About the optimization of memory consumption

[dylanwusee]

About the optimization of memory consumption

[DylanWusee]

Thank you for interesting in our work.

The part that you miss might be that the weight in 1x1 conv of Fig.5 is shared across all the points in the point cloud. However, the output of the MLP1' in Fig.5 is not. The reason for that is because the local region of a point cloud is not in a standard grid shape like images, refer to Fig.2(b) and (c) as an example.

So, in the original version of PointConv, the main memory consumption should be B x N x K x (C_in x C_out); And the efficient version should be B x N x K x C_mid + B x N x C_in x C_mid. The C_mid x C_in x C_out you mentioned is just a standard convolution kernel which is not the main memory consumption in this case.

ratio : B x N x (K + C_in) x C_mid / (B x N x K x C_in x C_out) = (K + C_in) x C_mid / (K x C_in x C_out)

The general value for C_in would be 641024, and K would be 832. So, (K + C_in) is the same magnitude as C_in.

So, the ratio becomes C_mid / (K x C_out) .

Hope that answered your question.

[KaneKun]

Your analysis is correct. Hope that you would add such detail into the main paper :)

By the way, in practice, I wonder roughly about how many memory footprint you could save using the efficient solution (e.g., for the classification network with a certain batch size and number of points)?

[DylanWusee]

That is simple.

In practice, we could not be able to train an Original Version of PointConv on a general GPU such as GTX1080 Ti. But, we can easily train an efficient version of PointConv with the same structure.

The memory save is:
untrainable/unrunnable -> trainable/runnable

[KaneKun]

Excellent! Thank you for your kind explanation.