Files
ascend-tools/pt2pb/pt2onnx.py
T
2020-10-14 08:55:07 +08:00

77 lines
2.6 KiB
Python

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#########################################################################
import os
import torch
import argparse
def parse_args():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--model_path', default=None,
help="""the pytorch model pth file path""")
parser.add_argument('--input_shape', nargs='+', type=int,
help="""the model input shape, e.g. 1 3 224 224""")
args, unknown_args = parser.parse_known_args()
if len(unknown_args) > 0:
for bad_arg in unknown_args:
print("ERROR: Unknown command line arg: %s" % bad_arg)
raise ValueError("Invalid command line arg(s)")
return args
def load_model(model_path, input_shape):
if not os.path.exists(model_path):
print("The pytorch model is not exist")
return None
#修改点1:放开导入模型的注释,并导入自己的模型实现接口.
#例如:模型实现代码目录为./resnet50,网络实现在resnet.py的class ResNet50类
#from resnet50.resnet import ResNet50
#修改点2:放开创建模型对象注释,并根据自己的模型接口创建模型对象
#model = ResNet50()
#修改点3:放开加载模型的注释
#model.load_state_dict(torch.load(model_file))
return model
def main():
args = parse_args()
print("model path ", args.model_path, ", shape ", args.input_shape)
#加载模型
model = load_model(args.model_path, args.input_shape)
if model is None:
print("Load model failed")
return
#将模型切换到推理状态
model.eval()
#创建输入张量
input = torch.randn(tuple(args.input_shape))
#生成的onnx文件存放在pytorch模型同级目录下,文件名相同,后缀为onnx
export_onnx_file = os.path.splitext(args.model_path)[0] + '.onnx'
# Export with ONNX
torch.onnx.export(model, input, export_onnx_file, verbose=True)
if __name__== "__main__":
main()