Support resuming a model during spacy train
@@ -53,7 +53,6 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
if dev_path and not dev_path.exists():
prints(dev_path, title="Development data not found", exits=1)
- lang_class = util.get_lang_class(lang)
pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
@@ -71,22 +70,22 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
util.env_opt('batch_to', 64),
util.env_opt('batch_compound', 1.001))
- if resume:
- prints(output_path / 'model9.pickle', title="Resuming training")
- nlp = dill.load((output_path / 'model9.pickle').open('rb'))
- else:
+ if not resume:
+ lang_class = util.get_lang_class(lang)
nlp = lang_class(pipeline=pipeline)
+ else:
+ print("Load resume")
+ nlp = _resume_model(lang, pipeline)
+ lang_class = nlp.__class__
+
corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
n_train_words = corpus.count_train()
-
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
nlp._optimizer = None
print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
try:
for i in range(n_iter):
- if resume:
- i += 20
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
gold_preproc=gold_preproc, max_length=0)
@@ -120,6 +119,17 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
dill.dump(nlp, file_, -1)
+def _resume_model(lang, pipeline):
+ nlp = util.load_model(lang)
+ pipes = {getattr(pipe, 'name', None) for pipe in nlp.pipeline}
+ for name in pipeline:
+ if name not in pipes:
+ factory = nlp.Defaults.factories[name]
+ nlp.pipeline.extend(factory(nlp))
+ nlp.meta['pipeline'] = pipeline
+ return nlp
+
+
def _render_parses(i, to_render):
to_render[0].user_data['title'] = "Batch %d" % i
with Path('/tmp/entities.html').open('w') as file_:
