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Minor fix

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FilippoOlivo
2025-03-17 22:23:34 +01:00
parent b92f39aead
commit e90be726da
2 changed files with 17 additions and 27 deletions
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19
pina/data/data_module.py
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@@ -81,16 +81,9 @@ class Collator:
: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 to enable 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 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.
"""
@@ -294,9 +287,9 @@ class PinaDataModule(LightningDataModule):
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``.
``__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