def _slogdet_one(a):
util._assert_rank2(a)
util._assert_nd_squareness(a)
dtype = a.dtype
handle = device.get_cusolver_handle()
m = len(a)
ipiv = cupy.empty(m, 'i')
info = cupy.empty((), 'i')
# Need to make a copy because getrf works inplace
a_copy = a.copy(order='F')
if dtype == 'f':
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrf = cusolver.sgetrf
#<-- MODIFIED
elif dtype == 'd':
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrf = cusolver.dgetrf
elif dtype == 'F':
getrf_bufferSize = cusolver.cgetrf_bufferSize
getrf = cusolver.cgetrf
else:
getrf_bufferSize = cusolver.zgetrf_bufferSize
getrf = cusolver.zgetrf
#<-- MODIFIED
buffersize = getrf_bufferSize(handle, m, m, a_copy.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
getrf(handle, m, m, a_copy.data.ptr, m, workspace.data.ptr, ipiv.data.ptr,
info.data.ptr)
if info[()] == 0:
diag = cupy.diag(a_copy)
# ipiv is 1-origin
non_zero = (cupy.count_nonzero(ipiv != cupy.arange(1, m + 1)) +
cupy.count_nonzero(diag < 0))
# Note: sign == -1 ** (non_zero % 2)
sign = (non_zero % 2) * -2 + 1
logdet = cupy.log(abs(diag)).sum()
else:
sign = cupy.array(0.0, dtype=dtype)
#ORIGINAL
# logdet = cupy.array(float('-inf'), dtype)
#<-- MODIFIED
if dtype in ['f', 'd']:
logdet = cupy.array(float('-inf'), dtype)
elif dtype == 'F':
logdet = cupy.array(float('-inf'), cupy.float32)
else:
logdet = cupy.array(float('-inf'), cupy.float64)
#<-- MODIFIED
return sign, logdet
def mean_peak_distance(peak_image, centroids, return_numpy=True):
"""
Calculate the mean peak distance in degrees between two corresponding peaks
for each line profile in an SLI image series.
Args:
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack
centroids: Use centroid calculation to better determine the peak position
regardless of the number of
measurements / illumination angles used.
return_numpy: Necessary if using `use_gpu`. Specifies if a CuPy or Numpy
array will be returned.
Returns:
NumPy array of floating point values containing the mean peak distance of
the line profiles in degrees.
"""
peak_distance_gpu = peak_distance(peak_image, centroids,
return_numpy=False)
peak_distance_gpu[peak_distance_gpu > 180] = 0
peak_distance_gpu = cupy.sum(peak_distance_gpu, axis=-1) / \
cupy.maximum(1, cupy.count_nonzero(peak_distance_gpu,
axis=-1))
if return_numpy:
peak_width_cpu = cupy.asnumpy(peak_distance_gpu)
del peak_distance_gpu
return peak_width_cpu
else:
return peak_distance_gpu
def batch_all_triplet_loss(embeddings, labels, margin=0.2, dist_type='l2'):
"""Build the triplet loss over a batch of embeddings.
We generate all the valid triplets and average the loss over the positive ones.
Args:
embeddings: Variable of shape=(batch_size, embed_dim)
labels: labels of the batch, of size=(batch_size,)
margin: margin for triplet loss
dist_type: definition of distance, 'l2' or 'cos'
Returns:
triplet_loss: scalar Variable containing the triplet loss
"""
# distance(f(xa), f(xp)) - distance(f(xa), f(xn)) + alpha
pairwise_dist = _pairwise_distances(embeddings, dist_type)
anchor_positive_dist = F.expand_dims(pairwise_dist, axis=2)
anchor_negative_dist = F.expand_dims(pairwise_dist, axis=1)
triplet_loss = anchor_positive_dist - anchor_negative_dist + margin
# Set invalid triplet [i, j, k] to 0.
mask = _get_triplet_mask(labels)
triplet_loss = mask * triplet_loss
# Ignore enough separated example pairs loss.
triplet_loss = F.relu(triplet_loss)
# Calculate mean of loss.
total = F.sum(triplet_loss)
count = xp.count_nonzero(triplet_loss.data)
return total / count if (count > 0.0) else chainer.Variable(
xp.array(0.0, dtype=xp.float32))
def _slogdet_one(a):
util._assert_rank2(a)
util._assert_nd_squareness(a)
dtype = a.dtype
handle = device.get_cusolver_handle()
m = len(a)
ipiv = cupy.empty(m, dtype=numpy.int32)
dev_info = cupy.empty((), dtype=numpy.int32)
# Need to make a copy because getrf works inplace
a_copy = a.copy(order='F')
if dtype == 'f':
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrf = cusolver.sgetrf
else:
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrf = cusolver.dgetrf
buffersize = getrf_bufferSize(handle, m, m, a_copy.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
getrf(handle, m, m, a_copy.data.ptr, m, workspace.data.ptr, ipiv.data.ptr,
dev_info.data.ptr)
