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import unittest |
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import torch |
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import torch.nn as nn |
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from pytorch3d import _C |
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from pytorch3d.ops.graph_conv import gather_scatter, gather_scatter_python, GraphConv |
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from pytorch3d.structures.meshes import Meshes |
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from pytorch3d.utils import ico_sphere |
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from .common_testing import get_random_cuda_device, TestCaseMixin |
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class TestGraphConv(TestCaseMixin, unittest.TestCase): |
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def test_undirected(self): |
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dtype = torch.float32 |
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device = get_random_cuda_device() |
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verts = torch.tensor( |
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[[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype, device=device |
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) |
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edges = torch.tensor([[0, 1], [0, 2]], device=device) |
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w0 = torch.tensor([[1, 1, 1]], dtype=dtype, device=device) |
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w1 = torch.tensor([[-1, -1, -1]], dtype=dtype, device=device) |
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expected_y = torch.tensor( |
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[ |
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[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9], |
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[4 + 5 + 6 - 1 - 2 - 3], |
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[7 + 8 + 9 - 1 - 2 - 3], |
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], |
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dtype=dtype, |
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device=device, |
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) |
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conv = GraphConv(3, 1, directed=False).to(device) |
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conv.w0.weight.data.copy_(w0) |
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conv.w0.bias.data.zero_() |
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conv.w1.weight.data.copy_(w1) |
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conv.w1.bias.data.zero_() |
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y = conv(verts, edges) |
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self.assertClose(y, expected_y) |
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def test_no_edges(self): |
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dtype = torch.float32 |
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verts = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype) |
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edges = torch.zeros(0, 2, dtype=torch.int64) |
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w0 = torch.tensor([[1, -1, -2]], dtype=dtype) |
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expected_y = torch.tensor( |
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[[1 - 2 - 2 * 3], [4 - 5 - 2 * 6], [7 - 8 - 2 * 9]], dtype=dtype |
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) |
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conv = GraphConv(3, 1).to(dtype) |
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conv.w0.weight.data.copy_(w0) |
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conv.w0.bias.data.zero_() |
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y = conv(verts, edges) |
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self.assertClose(y, expected_y) |
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def test_no_verts_and_edges(self): |
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dtype = torch.float32 |
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verts = torch.tensor([], dtype=dtype, requires_grad=True) |
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edges = torch.tensor([], dtype=dtype) |
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w0 = torch.tensor([[1, -1, -2]], dtype=dtype) |
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conv = GraphConv(3, 1).to(dtype) |
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conv.w0.weight.data.copy_(w0) |
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conv.w0.bias.data.zero_() |
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y = conv(verts, edges) |
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self.assertClose(y, torch.zeros((0, 1))) |
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self.assertTrue(y.requires_grad) |
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conv2 = GraphConv(3, 2).to(dtype) |
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conv2.w0.weight.data.copy_(w0.repeat(2, 1)) |
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conv2.w0.bias.data.zero_() |
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y = conv2(verts, edges) |
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self.assertClose(y, torch.zeros((0, 2))) |
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self.assertTrue(y.requires_grad) |
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def test_directed(self): |
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dtype = torch.float32 |
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verts = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype) |
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edges = torch.tensor([[0, 1], [0, 2]]) |
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w0 = torch.tensor([[1, 1, 1]], dtype=dtype) |
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w1 = torch.tensor([[-1, -1, -1]], dtype=dtype) |
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expected_y = torch.tensor( |
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[[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9], [4 + 5 + 6], [7 + 8 + 9]], dtype=dtype |
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) |
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conv = GraphConv(3, 1, directed=True).to(dtype) |
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conv.w0.weight.data.copy_(w0) |
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conv.w0.bias.data.zero_() |
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conv.w1.weight.data.copy_(w1) |
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conv.w1.bias.data.zero_() |
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y = conv(verts, edges) |
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self.assertClose(y, expected_y) |
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def test_backward(self): |
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device = get_random_cuda_device() |
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mesh = ico_sphere() |
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verts = mesh.verts_packed() |
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edges = mesh.edges_packed() |
