qujianwei
4 years ago
parent
78c733ffbe
commit
2fbde982f8
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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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"""
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convert resnet50 pretrain model to faster_rcnn backbone pretrain model
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"""
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import argparse
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from mindspore.train.serialization import load_checkpoint, save_checkpoint
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from mindspore.common.parameter import Parameter
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from mindspore.common.tensor import Tensor
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import mindspore.common.dtype as mstype
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parser = argparse.ArgumentParser(description='load_ckpt')
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parser.add_argument('--ckpt_file', type=str, default='', help='ckpt file path')
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args_opt = parser.parse_args()
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def load_weights(model_path, use_fp16_weight):
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"""
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load resnet50 pretrain checkpoint file.
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Args:
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model_path (str): resnet50 pretrain checkpoint file .
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use_fp16_weight(bool): whether save weight into float16.
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Returns:
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parameter list(list): pretrain model weight list.
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"""
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ms_ckpt = load_checkpoint(model_path)
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weights = {}
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for msname in ms_ckpt:
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if msname.startswith("layer") or msname.startswith("conv1") or msname.startswith("bn"):
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param_name = "backbone." + msname
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else:
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param_name = msname
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if "down_sample_layer.0" in param_name:
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param_name = param_name.replace("down_sample_layer.0", "conv_down_sample")
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if "down_sample-layer.1" in param_name:
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param_name = param_name.replace("down_sample_layer.1", "bn_down_sample")
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weights[param_name] = ms_ckpt[msname].data.asnumpy()
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if use_fp16_weight:
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dtype = mstype.float16
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else:
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dtype = mstype.float32
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parameter_dict = {}
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for name in weights:
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parameter_dict[name] = Parameter(Tensor(weights[name], dtype), name=name)
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param_list = []
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for key, value in parameter_dict.items():
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param_list.append({"name": key, "data": value})
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return param_list
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if __name__ == "__main__":
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parameter_list = load_weights(args_opt.ckpt_file, use_fp16_weight=True)
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save_checkpoint(parameter_list, "resnet50_backbone.ckpt")
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@ -0,0 +1,64 @@
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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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"""
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convert resnet50 pretrain model to faster_rcnn backbone pretrain model
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"""
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import argparse
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from mindspore.train.serialization import load_checkpoint, save_checkpoint
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from mindspore.common.parameter import Parameter
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from mindspore.common.tensor import Tensor
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import mindspore.common.dtype as mstype
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parser = argparse.ArgumentParser(description='load_ckpt')
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parser.add_argument('--ckpt_file', type=str, default='', help='ckpt file path')
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args_opt = parser.parse_args()
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def load_weights(model_path, use_fp16_weight):
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"""
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load resnet50 pretrain checkpoint file.
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Args:
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model_path (str): resnet50 pretrain checkpoint file .
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use_fp16_weight(bool): whether save weight into float16.
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Returns:
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parameter list(list): pretrain model weight list.
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"""
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ms_ckpt = load_checkpoint(model_path)
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weights = {}
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for msname in ms_ckpt:
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if msname.startswith("layer") or msname.startswith("conv1") or msname.startswith("bn"):
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param_name = "backbone." + msname
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else:
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param_name = msname
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if "down_sample_layer.0" in param_name:
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param_name = param_name.replace("down_sample_layer.0", "conv_down_sample")
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if "down_sample-layer.1" in param_name:
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param_name = param_name.replace("down_sample_layer.1", "bn_down_sample")
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weights[param_name] = ms_ckpt[msname].data.asnumpy()
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if use_fp16_weight:
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dtype = mstype.float16
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else:
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dtype = mstype.float32
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parameter_dict = {}
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for name in weights:
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parameter_dict[name] = Parameter(Tensor(weights[name], dtype), name=name)
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param_list = []
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for key, value in parameter_dict.items():
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param_list.append({"name": key, "data": value})
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return param_list
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if __name__ == "__main__":
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parameter_list = load_weights(args_opt.ckpt_file, use_fp16_weight=True)
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save_checkpoint(parameter_list, "resnet50_backbone.ckpt")
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