Different features for each run?

[katadam]

Hello and thanks for the terrific code.

I have one question, I am trying to use OcCo pretrained network on the semantic segmentation task to extract features and perform dense matching of 3d points (either Pointnet or pcn).

I have saved and I am loading the same h5 file to avoid alternations due to different sampling. However, if I run the encoder twice (with the same tensor as points), I do not get determenistic results and the returned feature tensors are different, without changing anything. I am in mode model.eval() to have a defined dropout. Could you elaborate please?

[katadam]

I have tried to load the occo weights for the pointnet model (for the semantic segmentation task). However, some weights are missing ({'module.conv4.weight', 'module.conv4.bias', 'module.feat.stn.conv1.weight', 'module.feat.conv1.weight'}
) when model is loaded in Torch. Thus, weights are randomly initialized and lead to non-deterministic descriptors for the same input. Would you mind updating the model with all the weights? Thanks!

[hansen7]

Hi @katadam, the provided pre-trained weight is from the completion pre-training task, for encoding an object you don't need to load the entire classification version of the PointNet

[katadam]

Thanks for the answer @hansen7 . Yes, I have noticed that, however, I want to extract features for a semantic segmentation. This is why I am loading PointNet (local and global features aggregated). To obtain the weights for this task, I should finetune on a semantic segmentation dataset (s3dis, Scannet), the models you are providing or pre-train the whole PointNet ?

[hansen7]

we have many downstream tasks, I cannot provide all the fine tuned weights for all of them。。。

[katadam]

@hansen7 thanks for your answer!
Yes I do get that about the fine-tuned weights, I was just asking if you have the models with OcCo Weights for the semantic segmentation task, as right now you are providing weights for classification and segmentation (so global descriptors for the 3 networks and not global+local as wanted for the semantic segmentation task.)

[hansen7]

It is on the same location of the google drive。。。namely, {modelname}_occo_seg.pth

[katadam]

Thanks @hansen7 , ok, it is clear now. I was wondering if you had a model for pre-trained on shape completion for the whole point net model (the one you are providing + the transformation networks). But I will use the one for part segmentation (namely, {modelname}_occo_seg.pth), finetune it on s3dis to get the weights for the extra layers and use that as an encoder to my other dataset to extract semantic segmentation features.