项目场景：pytorch torch.load ModuleNotFoundError No module named ‘models‘__项目场景规划

项目场景：pytorch torch.load ModuleNotFoundError: No module named ‘models’

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Collecting package metadata (repodata.json): \ / failed

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# 问题描述：

我把服务器训练完的模型拿过来在本地查看

目录接口

----infer

---- 当前文件.py

----yolov5

import torch
import argparse
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default='D:/inference_model/tangbao-citie/2021-12-31/best.pt', help='weights path')
    opt = parser.parse_args()
    # Load pytorch model
    print(opt.weights)
    model = torch.load(opt.weights, map_location=torch.device('cpu'))['model']
    for name, parameters in model.named_parameters():
        print(parameters.dtype)

报错

D:\install\anconda\envs\pytorch-test\python.exe D:/pytorch-work/pytorch_infer/cat_model.py
Traceback (most recent call last):
  File "D:\pytorch-work\pytorch_infer\cat_model.py", line 23, in <module>
    model = torch.load(opt.weights, map_location=torch.device('cpu'))['model']
  File "D:\install\anconda\envs\pytorch-test\lib\site-packages\torch\serialization.py", line 607, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File "D:\install\anconda\envs\pytorch-test\lib\site-packages\torch\serialization.py", line 882, in _load
    result = unpickler.load()
  File "D:\install\anconda\envs\pytorch-test\lib\site-packages\torch\serialization.py", line 875, in find_class
    return super().find_class(mod_name, name)
ModuleNotFoundError: No module named 'models'

原因分析：

需要放在yolov5目录下他会找yolov5目录下的models目录

https://github.com/ultralytics/yolov5/issues/353

解决方案：

import sys
sys.path.insert(0, 'D:/pytorch-work/yolov5')
print(sys.path)

import torch
import argparse
import sys
sys.path.insert(0, 'D:/pytorch-work/yolov5')
print(sys.path)
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default='D:/inference_model/tangbao-citie/2021-12-31/best.pt', help='weights path')
    opt = parser.parse_args()
    # Load pytorch model
    print(opt.weights)
    model = torch.load(opt.weights, map_location=torch.device('cpu'))['model']
    for name, parameters in model.named_parameters():
        print(parameters.dtype)

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