def forward(ctx, coords, ratio):
coords_float = coords[:, :3].float()
# following Minkowski engine
coords_new = torch.floor(torch.floor(coords_float / ratio) *
ratio).int()
coords_new = torch.cat([coords_new, coords[:, 3].view(-1, 1)], 1)
coords_new_hash = sphash(coords_new)
uq, inv, cnt = torch.unique(coords_new_hash,
return_inverse=True,
return_counts=True)
inv = inv.int()
cnt = cnt.int()
# rounding is necessary
# gpu
if 'cuda' in str(coords.device):
uq_coords = torch.round(
torchsparse_cuda.insertion_forward(coords_new.float(), inv,
cnt))
elif 'cpu' in str(coords.device):
uq_coords = torch.round(
torchsparse_cuda.cpu_insertion_forward(coords_new.float(), inv,
cnt))
else:
device = coords.device
uq_coords = torch.round(
torchsparse_cuda.cpu_insertion_forward(
coords_new.float().cpu(), inv.cpu(), cnt.cpu()))
uq_coords = uq_coords.to(device)
uq_coords = uq_coords.int()
# Notice: corrds_new_hash cannot be directly used
return uq_coords #, coords_new_hash
def forward(ctx, coords, ratio):
coords_float = coords[:, :-1].float()
ratio = torch.from_numpy(ratio).float().to(coords.device)
coords_new = torch.floor(torch.floor(coords_float / ratio) *
ratio).int()
coords_new = torch.cat([coords_new, coords[:, -1].view(-1, 1)], 1)
coords_new_hash = sphash(coords_new[:, 1:])
uq, inv, cnt = torch.unique(coords_new_hash,
return_inverse=True,
return_counts=True)
inv = inv.int()
cnt = cnt.int()
if 'cuda' in str(coords.device):
uq_coords = torch.round(
torchsparse_cuda.insertion_forward(coords_new.float(), inv,
cnt))
elif 'cpu' in str(coords.device):
uq_coords = torch.round(
torchsparse_cuda.cpu_insertion_forward(coords_new.float(), inv,
cnt))
else:
device = coords.device
uq_coords = torch.round(
torchsparse_cuda.cpu_insertion_forward(
coords_new.float().cpu(), inv.cpu(), cnt.cpu()))
uq_coords = uq_coords.to(device)
uq_coords = uq_coords.int()
return uq_coords
def forward(ctx, coords, ratio):
'''
Inputs:
coords: torch.Int32 tensor, N x 4
ratio: float, downsample ratio
Outputs:
coords_downsampled: torch.Int32 tensor, M x 4
Algorithm:
Using torch.unique to get **inverse** indices
Then use the insertion kernel.
TBD:
The insertion kernel w/o atomic op.
'''
#coords = coords.to('cpu')
coords_float = coords[:, :3].float()
# following Minkowski engine
coords_new = torch.floor(torch.floor(coords_float / ratio) *
ratio).int()
coords_new = torch.cat([coords_new, coords[:, 3].view(-1, 1)], 1)
coords_new_hash = sphash(coords_new)
uq, inv, cnt = torch.unique(coords_new_hash,
return_inverse=True,
return_counts=True)
inv = inv.int()
cnt = cnt.int()
# rounding is necessary
# gpu
if 'cuda' in str(coords.device):
uq_coords = torch.round(
torchsparse_cuda.insertion_forward(coords_new.float(), inv,
cnt))
elif 'cpu' in str(coords.device):
uq_coords = torch.round(
torchsparse_cuda.cpu_insertion_forward(coords_new.float(), inv,
cnt))
else:
device = coords.device
uq_coords = torch.round(
torchsparse_cuda.cpu_insertion_forward(
coords_new.float().cpu(), inv.cpu(), cnt.cpu()))
uq_coords = uq_coords.to(device)
uq_coords = uq_coords.int()
# Notice: corrds_new_hash cannot be directly used
return uq_coords #, coords_new_hash
def forward(ctx, feat, idx, cnt):
out = torchsparse_cuda.insertion_forward(feat.float().contiguous(),
idx.int().contiguous(), cnt)
ctx.for_backwards = (idx.int().contiguous(), cnt, feat.shape[0])
return out