def forward(self, x_orig):
xp = cupy.get_array_module(*x_orig)
ldim, cdim, rdim = self._internal_shape(x_orig[0])
x = x_orig[0].reshape(ldim, cdim, rdim)
if self.use_batch_mean:
mean = x.mean(axis=(0, 2), keepdims=True)
var = x.var(axis=(0, 2), keepdims=True)
var += self.eps
else:
mean = self.avg_mean
var = self.avg_var
self.std = xp.sqrt(var, dtype=var.dtype)
x_mu = x - mean
self.x_hat = x_mu / self.std
y = self.gamma * self.x_hat
y += self.beta
# Compute exponential moving average
if self.use_batch_mean and self.update_batch_estimations:
if self.is_finetune:
self.N[0] += 1
decay = 1. / self.N[0]
else:
decay = self.decay
m = ldim * rdim
adjust = m / max(m - 1., 1.) # unbiased estimation
self.avg_mean *= decay
self.avg_mean += (1 - decay) * mean
self.avg_var *= decay
self.avg_var += (1 - decay) * adjust * var
return y.reshape(x_orig[0].shape),
def transform(in_data, train=True, crop_size=32, padding=4):
img, label = in_data
img = img.copy()
xp = cp.get_array_module(img)
# Random flip & crop
if train:
# Random flip
if random.randint(0, 1):
img = img[:, :, ::-1]
# Random crop
pad_img = xp.pad(img, [(0, 0), (padding, padding), (padding, padding)],
'constant')
C, H, W = pad_img.shape
top = random.randint(0, H - crop_size - 1)
left = random.randint(0, W - crop_size - 1)
bottom = top + crop_size
right = left + crop_size
img = pad_img[:, top:bottom, left:right]
# Normalize
mean = xp.array([0.485, 0.456, 0.406])
std = xp.array([0.229, 0.224, 0.225])
img -= mean[:, None, None]
img /= std[:, None, None]
return img, label
def categorical_crossentropy(scores, labels):
xp = get_array_module(scores)
target = xp.zeros(scores.shape, dtype='float32')
loss = 0.
for i in range(len(labels)):
target[i, int(labels[i])] = 1.
loss += (1.0-scores[i, int(labels[i])])**2
return scores - target, loss
def forward(self, inputs):
xp = cupy.get_array_module(*inputs[0])
"""
return (1/N) * \sum_i^N \sum_j^L [py_ij * log(py_ij) - py_ij * log(py_tilde_ij)]
"""
py,py_tilde = inputs
kl = py * ( xp.log(py) - xp.log(py_tilde) )
kl_sum = kl.sum(axis=1,keepdims=True)
return kl_sum.mean(keepdims=True).reshape(()),
def to_categorical(y, nb_classes=None):
# From keras
xp = get_array_module(y)
if xp is cupy:
y = y.get()
y = numpy.array(y, dtype='int').ravel()
if not nb_classes:
nb_classes = numpy.max(y) + 1
n = y.shape[0]
categorical = numpy.zeros((n, nb_classes), dtype='float32')
categorical[numpy.arange(n), y] = 1
return xp.asarray(categorical)
def backward(self, inputs, grad_outputs):
xp = cupy.get_array_module(*inputs[0])
"""
(gradient w.r.t py) = log(py) + 1 - log(py_tilde)
(gradient w.r.t py_tilde) = - py/py_tilde
"""
py,py_tilde = inputs
coeff = xp.asarray(grad_outputs[0]/py.shape[0],'float32')
if(self.unchain_py):
ret_py = None
else:
ret_py = coeff * ( xp.log(py) - xp.log(py_tilde) + 1.0)
ret_py_tilde = -coeff * py/py_tilde
return ret_py,ret_py_tilde
def mean_pool(X_lengths, drop=0.):
X, lengths = X_lengths
xp = get_array_module(X)
output = xp.zeros((len(lengths), X.shape[1]), dtype='float32')
start = 0
for i, length in enumerate(lengths):
end = start + length
output[i] = X[start : end].mean(axis=0)
start = end
def finish_update(d_output, sgd=None):
d_X = xp.zeros((X.shape[0], X.shape[1]), dtype='float32')
start = 0
for i, length in enumerate(lengths):
end = start + length
d_X[start : end] += d_output[i] / (end-start)
start = end
return d_X
return output, finish_update
def max_pool(X_lengths, drop=0.):
X, lengths = X_lengths
xp = get_array_module(X)
maxes = xp.zeros((len(lengths), X.shape[1]), dtype='float32')
start = 0
for i, length in enumerate(lengths):
end = start + length
maxes[i] = X[start : end].max(axis=0)
start = end
def finish_update(d_maxes, sgd=None):
d_X = xp.zeros((X.shape[0], X.shape[1]), dtype='float32')
start = 0
for i, length in enumerate(lengths):
end = start + length
d_X[start : end] += d_maxes[i] * (d_maxes[i] == maxes[i])
start = end
return d_X
return maxes, finish_update
def get_array_module(*args):
"""Gets an appropriate one from :mod:`numpy` or :mod:`cupy`.
