mindspore/model_zoo/official/cv/deeplabv3/export.py

# 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 AIR file."""
import argparse
import numpy as np

from mindspore import Tensor
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.serialization import export

from src.nets import net_factory

context.set_context(mode=context.GRAPH_MODE, save_graphs=False)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='checkpoint export')
    parser.add_argument('--checkpoint', type=str.lower, default='', help='checkpoint of deeplabv3 (Default: None)')
    parser.add_argument('--model', type=str.lower, default='deeplab_v3_s8', choices=['deeplab_v3_s16', 'deeplab_v3_s8'],
                        help='Select model structure (Default: deeplab_v3_s8)')
    parser.add_argument('--num_classes', type=int, default=21, help='the number of classes (Default: 21)')
    args = parser.parse_args()

    if args.model == 'deeplab_v3_s16':
        network = net_factory.nets_map['deeplab_v3_s16']('eval', args.num_classes, 16, True)
    else:
        network = net_factory.nets_map['deeplab_v3_s8']('eval', args.num_classes, 8, True)
    param_dict = load_checkpoint(args.checkpoint)

    # load the parameter into net
    load_param_into_net(network, param_dict)
    input_data = np.random.uniform(0.0, 1.0, size=[32, 3, 513, 513]).astype(np.float32)
    export(network, Tensor(input_data), file_name=args.model+'-300_11.air', file_format='AIR')