Comments (4)
Hi,
This is because the maximum number of points are sampled for the shape but they are still unable to cover the entire shape. For example, you may need 20000 points to cover the entire shape but the upper bound set in the code is 16384, then you will get a "batch_size exceeded" warning.
You can safely ignore the warnings if only a small portion of the shapes have such warnings.
If you want to change the upper bounds, find the following lines of code at each script and change batch_size_2
and batch_size_3
to some larger numbers. (batch_size_1
is for 16^3 voxels and 16*16*16
is already the maximum possible amount.)
batch_size_1 = 16*16*16
batch_size_2 = 16*16*16
batch_size_3 = 16*16*16*4
from im-net.
Thank you very much for your quick and detailed reply! I got it at once.
So for the best performance of IM-Net and also for BSP-Net should I use the maximum possible amount to prevent "batch_size exceeded" regardless of the time of trainig?
And have you ever test how the growing batch_size will slow down the training of the network?
from im-net.
I have never tested other options so I am not sure how they can influence the training.
from im-net.
Then I'll try it by myself. Really appreciate your fast response. Great thanks!
from im-net.
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from im-net.