def test_convolve_filter_adjoint_full(self):
mode = 'full'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
for device in devices:
xp = device.xp
with device:
for dtype in dtypes:
with self.subTest(dtype=dtype, device=device):
data = xp.ones([1, 3], dtype=dtype)
output = xp.ones([1, 5], dtype=dtype)
filt_shape = [1, 3]
filt = backend.to_device(
conv.convolve_filter_adjoint(output,
data,
filt_shape,
mode=mode))
npt.assert_allclose(filt, [[3, 3, 3]], atol=1e-5)
data = xp.ones([1, 3], dtype=dtype)
output = xp.ones([1, 4], dtype=dtype)
filt_shape = [1, 2]
filt = backend.to_device(
conv.convolve_filter_adjoint(output,
data,
filt_shape,
mode=mode))
npt.assert_allclose(filt, [[3, 3]], atol=1e-5)
data = xp.ones([1, 1, 3], dtype=dtype)
output = xp.ones([2, 1, 5], dtype=dtype)
filt_shape = [2, 1, 1, 3]
filt = backend.to_device(
conv.convolve_filter_adjoint(output,
data,
filt_shape,
mode=mode,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(filt,
[[[[3, 3, 3]]], [[[3, 3, 3]]]],
atol=1e-5)
data = xp.ones([1, 1, 3], dtype=dtype)
output = xp.ones([2, 1, 3], dtype=dtype)
filt_shape = [2, 1, 1, 3]
strides = [1, 2]
filt = backend.to_device(
conv.convolve_filter_adjoint(output,
data,
filt_shape,
mode=mode,
strides=strides,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(filt,
[[[[2, 1, 2]]], [[[2, 1, 2]]]],
atol=1e-5)
def test_convolve_adjoint_input_full(self):
mode = 'full'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
for device in devices:
xp = device.xp
with device:
for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
y = xp.ones([1, 5], dtype=dtype)
W = xp.ones([1, 3], dtype=dtype)
x = backend.to_device(conv.convolve_adjoint_input(W, y, mode=mode), backend.cpu_device)
npt.assert_allclose(x, [[3, 3, 3]], atol=1e-5)
y = xp.ones([1, 4], dtype=dtype)
W = xp.ones([1, 2], dtype=dtype)
x = backend.to_device(conv.convolve_adjoint_input(W, y, mode=mode),
backend.cpu_device)
npt.assert_allclose(x, [[2, 2, 2]], atol=1e-5)
y = xp.ones([2, 1, 5], dtype=dtype)
W = xp.ones([2, 1, 3], dtype=dtype)
x = backend.to_device(conv.convolve_adjoint_input(W, y, mode=mode,
output_multi_channel=True),
backend.cpu_device)
npt.assert_allclose(x, [[6, 6, 6]], atol=1e-5)
def test_convolve_valid(self):
mode = 'valid'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
for D in [1, 2, 3]:
for device in devices:
xp = device.xp
with device:
for dtype in dtypes:
with self.subTest(D=D, dtype=dtype, device=device):
data = util.dirac([3] + [1] * (D - 1),
device=device, dtype=dtype)
filt = xp.ones([3] + [1] * (D - 1), dtype=dtype)
output = backend.to_device(conv.convolve(
data, filt, mode=mode))
npt.assert_allclose(
output,
np.ones([1] * D), atol=1e-5)
data = util.dirac([3] + [1] * (D - 1),
device=device, dtype=dtype)
filt = xp.ones([2] + [1] * (D - 1), dtype=dtype)
output = backend.to_device(conv.convolve(
data, filt, mode=mode))
npt.assert_allclose(
output,
np.ones([2] + [1] * (D - 1)), atol=1e-5)
data = util.dirac([1, 3] + [1] * (D - 1),
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 3] + [1] * (D - 1),
dtype=dtype)
output = backend.to_device(
conv.convolve(data, filt,
mode=mode,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(
output,
np.ones([2, 1] + [1] * (D - 1)),
atol=1e-5)
data = util.dirac([1, 3] + [1] * (D - 1),
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 3] + [1] * (D - 1),
dtype=dtype)
strides = [2] + [1] * (D - 1)
output = backend.to_device(
conv.convolve(data, filt,
mode=mode, strides=strides,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(
output,
np.ones([2, 1] + [1] * (D - 1)),
atol=1e-5)
def test_convolve_adjoint_filter_valid(self):
mode = 'valid'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
ndim = 2
for device in devices:
xp = device.xp
with device:
for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
x = xp.ones([1, 3], dtype=dtype)
y = xp.ones([1, 1], dtype=dtype)
W = backend.to_device(conv.convolve_adjoint_filter(x, y, ndim, mode=mode),
