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.
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.
- padding_lengths :
Dict[str, int]This dictionary will have the same keys that were produced in
get_padding_lengths. The values specify the lengths to use when padding each relevant dimension, aggregated across all instances in a batch.
ListField can pad the number of fields in a list (e.g., the number of answer
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
as_tensor, so you don't need to worry about
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
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
This is always called after :func: