def _triu(x, k=0):
m, n = x.shape
u = cupy.arange(m).reshape(m, 1)
v = cupy.arange(n).reshape(1, n)
mask = v - u >= k
x *= mask
return x
def test_cupy_to_chainerx_contiguous():
dtype = numpy.float32
a_cupy = cupy.arange(6, dtype=dtype).reshape((2, 3))
a_cupy_refcount_before = sys.getrefcount(a_cupy)
a_chx = _fromrawpointer(
a_cupy.data.mem.ptr,
a_cupy.shape,
a_cupy.dtype,
a_cupy.strides,
'cuda:0',
0,
a_cupy)
assert sys.getrefcount(a_cupy) == a_cupy_refcount_before + 1
assert a_chx.device.name == 'cuda:0'
chainerx.testing.assert_array_equal_ex(a_chx, a_cupy.get())
# Write to a_cupy
a_cupy[0, 1] = 8
chainerx.testing.assert_array_equal_ex(
a_chx, numpy.array([[0, 8, 2], [3, 4, 5]], dtype))
# Write to a_chx
a_chx += 1
chainerx.testing.assert_array_equal_ex(
a_cupy.get(), numpy.array([[1, 9, 3], [4, 5, 6]], dtype))
def test_getitem_int(self):
x = cupy.arange(24).reshape((2, 3, 4)).astype('i')
y = cupy.empty_like(x)
y = cupy.ElementwiseKernel(
'raw T x', 'int32 y', 'y = x[i]', 'test_carray_getitem_int',
)(x, y)
testing.assert_array_equal(y, x)
def test_cupy_to_chainerx_noncontiguous_with_offset():
dtype = numpy.float32
a_cupy = cupy.arange(12, dtype=dtype).reshape((2, 6))[::-1, ::2]
offset = a_cupy.data.ptr - a_cupy.data.mem.ptr
# test preconditions
assert offset > 0
assert not a_cupy.flags.c_contiguous
a_chx = _fromrawpointer(
a_cupy.data.mem.ptr,
a_cupy.shape,
a_cupy.dtype,
a_cupy.strides,
'cuda:0',
offset,
a_cupy)
assert a_chx.strides == a_cupy.strides
chainerx.testing.assert_array_equal_ex(
a_chx, a_cupy.get(), strides_check=False)
a_cupy[1, 1] = 53
assert a_chx.strides == a_cupy.strides
chainerx.testing.assert_array_equal_ex(
a_chx, a_cupy.get(), strides_check=False)
def test_cupy_to_chainerx_noncontiguous_without_offset():
# This test includes access to address before the given pointer (because of
# a negative stride).
dtype = numpy.float32
a_cupy = cupy.arange(12, dtype=dtype).reshape((2, 6))[::-1, ::2]
# test preconditons
assert a_cupy.data.mem.ptr < a_cupy.data.ptr
assert not a_cupy.flags.c_contiguous
a_chx = _fromrawpointer(
a_cupy.data.ptr,
a_cupy.shape,
a_cupy.dtype,
a_cupy.strides,
'cuda:0',
0,
a_cupy)
assert a_chx.strides == a_cupy.strides
chainerx.testing.assert_array_equal_ex(
a_chx, a_cupy.get(), strides_check=False)
a_cupy[1, 1] = 53
assert a_chx.strides == a_cupy.strides
chainerx.testing.assert_array_equal_ex(
a_chx, a_cupy.get(), strides_check=False)
def test_strides(self):
x = cupy.arange(6).reshape((2, 3)).astype('i')
y = cupy.ElementwiseKernel(
'raw int32 x', 'int32 y', 'y = x.strides()[i]',
'test_carray_strides',
)(x, size=2)
testing.assert_array_equal(y, (12, 4))
def test_getitem_idx(self):
x = cupy.arange(24).reshape((2, 3, 4)).astype('i')
y = cupy.empty_like(x)
y = cupy.ElementwiseKernel(
'raw T x', 'int32 y',
'int idx[] = {i / 12, i / 4 % 3, i % 4}; y = x[idx]',
'test_carray_getitem_idx',
)(x, y)
testing.assert_array_equal(y, x)
def test_scan(self, dtype):
element_num = 10000
if dtype in {cupy.int8, cupy.uint8}:
element_num = 100
a = cupy.ones((element_num,), dtype=dtype)
prefix_sum = cupy.core.core.scan(a)
expect = cupy.arange(start=1, stop=element_num + 1).astype(dtype)
testing.assert_array_equal(prefix_sum, expect)
def test_cupy_to_chainerx_invalid_device():
dtype = numpy.float32
with cupy.cuda.Device(1):
a_cupy = cupy.arange(6, dtype=dtype).reshape((2, 3))
with pytest.raises(chainerx.ChainerxError):
_fromrawpointer(
a_cupy.data.mem.ptr,
