allennlp.data.fields.array_field#

ArrayField#

ArrayField(
    self,
    array: numpy.ndarray,
    padding_value: int = 0,
    dtype: numpy.dtype = <class 'numpy.float32'>,
) -> None

A class representing an array, which could have arbitrary dimensions. A batch of these arrays are padded to the max dimension length in the batch for each dimension.

as_tensor#

ArrayField.as_tensor(self, padding_lengths:Dict[str, int]) -> torch.Tensor

Given a set of specified padding lengths, actually pad the data in this field and return a torch Tensor (or a more complex data structure) of the correct shape. We also take a couple of parameters that are important when constructing torch Tensors.

Parameters

  • padding_lengths : Dict[str, int] This dictionary will have the same keys that were produced in
  • :func:get_padding_lengths. The values specify the lengths to use when padding each relevant dimension, aggregated across all instances in a batch.

empty_field#

ArrayField.empty_field(self)

So that ListField can pad the number of fields in a list (e.g., the number of answer option TextFields), we need a representation of an empty field of each type. This returns that. This will only ever be called when we're to the point of calling :func:as_tensor, so you don't need to worry about get_padding_lengths, count_vocab_items, etc., being called on this empty field.

We make this an instance method instead of a static method so that if there is any state in the Field, we can copy it over (e.g., the token indexers in TextField).

get_padding_lengths#

ArrayField.get_padding_lengths(self) -> Dict[str, int]

If there are things in this field that need padding, note them here. In order to pad a batch of instance, we get all of the lengths from the batch, take the max, and pad everything to that length (or use a pre-specified maximum length). The return value is a dictionary mapping keys to lengths, like {'num_tokens': 13}.

This is always called after :func:index.