# dev_info < 0 means illegal value (in dimensions, strides, and etc.) that
# should never happen even if the matrix contains nan or inf.
# TODO(kataoka): assert dev_info >= 0 if synchronization is allowed for
# debugging purposes.
diag = cupy.diag(a_copy)
# ipiv is 1-origin
non_zero = (cupy.count_nonzero(ipiv != cupy.arange(1, m + 1)) +
cupy.count_nonzero(diag < 0))
# Note: sign == -1 ** (non_zero % 2)
sign = (non_zero % 2) * -2 + 1
logdet = cupy.log(abs(diag)).sum()
singular = dev_info > 0
return (
cupy.where(singular, dtype.type(0), sign),
cupy.where(singular, dtype.type('-inf'), logdet),
)
def _slogdet_one(a):
util._assert_rank2(a)
util._assert_nd_squareness(a)
dtype = a.dtype
handle = device.get_cusolver_handle()
m = len(a)
ipiv = cupy.empty(m, dtype=numpy.int32)
dev_info = cupy.empty(1, dtype=numpy.int32)
# Need to make a copy because getrf works inplace
a_copy = a.copy(order='F')
if dtype == 'f':
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrf = cusolver.sgetrf
else:
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrf = cusolver.dgetrf
buffersize = getrf_bufferSize(handle, m, m, a_copy.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
getrf(handle, m, m, a_copy.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, dev_info.data.ptr)
try:
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
getrf, dev_info)
diag = cupy.diag(a_copy)
# ipiv is 1-origin
non_zero = (cupy.count_nonzero(ipiv != cupy.arange(1, m + 1)) +
cupy.count_nonzero(diag < 0))
# Note: sign == -1 ** (non_zero % 2)
sign = (non_zero % 2) * -2 + 1
logdet = cupy.log(abs(diag)).sum()
except linalg.LinAlgError:
sign = cupy.array(0.0, dtype=dtype)
logdet = cupy.array(float('-inf'), dtype)
return sign, logdet
def count_nonzero(self):
"""Returns number of non-zero entries.
.. note::
This method counts the actual number of non-zero entories, which
does not include explicit zero entries.
Instead ``nnz`` returns the number of entries including explicit
zeros.
Returns:
Number of non-zero entries.
"""
return cupy.count_nonzero(self.data)
def inclination_sign(peak_image, centroids, correction_angle=0, return_numpy=True):
"""
Calculate the inclination sign from the peak positions.
The inclination sign is based on the peak distance between two peaks.
Explanation of the results:
-1: The minimal peak distance is behind the first peak (wrapping around)
0: This pixel / line profile has more than two peaks
1: The minimal peak distance is in front of the first peak.
Args:
peak_distance: 3D NumPy array - of the peak distance between two peaks
return_numpy: Necessary if using `use_gpu`. Specifies if a CuPy or
NumPy array will be returned.
Returns:
inclination_sign: 3D NumPy array
inclination sign
"""
gpu_peak_image = cupy.array(peak_image).astype('int8')
gpu_centroids = cupy.array(centroids).astype('float32')
result_img_gpu = cupy.empty(
(gpu_peak_image.shape[0], gpu_peak_image.shape[1]), dtype='float32')
number_of_peaks = cupy.count_nonzero(gpu_peak_image, axis=-1).astype(
'int8')
threads_per_block = (1, 1)
blocks_per_grid = gpu_peak_image.shape[:-1]
_inclination_sign[blocks_per_grid, threads_per_block](gpu_peak_image,
gpu_centroids,
number_of_peaks,
result_img_gpu,
correction_angle)
cuda.synchronize()
del number_of_peaks
if peak_image is None:
del gpu_peak_image
if return_numpy:
result_img_cpu = cupy.asnumpy(result_img_gpu)
del result_img_gpu
return result_img_cpu
else:
return result_img_gpu
def mean_peak_prominence(image, peak_image=None, kind_of_normalization=0,
return_numpy=True):
"""
Calculate the mean peak prominence of all given peak positions within a
line profile. The line profile will be normalized by dividing the line
profile through its mean value. Therefore, values above 1 are possible.
Args:
image: Original line profile used to detect all peaks. This array will be
further analyzed to better determine the peak positions.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack
kind_of_normalization: Normalize given line profile by using a
normalization technique based on the kind_of_normalization parameter.
0 : Scale line profile to be between 0 and 1
1 : Divide line profile through its mean value
return_numpy: Necessary if using `use_gpu`. Specifies if a CuPy or Numpy
array will be returned.
Returns:
Floating point value containing the mean peak prominence of the line
profile in degrees.
"""
if peak_image is not None:
gpu_peak_image = cupy.array(peak_image).astype('uint8')
else:
gpu_peak_image = peaks(image, return_numpy=False).astype('uint8')
peak_prominence_gpu = peak_prominence(image, peak_image,
kind_of_normalization,
return_numpy=False)
peak_prominence_gpu = cupy.sum(peak_prominence_gpu, axis=-1) / \
cupy.maximum(1, cupy.count_nonzero(gpu_peak_image,
axis=-1))
peak_prominence_gpu = peak_prominence_gpu.astype('float32')
del gpu_peak_image
if return_numpy:
peak_width_cpu = cupy.asnumpy(peak_prominence_gpu)
del peak_prominence_gpu
return peak_width_cpu
else:
return peak_prominence_gpu
def _get_median(data, n_zeros):
"""Compute the median of data with n_zeros additional zeros.
This function is used to support sparse matrices; it modifies data in-place
"""
n_elems = len(data) + n_zeros
if not n_elems:
return np.nan
n_negative = np.count_nonzero(data < 0)
middle, is_odd = divmod(n_elems, 2)
data.sort()
if is_odd:
return _get_elem_at_rank(middle, data, n_negative, n_zeros)
return (_get_elem_at_rank(middle - 1, data, n_negative, n_zeros) +
_get_elem_at_rank(middle, data, n_negative, n_zeros)) / 2.
def mean_peak_width(image, peak_image=None, target_height=0.5,
return_numpy=True):
"""
Calculate the mean peak width of all given peak positions within a line
profile.
Args:
image: Original line profile used to detect all peaks. This array will be
further analyzed to better determine the peak positions.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack
target_height: Relative peak height in relation to the prominence of the
given peak.
return_numpy: Necessary if using `use_gpu`. Specifies if a CuPy or Numpy
array will be returned.
