration.
@@ -265,6 +282,8 @@ def _check_input_consistency(
             test dataset.
         :param float val_size: The percentage of elements to include in the
             validation dataset.
+        :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.
@@ -274,6 +293,7 @@ def _check_input_consistency(
         check_consistency(train_size, float)
         check_consistency(test_size, float)
         check_consistency(val_size, float)
+        check_consistency(repeat, bool)
         if automatic_batching is not None:
             check_consistency(automatic_batching, bool)
         if compile is not None: