def load(self, dirpath):
""" Loads a trained model from local disk, given the dirpath
Parameters
----------
dirpath : str
a directory where model artifacts are saved.
Returns
-------
self
"""
if not os.path.exists(dirpath):
raise ValueError("Model directory not found: {:s}".format(dirpath))
weights_file = os.path.join(dirpath, "weights.h5")
params_file = os.path.join(dirpath, "params.json")
preprocessor_file = os.path.join(dirpath, "preprocessor.pkl")
if not (os.path.exists(weights_file) or
os.path.exists(params_file) or
os.path.exists(preprocessor_file)):
raise ValueError("Model files may be corrupted, exiting")
self.model_ = load_model(weights_file, params_file)
self.preprocessor_ = IndexTransformer.load(preprocessor_file)
self.tagger_ = Tagger(self.model_, preprocessor=self.preprocessor_)
return self
def load(cls, weights_file, params_file, preprocessor_file):
self = cls()
self.p = IndexTransformer.load(preprocessor_file)
self.model = load_model(weights_file, params_file)
# Added by Sonvx on Jan 14, 2021: fix issue ("<tensor> is not an element of this graph." when loading model)
self.model._make_predict_function()
return self
def __init__(self, process_proper_nouns=False):
super().__init__(process_proper_nouns)
model = load_model(os.path.join(ELMO_TAGGER_PATH, 'weights.h5'),
os.path.join(ELMO_TAGGER_PATH, 'params.json'))
it = IndexTransformer.load(
os.path.join(ELMO_TAGGER_PATH, 'preprocessor.pkl'))
self.pos_tagger = Tagger(model,
preprocessor=it,
tokenizer=wordpunct_tokenize)
def main(args):
print('Loading objects...')
# model = BiLSTMCRF.load(args.weights_file, args.params_file)
model = load_model(args.weights_file, args.params_file)
it = IndexTransformer.load(args.preprocessor_file)
tagger = Tagger(model, preprocessor=it)
print('Tagging a sentence...')
res = tagger.analyze(args.sent)
pprint(res)
def setUpClass(cls):
weights_file = os.path.join(SAVE_ROOT, 'weights.h5')
params_file = os.path.join(SAVE_ROOT, 'params.json')
preprocessor_file = os.path.join(SAVE_ROOT, 'preprocessor.pickle')
# Load preprocessor
p = IndexTransformer.load(preprocessor_file)
# Load the model.
model = load_model(weights_file, params_file)
# Build a tagger
cls.tagger = anago.Tagger(model, preprocessor=p)
cls.sent = 'President Obama is speaking at the White House.'
def test_save_and_load(self):
char_vocab_size = 100
word_vocab_size = 10000
num_labels = 10
model = BiLSTMCRF(char_vocab_size=char_vocab_size,
word_vocab_size=word_vocab_size,
num_labels=num_labels)
model, loss = model.build()
self.assertFalse(os.path.exists(self.weights_file))
self.assertFalse(os.path.exists(self.params_file))
save_model(model, self.weights_file, self.params_file)
self.assertTrue(os.path.exists(self.weights_file))
self.assertTrue(os.path.exists(self.params_file))
model = load_model(self.weights_file, self.params_file)
def load(cls, weights_file, params_file, preprocessor_file):
self = cls()
self.p = IndexTransformer.load(preprocessor_file)
self.model = load_model(weights_file, params_file)
return self