DeepLearningExamples/TensorFlow2/Segmentation/MaskRCNN/scripts/benchmark_inference.py
2020-06-27 11:52:08 +02:00

61 lines
2.1 KiB
Python

# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
""" Scripts that simplifies running evaluation benchmark """
import argparse
import os
import shutil
import subprocess
def main():
# CLI flags
parser = argparse.ArgumentParser(description="MaskRCNN evaluation benchmark")
parser.add_argument('--batch_size', type=int, required=True)
parser.add_argument('--amp', action='store_true')
parser.add_argument('--data_dir', type=str, default='/data')
parser.add_argument('--model_dir', type=str, default='/tmp/model')
parser.add_argument('--weights_dir', type=str, default='/model')
flags = parser.parse_args()
main_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../mask_rcnn_main.py'))
# build command
cmd = (
f'python {main_path}'
f' --mode eval'
f' --model_dir "{flags.model_dir}"'
f' --checkpoint "{os.path.join(flags.weights_dir, "resnet/resnet-nhwc-2018-10-14/model.ckpt-112602")}"'
f' --validation_file_pattern "{os.path.join(flags.data_dir, "val*.tfrecord")}"'
f' --val_json_file "{os.path.join(flags.data_dir, "annotations/instances_val2017.json")}"'
f' --num_steps_per_eval 200'
f' --eval_samples 1200'
f' --use_batched_nms'
f' --nouse_custom_box_proposals_op'
f' --xla'
f' --eval_batch_size {flags.batch_size}'
f' {"--amp" if flags.amp else ""}'
)
# print command
line = '-' * shutil.get_terminal_size()[0]
print(line, cmd, line, sep='\n')
# run model
subprocess.call(cmd, shell=True)
if __name__ == '__main__':
main()