mirror of
https://github.com/explosion/spaCy.git
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0de1f8bf73
* avoid nesting then flattening
* mypy fix
* Apply suggestions from code review
* Add type for indices
* Run full matrix for mypy
* Add back modified type: ignore
* Revert "Run full matrix for mypy"
This reverts commit e218873d04.
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Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
from typing import List, Tuple, Callable
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from thinc.api import Model, to_numpy
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from thinc.types import Ragged, Ints1d
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from ..util import registry
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@registry.layers("spacy.extract_spans.v1")
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def extract_spans() -> Model[Tuple[Ragged, Ragged], Ragged]:
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"""Extract spans from a sequence of source arrays, as specified by an array
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of (start, end) indices. The output is a ragged array of the
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extracted spans.
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"""
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return Model(
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"extract_spans", forward, layers=[], refs={}, attrs={}, dims={}, init=init
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)
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def init(model, X=None, Y=None):
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pass
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def forward(
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model: Model, source_spans: Tuple[Ragged, Ragged], is_train: bool
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) -> Tuple[Ragged, Callable]:
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"""Get subsequences from source vectors."""
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ops = model.ops
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X, spans = source_spans
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assert spans.dataXd.ndim == 2
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indices = _get_span_indices(ops, spans, X.lengths)
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if len(indices) > 0:
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Y = Ragged(X.dataXd[indices], spans.dataXd[:, 1] - spans.dataXd[:, 0]) # type: ignore[arg-type, index]
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else:
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Y = Ragged(
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ops.xp.zeros(X.dataXd.shape, dtype=X.dataXd.dtype),
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ops.xp.zeros((len(X.lengths),), dtype="i"),
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)
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x_shape = X.dataXd.shape
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x_lengths = X.lengths
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def backprop_windows(dY: Ragged) -> Tuple[Ragged, Ragged]:
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dX = Ragged(ops.alloc2f(*x_shape), x_lengths)
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ops.scatter_add(dX.dataXd, indices, dY.dataXd) # type: ignore[arg-type]
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return (dX, spans)
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return Y, backprop_windows
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def _get_span_indices(ops, spans: Ragged, lengths: Ints1d) -> Ints1d:
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"""Construct a flat array that has the indices we want to extract from the
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source data. For instance, if we want the spans (5, 9), (8, 10) the
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indices will be [5, 6, 7, 8, 8, 9].
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"""
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spans, lengths = _ensure_cpu(spans, lengths)
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indices: List[int] = []
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offset = 0
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for i, length in enumerate(lengths):
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spans_i = spans[i].dataXd + offset
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for j in range(spans_i.shape[0]):
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indices.extend(range(spans_i[j, 0], spans_i[j, 1])) # type: ignore[arg-type, call-overload]
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offset += length
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return ops.asarray1i(indices)
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def _ensure_cpu(spans: Ragged, lengths: Ints1d) -> Tuple[Ragged, Ints1d]:
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return Ragged(to_numpy(spans.dataXd), to_numpy(spans.lengths)), to_numpy(lengths)
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