pyg-team/pytorch_geometric

Segmentation fault (core dumped)

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#1,362 opened on Jun 23, 2020

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Description

❓ Questions & Help

Hi,

I implemented the program with reference to examples/pointnet2_classification.py and used Google Colablatory's GPU to learn the model.

I save the model that I learned in Colab and try to call that model in Jetson Xavier NX to make an inference, I get the Segmentation fault (core dumped).

I ran the same code on the Jetson Nano, but in that case Segmentation fault (core dumped) did not occur.

I think I'm getting Segmentation fault (core dumped) when I run test().

How do I solve this problem?

Thanks!

Enviroment

$ uname -a
Linux Jetson Xavier NX 4.9.140-tegra #1 SMP PREEMPT Wed Apr 8 18:15:20 PDT 2020 aarch64 aarch64 aarch64 GNU/Linux
Device Nano Xavier NX
OS Ubuntu 18.04 Ubuntu 18.04
Python 3.6.9 3.6.9
torch 1.4.0 1.4.0a0+7f73f1d
torch.version.cuda 10.0 10.2
nvcc -V 10.0 10.2
torch-scatter 2.0.4 2.0.4
torch-sparse 0.6.1 0.6.1
torch-cluster 1.5.4 1.5.4
torch-spline-conv 1.2.0 1.2.0
torch-geometric 1.4.3 1.4.3 
$ pointnet++.py
load model
Test Acc: 0.9238
Segmentation fault (core dumped)

Contributor guide

Research direction
Verify that the PyTorch and CUDA versions on Jetson Xavier NX are compatible with the model trained on Colab. Check for any architecture specific compilation issues (e.g., missing CUDA kernels for aarch64). Also ensure that torch scatter, torch sparse, torch cluster, torch spline conv, and torch geometric are built for the correct CUDA version and architecture.
Tech stack
python
Domain
machine learning
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
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Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonPyTorchCUDA
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
65