Returns:
NumPy array where each entry corresponds to the mean peak width of the
line profile. The values are in degree.
"""
if peak_image is not None:
gpu_peak_image = cupy.array(peak_image).astype('uint8')
else:
gpu_peak_image = peaks(image, return_numpy=False).astype('uint8')
peak_width_gpu = peak_width(image, gpu_peak_image, target_height,
return_numpy=False)
peak_width_gpu = cupy.sum(peak_width_gpu, axis=-1) / \
cupy.maximum(1, cupy.count_nonzero(gpu_peak_image,
axis=-1))
del gpu_peak_image
if return_numpy:
peak_width_cpu = cupy.asnumpy(peak_width_gpu)
del peak_width_gpu
return peak_width_cpu
else:
return peak_width_gpu
def deskew(image, angle, dz, pixel_size):
deskewed = deskewGPU(image, angle, dz, pixel_size)
image_cp = cp.array(image)
deskewed_cp = cp.array(deskewed)
pages, col, row = image_cp.shape
noise_size = cp.ceil(cp.max(cp.array([row, col])) * 0.1)
image_noise_patch = image_cp[0:noise_size,
col - (noise_size + 1):col - 1, :]
image_noise_patch = image_noise_patch.flatten()
fill_length = deskewed_cp.size - cp.count_nonzero(deskewed_cp)
repeat_frequency = cp.ceil(fill_length / image_noise_patch.size)
repeat_frequency = cp.asnumpy(repeat_frequency).flatten().astype(
dtype=np.uint16)[0]
noise = cp.tile(image_noise_patch, repeat_frequency + 1)
noise = noise[0:fill_length]
deskewed_cp[deskewed_cp == 0] = noise
return cp.asnumpy(deskewed_cp)
def num_peaks(image=None, peak_image=None, return_numpy=True):
"""
Calculate the number of peaks from each line profile in an SLI image series
by detecting all peaks and applying thresholds to remove unwanted peaks.
Args:
image: Full SLI measurement (series of images) which is prepared for the
pipeline using the SLIX toolbox methods.
peak_image: Boolean NumPy array specifying the peak positions in the full SLI stack
return_numpy: Necessary if using `use_gpu`. Specifies if a CuPy or Numpy
array will be returned.
Returns:
Array where each entry corresponds to the number of detected peaks within
the first dimension of the SLI image series.
"""
if peak_image is None and image is not None:
peak_image = peaks(image, return_numpy=False)
elif peak_image is not None:
peak_image = cupy.array(peak_image)
else:
raise ValueError('Either image or peak_image has to be defined.')
resulting_image = cupy.count_nonzero(peak_image, axis=-1) \
.astype(cupy.uint16)
if return_numpy:
resulting_image_cpu = cupy.asnumpy(resulting_image)
del resulting_image
return resulting_image_cpu
else:
return resulting_image
def compose_vector_fields(
d1,
d2,
premult_index,
premult_disp,
time_scaling,
comp=None,
order=1,
*,
coord_axis=-1,
omit_stats=False,
xcoords=None,
Y=None,
Z=None,
):
if comp is None:
comp = cupy.empty_like(d1, order="C")
# need vector elements on first axis, not last
if coord_axis != 0:
d1 = cupy.ascontiguousarray(cupy.moveaxis(d1, -1, 0))
d2 = cupy.ascontiguousarray(cupy.moveaxis(d2, -1, 0))
else:
if not d1.flags.c_contiguous:
d1 = cupy.ascontiguousarray(d1)
if not d2.flags.c_contiguous:
d2 = cupy.ascontiguousarray(d2)
ndim = d1.shape[0]
B = premult_disp
A = premult_index
t = time_scaling
if xcoords is None:
xcoords = cupy.meshgrid(
*[cupy.arange(s, dtype=d1.real.dtype) for s in d1.shape[1:]],
indexing="ij",
sparse=True,
)
# TODO: reduce number of temporary arrays
if ndim in [2, 3]:
if Y is None:
Y = cupy.empty_like(d1)
if A is None:
if B is None:
if ndim == 3:
composeNone_3d(
d1[0],
d1[1],
d1[2],
xcoords[0],
xcoords[1],
xcoords[2],
Y[0],
Y[1],
Y[2],
)
else:
composeNone_2d(d1[0], d1[1], xcoords[0], xcoords[1], Y[0],
Y[1])
else:
B = cupy.asarray(B[:ndim, :ndim], dtype=d1.dtype, order="C")
if ndim == 3:
composeB_3d(
d1[0],
d1[1],
d1[2],
xcoords[0],
xcoords[1],
xcoords[2],
B,
Y[0],
Y[1],
Y[2],
)
else:
composeB_2d(d1[0], d1[1], xcoords[0], xcoords[1], B, Y[0],
Y[1])
elif B is None:
A = cupy.asarray(A[:ndim, :], dtype=d1.dtype, order="C")
if ndim == 3:
composeA_3d(xcoords[0], xcoords[1], xcoords[2], A, Y[0], Y[1],
Y[2])
else:
composeA_2d(xcoords[0], xcoords[1], A, Y[0], Y[1])
else:
A = cupy.asarray(A[:ndim, :], dtype=d1.dtype, order="C")
B = cupy.asarray(B[:ndim, :ndim], dtype=d1.dtype, order="C")
if ndim == 3:
composeAB_3d(
d1[0],
d1[1],
d1[2],
xcoords[0],
xcoords[1],
xcoords[2],
B,
A,
Y[0],
Y[1],
Y[2],
)
else:
composeAB_2d(d1[0], d1[1], xcoords[0], xcoords[1], B, A, Y[0],
Y[1])
else:
if B is None:
d1tmp = d1.copy() # have to copy to avoid modification of d1
else:
d1tmp = _apply_affine_to_field(d1,
B[:ndim, :ndim],
include_translations=False,
coord_axis=0)
if A is None:
Y = d1tmp
for n in range(ndim):
Y[n] += xcoords[n]
else:
# Y = mul0(A, xcoords, sh, cupy, lastcol=1)
Y = _apply_affine_to_field(xcoords,
A[:ndim, :],
include_translations=True,
coord_axis=0)
Y += d1tmp
if Z is None:
Z = cupy.empty_like(Y)
for n in range(ndim):
Z[n, ...] = ndi.map_coordinates(d2[n], Y, order=1, mode="constant")
if coord_axis == 0:
res = comp
else:
res = cupy.empty_like(Z)
if omit_stats and ndim in [2, 3]:
_shape = cupy.asarray([d1.shape[1 + n] - 1 for n in range(ndim)],
dtype=cupy.int32)
if ndim == 3:
_comp_apply_masked_time_scaling_3d(
d1[0],
d1[1],
d1[2],
Y[0],
Y[1],
Y[2],
Z[0],
Z[1],
Z[2],
t,
_shape,
res[0],
res[1],
res[2],
)
else:
_comp_apply_masked_time_scaling_2d(d1[0], d1[1], Y[0], Y[1], Z[0],
Z[1], t, _shape, res[0], res[1])
else:
