1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
|
// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "pybind/core/tensor_converter.h"
#include "open3d/core/Tensor.h"
#include "open3d/utility/Logging.h"
#ifdef _MSC_VER
#pragma warning(disable : 4996) // Use of [[deprecated]] feature
#endif
#include "pybind/core/core.h"
#include "pybind/open3d_pybind.h"
#include "pybind/pybind_utils.h"
namespace open3d {
namespace core {
static Tensor CastOptionalDtypeDevice(const Tensor& t,
utility::optional<Dtype> dtype,
utility::optional<Device> device) {
Tensor t_cast = t;
if (dtype.has_value()) {
t_cast = t_cast.To(dtype.value());
}
if (device.has_value()) {
t_cast = t_cast.To(device.value());
}
return t_cast;
}
/// Convert Tensor class to py::array (Numpy array).
py::array TensorToPyArray(const Tensor& tensor) {
if (!tensor.IsCPU()) {
utility::LogError(
"Can only convert CPU Tensor to numpy. Copy Tensor to CPU "
"before converting to numpy.");
}
py::dtype py_dtype =
py::dtype(pybind_utils::DtypeToArrayFormat(tensor.GetDtype()));
py::array::ShapeContainer py_shape(tensor.GetShape());
SizeVector strides = tensor.GetStrides();
int64_t element_byte_size = tensor.GetDtype().ByteSize();
for (auto& s : strides) {
s *= element_byte_size;
}
py::array::StridesContainer py_strides(strides);
// `base_tensor` is a shallow copy of `tensor`. `base_tensor`
// is on the heap and is owned by py::capsule
// `base_tensor_capsule`. The capsule is referenced as the
// "base" of the numpy tensor returned by o3d.Tensor.numpy().
// When the "base" goes out-of-scope (e.g. when all numpy
// tensors referencing the base have gone out-of-scope), the
// deleter is called to free the `base_tensor`.
//
// This behavior is important when the original `tensor` goes
// out-of-scope while we still want to keep the data alive.
// e.g.
//
// ```python
// def get_np_tensor():
// o3d_t = o3d.Tensor(...)
// return o3d_t.numpy()
//
// # Now, `o3d_t` is out-of-scope, but `np_t` still
// # references the base tensor which references the
// # underlying data of `o3d_t`. Thus np_t is still valid.
// # When np_t goes out-of-scope, the underlying data will be
// # finally freed.
// np_t = get_np_tensor()
// ```
//
// See:
// https://stackoverflow.com/questions/44659924/returning-numpy-arrays-via-pybind11
Tensor* base_tensor = new Tensor(tensor);
// See PyTorch's torch/csrc/Module.cpp
auto capsule_destructor = [](PyObject* data) {
Tensor* base_tensor = reinterpret_cast<Tensor*>(
PyCapsule_GetPointer(data, "open3d::Tensor"));
if (base_tensor) {
delete base_tensor;
} else {
PyErr_Clear();
}
};
py::capsule base_tensor_capsule(base_tensor, "open3d::Tensor",
capsule_destructor);
return py::array(py_dtype, py_shape, py_strides, tensor.GetDataPtr(),
base_tensor_capsule);
}
Tensor PyArrayToTensor(py::array array, bool inplace) {
py::buffer_info info = array.request();
SizeVector shape(info.shape.begin(), info.shape.end());
SizeVector strides(info.strides.begin(), info.strides.end());
for (size_t i = 0; i < strides.size(); ++i) {
strides[i] /= info.itemsize;
}
Dtype dtype = pybind_utils::ArrayFormatToDtype(info.format, info.itemsize);
Device device("CPU:0");
auto shared_array = std::make_shared<py::array>(array);
std::function<void(void*)> deleter = [shared_array](void*) mutable -> void {
py::gil_scoped_acquire acquire;
shared_array.reset();
};
auto blob = std::make_shared<Blob>(device, info.ptr, deleter);
Tensor t_inplace(shape, strides, info.ptr, dtype, blob);
