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preprocess.py 2.53 KiB
# 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.
# ============================================================================
""" preprocess script """
import os
import numpy as np
from src.dataset import keypoint_dataset
from src.config import config
def get_bin():
""" get bin files"""
valid_dataset, _ = keypoint_dataset(
config,
bbox_file=config.TEST.COCO_BBOX_FILE,
train_mode=False,
num_parallel_workers=config.TEST.NUM_PARALLEL_WORKERS,
)
inputs_path = os.path.join(config.INFER.PRE_RESULT_PATH, "00_data")
os.makedirs(inputs_path)
center_path = os.path.join(config.INFER.PRE_RESULT_PATH, "center")
os.makedirs(center_path)
scale_path = os.path.join(config.INFER.PRE_RESULT_PATH, "scale")
os.makedirs(scale_path)
score_path = os.path.join(config.INFER.PRE_RESULT_PATH, "score")
os.makedirs(score_path)
id_path = os.path.join(config.INFER.PRE_RESULT_PATH, "id")
os.makedirs(id_path)
for i, item in enumerate(valid_dataset.create_dict_iterator(output_numpy=True)):
file_name = "sp_bs" + str(config.TEST.BATCH_SIZE) + "_" + str(i) + ".bin"
# input data
inputs = item['image']
inputs_file_path = os.path.join(inputs_path, file_name)
inputs.tofile(inputs_file_path)
if config.TEST.FLIP_TEST:
inputs_flipped = inputs[:, :, :, ::-1]
file_name = "sp_flip_bs" + str(config.TEST.BATCH_SIZE) + "_" + str(i) + ".bin"
inputs_file_path = os.path.join(inputs_path, file_name)
inputs_flipped.tofile(inputs_file_path)
file_name = "sp_bs" + str(config.TEST.BATCH_SIZE) + "_" + str(i) + ".npy"
np.save(os.path.join(center_path, file_name), item['center'])
np.save(os.path.join(scale_path, file_name), item['scale'])
np.save(os.path.join(score_path, file_name), item['score'])
np.save(os.path.join(id_path, file_name), item['id'])
print("=" * 20, "export bin files finished", "=" * 20)
if __name__ == '__main__':
get_bin()