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Merge pull request #27581 from dkurt:d.kuryaev/dlpack

Browse Source 
### Pull Request Readiness Checklist

resolves #16295

```
docker run --gpus 0 -v ~/opencv:/opencv -v ~/opencv_contrib:/opencv_contrib -it nvidia/cuda:12.8.1-cudnn-devel-ubuntu22.04
apt-get update && apt-get install -y cmake python3-dev python3-pip python3-venv &&
python3 -m venv .venv &&
source .venv/bin/activate &&
pip install -U pip &&
pip install -U numpy &&
pip install torch --index-url https://download.pytorch.org/whl/cu128 &&
cmake \
    -DWITH_OPENCL=OFF \
    -DCMAKE_BUILD_TYPE=Release \
    -DBUILD_DOCS=OFF \
    -DWITH_CUDA=ON \
    -DOPENCV_DNN_CUDA=ON \
    -DOPENCV_EXTRA_MODULES_PATH=/opencv_contrib/modules \
    -DBUILD_LIST=ts,cudev,python3 \
    -S /opencv -B /opencv_build &&
cmake --build /opencv_build -j16
export PYTHONPATH=/opencv_build/lib/python3/:$PYTHONPATH
```

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Dmitry Kurtaev 2025-08-20 11:43:41 +03:00 committed by GitHub
parent 1e37d84e3a
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/*!
* Copyright (c) 2017 by Contributors
* \file dlpack.h
* \brief The common header of DLPack.
*/
#ifndef DLPACK_DLPACK_H_
#define DLPACK_DLPACK_H_
/**
* \brief Compatibility with C++
*/
#ifdef __cplusplus
#define DLPACK_EXTERN_C extern "C"
#else
#define DLPACK_EXTERN_C
#endif
/*! \brief The current major version of dlpack */
#define DLPACK_MAJOR_VERSION 1
/*! \brief The current minor version of dlpack */
#define DLPACK_MINOR_VERSION 1
/*! \brief DLPACK_DLL prefix for windows */
#ifdef _WIN32
#ifdef DLPACK_EXPORTS
#define DLPACK_DLL __declspec(dllexport)
#else
#define DLPACK_DLL __declspec(dllimport)
#endif
#else
#define DLPACK_DLL
#endif
#include <stdint.h>
#include <stddef.h>
#ifdef __cplusplus
extern "C" {
#endif
/*!
* \brief The DLPack version.
*
* A change in major version indicates that we have changed the
* data layout of the ABI - DLManagedTensorVersioned.
*
* A change in minor version indicates that we have added new
* code, such as a new device type, but the ABI is kept the same.
*
* If an obtained DLPack tensor has a major version that disagrees
* with the version number specified in this header file
* (i.e. major != DLPACK_MAJOR_VERSION), the consumer must call the deleter
* (and it is safe to do so). It is not safe to access any other fields
* as the memory layout will have changed.
*
* In the case of a minor version mismatch, the tensor can be safely used as
* long as the consumer knows how to interpret all fields. Minor version
* updates indicate the addition of enumeration values.
*/
typedef struct {
/*! \brief DLPack major version. */
uint32_t major;
/*! \brief DLPack minor version. */
uint32_t minor;
} DLPackVersion;
/*!
* \brief The device type in DLDevice.
*/
#ifdef __cplusplus
typedef enum : int32_t {
#else
typedef enum {
#endif
/*! \brief CPU device */
kDLCPU = 1,
/*! \brief CUDA GPU device */
kDLCUDA = 2,
/*!
* \brief Pinned CUDA CPU memory by cudaMallocHost
*/
kDLCUDAHost = 3,
/*! \brief OpenCL devices. */
kDLOpenCL = 4,
/*! \brief Vulkan buffer for next generation graphics. */
kDLVulkan = 7,
/*! \brief Metal for Apple GPU. */
kDLMetal = 8,
/*! \brief Verilog simulator buffer */
kDLVPI = 9,
/*! \brief ROCm GPUs for AMD GPUs */
kDLROCM = 10,
/*!
* \brief Pinned ROCm CPU memory allocated by hipMallocHost
*/
kDLROCMHost = 11,
/*!
* \brief Reserved extension device type,
* used for quickly test extension device
* The semantics can differ depending on the implementation.
