def local_cov_bet_class_NN(self,key,label,nb_class,batchsize,k):
key_broadcast=cp.broadcast_to(key,(batchsize,batchsize,key.shape[1]))
key_broadcast_transpose=cp.transpose(cp.broadcast_to(key,(batchsize,batchsize,key.shape[1])),axes=(1,0,2))
sub_key_broadcast=key_broadcast-key_broadcast_transpose
norm_sub_broadcast=cp.linalg.norm(sub_key_broadcast,axis=2)
sorted_d=cp.sort(norm_sub_broadcast,axis=0)
kth_d=sorted_d[k]
kth_d=kth_d.reshape([batchsize,1])
sigma=cp.matmul(kth_d,cp.transpose(kth_d))
batchsize_per_class=batchsize//nb_class
index=cp.arange(key.shape[0])
xx,yy=cp.meshgrid(index,index)
sub=key[xx]-key[yy]
norm_sub=cp.linalg.norm(sub,axis=2)
a1=cp.exp(-norm_sub*norm_sub/sigma)
lindex=cp.arange(label.shape[0])
lx,ly=cp.meshgrid(lindex,lindex)
l=(label[lx]==label[ly])
a1=a1*l*(1.0/(batchsize*nb_class)-1.0/batchsize_per_class)
l2=(label[lx]!=label[ly])
a2=l2*(1.0/batchsize)
a=a1+a2
a=a.reshape([a.shape[0],a.shape[1],1])
a_sub=a*sub
Sb=cp.einsum('ijk,ijl->kl',a_sub,sub,dtype='float32')*0.5
return Sb
def run_all_to_all(rank, n_workers, dtype):
dev = cuda.Device(rank)
dev.use()
comm = NCCLBackend(n_workers, rank)
in_array = cupy.arange(n_workers * 10,
dtype='f').reshape(n_workers, 10)
out_array = cupy.zeros((n_workers, 10), dtype='f')
comm.all_to_all(in_array, out_array)
expected = (10 * rank) + cupy.broadcast_to(cupy.arange(10, dtype='f'),
(n_workers, 10))
testing.assert_allclose(out_array, expected)
def __call__(self,
data,
iters=5,
width=3.0,
weights=None,
mask=None,
keepdims=False):
data = cp.asarray(data, dtype=self.dtype)
filt = cp.ones_like(data)
if mask is not None:
mask = cp.asarray(mask, dtype=self.dtype)
elementwise_not(cp.broadcast_to(mask, data.shape), filt)
if weights is not None:
weights = cp.asarray(weights, dtype=self.dtype)
try:
filt *= weights
except ValueError:
if isinstance(self.axis, int):
ndim = data.ndim
axis = self.axis % ndim
if weights.size == data.shape[axis]:
filt *= weights.reshape(-1 if i == axis else 1
for i in range(ndim))
else:
raise ValueError(
'length of weights must be same as'
' the length of data along specified axis.')
else:
raise ValueError(
'If weights and data are not broadcastable, '
'axis must be specified as int.')
checkfinite(data, filt, filt)
iterator = count() if (iters is None) else range(iters)
csum = check_sum(filt, axis=self.axis)
for _ in iterator:
self.updatefilt(data, filt, width)
tsum = check_sum(filt, axis=self.axis)
if all_equal(csum, tsum):
break
else:
csum = tsum
if self.rtnmask:
result = elementwise_not(filt, filt)
else:
result = self.reduce(data, filt, keepdims=keepdims)
return result
def run_gather(rank, n_workers, root, dtype):
dev = cuda.Device(rank)
dev.use()
comm = NCCLBackend(n_workers, rank)
in_array = (rank + 1) * cupy.arange(10, dtype='f')
out_array = cupy.zeros((n_workers, 10), dtype='f')
comm.gather(in_array, out_array, root)
if rank == root:
expected = 1 + cupy.arange(n_workers).reshape(n_workers, 1)
expected = expected * cupy.broadcast_to(cupy.arange(10, dtype='f'),
(n_workers, 10))
testing.assert_allclose(out_array, expected)
def predict(self, input_x):
output = self.predictor(input_x)
batch_size, input_channel, input_h, input_w = input_x.shape
batch_size, _, grid_h, grid_w = output.shape
x, y, w, h, conf, prob = F.split_axis(F.reshape(
output, (batch_size, self.predictor.n_boxes,
self.predictor.n_classes + 5, grid_h, grid_w)),
(1, 2, 3, 4, 5),
axis=2)
x = F.sigmoid(x) # xのactivation
y = F.sigmoid(y) # yのactivation
conf = F.sigmoid(conf) # confのactivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
prob = F.softmax(prob) # probablitiyのacitivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
# x, y, w, hを絶対座標へ変換
x_shift = Variable(
xp.broadcast_to(xp.arange(grid_w, dtype=xp.float32), x.shape))
y_shift = Variable(
xp.broadcast_to(
xp.arange(grid_h, dtype=xp.float32).reshape(grid_h, 1),
y.shape))
w_anchor = Variable(
xp.broadcast_to(
xp.reshape(
xp.array(self.anchors, dtype=xp.float32)[:, 0],
(self.predictor.n_boxes, 1, 1, 1)), w.shape))
h_anchor = Variable(
xp.broadcast_to(
xp.reshape(
xp.array(self.anchors, dtype=xp.float32)[:, 1],
(self.predictor.n_boxes, 1, 1, 1)), h.shape))
# x_shift.to_gpu(), y_shift.to_gpu(), w_anchor.to_gpu(), h_anchor.to_gpu()
box_x = (x + x_shift) / grid_w
box_y = (y + y_shift) / grid_h
box_w = F.exp(w) * w_anchor / grid_w
box_h = F.exp(h) * h_anchor / grid_h
return box_x, box_y, box_w, box_h, conf, prob
def in1d(ar1, ar2, assume_unique=False, invert=False):
"""Tests whether each element of a 1-D array is also present in a second
array.
Returns a boolean array the same length as ``ar1`` that is ``True``
where an element of ``ar1`` is in ``ar2`` and ``False`` otherwise.
Args:
ar1 (cupy.ndarray): Input array.
ar2 (cupy.ndarray): The values against which to test each value of
``ar1``.
assume_unique (bool, optional): Ignored
invert (bool, optional): If ``True``, the values in the returned array
are inverted (that is, ``False`` where an element of ``ar1`` is in
``ar2`` and ``True`` otherwise). Default is ``False``.
Returns:
cupy.ndarray, bool: The values ``ar1[in1d]`` are in ``ar2``.
"""
# Ravel both arrays, behavior for the first array could be different
ar1 = ar1.ravel()
ar2 = ar2.ravel()
if ar1.size == 0 or ar2.size == 0:
if invert:
return cupy.ones(ar1.shape, dtype=cupy.bool_)
else:
return cupy.zeros(ar1.shape, dtype=cupy.bool_)
shape = (ar1.size, ar2.size)
ar1_broadcast = cupy.broadcast_to(ar1[..., cupy.newaxis], shape)
ar2_broadcast = cupy.broadcast_to(ar2, shape)
count = (ar1_broadcast == ar2_broadcast).sum(axis=1)
if invert:
return count == 0
else:
return count > 0
def triu_indices(n, k=0, m=None):
"""Returns the indices of the upper triangular matrix.
Here, the first group of elements contains row coordinates
of all indices and the second group of elements
contains column coordinates.
Parameters
----------
n : int
The size of the arrays for which the returned indices will
be valid.
k : int, optional
Refers to the diagonal offset. By default, `k = 0` i.e.
the main dialogal. The positive value of `k`
denotes the diagonals above the main diagonal, while the negative
value includes the diagonals below the main diagonal.
m : int, optional
The column dimension of the arrays for which the
returned arrays will be valid. By default, `m = n`.
Returns
-------
y : tuple of ndarrays
The indices for the triangle. The returned tuple
contains two arrays, each with the indices along
one dimension of the array.
See Also
--------
numpy.triu_indices
"""
tri_ = ~cupy.tri(n, m, k=k - 1, dtype=bool)
return tuple(
cupy.broadcast_to(inds, tri_.shape)[tri_]
for inds in cupy.indices(tri_.shape, dtype=int))
def tril_indices(n, k=0, m=None):
"""Returns the indices of the lower triangular matrix.
Here, the first group of elements contains row coordinates
of all indices and the second group of elements
contains column coordinates.
Parameters
----------
n : int
The row dimension of the arrays for which the returned
indices will be valid.
k : int, optional
Diagonal above which to zero elements. `k = 0`
(the default) is the main diagonal, `k < 0` is
below it and `k > 0` is above.
m : int, optional
The column dimension of the arrays for which the
returned arrays will be valid. By default, `m = n`.
