forked from mindspore-Ecosystem/mindspore
53 lines
2.2 KiB
Python
Executable File
53 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 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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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.model import DnCNN
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parser = argparse.ArgumentParser(description='DnCNN')
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument("--image_height", type=int, default=256, help="image_height")
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parser.add_argument("--image_width", type=int, default=256, help="image_width")
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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="DnCNN", help="output file name.")
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parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='MINDIR', help='file format')
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parser.add_argument('--model_type', type=str, default='DnCNN-S', \
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choices=['DnCNN-S', 'DnCNN-B', 'DnCNN-3'], help='type of DnCNN')
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args = parser.parse_args()
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if __name__ == '__main__':
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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if args.model_type == 'DnCNN-S':
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network = DnCNN(1, num_of_layers=17)
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elif args.model_type == 'DnCNN-3' or args.model_type == 'DnCNN-B':
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network = DnCNN(1, num_of_layers=20)
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else:
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print("wrong model type")
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exit()
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param_dict = load_checkpoint(args.ckpt_file)
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load_param_into_net(network, param_dict)
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input_arr = Tensor(np.ones([args.batch_size, 1, args.image_height, args.image_width]), ms.float32)
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export(network, input_arr, file_name=args.file_name, file_format=args.file_format)
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