# TODO: declare count as boolean?
count = cupy.zeros(Z.shape[1:], dtype=np.int32)
# We now compute:
# res = d1 + t * Z
# except that res = 0 where either coordinate in
# interpolating Y was outside the displacement extent
for n in range(ndim):
_comp_apply_masked_time_scaling_nd(d1[n], Y[n], Z[n], t,
d1.shape[1 + n] - 1, res[n],
count)
# nnz corresponds to the number of points in comp inside the domain
count = count > 0 # remove after init count as boolean
if not omit_stats:
nnz = res.size // ndim - cupy.count_nonzero(count)
res *= ~count[np.newaxis, ...]
if omit_stats:
stats = None
else:
# compute the stats
stats = cupy.empty((3, ), dtype=float)
nn = res[0] * res[0]
for n in range(1, ndim):
nn += res[n] * res[n]
# TODO: do we want stats to be a GPU array or CPU array?
stats[0] = cupy.sqrt(nn.max())
mean_norm = nn.sum() / nnz
stats[1] = cupy.sqrt(mean_norm)
nn *= nn
stats[2] = cupy.sqrt(nn.sum() / nnz - mean_norm * mean_norm)
if coord_axis != 0:
res = cupy.moveaxis(res, 0, -1)
comp[...] = res
return comp, stats
# dimensional arrays.
if x.ndim < 2:
raise np.linalg.LinAlgError(
"1-dimensional array given. Array must be at least two-dimensional"
)
S = np.linalg.svd(x._array, compute_uv=False)
if rtol is None:
tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * np.finfo(
S.dtype).eps
else:
if isinstance(rtol, Array):
rtol = rtol._array
# Note: this is different from np.linalg.matrix_rank, which does not multiply
# the tolerance by the largest singular value.
tol = S.max(axis=-1, keepdims=True) * np.asarray(rtol)[..., np.newaxis]
return Array._new(np.count_nonzero(S > tol, axis=-1))
# Note: this function is new in the array API spec. Unlike transpose, it only
# transposes the last two axes.
def matrix_transpose(x: Array, /) -> Array:
if x.ndim < 2:
raise ValueError(
"x must be at least 2-dimensional for matrix_transpose")
return Array._new(np.swapaxes(x._array, -1, -2))
# Note: outer is the numpy top-level namespace, not np.linalg
def outer(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.outer <numpy.outer>`.
def slogdet(a):
"""Returns sign and logarithm of the determinant of an array.
It calculates the natural logarithm of the determinant of a given value.
Args:
a (cupy.ndarray): The input matrix with dimension ``(..., N, N)``.
Returns:
tuple of :class:`~cupy.ndarray`:
It returns a tuple ``(sign, logdet)``. ``sign`` represents each
sign of the determinant as a real number ``0``, ``1`` or ``-1``.
'logdet' represents the natural logarithm of the absolute of the
determinant.
If the determinant is zero, ``sign`` will be ``0`` and ``logdet``
will be ``-inf``.
The shapes of both ``sign`` and ``logdet`` are equal to
``a.shape[:-2]``.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. warning::
To produce the same results as :func:`numpy.linalg.slogdet` for
singular inputs, set the `linalg` configuration to `raise`.
.. seealso:: :func:`numpy.linalg.slogdet`
"""
if a.ndim < 2:
msg = ('%d-dimensional array given. '
'Array must be at least two-dimensional' % a.ndim)
raise linalg.LinAlgError(msg)
_util._assert_nd_squareness(a)
dtype = numpy.promote_types(a.dtype.char, 'f')
real_dtype = numpy.dtype(dtype.char.lower())
if dtype not in (numpy.float32, numpy.float64,
numpy.complex64, numpy.complex128):
msg = ('dtype must be float32, float64, complex64, or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
a_shape = a.shape
shape = a_shape[:-2]
n = a_shape[-2]
if a.size == 0:
# empty batch (result is empty, too) or empty matrices det([[]]) == 1
sign = cupy.ones(shape, dtype)
logdet = cupy.zeros(shape, real_dtype)
return sign, logdet
lu, ipiv, dev_info = _decomposition._lu_factor(a, dtype)
