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val.py
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val.py
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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.
import argparse
import os
import paddle
from paddleseg.cvlibs import manager, Config
from paddleseg.core import evaluate
from paddleseg.utils import get_sys_env, logger, utils
def get_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 parse_args():
parser = argparse.ArgumentParser(description='Model evaluation')
# params of evaluate
parser.add_argument(
"--config", dest="cfg", help="The config file.", default=None, type=str)
parser.add_argument(
'--opts',
help='Update the key-value pairs of all options.',
default=None,
nargs='+')
parser.add_argument(
'--model_path',
dest='model_path',
help='The path of model for evaluation.',
type=str,
default=None)
parser.add_argument(
'--num_workers',
dest='num_workers',
help='Number of workers for data loader.',
type=int,
default=0)
# augment for evaluation
parser.add_argument(
'--aug_eval',
dest='aug_eval',
help='Whether to use mulit-scales and flip augment for evaluation.',
action='store_true')
parser.add_argument(
'--scales',
dest='scales',
nargs='+',
help='Scales for augment.',
type=float,
default=1.0)
parser.add_argument(
'--flip_horizontal',
dest='flip_horizontal',
help='Whether to use flip horizontally augment.',
action='store_true')
parser.add_argument(
'--flip_vertical',
dest='flip_vertical',
help='Whether to use flip vertically augment.',
action='store_true')
# sliding window evaluation
parser.add_argument(
'--is_slide',
dest='is_slide',
help='Whether to evaluate by sliding window.',
action='store_true')
parser.add_argument(
'--crop_size',
dest='crop_size',
nargs=2,
help='The crop size of sliding window, the first is width and the second is height.',
type=int,
default=None)
parser.add_argument(
'--stride',
dest='stride',
nargs=2,
help='The stride of sliding window, the first is width and the second is height.',
type=int,
default=None)
parser.add_argument(
'--data_format',
dest='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',
dest='add auc_roc metric',
help='Whether to use auc_roc metric.',
type=bool,
default=False)
parser.add_argument(
'--device',
dest='device',
help='Device place to be set, which can be gpu, xpu, npu, mlu or cpu.',
default='gpu',
choices=['cpu', 'gpu', 'xpu', 'npu'],
type=str)
return parser.parse_args()
def main(args):
env_info = get_sys_env()
if args.device == 'gpu' and env_info[
'Paddle compiled with cuda'] and env_info['GPUs used']:
place = 'gpu'
elif args.device == 'xpu' and paddle.is_compiled_with_xpu():
place = 'xpu'
elif args.device == 'npu' and paddle.is_compiled_with_npu():
place = 'npu'
elif args.device == 'mlu' and paddle.is_compiled_with_mlu():
place = 'mlu'
else:
place = 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg, opts=args.opts)
cfg.check_sync_info()
# 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
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
if args.model_path:
utils.load_entire_model(model, args.model_path)
logger.info('Loaded trained params of model successfully')
test_config = get_test_config(cfg, args)
evaluate(model, val_dataset, num_workers=args.num_workers, **test_config)
logger.warning("This `val.py` will be removed in version 2.8, "
"please use `tools/val.py`.")
if __name__ == '__main__':
args = parse_args()
main(args)