*/
kDLExtDev = 12,
/*!
* \brief CUDA managed/unified memory allocated by cudaMallocManaged
*/
kDLCUDAManaged = 13,
/*!
* \brief Unified shared memory allocated on a oneAPI non-partititioned
* device. Call to oneAPI runtime is required to determine the device
* type, the USM allocation type and the sycl context it is bound to.
*
*/
kDLOneAPI = 14,
/*! \brief GPU support for next generation WebGPU standard. */
kDLWebGPU = 15,
/*! \brief Qualcomm Hexagon DSP */
kDLHexagon = 16,
/*! \brief Microsoft MAIA devices */
kDLMAIA = 17,
} DLDeviceType;
/*!
* \brief A Device for Tensor and operator.
*/
typedef struct {
/*! \brief The device type used in the device. */
DLDeviceType device_type;
/*!
* \brief The device index.
* For vanilla CPU memory, pinned memory, or managed memory, this is set to 0.
*/
int32_t device_id;
} DLDevice;
/*!
* \brief The type code options DLDataType.
*/
typedef enum {
/*! \brief signed integer */
kDLInt = 0U,
/*! \brief unsigned integer */
kDLUInt = 1U,
/*! \brief IEEE floating point */
kDLFloat = 2U,
/*!
* \brief Opaque handle type, reserved for testing purposes.
* Frameworks need to agree on the handle data type for the exchange to be well-defined.
*/
kDLOpaqueHandle = 3U,
/*! \brief bfloat16 */
kDLBfloat = 4U,
/*!
* \brief complex number
* (C/C++/Python layout: compact struct per complex number)
*/
kDLComplex = 5U,
/*! \brief boolean */
kDLBool = 6U,
/*! \brief FP8 data types */
kDLFloat8_e3m4 = 7U,
kDLFloat8_e4m3 = 8U,
kDLFloat8_e4m3b11fnuz = 9U,
kDLFloat8_e4m3fn = 10U,
kDLFloat8_e4m3fnuz = 11U,
kDLFloat8_e5m2 = 12U,
kDLFloat8_e5m2fnuz = 13U,
kDLFloat8_e8m0fnu = 14U,
/*! \brief FP6 data types
* Setting bits != 6 is currently unspecified, and the producer must ensure it is set
* while the consumer must stop importing if the value is unexpected.
*/
kDLFloat6_e2m3fn = 15U,
kDLFloat6_e3m2fn = 16U,
/*! \brief FP4 data types
* Setting bits != 4 is currently unspecified, and the producer must ensure it is set
* while the consumer must stop importing if the value is unexpected.
*/
kDLFloat4_e2m1fn = 17U,
} DLDataTypeCode;
/*!
* \brief The data type the tensor can hold. The data type is assumed to follow the
* native endian-ness. An explicit error message should be raised when attempting to
* export an array with non-native endianness
*
* Examples
* - float: type_code = 2, bits = 32, lanes = 1
* - float4(vectorized 4 float): type_code = 2, bits = 32, lanes = 4
* - int8: type_code = 0, bits = 8, lanes = 1
* - std::complex<float>: type_code = 5, bits = 64, lanes = 1
* - bool: type_code = 6, bits = 8, lanes = 1 (as per common array library convention, the underlying storage size of bool is 8 bits)
* - float8_e4m3: type_code = 8, bits = 8, lanes = 1 (packed in memory)
* - float6_e3m2fn: type_code = 16, bits = 6, lanes = 1 (packed in memory)
* - float4_e2m1fn: type_code = 17, bits = 4, lanes = 1 (packed in memory)
*
* When a sub-byte type is packed, DLPack requires the data to be in little bit-endian, i.e.,
* for a packed data set D ((D >> (i * bits)) && bit_mask) stores the i-th element.
*/
typedef struct {
/*!
* \brief Type code of base types.
* We keep it uint8_t instead of DLDataTypeCode for minimal memory
* footprint, but the value should be one of DLDataTypeCode enum values.
* */
uint8_t code;
/*!
* \brief Number of bits, common choices are 8, 16, 32.
*/
uint8_t bits;
/*! \brief Number of lanes in the type, used for vector types. */
uint16_t lanes;
} DLDataType;
/*!
* \brief Plain C Tensor object, does not manage memory.
