removed unpack_data()

[salykova]

Hi @liruilong940607

I noticed that you removed the unpack functions due to the new backend. Is there a simple way to unpack tensors with python/pytorch? or this needs to be implemented with c++/cuda code?

[liruilong940607]

Hi unfortunately it requires some cuda programming which is not yet pop to the top of my priority list.

Simply borrow the relevant python&c&cuda code from nerfacc<=0.3.5 should be the easiest fix as this function had a pretty standalone implementation.

The new backend should enable a more optimized way of implementation which I have not yet do it.

[salykova]

nice, thanks for the hint!

[liruilong940607]

@salykovaa Hi I figured out a way to implement the function in pure python:

def unpack_data(packed_info, data, n_samples):
    n_rays = packed_info.shape[0]
    all_samples = data.shape[0]
    row_strides = torch.arange(n_rays, device=device, dtype=torch.long) * n_samples
    row_starts, row_lengths = torch.unbind(packed_info, dim=1)
    idxs = torch.arange(all_samples, device=device, dtype=torch.long) - row_starts.repeat_interleave(row_lengths) + row_strides.repeat_interleave(packed_info[:, 1])
    outputs = torch.zeros((n_rays * n_samples, data.shape[-1]), device=device, dtype=data.dtype)
    outputs[idxs, :] = data
    outputs = outputs.reshape((n_rays, n_samples, data.shape[-1]))
    return outputs

And it is verified to be equivalent to the old implementation we had. Though I'm not sure how efficient this is comparing to the fused CUDA implementation.

[salykova]

@liruilong940607 many thanks!!! As you advised, I took the old pack and cuda unpack functions :sweat_smile: I will test the cuda unpack this pytorch unpack in terms of speed. Let's see how fast it is :blush: