| import torchsparse.nn.functional as spf |
| from torchsparse.point_tensor import PointTensor |
| from torchsparse.utils.kernel_region import * |
| from torchsparse.utils.helpers import * |
|
|
|
|
| __all__ = ['initial_voxelize', 'point_to_voxel', 'voxel_to_point'] |
|
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|
| |
| |
| def initial_voxelize(z, init_res, after_res): |
| new_float_coord = torch.cat( |
| [(z.C[:, :3] * init_res) / after_res, z.C[:, -1].view(-1, 1)], 1) |
|
|
| pc_hash = spf.sphash(torch.floor(new_float_coord).int()) |
| sparse_hash = torch.unique(pc_hash) |
| idx_query = spf.sphashquery(pc_hash, sparse_hash) |
| counts = spf.spcount(idx_query.int(), len(sparse_hash)) |
|
|
| inserted_coords = spf.spvoxelize(torch.floor(new_float_coord), idx_query, |
| counts) |
| inserted_coords = torch.round(inserted_coords).int() |
| inserted_feat = spf.spvoxelize(z.F, idx_query, counts) |
|
|
| new_tensor = SparseTensor(inserted_feat, inserted_coords, 1) |
| new_tensor.check() |
| z.additional_features['idx_query'][1] = idx_query |
| z.additional_features['counts'][1] = counts |
| z.C = new_float_coord |
|
|
| return new_tensor |
|
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|
| |
| |
| def point_to_voxel(x, z): |
| if z.additional_features is None or z.additional_features.get('idx_query') is None\ |
| or z.additional_features['idx_query'].get(x.s) is None: |
| |
| pc_hash = spf.sphash( |
| torch.cat([ |
| torch.floor(z.C[:, :3] / x.s).int() * x.s, |
| z.C[:, -1].int().view(-1, 1) |
| ], 1)) |
| sparse_hash = spf.sphash(x.C) |
| idx_query = spf.sphashquery(pc_hash, sparse_hash) |
| counts = spf.spcount(idx_query.int(), x.C.shape[0]) |
| z.additional_features['idx_query'][x.s] = idx_query |
| z.additional_features['counts'][x.s] = counts |
| else: |
| idx_query = z.additional_features['idx_query'][x.s] |
| counts = z.additional_features['counts'][x.s] |
|
|
| inserted_feat = spf.spvoxelize(z.F, idx_query, counts) |
| new_tensor = SparseTensor(inserted_feat, x.C, x.s) |
| new_tensor.coord_maps = x.coord_maps |
| new_tensor.kernel_maps = x.kernel_maps |
|
|
| return new_tensor |
|
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|
|
| |
| |
| def voxel_to_point(x, z, nearest=False): |
| if z.idx_query is None or z.weights is None or z.idx_query.get( |
| x.s) is None or z.weights.get(x.s) is None: |
| kr = KernelRegion(2, x.s, 1) |
| off = kr.get_kernel_offset().to(z.F.device) |
| |
| old_hash = spf.sphash( |
| torch.cat([ |
| torch.floor(z.C[:, :3] / x.s).int() * x.s, |
| z.C[:, -1].int().view(-1, 1) |
| ], 1), off) |
| pc_hash = spf.sphash(x.C.to(z.F.device)) |
| idx_query = spf.sphashquery(old_hash, pc_hash) |
| weights = spf.calc_ti_weights(z.C, idx_query, |
| scale=x.s).transpose(0, 1).contiguous() |
| idx_query = idx_query.transpose(0, 1).contiguous() |
| if nearest: |
| weights[:, 1:] = 0. |
| idx_query[:, 1:] = -1 |
| new_feat = spf.spdevoxelize(x.F, idx_query, weights) |
| new_tensor = PointTensor(new_feat, |
| z.C, |
| idx_query=z.idx_query, |
| weights=z.weights) |
| new_tensor.additional_features = z.additional_features |
| new_tensor.idx_query[x.s] = idx_query |
| new_tensor.weights[x.s] = weights |
| z.idx_query[x.s] = idx_query |
| z.weights[x.s] = weights |
|
|
| else: |
| new_feat = spf.spdevoxelize(x.F, z.idx_query.get(x.s), z.weights.get(x.s)) |
| new_tensor = PointTensor(new_feat, |
| z.C, |
| idx_query=z.idx_query, |
| weights=z.weights) |
| new_tensor.additional_features = z.additional_features |
|
|
| return new_tensor |
|
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|