This is almost equivalent to :func:`cupy.get_array_module`. The only
difference is that this function can be used even if CUDA is not available.
Args:
args: Values to determine whether NumPy or CuPy should be used.
Returns:
module: :mod:`cupy` or :mod:`numpy` is returned based on the types of
the arguments.
"""
if available:
return cupy.get_array_module(*args)
else:
return numpy
def get_array_module(*args):
"""Gets an appropriate one from :mod:`numpy` or :mod:`cupy`.
This is almost equivalent to :func:`cupy.get_array_module`. The differences
are that this function can be used even if CUDA is not available and that
it will return their data arrays' array module for
:class:`~chainer.Variable` arguments.
Args:
args: Values to determine whether NumPy or CuPy should be used.
Returns:
module: :mod:`cupy` or :mod:`numpy` is returned based on the types of
the arguments.
"""
if available:
args = [arg.data if isinstance(arg, chainer.Variable) else arg
for arg in args]
return cupy.get_array_module(*args)
else:
return numpy
def get_array_module(*args):
"""Gets an appropriate one from :mod:`numpy` or :mod:`cupy`.
This is almost equivalent to :func:`cupy.get_array_module`. The differences
are that this function can be used even if CUDA is not available and that
it will return their data arrays' array module for
:class:`~chainer.Variable` arguments.
Args:
args: Values to determine whether NumPy or CuPy should be used.
Returns:
module: :mod:`cupy` or :mod:`numpy` is returned based on the types of
the arguments.
"""
if available:
args = [
arg.data if isinstance(arg, chainer.Variable) else arg
for arg in args
]
return cupy.get_array_module(*args)
else:
return numpy
def apply(self, M, sim_object, postime, t):
if HAS_CUPY and self.use_gpu:
xp = cp.get_array_module(M)
else:
xp = np
postime.calc_pos(t)
fov = xp.asarray(sim_object.fov[None, :], dtype=xp.float32)
rr = (postime.pos * self.dirvec * fov).sum(1)
theta = rr * self.M0 * 267.522
# print([postime.pos.min(), postime.pos.max(), fov])
# print([rr.min(), rr.max()])
# print([theta.min(), theta.max()])
M_new = xp.zeros_like(M)
M_new[:, 0] = M[:, 0] * xp.cos(theta) - M[:, 1] * xp.sin(theta)
M_new[:, 1] = M[:, 0] * xp.sin(theta) + M[:, 1] * xp.cos(theta)
M_new[:, 2] = M[:, 2]
M[:] = M_new
return None
def get_convolve(x):
"""Returns correct convolve module based on input
Parameters
----------
x : :obj:`numpy.ndarray`
Array
Returns
-------
mod : :obj:`func`
Module to be used to process array (:mod:`numpy` or :mod:`cupy`)
"""
if not deps.cupy_enabled:
return convolve
if cp.get_array_module(x) == np:
return convolve
else:
if deps.cusignal_enabled:
return cusignal.convolution.convolve
else:
raise ModuleNotFoundError(cusignal_message)
def as_single_prec(self):
"""
force single precision
:rtype: SFTData
"""
dict_single = dict()
xp = cp.get_array_module(self.Yenc)
for k, v in asdict(self).items():
if v is None:
continue
if v.dtype == xp.float64:
dict_single[k] = v.astype(xp.float32)
elif v.dtype == xp.complex128:
dict_single[k] = v.astype(xp.complex64)
elif v.dtype == xp.float32:
dict_single[k] = v
elif v.dtype == xp.complex64:
dict_single[k] = v
else:
raise NotImplementedError
return SFTData(**dict_single)
def draw(X, pred, means, covariances, output):
