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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
import torch.nn as nn
from pytorch3d import _C
from pytorch3d.ops.graph_conv import gather_scatter, gather_scatter_python, GraphConv
from pytorch3d.structures.meshes import Meshes
from pytorch3d.utils import ico_sphere
from .common_testing import get_random_cuda_device, TestCaseMixin
class TestGraphConv(TestCaseMixin, unittest.TestCase):
def test_undirected(self):
dtype = torch.float32
device = get_random_cuda_device()
verts = torch.tensor(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype, device=device
)
edges = torch.tensor([[0, 1], [0, 2]], device=device)
w0 = torch.tensor([[1, 1, 1]], dtype=dtype, device=device)
w1 = torch.tensor([[-1, -1, -1]], dtype=dtype, device=device)
expected_y = torch.tensor(
[
[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9],
[4 + 5 + 6 - 1 - 2 - 3],
[7 + 8 + 9 - 1 - 2 - 3],
],
dtype=dtype,
device=device,
)
conv = GraphConv(3, 1, directed=False).to(device)
conv.w0.weight.data.copy_(w0)
conv.w0.bias.data.zero_()
conv.w1.weight.data.copy_(w1)
conv.w1.bias.data.zero_()
y = conv(verts, edges)
self.assertClose(y, expected_y)
def test_no_edges(self):
dtype = torch.float32
verts = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype)
edges = torch.zeros(0, 2, dtype=torch.int64)
w0 = torch.tensor([[1, -1, -2]], dtype=dtype)
expected_y = torch.tensor(
[[1 - 2 - 2 * 3], [4 - 5 - 2 * 6], [7 - 8 - 2 * 9]], dtype=dtype
)
conv = GraphConv(3, 1).to(dtype)
conv.w0.weight.data.copy_(w0)
conv.w0.bias.data.zero_()
y = conv(verts, edges)
self.assertClose(y, expected_y)
def test_no_verts_and_edges(self):
dtype = torch.float32
verts = torch.tensor([], dtype=dtype, requires_grad=True)
edges = torch.tensor([], dtype=dtype)
w0 = torch.tensor([[1, -1, -2]], dtype=dtype)
conv = GraphConv(3, 1).to(dtype)
conv.w0.weight.data.copy_(w0)
conv.w0.bias.data.zero_()
y = conv(verts, edges)
self.assertClose(y, torch.zeros((0, 1)))
self.assertTrue(y.requires_grad)
conv2 = GraphConv(3, 2).to(dtype)
conv2.w0.weight.data.copy_(w0.repeat(2, 1))
conv2.w0.bias.data.zero_()
y = conv2(verts, edges)
self.assertClose(y, torch.zeros((0, 2)))
self.assertTrue(y.requires_grad)
def test_directed(self):
dtype = torch.float32
verts = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype)
edges = torch.tensor([[0, 1], [0, 2]])
w0 = torch.tensor([[1, 1, 1]], dtype=dtype)
w1 = torch.tensor([[-1, -1, -1]], dtype=dtype)
expected_y = torch.tensor(
[[1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9], [4 + 5 + 6], [7 + 8 + 9]], dtype=dtype
)
conv = GraphConv(3, 1, directed=True).to(dtype)
conv.w0.weight.data.copy_(w0)
conv.w0.bias.data.zero_()
conv.w1.weight.data.copy_(w1)
conv.w1.bias.data.zero_()
y = conv(verts, edges)
self.assertClose(y, expected_y)
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_repr(self):
conv = GraphConv(32, 64, directed=True)
self.assertEqual(repr(conv), "GraphConv(32 -> 64, directed=True)")
def test_cpu_cuda_tensor_error(self):
device = get_random_cuda_device()
verts = torch.tensor(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float32, device=device
)
edges = torch.tensor([[0, 1], [0, 2]])
conv = GraphConv(3, 1, directed=True).to(torch.float32)
with self.assertRaises(Exception) as err:
conv(verts, edges)
self.assertTrue("tensors must be on the same device." in str(err.exception))
def test_gather_scatter(self):
"""
Check gather_scatter cuda and python versions give the same results.
Check that gather_scatter cuda version throws an error if cpu tensors
are given as input.
"""
device = get_random_cuda_device()
mesh = ico_sphere()
verts = mesh.verts_packed()
edges = mesh.edges_packed()
w0 = nn.Linear(3, 1)
input = w0(verts)
# undirected
output_python = gather_scatter_python(input, edges, False)
output_cuda = _C.gather_scatter(
input.to(device=device), edges.to(device=device), False, False
)
self.assertClose(output_cuda.cpu(), output_python)
output_cpu = _C.gather_scatter(input.cpu(), edges.cpu(), False, False)
self.assertClose(output_cpu, output_python)
# directed
output_python = gather_scatter_python(input, edges, True)
output_cuda = _C.gather_scatter(
input.to(device=device), edges.to(device=device), True, False
)
self.assertClose(output_cuda.cpu(), output_python)
output_cpu = _C.gather_scatter(input.cpu(), edges.cpu(), True, False)
self.assertClose(output_cpu, output_python)
@staticmethod
def graph_conv_forward_backward(
gconv_dim,
num_meshes,
num_verts,
num_faces,
directed: bool,
backend: str = "cuda",
):
device = torch.device("cuda") if backend == "cuda" else "cpu"
verts_list = torch.tensor(num_verts * [[0.11, 0.22, 0.33]], device=device).view(
-1, 3
)
faces_list = torch.tensor(num_faces * [[1, 2, 3]], device=device).view(-1, 3)
meshes = Meshes(num_meshes * [verts_list], num_meshes * [faces_list])
gconv = GraphConv(gconv_dim, gconv_dim, directed=directed)
gconv.to(device)
edges = meshes.edges_packed()
total_verts = meshes.verts_packed().shape[0]
# Features.
x = torch.randn(total_verts, gconv_dim, device=device, requires_grad=True)
torch.cuda.synchronize()
def run_graph_conv():
y1 = gconv(x, edges)
y1.sum().backward()
torch.cuda.synchronize()
return run_graph_conv
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