a_cupy.shape,
a_cupy.dtype,
a_cupy.strides,
'cuda:0',
0,
a_cupy)
def test_scan_out(self, dtype):
element_num = 10000
if dtype in {cupy.int8, cupy.uint8, cupy.float16}:
element_num = 100
a = cupy.ones((element_num,), dtype=dtype)
b = cupy.zeros_like(a)
cupy.core.core.scan(a, b)
expect = cupy.arange(start=1, stop=element_num + 1).astype(dtype)
testing.assert_array_equal(b, expect)
cupy.core.core.scan(a, a)
testing.assert_array_equal(a, expect)
def test_cupy_to_chainerx_nondefault_device():
dtype = numpy.float32
with cupy.cuda.Device(1):
a_cupy = cupy.arange(6, dtype=dtype).reshape((2, 3))
a_chx = _fromrawpointer(
a_cupy.data.mem.ptr,
a_cupy.shape,
a_cupy.dtype,
a_cupy.strides,
'cuda:1',
0,
a_cupy)
assert a_chx.device.name == 'cuda:1'
chainerx.testing.assert_array_equal_ex(a_chx, a_cupy.get())
def test_reshape_contiguity(self):
a = cupy.arange(6).reshape(2, 3)
self.assertTrue(a.flags.c_contiguous)
self.assertFalse(a.flags.f_contiguous)
a = a.reshape(1, 6, 1)
self.assertTrue(a.flags.c_contiguous)
self.assertTrue(a.flags.f_contiguous)
b = a.T.reshape(1, 6, 1)
self.assertTrue(b.flags.c_contiguous)
self.assertTrue(b.flags.f_contiguous)
b = a.T.reshape(2, 3)
self.assertTrue(b.flags.c_contiguous)
self.assertFalse(b.flags.f_contiguous)
def test_cupy_to_chainerx_delete_chainerx_first():
dtype = numpy.float32
a_cupy = cupy.arange(6, dtype=dtype).reshape((2, 3))
a_chx = _fromrawpointer(
a_cupy.data.mem.ptr,
a_cupy.shape,
a_cupy.dtype,
a_cupy.strides,
'cuda:0',
0,
a_cupy)
del a_chx
a_cupy += 1
chainerx.testing.assert_array_equal_ex(
a_cupy.get(), numpy.array([[1, 2, 3], [4, 5, 6]], dtype))
def fliplr(a):
"""Flip array in the left/right direction.
Flip the entries in each row in the left/right direction. Columns
are preserved, but appear in a different order than before.
Args:
a (~cupy.ndarray): Input array.
Returns:
~cupy.ndarray: Output array.
.. seealso:: :func:`numpy.fliplr`
"""
if a.ndim < 2:
raise ValueError('Input must be >= 2-d')
return cupy.take(a, cupy.arange(a.shape[1] - 1, -1, -1), axis=1)
def flipud(a):
"""Flip array in the up/down direction.
Flip the entries in each column in the up/down direction. Rows are
preserved, but appear in a different order than before.
Args:
a (~cupy.ndarray): Input array.
Returns:
~cupy.ndarray: Output array.
.. seealso:: :func:`numpy.flipud`
"""
if a.ndim < 1:
raise ValueError('Input must be >= 1-d')
return cupy.take(a, cupy.arange(a.shape[0] - 1, -1, -1), axis=0)
def forward_gpu(self, inputs):
a, b = inputs
c = cp.zeros_like(a, 'float32')
chainer.cuda.elementwise(
'int32 j, raw T a, raw T b',
'raw T c',
'''
float* ap = &a[j * 3];
float* bp = &b[j * 3];
float* cp = &c[j * 3];
cp[0] = ap[1] * bp[2] - ap[2] * bp[1];
cp[1] = ap[2] * bp[0] - ap[0] * bp[2];
cp[2] = ap[0] * bp[1] - ap[1] * bp[0];
''',
'function',
)(
cp.arange(a.size / 3).astype('int32'), a, b, c,
)
return c,
def _non_maximum_suppression_gpu(bbox, thresh, score=None, limit=None):
if len(bbox) == 0:
return cp.zeros((0,), dtype=np.int32)
n_bbox = bbox.shape[0]
if score is not None:
order = score.argsort()[::-1].astype(np.int32)
else:
order = cp.arange(n_bbox, dtype=np.int32)
sorted_bbox = bbox[order, :]
selec, n_selec = _call_nms_kernel(
sorted_bbox, thresh)
selec = selec[:n_selec]
selec = order[selec]
if limit is not None:
selec = selec[:limit]
return selec
def take_along_axis(a, indices, axis):
"""Take values from the input array by matching 1d index and data slices.