Returns
-------
y : tuple of ndarrays
The indices for the triangle. The returned tuple
contains two arrays, each with the indices along
one dimension of the array.
See Also
--------
numpy.tril_indices
"""
tri_ = cupy.tri(n, m, k=k, dtype=bool)
return tuple(
cupy.broadcast_to(inds, tri_.shape)[tri_]
for inds in cupy.indices(tri_.shape, dtype=int))
def __call__(self, input_x, t):
output = self.predictor(input_x)
batch_size, _, grid_h, grid_w = output.shape
self.seen += batch_size
x, y, w, h, conf, prob = F.split_axis(F.reshape(
output, (batch_size, self.predictor.n_boxes,
self.predictor.n_classes + 5, grid_h, grid_w)),
(1, 2, 3, 4, 5),
axis=2)
x = F.sigmoid(x) # xのactivation
y = F.sigmoid(y) # yのactivation
conf = F.sigmoid(conf) # confのactivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
prob = F.softmax(prob) # probablitiyのacitivation
# 教師データの用意
tw = xp.zeros(
w.shape,
dtype=xp.float32) # wとhが0になるように学習(e^wとe^hは1に近づく -> 担当するbboxの倍率1)
th = xp.zeros(h.shape, dtype=xp.float32)
tx = xp.tile(0.5, x.shape).astype(xp.float32) # 活性化後のxとyが0.5になるように学習()
ty = xp.tile(0.5, y.shape).astype(xp.float32)
if self.seen < self.unstable_seen: # centerの存在しないbbox誤差学習スケールは基本0.1
box_learning_scale = xp.tile(0.1, x.shape).astype(xp.float32)
else:
box_learning_scale = xp.tile(0, x.shape).astype(xp.float32)
tconf = xp.zeros(
conf.shape, dtype=xp.float32
) # confidenceのtruthは基本0、iouがthresh以上のものは学習しない、ただしobjectの存在するgridのbest_boxのみ真のIOUに近づかせる
conf_learning_scale = xp.tile(0.1, conf.shape).astype(xp.float32)
tprob = prob.data.copy() # best_anchor以外は学習させない(自身との二乗和誤差 = 0)
# 全bboxとtruthのiouを計算(batch単位で計算する)
x_shift = Variable(
xp.broadcast_to(xp.arange(grid_w, dtype=xp.float32), x.shape[1:]))
y_shift = Variable(
xp.broadcast_to(
xp.arange(grid_h, dtype=xp.float32).reshape(grid_h, 1),
y.shape[1:]))
w_anchor = Variable(
xp.broadcast_to(
xp.reshape(
xp.array(self.anchors, dtype=xp.float32)[:, 0],
(self.predictor.n_boxes, 1, 1, 1)), w.shape[1:]))
h_anchor = Variable(
xp.broadcast_to(
xp.reshape(
xp.array(self.anchors, dtype=xp.float32)[:, 1],
(self.predictor.n_boxes, 1, 1, 1)), h.shape[1:]))
x_shift.to_gpu(), y_shift.to_gpu(), w_anchor.to_gpu(), h_anchor.to_gpu(
)
best_ious = []
for batch in range(batch_size):
n_truth_boxes = len(t[batch])
box_x = (x[batch] + x_shift) / grid_w
box_y = (y[batch] + y_shift) / grid_h
box_w = F.exp(w[batch]) * w_anchor / grid_w
box_h = F.exp(h[batch]) * h_anchor / grid_h
ious = []
for truth_index in range(n_truth_boxes):
truth_box_x = Variable(
xp.broadcast_to(
xp.array(t[batch][truth_index]["x"], dtype=xp.float32),
box_x.shape))
truth_box_y = Variable(
xp.broadcast_to(
xp.array(t[batch][truth_index]["y"], dtype=xp.float32),
box_y.shape))
truth_box_w = Variable(
xp.broadcast_to(
xp.array(t[batch][truth_index]["w"], dtype=xp.float32),
box_w.shape))
truth_box_h = Variable(
xp.broadcast_to(
xp.array(t[batch][truth_index]["h"], dtype=xp.float32),
box_h.shape))
truth_box_x.to_gpu(), truth_box_y.to_gpu(), truth_box_w.to_gpu(
), truth_box_h.to_gpu()
ious.append(
multi_box_iou(
Box(box_x, box_y, box_w, box_h),
Box(truth_box_x, truth_box_y, truth_box_w,
truth_box_h)).data.get())
ious = xp.array(ious)
best_ious.append(xp.max(ious, axis=0))
best_ious = xp.array(best_ious)
# 一定以上のiouを持つanchorに対しては、confを0に下げないようにする(truthの周りのgridはconfをそのまま維持)。
tconf[best_ious > self.thresh] = conf.data.get()[
best_ious > self.thresh]
conf_learning_scale[best_ious > self.thresh] = 0
# objectの存在するanchor boxのみ、x、y、w、h、conf、probを個別修正
abs_anchors = self.anchors / xp.array([grid_w, grid_h])
for batch in range(batch_size):
for truth_box in t[batch]:
truth_w = int(float(truth_box["x"]) * grid_w)
truth_h = int(float(truth_box["y"]) * grid_h)
truth_n = 0
best_iou = 0.0
for anchor_index, abs_anchor in enumerate(abs_anchors):
iou = box_iou(
Box(0, 0, float(truth_box["w"]),
float(truth_box["h"])),
Box(0, 0, abs_anchor[0], abs_anchor[1]))
if best_iou < iou:
best_iou = iou
truth_n = anchor_index
# objectの存在するanchorについて、centerを0.5ではなく、真の座標に近づかせる。anchorのスケールを1ではなく真のスケールに近づかせる。学習スケールを1にする。
box_learning_scale[batch, truth_n, :, truth_h, truth_w] = 1.0
tx[batch, truth_n, :, truth_h,
truth_w] = float(truth_box["x"]) * grid_w - truth_w
ty[batch, truth_n, :, truth_h,
truth_w] = float(truth_box["y"]) * grid_h - truth_h
tw[batch, truth_n, :, truth_h, truth_w] = xp.log(
float(truth_box["w"]) / abs_anchors[truth_n][0])
th[batch, truth_n, :, truth_h, truth_w] = xp.log(
float(truth_box["h"]) / abs_anchors[truth_n][1])
tprob[batch, :, truth_n, truth_h, truth_w] = 0
tprob[batch,
int(truth_box["label"]), truth_n, truth_h, truth_w] = 1
# IOUの観測
full_truth_box = Box(float(truth_box["x"]),
float(truth_box["y"]),
float(truth_box["w"]),
float(truth_box["h"]))
predicted_box = Box(
(x[batch][truth_n][0][truth_h][truth_w].data.get() +
truth_w) / grid_w,
(y[batch][truth_n][0][truth_h][truth_w].data.get() +
truth_h) / grid_h,
xp.exp(w[batch][truth_n][0][truth_h][truth_w].data.get()) *
abs_anchors[truth_n][0],
xp.exp(h[batch][truth_n][0][truth_h][truth_w].data.get()) *
abs_anchors[truth_n][1])
predicted_iou = box_iou(full_truth_box, predicted_box)
tconf[batch, truth_n, :, truth_h, truth_w] = predicted_iou
conf_learning_scale[batch, truth_n, :, truth_h, truth_w] = 10.0
# debug prints
maps = F.transpose(prob[batch], (2, 3, 1, 0)).data
print(
"best confidences and best conditional probability and predicted class of each grid:"
)
for i in range(grid_h):
for j in range(grid_w):
print("%2d" %
(int(conf[batch, :, :, i, j].data.max() * 100)),
end=" ")
print(" ", end="")
for j in range(grid_w):
print("%2d" % (maps[i][j][int(
maps[i][j].max(axis=1).argmax())].argmax()),
end=" ")
print(" ", end="")
for j in range(grid_w):
print("%2d" % (maps[i][j][int(
maps[i][j].max(axis=1).argmax())].max() * 100),
end=" ")
print()
print(
"best default iou: %.2f predicted iou: %.2f confidence: %.2f class: %s"
% (best_iou, predicted_iou,
conf[batch][truth_n][0][truth_h][truth_w].data,
t[batch][0]["label"]))
print("-------------------------------")
print("seen = %d" % self.seen)
# loss計算
tx, ty, tw, th, tconf, tprob = Variable(tx), Variable(ty), Variable(
tw), Variable(th), Variable(tconf), Variable(tprob)
box_learning_scale, conf_learning_scale = Variable(
box_learning_scale), Variable(conf_learning_scale)
tx.to_gpu(), ty.to_gpu(), tw.to_gpu(), th.to_gpu(), tconf.to_gpu(
), tprob.to_gpu()
box_learning_scale.to_gpu()
conf_learning_scale.to_gpu()
x_loss = F.sum((tx - x)**2 * box_learning_scale) / 2
y_loss = F.sum((ty - y)**2 * box_learning_scale) / 2
w_loss = F.sum((tw - w)**2 * box_learning_scale) / 2
h_loss = F.sum((th - h)**2 * box_learning_scale) / 2
c_loss = F.sum((tconf - conf)**2 * conf_learning_scale) / 2
p_loss = F.sum((tprob - prob)**2) / 2
print(
"x_loss: %f y_loss: %f w_loss: %f h_loss: %f c_loss: %f p_loss: %f"
% (F.sum(x_loss).data, F.sum(y_loss).data, F.sum(w_loss).data,
F.sum(h_loss).data, F.sum(c_loss).data, F.sum(p_loss).data))
loss = x_loss + y_loss + w_loss + h_loss + c_loss + p_loss
return loss
def test_broadcast_to_short_shape_numpy19(self, dtype):
# Note that broadcast_to is only supported on numpy>=1.10
a = testing.shaped_arange((1, 3, 4), cupy, dtype)
with self.assertRaises(ValueError):
cupy.broadcast_to(a, (3, 4))
return [
Array._new(array)
for array in np.broadcast_arrays(*[a._array for a in arrays])
]
def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array:
"""
Array API compatible wrapper for :py:func:`np.broadcast_to <numpy.broadcast_to>`.
