diff options
Diffstat (limited to 'src/silx/opencl/linalg.py')
| -rw-r--r-- | src/silx/opencl/linalg.py | 78 |
1 files changed, 38 insertions, 40 deletions
diff --git a/src/silx/opencl/linalg.py b/src/silx/opencl/linalg.py index a64122a..573ebce 100644 --- a/src/silx/opencl/linalg.py +++ b/src/silx/opencl/linalg.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# coding: utf-8 # /*########################################################################## # # Copyright (c) 2016 European Synchrotron Radiation Facility @@ -25,8 +24,6 @@ # ###########################################################################*/ """Module for basic linear algebra in OpenCL""" -from __future__ import absolute_import, print_function, with_statement, division - __authors__ = ["P. Paleo"] __license__ = "MIT" __date__ = "01/08/2019" @@ -37,14 +34,23 @@ from .common import pyopencl from .processing import EventDescription, OpenclProcessing import pyopencl.array as parray + cl = pyopencl class LinAlg(OpenclProcessing): - kernel_files = ["linalg.cl"] - def __init__(self, shape, do_checks=False, ctx=None, devicetype="all", platformid=None, deviceid=None, profile=False): + def __init__( + self, + shape, + do_checks=False, + ctx=None, + devicetype="all", + platformid=None, + deviceid=None, + profile=False, + ): """ Create a "Linear Algebra" plan for a given image shape. @@ -59,32 +65,34 @@ class LinAlg(OpenclProcessing): store profiling elements (makes code slightly slower) """ - OpenclProcessing.__init__(self, ctx=ctx, devicetype=devicetype, - platformid=platformid, deviceid=deviceid, - profile=profile) + OpenclProcessing.__init__( + self, + ctx=ctx, + devicetype=devicetype, + platformid=platformid, + deviceid=deviceid, + profile=profile, + ) self.d_gradient = parray.empty(self.queue, shape, np.complex64) self.d_gradient.fill(np.complex64(0.0)) self.d_image = parray.empty(self.queue, shape, np.float32) self.d_image.fill(np.float32(0.0)) - self.add_to_cl_mem({ - "d_gradient": self.d_gradient, - "d_image": self.d_image - }) + self.add_to_cl_mem({"d_gradient": self.d_gradient, "d_image": self.d_image}) self.wg2D = None self.shape = shape - self.ndrange2D = ( - int(self.shape[1]), - int(self.shape[0]) - ) + self.ndrange2D = (int(self.shape[1]), int(self.shape[0])) self.do_checks = bool(do_checks) OpenclProcessing.compile_kernels(self, self.kernel_files) @staticmethod def check_array(array, dtype, shape, arg_name): if array.shape != shape or array.dtype != dtype: - raise ValueError("%s should be a %s array of type %s" %(arg_name, str(shape), str(dtype))) + raise ValueError( + "%s should be a %s array of type %s" + % (arg_name, str(shape), str(dtype)) + ) def get_data_references(self, src, dst, default_src_ref, default_dst_ref): """ @@ -100,7 +108,9 @@ class LinAlg(OpenclProcessing): elif isinstance(dst, cl.Buffer): dst_ref = dst else: - raise ValueError("dst should be either pyopencl.array.Array or pyopencl.Buffer") + raise ValueError( + "dst should be either pyopencl.array.Array or pyopencl.Buffer" + ) else: dst_ref = default_dst_ref @@ -130,21 +140,15 @@ class LinAlg(OpenclProcessing): self.check_array(image, np.float32, self.shape, "image") if dst is not None: self.check_array(dst, np.complex64, self.shape, "dst") - img_ref, grad_ref = self.get_data_references(image, dst, self.d_image.data, self.d_gradient.data) + img_ref, grad_ref = self.get_data_references( + image, dst, self.d_image.data, self.d_gradient.data + ) # Prepare the kernel call - kernel_args = [ - img_ref, - grad_ref, - n_x, - n_y - ] + kernel_args = [img_ref, grad_ref, n_x, n_y] # Call the gradient kernel evt = self.kernels.kern_gradient2D( - self.queue, - self.ndrange2D, - self.wg2D, - *kernel_args + self.queue, self.ndrange2D, self.wg2D, *kernel_args ) self.events.append(EventDescription("gradient2D", evt)) # TODO: should the wait be done in any case ? @@ -187,21 +191,15 @@ class LinAlg(OpenclProcessing): self.check_array(gradient, np.complex64, self.shape, "gradient") if dst is not None: self.check_array(dst, np.float32, self.shape, "dst") - grad_ref, img_ref = self.get_data_references(gradient, dst, self.d_gradient.data, self.d_image.data) + grad_ref, img_ref = self.get_data_references( + gradient, dst, self.d_gradient.data, self.d_image.data + ) # Prepare the kernel call - kernel_args = [ - grad_ref, - img_ref, - n_x, - n_y - ] + kernel_args = [grad_ref, img_ref, n_x, n_y] # Call the gradient kernel evt = self.kernels.kern_divergence2D( - self.queue, - self.ndrange2D, - self.wg2D, - *kernel_args + self.queue, self.ndrange2D, self.wg2D, *kernel_args ) self.events.append(EventDescription("divergence2D", evt)) # TODO: should the wait be done in any case ? |