From 7f097b064b0338e2534d7ea265f6e1444cfc1355 Mon Sep 17 00:00:00 2001
From: Krzysztof Chalupka <chalupka@fb.com>
Date: Mon, 11 Apr 2022 16:27:53 -0700
Subject: [PATCH] Submesh 1/n: Implement mesh equality

Summary: Adding a mesh equality operator. Two Meshes objects m1, m2 are equal iff their vertex lists, face lists, and normals lists are equal. Textures meshes are not supported yet, but will be added for vertex textures down the stack.

Reviewed By: bottler, nikhilaravi

Differential Revision: D35440655

fbshipit-source-id: 69974a59c091416afdb2892896859a189f5ebf3a
---
 tests/test_meshes.py | 51 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)

diff --git a/tests/test_meshes.py b/tests/test_meshes.py
index a0e940df..afe6e941 100644
--- a/tests/test_meshes.py
+++ b/tests/test_meshes.py
@@ -127,6 +127,45 @@ def init_simple_mesh(device: str = "cpu"):
     return Meshes(verts=verts, faces=faces)
 
 
+def mesh_structures_equal(mesh1, mesh2) -> bool:
+    """
+    Two meshes are equal if they have identical verts_list and faces_list.
+
+    Use to_sorted() before passing into this function to obtain meshes invariant to
+    vertex permutations. Note that this operator treats two geometrically identical
+    meshes as different if their vertices are in different coordinate frames.
+    """
+    if mesh1.__class__ != mesh1.__class__:
+        return False
+
+    if mesh1.textures is not None or mesh2.textures is not None:
+        raise NotImplementedError(
+            "mesh equality is not implemented for textured meshes."
+        )
+
+    if len(mesh1.verts_list()) != len(mesh2.verts_list()) or not all(
+        torch.equal(verts_mesh1, verts_mesh2)
+        for (verts_mesh1, verts_mesh2) in zip(mesh1.verts_list(), mesh2.verts_list())
+    ):
+        return False
+
+    if len(mesh1.faces_list()) != len(mesh2.faces_list()) or not all(
+        torch.equal(faces_mesh1, faces_mesh2)
+        for (faces_mesh1, faces_mesh2) in zip(mesh1.faces_list(), mesh2.faces_list())
+    ):
+        return False
+
+    if len(mesh1.verts_normals_list()) != len(mesh2.verts_normals_list()) or not all(
+        torch.equal(normals_mesh1, normals_mesh2)
+        for (normals_mesh1, normals_mesh2) in zip(
+            mesh1.verts_normals_list(), mesh2.verts_normals_list()
+        )
+    ):
+        return False
+
+    return True
+
+
 class TestMeshes(TestCaseMixin, unittest.TestCase):
     def setUp(self) -> None:
         np.random.seed(42)
@@ -1172,6 +1211,18 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
             face_normals_cpu[nonzero], face_normals_cuda.cpu()[nonzero], atol=1e-6
         )
 
+    def test_equality(self):
+        meshes1 = init_mesh(num_meshes=2)
+        meshes2 = init_mesh(num_meshes=2)
+        meshes3 = init_mesh(num_meshes=3)
+        empty_mesh = Meshes([], [])
+        self.assertTrue(mesh_structures_equal(empty_mesh, Meshes([], [])))
+        self.assertTrue(mesh_structures_equal(meshes1, meshes1))
+        self.assertTrue(mesh_structures_equal(meshes1, meshes1.clone()))
+        self.assertFalse(mesh_structures_equal(empty_mesh, meshes1))
+        self.assertFalse(mesh_structures_equal(meshes1, meshes2))
+        self.assertFalse(mesh_structures_equal(meshes1, meshes3))
+
     @staticmethod
     def compute_packed_with_init(
         num_meshes: int = 10, max_v: int = 100, max_f: int = 300, device: str = "cpu"