# -*- coding: utf-8 -*-

# Copyright 2021 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



import os

import re

import argparse

import numpy as np

label = {0:'3526', 1:'7312', 2:'1088', 3:'32', 4:'460', 5:'7859', 6:'118', 7:'6848', 8:'8629', 9:'163', 10:'2416', 11:'3947', 12:'332', 13:'19', 14:'6272', 15:'7367', 16:'1898', 17:'3664', 18:'2136', 19:'4640', 20:'1867', 21:'1970', 22:'4680', 23:'226', 24:'5789', 25:'3242', 26:'667', 27:'1737'}



if __name__ == '__main__':

    '''

    参数说明：

        --data_info: 数据集信息

        --result_dir: 二进制推理结果目录

    '''



    # arg parser

    parser = argparse.ArgumentParser()

    parser.add_argument('--data_info', default='mini_librispeech_test.info')

    parser.add_argument('--result_dir', default='result')



    opt = parser.parse_args()

    error = 0

    total = 0



    with open('mini_librispeech_test.info', 'r') as f:

        for line in f.readlines():

            # line format example

            # 0 mini_librispeech_test_bin/4680-16042-0024.bin (1,1600,23)

            split = line.split(' ')

            index = split[0]

            input_file = split[1]

            target = re.search('/(\d*)-', input_file).group()[1:-1]

            

            # output result/index.0.bin => index range from 0 to 152

            output_file='result/'+index+'.0.bin'

            



            output = np.fromfile(output_file, np.float32)

            res = np.argmax(output)

            print('Predicted:', label[res], 'Target:', target)

            total += 1

            if label[res] != target:

                error += 1

    accuracy = float(total - error) / total * 100

    print('\nClassification Accuracy: {:.2f}%\n'.format(accuracy))