class Model_Performance():
'''
'''
def __init__(self, checkpoint_path, model_name):
self.chkpoint = checkpoint_path
self.app = Application(load_path=checkpoint_path,
model_name=model_name)
self.datahelper = DataHelper()
def CrossArchTestFast(self, arch1, arch2):
'''
:param arch1: path to sqlite database
:param arch2: path to sqlite database
:return:
'''
pairs = self.datahelper.get_cross_archtecture_pair(arch1, arch2)
result = []
lables = []
pool = mp.Pool(processes=10)
start_time = datetime.now()
pairs = pairs[0:10]
for (source, target, label) in tqdm(pairs, desc="Sim Computing."):
source_tree = source[-1]
target_tree = target[-1]
lables.append(label)
result.append(
pool.apply_async(self.app.similarity_tree, (
source_tree,
target_tree,
)))
pool.close()
pool.join()
predictions = []
for res in result:
predictions.append(res.get())
time_cost = (datetime.now() - start_time).seconds
fpr, tpr, thresholds = roc_curve(np.array(lables),
np.array(predictions))
logger.info("===> architecture info : %s vs %s" % (arch1, arch2))
logger.info("===> time: %s" % (datetime.now()))
logger.info("model: %s" % self.chkpoint)
logger.info(
"compute %d function pairs, which cost %d seconds; %f pairs per sec"
% (len(pairs), time_cost, len(pairs) * 1.0 / time_cost))
logger.info("predictions= %s" % json.dumps(predictions))
logger.info("labels= %s" % json.dumps(lables))
logger.info("fpr= %s" % json.dumps(fpr.tolist()))
logger.info("tpr= %s" % json.dumps(tpr.tolist()))
logger.info("thresholds= %s" % json.dumps(thresholds.tolist()))
