def _operator_and_matrix(self, build_info, dtype, use_placeholder):
shape = build_info.shape
# For this test class, we are creating Hermitian spectrums.
# We also want the spectrum to have eigenvalues bounded away from zero.
#
# pre_spectrum is bounded away from zero.
pre_spectrum = linear_operator_test_util.random_uniform(
shape=self._shape_to_spectrum_shape(shape), minval=1., maxval=2.)
pre_spectrum_c = _to_complex(pre_spectrum)
# Real{IFFT[pre_spectrum]}
# = IFFT[EvenPartOf[pre_spectrum]]
# is the IFFT of something that is also bounded away from zero.
# Therefore, FFT[pre_h] would be a well-conditioned spectrum.
pre_h = math_ops.ifft(pre_spectrum_c)
# A spectrum is Hermitian iff it is the DFT of a real convolution kernel.
# So we will make spectrum = FFT[h], for real valued h.
h = math_ops.real(pre_h)
h_c = _to_complex(h)
spectrum = math_ops.fft(h_c)
lin_op_spectrum = spectrum
if use_placeholder:
lin_op_spectrum = array_ops.placeholder_with_default(spectrum,
shape=None)
operator = linalg.LinearOperatorCirculant(lin_op_spectrum,
input_output_dtype=dtype)
mat = self._spectrum_to_circulant_1d(spectrum, shape, dtype=dtype)
return operator, mat
def _spectrum_to_circulant_1d(self, spectrum, shape, dtype):
"""Creates a circulant matrix from a spectrum.
Intentionally done in an explicit yet inefficient way. This provides a
cross check to the main code that uses fancy reshapes.
Args:
spectrum: Float or complex `Tensor`.
shape: Python list. Desired shape of returned matrix.
dtype: Type to cast the returned matrix to.
Returns:
Circulant (batch) matrix of desired `dtype`.
"""
spectrum = _to_complex(spectrum)
spectrum_shape = self._shape_to_spectrum_shape(shape)
domain_dimension = spectrum_shape[-1]
if not domain_dimension:
return array_ops.zeros(shape, dtype)
# Explicitly compute the action of spectrum on basis vectors.
matrix_rows = []
for m in range(domain_dimension):
x = np.zeros([domain_dimension])
# x is a basis vector.
x[m] = 1.0
fft_x = math_ops.fft(x.astype(np.complex64))
h_convolve_x = math_ops.ifft(spectrum * fft_x)
matrix_rows.append(h_convolve_x)
matrix = array_ops.stack(matrix_rows, axis=-1)
return math_ops.cast(matrix, dtype)
def _spectrum_to_circulant_1d(self, spectrum, shape, dtype):
"""Creates a circulant matrix from a spectrum.
Intentionally done in an explicit yet inefficient way. This provides a
cross check to the main code that uses fancy reshapes.
Args:
spectrum: Float or complex `Tensor`.
shape: Python list. Desired shape of returned matrix.
dtype: Type to cast the returned matrix to.
Returns:
Circulant (batch) matrix of desired `dtype`.
"""
spectrum = _to_complex(spectrum)
spectrum_shape = self._shape_to_spectrum_shape(shape)
domain_dimension = spectrum_shape[-1]
if not domain_dimension:
return array_ops.zeros(shape, dtype)
# Explicitly compute the action of spectrum on basis vectors.
matrix_rows = []
for m in range(domain_dimension):
x = np.zeros([domain_dimension])
# x is a basis vector.
x[m] = 1.0
fft_x = math_ops.fft(x.astype(np.complex64))
h_convolve_x = math_ops.ifft(spectrum * fft_x)
matrix_rows.append(h_convolve_x)
matrix = array_ops.stack(matrix_rows, axis=-1)
return math_ops.cast(matrix, dtype)
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
shape = build_info.shape
# For this test class, we are creating Hermitian spectrums.
# We also want the spectrum to have eigenvalues bounded away from zero.
#
# pre_spectrum is bounded away from zero.
pre_spectrum = linear_operator_test_util.random_uniform(
shape=self._shape_to_spectrum_shape(shape), minval=1., maxval=2.)