Args:
a (cupy.ndarray): Array to extract elements.
indices (cupy.ndarray): Indices to take along each 1d slice of ``a``.
axis (int): The axis to take 1d slices along.
Returns:
cupy.ndarray: The indexed result.
.. seealso:: :func:`numpy.take_along_axis`
"""
if indices.dtype.kind not in ('i', 'u'):
raise IndexError('`indices` must be an integer array')
if axis is None:
a = a.ravel()
axis = 0
ndim = a.ndim
axis = internal._normalize_axis_index(axis, ndim)
if ndim != indices.ndim:
raise ValueError(
'`indices` and `a` must have the same number of dimensions')
fancy_index = []
for i, n in enumerate(a.shape):
if i == axis:
fancy_index.append(indices)
else:
ind_shape = (1,) * i + (-1,) + (1,) * (ndim - i - 1)
fancy_index.append(cupy.arange(n).reshape(ind_shape))
return a[tuple(fancy_index)]
def _bincount_histogram(image, source_range):
"""
Efficient histogram calculation for an image of integers.
This function is significantly more efficient than cupy.histogram but
works only on images of integers. It is based on cupy.bincount.
Parameters
----------
image : array
Input image.
source_range : string
'image' determines the range from the input image.
'dtype' determines the range from the expected range of the images
of that data type.
Returns
-------
hist : array
The values of the histogram.
bin_centers : array
The values at the center of the bins.
"""
if source_range not in ['image', 'dtype']:
raise ValueError('Incorrect value for `source_range` argument: '
f'{source_range}')
if source_range == 'image':
image_min = int(image.min().astype(np.int64))
image_max = int(image.max().astype(np.int64))
elif source_range == 'dtype':
image_min, image_max = dtype_limits(image, clip_negative=False)
image, offset = _offset_array(image, image_min, image_max)
hist = cp.bincount(image.ravel(), minlength=image_max - image_min + 1)
bin_centers = cp.arange(image_min, image_max + 1)
if source_range == 'image':
idx = max(image_min, 0)
hist = hist[idx:]
return hist, bin_centers
def __init__(
self,
dataset,
cat_names,
cont_names,
label_names,
batch_size,
shuffle,
seed_fn=None,
parts_per_chunk=1,
device=None,
global_size=None,
global_rank=None,
drop_last=False,
):
self.data = dataset
self.indices = cp.arange(dataset.to_ddf().npartitions)
self.drop_last = drop_last
self.device = device or 0
self.global_size = global_size or 1
self.global_rank = global_rank or 0
self.cat_names = cat_names or []
self.cont_names = cont_names or []
self.label_names = label_names
self.batch_size = batch_size
self.shuffle = shuffle
self.seed_fn = seed_fn
self.num_rows_processed = 0
# we set size of chunk queue to 1 we only want one chunk in queue at a time.
self._buff = ChunkQueue(self, 1, num_parts=parts_per_chunk, shuffle=shuffle)
# run once instead of everytime len called
self._buff_len = len(self._buff)
self._batch_itr = None
self._workers = None
def find(self, data):
ret = cp.ascontiguousarray(cp.zeros(data.shape[:-1], 'int32')) - 1
data = cp.ascontiguousarray(data)
loop_indices = cp.arange(data.size / self.dim).astype('int32')
chainer.cuda.elementwise(
'int32 j, raw int32 data, raw int32 indices, raw int32 values, raw int32 ret',
'',
string.Template('''
/* */
int* value = &data[j * ${dim}];
/* compute initial key */
unsigned int key = 0;
for (int k = 0; k < ${dim}; k++) key = (key + value[k]) * ${hash_factor};
key = key % ${table_size};
while (1) {
if (indices[key] < 0) {
ret[j] = -1;
break;
}
bool match = true;
for (int k = 0; k < ${dim}; k++) if (values[key * ${dim} + k] != value[k]) match = false;
if (match) {
ret[j] = indices[key];
break;
} else {
key = (key + 1) % ${table_size};
}
}
''').substitute(
table_size=self.table_size,
hash_factor=self.hash_factor,
dim=self.dim,
),
'function',
)(loop_indices, data, self.indices, self.values, ret)
return ret
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
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, 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)
logdet = cupy.array(float('-inf'), dtype)
return sign, logdet
def indices(dimensions, dtype=int):
"""Returns an array representing the indices of a grid.
