def test_strided_tensor(self):
in_channels, D = 2, 2
tfield = torch.Tensor([
[0, 0.1, 2.7],
[0, 0.3, 2],
[1, 1.5, 2.5],
])
coords = torch.IntTensor([[0, 0, 2], [0, 0, 4], [0, 2, 4]])
feats = torch.rand(len(coords), 1)
input = SparseTensor(feats, coordinates=coords, tensor_stride=2)
interp = MinkowskiInterpolation()
output = interp(input, tfield)
print(input)
print(output)
def test(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels, batch_size=2)
feats = feats.double()
tfield = torch.Tensor(
[
[0, 0.1, 2.7],
[0, 0.3, 2],
[1, 1.5, 2.5],
]
).double()
feats.requires_grad_()
input = SparseTensor(feats, coordinates=coords)
interp = MinkowskiInterpolation(return_kernel_map=True, return_weights=False)
output, (in_map, out_map) = interp(input, tfield)
print(input)
print(output)
# Check backward
output.sum().backward()
fn = MinkowskiInterpolationFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
tfield,
input.coordinate_map_key,
input._manager,
),
)
)
for i in range(LEAK_TEST_ITER):
input = SparseTensor(feats, coordinates=coords)
tfield = torch.DoubleTensor(
[
[0, 0.1, 2.7],
[0, 0.3, 2],
[1, 1.5, 2.5],
],
)
output, _ = interp(input, tfield)
output.sum().backward()
def test_gpu(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels, batch_size=2)
feats = feats.double()
tfield = torch.cuda.DoubleTensor(
[
[0, 0.1, 2.7],
[0, 0.3, 2],
[1, 1.5, 2.5],
],
)
feats.requires_grad_()
input = SparseTensor(feats, coordinates=coords, device="cuda")
interp = MinkowskiInterpolation()
output = interp(input, tfield)
print(input)
print(output)
output.sum().backward()
# Check backward
fn = MinkowskiInterpolationFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
tfield,
input.coordinate_map_key,
input._manager,
),
)
)
for i in range(LEAK_TEST_ITER):
input = SparseTensor(feats, coordinates=coords, device="cuda")
tfield = torch.cuda.DoubleTensor(
[
[0, 0.1, 2.7],
[0, 0.3, 2],
[1, 1.5, 2.5],
],
)
output = interp(input, tfield)
output.sum().backward()
def test_zero(self):
# Issue #383 https://github.com/NVIDIA/MinkowskiEngine/issues/383
#
# create point and features, all with batch 0
pc = torch.randint(-10, 10, size=(32, 4), dtype=torch.float32, device='cuda')
pc[:, 0] = 0
feat = torch.randn(32, 3, dtype=torch.float32, device='cuda', requires_grad=True)
# feature to interpolate
x = SparseTensor(feat, pc, device='cuda')
interp = MinkowskiInterpolation()
# samples with original coordinates, OK for now
samples = pc
y = interp(x, samples)
print(y.shape, y.stride())
torch.sum(y).backward()
# samples with all zeros, shape is inconsistent and backward gives error
samples = torch.zeros_like(pc)
samples[:, 0] = 0
y = interp(x, samples)
print(y.shape, y.stride())
torch.sum(y).backward()