*/
typedef struct {
/*!
* \brief The data pointer points to the allocated data. This will be CUDA
* device pointer or cl_mem handle in OpenCL. It may be opaque on some device
* types. This pointer is always aligned to 256 bytes as in CUDA. The
* `byte_offset` field should be used to point to the beginning of the data.
*
* Note that as of Nov 2021, multiply libraries (CuPy, PyTorch, TensorFlow,
* TVM, perhaps others) do not adhere to this 256 byte aligment requirement
* on CPU/CUDA/ROCm, and always use `byte_offset=0`. This must be fixed
* (after which this note will be updated); at the moment it is recommended
* to not rely on the data pointer being correctly aligned.
*
* For given DLTensor, the size of memory required to store the contents of
* data is calculated as follows:
*
* \code{.c}
* static inline size_t GetDataSize(const DLTensor* t) {
* size_t size = 1;
* for (tvm_index_t i = 0; i < t->ndim; ++i) {
* size *= t->shape[i];
* }
* size *= (t->dtype.bits * t->dtype.lanes + 7) / 8;
* return size;
* }
* \endcode
*
* Note that if the tensor is of size zero, then the data pointer should be
* set to `NULL`.
*/
void* data;
/*! \brief The device of the tensor */
DLDevice device;
/*! \brief Number of dimensions */
int32_t ndim;
/*! \brief The data type of the pointer*/
DLDataType dtype;
/*! \brief The shape of the tensor */
int64_t* shape;
/*!
* \brief strides of the tensor (in number of elements, not bytes)
* can be NULL, indicating tensor is compact and row-majored.
*/
int64_t* strides;
/*! \brief The offset in bytes to the beginning pointer to data */
uint64_t byte_offset;
} DLTensor;
/*!
* \brief C Tensor object, manage memory of DLTensor. This data structure is
* intended to facilitate the borrowing of DLTensor by another framework. It is
* not meant to transfer the tensor. When the borrowing framework doesn't need
* the tensor, it should call the deleter to notify the host that the resource
* is no longer needed.
*
* \note This data structure is used as Legacy DLManagedTensor
* in DLPack exchange and is deprecated after DLPack v0.8
* Use DLManagedTensorVersioned instead.
* This data structure may get renamed or deleted in future versions.
*
* \sa DLManagedTensorVersioned
*/
typedef struct DLManagedTensor {
/*! \brief DLTensor which is being memory managed */
DLTensor dl_tensor;
/*! \brief the context of the original host framework of DLManagedTensor in
* which DLManagedTensor is used in the framework. It can also be NULL.
*/
void * manager_ctx;
/*!
* \brief Destructor - this should be called
* to destruct the manager_ctx which backs the DLManagedTensor. It can be
* NULL if there is no way for the caller to provide a reasonable destructor.
* The destructor deletes the argument self as well.
*/
void (*deleter)(struct DLManagedTensor * self);
} DLManagedTensor;
// bit masks used in in the DLManagedTensorVersioned
/*! \brief bit mask to indicate that the tensor is read only. */
#define DLPACK_FLAG_BITMASK_READ_ONLY (1UL << 0UL)
/*!
* \brief bit mask to indicate that the tensor is a copy made by the producer.
*
* If set, the tensor is considered solely owned throughout its lifetime by the
* consumer, until the producer-provided deleter is invoked.
*/
#define DLPACK_FLAG_BITMASK_IS_COPIED (1UL << 1UL)
/*
* \brief bit mask to indicate that whether a sub-byte type is packed or padded.
*
* The default for sub-byte types (ex: fp4/fp6) is assumed packed. This flag can
* be set by the producer to signal that a tensor of sub-byte type is padded.
*/
#define DLPACK_FLAG_BITMASK_IS_SUBBYTE_TYPE_PADDED (1UL << 2UL)
/*!
* \brief A versioned and managed C Tensor object, manage memory of DLTensor.
*
* This data structure is intended to facilitate the borrowing of DLTensor by
* another framework. It is not meant to transfer the tensor. When the borrowing
* framework doesn't need the tensor, it should call the deleter to notify the
* host that the resource is no longer needed.
*
* \note This is the current standard DLPack exchange data structure.
*/
struct DLManagedTensorVersioned {
/*!