Computes an array where the subarrays contain index values 0,1,...
varying only along the corresponding axis.
Args:
dimensions: The shape of the grid.
dtype: Data type specifier. It is int by default.
Returns:
ndarray:
The array of grid indices,
``grid.shape = (len(dimensions),) + tuple(dimensions)``.
Examples
--------
>>> grid = cupy.indices((2, 3))
>>> grid.shape
(2, 2, 3)
>>> grid[0] # row indices
array([[0, 0, 0],
[1, 1, 1]])
>>> grid[1] # column indices
array([[0, 1, 2],
[0, 1, 2]])
.. seealso:: :func:`numpy.indices`
"""
dimensions = tuple(dimensions)
N = len(dimensions)
shape = (1, ) * N
res = cupy.empty((N, ) + dimensions, dtype=dtype)
for i, dim in enumerate(dimensions):
res[i] = cupy.arange(dim, dtype=dtype).reshape(shape[:i] + (dim, ) +
shape[i + 1:])
return res
def select_groups(labels, groups_order_subset='all'):
"""Get subset of groups in adata.obs[key].
"""
adata_obs_key = labels
groups_order = labels.cat.categories
groups_masks = cp.zeros(
(len(labels.cat.categories), len(labels.cat.codes)), dtype=bool)
for iname, name in enumerate(labels.cat.categories):
# if the name is not found, fallback to index retrieval
if labels.cat.categories[iname] in labels.cat.codes:
mask = labels.cat.categories[iname] == labels.cat.codes
else:
mask = iname == labels.cat.codes
groups_masks[iname] = mask.values
groups_ids = list(range(len(groups_order)))
if groups_order_subset != 'all':
groups_ids = []
for name in groups_order_subset:
groups_ids.append(
cp.where(
cp.array(labels.cat.categories.to_array().astype("int32"))
== int(name))[0][0])
if len(groups_ids) == 0:
# fallback to index retrieval
groups_ids = cp.where(
cp.in1d(
cp.arange(len(labels.cat.categories)).astype(str),
cp.array(groups_order_subset),
))[0]
groups_ids = [groups_id.item() for groups_id in groups_ids]
groups_masks = groups_masks[groups_ids]
groups_order_subset = labels.cat.categories[groups_ids].to_array(
).astype(int)
else:
groups_order_subset = groups_order.to_array()
return groups_order_subset, groups_masks
def __call__(self, input_ids, input_mask, token_type_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights,
next_sentence_labels):
sequence_output, pooled_output = self.bert.get_sequence_output_and_pooled_output(
input_ids,
input_mask,
token_type_ids)
# parameterを直接取得してmatmulしてもbpされるか
embedding_table = self.bert.get_embedding_table()
"""Gathers the vectors at the specific positions over a minibatch."""
batch_size, seq_length, width = sequence_output.shape
flat_offsets = np.reshape(np.arange(0, batch_size, dtype=np.int32) * seq_length, [-1, 1])
flat_positions = np.reshape(masked_lm_positions + flat_offsets, [-1])
flat_sequence_output = np.reshape(sequence_output,
[batch_size * seq_length, width])
x = flat_sequence_output[[flat_positions]]
"""Get loss for the masked LM."""
normed = self.layer_norm(self.activate(self.masked_lm_dense(x)))
masked_lm_logits = F.matmul(normed, embedding_table.T) + self.mask_bias
label_ids = F.reshape(masked_lm_ids, [-1])
masked_lm_loss = F.softmax_cross_entropy(masked_lm_logits, label_ids, ignore_label=0)
chainer.report({'masked_lm_loss': masked_lm_loss}, self)
chainer.report({'masked_lm_accuracy': F.accuracy(masked_lm_logits, label_ids)}, self)
"""Get loss for the next sentence."""
