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53 lines
2.3 KiB
53 lines
2.3 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""export checkpoint file into air, onnx, mindir models"""
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import argparse
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import numpy as np
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import mindspore as ms
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from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
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from src.FasterRcnn.faster_rcnn_r50 import FasterRcnn_Infer
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from src.config import config
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parser = argparse.ArgumentParser(description='fasterrcnn_export')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--file_name", type=str, default="faster_rcnn", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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parser.add_argument('--ckpt_file', type=str, default='', help='fasterrcnn ckpt file.')
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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if __name__ == '__main__':
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net = FasterRcnn_Infer(config=config)
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param_dict = load_checkpoint(args.ckpt_file)
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param_dict_new = {}
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for key, value in param_dict.items():
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param_dict_new["network." + key] = value
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load_param_into_net(net, param_dict_new)
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img = Tensor(np.zeros([config.test_batch_size, 3, config.img_height, config.img_width]), ms.float32)
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img_metas = Tensor(np.random.uniform(0.0, 1.0, size=[config.test_batch_size, 4]), ms.float32)
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export(net, img, img_metas, file_name=args.file_name, file_format=args.file_format)
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