Training for custom dataset

[ttsesm]

Hi @yewzijian,

Thanks for sharing your work. I would like to ask you whether you could elaborate with some details about how someone could train the model for a custom dataset.

Thanks.

[yewzijian]

You can look at the implementation of the data loaders in src/datasets. Note that depending on your dataset, the network parameters may also need to be changed.

[ttsesm]

I see, thanks.

I am skipping through the code now to get a better understanding of the pipeline. I had a look on the dataloaders as you suggested and one question that derived is why the input data kind of differs on the two loaders?

3Dmatch:

        data_pair = {
            'src_xyz': torch.from_numpy(src_xyz).float(),
            'tgt_xyz': torch.from_numpy(tgt_xyz).float(),
            'src_overlap': torch.from_numpy(src_overlap_mask),
            'tgt_overlap': torch.from_numpy(tgt_overlap_mask),
            'correspondences': torch.from_numpy(src_tgt_corr),  # indices
            'pose': torch.from_numpy(pose).float(),
            'idx': item,
            'src_path': src_path,
            'tgt_path': tgt_path,
            'overlap_p': overlap_p,
        }

Modelnet:

        sample_out = {
            'src_xyz': torch.from_numpy(sample['points_src'][:, :3]),
            'tgt_xyz': torch.from_numpy(sample['points_ref'][:, :3]),
            'tgt_raw': torch.from_numpy(sample['points_raw'][:, :3]),
            'src_overlap': torch.from_numpy(sample['src_overlap']),
            'tgt_overlap': torch.from_numpy(sample['ref_overlap']),
            'correspondences': torch.from_numpy(sample['correspondences']),
            'pose': torch.from_numpy(sample['transform_gt']),
            'idx': torch.from_numpy(sample['idx']),
            'corr_xyz': torch.from_numpy(corr_xyz),
        }

There are some common elements but also some different ones, why is that?

[yewzijian]

Hi @ttsesm, the fields that are not in common are not necessary and most of them can be removed. However certain fields, e.g. tgt_raw might be used for computing the evaluation metrics.