next_sentence_logits = F.matmul(pooled_output, self.next_sentence_weights.T) + self.next_sentence_bias
labels = F.reshape(next_sentence_labels, [-1])
next_sentence_loss = F.softmax_cross_entropy(next_sentence_logits, labels)
chainer.report({'next_sentence_loss': next_sentence_loss}, self)
chainer.report({'next_sentence_accuracy': F.accuracy(next_sentence_logits, labels)}, self)
loss = masked_lm_loss + next_sentence_loss
chainer.report({'loss': loss}, self)
return loss
def step(self, state, momentum, rng, **args):
q = state.copy()
p = self.draw_momentum(rng)
q_new = deepcopy(q)
p_new = deepcopy(p)
epsilon = self.step_size
path_length = cp.ceil(2 * cp.random.rand() * self.path_length /
epsilon)
grad_q = self.model.grad(q, **args)
# SG-HMC leapfrog step
for _ in cp.arange(path_length - 1):
for var in self.start.keys():
dim = (cp.array(q_new[var])).size
rvar = rng.normal(0, 2 * epsilon, dim).reshape(q[var].shape)
q_new[var] += epsilon * p_new[var]
grad_q = self.model.grad(q_new, **args)
p_new[var] = (
1 - epsilon) * p_new[var] + epsilon * grad_q[var] + rvar
acceptprob = self.accept(q, q_new, p, p_new, **args)
if cp.isfinite(acceptprob) and (cp.random.rand() < acceptprob):
q = q_new.copy()
p = p_new.copy()
return q, p, acceptprob
def do_nadam(self, X, Y, update, learning_rate, **kwargs):
layers = len(self.structure) - 1
grads = self.calculate_grads(X, Y, kwargs["l2_reg_param"])
for ii in cp.arange(1, layers + 1):
update["mw" + str(ii)] = kwargs["beta1"] * update.get(
"mw" + str(ii), 0) + (1 - kwargs["beta1"]) * cp.sum(
grads["w" + str(ii)], axis=0)
update["mb" + str(ii)] = kwargs["beta1"] * update.get(
"mb" + str(ii), 0) + (1 - kwargs["beta1"]) * cp.sum(
grads["b" + str(ii)], axis=1).reshape(-1, 1)
update["vw" + str(ii)] = kwargs["beta2"] * update.get(
"vw" + str(ii), 0) + (1 - kwargs["beta2"]) * cp.square(
cp.sum(grads["w" + str(ii)], axis=0))
update["vb" + str(ii)] = kwargs["beta2"] * update.get(
"vb" + str(ii), 0) + (1 - kwargs["beta2"]) * cp.square(
cp.sum(grads["b" + str(ii)], axis=1).reshape(-1, 1))
self.params["w"+str(ii)] -= cp.multiply( (learning_rate / cp.sqrt(kwargs["epsilon"] + (update["vw"+str(ii)]/(1-kwargs["beta2"]**kwargs["step_num"])))),\
kwargs["beta1"]*(update["mw"+str(ii)]/(1-kwargs["beta1"]**kwargs["step_num"]) +\
((1-kwargs["beta1"])/(1-kwargs["beta1"]**kwargs["step_num"]))*cp.sum(grads["w"+str(ii)],axis=0) ))
self.params["b"+str(ii)] -= cp.multiply( (learning_rate / cp.sqrt(kwargs["epsilon"] + (update["vb"+str(ii)]/(1-kwargs["beta2"]**kwargs["step_num"])))),\
kwargs["beta1"]*(update["mb"+str(ii)]/(1-kwargs["beta1"]**kwargs["step_num"]) +\
((1-kwargs["beta1"])/(1-kwargs["beta1"]**kwargs["step_num"]))*cp.sum(grads["b"+str(ii)],axis=1).reshape(-1,1) ))
return update
def test_cupy_to_chainerx_noncontiguous_with_offset():
dtype = numpy.float32
a_cupy = cupy.arange(12, dtype=dtype).reshape((2, 6))[::-1, ::2]
offset = a_cupy.data.ptr - a_cupy.data.mem.ptr
# test preconditions
assert offset > 0
assert not a_cupy.flags.c_contiguous
a_chx = _fromrawpointer(a_cupy.data.mem.ptr, a_cupy.shape, a_cupy.dtype,
a_cupy.strides, 'cuda:0', offset, a_cupy)
assert a_chx.strides == a_cupy.strides
chainerx.testing.assert_array_equal_ex(a_chx,
a_cupy.get(),
strides_check=False)
a_cupy[1, 1] = 53