* \brief The API and ABI version of the current managed Tensor
*/
DLPackVersion version;
/*!
* \brief the context of the original host framework.
*
* Stores DLManagedTensorVersioned is used in the
* framework. It can also be NULL.
*/
void *manager_ctx;
/*!
* \brief Destructor.
*
* This should be called to destruct manager_ctx which holds the DLManagedTensorVersioned.
* It can be NULL if there is no way for the caller to provide a reasonable
* destructor. The destructor deletes the argument self as well.
*/
void (*deleter)(struct DLManagedTensorVersioned *self);
/*!
* \brief Additional bitmask flags information about the tensor.
*
* By default the flags should be set to 0.
*
* \note Future ABI changes should keep everything until this field
* stable, to ensure that deleter can be correctly called.
*
* \sa DLPACK_FLAG_BITMASK_READ_ONLY
* \sa DLPACK_FLAG_BITMASK_IS_COPIED
*/
uint64_t flags;
/*! \brief DLTensor which is being memory managed */
DLTensor dl_tensor;
};
#ifdef __cplusplus
} // DLPACK_EXTERN_C
#endif
#endif // DLPACK_DLPACK_H_

1
CMakeLists.txt
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@ -640,6 +640,7 @@ ocv_cmake_hook(POST_CMAKE_BUILD_OPTIONS)
# --- Python Support --- # --- Python Support ---
if(NOT IOS AND NOT XROS) if(NOT IOS AND NOT XROS)
include(cmake/OpenCVDetectPython.cmake) include(cmake/OpenCVDetectPython.cmake)
include(cmake/OpenCVDetectDLPack.cmake)
endif() endif()
include(cmake/OpenCVCompilerOptions.cmake) include(cmake/OpenCVCompilerOptions.cmake)

5
cmake/OpenCVDetectDLPack.cmake Normal file
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@ -0,0 +1,5 @@
find_package(dlpack QUIET)
if (NOT dlpack_FOUND)
ocv_include_directories("${OpenCV_SOURCE_DIR}/3rdparty/dlpack/include")
ocv_install_3rdparty_licenses(dlpack "${OpenCV_SOURCE_DIR}/3rdparty/dlpack/LICENSE")
endif()

197
modules/core/misc/python/pyopencv_core.hpp
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@ -3,6 +3,8 @@
#ifdef HAVE_OPENCV_CORE #ifdef HAVE_OPENCV_CORE
#include "dlpack/dlpack.h"
static PyObject* pycvMakeType(PyObject* , PyObject* args, PyObject* kw) { static PyObject* pycvMakeType(PyObject* , PyObject* args, PyObject* kw) {
const char *keywords[] = { "depth", "channels", NULL }; const char *keywords[] = { "depth", "channels", NULL };
@ -20,6 +22,201 @@ static PyObject* pycvMakeTypeCh(PyObject*, PyObject *value) {
return PyInt_FromLong(CV_MAKETYPE(depth, channels)); return PyInt_FromLong(CV_MAKETYPE(depth, channels));
} }
#define CV_DLPACK_CAPSULE_NAME "dltensor"
#define CV_DLPACK_USED_CAPSULE_NAME "used_dltensor"
template<typename T>
bool fillDLPackTensor(const T& src, DLManagedTensor* tensor, const DLDevice& device);
template<typename T>
bool parseDLPackTensor(DLManagedTensor* tensor, T& obj, bool copy);
template<typename T>
int GetNumDims(const T& src);
// source: https://github.com/dmlc/dlpack/blob/7f393bbb86a0ddd71fde3e700fc2affa5cdce72d/docs/source/python_spec.rst#L110
static void dlpack_capsule_deleter(PyObject *self){
if (PyCapsule_IsValid(self, CV_DLPACK_USED_CAPSULE_NAME)) {
return;
}
