spaCy/spacy/tests/test_tok2vec.py

73 lines
3.4 KiB
Python

import pytest
from spacy.ml.models.tok2vec import build_Tok2Vec_model
from spacy.vocab import Vocab
from spacy.tokens import Doc
from .util import get_batch
# This fails in Thinc v7.3.1. Need to push patch
@pytest.mark.xfail
def test_empty_doc():
width = 128
embed_size = 2000
vocab = Vocab()
doc = Doc(vocab, words=[])
# TODO: fix tok2vec arguments
tok2vec = build_Tok2Vec_model(width, embed_size, dropout=None)
vectors, backprop = tok2vec.begin_update([doc])
assert len(vectors) == 1
assert vectors[0].shape == (0, width)
@pytest.mark.parametrize(
"batch_size,width,embed_size", [[1, 128, 2000], [2, 128, 2000], [3, 8, 63]]
)
def test_tok2vec_batch_sizes(batch_size, width, embed_size):
batch = get_batch(batch_size)
tok2vec = build_Tok2Vec_model(
width,
embed_size,
pretrained_vectors=None,
conv_depth=4,
bilstm_depth=0,
window_size=1,
maxout_pieces=3,
subword_features=True,
char_embed=False,
nM=64,
nC=8,
dropout=None,
)
tok2vec.initialize()
vectors, backprop = tok2vec.begin_update(batch)
assert len(vectors) == len(batch)
for doc_vec, doc in zip(vectors, batch):
assert doc_vec.shape == (len(doc), width)
# fmt: off
@pytest.mark.parametrize(
"tok2vec_config",
[
{"width": 8, "embed_size": 100, "char_embed": False, "nM": 64, "nC": 8, "pretrained_vectors": None, "window_size": 1, "conv_depth": 2, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": True, "dropout": None},
{"width": 8, "embed_size": 100, "char_embed": True, "nM": 64, "nC": 8, "pretrained_vectors": None, "window_size": 1, "conv_depth": 2, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": True, "dropout": None},
{"width": 8, "embed_size": 100, "char_embed": False, "nM": 64, "nC": 8, "pretrained_vectors": None, "window_size": 1, "conv_depth": 6, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": True, "dropout": None},
{"width": 8, "embed_size": 100, "char_embed": False, "nM": 64, "nC": 8, "pretrained_vectors": None, "window_size": 1, "conv_depth": 6, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": True, "dropout": None},
{"width": 8, "embed_size": 100, "char_embed": False, "nM": 64, "nC": 8, "pretrained_vectors": None, "window_size": 1, "conv_depth": 2, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": False, "dropout": None},
{"width": 8, "embed_size": 100, "char_embed": False, "nM": 64, "nC": 8, "pretrained_vectors": None, "window_size": 3, "conv_depth": 2, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": False, "dropout": None},
{"width": 8, "embed_size": 100, "char_embed": True, "nM": 81, "nC": 8, "pretrained_vectors": None, "window_size": 3, "conv_depth": 2, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": False, "dropout": None},
{"width": 8, "embed_size": 100, "char_embed": True, "nM": 81, "nC": 9, "pretrained_vectors": None, "window_size": 3, "conv_depth": 2, "bilstm_depth": 0, "maxout_pieces": 3, "subword_features": False, "dropout": None},
],
)
# fmt: on
def test_tok2vec_configs(tok2vec_config):
docs = get_batch(3)
tok2vec = build_Tok2Vec_model(**tok2vec_config)
tok2vec.initialize(docs)
vectors, backprop = tok2vec.begin_update(docs)
assert len(vectors) == len(docs)
assert vectors[0].shape == (len(docs[0]), tok2vec_config["width"])
backprop(vectors)