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| import torch | |
| import torchsparse.nn.functional as F | |
| from torchsparse import PointTensor, SparseTensor | |
| from torchsparse.nn.utils import get_kernel_offsets | |
| __all__ = ['initial_voxelize', 'point_to_voxel', 'voxel_to_point'] | |
| # z: PointTensor | |
| # return: SparseTensor | |
| 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 = F.sphash(torch.floor(new_float_coord).int()) | |
| sparse_hash = torch.unique(pc_hash) | |
| idx_query = F.sphashquery(pc_hash, sparse_hash) | |
| counts = F.spcount(idx_query.int(), len(sparse_hash)) | |
| inserted_coords = F.spvoxelize(torch.floor(new_float_coord), idx_query, counts) | |
| inserted_coords = torch.round(inserted_coords).int() | |
| inserted_feat = F.spvoxelize(z.F, idx_query, counts) | |
| new_tensor = SparseTensor(inserted_feat, inserted_coords, 1) | |
| new_tensor.cmaps.setdefault(new_tensor.stride, new_tensor.coords) | |
| z.additional_features['idx_query'][1] = idx_query | |
| z.additional_features['counts'][1] = counts | |
| z.C = new_float_coord | |
| return new_tensor | |
| # x: SparseTensor, z: PointTensor | |
| # return: SparseTensor | |
| 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 = F.sphash( | |
| torch.cat([torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0], z.C[:, -1].int().view(-1, 1)], 1)) | |
| sparse_hash = F.sphash(x.C) | |
| idx_query = F.sphashquery(pc_hash, sparse_hash) | |
| counts = F.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 = F.spvoxelize(z.F, idx_query, counts) | |
| new_tensor = SparseTensor(inserted_feat, x.C, x.s) | |
| new_tensor.cmaps = x.cmaps | |
| new_tensor.kmaps = x.kmaps | |
| return new_tensor | |
| # x: SparseTensor, z: PointTensor | |
| # return: PointTensor | |
| 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: | |
| off = get_kernel_offsets(2, x.s, 1, device=z.F.device) | |
| old_hash = F.sphash( | |
| torch.cat([ | |
| torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0], | |
| z.C[:, -1].int().view(-1, 1)], 1), off) | |
| pc_hash = F.sphash(x.C.to(z.F.device)) | |
| idx_query = F.sphashquery(old_hash, pc_hash) | |
| weights = F.calc_ti_weights(z.C, idx_query, scale=x.s[0]).transpose(0, 1).contiguous() | |
| idx_query = idx_query.transpose(0, 1).contiguous() | |
| if nearest: | |
| weights[:, 1:] = 0. | |
| idx_query[:, 1:] = -1 | |
| new_feat = F.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 = F.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 |