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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 yolov3-resnet18"""
- import os
- import argparse
- import numpy as np
- from src.config import ConfigYOLOV3ResNet18
- from src.utils import metrics
-
- parser = argparse.ArgumentParser(description='Yolov3 postprocess')
- parser.add_argument("--batchsize", type=int, default=1, help="batchsize.")
- parser.add_argument("--anno_path", type=str, required=True, help="Annotation path.")
- parser.add_argument("--result_path", type=str, required=True, help="result files path.")
- args = parser.parse_args()
-
- if __name__ == '__main__':
- config = ConfigYOLOV3ResNet18()
- batchsize = args.batchsize
-
- anno_dict = {}
- for line in open(args.anno_path):
- line_list = line.split(' ')
- line_list[0] = line_list[0].split('/')[-1]
- anno_dict[line_list[0]] = line_list[1:]
-
- pred_data = []
- for key in anno_dict:
- result0 = os.path.join(args.result_path, key.split('.')[0] + '_0.bin')
- result1 = os.path.join(args.result_path, key.split('.')[0] + '_1.bin')
- output0 = np.fromfile(result0, np.float32).reshape(batchsize, 13860, 4)
- output1 = np.fromfile(result1, np.float32).reshape(batchsize, 13860, 2)
-
- anno_list = []
- for v in anno_dict[key]:
- v_list = v.split(',')
- anno_list.append(v_list)
- annotation = np.array(anno_list, np.int64)
-
- for batch_idx in range(batchsize):
- pred_data.append({"boxes": output0[batch_idx],
- "box_scores": output1[batch_idx],
- "annotation": annotation})
-
- precisions, recalls = metrics(pred_data)
- print("\n========================================\n")
- for i in range(config.num_classes):
- print("class {} precision is {:.2f}%, recall is {:.2f}%".format(i, precisions[i] * 100, recalls[i] * 100))
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