def main(task2_model_id, task2_model_path):
multi_config = MultiConfig()
multi_config.is_training = False
multi_config.dropout_rate = 0.0
print("loading data...")
dict_word2index = bpe.load_pickle(multi_config.word2index_path)
tests_id, test_data = bd.load_test_data(multi_config.test_path)
if task2_model_id != 4:
test_X = bd.build_test_data(test_data, dict_word2index, multi_config.max_text_len)
else:
test_X = bd.build_test_data_HAN(test_data, dict_word2index, multi_config.num_sentences, multi_config.sequence_length)
testset = MingLueTestData(test_X)
test_loader = DataLoader(dataset=testset,
batch_size=multi_config.batch_size,
shuffle=False,
num_workers=multi_config.num_workers)
multi_config.vocab_size = len(dict_word2index)
print("loading model...")
model2 = load_multi_model(task2_model_path, task2_model_id, multi_config)
print("model loaded")
print("predicting...")
predicted_multi_labels = [[]]
predicted_multi_labels = predict_multi_label(test_loader, model2, multi_config)
generate_result_json(tests_id, predicted_multi_labels, multi_config.result_path)
def main(task1_model_id, task1_model_path):
config = Config()
multi_config = MultiConfig()
config.is_training = False
config.dropout_rate = 0.0
multi_config.is_training = False
multi_config.dropout_rate = 0.0
# model_id = int(input("Please select a model(input model id):\n0: fastText\n1: TextCNN\n2: TextRCNN\nInput: "))
print("loading data...")
dict_word2index = bpe.load_pickle(config.word2index_path)
if task1_model_id != 4:
tests_id, test_data = bd.load_test_data(config.test_path)
test_X = bd.build_test_data(test_data, dict_word2index,
config.max_text_len)
else:
tests_id, test_data = bd.load_test_data(config.test_path)
test_X = bd.build_test_data_HAN(test_data, dict_word2index,
config.num_sentences,
config.sequence_length)
testset = MingLueTestData(test_X)
test_loader = DataLoader(dataset=testset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers)
config.vocab_size = len(dict_word2index)
multi_config.vocab_size = len(dict_word2index)
print("loading model...")
model1 = load_model(task1_model_path, task1_model_id, config)
print("model loaded")
print("predicting...")
predicted_labels = predict(test_loader, model1, config.has_cuda)
predicted_multi_labels = [[]]
generate_result_json(tests_id, predicted_labels, predicted_multi_labels,
config.result_path)
def main(rcnn_model_path, han_model_path):
config = Config()
config.is_training = False
config.dropout_rate = 0.0
print("loading data...")
dict_word2index = bpe.load_pickle(config.word2index_path)
tests_id, test_data = bd.load_test_data(config.test_path)
test_X = bd.build_test_data(test_data, dict_word2index, config.max_text_len)
testset = MingLueTestData(test_X)
test_loader = DataLoader(dataset=testset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers)
test_X_HAN = bd.build_test_data_HAN(test_data, dict_word2index, config.num_sentences, config.sequence_length)
testset = MingLueTestData(test_X_HAN)
test_loader_HAN = DataLoader(dataset=testset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers)
config.vocab_size = len(dict_word2index)
print("loading model...")
rcnn_model = load_model(rcnn_model_path, 2, config)
han_model = load_model(han_model_path, 4, config)
print("model loaded")
print("predicting...")
predicted_labels = predict(test_loader, test_loader_HAN, rcnn_model, han_model, config)
generate_result_json(tests_id, predicted_labels, config.result_path)