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():
# Config Loader
test_args = ConfigSection()
ConfigLoader("config").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, "class2id.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model
model = AdvSeqLabel(test_args)
try:
ModelLoader.load_pytorch(model, "./save/saved_model.pkl")
print('model loaded!')
except Exception as e:
print('cannot load model!')
raise
# Data Loader
raw_data_loader = BaseLoader(data_infer_path)
infer_data = raw_data_loader.load_lines()
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("./data_for_tests/config",
{"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, "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, "./data_for_tests/saved_model.pkl")
print("model loaded!")
# Data Loader
raw_data_loader = BaseLoader(data_infer_path)
infer_data = raw_data_loader.load_lines()
"""
Transform strings into list of list of strings.
[
[word_11, word_12, ...],
[word_21, word_22, ...],
...
]
In this case, each line in "people_infer.txt" is already a sentence. So load_lines() just splits them.
"""
# Inference interface
infer = Predictor(pickle_path)
results = infer.predict(model, infer_data)
print(results)
print("Inference finished!")