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Diffstat (limited to 'silx/opencl/test/test_linalg.py')
| -rw-r--r-- | silx/opencl/test/test_linalg.py | 216 |
1 files changed, 0 insertions, 216 deletions
diff --git a/silx/opencl/test/test_linalg.py b/silx/opencl/test/test_linalg.py deleted file mode 100644 index 0b6c730..0000000 --- a/silx/opencl/test/test_linalg.py +++ /dev/null @@ -1,216 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 -# /*########################################################################## -# -# Copyright (c) 2016 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. -# -# ###########################################################################*/ -"""Test of the linalg module""" - -from __future__ import division, print_function - -__authors__ = ["Pierre paleo"] -__license__ = "MIT" -__copyright__ = "2013-2017 European Synchrotron Radiation Facility, Grenoble, France" -__date__ = "01/08/2019" - - -import time -import logging -import numpy as np -import unittest -try: - import mako -except ImportError: - mako = None -from ..common import ocl -if ocl: - import pyopencl as cl - import pyopencl.array as parray - from .. import linalg -from silx.test.utils import utilstest - -logger = logging.getLogger(__name__) -try: - from scipy.ndimage.filters import laplace - _has_scipy = True -except ImportError: - _has_scipy = False - - -# TODO move this function in math or image ? -def gradient(img): - ''' - Compute the gradient of an image as a numpy array - Code from https://github.com/emmanuelle/tomo-tv/ - ''' - shape = [img.ndim, ] + list(img.shape) - gradient = np.zeros(shape, dtype=img.dtype) - slice_all = [0, slice(None, -1),] - for d in range(img.ndim): - gradient[tuple(slice_all)] = np.diff(img, axis=d) - slice_all[0] = d + 1 - slice_all.insert(1, slice(None)) - return gradient - - -# TODO move this function in math or image ? -def divergence(grad): - ''' - Compute the divergence of a gradient - Code from https://github.com/emmanuelle/tomo-tv/ - ''' - res = np.zeros(grad.shape[1:]) - for d in range(grad.shape[0]): - this_grad = np.rollaxis(grad[d], d) - this_res = np.rollaxis(res, d) - this_res[:-1] += this_grad[:-1] - this_res[1:-1] -= this_grad[:-2] - this_res[-1] -= this_grad[-2] - return res - - -@unittest.skipUnless(ocl and mako, "PyOpenCl is missing") -class TestLinAlg(unittest.TestCase): - - def setUp(self): - if ocl is None: - return - self.getfiles() - self.la = linalg.LinAlg(self.image.shape) - self.allocate_arrays() - - def allocate_arrays(self): - """ - Allocate various types of arrays for the tests - """ - # numpy images - self.grad = np.zeros(self.image.shape, dtype=np.complex64) - self.grad2 = np.zeros((2,) + self.image.shape, dtype=np.float32) - self.grad_ref = gradient(self.image) - self.div_ref = divergence(self.grad_ref) - self.image2 = np.zeros_like(self.image) - # Device images - self.gradient_parray = parray.empty(self.la.queue, self.image.shape, np.complex64) - self.gradient_parray.fill(0) - # we should be using cl.Buffer(self.la.ctx, cl.mem_flags.READ_WRITE, size=self.image.nbytes*2), - # but platforms not suporting openCL 1.2 have a problem with enqueue_fill_buffer, - # so we use the parray "fill" utility - self.gradient_buffer = self.gradient_parray.data - # Do the same for image - self.image_parray = parray.to_device(self.la.queue, self.image) - self.image_buffer = self.image_parray.data - # Refs - tmp = np.zeros(self.image.shape, dtype=np.complex64) - tmp.real = np.copy(self.grad_ref[0]) - tmp.imag = np.copy(self.grad_ref[1]) - self.grad_ref_parray = parray.to_device(self.la.queue, tmp) - self.grad_ref_buffer = self.grad_ref_parray.data - - def tearDown(self): - self.image = None - self.image2 = None - self.grad = None - self.grad2 = None - self.grad_ref = None - self.div_ref = None - self.gradient_parray.data.release() - self.gradient_parray = None - self.gradient_buffer = None - self.image_parray.data.release() - self.image_parray = None - self.image_buffer = None - self.grad_ref_parray.data.release() - self.grad_ref_parray = None - self.grad_ref_buffer = None - - def getfiles(self): - # load 512x512 MRI phantom - TODO include Lena or ascent once a .npz is available - self.image = np.load(utilstest.getfile("Brain512.npz"))["data"] - - def compare(self, result, reference, abstol, name): - errmax = np.max(np.abs(result - reference)) - logger.info("%s: Max error = %e" % (name, errmax)) - self.assertTrue(errmax < abstol, str("%s: Max error is too high" % name)) - - @unittest.skipUnless(ocl and mako, "pyopencl is missing") - def test_gradient(self): - arrays = { - "numpy.ndarray": self.image, - "buffer": self.image_buffer, - "parray": self.image_parray - } - for desc, image in arrays.items(): - # Test with dst on host (numpy.ndarray) - res = self.la.gradient(image, return_to_host=True) - self.compare(res, self.grad_ref, 1e-6, str("gradient[src=%s, dst=numpy.ndarray]" % desc)) - # Test with dst on device (pyopencl.Buffer) - self.la.gradient(image, dst=self.gradient_buffer) - cl.enqueue_copy(self.la.queue, self.grad, self.gradient_buffer) - self.grad2[0] = self.grad.real - self.grad2[1] = self.grad.imag - self.compare(self.grad2, self.grad_ref, 1e-6, str("gradient[src=%s, dst=buffer]" % desc)) - # Test with dst on device (pyopencl.Array) - self.la.gradient(image, dst=self.gradient_parray) - self.grad = self.gradient_parray.get() - self.grad2[0] = self.grad.real - self.grad2[1] = self.grad.imag - self.compare(self.grad2, self.grad_ref, 1e-6, str("gradient[src=%s, dst=parray]" % desc)) - - @unittest.skipUnless(ocl and mako, "pyopencl is missing") - def test_divergence(self): - arrays = { - "numpy.ndarray": self.grad_ref, - "buffer": self.grad_ref_buffer, - "parray": self.grad_ref_parray - } - for desc, grad in arrays.items(): - # Test with dst on host (numpy.ndarray) - res = self.la.divergence(grad, return_to_host=True) - self.compare(res, self.div_ref, 1e-6, str("divergence[src=%s, dst=numpy.ndarray]" % desc)) - # Test with dst on device (pyopencl.Buffer) - self.la.divergence(grad, dst=self.image_buffer) - cl.enqueue_copy(self.la.queue, self.image2, self.image_buffer) - self.compare(self.image2, self.div_ref, 1e-6, str("divergence[src=%s, dst=buffer]" % desc)) - # Test with dst on device (pyopencl.Array) - self.la.divergence(grad, dst=self.image_parray) - self.image2 = self.image_parray.get() - self.compare(self.image2, self.div_ref, 1e-6, str("divergence[src=%s, dst=parray]" % desc)) - - @unittest.skipUnless(ocl and mako and _has_scipy, "pyopencl and/or scipy is missing") - def test_laplacian(self): - laplacian_ref = laplace(self.image) - # Laplacian = div(grad) - self.la.gradient(self.image) - laplacian_ocl = self.la.divergence(self.la.d_gradient, return_to_host=True) - self.compare(laplacian_ocl, laplacian_ref, 1e-6, "laplacian") - - -def suite(): - testSuite = unittest.TestSuite() - testSuite.addTest(TestLinAlg("test_gradient")) - testSuite.addTest(TestLinAlg("test_divergence")) - testSuite.addTest(TestLinAlg("test_laplacian")) - return testSuite - - -if __name__ == '__main__': - unittest.main(defaultTest="suite") |