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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
import open3d as o3d
import open3d.core as o3c
import numpy as np
import pytest
import pickle
import tempfile
import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../..")
from open3d_test import list_devices
class WrongType():
pass
@pytest.mark.parametrize("device", list_devices(enable_sycl=True))
def test_tensormap(device):
dtype = o3c.float32
# Constructor.
tm = o3d.t.geometry.TensorMap("positions")
# Get primary key().
assert tm.primary_key == "positions"
# Map member access, assignment and "contains" check. This should be the
# preferred way to construct a TensorMap with values in python.
points = o3c.Tensor.ones((0, 3), dtype, device)
colors = o3c.Tensor.ones((0, 3), dtype, device)
tm = o3d.t.geometry.TensorMap("positions")
assert "positions" not in tm
tm.positions = points
assert "positions" in tm
assert "colors" not in tm
tm.colors = colors
assert "colors" in tm
# Constructor with tm values.
tm = o3d.t.geometry.TensorMap("positions", {
"positions": points,
"colors": colors
})
assert "positions" in tm
assert "colors" in tm
# __delitem__ operator.
with pytest.raises(RuntimeError) as excinfo:
del tm.positions
assert 'cannot be deleted' in str(excinfo.value)
# Test setter.
tm = o3d.t.geometry.TensorMap("positions")
# Set attributes.
tm.positions = o3c.Tensor.ones((2, 3), dtype, device)
tm.colors = o3c.Tensor.ones((2, 3), dtype, device)
# Set attributes with numpy array.
tm.positions = np.ones((3, 3), np.float32)
tm.colors = np.ones((3, 3), np.float32)
assert len(tm.positions) == 3
assert len(tm.colors) == 3
# Set existing attributes with wrong type.
with pytest.raises(TypeError) as e:
tm.positions = WrongType()
# Set new attributes with wrong type.
with pytest.raises(TypeError) as e:
tm.normals = WrongType()
# Set primary key.
with pytest.raises(KeyError) as e:
tm.primary_key = o3c.Tensor.ones((2, 3), dtype, device)
# Test getter.
tm = o3d.t.geometry.TensorMap("positions")
assert isinstance(tm, o3d.t.geometry.TensorMap)
# Set attributes.
tm.positions = o3c.Tensor.ones((2, 3), dtype, device)
tm.colors = o3c.Tensor.ones((2, 3), dtype, device)
# Get existing attributes.
colors = tm.colors
assert len(colors) == 2
# Get unknown attributes.
with pytest.raises(KeyError) as e:
normals = tm.normals
# Get primary key.
primary_key = tm.primary_key
assert primary_key == "positions"
@pytest.mark.parametrize("device", list_devices(enable_sycl=True))
def test_tensormap_modify(device):
# Assigning to the *elements* of an alias will change the value in the map.
# This tests that the alias shares the same memory as the tensor in the map.
tm = o3d.t.geometry.TensorMap("positions")
tm.a = o3c.Tensor([100], device=device)
a_alias = tm.a
a_alias[:] = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm.a.cpu().numpy(), [200])
tm = o3d.t.geometry.TensorMap("positions")
tm.a = o3c.Tensor([100], device=device)
a_alias = tm.a
tm.a[:] = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm.a.cpu().numpy(), [200])
# Assigning a new tensor to an alias should not change the tensor in the
# map. The alias name simply points to a new tensor.
tm = o3d.t.geometry.TensorMap("positions")
tm.a = o3c.Tensor([100], device=device)
a_alias = tm.a
a_alias = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm.a.cpu().numpy(), [100])
tm = o3d.t.geometry.TensorMap("positions")
tm.a = o3c.Tensor([100], device=device)
a_alias = tm.a
tm.a = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [100])
np.testing.assert_equal(tm.a.cpu().numpy(), [200])
# Pybind always returns a "shallow copy" of the tensor. This is a copy since
# the new variable points to a different tensor object, and thus the id() is
# different. The copy is shallow because the new tensor shares the same
# memory as the tensor in the map.
tm = o3d.t.geometry.TensorMap("positions")
tm.a = o3c.Tensor([100], device=device)
a_alias = tm.a
assert id(a_alias) != id(tm.a)
# After deleting the key-value from the map, the alias shall still be alive.
tm = o3d.t.geometry.TensorMap("positions")
tm.a = o3c.Tensor([100], device=device)
a_alias = tm.a
assert len(tm) == 1
del tm.a
assert len(tm) == 0
np.testing.assert_equal(a_alias.cpu().numpy(), [100])
a_alias[:] = 200
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
# With this, swapping elements are supported.
tm = o3d.t.geometry.TensorMap("positions")
tm.a = o3c.Tensor([100], device=device)
tm.b = o3c.Tensor([200], device=device)
a_alias = tm.a
b_alias = tm.b
tm.a, tm.b = tm.b, tm.a
np.testing.assert_equal(a_alias.cpu().numpy(), [100])
np.testing.assert_equal(b_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm.a.cpu().numpy(), [200])
np.testing.assert_equal(tm.b.cpu().numpy(), [100])
@pytest.mark.parametrize("device", list_devices(enable_sycl=True))
def test_tensor_dict_modify(device):
"""
Same as test_tensormap_modify(), but we put Tensors in a python dict.