# dev_info < 0 means illegal value (in dimensions, strides, and etc.) that
# should never happen even if the matrix contains nan or inf.
# TODO(kataoka): assert dev_info >= 0 if synchronization is allowed for
# debugging purposes.
diag = cupy.diagonal(lu, axis1=-2, axis2=-1)
logdet = cupy.log(cupy.abs(diag)).sum(axis=-1)
# ipiv is 1-origin
non_zero = cupy.count_nonzero(ipiv != cupy.arange(1, n + 1), axis=-1)
if dtype.kind == "f":
non_zero += cupy.count_nonzero(diag < 0, axis=-1)
# Note: sign == -1 ** (non_zero % 2)
sign = (non_zero % 2) * -2 + 1
if dtype.kind == "c":
sign = sign * cupy.prod(diag / cupy.abs(diag), axis=-1)
singular = dev_info > 0
return (
cupy.where(singular, dtype.type(0), sign.astype(dtype)).reshape(shape),
cupy.where(singular, real_dtype.type('-inf'), logdet).reshape(shape),
)
def _count_accurate_predictions(y_hat, y):
y_hat = rmm_cupy_ary(cp.asarray, y_hat, dtype=y_hat.dtype)
y = rmm_cupy_ary(cp.asarray, y, dtype=y.dtype)
return y.shape[0] - cp.count_nonzero(y - y_hat)
def _quantile_is_valid(q):
if cupy.count_nonzero(q < 0.0) or cupy.count_nonzero(q > 1.0):
return False
return True
def time_count_nonzero_multi_axis(self, numaxes, size, dtype):
if self.x.ndim >= 2:
np.count_nonzero(self.x, axis=(self.x.ndim - 1, self.x.ndim - 2))
def time_count_nonzero(self, numaxes, size, dtype):
np.count_nonzero(self.x)
def direction(peak_image, centroids, correction_angle=0,
number_of_directions=3, strategy='strict', return_numpy=True):
"""
Calculate up to `number_of_directions` direction angles based on the given
peak positions. If more than `number_of_directions*2` peaks are present, no
direction angle will be calculated to avoid errors. This will result in a
direction angle of BACKGROUND_COLOR. The peak positions are determined by
the position of the corresponding peak pairs (i.e. 6 peaks: 1+4, 2+5, 3+6).
If two peaks are too far away or too near (outside of 180°±35°), the
direction angle will be considered as invalid, resulting in a direction
angle of BACKGROUND_COLOR.
Args:
correction_angle: Correct the resulting direction angle by the value.
This is useful when the stack or camera was rotated.
peak_image: Boolean NumPy array specifying the peak positions in the full
SLI stack
centroids: Centroids resulting from `centroid_correction` for more accurate
results
number_of_directions: Number of directions which shall be generated.
strategy: Strategy to determine the direction angle. Possible values are
'strict', 'safe' and 'unsafe'. 'strict' will only calculate a direction
angle if all peak pairs are within 180°±35°. 'safe' will calculate a
direction angle if the peak pair is within 180°±35°. 'unsafe' will
calculate a direction angle independent of the peak pair distance.
return_numpy: Necessary if using `use_gpu`. Specifies if a CuPy or Numpy
array will be returned.
Returns:
NumPy array with the shape (x, y, `number_of_directions`) containing up to
`number_of_directions` direction angles. x equals the number of pixels of
the SLI image series. If a direction angle is invalid or missing, the
array entry will be BACKGROUND_COLOR instead.
"""
strategy_dict = {'strict': 0, 'safe': 1, 'unsafe': 2}
gpu_peak_image = cupy.array(peak_image).astype('int8')
gpu_centroids = cupy.array(centroids).astype('float32')
result_img_gpu = cupy.empty(
(gpu_peak_image.shape[0], gpu_peak_image.shape[1],
number_of_directions), dtype='float32')
number_of_peaks = cupy.count_nonzero(gpu_peak_image, axis=-1).astype(
'int8')
blocks_per_grid, threads_per_block = prepare_kernel_execution(gpu_peak_image)
_direction[blocks_per_grid, threads_per_block](gpu_peak_image,
gpu_centroids,
number_of_peaks,
result_img_gpu,
correction_angle,
strategy_dict[strategy])
cuda.synchronize()
del number_of_peaks
if peak_image is None:
del gpu_peak_image
if return_numpy:
result_img_cpu = cupy.asnumpy(result_img_gpu)
del result_img_gpu
return result_img_cpu
else:
return result_img_gpu
def update(self):
"""
Where the magic happens. Finds a threshold that will limit the number of params in the network
to the tracked_size, and resets those params to the initial value to emulate how DropBack would
work in real hardware.
Chainer will calculate all grads, and this updater inserts itself before the next
forward pass can occur to set the parameters back to what they should be. Only the params with the largest
current-initial value will not be reset to initial. This emulates the accumulated gradient updates of the actual
algorithm.