assert a_chx.strides == a_cupy.strides
chainerx.testing.assert_array_equal_ex(a_chx,
a_cupy.get(),
strides_check=False)
def test_view_as_windows_2D():
A = cp.arange(5 * 4).reshape(5, 4)
window_shape = (4, 3)
B = view_as_windows(A, window_shape)
assert B.shape == (2, 2, 4, 3)
# fmt: off
cp.testing.assert_array_equal(
B, cp.array([[[[0, 1, 2],
[4, 5, 6],
[8, 9, 10],
[12, 13, 14]],
[[1, 2, 3],
[5, 6, 7],
[9, 10, 11],
[13, 14, 15]]],
[[[4, 5, 6],
[8, 9, 10],
[12, 13, 14],
[16, 17, 18]],
[[5, 6, 7],
[9, 10, 11],
[13, 14, 15],
[17, 18, 19]]]]))
def forward(self, x, t):
if x.ndim == 2: # ミニバッチ使用時
x = x - x.max(axis=1, keepdims=True)
x = cp.exp(x)
y = x / x.sum(axis=1, keepdims=True)
elif x.ndim == 1:
x = x - cp.max(x)
y = cp.exp(x) / cp.sum(cp.exp(x))
if y.ndim == 1:
t = t.reshape(1, t.size)
y = y.reshape(1, y.size)
# 教師ラベルがone-hotベクトルの場合、正解のインデックスに変換
if t.size == y.size:
t = t.argmax(axis=1)
batch_size = y.shape[0]
loss = -1.0 * cp.sum(
t * cp.log(y[cp.arange(batch_size), t] + 1e-7)) / batch_size
self.y = y
self.t = t
return loss
def inverse_transform(self, y: cudf.Series) -> cudf.Series:
"""
Revert ordinal label to original label
Parameters
----------
y : cudf.Series, dtype=int32
Ordinal labels to be reverted
Returns
-------
reverted : cudf.Series
Reverted labels
"""
# check LabelEncoder is fitted
self._check_is_fitted()
# check input type is cudf.Series
if not isinstance(y, cudf.Series):
raise TypeError(
'Input of type {} is not cudf.Series'.format(type(y)))
# check if ord_label out of bound
ord_label = y.unique()
category_num = len(self.classes_)
if self.handle_unknown == 'error':
for ordi in ord_label.values_host:
if ordi < 0 or ordi >= category_num:
raise ValueError(
'y contains previously unseen label {}'.format(ordi))
y = y.astype(self.dtype)
ran_idx = cudf.Series(cp.arange(len(self.classes_))).astype(self.dtype)
reverted = y._column.find_and_replace(ran_idx, self.classes_, False)
return cudf.Series(reverted)
def ds(kx, ky, qx, qy, om, d):
topkq = -complex(0, 1) * V0 * ((kx + qx) - complex(0, 1) * (ky + qy))
botkq = complex(0, 1) * V0 * ((kx + qx) + complex(0, 1) * (ky + qy))
innkq = om + complex(0, 1) * Gamm - A * ((kx + qx)**2 + (ky + qy)**2) - V2
topk = -complex(0, 1) * V0 * (kx - complex(0, 1) * ky)
botk = complex(0, 1) * V0 * (kx + complex(0, 1) * ky)
innk = om + complex(0, 1) * Gamm - A * (kx**2 + ky**2) - V2
cent = cp.arange(-(N - 1) / 2, (N - 1) / 2 + 1, 1)
d = hOmg * cp.diag(cent, dtype=cp.float32)
return d
Ginkq = np.eye(N, N, k=1) * topkq + np.eye(
N, N, k=-1) * botkq + innkq * np.eye(N, N) - d
Gink = np.eye(N, N, k=1) * topk + np.eye(
N, N, k=-1) * botk + innk * np.eye(N, N) - d
Grkq = np.linalg.inv(Ginkq)
Gakq = np.transpose(np.conj(Grkq))
Grk = np.linalg.inv(Gink)
Gak = np.transpose(np.conj(Grk))
fer = np.heaviside(-(d + np.eye(N, N) * (om - mu)), 0)
in1 = np.matmul(Grkq, np.matmul(Grk, np.matmul(fer, Gak)))
in2 = np.matmul(Grkq, np.matmul(fer, np.matmul(Gakq, Gak)))
tr = np.trace(in1 + in2)
# HERE i will divide by DOS, multiply by 2 for spin, and divide by (2pi)^3
dchi = -(4) * Gamm * tr / math.pi**2
return dchi
def inverse_transform(self, y: cudf.Series) -> cudf.Series:
"""
Revert ordinal label to original label
Parameters
----------
y : cudf.Series, pandas.Series, cupy.ndarray or numpy.ndarray
dtype=int32
