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- # Copyright (c) 2022 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 pickle
- import sys
-
- parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
- sys.path.insert(0, parent_path)
-
- import paddle
- import yaml
-
- from medicalseg.cvlibs import Config
- from medicalseg.utils import logger
- import nnunet
-
-
- 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)
-
- return parser.parse_args()
-
-
- class SavedSegmentationNet(paddle.nn.Layer):
- def __init__(self, net):
- super().__init__()
- self.net = net
-
- def forward(self, x):
- outs = self.net(x)
- outputs = []
- for out in outs:
- out = paddle.nn.functional.softmax(out, axis=1)
- outputs.append(out)
- return outputs
-
-
- def main(args):
- os.environ['MEDICALSEG_EXPORT_STAGE'] = 'True'
-
- cfg = Config(args.cfg)
- net = cfg.model
-
- 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.')
-
- shape = [None, net.num_input_channels, *net.patch_size.tolist()]
- print("Export nnunet static model patch size: ", shape)
-
- # if args.input_shape is None:
- # shape = [None, 1, None, None, None]
- # else:
- # shape = args.input_shape
-
- new_net = SavedSegmentationNet(net)
-
- new_net.eval()
- new_net = paddle.jit.to_static(
- new_net,
- input_spec=[paddle.static.InputSpec(
- shape=shape, dtype='float32')]) # export is export to static graph
- save_path = os.path.join(args.save_dir, 'model')
- paddle.jit.save(new_net, save_path)
-
- yml_file = os.path.join(args.save_dir, 'deploy.yaml')
- with open(yml_file, 'w') as file:
- transforms = cfg.export_config.get('transforms', [{}])
- data = {
- 'Deploy': {
- 'transforms': transforms,
- 'model': 'model.pdmodel',
- 'params': 'model.pdiparams'
- }
- }
- yaml.dump(data, file)
-
- logger.info(f'Model is saved in {args.save_dir}.')
-
-
- if __name__ == '__main__':
- args = parse_args()
- main(args)
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