# 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 import sys import paddle import yaml import paddleseg from paddleseg.cvlibs import manager from paddleseg.utils import logger LOCAL_PATH = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(LOCAL_PATH, '..')) manager.BACKBONES._components_dict.clear() manager.TRANSFORMS._components_dict.clear() import ppmatting from ppmatting.utils import get_input_spec, Config, MatBuilder def parse_args(): parser = argparse.ArgumentParser(description='Model export.') # params of training parser.add_argument( "--config", dest="cfg", help="The config file.", default=None, type=str, required=True) parser.add_argument( '--save_dir', dest='save_dir', help='The directory for saving the exported model', type=str, default='./output') parser.add_argument( '--model_path', dest='model_path', help='The path of model for export', type=str, default=None) parser.add_argument( '--trimap', dest='trimap', help='Whether to input trimap', action='store_true') parser.add_argument( "--input_shape", nargs='+', help="Export the model with fixed input shape, such as 1 3 1024 1024.", type=int, default=None) return parser.parse_args() def main(args): assert args.cfg is not None, \ 'No configuration file specified, please set --config' cfg = Config(args.cfg) builder = MatBuilder(cfg) paddleseg.utils.show_env_info() paddleseg.utils.show_cfg_info(cfg) os.environ['PADDLESEG_EXPORT_STAGE'] = 'True' net = builder.model net.eval() if args.model_path: para_state_dict = paddle.load(args.model_path) net.set_dict(para_state_dict) logger.info('Loaded trained params of model successfully.') if args.input_shape is None: shape = [None, 3, None, None] else: shape = args.input_shape input_spec = get_input_spec( net.__class__.__name__, shape=shape, trimap=args.trimap) net = paddle.jit.to_static(net, input_spec=input_spec) save_path = os.path.join(args.save_dir, 'model') paddle.jit.save(net, save_path) yml_file = os.path.join(args.save_dir, 'deploy.yaml') with open(yml_file, 'w') as file: transforms = cfg.val_dataset_cfg.get('transforms', [{ 'type': 'Normalize' }]) data = { 'Deploy': { 'transforms': transforms, 'model': 'model.pdmodel', 'params': 'model.pdiparams', 'input_shape': shape }, 'ModelName': net.__class__.__name__ } yaml.dump(data, file) logger.info(f'Model is saved in {args.save_dir}.') if __name__ == '__main__': args = parse_args() main(args)