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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 script.
- '''
-
- import os
- import argparse
- import numpy as np
- from mindspore import Tensor
-
- parser = argparse.ArgumentParser(description="postprocess")
- parser.add_argument("--batch_size", type=int, default=1, help="Eval batch size, default is 1")
- parser.add_argument("--label_dir", type=str, default="", help="label data dir")
- parser.add_argument("--result_dir", type=str, default="./result_Files", help="infer result Files")
-
- args, _ = parser.parse_known_args()
-
- if __name__ == "__main__":
- file_name = os.listdir(args.label_dir)
- total_acc = 0
- total = 0
- for f in file_name:
- f_name = os.path.join(args.result_dir, f.split('.')[0] + '_0.bin')
- logits = np.fromfile(f_name, np.float32).reshape(args.batch_size, 2)
- logits = Tensor(logits).asnumpy()
- label_ids = np.fromfile(os.path.join(args.label_dir, f), np.int32)
- label_ids = Tensor(label_ids.reshape(args.batch_size, 1)).asnumpy().reshape(-1)
- preds = np.argmax(logits, axis=1).reshape(-1)
- acc = (preds == label_ids).sum()
- total += len(preds)
- total_acc += acc
- print(total_acc / total)
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