:return:
"""
if self.first_iter:
self.first_iter = False
self.params = [i for i in self.opt.target.params()]
for i, p in enumerate(self.params):
self.init_params.append(xp.copy(p.data))
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
xp.savez(
os.path.join(self.output_dir,
'init_params_{0}'.format(self.time_stamp)),
self.init_params)
if self.tracked_size:
self.frozen_masks = [None] * len(self.params)
super(DropBack, self).update()
if self.decay_init and not self.first_iter:
for i, _ in enumerate(self.init_params):
self.init_params[i] = self.init_params[i] * .90
if self.tracked_size:
if not self.freeze:
abs_values = []
for i, param in enumerate(self.params):
if param.name == 'b':
values = (xp.abs(param.data).flatten()).copy()
else:
values = (
xp.abs(param.data -
self.init_params[i]).flatten()).copy()
abs_values.append(values)
abs_vals = xp.concatenate(abs_values)
thresh = xp.partition(abs_vals,
self.tracked_size)[-self.tracked_size]
for i, param in enumerate(self.params):
if param.name == 'b':
if self.freeze:
mask = self.frozen_masks[i]
else:
mask = xp.abs(param.data) > thresh
param.data = mask * param.data
else:
if self.freeze:
mask = self.frozen_masks[i]
else:
mask = xp.abs(param.data -
self.init_params[i]) > thresh
param.data = mask * param.data + self.init_params[i] * ~mask
self.frozen_masks[i] = mask
if self.iteration == 3465:
print("Checking inv...")
total_sum = sum([
xp.count_nonzero(p.data != self.init_params[i])
for i, p in enumerate(self.params)
])
print(
"********\n\n Total non zero is: {}\n\n1*********".format(
total_sum))
assert total_sum <= self.tracked_size * 1.1
if self.track:
if (self.iteration - 1) % 100 == 0:
flat_now = xp.concatenate(
[i.array.ravel() for i in self.params])
flat_0 = xp.concatenate([i.ravel() for i in self.init_params])
xp.savez(
os.path.join(self.output_dir, f'l2_{self.iteration-1}'),
xp.linalg.norm(flat_now - flat_0))
xp.savez(
os.path.join(self.output_dir,
f'param_hist_{self.iteration-1}'),
xp.concatenate([
i.array.ravel() for i in self.params
if i.name == 'b' or i.name == 'W'
]))
def fit_genetic_algorithm(M_PROBABILITY, X_PROBABILITY, P_SIZE, N_GEN, SEL_M,
CROSS_M, MUT_METHOD, K, items, encoding,
BEST_CHR_CONV_LIM, PERCENT_CHR_CONV_LIM):
for user in tqdm.tqdm_notebook(range(items.shape[0])):
# define evaluations list to plot afterwards
U_EVALS_LIST = list()
# fetch user's rating top - k neighbors (k=10)
neighbors_data, neighbors_indeces = fetch_neighborhood(user, items, K)
# fetch optimal solution vector
optim_values, optim_indeces = fetch_optim(user, items,
neighbors_indeces)
# reformat optimal solution vector
# trim non neighbor users
optim_values = optim_values[neighbors_indeces]
# drop zero values
optim_mean = cupy.count_nonzero([optim_values], axis=1)
# find mean of all k vectors and flatten optimal solution to a single vector
optim_values = optim_values.sum(axis=0)
optim_indeces = optim_indeces[optim_values != 0]
optim_values = optim_values[optim_values != 0]
optim_mean = optim_mean[optim_mean != 0]
optim_values = optim_values / optim_mean
# round up mean
optim_values = cupy.ceil(optim_values).astype(cupy.int64)
# random initialize population (chromosomes)
chromosomes = cupy.random.choice(cupy.unique(optim_values),
(P_SIZE, optim_values.shape[0]))
# initialize error (best_chr) array
evaluation_best_chr = cupy.zeros(N_GEN)
# initialize error (gen) array
evaluation_overall_gen = cupy.zeros(N_GEN)
# initialize best_chr counter
BEST_CHR_CONV_CTR = 0
# initialize early stopping conditional
stop = False
# fit GA
for gen in tqdm.tqdm_notebook(range(N_GEN)):
# select and crossover population
chromosomes = crossover_chromosomes(X_PROBABILITY, CROSS_M, SEL_M,
chromosomes, optim_values,
encoding)
# mutate chromosomes
chromosomes = mutate_chromosomes(M_PROBABILITY, MUT_METHOD,
chromosomes, optim_values)
# check GA convergence
stop, evaluation_best_chr, evaluation_overall_gen, BEST_CHR_CONV_CTR, cause = \
genetic_algorithm_convergence(evaluation_best_chr, evaluation_overall_gen, gen,
chromosomes, optim_values, BEST_CHR_CONV_LIM, BEST_CHR_CONV_CTR,
PERCENT_CHR_CONV_LIM)
# if GA converges
if stop is True:
# print met GA convergence conditional
print("User [", user, "]\tGen [", gen, "]\t: GA converged (",
cause, ")")
# save population
save_population(user, chromosomes, optim_values)
# save different fitness metrics
save_evaluations(user, chromosomes, optim_values)
# stop fitting
break
# custom convergence conditional: evaluate by chromosome accuracy [90% accuracy convergence]
if cupy.mean(cupy.fromiter((evaluate_chromosome(chromosomes[i], optim_values)
for i in range(chromosomes.shape[0])), cupy.int64)) / \
chromosomes[0].shape[0] > 0.9:
# print population accuracy
print(
"Mean accuracy: ",
cupy.mean(
cupy.fromiter(
(evaluate_chromosome(chromosomes[i], optim_values)
for i in range(chromosomes.shape[0])),
cupy.int64)), "\tTotal chromosomes: ",
chromosomes[0].shape[0])
# save population
save_population(user, chromosomes, optim_values)
# save different fitness metrics
save_evaluations(user, chromosomes, optim_values)
# stop fitting
break
# append mean population fitness
U_EVALS_LIST.append(
cupy.mean(
cupy.fromiter(
(evaluate_chromosome(chromosomes[i], optim_values)
for i in range(chromosomes.shape[0])), cupy.int64)))
# save population
save_population(user, chromosomes, optim_values)
# save different fitness metrics
save_evaluations(user, chromosomes, optim_values)
# convert mean population fitness list to cupy array
U_EVALS_ARRAY = cupy.asarray(U_EVALS_LIST)
# define x axis of plot
x_axis = cupy.arange(U_EVALS_ARRAY.shape[0])
# plot mean population fitness array
matplotlib.pyplot.clf()
matplotlib.pyplot.plot(x_axis,
U_EVALS_ARRAY,
label="User " + str(user) + " - Evaluation")
matplotlib.pyplot.legend()
# matplotlib.pyplot.show()
# save figure
matplotlib.pyplot.savefig("user_" + str(user) + "-evaluation.png",
bbox_inches='tight')
def _count_accurate_predictions(y_hat_y):
y_hat, y = y_hat_y
y_hat = cp.asarray(y_hat, dtype=y_hat.dtype)
y = cp.asarray(y, dtype=y.dtype)
return y.shape[0] - cp.count_nonzero(y - y_hat)
def count_nan_inf(x):
bool_arr = cp.isinf(x) | cp.isnan(x)
return cp.count_nonzero(bool_arr)
def time_count_nonzero_axis(self, numaxes, size, dtype):
np.count_nonzero(self.x, axis=self.x.ndim - 1)
k = 0
times = 0
idx = cp.arange(train_datas)
x = list()
y = list()
print("開始訓練...")
