def predict(model_handle, text):
""" predict for text by model_handle """
batch_size = 1
[exe, place, final_score, final_ids, final_index, processors, id_dict_array] = model_handle
data_generator = processors.preprocessing_for_lines([text], batch_size=batch_size)
results = []
for batch_id, data in enumerate(data_generator()):
data_feed, sent_num = build_data_feed(data, place, batch_size=batch_size)
out = exe.run(feed=data_feed,
fetch_list=[final_score.name, final_ids.name, final_index.name])
batch_score = out[0]
batch_ids = out[1]
batch_pre_index = out[2]
batch_score_arr = np.split(batch_score, batch_size, axis=1)
batch_ids_arr = np.split(batch_ids, batch_size, axis=1)
batch_pre_index_arr = np.split(batch_pre_index, batch_size, axis=1)
index = 0
for (score, ids, pre_index) in zip(batch_score_arr, batch_ids_arr, batch_pre_index_arr):
trace_ids, trace_score = trace_fianl_result(score, ids, pre_index, topk=1, EOS=3)
results.append(id_to_text(trace_ids[0][:-1], id_dict_array))
index += 1
if index >= sent_num:
break
return results[0]
def test(config):
""" test """
batch_size = config.batch_size
config.vocab_size = len(open(config.vocab_path).readlines())
final_score, final_ids, final_index = knowledge_seq2seq(config)
final_score.persistable = True
final_ids.persistable = True
final_index.persistable = True
main_program = fluid.default_main_program()
if config.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
fluid.io.load_params(executor=exe, dirname=config.model_path,
main_program=main_program)
print("laod params finsihed")
# test data generator
processors = KnowledgeCorpus(
data_dir=config.data_dir,
data_prefix=config.data_prefix,
vocab_path=config.vocab_path,
min_len=config.min_len,
max_len=config.max_len)
test_generator = processors.data_generator(
batch_size=config.batch_size,
phase="test",
shuffle=False)
# load dict
id_dict_array = load_id2str_dict(config.vocab_path)
out_file = config.output
fout = open(out_file, 'w')
for batch_id, data in enumerate(test_generator()):
data_feed, sent_num = build_data_feed(data, place, batch_size=batch_size)
if data_feed is None:
break
out = exe.run(feed=data_feed,
fetch_list=[final_score.name, final_ids.name, final_index.name])
batch_score = out[0]
batch_ids = out[1]
batch_pre_index = out[2]
batch_score_arr = np.split(batch_score, batch_size, axis=1)
batch_ids_arr = np.split(batch_ids, batch_size, axis=1)
batch_pre_index_arr = np.split(batch_pre_index, batch_size, axis=1)
index = 0
for (score, ids, pre_index) in zip(batch_score_arr, batch_ids_arr, batch_pre_index_arr):
trace_ids, trace_score = trace_fianl_result(score, ids, pre_index, topk=1, EOS=3)
fout.write(id_to_text(trace_ids[0][:-1], id_dict_array))
fout.write('\n')
index += 1
if index >= sent_num:
break
fout.close()
