def test_backward(self):
device = get_random_cuda_device()
mesh = ico_sphere()
verts = mesh.verts_packed()
edges = mesh.edges_packed()
verts_cpu = verts.clone()
edges_cpu = edges.clone()
verts_cuda = verts.clone().to(device)
edges_cuda = edges.clone().to(device)
verts.requires_grad = True
verts_cpu.requires_grad = True
verts_cuda.requires_grad = True
neighbor_sums_cuda = gather_scatter(verts_cuda, edges_cuda, False)
neighbor_sums_cpu = gather_scatter(verts_cpu, edges_cpu, False)
neighbor_sums = gather_scatter_python(verts, edges, False)
randoms = torch.rand_like(neighbor_sums)
(neighbor_sums_cuda * randoms.to(device)).sum().backward()
(neighbor_sums_cpu * randoms).sum().backward()
(neighbor_sums * randoms).sum().backward()
self.assertClose(verts.grad, verts_cuda.grad.cpu())
self.assertClose(verts.grad, verts_cpu.grad)
def test_backward(self):
device = torch.device("cuda:0")
mesh = ico_sphere()
verts = mesh.verts_packed()
edges = mesh.edges_packed()
verts_cuda = verts.clone().to(device)
edges_cuda = edges.clone().to(device)
verts.requires_grad = True
verts_cuda.requires_grad = True
neighbor_sums_cuda = gather_scatter(verts_cuda, edges_cuda, False)
neighbor_sums = gather_scatter_python(verts, edges, False)
neighbor_sums_cuda.sum().backward()
neighbor_sums.sum().backward()
self.assertClose(verts.grad.cpu(), verts_cuda.grad.cpu())