See its docstring for more information.
"""
from ._array_object import Array
return Array._new(np.broadcast_to(x._array, shape))
def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
"""
Array API compatible wrapper for :py:func:`np.can_cast <numpy.can_cast>`.
See its docstring for more information.
"""
if isinstance(from_, Array):
from_ = from_.dtype
elif from_ not in _all_dtypes:
raise TypeError(f"{from_=}, but should be an array_api array or dtype")
if to not in _all_dtypes:
raise TypeError(f"{to=}, but should be a dtype")
# Note: We avoid np.can_cast() as it has discrepancies with the array API,
def average(a, axis=None, weights=None, returned=False):
"""Returns the weighted average along an axis.
Args:
a (cupy.ndarray): Array to compute average.
axis (int): Along which axis to compute average. The flattened array
is used by default.
weights (cupy.ndarray): Array of weights where each element
corresponds to the value in ``a``. If ``None``, all the values
in ``a`` have a weight equal to one.
returned (bool): If ``True``, a tuple of the average and the sum
of weights is returned, otherwise only the average is returned.
Returns:
cupy.ndarray or tuple of cupy.ndarray: The average of the input array
along the axis and the sum of weights.
.. seealso:: :func:`numpy.average`
"""
a = cupy.asarray(a)
if weights is None:
avg = a.mean(axis)
scl = avg.dtype.type(a.size / avg.size)
else:
wgt = cupy.asarray(weights)
if issubclass(a.dtype.type, (numpy.integer, numpy.bool_)):
result_dtype = numpy.result_type(a.dtype, wgt.dtype, 'f8')
else:
result_dtype = numpy.result_type(a.dtype, wgt.dtype)
# Sanity checks
if a.shape != wgt.shape:
if axis is None:
raise TypeError(
'Axis must be specified when shapes of a and weights '
'differ.')
if wgt.ndim != 1:
raise TypeError(
'1D weights expected when shapes of a and weights differ.')
if wgt.shape[0] != a.shape[axis]:
raise ValueError(
'Length of weights not compatible with specified axis.')
# setup wgt to broadcast along axis
wgt = cupy.broadcast_to(wgt, (a.ndim - 1) * (1, ) + wgt.shape)
wgt = wgt.swapaxes(-1, axis)
scl = wgt.sum(axis=axis, dtype=result_dtype)
if cupy.any(scl == 0.0):
raise ZeroDivisionError(
'Weights sum to zero, can\'t be normalized')
avg = cupy.multiply(a, wgt, dtype=result_dtype).sum(axis) / scl
if returned:
if scl.shape != avg.shape:
scl = cupy.broadcast_to(cupy.array(scl), avg.shape).copy()
return avg, scl
else:
return avg
def test_broadcast_to_short_shape_numpy19(self, dtype):
# Note that broadcast_to is only supported on numpy>=1.10
a = testing.shaped_arange((1, 3, 4), cupy, dtype)
with self.assertRaises(ValueError):
cupy.broadcast_to(a, (3, 4))
def duplication(a):
return cupy.concatenate(cupy.broadcast_to(a, (self.fs, a.shape[0], a.shape[1])), axis=1).reshape(a.shape[0]*self.fs, -1)
def cdf(y, x, bw_method='scott', weight=1):
'''
Nadaraya watson conditional probability estimation is a way to estimate the conditional probability of a
random variable y given random variable x in a non-parametric way. It works for both uni-variate and
multi-variate data. It includes automatic bandwidth determination. The estimation works best
for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed.
Parameters
dataset: array_like
Datapoints to estimate from. Currently, it only supports 1-D array.
bw_method:str, scalar or callable, optional
The method used to calculate the estimator bandwidth.
This can be ‘scott’, ‘silverman’, a scalar constant.
If a scalar, this will be used directly as kde.factor.
If None (default), ‘scott’ is used. See Notes for more details.
weights:array_like, optional
weights of datapoints. This must be the same shape as dataset.
If None (default), the samples are assumed to be equally weighted
'''
mempool = cp.get_default_memory_pool()
pinned_mempool = cp.get_default_pinned_memory_pool()
assert (x.ndim == 1) & (y.ndim == 1)
NN = y.size
d = 1
neff = (cp.ones(NN) * weight).sum()
if bw_method == 'scott':
h = neff**(-1. / (d + 4))
elif bw_method == 'silverman':
h = (neff * (d + 2) / 4.)**(-1. / (d + 4))
else:
h = bw_method
x = x.reshape((-1, 1))
x = cp.asarray(x / h, dtype='float32')
y = cp.asarray(y, dtype='float32')
XX = cp.broadcast_to(x, (NN, NN))
XXT = cp.broadcast_to(x.T, (NN, NN))
xx = cp.absolute(XX - XXT)
XX = None
XXT = None
xx2 = cp.copy(xx)
xx[xx2 < 1] = 70 / 81 * (1 - xx[xx < 1]**3)**3
xx[xx2 >= 1] = 0
xx2 = None
y = y.reshape((-1, 1))
yy = y <= y.T
kernel = cp.asarray(weight, dtype='float32')
kernel = cp.broadcast_to(kernel, (NN, NN))
kernel = xx * kernel
weight = kernel / kernel.sum(0, keepdims=True)
cdf = (weight * yy).sum(0, keepdims=True).T
#cv = cp.asnumpy((((yy-cdf)/(1-weight))**2*kk).mean())
weight = None
kernel = None
yy = None
cdf2 = cp.asnumpy(cdf)
cdf = None
mempool.free_all_blocks()
pinned_mempool.free_all_blocks()
return cdf2
def main(id):
model_path = "/efs/fMRI_AE/SimpleFCAE_E32D32/model/model_iter_108858"
gpu = 0
get_device_from_id(gpu).use()
"""NibDataset
def __init__(self, directory: str, crop: list):
"""
crop = [[9, 81], [11, 99], [0, 80]]
test_dataset = NibDataset("/data/test", crop=crop)
mask = load_mask_nib("/data/mask/average_optthr.nii", crop)
"""SimpleFCAE_E32D32
def __init__(self, mask, r: int, in_mask: str, out_mask: str):
"""
model = Model(mask, 2, "mask", "mask")
load_npz(model_path, model)
model.to_gpu()
# feature_idx = 0
# feature_idx = (0, 4, 5, 5) # == [0, 9/2, 11/2, 10/2]
# feature_idx = (0, 1, 1, 1)
feature_idx = (0, 2, 7, 4)
resample_size = 100
batch_size = 10
noise_level = 0.2
for i in range(len(test_dataset)):
if i % 8 != id:
continue
print("{:4}/{:4}".format(i, len(test_dataset)))
subject = test_dataset.get_subject(i)
frame = test_dataset.get_frame(i)
test_img = xp.asarray(test_dataset[i])
resample_remain = resample_size
resample_processed = 0
ret = xp.zeros(test_img.shape)
while resample_remain > 0:
batch_size_this_loop = min(batch_size, resample_remain)
resample_remain -= batch_size_this_loop
batch = xp.broadcast_to(
test_img, chain((batch_size_this_loop, ), test_img.shape))
sigma = noise_level / (xp.max(test_img) - xp.min(test_img))
batch += sigma * xp.random.randn(*batch.shape)
x = Variable(batch)
feature = model.extract(x)
assert feature.shape == (batch_size, 1, 9, 11, 10)
feature = F.sum(feature, axis=0)
assert feature.shape == (1, 9, 11, 10)
feature = F.get_item(feature, feature_idx)
feature.backward()
grad = xp.mean(x.grad, axis=0)
ret = (ret * resample_processed + grad * batch_size_this_loop) / (
resample_processed + batch_size_this_loop)
model.cleargrads()
xp.save(
"/efs/fMRI_AE/SimpleFCAE_E32D32/grad/sensitivity_map_feature_{}_{}_{}_subject{:03d}_frame{:03d}"
.format(feature_idx[1], feature_idx[2], feature_idx[3], subject,
frame), ret)
def choice(self, a, size=None, replace=True, p=None):
"""Returns an array of random values from a given 1-D array.