backend.cpu_device)
npt.assert_allclose(W, [[1, 1, 1]], atol=1e-5)
x = xp.ones([1, 3], dtype=dtype)
y = xp.ones([1, 2], dtype=dtype)
W = backend.to_device(conv.convolve_adjoint_filter(x, y, ndim, mode=mode),
backend.cpu_device)
npt.assert_allclose(W, [[2, 2]], atol=1e-5)
x = xp.ones([1, 1, 3], dtype=dtype)
y = xp.ones([2, 1, 1], dtype=dtype)
W = backend.to_device(conv.convolve_adjoint_filter(x, y, ndim, mode=mode,
output_multi_channel=True),
backend.cpu_device)
npt.assert_allclose(W, [[[1, 1, 1]],
[[1, 1, 1]]], atol=1e-5)
def test_convolve_full(self):
mode = 'full'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
for device in devices:
xp = device.xp
with device:
for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
x = util.dirac([1, 3], device=device, dtype=dtype)
W = xp.ones([1, 3], dtype=dtype)
y = backend.to_device(conv.convolve(x, W, mode=mode), backend.cpu_device)
npt.assert_allclose(y, [[0, 1, 1, 1, 0]], atol=1e-5)
x = util.dirac([1, 3], device=device, dtype=dtype)
W = xp.ones([1, 2], dtype=dtype)
y = backend.to_device(conv.convolve(x, W, mode=mode), backend.cpu_device)
npt.assert_allclose(y, [[0, 1, 1, 0]], atol=1e-5)
x = util.dirac([1, 3], device=device, dtype=dtype)
W = xp.ones([2, 1, 3], dtype=dtype)
y = backend.to_device(conv.convolve(x, W, mode=mode,
output_multi_channel=True), backend.cpu_device)
npt.assert_allclose(y, [[[0, 1, 1, 1, 0]],
[[0, 1, 1, 1, 0]]], atol=1e-5)
def convolve(data, filt, mode='full', strides=None, multi_channel=False):
r"""Convolution that supports multi-dimensional and multi-channel inputs.
This function follows the signal processing definition of convolution.
Args:
data (array): data array of shape:
:math:`[..., m_1, ..., m_D]` if multi_channel is False,
:math:`[..., c_i, m_1, ..., m_D]` otherwise.
filt (array): filter array of shape:
:math:`[n_1, ..., n_D]` if multi_channel is False
:math:`[c_o, c_i, n_1, ..., n_D]` otherwise.
mode (str): {'full', 'valid'}.
strides (None or tuple of ints): convolution strides of length D.
multi_channel (bool): specify if input/output has multiple channels.
Returns:
array: output array of shape:
:math:`[..., p_1, ..., p_D]` if multi_channel is False,
:math:`[..., c_o, p_1, ..., p_D]` otherwise.
"""
device = backend.get_device(data)
filt = backend.to_device(filt, device)
with device:
filt = filt.astype(data.dtype, copy=False)
if device == backend.cpu_device:
output = _convolve(data,
filt,
mode=mode,
strides=strides,
multi_channel=multi_channel)
else: # pragma: no cover
if config.cudnn_enabled:
if np.issubdtype(data.dtype, np.floating):
output = _convolve_cuda(data,
filt,
mode=mode,
strides=strides,
multi_channel=multi_channel)
else:
output = _complex(_convolve_cuda,
data,
filt,
mode=mode,
strides=strides,
multi_channel=multi_channel)
else:
data = backend.to_device(data)
filt = backend.to_device(filt)
output = _convolve_data_adjoint(data,
output,
mode=mode,
strides=strides,
multi_channel=multi_channel)
output = backend.to_device(output, device)
return output
def _apply(self, input):
device = backend.get_device(input)
coord = backend.to_device(self.coord, device)
kernel = backend.to_device(self.kernel, device)
with device:
return interp.gridding(input, self.oshape, self.width, kernel,
coord)
def test_convolve_data_adjoint_valid(self):
mode = 'valid'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
for device in devices:
xp = device.xp
with device:
for dtype in dtypes:
with self.subTest(dtype=dtype, device=device):
output = xp.ones([1, 1], dtype=dtype)
filt = xp.ones([1, 3], dtype=dtype)
data_shape = [1, 3]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode))
npt.assert_allclose(data, [[1, 1, 1]], atol=1e-5)
output = xp.ones([1, 2], dtype=dtype)
filt = xp.ones([1, 2], dtype=dtype)
data_shape = [1, 3]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode))
npt.assert_allclose(data, [[1, 2, 1]], atol=1e-5)
output = xp.ones([2, 1, 1], dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
