运行eval.py文件

[Aerian222]

谢谢您的工作，让我受益颇多。我尝试使用您公布的预训练权重进行测试,按照你的流程，我修改了如下文件eval.yaml,

defaults:

• visualization: eval
  num_workers: 0
  batch_size: 1
  cuda: 0
  weight_name: "PointGroup-PAPER" # Used during resume, select with model to load from [miou, macc, acc..., latest]
  enable_cudnn: True
  checkpoint_dir: "outputs" # "{your_output_log_path}/outputs/2020-01-28/11-04-13"
  model_name: PointGroup-PAPER
  precompute_multi_scale: True # Compute multiscate features on cpu for faster training / inference
  enable_dropout: False
  voting_runs: 1
  data:
  fold: ['data_set1_5classes/treeinsfused/raw/CULS/CULS_plot_2_annotated_test.ply']
  tracker_options: # Extra options for the tracker
  full_res: True
  make_submission: True
  ply_output: "vote1regular.ply"
  hydra:
  run:
  dir: ${checkpoint_dir}/eval/${now:%Y-%m-%d_%H-%M-%S}

但是当我运行python eval.py时，显示如下错误

[2024-11-07 14:28:29,657][torch_points3d.trainer][INFO] - DEVICE : cuda
[2024-11-07 14:28:29,664][torch_points3d.datasets.dataset_factory][ERROR] - This should happen only during testing
Traceback (most recent call last):
File "eval.py", line 13, in main
trainer = Trainer(cfg)
File "/root/autodl-tmp/ForAINet/PointCloudSegmentation/torch_points3d/trainer.py", line 48, in init
self._initialize_trainer()
File "/root/autodl-tmp/ForAINet/PointCloudSegmentation/torch_points3d/trainer.py", line 97, in _initialize_trainer
self._dataset: BaseDataset = instantiate_dataset(self._cfg.data)
File "/root/autodl-tmp/ForAINet/PointCloudSegmentation/torch_points3d/datasets/dataset_factory.py", line 45, in instantiate_dataset
dataset_cls = get_dataset_class(dataset_config)
File "/root/autodl-tmp/ForAINet/PointCloudSegmentation/torch_points3d/datasets/dataset_factory.py", line 20, in get_dataset_class
dataset_paths = dataset_class.split(".")
AttributeError: 'NoneType' object has no attribute 'split'

这似乎是 配置文件中缺失class参数，是eval.yaml需要加其他参数吗

[bxiang233]

另外我直接测试的步骤如下： 1.下载您提供的pretrained model权重，放在outputs文件夹下； 2.修改eval.yaml文件如下 defaults: visualization: eval num_workers: 0 batch_size: 1 cuda: 0 weight_name: "PointGroup-PAPER" # Used during resume, select with model to load from [miou, macc, acc..., latest] enable_cudnn: True checkpoint_dir: "outputs" # "{your_output_log_path}/outputs/2020-01-28/11-04-13" model_name: PointGroup-PAPER precompute_multi_scale: True # Compute multiscate features on cpu for faster training / inference enable_dropout: False voting_runs: 1 data: fold: ['data_set1_5classes/treeinsfused/raw/CULS/CULS_plot_2_annotated_test.ply'] tracker_options: # Extra options for the tracker full_res: True make_submission: True ply_output: "vote1regular.ply" hydra: run: dir: ${checkpoint_dir}/eval/${now:%Y-%m-%d_%H-%M-%S} 3.运行python eval.py指令

谢谢您的答复！

你好~我没见过这个错误……你试一下把模型的路径和数据的路径改成绝对路径呢？

[Aerian222]

另外我直接测试的步骤如下： 1.下载您提供的pretrained model权重，放在outputs文件夹下； 2.修改eval.yaml文件如下 defaults: visualization: eval num_workers: 0 batch_size: 1 cuda: 0 weight_name: "PointGroup-PAPER" # Used during resume, select with model to load from [miou, macc, acc..., latest] enable_cudnn: True checkpoint_dir: "outputs" # "{your_output_log_path}/outputs/2020-01-28/11-04-13" model_name: PointGroup-PAPER precompute_multi_scale: True # Compute multiscate features on cpu for faster training / inference enable_dropout: False voting_runs: 1 data: fold: ['data_set1_5classes/treeinsfused/raw/CULS/CULS_plot_2_annotated_test.ply'] tracker_options: # Extra options for the tracker full_res: True make_submission: True ply_output: "vote1regular.ply" hydra: run: dir: ${checkpoint_dir}/eval/${now:%Y-%m-%d_%H-%M-%S} 3.运行python eval.py指令
谢谢您的答复！

