detach for meshes, pointclouds, textures

Summary: Add `detach` for Meshes, Pointclouds, Textures

Reviewed By: nikhilaravi

Differential Revision: D23070418

fbshipit-source-id: 68671124ce114c4495d7ef3c944c9aac3d0db2d8

Author: gkioxari

pytorch3d/renderer/mesh/textures.py

@@ -242,6 +242,13 @@ def clone(self):
         """
         raise NotImplementedError()
 
+    def detach(self):
+        """
+        Each texture class should implement a method
+        to detach all necessary internal tensors.
+        """
+        raise NotImplementedError()
+
     def __getitem__(self, index):
         """
         Each texture class should implement a method
@@ -388,6 +395,8 @@ def __repr__(self):
 
     def clone(self):
         tex = self.__class__(atlas=self.atlas_padded().clone())
+        if self._atlas_list is not None:
+            tex._atlas_list = [atlas.clone() for atlas in self._atlas_list]
         num_faces = (
             self._num_faces_per_mesh.clone()
             if torch.is_tensor(self._num_faces_per_mesh)
@@ -397,6 +406,19 @@ def clone(self):
         tex._num_faces_per_mesh = num_faces
         return tex
 
+    def detach(self):
+        tex = self.__class__(atlas=self.atlas_padded().detach())
+        if self._atlas_list is not None:
+            tex._atlas_list = [atlas.detach() for atlas in self._atlas_list]
+        num_faces = (
+            self._num_faces_per_mesh.detach()
+            if torch.is_tensor(self._num_faces_per_mesh)
+            else self._num_faces_per_mesh
+        )
+        tex.valid = self.valid.detach()
+        tex._num_faces_per_mesh = num_faces
+        return tex
+
     def __getitem__(self, index):
         props = ["atlas_list", "_num_faces_per_mesh"]
         new_props = self._getitem(index, props=props)
@@ -656,6 +678,12 @@ def clone(self):
             self.faces_uvs_padded().clone(),
             self.verts_uvs_padded().clone(),
         )
+        if self._maps_list is not None:
+            tex._maps_list = [m.clone() for m in self._maps_list]
+        if self._verts_uvs_list is not None:
+            tex._verts_uvs_list = [v.clone() for v in self._verts_uvs_list]
+        if self._faces_uvs_list is not None:
+            tex._faces_uvs_list = [f.clone() for f in self._faces_uvs_list]
         num_faces = (
             self._num_faces_per_mesh.clone()
             if torch.is_tensor(self._num_faces_per_mesh)
@@ -665,6 +693,27 @@ def clone(self):
         tex.valid = self.valid.clone()
         return tex
 
+    def detach(self):
+        tex = self.__class__(
+            self.maps_padded().detach(),
+            self.faces_uvs_padded().detach(),
+            self.verts_uvs_padded().detach(),
+        )
+        if self._maps_list is not None:
+            tex._maps_list = [m.detach() for m in self._maps_list]
+        if self._verts_uvs_list is not None:
+            tex._verts_uvs_list = [v.detach() for v in self._verts_uvs_list]
+        if self._faces_uvs_list is not None:
+            tex._faces_uvs_list = [f.detach() for f in self._faces_uvs_list]
+        num_faces = (
+            self._num_faces_per_mesh.detach()
+            if torch.is_tensor(self._num_faces_per_mesh)
+            else self._num_faces_per_mesh
+        )
+        tex._num_faces_per_mesh = num_faces
+        tex.valid = self.valid.detach()
+        return tex
+
     def __getitem__(self, index):
         props = ["verts_uvs_list", "faces_uvs_list", "maps_list", "_num_faces_per_mesh"]
         new_props = self._getitem(index, props)
@@ -892,8 +941,8 @@ def __init__(
         has a D dimensional feature vector.
 