xp = cupy.get_array_module(X)
for i in six.moves.range(2):
labels = X[pred == i]
if xp is cupy:
labels = labels.get()
plt.scatter(labels[:, 0], labels[:, 1], c=np.random.rand(1, 3))
if xp is cupy:
means = means.get()
covariances = covariances.get()
plt.scatter(means[:, 0],
means[:, 1],
s=120,
marker='s',
facecolors='y',
edgecolors='k')
x = np.linspace(-5, 5, 1000)
y = np.linspace(-5, 5, 1000)
X, Y = np.meshgrid(x, y)
for i in six.moves.range(2):
dist = stats.multivariate_normal(means[i], covariances[i])
Z = dist.pdf(np.stack([X, Y], axis=-1))
plt.contour(X, Y, Z)
plt.savefig(output)
def _init_proj_matrix(self, init_sample, compressed_dim, proj_method):
"""
init the projection matrix
"""
if gpu_config.use_gpu:
xp = cp.get_array_module(init_sample[0])
else:
xp = np
x = [xp.reshape(x, (-1, x.shape[2])) for x in init_sample]
x = [z - z.mean(0) for z in x]
proj_matrix_ = []
if self.config.proj_init_method == 'pca':
for x_, compressed_dim_ in zip(x, compressed_dim):
proj_matrix, _, _ = xp.linalg.svd(x_.T.dot(x_))
proj_matrix = proj_matrix[:, :compressed_dim_]
proj_matrix_.append(proj_matrix)
elif self.config.proj_init_method == 'rand_uni':
for x_, compressed_dim_ in zip(x, compressed_dim):
proj_matrix = xp.random.uniform(size=(x_.shape[1],
compressed_dim_))
proj_matrix /= xp.sqrt(
xp.sum(proj_matrix**2, axis=0, keepdims=True))
proj_matrix_.append(proj_matrix)
return proj_matrix_
def __call__(self, arr: np.ndarray, gpu=False):
if self.p is None or self.p == 0:
return arr
if gpu:
arr = cp.asnumpy(arr)
elif cp.get_array_module(arr) == cp:
print("You forgot to set `gpu=True` for a Cupy array")
gpu = True
arr = cp.asnumpy(arr)
else:
arr = arr.copy()
if arr.dtype == np.uint8:
arr = np.apply_along_axis(self.intError, 1, arr)
# elif arr.dtype == np.float32:
# result = self.floatError(arr)
else:
raise ValueError("Only uint8 allowed")
if gpu:
arr = cp.array(arr)
return arr
def sample_fs(xf, grid_sz=None):
if gpu_config.use_gpu:
xp = cp.get_array_module(xf)
else:
xp = np
sz = xf.shape[:2]
if grid_sz is None or sz == grid_sz:
x = sz[0] * sz[1] * cifft2(xf)
else:
sz = np.array(sz)
grid_sz = np.array(grid_sz)
if np.any(grid_sz < sz):
raise (
"The grid size must be larger than or equal to the siganl size"
)
tot_pad = grid_sz - sz
pad_sz = np.ceil(tot_pad / 2).astype(np.int32)
xf_pad = xp.pad(xf, tuple(pad_sz), 'constant')
if np.any(tot_pad % 2 == 1):
xf_pad = xf_pad[:xf_pad.shape[0] -
(tot_pad[0] % 2), :xf_pad.shape[1] -
(tot_pad[1] % 2)]
x = grid_sz[0] * grid_sz[1] * cifft2(xf_pad)
return x
def lf(*args, **kwargs):
t0 = args[-1]
xp = cupy.get_array_module(t0)
t = xp.expand_dims(t0.astype(xp.float32), axis=1)
#t = xp.eye(self.n_label, dtype=np.float32)[t0]
x = args[0]
mu, ln_var = self.encode(x, t)
batchsize = len(mu.data)
# reconstruction loss
rec_loss = 0
for l in six.moves.range(k):
z = F.gaussian(mu, ln_var)
rec_loss += F.bernoulli_nll(x, self.decode(z, t, sigmoid=False)) \
/ (k * batchsize)
self.rec_loss = rec_loss
self.loss = self.rec_loss + \
C * gaussian_kl_divergence(mu, ln_var) / batchsize
chainer.report({
'rec_loss': rec_loss,
'loss': self.loss
},
observer=self)
return self.loss
def circ_mul_v(circ,v,eigs=None):
''' multiply a circulant matrix A by multi vector v.
