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export_model.py
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export_model.py
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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 os
import argparse
import pickle
import paddle
from models.resnet import ResNet_PaDiM
def parse_args():
parser = argparse.ArgumentParser(description='Model export.')
parser.add_argument("--depth", type=int, default=18, help="resnet depth")
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(
'--img_size',
help='The img size for export',
type=int,
default=224)
return parser.parse_args()
def main(args):
# build model
model = ResNet_PaDiM(depth=args.depth, pretrained=True)
if args.model_path is not None:
state = paddle.load(args.model_path)
model.model.set_dict(state["params"])
model.distribution = state["distribution"]
save_path = os.path.join(args.save_dir, 'distribution')
with open(save_path, 'wb') as f:
pickle.dump(model.distribution, f)
shape = [-1, 3, args.img_size, args.img_size]
new_net = model
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)
print(f'Model is saved in {args.save_dir}.')
if __name__ == '__main__':
args = parse_args()
main(args)