while cp.less(k, train_datas):
times = cp.add(times, 1)
output = train.TTTG_Network(
input_data, weight, baise, weight2, baise2, weight3, baise3, weight4, baise4, L, expect_data, True, True)
if times % 10000 == 0:
error = cp.sum(output[0])
result = expect_data[idx, output[1]]
k = cp.count_nonzero(result)
# result = cp.equal(expect_data, cp.asarray(output[1])) # AND閘
expect_idx = list()
for i in map(cp.nonzero, expect_data[result == 0]):
expect_idx.append(i[0].tolist())
print("輸入棋盤,輸出位置,預計輸出,實際輸出:", *list(zip(input_data[result == 0].tolist(
), output[1][result == 0].tolist(), expect_idx, output[2][result == 0].tolist())), sep='\n')
print("第%d次訓練(學習率:%f):" % (times, L))
print("總誤差:", error)
print("答對筆數/總筆數: %d/%d" % (k, train_datas))
# x.append(L)
# y.append(output[0])
# L = L * 10
# if L >= 1:
# plt.plot([5, 4, 3, 2, 1, 0], y)
# plt.show()
def _binary_erosion(input,
structure,
iterations,
mask,
output,
border_value,
origin,
invert,
brute_force=True):
try:
iterations = operator.index(iterations)
except TypeError:
raise TypeError('iterations parameter should be an integer')
if input.dtype.kind == 'c':
raise TypeError('Complex type not supported')
if structure is None:
structure = generate_binary_structure(input.ndim, 1)
all_weights_nonzero = input.ndim == 1
center_is_true = True
default_structure = True
else:
structure = structure.astype(dtype=bool, copy=False)
# transfer to CPU for use in determining if it is fully dense
# structure_cpu = cupy.asnumpy(structure)
default_structure = False
if structure.ndim != input.ndim:
raise RuntimeError('structure and input must have same dimensionality')
if not structure.flags.c_contiguous:
structure = cupy.ascontiguousarray(structure)
if structure.size < 1:
raise RuntimeError('structure must not be empty')
if mask is not None:
if mask.shape != input.shape:
raise RuntimeError('mask and input must have equal sizes')
if not mask.flags.c_contiguous:
mask = cupy.ascontiguousarray(mask)
masked = True
else:
masked = False
origin = _util._fix_sequence_arg(origin, input.ndim, 'origin', int)
if isinstance(output, cupy.ndarray):
if output.dtype.kind == 'c':
raise TypeError('Complex output type not supported')
else:
output = bool
output = _util._get_output(output, input)
temp_needed = cupy.shares_memory(output, input, 'MAY_SHARE_BOUNDS')
if temp_needed:
# input and output arrays cannot share memory
temp = output
output = _util._get_output(output.dtype, input)
if structure.ndim == 0:
# kernel doesn't handle ndim=0, so special case it here
if float(structure):
output[...] = cupy.asarray(input, dtype=bool)
else:
output[...] = ~cupy.asarray(input, dtype=bool)
return output
origin = tuple(origin)
int_type = _util._get_inttype(input)
offsets = _filters_core._origins_to_offsets(origin, structure.shape)
if not default_structure:
# synchronize required to determine if all weights are non-zero
nnz = int(cupy.count_nonzero(structure))
all_weights_nonzero = nnz == structure.size
if all_weights_nonzero:
center_is_true = True
else:
center_is_true = _center_is_true(structure, origin)
erode_kernel = _get_binary_erosion_kernel(
structure.shape,
int_type,
offsets,
center_is_true,
border_value,
invert,
masked,
all_weights_nonzero,
)
if iterations == 1:
if masked:
output = erode_kernel(input, structure, mask, output)
else:
output = erode_kernel(input, structure, output)
elif center_is_true and not brute_force:
raise NotImplementedError(
'only brute_force iteration has been implemented')
else:
if cupy.shares_memory(output, input, 'MAY_SHARE_BOUNDS'):
raise ValueError('output and input may not overlap in memory')
tmp_in = cupy.empty_like(input, dtype=output.dtype)
tmp_out = output
if iterations >= 1 and not iterations & 1:
tmp_in, tmp_out = tmp_out, tmp_in
if masked:
tmp_out = erode_kernel(input, structure, mask, tmp_out)
else:
tmp_out = erode_kernel(input, structure, tmp_out)
# TODO: kernel doesn't return the changed status, so determine it here
changed = not (input == tmp_out).all() # synchronize!
ii = 1
while ii < iterations or ((iterations < 1) and changed):
tmp_in, tmp_out = tmp_out, tmp_in
if masked:
tmp_out = erode_kernel(tmp_in, structure, mask, tmp_out)
else:
tmp_out = erode_kernel(tmp_in, structure, tmp_out)
changed = not (tmp_in == tmp_out).all()
ii += 1
if not changed and (not ii & 1): # synchronize!
