mindspore/model_zoo/official/cv/alexnet/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 checkpoint file into air and onnx models#################
python export.py
"""
import argparse
import numpy as np

import mindspore as ms
from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export

from src.config import alexnet_cifar10_cfg, alexnet_imagenet_cfg
from src.alexnet import AlexNet

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Classification')
    parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
                        help='please choose dataset: imagenet or cifar10.')
    parser.add_argument('--device_target', type=str, default="Ascend",
                        choices=['Ascend', 'GPU'],
                        help='device where the code will be implemented (default: Ascend)')
    parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
                                path where the trained ckpt file')
    args_opt = parser.parse_args()
    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)

    if args_opt.dataset_name == 'cifar10':
        cfg = alexnet_cifar10_cfg
    elif args_opt.dataset_name == 'imagenet':
        cfg = alexnet_imagenet_cfg
    else:
        raise ValueError("dataset is not support.")

    net = AlexNet(num_classes=cfg.num_classes)

    param_dict = load_checkpoint(args_opt.ckpt_path)
    load_param_into_net(net, param_dict)

    input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, cfg.image_height, cfg.image_width]), ms.float32)
    export(net, input_arr, file_name=cfg.air_name, file_format="AIR")