LSTR环境安装

ane Shape Prediction with Transformers  GitHub - liuruijin17/LSTR: This is an official repository of End-to-end Lane Shape Prediction with Transformers.出来虽然两年了但依然是一个非常优秀的车道线检测模型，可以同来拓展检测其他线形，而且占用资源较少推理速度非常快。但是，它的使用python3.6和torch1.5等软件版本比较老了，写导出的onnx脚本在该环境下导出的onnx用现在比较新的TensorRT8.4来解析生成engine时可能会报错，python3.6下即使把torch升级到1.10.1后也还是存在这个问题，例如用下面的脚本导出onnx:

torch.onnx.export(model,         
         images,       # model input (or a tuple for multiple inputs) 
         onnx_file,       
         export_params=True,  # store the trained parameter weights inside the model file 
         opset_version=11,    
         do_constant_folding=True,  # whether to execute constant folding for optimization 
         input_names = ['images', 'masks'],   # the model's input names 
         output_names = ['output_class', 'output_curve'])

会报错:

/root/anaconda3/envs/lstr/lib/python3.6/site-packages/torch/onnx/symbolic_helper.py:325: UserWarning: Type cannot be inferred, which might cause exported graph to produce incorrect results.
  warnings.warn("Type cannot be inferred, which might cause exported graph to produce incorrect results.")
[W shape_type_inference.cpp:434] Warning: Constant folding in symbolic shape inference fails: shape '[240, 1, 1, 240]' is invalid for input of size 240 (function ComputeConstantFolding)
[W shape_type_inference.cpp:434] Warning: Constant folding in symbolic shape inference fails: shape '[3, 1, 1, 240]' is invalid for input of size 240 (function ComputeConstantFolding)
[W shape_type_inference.cpp:434] Warning: Constant folding in symbolic shape inference fails: shape '[3, 1, 1, 240]' is invalid for input of size 240 (function ComputeConstantFolding)
Traceback (most recent call last):
  File "experiments/ex