.. seealso::
:func:`cupy.random.choice` for full document,
:meth:`numpy.random.choice`
"""
if a is None:
raise ValueError('a must be 1-dimensional or an integer')
if isinstance(a, cupy.ndarray) and a.ndim == 0:
raise NotImplementedError
if isinstance(a, six.integer_types):
a_size = a
if a_size <= 0:
raise ValueError('a must be greater than 0')
else:
a = cupy.array(a, copy=False)
if a.ndim != 1:
raise ValueError('a must be 1-dimensional or an integer')
else:
a_size = len(a)
if a_size == 0:
raise ValueError('a must be non-empty')
if p is not None:
p = cupy.array(p)
if p.ndim != 1:
raise ValueError('p must be 1-dimensional')
if len(p) != a_size:
raise ValueError('a and p must have same size')
if not (p >= 0).all():
raise ValueError('probabilities are not non-negative')
p_sum = cupy.sum(p).get()
if not numpy.allclose(p_sum, 1):
raise ValueError('probabilities do not sum to 1')
if not replace:
raise NotImplementedError
if size is None:
raise NotImplementedError
shape = size
size = numpy.prod(shape)
if p is not None:
p = cupy.broadcast_to(p, (size, a_size))
index = cupy.argmax(cupy.log(p) -
cupy.random.gumbel(size=(size, a_size)),
axis=1)
if not isinstance(shape, six.integer_types):
index = cupy.reshape(index, shape)
else:
index = cupy.random.randint(0, a_size, size=shape)
# Align the dtype with NumPy
index = index.astype(cupy.int64, copy=False)
if isinstance(a, six.integer_types):
return index
if index.ndim == 0:
return cupy.array(a[index], dtype=a.dtype)
return a[index]
def make_plasma(steps, cell_size, coarseness=3, fineness=2):
"""
Make coarse plasma initial state arrays and the arrays needed to intepolate
coarse plasma into fine plasma (``virt_params``).
Coarse is the one that will evolve and fine is the one to be bilinearly
interpolated from the coarse one based on the initial positions
(using 1 to 4 coarse plasma particles that initially were the closest).
"""
coarse_step = cell_size * coarseness
# Make two initial grids of plasma particles, coarse and fine.
# Coarse is the one that will evolve and fine is the one to be bilinearly
# interpolated from the coarse one based on the initial positions.
coarse_grid = make_coarse_plasma_grid(steps, cell_size, coarseness)
coarse_grid_xs, coarse_grid_ys = coarse_grid[:, None], coarse_grid[None, :]
fine_grid = make_fine_plasma_grid(steps, cell_size, fineness)
Nc = len(coarse_grid)
# Create plasma electrons on the coarse grid, the ones that really move
coarse_x_init = cp.broadcast_to(cp.asarray(coarse_grid_xs), (Nc, Nc))
coarse_y_init = cp.broadcast_to(cp.asarray(coarse_grid_ys), (Nc, Nc))
coarse_x_offt = cp.zeros((Nc, Nc))
coarse_y_offt = cp.zeros((Nc, Nc))
coarse_px = cp.zeros((Nc, Nc))
coarse_py = cp.zeros((Nc, Nc))
coarse_pz = cp.zeros((Nc, Nc))
coarse_m = cp.ones((Nc, Nc)) * ELECTRON_MASS * coarseness**2
coarse_q = cp.ones((Nc, Nc)) * ELECTRON_CHARGE * coarseness**2
# Calculate indices for coarse -> fine bilinear interpolation
# Neighbour indices array, 1D, same in both x and y direction.
indices = np.searchsorted(coarse_grid, fine_grid)
# example:
# coarse: [-2., -1., 0., 1., 2.]
# fine: [-2.4, -1.8, -1.2, -0.6, 0. , 0.6, 1.2, 1.8, 2.4]
# indices: [ 0 , 1 , 1 , 2 , 2 , 3 , 4 , 4 , 5 ]
# There is no coarse particle with index 5, so clip it to 4:
indices_next = np.clip(indices, 0, Nc - 1) # [0, 1, 1, 2, 2, 3, 4, 4, 4]
# Clip to zero for indices of prev particles as well:
indices_prev = np.clip(indices - 1, 0, Nc - 1) # [0, 0, 0, 1 ... 3, 3, 4]
# mixed from: [ 0&0 , 0&1 , 0&1 , 1&2 , 1&2 , 2&3 , 3&4 , 3&4, 4&4 ]
# Calculate weights for coarse->fine interpolation from initial positions.
# The further the fine particle is from closest right coarse particles,
# the more influence the left ones have.
influence_prev = (coarse_grid[indices_next] - fine_grid) / coarse_step
influence_next = (fine_grid - coarse_grid[indices_prev]) / coarse_step
# Fix for boundary cases of missing cornering particles.
influence_prev[indices_next == 0] = 0 # nothing on the left?
influence_next[indices_next == 0] = 1 # use right
influence_next[indices_prev == Nc - 1] = 0 # nothing on the right?
influence_prev[indices_prev == Nc - 1] = 1 # use left
# Same arrays are used for interpolating in y-direction.
# The virtualization formula is thus
# influence_prev[pi] * influence_prev[pj] * <bottom-left neighbour value> +
# influence_prev[pi] * influence_next[nj] * <top-left neighbour value> +
# influence_next[ni] * influence_prev[pj] * <bottom-right neighbour val> +
# influence_next[ni] * influence_next[nj] * <top-right neighbour value>
# where pi, pj are indices_prev[i], indices_prev[j],
# ni, nj are indices_next[i], indices_next[j] and
# i, j are indices of fine virtual particles
# This is what is employed inside mix() and deposit_kernel().
# An equivalent formula would be
# inf_prev[pi] * (inf_prev[pj] * <bot-left> + inf_next[nj] * <bot-right>) +
# inf_next[ni] * (inf_prev[pj] * <top-left> + inf_next[nj] * <top-right>)
# Values of m, q, px, py, pz should be scaled by 1/(fineness*coarseness)**2
virt_params = GPUArrays(
influence_prev=influence_prev,
influence_next=influence_next,
indices_prev=indices_prev,
indices_next=indices_next,
fine_grid=fine_grid,
)
return (coarse_x_init, coarse_y_init, coarse_x_offt, coarse_y_offt,
coarse_px, coarse_py, coarse_pz, coarse_m, coarse_q, virt_params)
def kde(dataset, bw_method='scott', weight=1):
#
# Representation of a kernel-density estimate using Gaussian kernels.
'''
Nadaraya watson Kernel density estimation is a way to estimate the probability density function (PDF) of a
random variable in a non-parametric way. The code currently only works for uni-variate data. It includes automatic
bandwidth determination. The estimation works best
for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed.
Parameters
dataset: array_like
Datapoints to estimate from. Currently, it only supports 1-D array.
bw_method:str, scalar or callable, optional
The method used to calculate the estimator bandwidth.
This can be ‘scott’, ‘silverman’, a scalar constant.
If a scalar, this will be used directly as kde.factor.
If None (default), ‘scott’ is used. See Notes for more details.
weights:array_like, optional
weights of datapoints. This must be the same shape as dataset.
