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 = hash_gpu(torch.floor(z.C).int())
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
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
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 = hash_gpu(torch.floor(z.C).int())
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