I created a PointNet module with libtorch. It has a point cloud input as {min batch size, 3, pointCount(about 500000)} shape. But, when I training the model, It consumes so many memory.
As I know, on training, ‘conv, linear, batchNormalize…’ hold it’s input tensor for backward. If input tensor shape is {4, 3, 500000} and float32 type, it has 24000000 (=4 x 3 x 500000 x 4) byte on memory. is it true? And if it is True. can I change storage for the values with libtorch?