         Args:
-            verts_features: (N, V, D) tensor giving a feature vector with
-                artbitrary dimensions for each vertex.
+            verts_features: list of (Vi, D) or (N, V, D) tensor giving a feature
+                vector with artbitrary dimensions for each vertex.
         """
         if isinstance(verts_features, (tuple, list)):
             correct_shape = all(
@@ -948,15 +997,28 @@ def clone(self):
         tex = self.__class__(self.verts_features_padded().clone())
         if self._verts_features_list is not None:
             tex._verts_features_list = [f.clone() for f in self._verts_features_list]
-        num_faces = (
+        num_verts = (
             self._num_verts_per_mesh.clone()
             if torch.is_tensor(self._num_verts_per_mesh)
             else self._num_verts_per_mesh
         )
-        tex._num_verts_per_mesh = num_faces
+        tex._num_verts_per_mesh = num_verts
         tex.valid = self.valid.clone()
         return tex
 
+    def detach(self):
+        tex = self.__class__(self.verts_features_padded().detach())
+        if self._verts_features_list is not None:
+            tex._verts_features_list = [f.detach() for f in self._verts_features_list]
+        num_verts = (
+            self._num_verts_per_mesh.detach()
+            if torch.is_tensor(self._num_verts_per_mesh)
+            else self._num_verts_per_mesh
+        )
+        tex._num_verts_per_mesh = num_verts
+        tex.valid = self.valid.detach()
+        return tex
+
     def __getitem__(self, index):
         props = ["verts_features_list", "_num_verts_per_mesh"]
         new_props = self._getitem(index, props)

pytorch3d/structures/meshes.py

@@ -1138,6 +1138,28 @@ def clone(self):
             other.textures = self.textures.clone()
         return other
 
+    def detach(self):
+        """
+        Detach Meshes object. All internal tensors are detached individually.
+
+        Returns:
+            new Meshes object.
+        """
+        verts_list = self.verts_list()
+        faces_list = self.faces_list()
+        new_verts_list = [v.detach() for v in verts_list]
+        new_faces_list = [f.detach() for f in faces_list]
+        other = self.__class__(verts=new_verts_list, faces=new_faces_list)
+        for k in self._INTERNAL_TENSORS:
+            v = getattr(self, k)
+            if torch.is_tensor(v):
+                setattr(other, k, v.detach())
+
+        # Textures is not a tensor but has a detach method
+        if self.textures is not None:
+            other.textures = self.textures.detach()
+        return other
+
     def to(self, device, copy: bool = False):
         """
         Match functionality of torch.Tensor.to()

pytorch3d/structures/pointclouds.py

@@ -655,6 +655,42 @@ def clone(self):
                 setattr(other, k, v.clone())
         return other
 
+    def detach(self):
+        """
+        Detach Pointclouds object. All internal tensors are detached
+        individually.
+
+        Returns:
+            new Pointclouds object.
+        """
+        # instantiate new pointcloud with the representation which is not None
+        # (either list or tensor) to save compute.
+        new_points, new_normals, new_features = None, None, None
+        if self._points_list is not None:
+            new_points = [v.detach() for v in self.points_list()]
+            normals_list = self.normals_list()
+            features_list = self.features_list()
+            if normals_list is not None:
+                new_normals = [n.detach() for n in normals_list]
+            if features_list is not None:
+                new_features = [f.detach() for f in features_list]
+        elif self._points_padded is not None:
+            new_points = self.points_padded().detach()
+            normals_padded = self.normals_padded()
+            features_padded = self.features_padded()
+            if normals_padded is not None:
+                new_normals = self.normals_padded().detach()
+            if features_padded is not None:
+                new_features = self.features_padded().detach()
+        other = self.__class__(
+            points=new_points, normals=new_normals, features=new_features
+        )
+        for k in self._INTERNAL_TENSORS:
+            v = getattr(self, k)
+            if torch.is_tensor(v):
+                setattr(other, k, v.detach())
+        return other
+
     def to(self, device, copy: bool = False):
         """
         Match functionality of torch.Tensor.to()

tests/test_meshes.py

@@ -20,6 +20,7 @@ def init_mesh(
         max_f: int = 300,
         lists_to_tensors: bool = False,
         device: str = "cpu",
+        requires_grad: bool = False,
     ):
         """
         Function to generate a Meshes object of N meshes with
@@ -57,7 +58,12 @@ def init_mesh(
 
