out-of-bounds w.r.t. dense side with broadcasted shape issue during promise12 demo training

[niftk]

out-of-bounds w.r.t. dense side with broadcasted shape issue during promise12 demo training

[ladislav-urban]

Hi Wenqi Li,
thanks a lot for the advice! The training is now working without errors.

[wyli]

Hi @ladislav-urban many thanks for the feedback! This could happen when the largest voxel value in the discrete segmentation maps is greater than num_classes in the config file (since the voxel values are used as sparse binary map indices). Could you check if that's the case?

[ladislav-urban]

Hi Wenqi Li,
thanks for your quick answer! I do use default promise12 dataset. I have checked segmentation files in directories: TrainingData_Part1 to TrainingData_Part3. These are files starting from Case00_segmentation.mhd till Case49_segmentation.mhd . They include only values 0 or 1. There is a settings num_classes = 2 in the config files.
Can the higher voxel numbers be created by data augmentation process?
Thanks a lot for your answer
Ladislav

[wyli]

Yes I think you're right, the interp_order parameter should be 0 for [label] section, which means using the nearest neighbour interpolation in the data augmentations. Thanks and I'll update the config files.

[qianjiangcn]

I have been running into the same issue.
InvalidArgumentError (see above for traceback): Provided indices are out-of-bounds w.r.t. dense side with broadcasted shape
[[{{node worker_0/loss_function/map/while/mul}} = SparseDenseCwiseMul[T=DT_FLOAT, _class=["loc:@worke...tackPushV2"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](worker_0/loss_function/map/while/SparseReshape, worker_0/loss_function/map/while/ones_like, worker_0/loss_function/map/while/SparseReshape:1, worker_0/loss_function/map/while/Softmax)]]

As stated, this could happen when the largest voxel value in the discrete segmentation maps is greater than num_classes in the config file.
what do you mean largest voxel value in the discrete segmentation maps?
my spatial window is 32 and the num_classes in the config is 160.

Thank you very much !