If None (default), the samples are assumed to be equally weighted
'''
mempool = cp.get_default_memory_pool()
pinned_mempool = cp.get_default_pinned_memory_pool()
assert dataset.ndim == 1
n = dataset.size
neff = (cp.ones(n) * weight).sum()
d = 1
#find band width
if bw_method == 'scott':
h = neff**(-1. / (d + 4))
elif bw_method == 'silverman':
h = (neff * (d + 2) / 4.)**(-1. / (d + 4))
else:
h = bw_method
dataset = cp.asarray(dataset / h, dtype='float32').T
dataset = cp.expand_dims(dataset, 1)
XX = cp.broadcast_to(dataset, (n, n))
XXT = cp.broadcast_to(dataset.T, (n, n))
norm = cp.absolute(XX - XXT)
XX = None
XXT = None
#find k((x-X)/h)
kxx = cp.copy(norm)
kxx[norm < 1] = 70 / 81 * (1 - norm[norm < 1]**3)**3
kxx[norm >= 1] = 0
norm = None
kernel = cp.asarray(weight, dtype='float32')
kernel = cp.broadcast_to(kernel, (n, n))
kernel = kxx * kernel
kde = kernel.mean(0, keepdims=False) / h
mempool.free_all_blocks()
pinned_mempool.free_all_blocks()
return kde
def diff(a, n=1, axis=-1, prepend=None, append=None):
"""Calculate the n-th discrete difference along the given axis.
Args:
a (cupy.ndarray): Input array.
n (int): The number of times values are differenced. If zero, the input
is returned as-is.
axis (int): The axis along which the difference is taken, default is
the last axis.
prepend (int, float, cupy.ndarray): Value to prepend to ``a``.
append (int, float, cupy.ndarray): Value to append to ``a``.
Returns:
cupy.ndarray: The result array.
.. seealso:: :func:`numpy.diff`
"""
if n == 0:
return a
if n < 0:
raise ValueError(
"order must be non-negative but got " + repr(n))
a = cupy.asanyarray(a)
nd = a.ndim
combined = []
if prepend is not None:
prepend = cupy.asanyarray(prepend)
if prepend.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
prepend = cupy.broadcast_to(prepend, tuple(shape))
combined.append(prepend)
combined.append(a)
if append is not None:
append = cupy.asanyarray(append)
if append.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
append = cupy.broadcast_to(append, tuple(shape))
combined.append(append)
if len(combined) > 1:
a = cupy.concatenate(combined, axis)
slice1 = [slice(None)] * nd
slice2 = [slice(None)] * nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
slice1 = tuple(slice1)
slice2 = tuple(slice2)
op = cupy.not_equal if a.dtype == numpy.bool_ else cupy.subtract
for _ in range(n):
a = op(a[slice1], a[slice2])
return a
def kernel_smoothing_ecdf_weighted(y,
x,
dampmin=1e-30,
maxit=500,
lam=0,
bw_method='scott',
weight=1):
mempool = cp.get_default_memory_pool()
pinned_mempool = cp.get_default_pinned_memory_pool()
assert (x.ndim == 1) & (y.ndim == 1)
NN = y.size
d = 1
neff = (cp.ones(NN) * weight).sum()
if bw_method == 'scott':
h = neff**(-1. / (d + 4))
elif bw_method == 'silverman':
h = (neff * (d + 2) / 4.)**(-1. / (d + 4))
else:
h = bw_method
NN = x.size
x = x.reshape((-1, 1))
x = cp.asarray(x / h, dtype='float32')
y = cp.asarray(y, dtype='float32')
XX = cp.broadcast_to(x, (NN, NN))
XXT = cp.broadcast_to(x.T, (NN, NN))
xx = XX - XXT
XX = None
XXT = None
#print(mempool.used_bytes())
kxx = cp.absolute(xx, dtype='float32')
kxx[kxx < 1] = 70 / 81 * (1 - kxx[kxx < 1]**3)**3
kxx[cp.absolute(xx, dtype='float32') >= 1] = 0
xx = xx * kxx
kernel = cp.asarray(weight, dtype='float32') #weight
kernel = cp.broadcast_to(kernel, (NN, NN))
#Levenberg Marquardt
whileii = 0
#lam = -1/(xx.max(0)+xx.max(0).mean())/2
lam = cp.zeros(xx.shape[0], dtype='float32') #-1/(xx.max(0))/2
max_change = 1
residual_rhs = 1e10
damp = 1e-2
# Levenberg Marquardt method of finding better weighting for adjusted Nadaraya waston
while ((max_change > 2e-100) |
(residual_rhs > 1e-100)) & (whileii < maxit):
whileii = whileii + 1
lam2 = cp.broadcast_to(lam, (NN, NN))
dpt_constraint = cp.asarray(xx / (1 + lam2 * xx), dtype='float64')
lam2 = None
ddpt_constraint = -dpt_constraint**2
ddpt_constraint = (kernel * ddpt_constraint).sum(0)
dpt_constraint = (kernel * dpt_constraint).sum(0)
residual_rhs_old = residual_rhs
residual_rhs = cp.absolute(dpt_constraint).mean() #calculate residual
change = dpt_constraint * ddpt_constraint / (ddpt_constraint**2 + damp)
max_change = cp.absolute(change).max()
dpt_constraint = None
ddpt_constraint = None
'''
lam2 = cp.broadcast_to(lam,(NN,NN))
lam2 = cp.logical_not(((1+lam2*xx)>=0).prod(0))
#lam2 = None
lam[lam2] = lam[lam2]/100
if cp.any(lam>0):
lam[lam>0] = -cp.random.rand(int((lam>0).sum()))/(xx[:,lam>0].max(0))
#lam = cp.maximum(-1/(xx+1e-4),lam)
#obj = cp.log(1+lam*xx+1e-4).sum()
'''
if (residual_rhs_old >= residual_rhs):
lam = lam - change
if ((whileii % 20) == 0):
print(max_change, ' ', residual_rhs, ' ', damp, ' ', lam.max(),
lam.min(), ' any NA ',
cp.isnan(change).any())
if (damp > dampmin): damp = damp / 2
change = None
elif (residual_rhs_old < residual_rhs):
damp = damp * 4
residual_rhs = None
p = 1 / (1 + lam * xx) * kernel
p = cp.asarray(p, dtype='float64')
p = p / p.sum(0)
if cp.any(p < -1e-3):
print(
'kernel smoothing weighting is not converging in finding outlier, should be all positive'
)
p[p < 0] = 0
p = p / p.sum(0)
kernel = cp.asarray(kxx * p, dtype='float32')
print(lam.max(), lam.min(), p.max(), p.min())
print('this should be zero. actual residual:',
cp.absolute((xx * p).sum(0)).max())
print(
'sum of probability should be 1, so this should be 0. Actual residual:',
cp.absolute(sum(p) - 1).mean())
xx = None
lam = None
kxx = cp.asarray(kxx * p, dtype='float32')
#xx2 =None
p = None
kernel = kxx * kernel
kernel_de = cp.broadcast_to(kernel.sum(0, keepdims=True), (NN, NN))
y = y.reshape((-1, 1))
yy = y <= y.T
weight = kernel / kernel_de
cdf = (weight * yy).sum(0, keepdims=True).T
#cv = cp.asnumpy((((yy-cdf)/(1-weight))**2*kk).mean())
weight = None
kernel = None
yy = None
cdf2 = cp.asnumpy(cdf)
cdf = None
mempool.free_all_blocks()
pinned_mempool.free_all_blocks()
return cdf2
def SetGeometry(self):
if self.verbosity >= 1:
print("Prepating the domain data (shape,metric,...)")
eikonal = self.kernel_data['eikonal']
policy = eikonal.policy
# Domain shape and grid scale
self.shape = tuple(
self.GetValue(
'dims',
help="dimensions (shape) of the computational domain").astype(int))
self.periodic_default = (
False, False, True) if self.isCurvature else (False, ) * self.ndim
self.periodic = self.GetValue(
'periodic',
default=self.periodic_default,
help="Apply periodic boundary conditions on some axes")
self.shape_o = tuple(misc.round_up(self.shape, self.shape_i))
if policy.bound_active_blocks is True:
policy.bound_active_blocks = 12 * np.prod(self.shape_o) / np.max(
self.shape_o)
# Set the discretization gridScale(s)
if self.isCurvature:
self.h_base = self.GetValue(
'gridScale',
array_float=True,
help="Scale of the physical (not angular) grid.")
