def infer():
# Config Loader
test_args = ConfigSection()
ConfigLoader().load_config(cfgfile, {"POS_test": test_args})
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "label2id.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model
model = AdvSeqLabel(test_args)
try:
ModelLoader.load_pytorch(model, "./save/trained_model.pkl")
print('model loaded!')
except Exception as e:
print('cannot load model!')
raise
# Data Loader
infer_data = SeqLabelDataSet(load_func=BaseLoader.load_lines)
infer_data.load(data_infer_path,
vocabs={"word_vocab": word2index},
infer=True)
print('data loaded')
# Inference interface
infer = SeqLabelInfer(pickle_path)
results = infer.predict(model, infer_data)
print(results)
print("Inference finished!")
def infer():
# Load infer configuration, the same as test
test_args = ConfigSection()
ConfigLoader("config.cfg").load_config(config_dir,
{"POS_infer": test_args})
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "id2class.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model
model = SeqLabeling(test_args)
# Dump trained parameters into the model
ModelLoader.load_pytorch(model, os.path.join(pickle_path, model_name))
print("model loaded!")
# Data Loader
raw_data_loader = BaseLoader(data_infer_path)
infer_data = raw_data_loader.load_lines()
# Inference interface
infer = SeqLabelInfer(pickle_path)
results = infer.predict(model, infer_data)
for res in results:
print(res)
print("Inference finished!")
def infer():
# Load infer configuration, the same as test
test_args = ConfigSection()
ConfigLoader().load_config(config_path, {"POS_infer": test_args})
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "label2id.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model
model = SeqLabeling(test_args)
# Dump trained parameters into the model
ModelLoader.load_pytorch(model, "./save/saved_model.pkl")
print("model loaded!")
# Load infer data
infer_data = SeqLabelDataSet(load_func=BaseLoader.load)
infer_data.load(data_infer_path, vocabs={"word_vocab": word2index}, infer=True)
# inference
infer = SeqLabelInfer(pickle_path)
results = infer.predict(model, infer_data)
print(results)
def _create_inference(self, model_dir):
if self.infer_type == "seq_label":
return SeqLabelInfer(model_dir)
elif self.infer_type == "text_class":
return ClassificationInfer(model_dir)
else:
raise ValueError("fail to create inference instance")
def _create_inference(self, model_dir):
"""Specify which task to perform.
:param model_dir:
:return:
"""
if self.infer_type == "seq_label":
return SeqLabelInfer(model_dir)
elif self.infer_type == "text_class":
return ClassificationInfer(model_dir)
else:
raise ValueError("fail to create inference instance")
def infer():
# Load infer configuration, the same as test
test_args = ConfigSection()
ConfigLoader().load_config(config_dir, {"POS_infer": test_args})
# fetch dictionary size and number of labels from pickle files
word_vocab = load_pickle(pickle_path, "word2id.pkl")
label_vocab = load_pickle(pickle_path, "label2id.pkl")
test_args["vocab_size"] = len(word_vocab)
test_args["num_classes"] = len(label_vocab)
print("vocabularies loaded")
# Define the same model
model = SeqLabeling(test_args)
print("model defined")
# Dump trained parameters into the model
ModelLoader.load_pytorch(model, os.path.join(pickle_path, model_name))
print("model loaded!")
# Data Loader
infer_data = SeqLabelDataSet(load_func=BaseLoader.load)
infer_data.load(data_infer_path,
vocabs={
"word_vocab": word_vocab,
"label_vocab": label_vocab
},
infer=True)
print("data set prepared")
# Inference interface
infer = SeqLabelInfer(pickle_path)
results = infer.predict(model, infer_data)
for res in results:
print(res)
print("Inference finished!")