docstr: Test the moving of the volumes from/to gpu and cpu
docstr_tok: ["Test","the","moving","of","the","volumes","from\/to","gpu","and","cpu"]
code: def test_to(
      self,
      num_volumes=3,
      num_channels=4,
      size=(6, 8, 10),
      dtype=torch.float32,
  ):
      """
      Test the moving of the volumes from/to gpu and cpu
      """
  
      features = torch.randn(
          size=[num_volumes, num_channels, *size], dtype=torch.float32
      )
      densities = torch.rand(size=[num_volumes, 1, *size], dtype=dtype)
      volumes = Volumes(densities=densities, features=features)
      locator = VolumeLocator(
          batch_size=5, grid_sizes=(3, 5, 7), device=volumes.device
      )
  
      for name, obj in (
          ("VolumeLocator", locator),
          ("Volumes", volumes),
      ):
          with self.subTest(f"Moving {name} from/to gpu and cpu"):
              # Test support for str and torch.device
              cpu_device = torch.device("cpu")
  
              converted_obj = obj.to("cpu")
              self.assertEqual(cpu_device, converted_obj.device)
              self.assertEqual(cpu_device, obj.device)
              self.assertIs(obj, converted_obj)
  
              converted_obj = obj.to(cpu_device)
              self.assertEqual(cpu_device, converted_obj.device)
              self.assertEqual(cpu_device, obj.device)
              self.assertIs(obj, converted_obj)
  
              cuda_device = torch.device("cuda:0")
  
              converted_obj = obj.to("cuda:0")
              self.assertEqual(cuda_device, converted_obj.device)
              self.assertEqual(cpu_device, obj.device)
              self.assertIsNot(obj, converted_obj)
  
              converted_obj = obj.to(cuda_device)
              self.assertEqual(cuda_device, converted_obj.device)
              self.assertEqual(cpu_device, obj.device)
              self.assertIsNot(obj, converted_obj)
  
      with self.subTest(
          "Test device placement of internal tensors of Volumes"
      ):
          features = features.to(cuda_device)
          densities = features.to(cuda_device)
  
          for features_ in (features, None):
              volumes = Volumes(densities=densities, features=features_)
  
              cpu_volumes = volumes.cpu()
              cuda_volumes = cpu_volumes.cuda()
              cuda_volumes2 = cuda_volumes.cuda()
              cpu_volumes2 = cuda_volumes2.cpu()
  
              for volumes1, volumes2 in itertools.combinations(
                  (
                      volumes,
                      cpu_volumes,
                      cpu_volumes2,
                      cuda_volumes,
                      cuda_volumes2,
                  ),
                  2,
              ):
                  if (
                      volumes1 is cuda_volumes
                      and volumes2 is cuda_volumes2
                  ):
                      # checks that we do not copy if the devices stay the same
                      assert_fun = self.assertIs
                  else:
                      assert_fun = self.assertSeparate
                  assert_fun(volumes1._densities, volumes2._densities)
                  if features_ is not None:
                      assert_fun(volumes1._features, volumes2._features)
                  for volumes_ in (volumes1, volumes2):
                      if volumes_ in (cpu_volumes, cpu_volumes2):
                          self._check_vars_on_device(
                              volumes_, cpu_device
                          )
                      else:
                          self._check_vars_on_device(
                              volumes_, cuda_device
                          )
  
      with self.subTest(
          "Test device placement of internal tensors of VolumeLocator"
      ):
          for device1, device2 in itertools.combinations(
              (torch.device("cpu"), torch.device("cuda:0")), 2
          ):
              locator = locator.to(device1)
              locator = locator.to(device2)
              self.assertEqual(locator._grid_sizes.device, device2)
              self.assertEqual(
                  locator._local_to_world_transform.device, device2
              )
  
imports: import copy
  import itertools
  import random
  import unittest
  import numpy
  import torch
  from pytorch3d.structures.volumes import VolumeLocator, Volumes
  from pytorch3d.transforms import Scale
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 12 for calling the TestVolumes obj's underlying member method code with example usage: obj.test_to(num_volumes, num_channels, size, dtype) without return types