def test_tensor_from_to_pytorch(device):
if not core_test_utils.torch_available():
return
device_id = device.get_id()
device_type = device.get_type()
# a, b, c share memory
a = torch.ones((2, 2))
if device_type == o3d.core.Device.DeviceType.CUDA:
a = a.cuda(device_id)
b = o3d.core.Tensor.from_dlpack(torch.utils.dlpack.to_dlpack(a))
c = torch.utils.dlpack.from_dlpack(b.to_dlpack())
a[0, 0] = 100
c[0, 1] = 200
r = np.array([[100., 200.], [1., 1.]])
np.testing.assert_equal(r, a.cpu().numpy())
np.testing.assert_equal(r, b.cpu().numpy())
np.testing.assert_equal(r, c.cpu().numpy())
# Special strides
np_r = np.random.randint(10, size=(10, 10)).astype(np.int32)
th_r = torch.Tensor(np_r)
th_t = th_r[1:10:2, 1:10:3].T
if device_type == o3d.core.Device.DeviceType.CUDA:
th_t = th_t.cuda(device_id)
o3_t = o3d.core.Tensor.from_dlpack(torch.utils.dlpack.to_dlpack(th_t))
np.testing.assert_equal(th_t.cpu().numpy(), o3_t.cpu().numpy())
th_t[0, 0] = 100
np.testing.assert_equal(th_t.cpu().numpy(), o3_t.cpu().numpy())
def test_tensor_to_pytorch_scope(device):
if not core_test_utils.torch_available():
return
src_t = np.array([[10, 11, 12.], [13., 14., 15.]])
def get_dst_t():
o3d_t = o3d.core.Tensor(src_t, device=device) # Copy
dst_t = torch.utils.dlpack.from_dlpack(o3d_t.to_dlpack())
return dst_t
dst_t = get_dst_t().cpu().numpy()
np.testing.assert_equal(dst_t, src_t)
def test_tensor_numpy_to_open3d_to_pytorch():
if not core_test_utils.torch_available():
return
# Numpy -> Open3D -> PyTorch all share the same memory
a = np.ones((2, 2))
b = o3d.core.Tensor.from_numpy(a)
c = torch.utils.dlpack.from_dlpack(b.to_dlpack())
a[0, 0] = 100
c[0, 1] = 200
r = np.array([[100., 200.], [1., 1.]])
np.testing.assert_equal(r, a)
np.testing.assert_equal(r, b.cpu().numpy())
np.testing.assert_equal(r, c.cpu().numpy())
def test_tensor_from_to_pytorch(device):
if not core_test_utils.torch_available():
return
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
# ----------------------------------------------------------------------------
import open3d as o3d
import numpy as np
import pytest
import core_test_utils
if core_test_utils.torch_available():
import torch
import torch.utils.dlpack
@pytest.mark.parametrize("device", core_test_utils.list_devices())
def test_creation(device):
# Shape takes tuple, list or o3d.core.SizeVector
t = o3d.core.Tensor.empty((2, 3), o3d.core.Dtype.Float32, device=device)
assert t.shape == o3d.core.SizeVector([2, 3])
t = o3d.core.Tensor.empty([2, 3], o3d.core.Dtype.Float32, device=device)
assert t.shape == o3d.core.SizeVector([2, 3])
t = o3d.core.Tensor.empty(o3d.core.SizeVector([2, 3]),
o3d.core.Dtype.Float32,
device=device)
assert t.shape == o3d.core.SizeVector([2, 3])
