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