DLManagedTensor *managed = (DLManagedTensor *)PyCapsule_GetPointer(self, CV_DLPACK_CAPSULE_NAME);
if (managed == NULL) {
PyErr_WriteUnraisable(self);
return;
}
if (managed->deleter) {
managed->deleter(managed);
}
}
static void array_dlpack_deleter(DLManagedTensor *self)
{
if (!Py_IsInitialized()) {
return;
}
PyGILState_STATE state = PyGILState_Ensure();
PyObject *array = (PyObject *)self->manager_ctx;
PyMem_Free(self);
Py_XDECREF(array);
PyGILState_Release(state);
}
template<typename T>
static PyObject* to_dlpack(const T& src, PyObject* self, PyObject* py_args, PyObject* kw)
{
int stream = 0;
PyObject* maxVersion = nullptr;
PyObject* dlDevice = nullptr;
bool copy = false;
const char* keywords[] = { "stream", "max_version", "dl_device", "copy", NULL };
if (!PyArg_ParseTupleAndKeywords(py_args, kw, "|iOOp:__dlpack__", (char**)keywords, &stream, &maxVersion, &dlDevice, &copy))
return nullptr;
DLDevice device = {(DLDeviceType)-1, 0};
if (dlDevice && dlDevice != Py_None && PyTuple_Check(dlDevice))
{
device.device_type = static_cast<DLDeviceType>(PyLong_AsLong(PyTuple_GetItem(dlDevice, 0)));
device.device_id = PyLong_AsLong(PyTuple_GetItem(dlDevice, 1));
}
int ndim = GetNumDims(src);
void* ptr = PyMem_Malloc(sizeof(DLManagedTensor) + sizeof(int64_t) * ndim * 2);
if (!ptr) {
PyErr_NoMemory();
return nullptr;
}
DLManagedTensor* tensor = reinterpret_cast<DLManagedTensor*>(ptr);
tensor->manager_ctx = self;
tensor->deleter = array_dlpack_deleter;
tensor->dl_tensor.ndim = ndim;
tensor->dl_tensor.shape = reinterpret_cast<int64_t*>(reinterpret_cast<char*>(ptr) + sizeof(DLManagedTensor));
tensor->dl_tensor.strides = tensor->dl_tensor.shape + ndim;
fillDLPackTensor(src, tensor, device);
PyObject* capsule = PyCapsule_New(ptr, CV_DLPACK_CAPSULE_NAME, dlpack_capsule_deleter);
if (!capsule) {
PyMem_Free(ptr);
return nullptr;
}
// the capsule holds a reference
Py_INCREF(self);
return capsule;
}
template<typename T>
static PyObject* from_dlpack(PyObject* py_args, PyObject* kw)
{
PyObject* arr = nullptr;
PyObject* device = nullptr;
bool copy = false;
const char* keywords[] = { "device", "copy", NULL };
if (!PyArg_ParseTupleAndKeywords(py_args, kw, "O|Op:from_dlpack", (char**)keywords, &arr, &device, &copy))
return nullptr;
PyObject* capsule = nullptr;
if (PyCapsule_CheckExact(arr))
{
capsule = arr;
}
else
{
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
capsule = PyObject_CallMethodObjArgs(arr, PyString_FromString("__dlpack__"), NULL);
PyGILState_Release(gstate);
}
DLManagedTensor* tensor = reinterpret_cast<DLManagedTensor*>(PyCapsule_GetPointer(capsule, CV_DLPACK_CAPSULE_NAME));
if (tensor == nullptr)
{
if (capsule != arr)
Py_DECREF(capsule);
return nullptr;
}
T retval;
bool success = parseDLPackTensor(tensor, retval, copy);
if (success)
{
PyCapsule_SetName(capsule, CV_DLPACK_USED_CAPSULE_NAME);
}
if (capsule != arr)
Py_DECREF(capsule);
return success ? pyopencv_from(retval) : nullptr;
}
static DLDataType GetDLPackType(size_t elemSize1, int depth) {
DLDataType dtype;
dtype.bits = static_cast<uint8_t>(8 * elemSize1);
dtype.lanes = 1;
switch (depth)
{
