关于使用rangenet的问题 (Problem when using rangenet)

[pikaqiu0131]

您好，我在使用您的代码时，infer这步出现了如下问题：
Couldn't load backbone, using random weights. Error: ../../../data/model_rangenet_residual_1/backbone is a zip archive (did you mean to use torch.jit.load()?)
Couldn't load decoder, using random weights. Error: ../../../data/model_rangenet_residual_1/segmentation_decoder is a zip archive (did you mean to use torch.jit.load()?)
Couldn't load head, using random weights. Error: ../../../data/model_rangenet_residual_1/segmentation_head is a zip archive (did you mean to use torch.jit.load()?)

Infering in device: cuda
OMP: Warning #191: Forking a process while a parallel region is active is potentially unsafe.
OMP: Warning #191: Forking a process while a parallel region is active is potentially unsafe.
OMP: Warning #191: Forking a process while a parallel region is active is potentially unsafe.

但之后还可以生成prediction文件，并且在评估iou时，08序列res=1时为0.005（这个估计是错误的）您知道怎么回事吗

[Chen-Xieyuanli]

感谢使用我们的代码，这应该是torch版本的问题，可以参看rangenet的安装流程或者尝试我们的salsanext版本.

[pikaqiu0131]

刚刚看了一下我的torch：

import torch

print(torch.version)
1.1.0
import torchvision
print(torchvision.version)
0.2.2

然后咱们的requirement是：
torch==1.1.0
tensorflow==1.13.1
vispy==0.5.3
torchvision==0.2.2.post3

应该是一致的把

[Chen-Xieyuanli]

pretrained模型下载了之后要先解压，从log文件来看，你好像在加载zip文件

[pikaqiu0131]

您好，我使用这篇文章https://zhuanlan.zhihu.com/p/454415689，更新了一下pytorch版本，现在已经成功运行，就是运行的时候使用了cpu，您知道怎么切换回cuda吗
model folder exists! Using model from ../../../data/model_rangenet_residual_1
Sequences folder exists! Using sequences from ../../../data/sequences
parsing seq 08
Using 4661 scans from sequences [8]
Using DarknetNet53 Backbone
Depth of backbone input = 6
Original OS: 32
New OS: 32
Strides: [2, 2, 2, 2, 2]
Decoder original OS: 32
Decoder new OS: 32
Decoder strides: [2, 2, 2, 2, 2]
Total number of parameters: 50372739
Total number of parameters requires_grad: 50372739
Param encoder 40585792
Param decoder 9786080
Param head 867
Successfully loaded model backbone weights
Successfully loaded model decoder weights
Successfully loaded model head weights

---

Cleaning point-clouds with kNN post-processing
kNN parameters:
knn: 5
search: 5
sigma: 1.0
cutoff: 1.0
nclasses: 3

---

Infering in device: cpu
Infered seq 08 scan 000000.label in 10.995035409927368 sec
Infered seq 08 scan 000001.label in 9.829725742340088 sec