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- # 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.
- # ============================================================================
- """
- mobilenetv2 export file.
- """
- import argparse
- import numpy as np
- from mindspore import Tensor, export, context
- from src.config import set_config
- from src.models import define_net, load_ckpt
- from src.utils import set_context
-
- parser = argparse.ArgumentParser(description="mobilenetv2 export")
- parser.add_argument("--device_id", type=int, default=0, help="Device id")
- parser.add_argument("--batch_size", type=int, default=1, help="batch size")
- parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
- parser.add_argument("--file_name", type=str, default="mobilenetv2", help="output file name.")
- parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
- parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
- help='run platform, only support GPU, CPU and Ascend')
- args = parser.parse_args()
- args.is_training = False
- args.run_distribute = False
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
- if args.platform == "Ascend":
- context.set_context(device_id=args.device_id)
-
- if __name__ == '__main__':
- cfg = set_config(args)
- set_context(cfg)
- _, _, net = define_net(cfg, args.is_training)
-
- load_ckpt(net, args.ckpt_file)
- input_shp = [args.batch_size, 3, cfg.image_height, cfg.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.file_name, file_format=args.file_format)
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