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Tidy uses of torch.device in Volumes

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Summary:
Tidy uses of `torch.device` in `Volumes`:
- Allow `str` or `torch.device` in `Volumes.to()` method
- Consistently use `torch.device` for internal type
- Fix comparison of devices

Reviewed By: nikhilaravi

Differential Revision: D28970876

fbshipit-source-id: c640cc22ced684a54cc450ac38a0f4b3435d47be
This commit is contained in:
Patrick Labatut 2021-06-09 15:48:56 -07:00 committed by Facebook GitHub Bot
parent 1db40ac566
commit 1f9661e150
2 changed files with 69 additions and 37 deletions
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41
pytorch3d/structures/volumes.py
View File

@ -5,6 +5,7 @@ from typing import List, Optional, Tuple, Union
import torch
from ..common.types import Device, make_device
from ..transforms import Scale, Transform3d
from . import utils as struct_utils
@ -694,7 +695,7 @@ class Volumes:
"""
return copy.deepcopy(self)
def to(self, device, copy: bool = False) -> "Volumes":
def to(self, device: Device, copy: bool = False) -> "Volumes":
"""
Match the functionality of torch.Tensor.to()
If copy = True or the self Tensor is on a different device, the
@ -703,30 +704,34 @@ class Volumes:
then self is returned.
Args:
**device**: Device id for the new tensor.
**copy**: Boolean indicator whether or not to clone self. Default False.
device: Device (as str or torch.device) for the new tensor.
copy: Boolean indicator whether or not to clone self. Default False.
Returns:
Volumes object.
Volumes object.
"""
if not copy and self.device == device:
device_ = make_device(device)
if not copy and self.device == device_:
return self
other = self.clone()
if self.device != device:
other.device = device
# pyre-fixme[16]: `List` has no attribute `to`.
other._densities = self._densities.to(device)
if self._features is not None:
# pyre-fixme[16]: `Optional` has no attribute `to`.
other._features = self.features().to(device)
other._local_to_world_transform = (
self.get_local_to_world_coords_transform().to(device)
)
other._grid_sizes = self._grid_sizes.to(device)
if self.device == device_:
return other
other.device = device_
# pyre-fixme[16]: `List` has no attribute `to`.
other._densities = self._densities.to(device_)
if self._features is not None:
# pyre-fixme[16]: `Optional` has no attribute `to`.
other._features = self.features().to(device_)
other._local_to_world_transform = self.get_local_to_world_coords_transform().to(
device_
)
other._grid_sizes = self._grid_sizes.to(device_)
return other
def cpu(self) -> "Volumes":
return self.to(torch.device("cpu"))
return self.to("cpu")
def cuda(self) -> "Volumes":
return self.to(torch.device("cuda"))
return self.to("cuda")

65
tests/test_volumes.py
View File

@ -445,38 +445,65 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
Test the moving of the volumes from/to gpu and cpu
"""
device = torch.device("cuda:0")
device_cpu = torch.device("cpu")
features = torch.randn(
size=[num_volumes, num_channels, *size], device=device, dtype=torch.float32
size=[num_volumes, num_channels, *size], dtype=torch.float32
)
densities = torch.rand(size=[num_volumes, 1, *size], device=device, dtype=dtype)
densities = torch.rand(size=[num_volumes, 1, *size], dtype=dtype)
volumes = Volumes(densities=densities, features=features)
# Test support for str and torch.device
cpu_device = torch.device("cpu")
converted_volumes = volumes.to("cpu")
self.assertEqual(cpu_device, converted_volumes.device)
self.assertEqual(cpu_device, volumes.device)
self.assertIs(volumes, converted_volumes)
converted_volumes = volumes.to(cpu_device)
self.assertEqual(cpu_device, converted_volumes.device)
self.assertEqual(cpu_device, volumes.device)
self.assertIs(volumes, converted_volumes)
cuda_device = torch.device("cuda:0")
converted_volumes = volumes.to("cuda:0")
self.assertEqual(cuda_device, converted_volumes.device)
self.assertEqual(cpu_device, volumes.device)
self.assertIsNot(volumes, converted_volumes)
converted_volumes = volumes.to(cuda_device)
self.assertEqual(cuda_device, converted_volumes.device)
self.assertEqual(cpu_device, volumes.device)
self.assertIsNot(volumes, converted_volumes)
# Test device placement of internal tensors
features = features.to(cuda_device)
densities = features.to(cuda_device)
for features_ in (features, None):
v = Volumes(densities=densities, features=features_)
volumes = Volumes(densities=densities, features=features_)
v_cpu = v.cpu()
v_cuda = v_cpu.cuda()
v_cuda_2 = v_cuda.cuda()
v_cpu_2 = v_cuda_2.cpu()
cpu_volumes = volumes.cpu()
cuda_volumes = cpu_volumes.cuda()
cuda_volumes2 = cuda_volumes.cuda()
cpu_volumes2 = cuda_volumes2.cpu()
for v1, v2 in itertools.combinations(
(v, v_cpu, v_cpu_2, v_cuda, v_cuda_2), 2
for volumes1, volumes2 in itertools.combinations(
(volumes, cpu_volumes, cpu_volumes2, cuda_volumes, cuda_volumes2), 2
):
if v1 is v_cuda and v2 is v_cuda_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(v1._densities, v2._densities)
assert_fun(volumes1._densities, volumes2._densities)
if features_ is not None:
assert_fun(v1._features, v2._features)
for v_ in (v1, v2):
if v_ in (v_cpu, v_cpu_2):
self._check_vars_on_device(v_, device_cpu)
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(v_, device)
self._check_vars_on_device(volumes_, cuda_device)
def _check_padded(self, x_pad, x_list, grid_sizes):
"""