spaCy/spacy/tests/serialize/test_serialize_config.py
2020-02-28 11:57:41 +01:00

139 lines
3.6 KiB
Python

from thinc.api import Config
import spacy
from spacy import util
from spacy.lang.en import English
from spacy.util import registry
from ..util import make_tempdir
from ...ml.models import build_Tok2Vec_model, build_tb_parser_model
nlp_config_string = """
[nlp]
lang = "en"
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 342
depth = 4
window_size = 1
embed_size = 2000
maxout_pieces = 3
subword_features = true
[nlp.pipeline.tagger]
factory = "tagger"
[nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
"""
parser_config_string = """
[model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 99
hidden_width = 66
maxout_pieces = 2
[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 333
depth = 4
embed_size = 5555
window_size = 1
maxout_pieces = 7
subword_features = false
"""
@registry.architectures.register("my_test_parser")
def my_parser():
tok2vec = build_Tok2Vec_model(
width=321,
embed_size=5432,
pretrained_vectors=None,
window_size=3,
maxout_pieces=4,
subword_features=True,
char_embed=True,
nM=64,
nC=8,
conv_depth=2,
bilstm_depth=0,
)
parser = build_tb_parser_model(
tok2vec=tok2vec, nr_feature_tokens=7, hidden_width=65, maxout_pieces=5
)
return parser
def test_serialize_nlp():
""" Create a custom nlp pipeline from config and ensure it serializes it correctly """
nlp_config = Config().from_str(nlp_config_string)
nlp = util.load_model_from_config(nlp_config["nlp"])
nlp.begin_training()
assert "tok2vec" in nlp.pipe_names
assert "tagger" in nlp.pipe_names
assert "parser" not in nlp.pipe_names
assert nlp.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
assert "tok2vec" in nlp2.pipe_names
assert "tagger" in nlp2.pipe_names
assert "parser" not in nlp2.pipe_names
assert nlp2.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
def test_serialize_custom_nlp():
""" Create a custom nlp pipeline and ensure it serializes it correctly"""
nlp = English()
parser_cfg = dict()
parser_cfg["model"] = {"@architectures": "my_test_parser"}
parser = nlp.create_pipe("parser", parser_cfg)
nlp.add_pipe(parser)
nlp.begin_training()
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
model = nlp2.get_pipe("parser").model
tok2vec = model.get_ref("tok2vec")
upper = model.upper
# check that we have the correct settings, not the default ones
assert tok2vec.get_dim("nO") == 321
assert upper.get_dim("nI") == 65
def test_serialize_parser():
""" Create a non-default parser config to check nlp serializes it correctly """
nlp = English()
model_config = Config().from_str(parser_config_string)
parser = nlp.create_pipe("parser", config=model_config)
parser.add_label("nsubj")
nlp.add_pipe(parser)
nlp.begin_training()
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
model = nlp2.get_pipe("parser").model
tok2vec = model.get_ref("tok2vec")
upper = model.upper
# check that we have the correct settings, not the default ones
assert upper.get_dim("nI") == 66
assert tok2vec.get_dim("nO") == 333