diff options
Diffstat (limited to 'src/silx/opencl/test/test_linalg.py')
| -rw-r--r-- | src/silx/opencl/test/test_linalg.py | 82 |
1 files changed, 61 insertions, 21 deletions
diff --git a/src/silx/opencl/test/test_linalg.py b/src/silx/opencl/test/test_linalg.py index da99480..0b0a443 100644 --- a/src/silx/opencl/test/test_linalg.py +++ b/src/silx/opencl/test/test_linalg.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # /*########################################################################## # -# Copyright (c) 2016 European Synchrotron Radiation Facility +# Copyright (c) 2016-2022 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 @@ -30,15 +30,16 @@ __copyright__ = "2013-2017 European Synchrotron Radiation Facility, Grenoble, Fr __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 @@ -47,7 +48,8 @@ from silx.test.utils import utilstest logger = logging.getLogger(__name__) try: - from scipy.ndimage.filters import laplace + from scipy.ndimage import laplace + _has_scipy = True except ImportError: _has_scipy = False @@ -55,13 +57,18 @@ except ImportError: # 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) + """ + shape = [ + img.ndim, + ] + list(img.shape) gradient = np.zeros(shape, dtype=img.dtype) - slice_all = [0, slice(None, -1),] + 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 @@ -71,10 +78,10 @@ def gradient(img): # 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) @@ -87,7 +94,6 @@ def divergence(grad): @unittest.skipUnless(ocl and mako, "PyOpenCl is missing") class TestLinAlg(unittest.TestCase): - def setUp(self): if ocl is None: return @@ -106,7 +112,9 @@ class TestLinAlg(unittest.TestCase): 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 = 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, @@ -153,46 +161,78 @@ class TestLinAlg(unittest.TestCase): arrays = { "numpy.ndarray": self.image, "buffer": self.image_buffer, - "parray": self.image_parray + "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)) + 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)) + 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)) + 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 + "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)) + 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)) + 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") + 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) |