eval.py

import argparse
import functools

import paddle

from utils.utils import add_arguments, print_arguments, get_lfw_list
from utils.utils import get_features, get_feature_dict, test_performance

parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
add_arg('batch_size',               int,     64,                            '训练的批量大小')
add_arg('model_path',               str,     'models/infer/model',          '预测模型的路径')
add_arg('test_list_path',           str,     'dataset/lfw_test.txt',        '测试数据的数据列表路径')
args = parser.parse_args()
print_arguments(args)

# 加载模型
model = paddle.jit.load(args.model_path)
model.eval()

# 获取测试数据
img_paths = get_lfw_list(args.test_list_path)
features = get_features(model, img_paths, batch_size=args.batch_size)
fe_dict = get_feature_dict(img_paths, features)
accuracy, threshold = test_performance(fe_dict, args.test_list_path)

print('准确率为：%f, 最优阈值为：%f' % (accuracy, threshold))