detach for meshes, pointclouds, textures

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

Reviewed By: nikhilaravi

Differential Revision: D23070418

fbshipit-source-id: 68671124ce114c4495d7ef3c944c9aac3d0db2d8

pytorch3d/structures/meshes.py

@@ -1138,6 +1138,28 @@ class Meshes(object):
             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 @@ class Pointclouds(object):
                 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()