self.h_per = self.caster(2. * np.pi / self.shape[2])
self.h = self.caster((self.h_base, self.h_base, self.h_per))
elif self.HasValue('gridScale') or self.isCurvature:
self.h = cp.broadcast_to(
self.GetValue('gridScale',
array_float=True,
help="Scale of the computational grid"),
(self.ndim, ))
else:
self.h = self.GetValue(
'gridScales',
array_float=True,
help="Axis independent scales of the computational grid")
self.h_broadcasted = fd.as_field(self.h, self.shape, depth=1)
# Get the metric
if self.model_ == 'Diagonal': metricClass = Metrics.Diagonal
elif self.model_ == 'Riemann': metricClass = Metrics.Riemann
elif self.model_ == 'Rander': metricClass = Metrics.Rander
elif self.model_ == 'TTI': metricClass = Metrics.Seismic.Reduced
if self.model_ == 'Isotropic':
self._metric = Metrics.Diagonal(cp.ones(self.ndim, dtype=self.float_t))
self._dualMetric = None
elif self.isCurvature:
pass
else:
self._metric = self.GetValue('metric',
default=None,
verbosity=3,
help="Metric of the minimal path model")
self._dualMetric = self.GetValue(
'dualMetric',
default=None,
verbosity=3,
help="Dual metric of the minimal path model")
for key, value in (('_metric', self._metric), ('_dualMetric',
self._dualMetric)):
if ad.cupy_generic.isndarray(value):
setattr(self, key, metricClass.from_HFM(value))
self.drift = self.GetValue(
'drift',
default=None,
verbosity=3,
array_float=True,
help=
"Drift introduced in the eikonal equation, becoming F^*(grad u - drift)=1"
)
# Set the geometry
if self.isCurvature:
# Geometry defined using the xi, kappa and theta parameters
self.xi = self.GetValue(
'xi',
array_float=True,
help="Cost of rotation for the curvature penalized models")
self.kappa = self.GetValue(
'kappa',
default=0.,
array_float=True,
help="Rotation bias for the curvature penalized models")
self.theta = self.GetValue(
'theta',
default=0.,
verbosity=3,
array_float=True,
help=
"Deviation from horizontality, for the curvature penalized models")
# Scale h_base is taken care of through the 'cost' field
h_ratio = self.h_per / self.h_base
self.xi *= h_ratio
self.kappa /= h_ratio
# Large arrays are passed as geometry data, and scalar entries as module constants
geom = []
def is_var(e):
return isinstance(e, cp.ndarray) and e.ndim > 0
traits = eikonal.traits
traits['xi_var_macro'] = int(is_var(self.xi))
traits['kappa_var_macro'] = int(is_var(self.kappa))
traits['theta_var_macro'] = int(is_var(self.theta))
if not is_var(self.theta): traits['nTheta'] = self.shape[2]
if all(traits[e] == 0 for e in ('xi_var_macro', 'kappa_var_macro',
'theta_var_macro')):
traits['precomputed_scheme_macro'] = 1
geom = [
e for e in (1. / self.xi, self.kappa, np.cos(self.theta),
np.sin(self.theta)) if is_var(e)
]
if len(geom) > 0: self.geom = ad.array(geom)
else: self.geom = cp.zeros((0, ) + self.shape, dtype=self.float_t)
else:
if self._metric is not None:
self._metric = self._metric.with_costs(self.h)
if self._dualMetric is not None:
self._dualMetric = self._dualMetric.with_speeds(self.h)
if self.drift is not None: self.drift *= self.h_broadcasted
if self.model_ == 'Isotropic':
# No geometry field. Metric passed as a module constant
self.geom = cp.array(0., dtype=self.float_t)
elif self.model_ == 'Diagonal':
self.geom = self.dualMetric.costs**2
elif self.model_ == 'Riemann':
self.geom = self.dualMetric.flatten()
elif self.model_ == 'Rander':
self.geom = Metrics.Riemann(self.metric.m).dual().flatten()
if self.drift is None: self.drift = self.float_t(0.)
self.drift += self.metric.w
elif self.model_ == 'TTI':
self.geom = self.metric.flatten(transposed_transformation=True)
eikonal.args['geom'] = misc.block_expand(fd.as_field(
self.geom, self.shape),
self.shape_i,
mode='constant',
constant_values=np.inf,
contiguous=True)
if self.drift is not None:
eikonal.args['drift'] = misc.block_expand(fd.as_field(
self.drift, self.shape),
self.shape_i,
mode='constant',
constant_values=np.nan,
contiguous=True)
# geometrical data related with geodesics
self.exportGeodesicFlow = self.GetValue(
'exportGeodesicFlow',
default=False,
help="Export the upwind geodesic flow (direction of the geodesics)")
self.tips = self.GetValue(
'tips',
default=None,
array_float=True,
help="Tips from which to compute the minimal geodesics")
if self.isCurvature:
self.unorientedTips = self.GetValue(
'unorientedTips',
default=None,
array_float=True,
help="Compute a geodesic from the most favorable orientation")
self.hasTips = (self.tips is not None
or (self.isCurvature and self.unorientedTips is not None))
# Cost function
if self.HasValue('speed'):
self.cost = 1. / self.GetValue(
'speed',
array_float=True,
help="speed = 1/cost (scales the metric, accepts AD)")
else:
self.cost = self.GetValue(
'cost',
array_float=True,
default=None,
help="cost = 1/speed (scales the metric, accepts AD)")
if self.cost is None:
self.cost = cp.ones(self.shape, dtype=self.float_t)
if not ad.is_ad(self.cost):
costVariation = self.GetValue(
'costVariation',
default=None,
help="First order variation of the cost function")
if costVariation is not None:
self.cost = ad.Dense.new(self.cost, costVariation)
if self.isCurvature: self.cost *= self.h_base
self.cost = np.broadcast_to(self.cost, self.shape)
# Cost related parameters
if self.HasValue('atol') and self.HasValue('rtol'): tol = None
else:
tol = self.GetValue(
'tol',
default="_Dummy",
array_float=True,
help=
"Convergence tolerance for the fixed point solver (determines atol, rtol)"
)
float_resolution = np.finfo(self.float_t).resolution
if isinstance(tol, str) and tol == "_Dummy":
cost_bound = ad.remove_ad(self.cost)
if not self.isCurvature:
cost_bound = cost_bound * self.metric.cost_bound()
mean_cost_bound = np.mean(cost_bound)
tol = mean_cost_bound * float_resolution * 5.
self.hfmOut['keys']['default']['tol'] = self.float_t(float(tol))
policy.atol = self.GetValue(
'atol',
default=tol,
array_float=True,
help="Absolute convergence tolerance for the fixed point solver")
rtol_default = 0. if policy.multiprecision else float_resolution * 5.
policy.rtol = self.GetValue(
'rtol',
default=rtol_default,
array_float=True,
help="Relative convergence tolerance for the fixed point solver")
if policy.bound_active_blocks:
policy.minChg_delta_min = self.GetValue(
'minChg_delta_min',
default=float(np.min(self.h)) / 10.,
help="Minimal threshold increase with bound_active_blocks method")
# Walls
walls = self.GetValue('walls',
default=None,
help='Obstacles in the domain')
if walls is not None:
wallDist_t = np.uint8
wallDistBound = self.GetValue(
'wallsDistBound',
default=10,
help="Bound on the computed distance to the obstacles.\n"
"(Ideally a sharp upper bound on the stencil width.)")
wallDistMax_t = np.iinfo(wallDist_t).max
wallDist = cp.full(self.shape, wallDistMax_t, dtype=wallDist_t)
wallDist[walls] = 0
l1Kernel = inf_convolution.distance_kernel(1,
self.ndim,
dtype=wallDist_t,
ord=1)
wallDist = inf_convolution.inf_convolution(wallDist,
l1Kernel,
niter=wallDistBound,
periodic=self.periodic,
overwrite=True)
# This value indicates 'far from wall', and visibility computation is bypassed
wallDist[wallDist > wallsDistBound] = wallDistMax_t
self.wallDist = wallDist
eikonal.args['wallDist'] = misc.block_expand(
wallDist,
self.shape_i,
mode='constant',
constant_values=np.iinfo(wallDist_t).max)
def roll(a, shift, axis=None):
"""Roll array elements along a given axis.
Elements that roll beyond the last position are re-introduced at the first.
Args:
a (~cupy.ndarray): Array to be rolled.
shift (int or tuple of int): The number of places by which elements are
shifted. If a tuple, then `axis` must be a tuple of the same size,
and each of the given axes is shifted by the corresponding number.
If an int while `axis` is a tuple of ints, then the same value is
used for all given axes.
axis (int or tuple of int or None): The axis along which elements are
shifted. By default, the array is flattened before shifting, after
which the original shape is restored.
Returns:
~cupy.ndarray: Output array.