case CV_8S: case CV_16S: case CV_32S: dtype.code = kDLInt; break;
case CV_8U: case CV_16U: dtype.code = kDLUInt; break;
case CV_16F: case CV_32F: case CV_64F: dtype.code = kDLFloat; break;
default:
CV_Error(Error::StsNotImplemented, "__dlpack__ data type");
}
return dtype;
}
static int DLPackTypeToCVType(const DLDataType& dtype, int channels) {
if (dtype.code == kDLInt)
{
switch (dtype.bits)
{
case 8: return CV_8SC(channels);
case 16: return CV_16SC(channels);
case 32: return CV_32SC(channels);
default:
{
PyErr_SetString(PyExc_BufferError,
format("Unsupported int dlpack depth: %d", dtype.bits).c_str());
return -1;
}
}
}
if (dtype.code == kDLUInt)
{
switch (dtype.bits)
{
case 8: return CV_8UC(channels);
case 16: return CV_16UC(channels);
default:
{
PyErr_SetString(PyExc_BufferError,
format("Unsupported uint dlpack depth: %d", dtype.bits).c_str());
return -1;
}
}
}
if (dtype.code == kDLFloat)
{
switch (dtype.bits)
{
case 16: return CV_16FC(channels);
case 32: return CV_32FC(channels);
case 64: return CV_64FC(channels);
default:
{
PyErr_SetString(PyExc_BufferError,
format("Unsupported float dlpack depth: %d", dtype.bits).c_str());
return -1;
}
}
}
PyErr_SetString(PyExc_BufferError, format("Unsupported dlpack data type: %d", dtype.code).c_str());
return -1;
}
#define PYOPENCV_EXTRA_METHODS_CV \ #define PYOPENCV_EXTRA_METHODS_CV \
{"CV_MAKETYPE", CV_PY_FN_WITH_KW(pycvMakeType), "CV_MAKETYPE(depth, channels) -> retval"}, \ {"CV_MAKETYPE", CV_PY_FN_WITH_KW(pycvMakeType), "CV_MAKETYPE(depth, channels) -> retval"}, \
{"CV_8UC", (PyCFunction)(pycvMakeTypeCh<CV_8U>), METH_O, "CV_8UC(channels) -> retval"}, \ {"CV_8UC", (PyCFunction)(pycvMakeTypeCh<CV_8U>), METH_O, "CV_8UC(channels) -> retval"}, \

158
modules/core/misc/python/pyopencv_cuda.hpp
View File

@ -21,17 +21,175 @@ template<> struct pyopencvVecConverter<cuda::GpuMat>
}; };
CV_PY_TO_CLASS(cuda::GpuMat) CV_PY_TO_CLASS(cuda::GpuMat)
CV_PY_TO_CLASS(cuda::GpuMatND)
CV_PY_TO_CLASS(cuda::Stream) CV_PY_TO_CLASS(cuda::Stream)
CV_PY_TO_CLASS(cuda::Event) CV_PY_TO_CLASS(cuda::Event)
CV_PY_TO_CLASS(cuda::HostMem) CV_PY_TO_CLASS(cuda::HostMem)
CV_PY_TO_CLASS_PTR(cuda::GpuMat) CV_PY_TO_CLASS_PTR(cuda::GpuMat)
CV_PY_TO_CLASS_PTR(cuda::GpuMatND)
CV_PY_TO_CLASS_PTR(cuda::GpuMat::Allocator) CV_PY_TO_CLASS_PTR(cuda::GpuMat::Allocator)
CV_PY_FROM_CLASS(cuda::GpuMat) CV_PY_FROM_CLASS(cuda::GpuMat)
CV_PY_FROM_CLASS(cuda::GpuMatND)
CV_PY_FROM_CLASS(cuda::Stream) CV_PY_FROM_CLASS(cuda::Stream)
CV_PY_FROM_CLASS(cuda::HostMem) CV_PY_FROM_CLASS(cuda::HostMem)
CV_PY_FROM_CLASS_PTR(cuda::GpuMat::Allocator) CV_PY_FROM_CLASS_PTR(cuda::GpuMat::Allocator)
template<>
bool fillDLPackTensor(const Ptr<cv::cuda::GpuMat>& src, DLManagedTensor* tensor, const DLDevice& device)
{
if ((device.device_type != -1 && device.device_type != kDLCUDA) || device.device_id != 0)
{
PyErr_SetString(PyExc_BufferError, "GpuMat can be exported only on GPU:0");
return false;
}
tensor->dl_tensor.data = src->cudaPtr();
tensor->dl_tensor.device.device_type = kDLCUDA;
tensor->dl_tensor.device.device_id = 0;
tensor->dl_tensor.dtype = GetDLPackType(src->elemSize1(), src->depth());
