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- # 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.
- # ============================================================================
- """post process for 310 inference"""
-
- import os
- import json
- import argparse
- import numpy as np
-
- batch_size = 1
- parser = argparse.ArgumentParser(description="resnet inference")
- parser.add_argument("--dataset", type=str, required=True, help="dataset type.")
- parser.add_argument("--result_path", type=str, required=True, help="result files path.")
- parser.add_argument("--label_path", type=str, required=True, help="image file path.")
- args = parser.parse_args()
-
- def cal_acc_imagenet(result_path, label_path):
- """cal_acc_imagenet"""
- files = os.listdir(result_path)
- with open(label_path, "r") as label:
- labels = json.load(label)
- result_shape = (1, 1001)
- top1 = 0
- top5 = 0
- total_data = len(files)
- for file in files:
- img_ids_name = file.split('_0.')[0]
- data_path = os.path.join(result_path, img_ids_name + "_0.bin")
- result = np.fromfile(data_path, dtype=np.float32).reshape(result_shape)
- for batch in range(batch_size):
- predict = np.argsort(-result[batch], axis=-1)
- if labels[img_ids_name+".JPEG"] == predict[0]:
- top1 += 1
- if labels[img_ids_name+".JPEG"] in predict[:5]:
- top5 += 1
- print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.")
-
-
- if __name__ == '__main__':
- cal_acc_imagenet(args.result_path, args.label_path)
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