def test_numpy_fft(self):
"""
Test the numpy backend against native fft.
Results should be exactly the same.
"""
trinfos = self.param["transform_infos"]
trdim = self.param["trdim"]
ndim = len(self.param["size"])
input_data = self.param["test_data"].data_refs[ndim].astype(trinfos.modes[self.param["mode"]])
np_fft, np_ifft = self.transforms[trdim][np.isrealobj(input_data)]
F = FFT(
template=input_data,
axes=trinfos.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 test_numpy_fft(self):
"""
Test the numpy backend against native fft.
Results should be exactly the same.
"""
trinfos = self.param["transform_infos"]
trdim = self.param["trdim"]
ndim = len(self.param["size"])
input_data = self.param["test_data"].data_refs[ndim].astype(trinfos.modes[self.param["mode"]])
np_fft, np_ifft = self.transforms[trdim][np.isrealobj(input_data)]
F = FFT(
template=input_data,
axes=trinfos.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 test_fft(self):
err = self.check_current_backend()
if err is not None:
self.skipTest(err)
if self.size == "3D" and test_options.TEST_LOW_MEM:
self.skipTest("low mem")
ndim = len(self.size)
input_data = self.test_data.data_refs[ndim].astype(self.transform_infos.modes[self.mode])
tol = self.tol[np.dtype(input_data.dtype)]
if self.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[self.trdim],
"backend": self.backend,
}
fft_args.update(self.extra_args)
F = FFT(
**fft_args
)
F_np = FFT(
template=input_data,
axes=self.transform_infos.axes[self.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" % (self.mode, self.trdim, self.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" % (self.mode, self.trdim, self.backend, mae)
)
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 __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 self.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
# It seems that cuda has problems with C2D batched 1D
if trdim == "batched_1D" and backend == "cuda" and mode == "C2C":
tol *= 10
# 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)
all_close = np.allclose(res, res_np, atol=tol, rtol=tol),
self.assertTrue(
all_close,
"FFT %s:%s, MAE(%s, numpy) = %f (tol = %.2e)" % (mode, trdim, backend, mae, tol)
)
# 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)
)
def test_fft(self):
err = self.check_current_backend()
if err is not None:
self.skipTest(err)
if self.size == "3D" and test_options.TEST_LOW_MEM:
self.skipTest("low mem")
ndim = len(self.size)
input_data = self.test_data.data_refs[ndim].astype(self.transform_infos.modes[self.mode])
tol = self.tol[np.dtype(input_data.dtype)]
if self.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[self.trdim],
"backend": self.backend,
}
fft_args.update(self.extra_args)
F = FFT(
**fft_args
)
F_np = FFT(
template=input_data,
axes=self.transform_infos.axes[self.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" % (self.mode, self.trdim, self.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" % (self.mode, self.trdim, self.backend, mae)
)