error in pointops.interpolation

[SidneyRey]

Thank you for your code!
When running the test script, the program hangs in the decoder module within the pointops.interpolation function. The main issue arises specifically in the knnquery part, where values in the idx variable are abnormal. The error message reads as follows:

how to fix it?

[nicehuster]

Thank you for your interest in our work. I will check the issue later

[chunibyo-wly]

same issues, thanks for your work

Thank you for your interest in our work. I will check the issue later

[nicehuster]

I'm sorry for the late response. After careful inspection, it was an environmental issue and I switched to the environment where I conducted the experiment. It works fine, but I still can't run it on the newly rebuilt environment. I don't have the energy to accurately locate the problem at the moment. But, I uploaded my conda environment to this link. You can download it and use 'conda activate sp ' to activate the environment.

[revenamar1]

I also had it (run the code using Google Colab, 1 GPU), and I had to change some config to make it work.

• GPU number need to be set to X @ test_dist (for me 1)
• If you're run the code with only 1 GPU, make sure you comment the multiple GPU init on test.py (lines 48-49)
• You need to set the max feat size related to the hardware you use (512 for what I selected at Colab), and change this function at modules->pointops->functions->pointops.py to be:

def interpolation(xyz, new_xyz, feat, offset, new_offset, k=3):
"""
input: xyz: (m, 3), new_xyz: (n, 3), feat: (m, c), offset: (b), new_offset: (b)
output: (n, c)
"""
assert xyz.is_contiguous() and new_xyz.is_contiguous() and feat.is_contiguous()
idx, dist = knnquery(k, xyz, new_xyz, offset, new_offset) # (n, k), (n, k)

# Ensure indices are within the valid range
idx = torch.clamp(idx, 0, feat.size(0) - 1)

dist_recip = 1.0 / (dist + 1e-8)  # (n, k)
norm = torch.sum(dist_recip, dim=1, keepdim=True)
weight = dist_recip / norm  # (n, k)

new_feat = torch.cuda.FloatTensor(new_xyz.shape[0], feat.shape[1]).zero_()
for i in range(k):
    new_feat += feat[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1)
return new_feat