|
- # 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.
-
- import argparse
- import os
-
- import paddle
-
- from paddleseg.cvlibs import manager, Config, SegBuilder
- from paddleseg.core import evaluate
- from paddleseg.utils import get_sys_env, logger, utils
-
-
- def parse_args():
- parser = argparse.ArgumentParser(description='Model evaluation')
-
- # Common params
- parser.add_argument("--config", help="The path of config file.", type=str)
- parser.add_argument(
- '--model_path',
- help='The path of trained weights to be loaded for evaluation.',
- type=str)
- parser.add_argument(
- '--num_workers',
- help='Number of workers for data loader. Bigger num_workers can speed up data processing.',
- type=int,
- default=0)
- parser.add_argument(
- '--device',
- help='Set the device place for evaluating model.',
- default='gpu',
- choices=['cpu', 'gpu', 'xpu', 'npu', 'mlu'],
- type=str)
-
- # Data augment params
- parser.add_argument(
- '--aug_eval',
- help='Whether to use mulit-scales and flip augment for evaluation.',
- action='store_true')
- parser.add_argument(
- '--scales',
- nargs='+',
- help='Scales for data augment.',
- type=float,
- default=1.0)
- parser.add_argument(
- '--flip_horizontal',
- help='Whether to use flip horizontally augment.',
- action='store_true')
- parser.add_argument(
- '--flip_vertical',
- help='Whether to use flip vertically augment.',
- action='store_true')
-
- # Sliding window evaluation params
- parser.add_argument(
- '--is_slide',
- help='Whether to evaluate images in sliding window method.',
- action='store_true')
- parser.add_argument(
- '--crop_size',
- nargs=2,
- help='The crop size of sliding window, the first is width and the second is height.'
- 'For example, `--crop_size 512 512`',
- type=int)
- parser.add_argument(
- '--stride',
- nargs=2,
- help='The stride of sliding window, the first is width and the second is height.'
- 'For example, `--stride 512 512`',
- type=int)
-
- # Other params
- parser.add_argument(
- '--data_format',
- help='Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".',
- type=str,
- default='NCHW')
- parser.add_argument(
- '--auc_roc',
- help='Whether to use auc_roc metric.',
- type=bool,
- default=False)
- parser.add_argument(
- '--opts',
- help='Update the key-value pairs of all options.',
- default=None,
- nargs='+')
-
- return parser.parse_args()
-
-
- def merge_test_config(cfg, args):
- test_config = cfg.test_config
- if args.aug_eval:
- test_config['aug_eval'] = args.aug_eval
- test_config['scales'] = args.scales
- test_config['flip_horizontal'] = args.flip_horizontal
- test_config['flip_vertical'] = args.flip_vertical
- if args.is_slide:
- test_config['is_slide'] = args.is_slide
- test_config['crop_size'] = args.crop_size
- test_config['stride'] = args.stride
- return test_config
-
-
- def main(args):
- assert args.config is not None, \
- 'No configuration file specified, please set --config'
- cfg = Config(args.config, opts=args.opts)
- builder = SegBuilder(cfg)
- test_config = merge_test_config(cfg, args)
-
- utils.show_env_info()
- utils.show_cfg_info(cfg)
- utils.set_device(args.device)
-
- # TODO refactor
- # Only support for the DeepLabv3+ model
- if args.data_format == 'NHWC':
- if cfg.dic['model']['type'] != 'DeepLabV3P':
- raise ValueError(
- 'The "NHWC" data format only support the DeepLabV3P model!')
- cfg.dic['model']['data_format'] = args.data_format
- cfg.dic['model']['backbone']['data_format'] = args.data_format
- loss_len = len(cfg.dic['loss']['types'])
- for i in range(loss_len):
- cfg.dic['loss']['types'][i]['data_format'] = args.data_format
-
- model = builder.model
- if args.model_path:
- utils.load_entire_model(model, args.model_path)
- logger.info('Loaded trained weights successfully.')
- val_dataset = builder.val_dataset
-
- evaluate(model, val_dataset, num_workers=args.num_workers, **test_config)
-
-
- if __name__ == '__main__':
- args = parse_args()
- main(args)
|