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# -*- coding: utf-8 -*-
# /*##########################################################################
# Copyright (C) 2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ############################################################################*/
"""
Project: Sift implementation in Python + OpenCL
https://github.com/silx-kit/silx
"""
from __future__ import division
__authors__ = ["Jérôme Kieffer", "Pierre Paleo"]
__contact__ = "jerome.kieffer@esrf.eu"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "06/09/2017"
__status__ = "Production"
import os
import numpy
from .. import resources
from math import log, ceil
def calc_size(shape, blocksize):
"""
Calculate the optimal size for a kernel according to the workgroup size
"""
if "__len__" in dir(blocksize):
return tuple((int(i) + int(j) - 1) & ~(int(j) - 1) for i, j in zip(shape, blocksize))
else:
return tuple((int(i) + int(blocksize) - 1) & ~(int(blocksize) - 1) for i in shape)
def nextpower(n):
"""Calculate the power of two
:param n: an integer, for example 100
:return: another integer, 100-> 128
"""
return 1 << int(ceil(log(n, 2)))
def sizeof(shape, dtype="uint8"):
"""
Calculate the number of bytes needed to allocate for a given structure
:param shape: size or tuple of sizes
:param dtype: data type
"""
itemsize = numpy.dtype(dtype).itemsize
cnt = 1
if "__len__" in dir(shape):
for dim in shape:
cnt *= dim
else:
cnt = int(shape)
return cnt * itemsize
def get_cl_file(resource):
"""get the full path of a openCL resource file
The resource name can be prefixed by the name of a resource directory. For
example "silx:foo.png" identify the resource "foo.png" from the resource
directory "silx".
See also :func:`silx.resources.register_resource_directory`.
:param str resource: Resource name. File name contained if the `opencl`
directory of the resources.
:return: the full path of the openCL source file
"""
if not resource.endswith(".cl"):
resource += ".cl"
return resources._resource_filename(resource,
default_directory="opencl")
def read_cl_file(filename):
"""
:param filename: read an OpenCL file and apply a preprocessor
:return: preprocessed source code
"""
with open(get_cl_file(filename), "r") as f:
# Dummy preprocessor which removes the #include
lines = [i for i in f.readlines() if not i.startswith("#include ")]
return "".join(lines)
get_opencl_code = read_cl_file
def concatenate_cl_kernel(filenames):
"""Concatenates all the kernel from the list of files
:param filenames: filenames containing the kernels
:type filenames: list of str which can be filename of kernel as a string.
:return: a string with all kernels concatenated
this method concatenates all the kernel from the list
"""
return os.linesep.join(read_cl_file(fn) for fn in filenames)
class ConvolutionInfos(object):
allowed_axes = {
"1D": [None],
"separable_2D_1D_2D": [None, (0, 1), (1, 0)],
"batched_1D_2D": [(0,), (1,)],
"separable_3D_1D_3D": [
None,
(0, 1, 2),
(1, 2, 0),
(2, 0, 1),
(2, 1, 0),
(1, 0, 2),
(0, 2, 1)
],
"batched_1D_3D": [(0,), (1,), (2,)],
"batched_separable_2D_1D_3D": [(0,), (1,), (2,)], # unsupported (?)
"2D": [None],
"batched_2D_3D": [(0,), (1,), (2,)],
"separable_3D_2D_3D": [
(1, 0),
(0, 1),
(2, 0),
(0, 2),
(1, 2),
(2, 1),
],
"3D": [None],
}
use_cases = {
(1, 1): {
"1D": {
"name": "1D convolution on 1D data",
"kernels": ["convol_1D_X"],
},
},
(2, 2): {
"2D": {
"name": "2D convolution on 2D data",
"kernels": ["convol_2D_XY"],
},
},
(3, 3): {
"3D": {
"name": "3D convolution on 3D data",
"kernels": ["convol_3D_XYZ"],
},
},
(2, 1): {
"separable_2D_1D_2D": {
"name": "Separable (2D->1D) convolution on 2D data",
"kernels": ["convol_1D_X", "convol_1D_Y"],
},
"batched_1D_2D": {
"name": "Batched 1D convolution on 2D data",
"kernels": ["convol_1D_X", "convol_1D_Y"],
},
},
(3, 1): {
"separable_3D_1D_3D": {
"name": "Separable (3D->1D) convolution on 3D data",
"kernels": ["convol_1D_X", "convol_1D_Y", "convol_1D_Z"],
},
"batched_1D_3D": {
"name": "Batched 1D convolution on 3D data",
"kernels": ["convol_1D_X", "convol_1D_Y", "convol_1D_Z"],
},
"batched_separable_2D_1D_3D": {
"name": "Batched separable (2D->1D) convolution on 3D data",
"kernels": ["convol_1D_X", "convol_1D_Y", "convol_1D_Z"],
},
},
(3, 2): {
"separable_3D_2D_3D": {
"name": "Separable (3D->2D) convolution on 3D data",
"kernels": ["convol_2D_XY", "convol_2D_XZ", "convol_2D_YZ"],
},
"batched_2D_3D": {
"name": "Batched 2D convolution on 3D data",
"kernels": ["convol_2D_XY", "convol_2D_XZ", "convol_2D_YZ"],
},
},
}
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