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52 lines
1.9 KiB
52 lines
1.9 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# less required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""post process for 310 inference"""
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import os
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import json
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import argparse
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import numpy as np
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from src.config import config2 as config
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batch_size = 1
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parser = argparse.ArgumentParser(description="resnet inference")
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parser.add_argument("--result_path", type=str, required=True, help="result files path.")
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parser.add_argument("--label_path", type=str, required=True, help="image file path.")
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args = parser.parse_args()
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def get_result(result_path, label_path):
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files = os.listdir(result_path)
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with open(label_path, "r") as label:
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labels = json.load(label)
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top1 = 0
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top5 = 0
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total_data = len(files)
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for file in files:
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img_ids_name = file.split('_0.')[0]
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data_path = os.path.join(result_path, img_ids_name + "_0.bin")
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result = np.fromfile(data_path, dtype=np.float32).reshape(batch_size, config.class_num)
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for batch in range(batch_size):
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predict = np.argsort(-result[batch], axis=-1)
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if labels[img_ids_name+".JPEG"] == predict[0]:
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top1 += 1
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if labels[img_ids_name+".JPEG"] in predict[:5]:
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top5 += 1
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print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.")
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if __name__ == '__main__':
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get_result(args.result_path, args.label_path)
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