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- # Copyright (c) 2020 PaddlePaddle Authors. 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.
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import os, sys
- # add python path of PadleDetection to sys.path
- parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
- if parent_path not in sys.path:
- sys.path.append(parent_path)
-
- # ignore warning log
- import warnings
- warnings.filterwarnings('ignore')
-
- import paddle
-
- from model.core.workspace import load_config, merge_config
- from utils.utils.check import check_gpu, check_version, check_config
- from utils.utils.cli import ArgsParser
- from model.engine import Trainer, init_parallel_env
- from model.metrics.coco_utils import json_eval_results
-
- from utils.utils.logger import setup_logger
- logger = setup_logger('eval')
-
-
- def parse_args():
- parser = ArgsParser()
- parser.add_argument(
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation directory, default is current directory.")
-
- parser.add_argument(
- '--json_eval',
- action='store_true',
- default=False,
- help='Whether to re eval with already exists bbox.json or mask.json')
-
- # TODO: bias should be unified
- parser.add_argument(
- "--bias",
- action="store_true",
- help="whether add bias or not while getting w and h")
-
- parser.add_argument(
- "--classwise",
- action="store_true",
- help="whether per-category AP and draw P-R Curve or not.")
-
- parser.add_argument(
- '--save_prediction_only',
- action='store_true',
- default=False,
- help='Whether to save the evaluation results only')
-
- args = parser.parse_args()
- return args
-
-
- def run(FLAGS, cfg):
- if FLAGS.json_eval:
- logger.info(
- "In json_eval mode, PaddleDetection will evaluate json files in "
- "output_eval directly. And proposal.json, bbox.json and mask.json "
- "will be detected by default.")
- json_eval_results(
- cfg.metric,
- json_directory=FLAGS.output_eval,
- dataset=cfg['EvalDataset'])
- return
-
- # init parallel environment if nranks > 1
- init_parallel_env()
-
- # build trainer
- trainer = Trainer(cfg, mode='eval')
-
- # load weights
- trainer.load_weights(cfg.weights)
-
- # training
- trainer.evaluate()
-
-
- def main():
- FLAGS = parse_args()
- cfg = load_config(FLAGS.config)
- # TODO: bias should be unified
- cfg['bias'] = 1 if FLAGS.bias else 0
- cfg['classwise'] = True if FLAGS.classwise else False
- cfg['output_eval'] = FLAGS.output_eval
- cfg['save_prediction_only'] = FLAGS.save_prediction_only
- merge_config(FLAGS.opt)
-
- place = paddle.set_device('gpu' if cfg.use_gpu else 'cpu')
-
- if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
- cfg['norm_type'] = 'bn'
-
- check_config(cfg)
- check_gpu(cfg.use_gpu)
- check_version()
-
- run(FLAGS, cfg)
-
-
- if __name__ == '__main__':
- main()
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