Method test_network

tests/python/dense.py:111–136

(self)

        self.assertEqual(sparse_tensor.F.size(1), 6)

    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