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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.utils import get_sys_env, logger, get_image_list
from paddleseg.core import predict
from paddleseg.transforms import Compose
def parse_args():
parser = argparse.ArgumentParser(description='Model prediction')
# params of prediction
parser.add_argument(
"--config", dest="cfg", help="The config file.", default=None, type=str)
parser.add_argument(
'--model_path',
dest='model_path',
help='The path of model for prediction',
type=str,
default=None)
parser.add_argument(
'--image_path',
dest='image_path',
help='The image to predict, which can be a path of image, or a file list containing image paths, or a directory including images',
type=str,
default=None)
parser.add_argument(
'--save_dir',
dest='save_dir',
help='The directory for saving the predicted results',
type=str,
default='./output/result')
# augment for prediction
parser.add_argument(
'--aug_pred',
dest='aug_pred',
help='Whether to use mulit-scales and flip augment for prediction',
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 prediction
parser.add_argument(
'--is_slide',
dest='is_slide',
help='Whether to prediction 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)
# custom color map
parser.add_argument(
'--custom_color',
dest='custom_color',
nargs='+',
help='Save images with a custom color map. Default: None, use paddleseg\'s default color map.',
type=int,
default=None)
# set device
parser.add_argument(
'--device',
dest='device',
help='Device place to be set, which can be GPU, XPU, NPU, CPU',
default='gpu',
type=str)
return parser.parse_args()
def get_test_config(cfg, args):
test_config = cfg.test_config
if 'aug_eval' in test_config:
test_config.pop('aug_eval')
if args.aug_pred:
test_config['aug_pred'] = args.aug_pred
test_config['scales'] = args.scales
if args.flip_horizontal:
test_config['flip_horizontal'] = args.flip_horizontal
if args.flip_vertical:
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
if args.custom_color:
test_config['custom_color'] = args.custom_color
return test_config
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'
else:
place = 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = Compose(cfg.val_transforms)
image_list, image_dir = get_image_list(args.image_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
test_config = get_test_config(cfg, args)
predict(
model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir,
**test_config)
if __name__ == '__main__':
args = parse_args()
main(args)
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