Args:
circ (ndarray): representation of the multilevel circulant matrix A, i.e.
the first column of A in proper shape.
v (ndarray): vector to be multiplied. Should be reshaped to the same shape
as circ. Should be the same reshape order (column first/row first) as circ.
Returns:
result of multiplication.
'''
if use_gpu > 0:
import cupy
xp = cupy.get_array_module(circ)
else:
xp = np
if eigs is None:
eigs = circ_eigs(circ)
tmp = xp.real(xp.fft.ifft2(xp.fft.fft2(v,norm='ortho')*eigs,norm='ortho'))
if xp is cupy:
return tmp.astype(xp.float32)
else:
return tmp
def _gibbs_removal_1d(x, axis=0, n_points=3, xp=None):
"""Suppresses Gibbs ringing along a given axis using fourier sub-shifts.
Parameters
----------
x : 2D ndarray
Matrix x.
axis : int (0 or 1)
Axis in which Gibbs oscillations will be suppressed.
Default is set to 0.
n_points : int, optional
Number of neighbours to access local TV (see note).
Default is set to 3.
Returns
-------
xc : 2D ndarray
Matrix with suppressed Gibbs oscillations along the given axis.
Notes
-----
This function suppresses the effects of Gibbs oscillations based on the
analysis of local total variation (TV). Although artefact correction is
done based on two adjacent points for each voxel, total variation should be
accessed in a larger range of neighbours. The number of neighbours to be
considered in TV calculation can be adjusted using the parameter n_points.
"""
if xp is None:
xp = cp.get_array_module(x)
float_dtype = xp.promote_types(x.dtype, np.float32)
ssamp = xp.linspace(0.02, 0.9, num=45, dtype=float_dtype)
xs = xp.moveaxis(x, axis, -1).copy()
h = xp.ones(n_points, dtype=x.real.dtype) # filter used in _image_tv
# TV for shift zero (baseline)
tvr, tvl = _image_tv(xs, h, axis=-1)
tvp = xp.minimum(tvr, tvl)
tvn = tvp.copy()
# Find optimal shift for gibbs removal
isp = xs.copy()
isn = xs.copy()
sp = xp.zeros(xs.shape, dtype=float_dtype)
sn = xp.zeros(xs.shape, dtype=float_dtype)
n = xs.shape[-1]
c = xp.fft.fft(xs, axis=-1)
k = xp.fft.fftfreq(n, 1 / (2.0j * np.pi))
k = k.astype(xp.promote_types(xs.dtype, xp.complex64), copy=False)
if xs.ndim == 2:
k = k[np.newaxis, :]
ssamp_nd = ssamp[:, np.newaxis]
elif xs.ndim == 3:
k = k[np.newaxis, np.newaxis, :]
ssamp_nd = ssamp[:, np.newaxis, np.newaxis]
all_eks = ssamp_nd * k
xp.exp(all_eks, out=all_eks)
for s, eks in zip(ssamp, all_eks):
eks = eks[np.newaxis, ...]