data_shape = [1, 1, 3]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(data, [[[2, 2, 2]]], atol=1e-5)
output = xp.ones([2, 1, 1], dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
data_shape = [1, 1, 4]
strides = [1, 2]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode,
strides=strides,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(data, [[[2, 2, 2, 0]]], atol=1e-5)
def _apply(self, input):
device = backend.get_device(input)
coord = backend.to_device(self.coord, device)
kernel = backend.to_device(self.kernel, device)
shift = backend.to_device(self.shift, device)
with device:
return interp.interpolate(input, self.width, kernel,
coord * self.scale + shift)
def _scale_coord(coord, shape, oversamp):
ndim = coord.shape[-1]
device = backend.get_device(coord)
scale = backend.to_device(
[_get_ugly_number(oversamp * i) / i for i in shape[-ndim:]], device)
shift = backend.to_device(
[_get_ugly_number(oversamp * i) // 2 for i in shape[-ndim:]], device)
with device:
coord = scale * coord + shift
return coord
def test_convolve_full(self):
mode = 'full'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
dtypes = [np.float32, np.float64, np.complex64, np.complex128]
for device in devices:
xp = device.xp
with device:
for dtype in dtypes:
with self.subTest(dtype=dtype, device=device):
data = util.dirac([1, 3], device=device, dtype=dtype)
filt = xp.ones([1, 3], dtype=dtype)
output = backend.to_device(
conv.convolve(data, filt, mode=mode))
npt.assert_allclose(output, [[0, 1, 1, 1, 0]],
atol=1e-5)
data = util.dirac([1, 3], device=device, dtype=dtype)
filt = xp.ones([1, 2], dtype=dtype)
output = backend.to_device(
conv.convolve(data, filt, mode=mode))
npt.assert_allclose(output, [[0, 1, 1, 0]], atol=1e-5)
data = util.dirac([1, 1, 3],
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
output = backend.to_device(
conv.convolve(data,
filt,
mode=mode,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(
output, [[[0, 1, 1, 1, 0]], [[0, 1, 1, 1, 0]]],
atol=1e-5)
data = util.dirac([1, 1, 3],
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
strides = [1, 2]
output = backend.to_device(
conv.convolve(data,
filt,
mode=mode,
strides=strides,
multi_channel=True))
npt.assert_allclose(output, [[[0, 1, 0]], [[0, 1, 0]]],
atol=1e-5)
def _apply(self, input):
data = backend.to_device(self.data, backend.get_device(input))
return conv.convolve(data,
input,
mode=self.mode,
strides=self.strides,
multi_channel=self.multi_channel)
def _apply(self, input):
device = backend.get_device(input)
with device:
coord = backend.to_device(self.coord, device)
return interp.interpolate(input, coord,
kernel=self.kernel,
width=self.width, param=self.param)
def _apply(self, input):
device = backend.get_device(input)
filt = backend.to_device(self.filt, device)
with device:
return conv.convolve(input, filt, mode=self.mode,
strides=self.strides,
multi_channel=self.multi_channel)
def _apply(self, input):
device = backend.get_device(input)
with device:
coord = backend.to_device(self.coord, device)
return fourier.nufft_adjoint(
input, coord, self.oshape,
oversamp=self.oversamp, width=self.width)
def soft_thresh(lamda, input):
r"""Soft threshold.
Performs:
.. math::
(| x | - \lambda)_+ \text{sgn}(x)
Args:
lamda (float, or array): Threshold parameter.
input (array)
Returns:
array: soft-thresholded result.
"""
device = backend.get_device(input)
xp = device.xp
if xp == np:
return _soft_thresh(lamda, input)
else: # pragma: no cover
if np.isscalar(lamda):
lamda = backend.to_device(lamda, device)
return _soft_thresh_cuda(lamda, input)
def _apply(self, input):
device = backend.get_device(input)
data = backend.to_device(self.data, device)
with device:
return conv.convolve_filter_adjoint(
input, data, self.oshape,
mode=self.mode, strides=self.strides,
multi_channel=self.multi_channel)
def axpy(y, a, x):
"""Compute y = a * x + y.
Args:
y (array): Output array.
a (scalar): Input scalar.
x (array): Input array.
