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predict.py
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predict.py
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# Copyright (c) 2021 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
from paddleseg.cvlibs import manager, Config
from paddleseg.utils import get_sys_env, logger
from core import predict
from model import *
from dataset import MattingDataset
from transforms import Compose
from utils import get_image_list
def parse_args():
parser = argparse.ArgumentParser(description='Model training')
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 path of image, it can be a file or a directory including images',
type=str,
default=None)
parser.add_argument(
'--trimap_path',
dest='trimap_path',
help='The path of trimap, it can be a file or a directory including images. '
'The image should be the same as image when it is a directory.',
type=str,
default=None)
parser.add_argument(
'--save_dir',
dest='save_dir',
help='The directory for saving the model snapshot',
type=str,
default='./output/results')
parser.add_argument(
'--fg_estimate',
default=True,
type=eval,
choices=[True, False],
help='Whether to estimate foreground when predicting.')
return parser.parse_args()
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else '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)
if args.trimap_path is None:
trimap_list = None
else:
trimap_list, _ = get_image_list(args.trimap_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
predict(
model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
trimap_list=trimap_list,
save_dir=args.save_dir,
fg_estimate=args.fg_estimate)
if __name__ == '__main__':
args = parse_args()
main(args)