# Access positive shift for given s
img_p = c * eks
img_p = xp.fft.ifft(img_p, axis=-1)
xp.abs(img_p, out=img_p)
tvsr, tvsl = _image_tv(img_p, h, axis=-1)
tvs_p = xp.minimum(tvsr, tvsl)
# Access negative shift for given s
img_n = c * xp.conj(eks) # xp.exp(-ks)
img_n = xp.fft.ifft(img_n, axis=-1)
xp.abs(img_n, out=img_n)
tvsr, tvsl = _image_tv(img_n, h, axis=-1)
tvs_n = xp.minimum(tvsr, tvsl)
maskp = tvp > tvs_p
maskn = tvn > tvs_n
# Update positive shift params
isp[maskp] = img_p[maskp].real
sp[maskp] = s
tvp[maskp] = tvs_p[maskp]
# Update negative shift params
isn[maskn] = img_n[maskn].real
sn[maskn] = s
tvn[maskn] = tvs_n[maskn]
# check non-zero sub-voxel shifts
idx = xp.nonzero(sp + sn)
# use positive and negative optimal sub-voxel shifts to interpolate to
# original grid points
sn_i = sn[idx]
isn_i = isn[idx]
tmp = isp[idx] - isn_i
tmp /= sp[idx] + sn_i
tmp *= sn_i
tmp += isn_i
xs[idx] = tmp
return xp.moveaxis(xs, -1, axis)
def normalize_axis1(x):
xp = cupy.get_array_module(*x)
abs_x = abs(x)
x = x / (1e-6 + abs_x.max(axis=1,keepdims=True))
x_norm_2 = x**2
return x / xp.sqrt(1e-6 + x_norm_2.sum(axis=1,keepdims=True))
def sample_function(x, y, z):
xp = cupy.get_array_module(x, y, z)
return xp.square(xp.add(x, y))
def f(x):
xp = cupy.get_array_module(x)
return xp.square(x)
def flatten_sequences(sequences, drop=0.0): # pragma: no cover
xp = get_array_module(sequences[0])
return xp.concatenate(sequences), None
def L1_distance(vec1, vec2, labels, margin=0.2):
xp = get_array_module(vec1)
dist = xp.abs(vec1 - vec2).sum(axis=1)
loss = (dist > margin) - labels
return (sent1-sent2) * loss, (sent2-sent1) * loss, loss
def resample(x, sr_orig, sr_new, axis=-1, filter='kaiser_best', **kwargs):
'''Resample a signal x from sr_orig to sr_new along a given axis.
Parameters
----------
x : np.ndarray, dtype=np.float*
The input signal(s) to resample.
sr_orig : int > 0
The sampling rate of x
sr_new : int > 0
The target sampling rate of the output signal(s)
axis : int
The target axis along which to resample `x`
filter : optional, str or callable
The resampling filter to use.
By default, uses the `kaiser_best` (pre-computed filter).
kwargs
additional keyword arguments provided to the specified filter
Returns
-------
y : np.ndarray
`x` resampled to `sr_new`
Raises
------
ValueError
if `sr_orig` or `sr_new` is not positive
TypeError
if the input signal `x` has an unsupported data type.
Examples
--------
>>> # Generate a sine wave at 440 Hz for 5 seconds
>>> sr_orig = 44100.0
>>> x = np.sin(2 * np.pi * 440.0 / sr_orig * np.arange(5 * sr_orig))
>>> x
array([ 0. , 0.063, ..., -0.125, -0.063])
>>> # Resample to 22050 with default parameters
>>> resampy.resample(x, sr_orig, 22050)
array([ 0.011, 0.123, ..., -0.193, -0.103])
>>> # Resample using the fast (low-quality) filter
>>> resampy.resample(x, sr_orig, 22050, filter='kaiser_fast')
array([ 0.013, 0.121, ..., -0.189, -0.102])
>>> # Resample using a high-quality filter
>>> resampy.resample(x, sr_orig, 22050, filter='kaiser_best')
array([ 0.011, 0.123, ..., -0.193, -0.103])
>>> # Resample using a Hann-windowed sinc filter
>>> resampy.resample(x, sr_orig, 22050, filter='sinc_window',
... window=scipy.signal.hann)
array([ 0.011, 0.123, ..., -0.193, -0.103])
>>> # Generate stereo data
>>> x_right = np.sin(2 * np.pi * 880.0 / sr_orig * np.arange(len(x)))])
