syna.utils.dataloaders module

Data loader utilities.

Provides simple iterable data loaders used by training loops: a batched DataLoader and a sequence-aware SeqDataLoader.

class syna.utils.dataloaders.DataLoader(dataset: Iterable[Tuple[Any, Any]], batch_size: int, shuffle: bool = True)

Bases: object

Simple iterable data loader.

Args:

dataset: Sequence of (input, target) pairs. batch_size: Number of samples per batch. shuffle: If True, shuffle dataset order at the start of each epoch.

Behavior:

• Iterates over dataset in batches. The last batch may be smaller.

• Iteration raises StopIteration at epoch end and automatically resets for the next epoch.

next()

reset() → None

Reset iteration counters and prepare indices for the next epoch.

class syna.utils.dataloaders.SeqDataLoader(dataset: Iterable[Tuple[Any, Any]], batch_size: int)

Bases: DataLoader

Sequence-aware loader that yields batch_size sequences in parallel.

The loader divides the dataset into batch_size streams by computing a ‘jump’ = data_size // batch_size and, for each iteration step k, takes the elements at positions (i*jump + k) % data_size for i in range(batch_size).

Args:

dataset: Sequence of (input, target) pairs. batch_size: Number of parallel sequences (streams) per batch.

Behavior:

• shuffle is always disabled for sequence loader.

• Iteration yields exactly data_size // jump steps (i.e., max_iter inherited).