The only difference is that the id of the alias will be the same.
"""
# Assign to elements.
tm = dict()
tm["a"] = o3c.Tensor([100], device=device)
a_alias = tm["a"]
a_alias[:] = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm["a"].cpu().numpy(), [200])
tm = dict()
tm["a"] = o3c.Tensor([100], device=device)
a_alias = tm["a"]
tm["a"][:] = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm["a"].cpu().numpy(), [200])
# Assign a new tensor.
tm = dict()
tm["a"] = o3c.Tensor([100], device=device)
a_alias = tm["a"]
a_alias = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm["a"].cpu().numpy(), [100])
tm = dict()
tm["a"] = o3c.Tensor([100], device=device)
a_alias = tm["a"]
tm["a"] = o3c.Tensor([200], device=device)
np.testing.assert_equal(a_alias.cpu().numpy(), [100])
np.testing.assert_equal(tm["a"].cpu().numpy(), [200])
# Object id.
tm = dict()
tm["a"] = o3c.Tensor([100], device=device)
a_alias = tm["a"]
assert id(a_alias) == id(tm["a"])
# Liveness of alias.
tm = dict()
tm["a"] = o3c.Tensor([100], device=device)
a_alias = tm["a"]
assert len(tm) == 1
del tm["a"]
assert len(tm) == 0
np.testing.assert_equal(a_alias.cpu().numpy(), [100])
a_alias[:] = 200
np.testing.assert_equal(a_alias.cpu().numpy(), [200])
# Swap.
tm = dict()
tm["a"] = o3c.Tensor([100], device=device)
tm["b"] = o3c.Tensor([200], device=device)
a_alias = tm["a"]
b_alias = tm["b"]
tm["a"], tm["b"] = tm["b"], tm["a"]
np.testing.assert_equal(a_alias.cpu().numpy(), [100])
np.testing.assert_equal(b_alias.cpu().numpy(), [200])
np.testing.assert_equal(tm["a"].cpu().numpy(), [200])
np.testing.assert_equal(tm["b"].cpu().numpy(), [100])
def test_numpy_dict_modify():
"""
Same as test_tensor_dict_modify(), but we put numpy arrays in a python dict.
The id of the alias will be the same.
"""
# Assign to elements.
tm = dict()
tm["a"] = np.array([100])
a_alias = tm["a"]
a_alias[:] = np.array([200])
np.testing.assert_equal(a_alias, [200])
np.testing.assert_equal(tm["a"], [200])
tm = dict()
tm["a"] = np.array([100])
a_alias = tm["a"]
tm["a"][:] = np.array([200])
np.testing.assert_equal(a_alias, [200])
np.testing.assert_equal(tm["a"], [200])
# Assign a new tensor.
tm = dict()
tm["a"] = np.array([100])
a_alias = tm["a"]
tm["a"] = np.array([200])
np.testing.assert_equal(a_alias, [100])
np.testing.assert_equal(tm["a"], [200])
tm = dict()
tm["a"] = np.array([100])
a_alias = tm["a"]
a_alias = np.array([200])
np.testing.assert_equal(a_alias, [200])
np.testing.assert_equal(tm["a"], [100])
# Object id.
tm = dict()
tm["a"] = np.array([100])
a_alias = tm["a"]
assert id(a_alias) == id(tm["a"])
# Liveness of alias.
tm = dict()
tm["a"] = np.array([100])
a_alias = tm["a"]
assert len(tm) == 1
del tm["a"]
assert len(tm) == 0
np.testing.assert_equal(a_alias, [100])
a_alias[:] = 200
np.testing.assert_equal(a_alias, [200])
# Swap.
tm = dict()
tm["a"] = np.array([100])
tm["b"] = np.array([200])
a_alias = tm["a"]
b_alias = tm["b"]
tm["a"], tm["b"] = tm["b"], tm["a"]
np.testing.assert_equal(a_alias, [100])
np.testing.assert_equal(b_alias, [200])
np.testing.assert_equal(tm["a"], [200])
np.testing.assert_equal(tm["b"], [100])
@pytest.mark.parametrize("device", list_devices(enable_sycl=True))
def test_pickle(device):
tm = o3d.t.geometry.TensorMap("positions")
with tempfile.TemporaryDirectory() as temp_dir:
file_name = f"{temp_dir}/tm.pkl"
tm.positions = o3c.Tensor.ones((10, 3), o3c.float32, device=device)
pickle.dump(tm, open(file_name, "wb"))
tm_load = pickle.load(open(file_name, "rb"))
assert tm_load.positions.device == device and tm_load.positions.dtype == o3c.float32
np.testing.assert_equal(tm.positions.cpu().numpy(),
tm_load.positions.cpu().numpy())
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