Comments (2)
This is because 0
is used as the nolabel class during training (stairs are not evaluated). Since the nolabel class is never predicted, we shift the labels by one when evaluating.
To see this, remember that the columns of target
for a superpoint are as follows, (see here):
- col0: label mode, ie the most common label the superpoint
- col1: number of unannotated points
- col2+: number of point of each label
When training, we use the cross entropy with the label mode (target[0]), see here. When evaluating, we compute the precision of the point-level predictions (target[2:]), see here.
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Thanks @loicland for the explanation. I found my thinking mistake..
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Related Issues (20)
- S3DIS superpoints generated by SPG does not look right HOT 1
- how to generate the trainset'superpoint-graph after oversegment? HOT 1
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation HOT 5
- cuda acceleration for partition HOT 4
- TypeError in 'NearestNeighbors(1, algorithm='kd_tree').fit(xyz)' HOT 2
- CUDA error when training for Semantic3D HOT 1
- the version of metrics HOT 2
- When making ply_c, fatal error: numpy/ndarrayobject.h: No such file or directory HOT 3
- How to control the number of superpoints in a room? HOT 6
- Segmentation fault (core dumped) HOT 2
- Running on Stanford3dDataset_v1.2_Aligned_Version, the error occurs. HOT 6
- CMake error HOT 1
- Which version of Pytorch is needed for this code? HOT 1
- ModuleNotFoundError: No module named 'torchnet' HOT 3
- RuntimeError: scan failed to synchronize: an illegal memory access was encountered HOT 2
- L0-cut pursuit partition algorithm HOT 3
- cupy_backends.cuda.api.driver.CUDADriverError: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered HOT 3
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