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86 lines
3.8 KiB
Python
86 lines
3.8 KiB
Python
import pytest
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from spacy.ml.models.tok2vec import build_Tok2Vec_model
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from spacy.ml.models.tok2vec import MultiHashEmbed, MaxoutWindowEncoder
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from spacy.vocab import Vocab
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from spacy.tokens import Doc
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from .util import get_batch
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def test_empty_doc():
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width = 128
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embed_size = 2000
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vocab = Vocab()
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doc = Doc(vocab, words=[])
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tok2vec = build_Tok2Vec_model(
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MultiHashEmbed(
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width=width,
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rows=embed_size,
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also_use_static_vectors=False,
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also_embed_subwords=True
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),
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MaxoutWindowEncoder(
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width=width,
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depth=4,
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window_size=1,
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maxout_pieces=3
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)
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)
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tok2vec.initialize()
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vectors, backprop = tok2vec.begin_update([doc])
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assert len(vectors) == 1
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assert vectors[0].shape == (0, width)
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@pytest.mark.parametrize(
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"batch_size,width,embed_size", [[1, 128, 2000], [2, 128, 2000], [3, 8, 63]]
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)
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def test_tok2vec_batch_sizes(batch_size, width, embed_size):
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batch = get_batch(batch_size)
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tok2vec = build_Tok2Vec_model(
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MultiHashEmbed(
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width=width,
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rows=embed_size,
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also_use_static_vectors=False,
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also_embed_subwords=True
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),
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MaxoutWindowEncoder(
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width=width,
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depth=4,
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window_size=1,
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maxout_pieces=3,
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)
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)
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tok2vec.initialize()
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vectors, backprop = tok2vec.begin_update(batch)
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assert len(vectors) == len(batch)
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for doc_vec, doc in zip(vectors, batch):
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assert doc_vec.shape == (len(doc), width)
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# fmt: off
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@pytest.mark.xfail(reason="TODO: Update for new signature")
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@pytest.mark.parametrize(
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"tok2vec_config",
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[
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{"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},
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{"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},
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{"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},
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{"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},
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{"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},
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{"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},
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{"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},
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{"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},
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],
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)
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# fmt: on
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def test_tok2vec_configs(tok2vec_config):
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docs = get_batch(3)
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tok2vec = build_Tok2Vec_model_from_old_args(**tok2vec_config)
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tok2vec.initialize(docs)
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vectors, backprop = tok2vec.begin_update(docs)
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assert len(vectors) == len(docs)
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assert vectors[0].shape == (len(docs[0]), tok2vec_config["width"])
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backprop(vectors)
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