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#!/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 FFT module"""
import numpy as np
import unittest
import logging
try:
from scipy.misc import ascent
__have_scipy = True
except ImportError:
__have_scipy = False
from silx.utils.testutils import ParametricTestCase
from silx.math.fft.fft import FFT
from silx.math.fft.clfft import __have_clfft__
from silx.math.fft.cufft import __have_cufft__
from silx.math.fft.fftw import __have_fftw__
from silx.test.utils import test_options
logger = logging.getLogger(__name__)
class TransformInfos(object):
def __init__(self):
self.dimensions = [
"1D",
"batched_1D",
"2D",
"batched_2D",
"3D",
]
self.modes = {
"R2C": np.float32,
"R2C_double": np.float64,
"C2C": np.complex64,
"C2C_double": np.complex128,
}
self.sizes = {
"1D": [(512,), (511,)],
"2D": [(512, 512), (512, 511), (511, 512), (511, 511)],
"3D": [(128, 128, 128), (128, 128, 127), (128, 127, 128), (127, 128, 128),
(128, 127, 127), (127, 128, 127), (127, 127, 128), (127, 127, 127)]
}
self.axes = {
"1D": None,
"batched_1D": (-1,),
"2D": None,
"batched_2D": (-2, -1),
"3D": None,
}
self.sizes["batched_1D"] = self.sizes["2D"]
self.sizes["batched_2D"] = self.sizes["3D"]
class TestData(object):
def __init__(self):
self.data = ascent().astype("float32")
self.data1d = self.data[:, 0] # non-contiguous data
self.data3d = np.tile(self.data[:128, :128], (128, 1, 1))
self.data_refs = {
1: self.data1d,
2: self.data,
3: self.data3d,
}
@unittest.skipUnless(__have_scipy, "scipy is missing")
class TestFFT(ParametricTestCase):
"""Test cuda/opencl/fftw backends of FFT"""
def setUp(self):
self.tol = {
np.dtype("float32"): 1e-3,
np.dtype("float64"): 1e-9,
np.dtype("complex64"): 1e-3,
np.dtype("complex128"): 1e-9,
}
self.transform_infos = TransformInfos()
self.test_data = TestData()
@staticmethod
def calc_mae(arr1, arr2):
"""
Compute the Max Absolute Error between two arrays
"""
return np.max(np.abs(arr1 - arr2))
@unittest.skipIf(not __have_cufft__,
"cuda back-end requires pycuda and scikit-cuda")
def test_cuda(self):
import pycuda.autoinit
# Error is higher when using cuda. fast_math mode ?
self.tol[np.dtype("float32")] *= 2
self.__run_tests(backend="cuda")
@unittest.skipIf(not __have_clfft__,
"opencl back-end requires pyopencl and gpyfft")
def test_opencl(self):
from silx.opencl.common import ocl
self.__run_tests(backend="opencl", ctx=ocl.create_context())
@unittest.skipIf(not __have_fftw__,
"fftw back-end requires pyfftw")
def test_fftw(self):
self.__run_tests(backend="fftw")
def __run_tests(self, backend, **extra_args):
"""Run all tests with the given backend
:param str backend:
:param dict extra_args: Additional arguments to provide to FFT
"""
for trdim in self.transform_infos.dimensions:
for mode in self.transform_infos.modes:
for size in self.transform_infos.sizes[trdim]:
with self.subTest(trdim=trdim, mode=mode, size=size):
self.__test(backend, trdim, mode, size, **extra_args)
def __test(self, backend, trdim, mode, size, **extra_args):
"""Compare given backend with numpy for given conditions"""
logger.debug("backend: %s, trdim: %s, mode: %s, size: %s",
backend, trdim, mode, str(size))
if size == "3D" and test_options.TEST_LOW_MEM:
self.skipTest("low mem")
ndim = len(size)
input_data = self.test_data.data_refs[ndim].astype(
self.transform_infos.modes[mode])
tol = self.tol[np.dtype(input_data.dtype)]
if trdim == "3D":
tol *= 10 # Error is relatively high in high dimensions
# Python < 3.5 does not want to mix **extra_args with existing kwargs
fft_args = {
"template": input_data,
"axes": self.transform_infos.axes[trdim],
"backend": backend,
}
fft_args.update(extra_args)
F = FFT(
**fft_args
)
F_np = FFT(
template=input_data,
axes=self.transform_infos.axes[trdim],
backend="numpy"
)
# Forward FFT
res = F.fft(input_data)
res_np = F_np.fft(input_data)
mae = self.calc_mae(res, res_np)
self.assertTrue(
mae < np.abs(input_data.max()) * tol,
"FFT %s:%s, MAE(%s, numpy) = %f" % (mode, trdim, backend, mae)
)
# Inverse FFT
res2 = F.ifft(res)
mae = self.calc_mae(res2, input_data)
self.assertTrue(
mae < tol,
"IFFT %s:%s, MAE(%s, numpy) = %f" % (mode, trdim, backend, mae)
)
@unittest.skipUnless(__have_scipy, "scipy is missing")
class TestNumpyFFT(ParametricTestCase):
"""
Test the Numpy backend individually.
"""
def setUp(self):
transforms = {
"1D": {
True: (np.fft.rfft, np.fft.irfft),
False: (np.fft.fft, np.fft.ifft),
},
"2D": {
True: (np.fft.rfft2, np.fft.irfft2),
False: (np.fft.fft2, np.fft.ifft2),
},
"3D": {
True: (np.fft.rfftn, np.fft.irfftn),
False: (np.fft.fftn, np.fft.ifftn),
},
}
transforms["batched_1D"] = transforms["1D"]
transforms["batched_2D"] = transforms["2D"]
self.transforms = transforms
self.transform_infos = TransformInfos()
self.test_data = TestData()
def test(self):
"""Test the numpy backend against native fft.
Results should be exactly the same.
"""
for trdim in self.transform_infos.dimensions:
for mode in self.transform_infos.modes:
for size in self.transform_infos.sizes[trdim]:
with self.subTest(trdim=trdim, mode=mode, size=size):
self.__test(trdim, mode, size)
def __test(self, trdim, mode, size):
logger.debug("trdim: %s, mode: %s, size: %s", trdim, mode, str(size))
ndim = len(size)
input_data = self.test_data.data_refs[ndim].astype(
self.transform_infos.modes[mode])
np_fft, np_ifft = self.transforms[trdim][np.isrealobj(input_data)]
F = FFT(
template=input_data,
axes=self.transform_infos.axes[trdim],
backend="numpy"
)
# Test FFT
res = F.fft(input_data)
ref = np_fft(input_data)
self.assertTrue(np.allclose(res, ref))
# Test IFFT
res2 = F.ifft(res)
ref2 = np_ifft(ref)
self.assertTrue(np.allclose(res2, ref2))
def suite():
suite = unittest.TestSuite()
for cls in (TestNumpyFFT, TestFFT):
suite.addTest(
unittest.defaultTestLoader.loadTestsFromTestCase(cls))
return suite
if __name__ == '__main__':
unittest.main(defaultTest="suite")
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