         # Generate the actual vertices and faces.
         for i in range(num_meshes):
-            verts = torch.rand((v[i], 3), dtype=torch.float32, device=device)
+            verts = torch.rand(
+                (v[i], 3),
+                dtype=torch.float32,
+                device=device,
+                requires_grad=requires_grad,
+            )
             faces = torch.randint(
                 v[i], size=(f[i], 3), dtype=torch.int64, device=device
             )
@@ -353,6 +359,26 @@ def test_clone(self):
             self.assertSeparate(new_mesh.faces_padded(), mesh.faces_padded())
             self.assertSeparate(new_mesh.edges_packed(), mesh.edges_packed())
 
+    def test_detach(self):
+        N = 5
+        mesh = TestMeshes.init_mesh(N, 10, 100, requires_grad=True)
+        for force in [0, 1]:
+            if force:
+                # force mesh to have computed attributes
+                mesh.verts_packed()
+                mesh.edges_packed()
+                mesh.verts_padded()
+
+            new_mesh = mesh.detach()
+
+            self.assertFalse(new_mesh.verts_packed().requires_grad)
+            self.assertClose(new_mesh.verts_packed(), mesh.verts_packed())
+            self.assertTrue(new_mesh.verts_padded().requires_grad == False)
+            self.assertClose(new_mesh.verts_padded(), mesh.verts_padded())
+            for v, newv in zip(mesh.verts_list(), new_mesh.verts_list()):
+                self.assertTrue(newv.requires_grad == False)
+                self.assertClose(newv, v)
+
     def test_laplacian_packed(self):
         def naive_laplacian_packed(meshes):
             verts_packed = meshes.verts_packed()

tests/test_pointclouds.py

@@ -24,6 +24,7 @@ def init_cloud(
         with_normals: bool = True,
         with_features: bool = True,
         min_points: int = 0,
+        requires_grad: bool = False,
     ):
         """
         Function to generate a Pointclouds object of N meshes with
@@ -49,16 +50,31 @@ def init_cloud(
             p.fill_(p[0])
 
         points_list = [
-            torch.rand((i, 3), device=device, dtype=torch.float32) for i in p
+            torch.rand(
+                (i, 3), device=device, dtype=torch.float32, requires_grad=requires_grad
+            )
+            for i in p
         ]
         normals_list, features_list = None, None
         if with_normals:
             normals_list = [
-                torch.rand((i, 3), device=device, dtype=torch.float32) for i in p
+                torch.rand(
+                    (i, 3),
+                    device=device,
+                    dtype=torch.float32,
+                    requires_grad=requires_grad,
+                )
+                for i in p
             ]
         if with_features:
             features_list = [
-                torch.rand((i, channels), device=device, dtype=torch.float32) for i in p
+                torch.rand(
+                    (i, channels),
+                    device=device,
+                    dtype=torch.float32,
+                    requires_grad=requires_grad,
+                )
+                for i in p
             ]
 
         if lists_to_tensors:
@@ -382,6 +398,39 @@ def test_clone_tensor(self):
 
             self.assertCloudsEqual(clouds, new_clouds)
 
+    def test_detach(self):
+        N = 5
+        for lists_to_tensors in (True, False):
+            clouds = self.init_cloud(
+                N, 100, 5, lists_to_tensors=lists_to_tensors, requires_grad=True
+            )
+            for force in (False, True):
+                if force:
+                    clouds.points_packed()
+
+                new_clouds = clouds.detach()
+
+                for cloud in new_clouds.points_list():
+                    self.assertTrue(cloud.requires_grad == False)
+                for normal in new_clouds.normals_list():
+                    self.assertTrue(normal.requires_grad == False)
+                for feats in new_clouds.features_list():
+                    self.assertTrue(feats.requires_grad == False)
+
+                for attrib in [
+                    "points_packed",
+                    "normals_packed",
+                    "features_packed",
+                    "points_padded",
+                    "normals_padded",
+                    "features_padded",
+                ]:
+                    self.assertTrue(
+                        getattr(new_clouds, attrib)().requires_grad == False
+                    )
+
+                self.assertCloudsEqual(clouds, new_clouds)
+
     def assertCloudsEqual(self, cloud1, cloud2):
         N = len(cloud1)
         self.assertEqual(N, len(cloud2))