pre_spectrum_c = _to_complex(pre_spectrum)
# Real{IFFT[pre_spectrum]}
# = IFFT[EvenPartOf[pre_spectrum]]
# is the IFFT of something that is also bounded away from zero.
# Therefore, FFT[pre_h] would be a well-conditioned spectrum.
pre_h = math_ops.ifft(pre_spectrum_c)
# A spectrum is Hermitian iff it is the DFT of a real convolution kernel.
# So we will make spectrum = FFT[h], for real valued h.
h = math_ops.real(pre_h)
h_c = _to_complex(h)
spectrum = math_ops.fft(h_c)
lin_op_spectrum = spectrum
if use_placeholder:
lin_op_spectrum = array_ops.placeholder_with_default(spectrum, shape=None)
operator = linalg.LinearOperatorCirculant(
lin_op_spectrum, input_output_dtype=dtype)
mat = self._spectrum_to_circulant_1d(spectrum, shape, dtype=dtype)
return operator, mat
def _operator_and_mat_and_feed_dict(self, build_info, dtype,
use_placeholder):
shape = build_info.shape
# For this test class, we are creating Hermitian spectrums.
# We also want the spectrum to have eigenvalues bounded away from zero.
#
# pre_spectrum is bounded away from zero.
pre_spectrum = linear_operator_test_util.random_uniform(
shape=self._shape_to_spectrum_shape(shape), minval=1., maxval=2.)
pre_spectrum_c = _to_complex(pre_spectrum)
# Real{IFFT[pre_spectrum]}
# = IFFT[EvenPartOf[pre_spectrum]]
# is the IFFT of something that is also bounded away from zero.
# Therefore, FFT[pre_h] would be a well-conditioned spectrum.
pre_h = math_ops.ifft(pre_spectrum_c)
# A spectrum is Hermitian iff it is the DFT of a real convolution kernel.
# So we will make spectrum = FFT[h], for real valued h.
h = math_ops.real(pre_h)
h_c = _to_complex(h)
spectrum = math_ops.fft(h_c)
if use_placeholder:
spectrum_ph = array_ops.placeholder(dtypes.complex64)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# it is random and we want the same value used for both mat and feed_dict.
spectrum = spectrum.eval()
operator = linalg.LinearOperatorCirculant(spectrum_ph,
input_output_dtype=dtype)
feed_dict = {spectrum_ph: spectrum}
else:
operator = linalg.LinearOperatorCirculant(spectrum,
input_output_dtype=dtype)
feed_dict = None
mat = self._spectrum_to_circulant_1d(spectrum, shape, dtype=dtype)
return operator, mat, feed_dict
def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder):
shape = build_info.shape
# For this test class, we are creating Hermitian spectrums.
# We also want the spectrum to have eigenvalues bounded away from zero.
#
# pre_spectrum is bounded away from zero.
pre_spectrum = linear_operator_test_util.random_uniform(
shape=self._shape_to_spectrum_shape(shape), minval=1., maxval=2.)
pre_spectrum_c = _to_complex(pre_spectrum)
# Real{IFFT[pre_spectrum]}
# = IFFT[EvenPartOf[pre_spectrum]]
# is the IFFT of something that is also bounded away from zero.
# Therefore, FFT[pre_h] would be a well-conditioned spectrum.
pre_h = math_ops.ifft(pre_spectrum_c)
# A spectrum is Hermitian iff it is the DFT of a real convolution kernel.
# So we will make spectrum = FFT[h], for real valued h.
h = math_ops.real(pre_h)
h_c = _to_complex(h)
spectrum = math_ops.fft(h_c)
if use_placeholder:
spectrum_ph = array_ops.placeholder(dtypes.complex64)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# it is random and we want the same value used for both mat and feed_dict.
spectrum = spectrum.eval()
operator = linalg.LinearOperatorCirculant(
spectrum_ph, input_output_dtype=dtype)
feed_dict = {spectrum_ph: spectrum}
else:
operator = linalg.LinearOperatorCirculant(
spectrum, input_output_dtype=dtype)
feed_dict = None
mat = self._spectrum_to_circulant_1d(spectrum, shape, dtype=dtype)
return operator, mat, feed_dict
def _FFTGrad(_, grad):
size = math_ops.cast(array_ops.size(grad), dtypes.float32)
return math_ops.ifft(grad) * math_ops.complex(size, 0.)
def _FFTGrad(_, grad): size = math_ops.cast(_FFTSizeForGrad(grad, 1), dtypes.float32) return math_ops.ifft(grad) * math_ops.complex(size, 0.)
def _FFTGrad(_, grad): size = math_ops.cast(array_ops.size(grad), dtypes.float32) return math_ops.ifft(grad) * math_ops.complex(size, 0.)
def _FFTGrad(_, grad):
size = math_ops.cast(_FFTSizeForGrad(grad, 1), dtypes.float32)
return math_ops.ifft(grad) * math_ops.complex(size, 0.)