Update tests

This commit is contained in:
Ines Montani 2021-01-29 19:38:09 +11:00
parent 325f47500d
commit bc089b693c

View File

@ -6,12 +6,13 @@ from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
from spacy.vocab import Vocab
from spacy.tokens import Doc
from spacy.training import Example
from spacy.training.initialize import init_nlp
from spacy import util
from spacy.lang.en import English
from thinc.api import Config
from numpy.testing import assert_equal
from ..util import get_batch
from ..util import get_batch, make_tempdir
def test_empty_doc():
@ -55,17 +56,17 @@ def test_tok2vec_batch_sizes(batch_size, width, embed_size):
assert doc_vec.shape == (len(doc), width)
# fmt: off
@pytest.mark.parametrize(
"width,embed_arch,embed_config,encode_arch,encode_config",
# fmt: off
[
(8, MultiHashEmbed, {"rows": [100, 100], "attrs": ["SHAPE", "LOWER"], "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
(8, MultiHashEmbed, {"rows": [100, 20], "attrs": ["ORTH", "PREFIX"], "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}),
(8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8, "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}),
(8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2, "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 3}),
],
# fmt: on
)
# fmt: on
def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_config):
embed_config["width"] = width
encode_config["width"] = width
@ -196,8 +197,14 @@ def test_replace_listeners():
tagger = nlp.get_pipe("tagger")
assert isinstance(tagger.model.layers[0], Tok2VecListener)
assert tok2vec.listener_map["tagger"][0] == tagger.model.layers[0]
assert nlp.config["components"]["tok2vec"]["model"]["@architectures"] == "spacy.Tok2Vec.v2"
assert nlp.config["components"]["tagger"]["model"]["tok2vec"]["@architectures"] == "spacy.Tok2VecListener.v1"
assert (
nlp.config["components"]["tok2vec"]["model"]["@architectures"]
== "spacy.Tok2Vec.v2"
)
assert (
nlp.config["components"]["tagger"]["model"]["tok2vec"]["@architectures"]
== "spacy.Tok2VecListener.v1"
)
nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec"])
assert not isinstance(tagger.model.layers[0], Tok2VecListener)
t2v_cfg = nlp.config["components"]["tok2vec"]["model"]
@ -211,3 +218,96 @@ def test_replace_listeners():
nlp.replace_listeners("tok2vec", "tagger", ["model.yolo"])
with pytest.raises(ValueError):
nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec", "model.yolo"])
cfg_string_multi = """
[nlp]
lang = "en"
pipeline = ["tok2vec","tagger", "ner"]
[components]
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v1"
nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v2"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode.width}
rows = [2000, 1000, 1000, 1000]
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 96
depth = 4
window_size = 1
maxout_pieces = 3
"""
def test_replace_listeners_from_config():
orig_config = Config().from_str(cfg_string_multi)
nlp = util.load_model_from_config(orig_config, auto_fill=True)
annots = {"tags": ["V", "Z"], "entities": [(0, 1, "A"), (1, 2, "B")]}
examples = [Example.from_dict(nlp.make_doc("x y"), annots)]
nlp.initialize(lambda: examples)
tok2vec = nlp.get_pipe("tok2vec")
tagger = nlp.get_pipe("tagger")
ner = nlp.get_pipe("ner")
assert tok2vec.listening_components == ["tagger", "ner"]
assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
assert any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
with make_tempdir() as dir_path:
nlp.to_disk(dir_path)
base_model = str(dir_path)
new_config = {
"nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]},
"components": {
"tok2vec": {"source": base_model},
"tagger": {
"source": base_model,
"replace_listeners": ["model.tok2vec"],
},
"ner": {"source": base_model},
},
}
new_nlp = util.load_model_from_config(new_config, auto_fill=True)
new_nlp.initialize(lambda: examples)
tok2vec = new_nlp.get_pipe("tok2vec")
tagger = new_nlp.get_pipe("tagger")
ner = new_nlp.get_pipe("ner")
assert tok2vec.listening_components == ["ner"]
assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
assert not any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
t2v_cfg = new_nlp.config["components"]["tok2vec"]["model"]
assert t2v_cfg["@architectures"] == "spacy.Tok2Vec.v2"
assert new_nlp.config["components"]["tagger"]["model"]["tok2vec"] == t2v_cfg
assert (
new_nlp.config["components"]["ner"]["model"]["tok2vec"]["@architectures"]
== "spacy.Tok2VecListener.v1"
)