44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (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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# https://opensource.org/licenses/BSD-3-Clause
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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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import argparse
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import torch
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def add_parser_arguments(parser):
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parser.add_argument(
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"--checkpoint-path", metavar="<path>", help="checkpoint filename"
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)
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parser.add_argument(
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"--weight-path", metavar="<path>", help="name of file in which to store weights"
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)
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parser.add_argument("--ema", action="store_true", default=False)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="PyTorch ImageNet Training")
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add_parser_arguments(parser)
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args = parser.parse_args()
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checkpoint = torch.load(args.checkpoint_path, map_location=torch.device("cpu"))
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key = "state_dict" if not args.ema else "ema_state_dict"
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model_state_dict = {
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k[len("module.") :] if "module." in k else k: v
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for k, v in checkpoint["state_dict"].items()
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}
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print(f"Loaded model, acc : {checkpoint['best_prec1']}")
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torch.save(model_state_dict, args.weight_path)
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