drprojects
commented
1 year ago Hi @kotetsu-n, thanks for your interest in this project !
Quick answer
It is likely that some voxels in your Data object have no labels in their label histogram y, a way of testing it:
assert a.sum(dim=1).gt(0).all(), f"Some points in the label histogram `self.y` do not have any labels.place this :point_up: in here to check if this hypothesis is correct.
Detailed answer
The error stems when trying to save the labels of your preprocessed NAG. More precisely, it is when trying to save your Data to disk (for details on the Data and NAG data structures used in this project, please have a look at the docs and the extensive docstrings and comments in the code of the relevant classes). Even more precisely, the Data objects hold the semantic annotations in their y attribute as a 2D histogram of shape (num_nodes, num_classes + 1), where num_nodes is the number of nodes in your Data object and num_classes is the number of classes in your dataset. These are histograms and not simply 1D tensors holding a single label per node, because we want to keep track of all labels in your raw point cloud, even after voxelization, and superpoint partition (this allows computing exact full-resolution metrics and losses without loading the entire raw point clouds).
Now, when saving these 2D histograms, we convert them to CSR format to save some disk space and I/O speed. The error you are encountering is happening when converting a 2D histogram to CSR format.
One reason I can think of for things to go wrong at this point is that, somehow, one of your points/voxels/superpoints (depends on the partition level in your NAG) has no labels at all in its histogram.
I don't know how this could happen, but it definitely comes from your dataset reader. Can you think of a reason why a point would not have any labels ?
Have you followed the guidelines for creating your own dataset ?
An important rule (that I will make clearer in future releases) for creating your own dataset is that your points must have labels within $[0, C]$, where:
- $C$ is your
BaseDataset'snum_classes - giving the $C$ label to a point marks it as void/ignored/unlabeled (whichever you call it) and will exclude it from metrics and losses computation
- all labels $[0, C - 1]$ are assumed to be present in your dataset and will be used in metrics and losses computation
To this end, I recommend you make sure the output of your read_single_raw_cloud method never has labels outside of your $[0, C]$ range. Besides, if some labels in $[0, C - 1]$ are not useful to you (ie absent from your dataset), I recommend you densely remap your labels to another $[0, C_2 - 1]$ range (you can use torch_geometric.nn.pool.consecutive.consecutive_cluster for that, for instance), while making sure you only use the label $C_2$ for void/ignored/unlabeled points.
Best,
Damien
tkm-n
Hi, thank you for sharing the code of your promising paper! I want to try the code with my own dataset, but I got an error during preprocess. When does this AssertionError happen? I believe I made a custom loader to load my dataset referring to s3dis's loader, but it or my own dataset seem to be causing the error. It seems properly to load dataset for a while during the preprocess, but stops in the middle of it. The following is the traceback or the error. Could you tell me what the cause would be for this error?