>>> x_stereo = np.stack([x, x_right])
>>> x_stereo.shape
(2, 220500)
>>> # Resample along the time axis (1)
>>> y_stereo = resampy.resample(x, sr_orig, 22050, axis=1)
>>> y_stereo.shape
(2, 110250)
'''
if sr_orig <= 0:
raise ValueError('Invalid sample rate: sr_orig={}'.format(sr_orig))
if sr_new <= 0:
raise ValueError('Invalid sample rate: sr_new={}'.format(sr_new))
sample_ratio = float(sr_new) / sr_orig
# Set up the output shape
shape = list(x.shape)
shape[axis] = int(shape[axis] * sample_ratio)
if shape[axis] < 1:
raise ValueError('Input signal length={} is too small to '
'resample from {}->{}'.format(x.shape[axis], sr_orig,
sr_new))
# Preserve contiguity of input (if it exists)
# If not, revert to C-contiguity by default
if x.flags['F_CONTIGUOUS']:
order = 'F'
else:
order = 'C'
xp = cp.get_array_module(x)
y = xp.zeros(shape, dtype=x.dtype, order=order)
interp_win, precision, _ = get_filter(filter, **kwargs)
interp_win = xp.asarray(interp_win)
if sample_ratio < 1:
interp_win *= sample_ratio
interp_delta = xp.zeros_like(interp_win)
interp_delta[:-1] = xp.diff(interp_win)
# Construct 2d views of the data with the resampling axis on the first dimension
x_2d = x.swapaxes(0, axis).reshape((x.shape[axis], -1))
y_2d = y.swapaxes(0, axis).reshape((y.shape[axis], -1))
resample_f(x_2d, y_2d, sample_ratio, interp_win, interp_delta,
int(precision))
return y
def argsort(self, a, axis=-1):
if self.external:
xp = cupy.get_array_module(a)
return xp.argsort(a, axis=axis)
else:
return a.argsort(axis=axis)
def sum(self):
return cp.get_array_module(self.values).sum(self.values)
def wrap_take(array, *args, **kwargs):
if get_array_module(array) == numpy:
kwargs["mode"] = "wrap"
return array.take(*args, **kwargs)
def flatten_sequences(sequences, drop=0.): # pragma: no cover
xp = get_array_module(sequences[0])
return xp.concatenate(sequences), None
def init_gx(self, inputs):
xp = cupy.get_array_module(*inputs.data)
self.gx = as_mat(xp.zeros_like(inputs.data))
def collide(self, input_f):
xp = cp.get_array_module(input_f)
f = input_f
vaxis = tuple(-(i + 1) for i in range(self.num_dim))
return xp.sum(f * self.weights, axis=vaxis, keepdims=True) - f
def argpartition(self, a, kth, axis=-1):
if self.external:
xp = cupy.get_array_module(a)
return xp.argpartition(a, kth, axis=axis)
else:
return a.argpartition(kth, axis=axis)
def add_noise_c(h, sigma=0.2):
xp = cp.get_array_module(h.data)
if chainer.config.train:
return h + sigma * xp.random.randn(*h.data.shape, dtype=cp.float32)
else:
return h
def wrap_take(array, *args, **kwargs):
if get_array_module(array) == numpy:
kwargs["mode"] = "wrap"
return array.take(*args, **kwargs)
def predict(X, inv_cov, means, weights):
xp = cupy.get_array_module(X)
log_prob = estimate_log_prob(X, inv_cov, means)
return (log_prob + xp.log(weights)).argmax(axis=1)
def dbeta(self, upstream_dx):
xp = cp.get_array_module(upstream_dx)
dbeta = xp.sum(upstream_dx, axis=self.av_axis)
if self.input_dimension == 4:
dbeta = dbeta[xp.newaxis, :, xp.newaxis, xp.newaxis]
return dbeta
def forward(self, X):
xp = cp.get_array_module(X)
return 0.5*self.strength*xp.sum(xp.power(X,2))
def forward(self, X, test_mode=False, use_express=False):
"""
X.shape = (batch_size, channel, height, width)
Note that when following a convolution layer, we learn a (gamma, beta)
for each of the channels (feature maps).
