def backward(ctx, grad_dists):
grad_dists = grad_dists.contiguous()
points, segms, idxs = ctx.saved_tensors
grad_points, grad_segms = _C.point_edge_dist_backward(
points, segms, idxs, grad_dists
)
return grad_points, None, grad_segms, None, None
def test_point_edge_distance(self):
"""
Test CUDA implementation for PointEdgeDistanceForward
& PointEdgeDistanceBackward
"""
device = get_random_cuda_device()
N, V, F, P = 4, 32, 16, 24
meshes, pcls = self.init_meshes_clouds(N, V, F, P, device=device)
# make points packed a leaf node
points_packed = pcls.points_packed().detach().clone() # (P, 3)
points_first_idx = pcls.cloud_to_packed_first_idx()
max_p = pcls.num_points_per_cloud().max().item()
# make edges packed a leaf node
verts_packed = meshes.verts_packed()
edges_packed = verts_packed[meshes.edges_packed()] # (E, 2, 3)
edges_packed = edges_packed.clone().detach()
edges_first_idx = meshes.mesh_to_edges_packed_first_idx()
# leaf nodes
points_packed.requires_grad = True
edges_packed.requires_grad = True
grad_dists = torch.rand(
(points_packed.shape[0],), dtype=torch.float32, device=device
)
# Cuda Implementation: forward
dists_cuda, idx_cuda = _C.point_edge_dist_forward(
points_packed, points_first_idx, edges_packed, edges_first_idx, max_p
)
# Cuda Implementation: backward
grad_points_cuda, grad_edges_cuda = _C.point_edge_dist_backward(
points_packed, edges_packed, idx_cuda, grad_dists
)
# Cpu Implementation: forward
dists_cpu, idx_cpu = _C.point_edge_dist_forward(
points_packed.cpu(),
points_first_idx.cpu(),
edges_packed.cpu(),
edges_first_idx.cpu(),
max_p,
)
# Cpu Implementation: backward
# Note that using idx_cpu doesn't pass - there seems to be a problem with tied results.
grad_points_cpu, grad_edges_cpu = _C.point_edge_dist_backward(
points_packed.cpu(), edges_packed.cpu(), idx_cuda.cpu(), grad_dists.cpu()
)
# Naive Implementation: forward
edges_list = packed_to_list(edges_packed, meshes.num_edges_per_mesh().tolist())
dists_naive = []
for i in range(N):
points = pcls.points_list()[i]
edges = edges_list[i]
dists_temp = torch.zeros(
(points.shape[0], edges.shape[0]), dtype=torch.float32, device=device
)
for p in range(points.shape[0]):
for e in range(edges.shape[0]):
dist = self._point_to_edge_distance(points[p], edges[e])
dists_temp[p, e] = dist
# torch.min() doesn't necessarily return the first index of the
# smallest value, our warp_reduce does. So it's not straightforward
# to directly compare indices, nor the gradients of grad_edges which
# also depend on the indices of the minimum value.
# To be able to compare, we will compare dists_temp.min(1) and
# then feed the cuda indices to the naive output
start = points_first_idx[i]
end = points_first_idx[i + 1] if i < N - 1 else points_packed.shape[0]
min_idx = idx_cuda[start:end] - edges_first_idx[i]
iidx = torch.arange(points.shape[0], device=device)
min_dist = dists_temp[iidx, min_idx]
dists_naive.append(min_dist)
dists_naive = torch.cat(dists_naive)
# Compare
self.assertClose(dists_naive.cpu(), dists_cuda.cpu())
self.assertClose(dists_naive.cpu(), dists_cpu)
# Naive Implementation: backward
dists_naive.backward(grad_dists)
grad_points_naive = torch.cat([cloud.grad for cloud in pcls.points_list()])
grad_edges_naive = edges_packed.grad.cpu()
# Compare
self.assertClose(grad_points_naive.cpu(), grad_points_cuda.cpu(), atol=1e-7)
self.assertClose(grad_edges_naive, grad_edges_cuda.cpu(), atol=5e-7)
self.assertClose(grad_points_naive.cpu(), grad_points_cpu, atol=1e-7)
self.assertClose(grad_edges_naive, grad_edges_cpu, atol=5e-7)