def test_network(self):
dense_tensor = torch.rand(3, 4, 11, 11, 11, 11) # BxCxD1xD2x....xDN
dense_tensor.requires_grad = True
# Since the shape is fixed, cache the coordinates for faster inference
coordinates = dense_coordinates(dense_tensor.shape)
network = nn.Sequential(
# Add layers that can be applied on a regular pytorch tensor
nn.ReLU(),
MinkowskiToSparseTensor(remove_zeros=False,
coordinates=coordinates),
MinkowskiConvolution(4, 5, stride=2, kernel_size=3, dimension=4),
MinkowskiBatchNorm(5),
MinkowskiReLU(),
MinkowskiConvolutionTranspose(5,
6,
stride=2,
kernel_size=3,
dimension=4),
MinkowskiToDenseTensor(
dense_tensor.shape), # must have the same tensor stride.
)
for i in range(5):
print(f"Iteration: {i}")
output = network(dense_tensor)
output.sum().backward()
assert dense_tensor.grad is not None
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 stride_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(
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)
ofield = otensor.slice(tfield)
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))
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))
