|
- # 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.
- # ============================================================================
- """
- resnext export mindir.
- """
- import argparse
- import numpy as np
- from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
- from src.config import config
- from src.image_classification import get_network
-
-
- def parse_args():
- """parse_args"""
- parser = argparse.ArgumentParser('mindspore classification test')
- parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform')
-
- parser.add_argument('--pretrained', type=str, required=True, help='fully path of pretrained model to load. '
- 'If it is a direction, it will test all ckpt')
-
- args, _ = parser.parse_known_args()
- args.image_size = config.image_size
- args.num_classes = config.num_classes
-
- args.image_size = list(map(int, config.image_size.split(',')))
- args.image_height = args.image_size[0]
- args.image_width = args.image_size[1]
- args.export_format = config.export_format
- args.export_file = config.export_file
- return args
-
- if __name__ == '__main__':
- args_export = parse_args()
- context.set_context(mode=context.GRAPH_MODE, device_target=args_export.platform)
-
- net = get_network(num_classes=args_export.num_classes, platform=args_export.platform)
-
- param_dict = load_checkpoint(args_export.pretrained)
- load_param_into_net(net, param_dict)
- input_shp = [1, 3, args_export.image_height, args_export.image_width]
- input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
- export(net, input_array, file_name=args_export.export_file, file_format=args_export.export_format)
|
