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99 lines
3.6 KiB
99 lines
3.6 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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# Unless 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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"""YoloV4 310 infer."""
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import os
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import argparse
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import datetime
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import time
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import numpy as np
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from pycocotools.coco import COCO
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from src.logger import get_logger
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from eval import DetectionEngine
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parser = argparse.ArgumentParser('mindspore coco testing')
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# dataset related
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parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
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# logging related
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parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
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# detect_related
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parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
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parser.add_argument('--ann_file', type=str, default='', help='path to annotation')
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parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
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parser.add_argument('--img_id_file_path', type=str, default='', help='path of image dataset')
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parser.add_argument('--result_files', type=str, default='./result_Files', help='path to 310 infer result floder')
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args, _ = parser.parse_known_args()
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class Redirct:
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def __init__(self):
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self.content = ""
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def write(self, content):
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self.content += content
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def flush(self):
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self.content = ""
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if __name__ == "__main__":
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start_time = time.time()
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args.outputs_dir = os.path.join(args.log_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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args.logger = get_logger(args.outputs_dir, 0)
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# init detection engine
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detection = DetectionEngine(args)
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coco = COCO(args.ann_file)
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result_path = args.result_files
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files = os.listdir(args.img_id_file_path)
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for file in files:
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img_ids_name = file.split('.')[0]
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img_id = int(np.squeeze(img_ids_name))
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imgIds = coco.getImgIds(imgIds=[img_id])
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img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
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image_shape = ((img['width'], img['height']),)
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img_id = (np.squeeze(img_ids_name),)
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result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
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result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
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result_path_2 = os.path.join(result_path, img_ids_name + "_2.bin")
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output_small = np.fromfile(result_path_0, dtype=np.float32).reshape(1, 19, 19, 3, 85)
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output_me = np.fromfile(result_path_1, dtype=np.float32).reshape(1, 38, 38, 3, 85)
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output_big = np.fromfile(result_path_2, dtype=np.float32).reshape(1, 76, 76, 3, 85)
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detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape, img_id)
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args.logger.info('Calculating mAP...')
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detection.do_nms_for_results()
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result_file_path = detection.write_result()
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args.logger.info('result file path: {}'.format(result_file_path))
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eval_result = detection.get_eval_result()
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cost_time = time.time() - start_time
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args.logger.info('\n=============coco eval reulst=========\n' + eval_result)
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args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
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