Method test_device

tests/python/pruning.py:69–80

(self)

        )

    def test_device(self):
        in_channels = 2
        coords, feats, labels = data_loader(in_channels, batch_size=1)
        feats = feats.double()
        feats.requires_grad_()
        input = SparseTensor(feats, coords, device="cuda")
        use_feat = torch.rand(feats.size(0)) < 0.5
        pruning = MinkowskiPruning()
        output = pruning(input, use_feat.cuda())
        print(input)
        print(use_feat)
        print(output)

    def test_empty(self):
        in_channels = 2