"""
device = backend.get_device(x)
x = backend.to_device(x, device)
a = backend.to_device(a, device)
with device:
if device == backend.cpu_device:
_axpy(y, a, x, out=y)
else:
_axpy_cuda(y, a, x)
def xpay(y, a, x):
"""Compute y = x + a * y.
Args:
y (array): Output array.
a (scalar): Input scalar.
x (array): Input array.
"""
device = backend.get_device(y)
x = backend.to_device(x, device)
a = backend.to_device(a, device)
with device:
if device == backend.cpu_device:
_xpay(y, a, x, out=y)
else:
_xpay_cuda(a, x, y)
def _apply(self, input):
device = backend.get_device(input)
xp = device.xp
mat = backend.to_device(self.mat, device)
with device:
if self.adjoint:
mat = xp.conj(mat).swapaxes(-1, -2)
return xp.matmul(input, mat)
def nufft(input, coord, oversamp=1.25, width=4.0, n=128):
"""Non-uniform Fast Fourier Transform.
Args:
input (array): input signal domain array of shape
(..., n_{ndim - 1}, ..., n_1, n_0),
where ndim is specified by coord.shape[-1]. The nufft
is applied on the last ndim axes, and looped over
the remaining axes.
coord (array): Fourier domain coordinate array of shape (..., ndim).
ndim determines the number of dimensions to apply the nufft.
coord[..., i] should be scaled to have its range between
-n_i // 2, and n_i // 2.
oversamp (float): oversampling factor.
width (float): interpolation kernel full-width in terms of
oversampled grid.
n (int): number of sampling points of the interpolation kernel.
Returns:
array: Fourier domain data of shape
input.shape[:-ndim] + coord.shape[:-1].
References:
Fessler, J. A., & Sutton, B. P. (2003).
Nonuniform fast Fourier transforms using min-max interpolation
IEEE Transactions on Signal Processing, 51(2), 560-574.
Beatty, P. J., Nishimura, D. G., & Pauly, J. M. (2005).
Rapid gridding reconstruction with a minimal oversampling ratio.
IEEE transactions on medical imaging, 24(6), 799-808.
"""
device = backend.get_device(input)
ndim = coord.shape[-1]
beta = np.pi * (((width / oversamp) * (oversamp - 0.5))**2 - 0.8)**0.5
os_shape = _get_oversamp_shape(input.shape, ndim, oversamp)
with device:
output = input.copy()
# Apodize
_apodize(output, ndim, oversamp, width, beta)
# Zero-pad
output /= util.prod(input.shape[-ndim:])**0.5
output = util.resize(output, os_shape)
# FFT
output = fft(output, axes=range(-ndim, 0), norm=None)
# Interpolate
coord = _scale_coord(backend.to_device(
coord, device), input.shape, oversamp)
kernel = _get_kaiser_bessel_kernel(n, width, beta, coord.dtype, device)
output = interp.interpolate(output, width, kernel, coord)
return output
def nufft_adjoint(input, coord, oshape=None, oversamp=1.25, width=4.0, n=128):
"""Adjoint non-uniform Fast Fourier Transform.
Args:
input (array): input Fourier domain array of shape
(...) + coord.shape[:-1]. That is, the last dimensions
of input must match the first dimensions of coord.
The nufft_adjoint is applied on the last coord.ndim - 1 axes,
and looped over the remaining axes.
coord (array): Fourier domain coordinate array of shape (..., ndim).
ndim determines the number of dimension to apply nufft adjoint.
coord[..., i] should be scaled to have its range between
-n_i // 2, and n_i // 2.
oshape (tuple of ints): output shape of the form
(..., n_{ndim - 1}, ..., n_1, n_0).
oversamp (float): oversampling factor.
width (float): interpolation kernel full-width in terms of
oversampled grid.
n (int): number of sampling points of the interpolation kernel.
Returns:
array: signal domain array with shape specified by oshape.
See Also:
:func:`sigpy.nufft.nufft`
"""
device = backend.get_device(input)
ndim = coord.shape[-1]
beta = np.pi * (((width / oversamp) * (oversamp - 0.5))**2 - 0.8)**0.5
if oshape is None:
oshape = list(input.shape[:-coord.ndim + 1]) + estimate_shape(coord)
else:
oshape = list(oshape)
os_shape = _get_oversamp_shape(oshape, ndim, oversamp)
with device:
# Gridding
coord = _scale_coord(backend.to_device(
coord, device), oshape, oversamp)
kernel = _get_kaiser_bessel_kernel(n, width, beta, coord.dtype, device)
output = interp.gridding(input, os_shape, width, kernel, coord)
# IFFT
output = ifft(output, axes=range(-ndim, 0), norm=None)
# Crop
output = util.resize(output, oshape)
output *= util.prod(os_shape[-ndim:]) / util.prod(oshape[-ndim:])**0.5
# Apodize
_apodize(output, ndim, oversamp, width, beta)
return output
def asscalar(input):
"""Returns input array as scalar.
