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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
-
- import paddle
- import yaml
-
- from paddleseg.cvlibs import Config
- from paddleseg.utils import logger
-
-
- def parse_args():
- parser = argparse.ArgumentParser(description='Model export.')
- parser.add_argument(
- "--config", help="The config file.", type=str, required=True)
- parser.add_argument(
- '--model_path', help='The path of model for export', type=str)
- parser.add_argument(
- '--save_dir',
- help='The directory for saving the exported model',
- type=str,
- default='./output/inference_model')
- parser.add_argument(
- '--output_op',
- choices=['argmax', 'softmax', 'none'],
- default="argmax",
- help="Select which op to be appended to output result, default: argmax")
- parser.add_argument(
- '--without_argmax',
- help='Do not add the argmax operation at the end of the network. [Deprecated]',
- action='store_true')
- parser.add_argument(
- '--with_softmax',
- help='Add the softmax operation at the end of the network. [Deprecated]',
- 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()
-
-
- class SavedSegmentationNet(paddle.nn.Layer):
- def __init__(self, net, output_op):
- super().__init__()
- self.net = net
- self.output_op = output_op
- assert output_op in ['argmax', 'softmax'], \
- "output_op should in ['argmax', 'softmax']"
-
- def forward(self, x):
- outs = self.net(x)
-
- new_outs = []
- for out in outs:
- if self.output_op == 'argmax':
- out = paddle.argmax(out, axis=1, dtype='int32')
- elif self.output_op == 'softmax':
- out = paddle.nn.functional.softmax(out, axis=1)
- new_outs.append(out)
- return new_outs
-
-
- def main(args):
- os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
- cfg = Config(args.config)
- cfg.check_sync_info()
- net = cfg.model
-
- if args.model_path is not None:
- 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
-
- output_op = args.output_op
- if args.without_argmax:
- logger.warning(
- '--without_argmax will be deprecated, please use --output_op')
- output_op = 'none'
- if args.with_softmax:
- logger.warning(
- '--with_softmax will be deprecated, please use --output_op')
- output_op = 'softmax'
-
- new_net = net if output_op == 'none' else SavedSegmentationNet(net,
- output_op)
- new_net.eval()
- new_net = paddle.jit.to_static(
- new_net,
- input_spec=[paddle.static.InputSpec(
- shape=shape, dtype='float32')])
-
- 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', [{
- 'type': 'Normalize'
- }])
- output_dtype = 'int32' if output_op == 'argmax' else 'float32'
- data = {
- 'Deploy': {
- 'model': 'model.pdmodel',
- 'params': 'model.pdiparams',
- 'transforms': transforms,
- 'input_shape': shape,
- 'output_op': output_op,
- 'output_dtype': output_dtype
- }
- }
- yaml.dump(data, file)
-
- logger.info(f'The inference model is saved in {args.save_dir}')
-
- logger.warning("This `export.py` will be removed in version 2.8, "
- "please use `tools/export.py`.")
-
-
- if __name__ == '__main__':
- args = parse_args()
- main(args)
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