tensor->dl_tensor.shape[0] = src->rows;
tensor->dl_tensor.shape[1] = src->cols;
tensor->dl_tensor.shape[2] = src->channels();
tensor->dl_tensor.strides[0] = src->step1();
tensor->dl_tensor.strides[1] = src->channels();
tensor->dl_tensor.strides[2] = 1;
tensor->dl_tensor.byte_offset = 0;
return true;
}
template<>
bool fillDLPackTensor(const Ptr<cv::cuda::GpuMatND>& src, DLManagedTensor* tensor, const DLDevice& device)
{
if ((device.device_type != -1 && device.device_type != kDLCUDA) || device.device_id != 0)
{
PyErr_SetString(PyExc_BufferError, "GpuMatND can be exported only on GPU:0");
return false;
}
tensor->dl_tensor.data = src->getDevicePtr();
tensor->dl_tensor.device.device_type = kDLCUDA;
tensor->dl_tensor.device.device_id = 0;
tensor->dl_tensor.dtype = GetDLPackType(src->elemSize1(), CV_MAT_DEPTH(src->flags));
for (int i = 0; i < src->dims; ++i)
tensor->dl_tensor.shape[i] = src->size[i];
for (int i = 0; i < src->dims; ++i)
tensor->dl_tensor.strides[i] = src->step[i];
tensor->dl_tensor.byte_offset = 0;
return true;
}
template<>
bool parseDLPackTensor(DLManagedTensor* tensor, cv::cuda::GpuMat& obj, bool copy)
{
if (tensor->dl_tensor.byte_offset != 0)
{
PyErr_SetString(PyExc_BufferError, "Unimplemented from_dlpack for GpuMat with memory offset");
return false;
}
if (tensor->dl_tensor.ndim != 3)
{
PyErr_SetString(PyExc_BufferError, "cuda_GpuMat.from_dlpack expects a 3D tensor. Use cuda_GpuMatND.from_dlpack instead");
return false;
}
if (tensor->dl_tensor.device.device_type != kDLCUDA)
{
PyErr_SetString(PyExc_BufferError, "cuda_GpuMat.from_dlpack expects a tensor on CUDA device");
return false;
}
if (tensor->dl_tensor.strides[1] != tensor->dl_tensor.shape[2] ||
tensor->dl_tensor.strides[2] != 1)
{
PyErr_SetString(PyExc_BufferError, "Unexpected strides for image. Try use GpuMatND");
return false;
}
int type = DLPackTypeToCVType(tensor->dl_tensor.dtype, (int)tensor->dl_tensor.shape[2]);
if (type == -1)
return false;
obj = cv::cuda::GpuMat(
static_cast<int>(tensor->dl_tensor.shape[0]),
static_cast<int>(tensor->dl_tensor.shape[1]),
type,
tensor->dl_tensor.data,
tensor->dl_tensor.strides[0] * tensor->dl_tensor.dtype.bits / 8
);
if (copy)
obj = obj.clone();
return true;
}
template<>
bool parseDLPackTensor(DLManagedTensor* tensor, cv::cuda::GpuMatND& obj, bool copy)
{
if (tensor->dl_tensor.byte_offset != 0)
{
PyErr_SetString(PyExc_BufferError, "Unimplemented from_dlpack for GpuMat with memory offset");
return false;
}
if (tensor->dl_tensor.device.device_type != kDLCUDA)
{
PyErr_SetString(PyExc_BufferError, "cuda_GpuMat.from_dlpack expects a tensor on CUDA device");
return false;
}
int type = DLPackTypeToCVType(tensor->dl_tensor.dtype, (int)tensor->dl_tensor.shape[2]);
if (type == -1)
return false;
std::vector<size_t> steps(tensor->dl_tensor.ndim - 1);
std::vector<int> sizes(tensor->dl_tensor.ndim);
for (int i = 0; i < tensor->dl_tensor.ndim - 1; ++i)
{
steps[i] = tensor->dl_tensor.strides[i] * tensor->dl_tensor.dtype.bits / 8;
sizes[i] = static_cast<int>(tensor->dl_tensor.shape[i]);
}
sizes.back() = static_cast<int>(tensor->dl_tensor.shape[tensor->dl_tensor.ndim - 1]);
