#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
Interop with other python packages.
This module provides interoperability with the following python packages.
1. numpy
2. pycuda
3. pyopencl
"""
from .array import *
from .device import *
try:
import numpy as np
from numpy import ndarray as NumpyArray
from .data import reorder
AF_NUMPY_FOUND=True
def np_to_af_array(np_arr):
"""
Convert numpy.ndarray to arrayfire.Array.
Parameters
----------
np_arr : numpy.ndarray()
Returns
---------
af_arr : arrayfire.Array()
"""
in_shape = np_arr.shape
in_ptr = np_arr.ctypes.data_as(ct.c_void_p)
in_dtype = np_arr.dtype.char
if (np_arr.flags['F_CONTIGUOUS']):
return Array(in_ptr, in_shape, in_dtype)
elif (np_arr.flags['C_CONTIGUOUS']):
if np_arr.ndim == 1:
return Array(in_ptr, in_shape, in_dtype)
elif np_arr.ndim == 2:
shape = (in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype)
return reorder(res, 1, 0)
elif np_arr.ndim == 3:
shape = (in_shape[2], in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype)
return reorder(res, 2, 1, 0)
elif np_arr.ndim == 4:
shape = (in_shape[3], in_shape[2], in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype)
return reorder(res, 3, 2, 1, 0)
else:
raise RuntimeError("Unsupported ndim")
else:
return np_to_af_array(np.asfortranarray(np_arr))
from_ndarray = np_to_af_array
except:
AF_NUMPY_FOUND=False
try:
import pycuda.gpuarray
from pycuda.gpuarray import GPUArray as CudaArray
AF_PYCUDA_FOUND=True
def pycuda_to_af_array(pycu_arr):
"""
Convert pycuda.gpuarray to arrayfire.Array
Parameters
-----------
pycu_arr : pycuda.GPUArray()
Returns
----------
af_arr : arrayfire.Array()
Note
----------
The input array is copied to af.Array
"""
in_ptr = pycu_arr.ptr
in_shape = pycu_arr.shape
in_dtype = pycu_arr.dtype.char
if (pycu_arr.flags.f_contiguous):
res = Array(in_ptr, in_shape, in_dtype, is_device=True)
lock_array(res)
res = res.copy()
return res
elif (pycu_arr.flags.c_contiguous):
if pycu_arr.ndim == 1:
return Array(in_ptr, in_shape, in_dtype, is_device=True)
elif pycu_arr.ndim == 2:
shape = (in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype, is_device=True)
lock_array(res)
return reorder(res, 1, 0)
elif pycu_arr.ndim == 3:
shape = (in_shape[2], in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype, is_device=True)
lock_array(res)
return reorder(res, 2, 1, 0)
elif pycu_arr.ndim == 4:
shape = (in_shape[3], in_shape[2], in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype, is_device=True)
lock_array(res)
return reorder(res, 3, 2, 1, 0)
else:
raise RuntimeError("Unsupported ndim")
else:
return pycuda_to_af_array(pycu_arr.copy())
except:
AF_PYCUDA_FOUND=False
try:
from pyopencl.array import Array as OpenclArray
from .opencl import add_device_context as _add_device_context
from .opencl import set_device_context as _set_device_context
from .opencl import get_device_id as _get_device_id
from .opencl import get_context as _get_context
AF_PYOPENCL_FOUND=True
def pyopencl_to_af_array(pycl_arr):
"""
Convert pyopencl.gpuarray to arrayfire.Array
Parameters
-----------
pycl_arr : pyopencl.Array()
Returns
----------
af_arr : arrayfire.Array()
Note
----------
The input array is copied to af.Array
"""
ctx = pycl_arr.context.int_ptr
que = pycl_arr.queue.int_ptr
dev = pycl_arr.queue.device.int_ptr
dev_idx = None
ctx_idx = None
for n in range(get_device_count()):
set_device(n)
dev_idx = _get_device_id()
ctx_idx = _get_context()
if (dev_idx == dev and ctx_idx == ctx):
break
if (dev_idx == None or ctx_idx == None or
dev_idx != dev or ctx_idx != ctx):
_add_device_context(dev, ctx, que)
_set_device_context(dev, ctx)
in_ptr = pycl_arr.base_data.int_ptr
in_shape = pycl_arr.shape
in_dtype = pycl_arr.dtype.char
if (pycl_arr.flags.f_contiguous):
res = Array(in_ptr, in_shape, in_dtype, is_device=True)
lock_array(res)
return res
elif (pycl_arr.flags.c_contiguous):
if pycl_arr.ndim == 1:
return Array(in_ptr, in_shape, in_dtype, is_device=True)
elif pycl_arr.ndim == 2:
shape = (in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype, is_device=True)
lock_array(res)
return reorder(res, 1, 0)
elif pycl_arr.ndim == 3:
shape = (in_shape[2], in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype, is_device=True)
lock_array(res)
return reorder(res, 2, 1, 0)
elif pycl_arr.ndim == 4:
shape = (in_shape[3], in_shape[2], in_shape[1], in_shape[0])
res = Array(in_ptr, shape, in_dtype, is_device=True)
lock_array(res)
return reorder(res, 3, 2, 1, 0)
else:
raise RuntimeError("Unsupported ndim")
else:
return pyopencl_to_af_array(pycl_arr.copy())
except:
AF_PYOPENCL_FOUND=False
[docs]def to_array(in_array):
"""
Helper function to convert input from a different module to af.Array
Parameters
-------------
in_array : array like object
Can be one of numpy.ndarray, pycuda.GPUArray, pyopencl.Array, array.array, list
Returns
--------------
af.Array of same dimensions as input after copying the data from the input
"""
if AF_NUMPY_FOUND and isinstance(in_array, NumpyArray):
return np_to_af_array(in_array)
if AF_PYCUDA_FOUND and isinstance(in_array, CudaArray):
return pycuda_to_af_array(in_array)
if AF_PYOPENCL_FOUND and isinstance(in_array, OpenclArray):
return pyopencl_to_af_array(in_array)
return Array(src=in_array)