Args:
input (array): Input array
Returns:
scalar.
"""
return np.asscalar(backend.to_device(input, backend.cpu_device))
def fwt(input, wave_name='db4', axes=None, level=None):
"""Forward wavelet transform.
Args:
input (array): Input array.
axes (None or tuple of int): Axes to perform wavelet transform.
wave_name (str): Wavelet name.
level (None or int): Number of wavelet levels.
"""
device = backend.get_device(input)
input = backend.to_device(input, backend.cpu_device)
zshape = [((i + 1) // 2) * 2 for i in input.shape]
zinput = util.resize(input, zshape)
coeffs = pywt.wavedecn(zinput, wave_name, mode='zero', axes=axes, level=level)
output, _ = pywt.coeffs_to_array(coeffs, axes=axes)
output = backend.to_device(output, device)
return output
def iwt(input, oshape, coeff_slices, wave_name='db4', axes=None, level=None):
"""Inverse wavelet transform.
Args:
input (array): Input array.
oshape (tuple of ints): Output shape.
coeff_slices (list of slice): Slices to split coefficients.
axes (None or tuple of int): Axes to perform wavelet transform.
wave_name (str): Wavelet name.
level (None or int): Number of wavelet levels.
"""
device = backend.get_device(input)
input = backend.to_device(input, backend.cpu_device)
input = pywt.array_to_coeffs(input, coeff_slices, output_format='wavedecn')
output = pywt.waverecn(input, wave_name, mode='zero', axes=axes)
output = util.resize(output, oshape)
output = backend.to_device(output, device)
return output
def _summarize(self):
if self.save_objective_values:
self.objective_values.append(self.objective())
if self.show_pbar:
if self.save_objective_values:
self.pbar.set_postfix(
obj='{0:.2E}'.format(self.objective_values[-1]))
else:
self.pbar.set_postfix(resid='{0:.2E}'.format(
backend.to_device(self.alg.resid, backend.cpu_device)))
def _apply(self, input):
device = backend.get_device(input)
x = backend.to_device(self.x, backend.get_device(input))
with device:
x = x.astype(input.dtype, copy=False)
return conv.convolve(x,
input,
mode=self.mode,
input_multi_channel=self.input_multi_channel,
output_multi_channel=self.output_multi_channel)
def _apply(self, input):
device = backend.get_device(input)
W = backend.to_device(self.W, backend.get_device(input))
with device:
W = W.astype(input.dtype, copy=False)
return conv.convolve_adjoint_input(
W,
input,
mode=self.mode,
input_multi_channel=self.input_multi_channel,
output_multi_channel=self.output_multi_channel)
def interpolate(input, width, kernel, coord):
"""Interpolation from array to points specified by coordinates.
Args:
input (array): Input array of shape [..., ny, nx]
width (float): Interpolation kernel width.
kernel (array): Interpolation kernel.
coord (array): Coordinate array of shape [..., ndim]
Returns:
output (array): Output array of coord.shape[:-1]
"""
ndim = coord.shape[-1]
batch_shape = input.shape[:-ndim]
batch_size = util.prod(batch_shape)
pts_shape = coord.shape[:-1]
npts = util.prod(pts_shape)
device = backend.get_device(input)
xp = device.xp
isreal = np.issubdtype(input.dtype, np.floating)
coord = backend.to_device(coord, device)
kernel = backend.to_device(kernel, device)
with device:
input = input.reshape([batch_size] + list(input.shape[-ndim:]))
coord = coord.reshape([npts, ndim])
output = xp.zeros([batch_size, npts], dtype=input.dtype)
_interpolate = _select_interpolate(ndim, npts, device, isreal)
if device == backend.cpu_device:
_interpolate(output, input, width, kernel, coord)
else: # pragma: no cover
_interpolate(input, width, kernel, coord, output, size=npts)
return output.reshape(batch_shape + pts_shape)
def xpay(y, a, x):
"""Compute y = x + a * y.
Args:
y (array): Output array.
a (scalar or array): Input scalar.
x (array): Input array.
"""
device = backend.get_device(y)
xp = device.xp
if xp == np:
_xpay(y, a, x, out=y)
else:
if np.isscalar(a):
a = backend.to_device(a, device)
_xpay_cuda(a, x, y)
