You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
55 lines
2.3 KiB
55 lines
2.3 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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.
|
|
# ============================================================================
|
|
"""
|
|
##############export checkpoint file into air and onnx models#################
|
|
python export.py --net squeezenet --dataset cifar10 --checkpoint_path squeezenet_cifar10-120_1562.ckpt
|
|
"""
|
|
|
|
import argparse
|
|
import numpy as np
|
|
from mindspore import Tensor
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Image classification')
|
|
parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
|
|
help='Model.')
|
|
parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
|
|
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
|
|
args_opt = parser.parse_args()
|
|
|
|
if args_opt.net == "squeezenet":
|
|
from src.squeezenet import SqueezeNet as squeezenet
|
|
else:
|
|
from src.squeezenet import SqueezeNet_Residual as squeezenet
|
|
if args_opt.dataset == "cifar10":
|
|
num_classes = 10
|
|
else:
|
|
num_classes = 1000
|
|
|
|
onnx_filename = args_opt.net + '_' + args_opt.dataset
|
|
air_filename = args_opt.net + '_' + args_opt.dataset
|
|
|
|
net = squeezenet(num_classes=num_classes)
|
|
|
|
assert args_opt.checkpoint_path is not None, "checkpoint_path is None."
|
|
|
|
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
|
load_param_into_net(net, param_dict)
|
|
|
|
input_arr = Tensor(np.zeros([1, 3, 227, 227], np.float32))
|
|
export(net, input_arr, file_name=onnx_filename, file_format="ONNX")
|
|
export(net, input_arr, file_name=air_filename, file_format="AIR")
|