"""
self.input_shape = X.shape
xp = cp.get_array_module(X)
if not test_mode:
if len(X.shape) == 4 and not self.is_on_gpu:
mean, var = batch_norm_stats_cy.channelwise_mean_and_var_4d(X)
else:
mean = xp.mean(X, axis=self.av_axis)
var = xp.var(X, axis=self.av_axis)
self.std = xp.sqrt(var + self.eps)
if len(X.shape) == 4:
self.std = self.std[xp.newaxis, :, xp.newaxis, xp.newaxis]
mean = mean[xp.newaxis, :, xp.newaxis, xp.newaxis]
self.X_demean = X - mean
self.X_hat = self.X_demean / self.std
if self.non_learned_params["running_mean"] is not None:
self.non_learned_params["running_mean"] = (
self.run_momentum * self.non_learned_params["running_mean"]
+ (1 - self.run_momentum) * mean)
else:
self.non_learned_params["running_mean"] = mean
if self.non_learned_params["running_std"] is not None:
self.non_learned_params["running_std"] = (
self.run_momentum * self.non_learned_params["running_std"]
+ (1 - self.run_momentum) * self.std)
else:
self.non_learned_params["running_std"] = self.std
if use_express:
return (ne.evaluate("gamma*X_hat + beta",
local_dict={
**vars(self), 'gamma':
self.learned_params['gamma'],
'beta':
self.learned_params['beta']
}))
else:
return (self.learned_params['gamma'] * self.X_hat +
self.learned_params['beta'])
else: # test_mode
if use_express:
X_hat = ne.evaluate(
"(X - running_mean)/running_std",
local_dict={
'running_mean':
self.non_learned_params["running_mean"],
'running_std': self.non_learned_params["running_std"]
})
return (ne.evaluate("gamma*X_hat + beta",
local_dict={
'gamma': self.learned_params['gamma'],
'beta': self.learned_params['beta']
}))
else:
X_hat = (X - self.non_learned_params["running_mean"]
) / self.non_learned_params["running_std"]
return (self.learned_params['gamma'] * X_hat +
self.learned_params['beta'])
def getNorm(self, psiSquared):
"""
Finds norm for a given state of psi squared along the z-axis
"""
xp = cp.get_array_module(psiSquared)
return xp.sqrt(xp.sum(psiSquared*self.hz))
def _gibbs_removal_2d_or_3d(image, n_points=3, G0=None, G1=None, *, xp=None):
""" Suppress Gibbs ringing of a 2D image.
Parameters
----------
image : 2D ndarray
Matrix containing the 2D image.
n_points : int, optional
Number of neighbours to access local TV (see note). Default is
set to 3.
G0 : 2D ndarray, optional.
Weights for the image corrected along axis 0. If not given, the
function estimates them using the function :func:`_weights`.
G1 : 2D ndarray
Weights for the image corrected along axis 1. If not given, the
function estimates them using the function :func:`_weights`.
Returns
-------
imagec : 2D ndarray
Matrix with Gibbs oscillations reduced along axis a.
Notes
-----
This function suppresses the effects of Gibbs oscillations based on the
analysis of local total variation (TV). Although artefact correction is
done based on two adjacent points for each voxel, total variation should be
accessed in a larger range of neighbours. The number of neighbours to be
considered in TV calculation can be adjusted using the parameter n_points.
References
----------
Please cite the following articles
.. [1] Neto Henriques, R., 2018. Advanced Methods for Diffusion MRI Data
Analysis and their Application to the Healthy Ageing Brain
(Doctoral thesis). https://doi.org/10.17863/CAM.29356
.. [2] Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact
removal based on local subvoxel-shifts. Magn Reson Med. 2016
doi: 10.1002/mrm.26054.
"""
if xp is None:
xp = cp.get_array_module(image)
if G0 is None or G1 is None:
G0, G1 = _weights(image.shape[:2], image.dtype, xp=xp)
if image.ndim > 2:
G0 = G0[..., np.newaxis]
G1 = G1[..., np.newaxis]
if image.ndim not in [2, 3]:
raise ValueError(
"expected a 2D image or a 3D array corresponding to a batch of 2D "
"images stacked along the last axis")
img_c1 = _gibbs_removal_1d(image, axis=1, n_points=n_points)
img_c0 = _gibbs_removal_1d(image, axis=0, n_points=n_points)
C1 = xp.fft.fftn(img_c1, axes=(0, 1))
C0 = xp.fft.fftn(img_c0, axes=(0, 1))
imagec = xp.fft.fftshift(C1, axes=(0, 1)) * G1
imagec += xp.fft.fftshift(C0, axes=(0, 1)) * G0
imagec = xp.fft.ifftn(imagec, axes=(0, 1))
imagec = xp.abs(imagec)
return imagec
def g(x):
xp = cupy.get_array_module(x)
return xp.frexp(x)
def gibbs_removal(vol,
slice_axis=2,
n_points=3,
inplace=False,
num_threads=None,
*,
xp=None):
"""Suppresses Gibbs ringing artefacts of images volumes.
