[ allennlp.data.batch ]
Batch represents a collection of
Instance s to be fed
through a model.
class Batch(Iterable): | def __init__(self, instances: Iterable[Instance]) -> None
A batch of Instances. In addition to containing the instances themselves, it contains helper functions for converting the data into tensors.
A Batch just takes an iterable of instances in its constructor and hangs onto them in a list.
class Batch(Iterable): | ... | def get_padding_lengths(self) -> Dict[str, Dict[str, int]]
Gets the maximum padding lengths from all
Instances in this batch. Each
Fields, and each
Field could have multiple things that need padding.
We look at all fields in all instances, and find the max values for each (field_name,
padding_key) pair, returning them in a dictionary.
This can then be used to convert this batch into arrays of consistent length, or to set model parameters, etc.
class Batch(Iterable): | ... | def as_tensor_dict( | self, | padding_lengths: Dict[str, Dict[str, int]] = None, | verbose: bool = False | ) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]]
This method converts this
Batch into a set of pytorch Tensors that can be passed
through a model. In order for the tensors to be valid tensors, all
Instances in this
batch need to be padded to the same lengths wherever padding is necessary, so we do that
first, then we combine all of the tensors for each field in each instance into a set of
batched tensors for each field.
Dict[str, Dict[str, int]]
If a key is present in this dictionary with a non-
Nonevalue, we will pad to that length instead of the length calculated from the data. This lets you, e.g., set a maximum value for sentence length if you want to throw out long sequences.
Entries in this dictionary are keyed first by field name (e.g., "question"), then by padding key (e.g., "num_tokens").
bool, optional (default =
Should we output logging information when we're doing this padding? If the batch is large, this is nice to have, because padding a large batch could take a long time. But if you're doing this inside of a data generator, having all of this output per batch is a bit obnoxious (and really slow).
- tensors :
A dictionary of tensors, keyed by field name, suitable for passing as input to a model. This is a
batchof instances, so, e.g., if the instances have a "question" field and an "answer" field, the "question" fields for all of the instances will be grouped together into a single tensor, and the "answer" fields for all instances will be similarly grouped in a parallel set of tensors, for batched computation. Additionally, for complex
Fields, the value of the dictionary key is not necessarily a single tensor. For example, with the
TextField, the output is a dictionary mapping
TokenIndexerkeys to tensors. The number of elements in this sub-dictionary therefore corresponds to the number of
TokenIndexersused to index the
Fieldclass is responsible for batching its own output.
class Batch(Iterable): | ... | def index_instances(self, vocab: Vocabulary) -> None
class Batch(Iterable): | ... | def print_statistics(self) -> None
Make sure if has been indexed first