Wrong normalization on Kitti?

[autosquid]

Around https://github.com/chenfengxu714/SqueezeSegV3/blob/master/src/tasks/semantic/dataset/kitti/parser.py#L171:

proj = torch.cat([proj_range.unsqueeze(0).clone(),
                      proj_xyz.clone().permute(2,0,1),
                      proj_remission.unsqueeze(0).clone()])
proj = (proj - self.sensor_img_means[:, None, None]) / self.sensor_img_stds[:, None, None]

The order of channels in proj is (range, z, x, y, remission), but sensor_image_means is in the order of (range,x,y,z,signal) or something else (based if it's V321 or V353)?

So I guess here's something wrong?

[chenfengxu714]

Sorry for my late reply, I did't notice the issue recently. Yes, I made a small mistake here, while we also find the normalization on the KITTI doesn't have much influence and just slight drops the performance. It is true that right normalization with accurate mean and std are better. We will update this soon.