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Diffstat (limited to 'silx/opencl/test/test_sparse.py')
| -rw-r--r-- | silx/opencl/test/test_sparse.py | 203 |
1 files changed, 0 insertions, 203 deletions
diff --git a/silx/opencl/test/test_sparse.py b/silx/opencl/test/test_sparse.py deleted file mode 100644 index 76a6a0a..0000000 --- a/silx/opencl/test/test_sparse.py +++ /dev/null @@ -1,203 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 -# /*########################################################################## -# -# Copyright (c) 2018-2019 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 sparse module""" - -import numpy as np -import unittest -import logging -from itertools import product -from ..common import ocl -if ocl: - import pyopencl.array as parray - from silx.opencl.sparse import CSR -try: - import scipy.sparse as sp -except ImportError: - sp = None -logger = logging.getLogger(__name__) - - - -def generate_sparse_random_data( - shape=(1000,), - data_min=0, data_max=100, - density=0.1, - use_only_integers=True, - dtype="f"): - """ - Generate random sparse data where. - - Parameters - ------------ - shape: tuple - Output data shape. - data_min: int or float - Minimum value of data - data_max: int or float - Maximum value of data - density: float - Density of non-zero elements in the output data. - Low value of density mean low number of non-zero elements. - use_only_integers: bool - If set to True, the output data items will be primarily integers, - possibly casted to float if dtype is a floating-point type. - This can be used for ease of debugging. - dtype: str or numpy.dtype - Output data type - """ - mask = np.random.binomial(1, density, size=shape) - if use_only_integers: - d = np.random.randint(data_min, high=data_max, size=shape) - else: - d = data_min + (data_max - data_min) * np.random.rand(*shape) - return (d * mask).astype(dtype) - - - -@unittest.skipUnless(ocl and sp, "PyOpenCl/scipy is missing") -class TestCSR(unittest.TestCase): - """Test CSR format""" - - def setUp(self): - # Test possible configurations - input_on_device = [False, True] - output_on_device = [False, True] - dtypes = [np.float32, np.int32, np.uint16] - self._test_configs = list(product(input_on_device, output_on_device, dtypes)) - - - def compute_ref_sparsification(self, array): - ref_sparse = sp.csr_matrix(array) - return ref_sparse - - - def test_sparsification(self): - for input_on_device, output_on_device, dtype in self._test_configs: - self._test_sparsification(input_on_device, output_on_device, dtype) - - - def _test_sparsification(self, input_on_device, output_on_device, dtype): - current_config = "input on device: %s, output on device: %s, dtype: %s" % ( - str(input_on_device), str(output_on_device), str(dtype) - ) - logger.debug("CSR: %s" % current_config) - # Generate data and reference CSR - array = generate_sparse_random_data(shape=(512, 511), dtype=dtype) - ref_sparse = self.compute_ref_sparsification(array) - # Sparsify on device - csr = CSR(array.shape, dtype=dtype) - if input_on_device: - # The array has to be flattened - arr = parray.to_device(csr.queue, array.ravel()) - else: - arr = array - if output_on_device: - d_data = parray.empty_like(csr.data) - d_indices = parray.empty_like(csr.indices) - d_indptr = parray.empty_like(csr.indptr) - d_data.fill(0) - d_indices.fill(0) - d_indptr.fill(0) - output = (d_data, d_indices, d_indptr) - else: - output = None - data, indices, indptr = csr.sparsify(arr, output=output) - if output_on_device: - data = data.get() - indices = indices.get() - indptr = indptr.get() - # Compare - nnz = ref_sparse.nnz - self.assertTrue( - np.allclose(data[:nnz], ref_sparse.data), - "something wrong with sparsified data (%s)" - % current_config - ) - self.assertTrue( - np.allclose(indices[:nnz], ref_sparse.indices), - "something wrong with sparsified indices (%s)" - % current_config - ) - self.assertTrue( - np.allclose(indptr, ref_sparse.indptr), - "something wrong with sparsified indices pointers (indptr) (%s)" - % current_config - ) - - - def test_desparsification(self): - for input_on_device, output_on_device, dtype in self._test_configs: - self._test_desparsification(input_on_device, output_on_device, dtype) - - - def _test_desparsification(self, input_on_device, output_on_device, dtype): - current_config = "input on device: %s, output on device: %s, dtype: %s" % ( - str(input_on_device), str(output_on_device), str(dtype) - ) - logger.debug("CSR: %s" % current_config) - # Generate data and reference CSR - array = generate_sparse_random_data(shape=(512, 511), dtype=dtype) - ref_sparse = self.compute_ref_sparsification(array) - # De-sparsify on device - csr = CSR(array.shape, dtype=dtype, max_nnz=ref_sparse.nnz) - if input_on_device: - data = parray.to_device(csr.queue, ref_sparse.data) - indices = parray.to_device(csr.queue, ref_sparse.indices) - indptr = parray.to_device(csr.queue, ref_sparse.indptr) - else: - data = ref_sparse.data - indices = ref_sparse.indices - indptr = ref_sparse.indptr - if output_on_device: - d_arr = parray.empty_like(csr.array) - d_arr.fill(0) - output = d_arr - else: - output = None - arr = csr.densify(data, indices, indptr, output=output) - if output_on_device: - arr = arr.get() - # Compare - self.assertTrue( - np.allclose(arr.reshape(array.shape), array), - "something wrong with densified data (%s)" - % current_config - ) - - - -def suite(): - suite = unittest.TestSuite() - suite.addTest( - unittest.defaultTestLoader.loadTestsFromTestCase(TestCSR) - ) - return suite - - -if __name__ == '__main__': - unittest.main(defaultTest="suite") - - |