obj = cv::cuda::GpuMatND(sizes, type, tensor->dl_tensor.data, steps);
if (copy)
obj = obj.clone();
return true;
}
template<>
int GetNumDims(const Ptr<cv::cuda::GpuMat>& src) { return 3; }
template<>
int GetNumDims(const Ptr<cv::cuda::GpuMatND>& src) { return src->dims; }
static PyObject* pyDLPackGpuMat(PyObject* self, PyObject* py_args, PyObject* kw) {
Ptr<cv::cuda::GpuMat> * self1 = 0;
if (!pyopencv_cuda_GpuMat_getp(self, self1))
return failmsgp("Incorrect type of self (must be 'cuda_GpuMat' or its derivative)");
return to_dlpack(*(self1), self, py_args, kw);
}
static PyObject* pyDLPackGpuMatND(PyObject* self, PyObject* py_args, PyObject* kw) {
Ptr<cv::cuda::GpuMatND> * self1 = 0;
if (!pyopencv_cuda_GpuMatND_getp(self, self1))
return failmsgp("Incorrect type of self (must be 'cuda_GpuMatND' or its derivative)");
return to_dlpack(*(self1), self, py_args, kw);
}
static PyObject* pyDLPackDeviceCUDA(PyObject*, PyObject*, PyObject*) {
return pyopencv_from(std::tuple<int, int>(kDLCUDA, 0));
}
static PyObject* pyGpuMatFromDLPack(PyObject*, PyObject* py_args, PyObject* kw) {
return from_dlpack<cv::cuda::GpuMat>(py_args, kw);
}
static PyObject* pyGpuMatNDFromDLPack(PyObject*, PyObject* py_args, PyObject* kw) {
return from_dlpack<cv::cuda::GpuMatND>(py_args, kw);
}
#define PYOPENCV_EXTRA_METHODS_cuda_GpuMat \
{"__dlpack__", CV_PY_FN_WITH_KW(pyDLPackGpuMat), ""}, \
{"__dlpack_device__", CV_PY_FN_WITH_KW(pyDLPackDeviceCUDA), ""}, \
{"from_dlpack", CV_PY_FN_WITH_KW_(pyGpuMatFromDLPack, METH_STATIC), ""}, \
#define PYOPENCV_EXTRA_METHODS_cuda_GpuMatND \
{"__dlpack__", CV_PY_FN_WITH_KW(pyDLPackGpuMatND), ""}, \
{"__dlpack_device__", CV_PY_FN_WITH_KW(pyDLPackDeviceCUDA), ""}, \
{"from_dlpack", CV_PY_FN_WITH_KW_(pyGpuMatNDFromDLPack, METH_STATIC), ""}, \
#endif #endif

3
modules/python/src2/gen2.py
View File

@ -133,6 +133,9 @@ static PyGetSetDef pyopencv_${name}_getseters[] =
static PyMethodDef pyopencv_${name}_methods[] = static PyMethodDef pyopencv_${name}_methods[] =
{ {
#ifdef PYOPENCV_EXTRA_METHODS_${name}
PYOPENCV_EXTRA_METHODS_${name}
#endif
${methods_inits} ${methods_inits}
{NULL, NULL} {NULL, NULL}
}; };

13
modules/python/test/test_cuda.py
View File

@ -145,5 +145,18 @@ class cuda_test(NewOpenCVTests):
self.assertEqual(True, hasattr(cv.cuda, 'fastNlMeansDenoisingColored')) self.assertEqual(True, hasattr(cv.cuda, 'fastNlMeansDenoisingColored'))
self.assertEqual(True, hasattr(cv.cuda, 'nonLocalMeans')) self.assertEqual(True, hasattr(cv.cuda, 'nonLocalMeans'))
def test_dlpack_GpuMat(self):
for dtype in [np.int8, np.uint8, np.int16, np.uint16, np.float16, np.int32, np.float32, np.float64]:
for channels in [2, 3, 5]:
ref = (np.random.random((64, 128, channels)) * 255).astype(dtype)
src = cv.cuda_GpuMat()
src.upload(ref)
dst = cv.cuda_GpuMat.from_dlpack(src)
test = dst.download()
equal = np.array_equal(ref, test)
if not equal:
print(f"Failed test with dtype {dtype} and {channels} channels")
self.assertTrue(equal)
if __name__ == '__main__': if __name__ == '__main__':
NewOpenCVTests.bootstrap() NewOpenCVTests.bootstrap()