if (inplace) {
return t_inplace;
} else {
return t_inplace.Clone();
}
}
Tensor PyListToTensor(const py::list& list,
utility::optional<Dtype> dtype,
utility::optional<Device> device) {
py::object numpy = py::module::import("numpy");
py::array np_array = numpy.attr("array")(list);
Tensor t = PyArrayToTensor(np_array, false);
return CastOptionalDtypeDevice(t, dtype, device);
}
Tensor PyTupleToTensor(const py::tuple& tuple,
utility::optional<Dtype> dtype,
utility::optional<Device> device) {
py::object numpy = py::module::import("numpy");
py::array np_array = numpy.attr("array")(tuple);
Tensor t = PyArrayToTensor(np_array, false);
return CastOptionalDtypeDevice(t, dtype, device);
}
Tensor DoubleToTensor(double scalar_value,
utility::optional<Dtype> dtype,
utility::optional<Device> device) {
Dtype dtype_value = core::Float64;
if (dtype.has_value()) {
dtype_value = dtype.value();
}
Device device_value("CPU:0");
if (device.has_value()) {
device_value = device.value();
}
return Tensor(std::vector<double>{scalar_value}, {}, core::Float64,
device_value)
.To(dtype_value);
}
Tensor IntToTensor(int64_t scalar_value,
utility::optional<Dtype> dtype,
utility::optional<Device> device) {
Dtype dtype_value = core::Int64;
if (dtype.has_value()) {
dtype_value = dtype.value();
}
Device device_value("CPU:0");
if (device.has_value()) {
device_value = device.value();
}
return Tensor(std::vector<int64_t>{scalar_value}, {}, core::Int64,
device_value)
.To(dtype_value);
}
Tensor BoolToTensor(bool scalar_value,
utility::optional<Dtype> dtype,
utility::optional<Device> device) {
Dtype dtype_value = core::Bool;
if (dtype.has_value()) {
dtype_value = dtype.value();
}
Device device_value("CPU:0");
if (device.has_value()) {
device_value = device.value();
}
return Tensor(std::vector<bool>{scalar_value}, {}, core::Bool, device_value)
.To(dtype_value);
}
Tensor PyHandleToTensor(const py::handle& handle,
utility::optional<Dtype> dtype,
utility::optional<Device> device,
bool force_copy) {
// 1) bool
// 2) int
// 3) float (double)
// 4) list
// 5) tuple
// 6) numpy.ndarray (value will be copied)
// 7) Tensor (value will be copied)
std::string class_name(py::str(handle.get_type()));
if (class_name == "<class 'bool'>") {
return BoolToTensor(static_cast<bool>(handle.cast<py::bool_>()), dtype,
device);
} else if (class_name == "<class 'int'>") {
return IntToTensor(static_cast<int64_t>(handle.cast<py::int_>()), dtype,
device);
} else if (class_name == "<class 'float'>") {
return DoubleToTensor(static_cast<double>(handle.cast<py::float_>()),
dtype, device);
} else if (class_name == "<class 'list'>") {
return PyListToTensor(handle.cast<py::list>(), dtype, device);
} else if (class_name == "<class 'tuple'>") {
return PyTupleToTensor(handle.cast<py::tuple>(), dtype, device);
} else if (class_name == "<class 'numpy.ndarray'>") {
return CastOptionalDtypeDevice(PyArrayToTensor(handle.cast<py::array>(),
/*inplace=*/!force_copy),
dtype, device);
} else if (class_name.find("open3d") != std::string::npos &&
class_name.find("Tensor") != std::string::npos) {
try {
Tensor* tensor = handle.cast<Tensor*>();
if (force_copy) {
return CastOptionalDtypeDevice(tensor->Clone(), dtype, device);
} else {
return CastOptionalDtypeDevice(*tensor, dtype, device);
}
} catch (...) {
utility::LogError("Cannot cast index to Tensor.");
}
} else {
utility::LogError("PyHandleToTensor has invalid input type {}.",
class_name);
}
}
} // namespace core
} // namespace open3d
|
