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 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 = kernel_hash_gpu(torch.floor(z.C).int(), off)
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
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
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
