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58 lines
3.0 KiB
58 lines
3.0 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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from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
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from eval import BuildEvalNetwork
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from src.nets import net_factory
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parser = argparse.ArgumentParser(description='checkpoint export')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument("--input_size", type=int, default=513, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="deeplabv3", help="output file name.")
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parser.add_argument('--file_format', type=str, choices=["AIR", "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('--model', type=str.lower, default='deeplab_v3_s8', choices=['deeplab_v3_s16', 'deeplab_v3_s8'],
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help='Select model structure (Default: deeplab_v3_s8)')
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parser.add_argument('--num_classes', type=int, default=21, help='the number of classes (Default: 21)')
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parser.add_argument("--input_format", type=str, choices=["NCHW", "NHWC"], default="NCHW",
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help="NCHW or NHWC")
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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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if args.model == 'deeplab_v3_s16':
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network = net_factory.nets_map['deeplab_v3_s16']('eval', args.num_classes, 16, True)
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else:
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network = net_factory.nets_map['deeplab_v3_s8']('eval', args.num_classes, 8, True)
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network = BuildEvalNetwork(network, args.input_format)
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param_dict = load_checkpoint(args.ckpt_file)
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# load the parameter into net
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load_param_into_net(network, param_dict)
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if args.input_format == "NHWC":
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input_data = Tensor(np.ones([args.batch_size, args.input_size, args.input_size, 3]).astype(np.float32))
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else:
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input_data = Tensor(np.ones([args.batch_size, 3, args.input_size, args.input_size]).astype(np.float32))
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export(network, input_data, file_name=args.file_name, file_format=args.file_format)
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