[Fix] Replace hardcoded .cuda() with device-aware .to() in MinkUNet voxelization

[Mr-Neutr0n]

Motivation

In Det3DDataPreprocessor.voxelize(), the minkunet voxelization branch converts numpy arrays back to tensors using torch.from_numpy(...).cuda(). This hardcodes the CUDA device assumption, which causes:

1. RuntimeError on CPU-only environments — users without a GPU cannot use MinkUNet-based models at all
2. Incorrect device placement in multi-GPU setups — tensors always land on cuda:0 regardless of which device the input data resides on (e.g., cuda:1), leading to device mismatch errors during subsequent operations

Modification

Replace .cuda() with .to(res.device) for both point2voxel_map and inds tensors, where res is the input point cloud tensor already on the correct device. This is consistent with how device handling is done elsewhere in the same file (e.g., using .new_tensor() and F.pad() which inherit the device from existing tensors).

Before:

point2voxel_map = torch.from_numpy(point2voxel_map).cuda()
...
inds = torch.from_numpy(inds).cuda()

After:

point2voxel_map = torch.from_numpy(point2voxel_map).to(res.device)
...
inds = torch.from_numpy(inds).to(res.device)

BC-breaking (No)

This is a backward-compatible fix. On CUDA environments, res.device will be cuda:X (matching the previous behavior when on cuda:0), and it additionally supports CPU and multi-GPU scenarios correctly.

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[Mr-Neutr0n]

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