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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.
- # ============================================================================
- """postprocess for 310 inference"""
- import os
- import argparse
- import json
- import numpy as np
- from mindspore.nn import Top1CategoricalAccuracy, Top5CategoricalAccuracy
- parser = argparse.ArgumentParser(description="postprocess")
- parser.add_argument("--result_dir", type=str, default="./result_Files", help="result files path.")
- parser.add_argument('--dataset_name', type=str, choices=["imagenet2012"], default="imagenet2012")
- args = parser.parse_args()
-
- def calcul_acc(lab, preds):
- return sum(1 for x, y in zip(lab, preds) if x == y) / len(lab)
-
-
- if __name__ == '__main__':
- batch_size = 1
- top1_acc = Top1CategoricalAccuracy()
- rst_path = args.result_dir
- label_list = []
- pred_list = []
- file_list = os.listdir(rst_path)
- top5_acc = Top5CategoricalAccuracy()
- with open('./preprocess_Result/imagenet_label.json', "r") as label:
- labels = json.load(label)
- for f in file_list:
- label = f.split("_0.bin")[0] + ".JPEG"
- label_list.append(labels[label])
- pred = np.fromfile(os.path.join(rst_path, f), np.float32)
- pred = pred.reshape(batch_size, int(pred.shape[0] / batch_size))
- top1_acc.update(pred, [labels[label],])
- top5_acc.update(pred, [labels[label],])
- print("Top1 acc: ", top1_acc.eval())
- print("Top5 acc: ", top5_acc.eval())
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