.. seealso:: :func:`numpy.roll`
"""
if axis is None:
return roll(a.ravel(), shift, 0).reshape(a.shape)
elif isinstance(shift, cupy.ndarray):
shift = shift.ravel()
axes = _reduction._get_axis(axis, a.ndim)[0]
n_axes = max(len(axes), shift.size)
axes = numpy.broadcast_to(axes, (n_axes,))
shift = cupy.broadcast_to(shift, (n_axes,))
# TODO(asi1024): Improve after issue #4799 is resolved.
indices = []
for ax in range(a.ndim):
ind_shape = [1] * a.ndim
ind_shape[ax] = a.shape[ax]
indices.append(cupy.arange(a.shape[ax]).reshape(ind_shape))
for ax, s in zip(axes, shift):
indices[ax] -= s
indices[ax] %= a.shape[ax]
for ax in range(a.ndim):
indices[ax] = cupy.broadcast_to(indices[ax], a.shape)
return a[tuple(indices)]
else:
axis = _reduction._get_axis(axis, a.ndim)[0]
broadcasted = numpy.broadcast(shift, axis)
if broadcasted.nd > 1:
raise ValueError(
'\'shift\' and \'axis\' should be scalars or 1D sequences')
shifts = {ax: 0 for ax in range(a.ndim)}
for sh, ax in broadcasted:
shifts[ax] += sh
rolls = [((slice(None), slice(None)),)] * a.ndim
for ax, offset in shifts.items():
offset %= a.shape[ax] or 1 # If `a` is empty, nothing matters.
if offset:
# (original, result), (original, result)
rolls[ax] = ((slice(None, -offset), slice(offset, None)),
(slice(-offset, None), slice(None, offset)))
result = cupy.empty_like(a)
for indices in itertools.product(*rolls):
arr_index, res_index = zip(*indices)
result[res_index] = a[arr_index]
return result
def choice(self, a, size=None, replace=True, p=None):
"""Returns an array of random values from a given 1-D array.
.. seealso::
:func:`cupy.random.choice` for full document,
:meth:`numpy.random.choice`
"""
if a is None:
raise ValueError('a must be 1-dimensional or an integer')
if isinstance(a, cupy.ndarray) and a.ndim == 0:
raise NotImplementedError
if isinstance(a, six.integer_types):
a_size = a
if a_size <= 0:
raise ValueError('a must be greater than 0')
else:
a = cupy.array(a, copy=False)
if a.ndim != 1:
raise ValueError('a must be 1-dimensional or an integer')
else:
a_size = len(a)
if a_size == 0:
raise ValueError('a must be non-empty')
if p is not None:
p = cupy.array(p)
if p.ndim != 1:
raise ValueError('p must be 1-dimensional')
if len(p) != a_size:
raise ValueError('a and p must have same size')
if not (p >= 0).all():
raise ValueError('probabilities are not non-negative')
p_sum = cupy.sum(p).get()
if not numpy.allclose(p_sum, 1):
raise ValueError('probabilities do not sum to 1')
if size is None:
raise NotImplementedError
shape = size
size = numpy.prod(shape)
if not replace and p is None:
if a_size < size:
raise ValueError(
'Cannot take a larger sample than population when '
'\'replace=False\'')
if isinstance(a, six.integer_types):
indices = cupy.arange(a, dtype='l')
else:
indices = a.copy()
self.shuffle(indices)
return indices[:size].reshape(shape)
if not replace:
raise NotImplementedError
if p is not None:
p = cupy.broadcast_to(p, (size, a_size))
index = cupy.argmax(cupy.log(p) +
cupy.random.gumbel(size=(size, a_size)),
axis=1)
if not isinstance(shape, six.integer_types):
index = cupy.reshape(index, shape)
else:
index = cupy.random.randint(0, a_size, size=shape)
# Align the dtype with NumPy
index = index.astype(cupy.int64, copy=False)
if isinstance(a, six.integer_types):
return index
if index.ndim == 0:
return cupy.array(a[index], dtype=a.dtype)
return a[index]
def SetGeometry(self):
if self.verbosity >= 1:
print("Preparing the domain data (shape,metric,...)")
eikonal = self.kernel_data['eikonal']
policy = eikonal.policy
# These options allow to delete the metric and dual metric, when they are converted
self._metric_delete_dual = False
self._CostMetric_delete_dual = False
self._metric = None
self._dualMetric = None
self._CostMetric = None
# Domain shape and grid scale
self.shape = self.hfmIn.shape
if self.isCurvature and self.ndim_phys == 2:
periodic_default = (False, False, True)
else:
periodic_default = (False, ) * self.ndim
self.periodic = self.GetValue(
'periodic',
default=periodic_default,
help="Apply periodic boundary conditions on some axes")
self.shape_o = tuple(fd.round_up_ratio(self.shape, self.shape_i))
if policy.bound_active_blocks is True:
policy.bound_active_blocks = 12 * np.prod(self.shape_o) / np.max(
self.shape_o)
# Set the discretization gridScale(s)
if self.isCurvature and self.ndim_phys == 2:
self.h_base = self.GetValue(
'gridScale',
array_float=tuple(),
help="Scale of the physical (not angular) grid.")
self.h_per = self.hfmIn.Axes()[2][
1] #self.caster(2.*np.pi / self.shape[2] )
self.h = self.caster((self.h_base, self.h_base, self.h_per))
elif self.HasValue('gridScale'):
self.h = cp.broadcast_to(
self.GetValue('gridScale',
array_float=tuple(),
help="Scale of the computational grid"),
(self.ndim, ))
else:
self.h = self.GetValue(
'gridScales',
array_float=(self.ndim, ),
help="Axis independent scales of the computational grid")
if self.isCurvature:
if self.ndim_phys == 3:
self.h_base = self.h[0]
self.h_per = self.h[3]
h_ratio = self.h_per / self.h_base
if policy.multiprecision:
# Choose power of two, significantly less than h
hmin = float(np.min(self.h))
self.multip_step = 2.**np.floor(np.log2(hmin / 10))
self.multip_max = np.iinfo(self.int_t).max * self.multip_step / 2
self.h_broadcasted = fd.as_field(self.h, self.shape, depth=1)
# Get the metric
if self.model_ == 'Diagonal': metricClass = Metrics.Diagonal
elif self.model_ == 'Riemann': metricClass = Metrics.Riemann
elif self.model_ == 'Rander': metricClass = Metrics.Rander
elif self.model_ == 'TTI': metricClass = Metrics.Seismic.TTI
elif self.model_ == 'AsymmetricQuadratic': metricClass = Metrics.AsymQuad
if self.model_ == 'Isotropic':
self._metric = Metrics.Diagonal(cp.ones(self.ndim, dtype=self.float_t))
self._dualMetric = None
elif self.isCurvature:
pass
else:
self._metric = self.GetValue('metric',
default=None,
verbosity=3,
help="Metric of the minimal path model")
self._dualMetric = self.GetValue(
'dualMetric',
default=None,
verbosity=3,
help="Dual metric of the minimal path model")
for key, value in (('_metric', self._metric), ('_dualMetric',
self._dualMetric)):
if ad.cupy_generic.isndarray(value):
setattr(self, key, metricClass.from_HFM(value))
# Set the geometry
if self.isCurvature and self.ndim_phys == 2:
# Geometry defined using the xi, kappa and theta parameters
xi = self.GetValue(
'xi',
array_float=True,
help="Cost of rotation for the curvature penalized models")
kappa = self.GetValue(
'kappa',
default=0.,
array_float=True,
help="Rotation bias for the curvature penalized models")
self.theta = self.GetValue(
'theta',
default=0.,
verbosity=3,
array_float=True,
help=
"Deviation from horizontality, for the curvature penalized models")
# Scale h_base is taken care of through the 'cost' field
self.ixi = 1 / (xi * h_ratio)
self.kappa = kappa / h_ratio
# Large arrays are passed as geometry data, and scalar entries as module constants
geom = []
traits = eikonal.traits
traits['xi_var_macro'] = self.ixi.ndim > 0
traits['kappa_var_macro'] = self.kappa.ndim > 0
traits['theta_var_macro'] = self.theta.ndim > 0
if self.theta.ndim == 0: traits['nTheta'] = self.shape[2]
if self.ixi.ndim > 0: self.ixi = self.as_field(self.ixi, 'xi')
if self.kappa.ndim > 0: self.kappa = self.as_field(self.kappa, 'kappa')
if self.theta.ndim > 0: self.theta = self.as_field(self.theta, 'theta')
geom = [
e for e in (self.ixi, self.kappa, np.cos(self.theta),
np.sin(self.theta)) if e.ndim > 0
]
if len(geom) > 0: self.geom = ad.array(geom)
else: self.geom = cp.zeros((0, self.shape[2]), dtype=self.float_t)
elif self.isCurvature and self.ndim_phys == 3:
# No geometry field. Metric is built in
self.geom = cp.zeros((0, *self.shape[3:]), dtype=self.float_t) # Dummy
self.ixi = 1 / (h_ratio * self.GetValue(
'xi',
array_float=True,
help="Cost of rotation for the curvature penalized models"))
self.sphere_radius = self.h_per * self.shape[-1]
if self.shape[-1] == self.shape[-2]: self.separation_radius = None
else:
self.separation_radius = self.h_per * (self.shape[-2] / 2 -
self.shape[-1])
traits = eikonal.traits
traits['sphere_macro'] = self.separation_radius is not None
traits['dual_macro'] = self.GetValue(
'dual', default=False, help="Use the Reeds-Shepp dual model")
if traits['forward_macro'] and (traits['dual_macro']
or not traits['sphere_macro']):
raise ValueError("Incompatible traits for the Reeds-Shepp model.")
