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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.
- # ============================================================================
- """post process for 310 inference"""
- import os
- import argparse
- import numpy as np
- from PIL import Image
-
- from src.config import config
- from src.eval_utils import metrics
-
- batch_size = 1
- parser = argparse.ArgumentParser(description="ssd_mobilenet_v1_fpn inference")
- parser.add_argument("--result_path", type=str, required=True, help="result files path.")
- parser.add_argument("--img_path", type=str, required=True, help="image file path.")
- args = parser.parse_args()
-
- def get_imgSize(file_name):
- img = Image.open(file_name)
- return img.size
-
- def get_result(result_path, img_id_file_path):
- anno_json = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
- files = os.listdir(img_id_file_path)
- pred_data = []
-
- for file in files:
- img_ids_name = file.split('.')[0]
- img_id = int(np.squeeze(img_ids_name))
- img_size = get_imgSize(os.path.join(img_id_file_path, file))
- image_shape = np.array([img_size[1], img_size[0]])
-
- result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
- result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
-
- boxes = np.fromfile(result_path_0, dtype=np.float32).reshape(51150, 4)
- box_scores = np.fromfile(result_path_1, dtype=np.float32).reshape(51150, 81)
-
- pred_data.append({
- "boxes": boxes,
- "box_scores": box_scores,
- "img_id": img_id,
- "image_shape": image_shape
- })
- mAP = metrics(pred_data, anno_json)
- print(f" mAP:{mAP}")
-
- if __name__ == '__main__':
- get_result(args.result_path, args.img_path)
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