Method test_expansion

tests/python/convolution.py:52–90

(self)

class TestConvolution(unittest.TestCase):
    def test_expansion(self):
        print(f"{self.__class__.__name__}: test_expansion")
        in_channels, out_channels, D = 2, 2, 2
        coords, feats, labels = data_loader(in_channels)
        feats = feats.double()
        feats.requires_grad_()

        # Initialize context
        conv = MinkowskiConvolution(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=2,
            bias=False,
            expand_coordinates=True,
            dimension=D,
        ).double()

        input = SparseTensor(
            feats,
            coordinates=coords,
            minkowski_algorithm=MinkowskiAlgorithm.SPEED_OPTIMIZED,
        )
        print(input)
        output = conv(input)
        print(output)
        if not torch.cuda.is_available():
            return

        input = SparseTensor(
            feats,
            coordinates=coords,
            minkowski_algorithm=MinkowskiAlgorithm.SPEED_OPTIMIZED,
            device="cuda",
        )
        conv = conv.to("cuda")
        print(input)
        output = conv(input)
        print(output)

    def test_kernel_map(self):
        print(f"{self.__class__.__name__}: test_gpu")