def test_device(self):
in_channels, D = 2, 2
device = torch.device("cuda")
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
use_feat = (torch.rand(feats.size(0)) < 0.5).to(device)
pruning = MinkowskiPruning()
input = SparseTensor(feats, coords, device=device)
output = pruning(input, use_feat)
print(input)
print(output)
fn = MinkowskiPruningFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
use_feat,
input.coordinate_map_key,
output.coordinate_map_key,
input.coordinate_manager,
),
))
def test_empty(self):
in_channels = 2
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords)
use_feat = torch.BoolTensor(len(input))
use_feat.zero_()
pruning = MinkowskiPruning()
output = pruning(input, use_feat)
print(input)
print(use_feat)
print(output)
# Check backward
fn = MinkowskiPruningFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
use_feat,
input.coordinate_map_key,
output.coordinate_map_key,
input.coordinate_manager,
),
))
def test_pruning(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords)
use_feat = torch.rand(feats.size(0)) < 0.5
pruning = MinkowskiPruning(D)
output = pruning(input, use_feat)
print(use_feat, output)
# Check backward
fn = MinkowskiPruningFunction()
self.assertTrue(
gradcheck(fn, (input.F, use_feat, input.coords_key,
output.coords_key, input.coords_man)))
device = torch.device('cuda')
with torch.cuda.device(0):
input = input.to(device)
output = pruning(input, use_feat)
print(output)
self.assertTrue(
gradcheck(fn, (input.F, use_feat, input.coords_key,
output.coords_key, input.coords_man)))