Parameters
----------
vol : ndarray ([X, Y]), ([X, Y, Z]) or ([X, Y, Z, g])
Matrix containing one volume (3D) or multiple (4D) volumes of images.
slice_axis : int (0, 1, or 2)
Data axis corresponding to the number of acquired slices.
Default is set to the third axis.
n_points : int, optional
Number of neighbour points to access local TV (see note).
Default is set to 3.
inplace : bool, optional
unimplemented option on the GPU
num_threads : int or None, optional
unsupported option on the GPU
Returns
-------
vol : ndarray ([X, Y]), ([X, Y, Z]) or ([X, Y, Z, g])
Matrix containing one volume (3D) or multiple (4D) volumes of corrected
images.
Notes
-----
For 4D matrix last element should always correspond to the number of
diffusion gradient directions.
References
----------
Please cite the following articles
.. [1] Neto Henriques, R., 2018. Advanced Methods for Diffusion MRI Data
Analysis and their Application to the Healthy Ageing Brain
(Doctoral thesis). https://doi.org/10.17863/CAM.29356
.. [2] Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact
removal based on local subvoxel-shifts. Magn Reson Med. 2016
doi: 10.1002/mrm.26054.
"""
nd = vol.ndim
if xp is None:
xp = cp.get_array_module(vol)
if xp is np:
# The implementation here was refactored for the GPU
# Dipy's version is faster on the CPU, so fall back to it in that case.
from dipy.denoise.gibbs import gibbs_removal as gibbs_removal_cpu
try:
return gibbs_removal_cpu(vol,
slice_axis=slice_axis,
n_points=n_points,
inplace=inplace,
num_threads=num_threads)
except TypeError:
warnings.warn("inplace and num_threads arguments ignored")
# older DIPY did not have inplace or num_threads kwargs
return gibbs_removal_cpu(vol,
slice_axis=slice_axis,
n_points=n_points)
if not isinstance(inplace, bool):
raise TypeError("inplace must be a boolean.")
if num_threads is not None:
warnings.warn("num_threads is ignored by the GPU operation")
# check the axis corresponding to different slices
# 1) This axis cannot be larger than 2
if slice_axis > 2:
raise ValueError("Different slices have to be organized along" +
"one of the 3 first matrix dimensions")
# 2) If this is not 2, swap axes so that different slices are ordered
# along axis 2. Note that swapping is not required if data is already a
# single image
elif slice_axis < 2 and nd > 2:
vol = xp.swapaxes(vol, slice_axis, 2)
# check matrix dimension
if nd == 4:
inishap = vol.shape
vol = vol.reshape((inishap[0], inishap[1], inishap[2] * inishap[3]))
elif nd > 4:
raise ValueError("Data have to be a 4D, 3D or 2D matrix")
elif nd < 2:
raise ValueError("Data is not an image")
# Produce weigthing functions for 2D Gibbs removal
shap = vol.shape
G0, G1 = _weights(shap[:2], vol.dtype, xp=xp)
if inplace:
raise NotImplementedError("inplace restoration not supported")
# Run Gibbs removal of 2D images
if nd > 2:
G0 = G0[..., np.newaxis]
G1 = G1[..., np.newaxis]
vol = _gibbs_removal_2d_or_3d(vol, n_points=n_points, G0=G0, G1=G1, xp=xp)
# Reshape data to original format
if nd == 4:
vol = vol.reshape(inishap)
if slice_axis < 2 and nd > 2:
vol = xp.swapaxes(vol, slice_axis, 2)
return vol
def wrap_take(array, *args, **kwargs):
if get_array_module(array) == numpy:
kwargs['mode'] = 'wrap'
return array.take(*args, **kwargs)
def perturbation_with_max_norm_constraint(x,norm):
xp = cupy.get_array_module(*x)
return norm * xp.sign(x)
