classy.data.dataset.base
Functions
batchify_matrices
Classes
BaseDataset
An iterable Dataset.
All datasets that represent an iterable of data samples should subclass it. Such form of datasets is particularly useful when data come from a stream.
All subclasses should overwrite :meth:__iter__, which would return an iterator of samples in this dataset.
When a subclass is used with :class:~torch.utils.data.DataLoader, each item in the dataset will be yielded from the :class:~torch.utils.data.DataLoaderiterator. When :attr:num_workers > 0, each worker process will have a different copy of the dataset object, so it is often desired to configure each copy independently to avoid having duplicate data returned from the workers. :func:~torch.utils.data.get_worker_info, when called in a worker process, returns information about the worker. It can be used in either the dataset's :meth:__iter__ method or the :class:~torch.utils.data.DataLoader 's :attr:worker_init_fn option to modify each copy's behavior.
Example 1: splitting workload across all workers in :meth:__iter__::
>>> class MyIterableDataset(torch.utils.data.IterableDataset): ... def __init__(self, start, end): ... super(MyIterableDataset).__init__() ... assert end > start, "this example code only works with end >= start" ... self.start = start ... self.end = end ... ... def __iter__(self): ... worker_info = torch.utils.data.get_worker_info() ... if worker_info is None: # single-process data loading, return the full iterator ... iter_start = self.start ... iter_end = self.end ... else: # in a worker process ... # split workload ... per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers))) ... worker_id = worker_info.id ... iter_start = self.start + worker_id * per_worker ... iter_end = min(iter_start + per_worker, self.end) ... return iter(range(iter_start, iter_end)) ... >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6]. >>> ds = MyIterableDataset(start=3, end=7)>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]
>>> # Mult-process loading with two worker processes
>>> # Worker 0 fetched [3, 4]. Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 5, 4, 6]
>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20)))
[3, 4, 5, 6]
Example 2: splitting workload across all workers using :attr:worker_init_fn::
>>> class MyIterableDataset(torch.utils.data.IterableDataset): ... def __init__(self, start, end): ... super(MyIterableDataset).__init__() ... assert end > start, "this example code only works with end >= start" ... self.start = start ... self.end = end ... ... def __iter__(self): ... return iter(range(self.start, self.end)) ... >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6]. >>> ds = MyIterableDataset(start=3, end=7)>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]
>>>
>>> # Directly doing multi-process loading yields duplicate data
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 3, 4, 4, 5, 5, 6, 6]
>>> # Define a <code>worker_init_fn</code> that configures each dataset copy differently
>>> def worker_init_fn(worker_id):
... worker_info = torch.utils.data.get_worker_info()
... dataset = worker_info.dataset # the dataset copy in this worker process
... overall_start = dataset.start
... overall_end = dataset.end
... # configure the dataset to only process the split workload
... per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
... worker_id = worker_info.id
... dataset.start = overall_start + worker_id * per_worker
... dataset.end = min(dataset.start + per_worker, overall_end)
...
>>> # Mult-process loading with the custom <code>worker_init_fn</code>
>>> # Worker 0 fetched [3, 4]. Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
[3, 5, 4, 6]
>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20, worker_init_fn=worker_init_fn)))
[3, 4, 5, 6]
Subclasses (1)
__init__
samples_iterator: Callable[[], Iterator[ClassySample]],
vocabulary: Vocabulary,
for_inference: bool,
batch_size: Optional[int] = None,
tokens_per_batch: Optional[int] = None,
max_batch_size: Optional[int] = None,
batching_fields: Optional[List[str]] = None,
section_size: Optional[int] = None,
prebatch: bool = False,
materialize: bool = False,
drop_last: bool = False,
min_length: int = -1,
max_length: int = -1,
)
materialize_batches
self,
dataset_elements: List[Dict[str, Any]],
) ‑> Generator[Dict[str, Any], None, None]
adapt_dataset_from
training_dataset: omegaconf.dictconfig.DictConfig,
setting: str,
)
from_file
path: Union[str, Dict[str, DataDriver]],
data_driver: Optional[DataDriver] = None,
vocabulary: Vocabulary = None,
**kwargs,
) ‑> BaseDataset