你好~我没见过这个错误……你试一下把模型的路径和数据的路径改成绝对路径呢？

非常感谢您的建议，现在可以运行了！在运行后的结果中，Instance_results_withColor_0.ply是单木实例分割的结果，vote1regular.ply_0.ply是语义分割的结果？但是为什么两个分割文件的点数不同，甚至语义分割的点数低于实例分割结果好几倍！此外，在可视化后能清楚的看见二者点密度相差很多，这是什么原因呢？再次谢谢您的工作，非常棒！

[bxiang233]

另外我直接测试的步骤如下： 1.下载您提供的pretrained model权重，放在outputs文件夹下； 2.修改eval.yaml文件如下 defaults: visualization: eval num_workers: 0 batch_size: 1 cuda: 0 weight_name: "PointGroup-PAPER" # Used during resume, select with model to load from [miou, macc, acc..., latest] enable_cudnn: True checkpoint_dir: "outputs" # "{your_output_log_path}/outputs/2020-01-28/11-04-13" model_name: PointGroup-PAPER precompute_multi_scale: True # Compute multiscate features on cpu for faster training / inference enable_dropout: False voting_runs: 1 data: fold: ['data_set1_5classes/treeinsfused/raw/CULS/CULS_plot_2_annotated_test.ply'] tracker_options: # Extra options for the tracker full_res: True make_submission: True ply_output: "vote1regular.ply" hydra: run: dir: ${checkpoint_dir}/eval/${now:%Y-%m-%d_%H-%M-%S} 3.运行python eval.py指令
谢谢您的答复！

你好~我没见过这个错误……你试一下把模型的路径和数据的路径改成绝对路径呢？

非常感谢您的建议，现在可以运行了！在运行后的结果中，Instance_results_withColor_0.ply是单木实例分割的结果，vote1regular.ply_0.ply是语义分割的结果？但是为什么两个分割文件的点数不同，甚至语义分割的点数低于实例分割结果好几倍！此外，在可视化后能清楚的看见二者点密度相差很多，这是什么原因呢？再次谢谢您的工作，非常棒！

哈喽，vote1regular是voxolization之后的，Semantic_results_forEval_{}.ply是和Instance_Results_forEval_{}.ply是和输入的点云点数一致的结果。

计算最终的metrics也是用完整点云计算的：
（评估最终精度的代码：）PointCloudSegmentation/evaluation_stats_FOR.py

[Aerian222]

另外我直接测试的步骤如下： 1.下载您提供的pretrained model权重，放在outputs文件夹下； 2.修改eval.yaml文件如下 defaults: visualization: eval num_workers: 0 batch_size: 1 cuda: 0 weight_name: "PointGroup-PAPER" # Used during resume, select with model to load from [miou, macc, acc..., latest] enable_cudnn: True checkpoint_dir: "outputs" # "{your_output_log_path}/outputs/2020-01-28/11-04-13" model_name: PointGroup-PAPER precompute_multi_scale: True # Compute multiscate features on cpu for faster training / inference enable_dropout: False voting_runs: 1 data: fold: ['data_set1_5classes/treeinsfused/raw/CULS/CULS_plot_2_annotated_test.ply'] tracker_options: # Extra options for the tracker full_res: True make_submission: True ply_output: "vote1regular.ply" hydra: run: dir: ${checkpoint_dir}/eval/${now:%Y-%m-%d_%H-%M-%S} 3.运行python eval.py指令
谢谢您的答复！

你好~我没见过这个错误……你试一下把模型的路径和数据的路径改成绝对路径呢？

非常感谢您的建议，现在可以运行了！在运行后的结果中，Instance_results_withColor_0.ply是单木实例分割的结果，vote1regular.ply_0.ply是语义分割的结果？但是为什么两个分割文件的点数不同，甚至语义分割的点数低于实例分割结果好几倍！此外，在可视化后能清楚的看见二者点密度相差很多，这是什么原因呢？再次谢谢您的工作，非常棒！

哈喽，vote1regular是voxolization之后的，Semantic_results_forEval_{}.ply是和Instance_Results_forEval_{}.ply是和输入的点云点数一致的结果。

计算最终的metrics也是用完整点云计算的： （评估最终精度的代码：）PointCloudSegmentation/evaluation_stats_FOR.py

谢谢您，我明白了！您回复的很快，祝好！非常棒的工作！