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verts_cpu = verts.clone() |
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edges_cpu = edges.clone() |
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verts_cuda = verts.clone().to(device) |
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edges_cuda = edges.clone().to(device) |
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verts.requires_grad = True |
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verts_cpu.requires_grad = True |
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verts_cuda.requires_grad = True |
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neighbor_sums_cuda = gather_scatter(verts_cuda, edges_cuda, False) |
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neighbor_sums_cpu = gather_scatter(verts_cpu, edges_cpu, False) |
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neighbor_sums = gather_scatter_python(verts, edges, False) |
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randoms = torch.rand_like(neighbor_sums) |
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(neighbor_sums_cuda * randoms.to(device)).sum().backward() |
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(neighbor_sums_cpu * randoms).sum().backward() |
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(neighbor_sums * randoms).sum().backward() |
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self.assertClose(verts.grad, verts_cuda.grad.cpu()) |
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self.assertClose(verts.grad, verts_cpu.grad) |
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def test_repr(self): |
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conv = GraphConv(32, 64, directed=True) |
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self.assertEqual(repr(conv), "GraphConv(32 -> 64, directed=True)") |
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def test_cpu_cuda_tensor_error(self): |
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device = get_random_cuda_device() |
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verts = torch.tensor( |
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[[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float32, device=device |
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) |
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edges = torch.tensor([[0, 1], [0, 2]]) |
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conv = GraphConv(3, 1, directed=True).to(torch.float32) |
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with self.assertRaises(Exception) as err: |
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conv(verts, edges) |
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self.assertTrue("tensors must be on the same device." in str(err.exception)) |
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def test_gather_scatter(self): |
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""" |
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Check gather_scatter cuda and python versions give the same results. |
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Check that gather_scatter cuda version throws an error if cpu tensors |
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are given as input. |
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""" |
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device = get_random_cuda_device() |
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mesh = ico_sphere() |
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verts = mesh.verts_packed() |
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edges = mesh.edges_packed() |
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w0 = nn.Linear(3, 1) |
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input = w0(verts) |
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output_python = gather_scatter_python(input, edges, False) |
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output_cuda = _C.gather_scatter( |
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input.to(device=device), edges.to(device=device), False, False |
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) |
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self.assertClose(output_cuda.cpu(), output_python) |
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output_cpu = _C.gather_scatter(input.cpu(), edges.cpu(), False, False) |
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self.assertClose(output_cpu, output_python) |
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output_python = gather_scatter_python(input, edges, True) |
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output_cuda = _C.gather_scatter( |
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input.to(device=device), edges.to(device=device), True, False |
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) |
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self.assertClose(output_cuda.cpu(), output_python) |
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output_cpu = _C.gather_scatter(input.cpu(), edges.cpu(), True, False) |
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self.assertClose(output_cpu, output_python) |
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@staticmethod |
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def graph_conv_forward_backward( |
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gconv_dim, |
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num_meshes, |
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num_verts, |
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num_faces, |
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directed: bool, |
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backend: str = "cuda", |
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): |
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device = torch.device("cuda") if backend == "cuda" else "cpu" |
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verts_list = torch.tensor(num_verts * [[0.11, 0.22, 0.33]], device=device).view( |
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-1, 3 |
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) |
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faces_list = torch.tensor(num_faces * [[1, 2, 3]], device=device).view(-1, 3) |
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meshes = Meshes(num_meshes * [verts_list], num_meshes * [faces_list]) |
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gconv = GraphConv(gconv_dim, gconv_dim, directed=directed) |
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gconv.to(device) |
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edges = meshes.edges_packed() |
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total_verts = meshes.verts_packed().shape[0] |
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x = torch.randn(total_verts, gconv_dim, device=device, requires_grad=True) |
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torch.cuda.synchronize() |
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def run_graph_conv(): |
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y1 = gconv(x, edges) |
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y1.sum().backward() |
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torch.cuda.synchronize() |
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return run_graph_conv |
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