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59 lines
2.8 KiB
59 lines
2.8 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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import argparse
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import numpy as np
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from mindspore import Tensor, export, load_checkpoint, load_param_into_net, context
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from src.unet_medical.unet_model import UNetMedical
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from src.unet_nested import NestedUNet, UNet
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from src.config import cfg_unet as cfg
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from src.utils import UnetEval
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parser = argparse.ArgumentParser(description='unet 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("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument('--width', type=int, default=572, help='input width')
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parser.add_argument('--height', type=int, default=572, help='input height')
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parser.add_argument("--file_name", type=str, default="unet", 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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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 cfg['model'] == 'unet_medical':
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net = UNetMedical(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
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elif cfg['model'] == 'unet_nested':
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net = NestedUNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'], use_deconv=cfg['use_deconv'],
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use_bn=cfg['use_bn'], use_ds=False)
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elif cfg['model'] == 'unet_simple':
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net = UNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'])
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else:
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raise ValueError("Unsupported model: {}".format(cfg['model']))
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# return a parameter dict for model
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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(net, param_dict)
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net = UnetEval(net)
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input_data = Tensor(np.ones([args.batch_size, cfg["num_channels"], args.height, args.width]).astype(np.float32))
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export(net, input_data, file_name=args.file_name, file_format=args.file_format)
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