Method test

tests/python/convolution.py:170–224

(self)

        )

    def test(self):
        print(f"{self.__class__.__name__}: test")
        in_channels, out_channels, D = 2, 3, 2
        coords, feats, labels = data_loader(in_channels)
        feats = feats.double()
        feats.requires_grad_()
        input = SparseTensor(feats, coordinates=coords)
        # Initialize context
        conv = MinkowskiConvolution(
            in_channels, out_channels, kernel_size=3, stride=2, bias=True, dimension=D
        )
        conv = conv.double()
        output = conv(input)
        print(output)

        self.assertEqual(input.coordinate_map_key.get_tensor_stride(), [1, 1])
        self.assertEqual(output.coordinate_map_key.get_tensor_stride(), [2, 2])

        if torch.cuda.is_available():
            input_gpu = SparseTensor(feats, coordinates=coords, device="cuda")
            conv_gpu = conv.cuda()
            output_gpu = conv_gpu(input_gpu)
            self.assertTrue(torch.allclose(output_gpu.F.var(0).cpu(), output.F.var(0)))
            self.assertTrue(
                torch.allclose(output_gpu.F.mean(0).cpu(), output.F.mean(0))
            )

        # kernel_map = input.coords_man.kernel_map(
        #     1, 2, stride=2, kernel_size=3)
        # print(kernel_map)

        # Check backward
        fn = MinkowskiConvolutionFunction()

        conv = conv.cpu()
        self.assertTrue(
            gradcheck(
                fn,
                (
                    input.F,
                    conv.kernel,
                    conv.kernel_generator,
                    conv.convolution_mode,
                    input.coordinate_map_key,
                    output.coordinate_map_key,
                    input.coordinate_manager,
                ),
            )
        )

        for i in range(LEAK_TEST_ITER):
            input = SparseTensor(feats, coordinates=coords)
            conv(input).F.sum().backward()
            if i % 1000 == 0:
                print(i)

    def test_analytic(self):
        print(f"{self.__class__.__name__}: test")