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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.
- # ============================================================================
- """YoloV3 postprocess."""
- import os
- import argparse
- import datetime
- import numpy as np
- from PIL import Image
- from eval import DetectionEngine
-
- def get_img_size(file_name):
- img = Image.open(file_name)
- return img.size
-
- parser = argparse.ArgumentParser('YoloV3 postprocess')
- parser.add_argument('--result_path', type=str, required=True, help='result files path.')
- parser.add_argument('--img_path', type=str, required=True, help='train data dir.')
- parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
- parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
- parser.add_argument('--annFile', type=str, default='', help='path to annotation')
- parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
- parser.add_argument('--log_path', type=str, default='outputs/', help='inference result save location')
-
- args, _ = parser.parse_known_args()
-
- if __name__ == "__main__":
- args.outputs_dir = os.path.join(args.log_path,
- datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
- if not os.path.exists(args.outputs_dir):
- os.makedirs(args.outputs_dir)
-
- detection = DetectionEngine(args)
- bs = args.per_batch_size
-
- f_list = os.listdir(args.img_path)
- for f in f_list:
- image_size = get_img_size(os.path.join(args.img_path, f))
- f = f.split('.')[0]
- output_big = np.fromfile(os.path.join(args.result_path, f + '_0.bin'), np.float32).reshape(bs, 13, 13, 3, 85)
- output_me = np.fromfile(os.path.join(args.result_path, f + '_1.bin'), np.float32).reshape(bs, 26, 26, 3, 85)
- output_small = np.fromfile(os.path.join(args.result_path, f + '_2.bin'), np.float32).reshape(bs, 52, 52, 3, 85)
- image_id = [int(f.split('_')[-1])]
- image_shape = [[image_size[0], image_size[1]]]
-
- detection.detect([output_small, output_me, output_big], bs, image_shape, image_id)
-
- detection.do_nms_for_results()
- result_file_path = detection.write_result()
- eval_result = detection.get_eval_result()
-
- print('\n=============coco eval result=========\n' + eval_result)
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