else:
if self._metric is not None:
self._metric = self._metric.with_costs(self.h)
if self._dualMetric is not None:
self._dualMetric = self._dualMetric.with_speeds(self.h)
# if self.drift is not None: self.drift *= self.h_broadcasted
if self.model_ == 'Isotropic':
# No geometry field. Metric passed as a module constant
self.geom = cp.array(0., dtype=self.float_t)
elif self.model_ == 'Diagonal':
self.geom = self.dualMetric.costs**2
elif self.model_ == 'Riemann':
self.geom = self.dualMetric.flatten()
elif self.model_ == 'Rander':
self.geom = self.metric.flatten(inverse_m=True)
elif self.model_ == 'TTI':
self.geom = self.metric.flatten(transposed_transformation=True)
elif self.model_ == 'AsymmetricQuadratic':
self.geom = self.dualMetric.flatten(solve_w=True)
elif self.model_ == 'SubRiemann':
pruning_metric = self.GetValue(
'pruning_metric',
default=None,
help="""Finite difference offset is discarded """
"""if this norm exceeds the Euclidean norm.""")
if pruning_metric is None:
pruning_eps = self.GetValue(
'pruning_eps',
default=None,
help=
"""Approximation of the Riemannian relaxation parameter,"""
""" used for pruning the finite difference offsets.""")
rho = np.sqrt(
lp.trace(self.dualMetric.m) /
self.dualMetric.vdim) * pruning_eps
pruning_metric = self.metric.with_cost(rho)
self.geom = np.stack(
[self.dualMetric.flatten(),
pruning_metric.flatten()], axis=0)
eikonal.traits['SubRiemann_Pruning_macro'] = 1
else:
raise ValueError("Unrecognized model")
# Dual metric is useless now, except for generating the primal one
if self._metric is not None:
self._dualMetric = (None, "Deleted in SetGeometry")
self._metric_delete_dual = True
# Check wether the geometry only depends on a subset of the coordinates
geom_shape = self.geom.shape[1:]
self.geom_indep = len(self.shape) - len(geom_shape)
eikonal.traits['geom_indep_macro'] = self.geom_indep
if geom_shape != self.shape[self.geom_indep:]:
raise ValueError(
"Inconsistent dimensions for geometry data. "
"It should match (the last coordinates of) domain shape.")
if self.isCurvature: assert self.geom_indep <= self.ndim_phys
block_geom = fd.block_expand(self.geom,
self.shape_i[self.geom_indep:],
mode='constant',
constant_values=np.inf)
if not eikonal.traits[
'geom_first_macro'] and block_geom.ndim == 2 * self.ndim + 1:
block_geom = np.moveaxis(block_geom, 0, -1)
eikonal.args['geom'] = cp.ascontiguousarray(block_geom)
block_geom = None
self.geom = (None, "Deleted in SetGeometry")
precompute_excluded_schemes = (
'Isotropic',
'Diagonal', # Precomputation is useless, since stencil is trivial
'AsymmetricQuadratic',
'Rander', # TODO (?) precomputation does not handle drift yet
'TTI' # TODO (?) precomputation does not handle adaptive mix_is_min yet
)
if self.model_ in precompute_excluded_schemes:
self.precompute_scheme = False
else:
self.precompute_scheme = self.GetValue(
'precompute_scheme',
default=self.geom_indep > 0,
help="Precompute and store the finite difference scheme stencils")
# geometrical data related with geodesics
self.exportGeodesicFlow = self.GetValue(
'exportGeodesicFlow',
default=False,
help="Export the upwind geodesic flow (direction of the geodesics)")
self.tips = self.GetValue(
'tips',
default=None,
array_float=(-1, self.ndim),
help="Tips from which to compute the minimal geodesics")
if self.isCurvature:
self.tips_Unoriented = self.GetValue(
'tips_Unoriented',
default=None,
array_float=(-1, self.ndim_phys),
help="Compute a geodesic from the most favorable orientation")
self.hasTips = (self.tips is not None
or (self.isCurvature and self.tips_Unoriented is not None))
# Cost function
if self.HasValue('speed'):
self.cost = 1. / self.GetValue(
'speed',
array_float=True,
help="speed = 1/cost (scales the metric, accepts AD)")
else:
self.cost = self.GetValue(
'cost',
array_float=True,
default=None,
help="cost = 1/speed (scales the metric, accepts AD)")
if self.cost is None:
self.cost = cp.ones(self.shape, dtype=self.float_t)
if not ad.is_ad(self.cost):
costVariation = self.GetValue(
'costVariation',
default=None,
help="First order variation of the cost function")
if costVariation is not None:
self.cost = ad.Dense.new(self.cost, costVariation)
if self.isCurvature: self.cost = self.cost * self.h_base
self.cost = self.as_field(self.cost, 'cost')
# Cost related parameters
if self.HasValue('atol') and self.HasValue('rtol'): tol = None
else:
tol = self.GetValue(
'tol',
default="_Dummy",
array_float=tuple(),
help=
"Convergence tolerance for the fixed point solver (determines atol, rtol)"
)
float_resolution = np.finfo(self.float_t).resolution
if isinstance(tol, str) and tol == "_Dummy":
cost_bound = ad.remove_ad(self.cost)
if not self.isCurvature:
cost_bound = cost_bound * self.metric.cost_bound()
mean_cost_bound = np.nanmean(cost_bound)
tol = mean_cost_bound * float_resolution * 5.
self.hfmOut['keys']['default']['tol'] = self.float_t(float(tol))
policy.atol = self.GetValue(
'atol',
default=tol,
array_float=tuple(),
help="Absolute convergence tolerance for the fixed point solver")
rtol_default = 0. if policy.multiprecision else float_resolution * 5.
policy.rtol = self.GetValue(
'rtol',
default=rtol_default,
array_float=tuple(),
help="Relative convergence tolerance for the fixed point solver")
if policy.bound_active_blocks:
policy.minChg_delta_min = self.GetValue(
'minChg_delta_min',
default=float(np.min(self.h)) / 10.,
help="Minimal threshold increase with bound_active_blocks method")
self._CostMetric_delete_metric = not self.drift_model # Metric will not be needed anymore
# Walls
walls = self.GetValue('walls',
default=None,
help='Obstacles in the domain')
if walls is not None:
if self.isCurvature: walls = self.as_field(walls, 'walls')
wallDist_t = np.uint8
wallDistBound = self.GetValue(
'wallDistBound',
default=10,
help="Bound on the computed distance to the obstacles.\n"
"(Ideally a sharp upper bound on the stencil width.)")
wallDistMax_t = np.iinfo(wallDist_t).max
wallDist = cp.full(self.shape, wallDistMax_t, dtype=wallDist_t)
wallDist[walls] = 0
l1Kernel = inf_convolution.distance_kernel(1,
self.ndim,
dtype=wallDist_t,
ord=1)
wallDist = inf_convolution.inf_convolution(
wallDist,
l1Kernel,
niter=wallDistBound,
periodic=self.periodic,
overwrite=True,
upper_saturation=wallDistMax_t)
# This value indicates 'far from wall', and visibility computation is bypassed
wallDist[wallDist > wallDistBound] = wallDistMax_t
self.wallDist = wallDist
eikonal.args['wallDist'] = cp.ascontiguousarray(
fd.block_expand(wallDist,
self.shape_i,
mode='constant',
constant_values=wallDistMax_t))
self.walls = walls
