def slice(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(
MinkowskiLinear(3, 16),
MinkowskiBatchNorm(16),
MinkowskiReLU(),
MinkowskiLinear(16, 32),
MinkowskiBatchNorm(32),
MinkowskiReLU(),
MinkowskiToSparseTensor(),
MinkowskiConvolution(32, 64, kernel_size=3, stride=2, dimension=3),
MinkowskiConvolutionTranspose(64,
32,
kernel_size=3,
stride=2,
dimension=3),
)
otensor = network(tfield)
ofield = otensor.slice(tfield)
self.assertEqual(len(tfield), len(ofield))
self.assertEqual(ofield.F.size(1), otensor.F.size(1))
ofield = otensor.cat_slice(tfield)
self.assertEqual(len(tfield), len(ofield))
self.assertEqual(ofield.F.size(1),
(otensor.F.size(1) + tfield.F.size(1)))
def test_network_device(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, device=0).float()
network = nn.Sequential(
MinkowskiLinear(3, 16),
MinkowskiBatchNorm(16),
MinkowskiReLU(),
MinkowskiLinear(16, 32),
MinkowskiBatchNorm(32),
MinkowskiReLU(),
MinkowskiToSparseTensor(),
MinkowskiConvolution(32, 64, kernel_size=3, stride=2, dimension=3),
).to(0)
print(network(tfield))