def test(self):
rows = torch.Tensor([0, 0, 1, 1]).int()
cols = torch.Tensor([0, 1, 2, 3]).int()
vals = torch.ones(4).double()
size = [2, 4]
mat = torch.rand(4, 3).double()
mat.requires_grad_()
spmm_fn = MinkowskiSPMMFunction()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)
loss = out.sum()
loss.backward()
print(mat.grad)
self.assertTrue(gradcheck(spmm_fn, (rows, cols, vals, size, mat)))
rows = rows.cuda()
cols = cols.cuda()
vals = vals.cuda()
mat = mat.cuda()
mat.requires_grad_()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)
loss = out.sum()
loss.backward()
print(mat.grad)
self.assertTrue(gradcheck(spmm_fn, (rows, cols, vals, size, mat)))
def test_dtype(self):
rows = torch.Tensor([0, 0, 1, 1]).float()
cols = torch.Tensor([0, 1, 2, 3]).double()
vals = torch.ones(4).double()
size = [2, 4]
mat = torch.rand(4, 3).double()
mat.requires_grad_()
spmm_fn = MinkowskiSPMMFunction()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)
if not torch.cuda.is_available():
return
rows = torch.cuda.IntTensor([0, 0, 1, 1])
cols = torch.cuda.IntTensor([0, 1, 2, 3])
vals = torch.ones(4).double().to(0)
size = [2, 4]
mat = mat.to(0)
mat.requires_grad_()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)