Hi everyone,
I have a point cloud learning problem where the data from each sample (point cloud) is encoded as an a x b x c tensor where b and c are fixed across the samples, but a is not. Because of this I can’t use the standard collate function that stacks the data to a 4-dimensional tensor. Instead I put them in a simple python list. When I train as usual using DataParallel, all data ends up in GPU0, so I can’t utilize all GPUs on my system.
Is there a way to make the DataParallel chunk the data based on list index rather than the batch dimension?
thanks!
doc: http://pytorch.org/docs/master/nn.html#dataparallel-layers-multi-gpu-distributed 37