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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.
- # ============================================================================
- import argparse
- import numpy as np
-
- import mindspore
- from mindspore import context, Tensor
- from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
-
- from src.yolo import YOLOV4CspDarkNet53
-
- parser = argparse.ArgumentParser(description='yolov4 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("--testing_shape", type=int, default=608, help="test shape")
- parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
- parser.add_argument("--file_name", type=str, default="yolov4", help="output file name.")
- parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
- parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
- help="device target")
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
- if args.device_target == "Ascend":
- context.set_context(device_id=args.device_id)
-
- if __name__ == "__main__":
- ts_shape = args.testing_shape
-
- network = YOLOV4CspDarkNet53(is_training=False)
-
- param_dict = load_checkpoint(args.ckpt_file)
- load_param_into_net(network, param_dict)
-
- input_shape = Tensor(tuple([ts_shape, ts_shape]), mindspore.float32)
- input_data = Tensor(np.zeros([args.batch_size, 3, ts_shape, ts_shape]), mindspore.float32)
-
- export(network, input_data, input_shape, file_name=args.file_name, file_format=args.file_format)
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