# can exit early if nothing changed
# (only do this after even number of tmp_in/out swaps)
break
output = tmp_out
if temp_needed:
temp[...] = output
output = temp
return output
m=m+1
x=[]
continue
elif len(line)==1 and m==3:
#print(x)
nsw=n-nbus-nbrch-ngen
ipt_switch=x
continue
x.append(line)
n=n+1
ipt_bus=cp.array(ipt_bus,dtype=cp.float64)
ipt_gen=cp.array(ipt_gen,dtype=cp.float64)
ipt_brch= cp.array(ipt_brch,dtype=cp.float64)
ipt_switch=cp.array(ipt_switch,dtype=cp.float64)
nPV=cp.count_nonzero(ipt_bus[:,9]==2)
#print("Data read in successfully!")
#print("nbus:", nbus, " nbrch:", nbrch, " ngen:", ngen)
# assign bus data
bus_int=ipt_bus[:,0]
V=ipt_bus[:,1]
b_ang=ipt_bus[:,2]
b_pg=ipt_bus[:,3]
b_qg=ipt_bus[:,4]
Pl=ipt_bus[:,5]
Ql=ipt_bus[:,6]
Gb=ipt_bus[:,7]
Bb=ipt_bus[:,8]
b_type=ipt_bus[:,9]
# assign branch data
from_bus=ipt_brch[:,0].astype(cp.int)
def accuracy(a_hat, a_true):
result = cp.equal(a_hat, a_true)
right = cp.count_nonzero(result)
return right / a_hat.shape[1]
def threshold_multiotsu(image, classes=3, nbins=256):
r"""Generate `classes`-1 threshold values to divide gray levels in `image`.
The threshold values are chosen to maximize the total sum of pairwise
variances between the thresholded graylevel classes. See Notes and [1]_
for more details.
Parameters
----------
image : (N, M) ndarray
Grayscale input image.
classes : int, optional
Number of classes to be thresholded, i.e. the number of resulting
regions.
nbins : int, optional
Number of bins used to calculate the histogram. This value is ignored
for integer arrays.
Returns
-------
thresh : array
Array containing the threshold values for the desired classes.
Raises
------
ValueError
If ``image`` contains less grayscale value then the desired
number of classes.
Notes
-----
This implementation relies on a Cython function whose complexity
is :math:`O\left(\frac{Ch^{C-1}}{(C-1)!}\right)`, where :math:`h`
is the number of histogram bins and :math:`C` is the number of
classes desired.
The input image must be grayscale.
References
----------
.. [1] Liao, P-S., Chen, T-S. and Chung, P-C., "A fast algorithm for
multilevel thresholding", Journal of Information Science and
Engineering 17 (5): 713-727, 2001. Available at:
<https://ftp.iis.sinica.edu.tw/JISE/2001/200109_01.pdf>
:DOI:`10.6688/JISE.2001.17.5.1`
.. [2] Tosa, Y., "Multi-Otsu Threshold", a java plugin for ImageJ.
Available at:
<http://imagej.net/plugins/download/Multi_OtsuThreshold.java>
Examples
--------
>>> import cupy as cp
>>> from cucim.skimage.color import label2rgb
>>> from skimage import data
>>> image = cp.asarray(data.camera())
>>> thresholds = threshold_multiotsu(image)
>>> regions = cp.digitize(image, bins=thresholds)
>>> regions_colorized = label2rgb(regions)
"""
try:
from skimage.filters._multiotsu import (
_get_multiotsu_thresh_indices, _get_multiotsu_thresh_indices_lut)
except ImportError:
raise ImportError(
"could not the required (private) multi-otsu helper functions "
"from scikit-image")
if len(image.shape) > 2 and image.shape[-1] in (3, 4):
msg = ("threshold_multiotsu is expected to work correctly only for "
"grayscale images; image shape {0} looks like an RGB image")
warn(msg.format(image.shape))
# calculating the histogram and the probability of each gray level.
prob, bin_centers = histogram(image.ravel(),
nbins=nbins,
source_range='image',
normalize=True)
nvalues = cp.count_nonzero(prob)
if nvalues < classes:
msg = ("The input image has only {} different values. "
"It can not be thresholded in {} classes")
raise ValueError(msg.format(nvalues, classes))
elif nvalues == classes:
thresh_idx = cp.where(prob > 0)[0][:-1]
else:
# Need probabilities on the CPU to use the Cython code
# CuPy Backend: prob is small, so CPU computations should be faster
prob = cp.asnumpy(prob) # synchronization!
prob = prob.astype("float32")
# Get threshold indices
try:
thresh_idx = _get_multiotsu_thresh_indices_lut(prob, classes - 1)
except MemoryError:
# Don't use LUT if the number of bins is too large (if the
# image is uint16 for example): in this case, the
# allocated memory is too large.
thresh_idx = _get_multiotsu_thresh_indices(prob, classes - 1)
# transfer indices back to the GPU
thresh_idx = cp.asarray(thresh_idx) # synchronization!
thresh = bin_centers[thresh_idx]
return thresh
