def test_maxpool(self):
coords = torch.IntTensor([[0, 1], [0, 1], [0, 2], [0, 2], [1, 0],
[1, 0], [1, 1]])
feats = torch.FloatTensor([[0, 1, 2, 3, 5, 6, 7]]).T
sfield = TensorField(feats, coords)
# Convert to a sparse tensor
stensor = sfield.sparse(
quantization_mode=SparseTensorQuantizationMode.MAX_POOL)
print(stensor)
self.assertTrue(
{1, 3, 6, 7} == {a
for a in stensor.F.squeeze().detach().numpy()})
# device cuda
if not torch.cuda.is_available():
return
sfield = TensorField(feats, coords, device="cuda")
# Convert to a sparse tensor
stensor = sfield.sparse(
quantization_mode=SparseTensorQuantizationMode.MAX_POOL)
print(stensor)
self.assertTrue(
{1, 3, 6, 7
} == {a
for a in stensor.F.squeeze().detach().cpu().numpy()})
def test_pcd(self):
coords, colors, pcd = load_file("1.ply")
voxel_size = 0.02
colors = torch.from_numpy(colors)
bcoords = batched_coordinates([coords / voxel_size])
tfield = TensorField(colors, bcoords)
self.assertTrue(len(tfield) == len(colors))
stensor = tfield.sparse()
print(stensor)
def test(self):
coords = torch.IntTensor([[0, 1], [0, 1], [0, 2], [0, 2], [1, 0],
[1, 0], [1, 1]])
feats = torch.FloatTensor([[0, 1, 2, 3, 5, 6, 7]]).T
sfield = TensorField(feats, coords, device=feats.device)
# Convert to a sparse tensor
stensor = sfield.sparse()
print(stensor)
self.assertTrue(
{0.5, 2.5, 5.5, 7} == {a
for a in stensor.F.squeeze().numpy()})
def test(self):
coords = torch.IntTensor([[0, 1], [0, 1], [0, 2], [0, 2], [1, 0],
[1, 0], [1, 1]])
feats = torch.FloatTensor([[0, 1, 2, 3, 5, 6, 7]]).T
sfield = TensorField(feats, coords, device=feats.device)
# Convert to a sparse tensor
stensor = sfield.sparse(
quantization_mode=SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE)
print(stensor)
self.assertTrue({0.5, 2.5, 5.5, 7} ==
{a
for a in stensor.F.squeeze().detach().numpy()})
def field_to_sparse(self):
coords, colors, pcd = load_file("1.ply")
voxel_size = 0.02
colors = torch.from_numpy(colors).float()
bcoords = batched_coordinates([coords / voxel_size],
dtype=torch.float32)
tfield = TensorField(colors, bcoords)
network = nn.Sequential(
MinkowskiToSparseTensor(),
MinkowskiConvolution(3, 8, kernel_size=3, stride=4, dimension=3),
MinkowskiReLU(),
MinkowskiConvolution(8, 16, kernel_size=3, stride=4, dimension=3),
)
otensor = network(tfield)
field_to_sparse = tfield.sparse(
coordinate_map_key=otensor.coordinate_map_key)
self.assertTrue(len(field_to_sparse.F) == len(otensor))
