GeezerVisonThe World Wide Web, restored to 800 pixels of glory. |
| Home|About|Gaming cabinet|Archives|How it works|Starter sites |
GeezerVison is showing github.com — xtensor-python/include/xtensor-python/pytensor.hpp at master · xtensor-stack/xtensor-python Expand file tree/ Copy pathpytensor.hppMore file actions 556 lines (482 loc) · 18.7 KB / Copy pathpytensor.hppFile metadata and controls556 lines (482 loc) · 18.7 KB 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 /*************************************************************************** * Copyright (c) Wolf Vollprecht, Johan Mabille and Sylvain Corlay * * Copyright (c) QuantStack * * * * Distributed under the terms of the BSD 3-Clause License. * * * * The full license is in the file LICENSE, distributed with this software. * ****************************************************************************/ #ifndef PY_TENSOR_HPP #define PY_TENSOR_HPP #include <algorithm> #include <array> #include <cstddef> #include "xtensor/containers/xbuffer_adaptor.hpp" #include "xtensor/core/xiterator.hpp" #include "xtensor/core/xsemantic.hpp" #include "xtensor/utils/xutils.hpp" #include "pycontainer.hpp" #include "pystrides_adaptor.hpp" #include "pynative_casters.hpp" #include "xtensor_type_caster_base.hpp" #include "xtensor_python_config.hpp" namespace xt { template <class T, std::size_t N, layout_type L = layout_type::dynamic> class pytensor; } namespace pybind11 { namespace detail { #ifdef PYBIND11_DESCR // The macro is removed from pybind11 since 2.3 template <class T, std::size_t N, xt::layout_type L> struct handle_type_name<xt::pytensor<T, N, L>> { static PYBIND11_DESCR name() { return _("numpy.ndarray[") + npy_format_descriptor<T>::name() + _("]"); } }; #endif template <class T, std::size_t N, xt::layout_type L> struct pyobject_caster<xt::pytensor<T, N, L>> { using type = xt::pytensor<T, N, L>; bool load(handle src, bool convert) { if (!convert) { if (!xt::detail::check_array<T>(src)) { return false; } } try { value = type::ensure(src); } catch (const std::runtime_error&) { return false; } return static_cast<bool>(value); } static handle cast(const handle& src, return_value_policy, handle) { return src.inc_ref(); } #ifdef PYBIND11_DESCR // The macro is removed from pybind11 since 2.3 PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name()); #else PYBIND11_TYPE_CASTER(type, _("numpy.ndarray[") + npy_format_descriptor<T>::name + _("]")); #endif }; // Type caster for casting ndarray to xexpression<pytensor> template <class T, std::size_t N, xt::layout_type L> struct type_caster<xt::xexpression<xt::pytensor<T, N, L>>> : pyobject_caster<xt::pytensor<T, N, L>> { using Type = xt::xexpression<xt::pytensor<T, N, L>>; operator Type&() { return this->value; } operator const Type&() { return this->value; } }; } // namespace detail } namespace xt { namespace detail { template <std::size_t N> struct numpy_strides { npy_intp value[N]; }; template <> struct numpy_strides<0> { npy_intp* value = nullptr; }; } // namespace detail template <class T, std::size_t N, layout_type L> struct xiterable_inner_types<pytensor<T, N, L>> : xcontainer_iterable_types<pytensor<T, N, L>> { }; template <class T, std::size_t N, layout_type L> struct xcontainer_inner_types<pytensor<T, N, L>> { using storage_type = xbuffer_adaptor<T*>; using reference = typename storage_type::reference; using const_reference = typename storage_type::const_reference; using size_type = typename storage_type::size_type; using shape_type = std::array<npy_intp, N>; using strides_type = shape_type; using backstrides_type = shape_type; using inner_shape_type = shape_type; using inner_strides_type = strides_type; using inner_backstrides_type = backstrides_type; using temporary_type = pytensor<T, N, L>; static constexpr layout_type layout = L; }; /** * @class pytensor * @brief Multidimensional container providing the xtensor container semantics wrapping a numpy array. * * pytensor is similar to the xtensor container in that it has a static dimensionality. * * Unlike the pyarray container, pytensor cannot be reshaped with a different number of dimensions * and reshapes are not reflected on the Python side. However, pytensor has benefits compared to pyarray * in terms of performances. pytensor shapes are stack-allocated which makes iteration upon pytensor * faster than with pyarray. * * @tparam T The type of the element stored in the pyarray. * @sa pyarray */ template <class T, std::size_t N, layout_type L> class pytensor : public pycontainer<pytensor<T, N, L>>, public xcontainer_semantic<pytensor<T, N, L>> { public: using self_type = pytensor<T, N, L>; using semantic_base = xcontainer_semantic<self_type>; using base_type = pycontainer<self_type>; using storage_type = typename base_type::storage_type; using value_type = typename base_type::value_type; using reference = typename base_type::reference; using const_reference = typename base_type::const_reference; using pointer = typename base_type::pointer; using size_type = typename base_type::size_type; using shape_type = typename base_type::shape_type; using strides_type = typename base_type::strides_type; using backstrides_type = typename base_type::backstrides_type; using inner_shape_type = typename base_type::inner_shape_type; using inner_strides_type = typename base_type::inner_strides_type; using inner_backstrides_type = typename base_type::inner_backstrides_type; constexpr static std::size_t rank = N; pytensor(); pytensor(nested_initializer_list_t<T, N> t); pytensor(pybind11::handle h, pybind11::object::borrowed_t); pytensor(pybind11::handle h, pybind11::object::stolen_t); pytensor(const pybind11::object& o); explicit pytensor(const shape_type& shape, layout_type l = layout_type::row_major); explicit pytensor(const shape_type& shape, const_reference value, layout_type l = layout_type::row_major); explicit pytensor(const shape_type& shape, const strides_type& strides, const_reference value); explicit pytensor(const shape_type& shape, const strides_type& strides); template <class S = shape_type> static pytensor from_shape(S&& shape); pytensor(const self_type& rhs); self_type& operator=(const self_type& rhs); pytensor(self_type&&) = default; self_type& operator=(self_type&& e) = default; template <class E> pytensor(const xexpression<E>& e); template <class E> self_type& operator=(const xexpression<E>& e); using base_type::begin; using base_type::end; static self_type ensure(pybind11::handle h); static bool check_(pybind11::handle h); #if (PYBIND11_VERSION_MAJOR == 2 && PYBIND11_VERSION_MINOR >= 3) || (PYBIND11_VERSION_MAJOR >= 3) // Prevent ambiguous overload resolution for operators defined for // both xt::xcontainer_semantic and pybind11::object. using semantic_base::operator+=; using semantic_base::operator-=; using semantic_base::operator*=; using semantic_base::operator/=; using semantic_base::operator|=; using semantic_base::operator&=; using semantic_base::operator^=; // using semantic_base::operator<<=; // using semantic_base::operator>>=; #endif private: inner_shape_type m_shape; inner_strides_type m_strides; inner_backstrides_type m_backstrides; storage_type m_storage; void init_tensor(const shape_type& shape, const strides_type& strides); void init_from_python(); inner_shape_type& shape_impl() noexcept; const inner_shape_type& shape_impl() const noexcept; inner_strides_type& strides_impl() noexcept; const inner_strides_type& strides_impl() const noexcept; inner_backstrides_type& backstrides_impl() noexcept; const inner_backstrides_type& backstrides_impl() const noexcept; storage_type& storage_impl() noexcept; const storage_type& storage_impl() const noexcept; friend class xcontainer<pytensor<T, N, L>>; friend class pycontainer<pytensor<T, N, L>>; }; /*************************** * pytensor implementation * ***************************/ /** * @name Constructors */ //@{ /** * Allocates an uninitialized pytensor that holds 1 element. */ template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor() : base_type() { m_shape = xtl::make_sequence<shape_type>(N, size_type(1)); m_strides = xtl::make_sequence<strides_type>(N, size_type(0)); init_tensor(m_shape, m_strides); detail::default_initialize(m_storage); } /** * Allocates a pytensor with a nested initializer list. */ template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(nested_initializer_list_t<T, N> t) : base_type() { base_type::resize(xt::shape<shape_type>(t), layout_type::row_major); nested_copy(m_storage.begin(), t); } template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(pybind11::handle h, pybind11::object::borrowed_t b) : base_type(h, b) { init_from_python(); } template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(pybind11::handle h, pybind11::object::stolen_t s) : base_type(h, s) { init_from_python(); } template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(const pybind11::object& o) : base_type(o) { init_from_python(); } /** * Allocates an uninitialized pytensor with the specified shape and * layout. * @param shape the shape of the pytensor * @param l the layout_type of the pytensor */ template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(const shape_type& shape, layout_type l) { compute_strides(shape, l, m_strides); init_tensor(shape, m_strides); } /** * Allocates a pytensor with the specified shape and layout. Elements * are initialized to the specified value. * @param shape the shape of the pytensor * @param value the value of the elements * @param l the layout_type of the pytensor */ template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(const shape_type& shape, const_reference value, layout_type l) { compute_strides(shape, l, m_strides); init_tensor(shape, m_strides); std::fill(m_storage.begin(), m_storage.end(), value); } /** * Allocates an uninitialized pytensor with the specified shape and strides. * Elements are initialized to the specified value. * @param shape the shape of the pytensor * @param strides the strides of the pytensor * @param value the value of the elements */ template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(const shape_type& shape, const strides_type& strides, const_reference value) { init_tensor(shape, strides); std::fill(m_storage.begin(), m_storage.end(), value); } /** * Allocates an uninitialized pytensor with the specified shape and strides. * @param shape the shape of the pytensor * @param strides the strides of the pytensor */ template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(const shape_type& shape, const strides_type& strides) { init_tensor(shape, strides); } /** * Allocates and returns an pytensor with the specified shape. * @param shape the shape of the pytensor */ template <class T, std::size_t N, layout_type L> template <class S> inline pytensor<T, N, L> pytensor<T, N, L>::from_shape(S&& shape) { detail::check_dims<shape_type>::run(shape.size()); auto shp = xtl::forward_sequence<shape_type, S>(shape); return self_type(shp); } //@} /** * @name Copy semantic */ //@{ /** * The copy constructor. */ template <class T, std::size_t N, layout_type L> inline pytensor<T, N, L>::pytensor(const self_type& rhs) : base_type(), semantic_base(rhs) { init_tensor(rhs.shape(), rhs.strides()); std::copy(rhs.storage().cbegin(), rhs.storage().cend(), this->storage().begin()); } /** * The assignment operator. */ template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::operator=(const self_type& rhs) -> self_type& { self_type tmp(rhs); *this = std::move(tmp); return *this; } //@} /** * @name Extended copy semantic */ //@{ /** * The extended copy constructor. */ template <class T, std::size_t N, layout_type L> template <class E> inline pytensor<T, N, L>::pytensor(const xexpression<E>& e) : base_type() { shape_type shape = xtl::forward_sequence<shape_type, decltype(e.derived_cast().shape())>(e.derived_cast().shape()); strides_type strides = xtl::make_sequence<strides_type>(N, size_type(0)); compute_strides(shape, layout_type::row_major, strides); init_tensor(shape, strides); semantic_base::assign(e); } /** * The extended assignment operator. */ template <class T, std::size_t N, layout_type L> template <class E> inline auto pytensor<T, N, L>::operator=(const xexpression<E>& e) -> self_type& { return semantic_base::operator=(e); } //@} template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::ensure(pybind11::handle h) -> self_type { return base_type::ensure(h); } template <class T, std::size_t N, layout_type L> inline bool pytensor<T, N, L>::check_(pybind11::handle h) { return base_type::check_(h); } template <class T, std::size_t N, layout_type L> inline void pytensor<T, N, L>::init_tensor(const shape_type& shape, const strides_type& strides) { detail::numpy_strides<N> python_strides; std::transform(strides.begin(), strides.end(), python_strides.value, [](auto v) { return sizeof(T) * v; }); int flags = NPY_ARRAY_ALIGNED; if (!std::is_const<T>::value) { flags |= NPY_ARRAY_WRITEABLE; } auto dtype = pybind11::detail::npy_format_descriptor<T>::dtype(); auto tmp = pybind11::reinterpret_steal<pybind11::object>( PyArray_NewFromDescr(&PyArray_Type, (PyArray_Descr*) dtype.release().ptr(), static_cast<int>(shape.size()), const_cast<npy_intp*>(shape.data()), python_strides.value, nullptr, flags, nullptr)); if (!tmp) { throw std::runtime_error("NumPy: unable to create ndarray"); } this->m_ptr = tmp.release().ptr(); m_shape = shape; m_strides = strides; adapt_strides(m_shape, m_strides, m_backstrides); m_storage = storage_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())), static_cast<size_type>(PyArray_SIZE(this->python_array()))); } template <class T, std::size_t N, layout_type L> inline void pytensor<T, N, L>::init_from_python() { if (!static_cast<bool>(*this)) { return; } if (PyArray_NDIM(this->python_array()) != N) { throw std::runtime_error("NumPy: ndarray has incorrect number of dimensions"); } std::copy(PyArray_DIMS(this->python_array()), PyArray_DIMS(this->python_array()) + N, m_shape.begin()); std::transform(PyArray_STRIDES(this->python_array()), PyArray_STRIDES(this->python_array()) + N, m_strides.begin(), [](auto v) { return v / sizeof(T); }); adapt_strides(m_shape, m_strides, m_backstrides); if (L != layout_type::dynamic && !do_strides_match(m_shape, m_strides, L, 1)) { throw std::runtime_error("NumPy: passing container with bad strides for layout (is it a view?)."); } m_storage = storage_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())), this->get_buffer_size()); } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::shape_impl() noexcept -> inner_shape_type& { return m_shape; } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::shape_impl() const noexcept -> const inner_shape_type& { return m_shape; } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::strides_impl() noexcept -> inner_strides_type& { return m_strides; } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::strides_impl() const noexcept -> const inner_strides_type& { return m_strides; } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::backstrides_impl() noexcept -> inner_backstrides_type& { return m_backstrides; } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::backstrides_impl() const noexcept -> const inner_backstrides_type& { return m_backstrides; } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::storage_impl() noexcept -> storage_type& { return m_storage; } template <class T, std::size_t N, layout_type L> inline auto pytensor<T, N, L>::storage_impl() const noexcept -> const storage_type& { return m_storage; } } #endif |
| You are visitor 01402503 — GeezerVison does not store the pages you look up. Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required. |