Ordinal labels to be reverted
Returns
-------
reverted : the same type as y
Reverted labels
"""
# check LabelEncoder is fitted
self._check_is_fitted()
# check input type is cudf.Series
y = self._to_cudf_series(y)
# check if ord_label out of bound
ord_label = y.unique()
category_num = len(self.classes_)
if self.handle_unknown == 'error':
for ordi in ord_label.values_host:
if ordi < 0 or ordi >= category_num:
raise ValueError(
'y contains previously unseen label {}'.format(ordi))
y = y.astype(self.dtype)
ran_idx = cudf.Series(cp.arange(len(self.classes_))).astype(self.dtype)
reverted = y._column.find_and_replace(ran_idx, self.classes_, False)
res = cudf.Series(reverted)
return res
def test_mask():
vol = cp.zeros((30, 30, 30))
vol[15, 15, 15] = 1
struct = generate_binary_structure(3, 1)
# TODO: remove brute_force=True once non-brute force implemented for CuPy
voln = binary_dilation(vol,
structure=struct,
iterations=4,
brute_force=True).astype("f4")
initial = cp.sum(voln > 0)
mask = voln.copy()
thresh = otsu(mask)
mask = mask > thresh
initial_otsu = cp.sum(mask > 0)
assert_array_equal(initial_otsu, initial)
mins, maxs = bounding_box(mask)
voln_crop = crop(mask, mins, maxs)
initial_crop = cp.sum(voln_crop > 0)
assert_array_equal(initial_crop, initial)
applymask(voln, mask)
final = cp.sum(voln > 0)
assert_array_equal(final, initial)
# Test multi_median.
img = cp.arange(25).reshape(5, 5)
img_copy = img.copy()
medianradius = 2
median_test = multi_median(img, medianradius, 3)
assert_array_equal(img, img_copy)
medarr = ((medianradius * 2) + 1, ) * img.ndim
median_control = median_filter(img, medarr)
median_control = median_filter(median_control, medarr)
median_control = median_filter(median_control, medarr)
assert_array_equal(median_test, median_control)
def _run_cupy_natural_break(data, num_sample, k):
num_data = data.size
if num_sample is not None and num_sample < num_data:
generator = cupy.random.RandomState(1234567890)
idx = [i for i in range(0, data.size)]
generator.shuffle(idx)
sample_idx = idx[:num_sample]
sample_data = data.flatten()[sample_idx]
else:
sample_data = data.flatten()
# warning if number of total data points to fit the model bigger than 40k
if sample_data.size >= 40000:
warnings.warn(
'natural_breaks Warning: Natural break classification '
'(Jenks) has a complexity of O(n^2), '
'your classification with {} data points may take '
'a long time.'.format(sample_data.size), Warning)
uv = cupy.unique(sample_data)
uvk = len(uv)
if uvk < k:
warnings.warn(
'natural_breaks Warning: Not enough unique values '
'in data array for {} classes. '
'n_samples={} should be >= n_clusters={}. '
'Using k={} instead.'.format(k, uvk, k, uvk), Warning)
uv.sort()
bins = uv
else:
centroids = _run_cupy_jenks(sample_data, k)
bins = cupy.array(centroids[1:])
out = _bin(data, bins, cupy.arange(uvk))
return out
def transform(self, columns: ColumnNames,
gdf: cudf.DataFrame) -> cudf.DataFrame:
# Add temporary column for sorting
tmp = "__tmp__"
gdf[tmp] = cupy.arange(len(gdf), dtype="int32")
fit_folds = self.kfold > 1
if fit_folds:
gdf[self.fold_name] = _add_fold(gdf.index, self.kfold,
self.fold_seed)
# Need mean of contiuous target column
y_mean = self.target_mean or self.means
# Loop over categorical-column groups and apply logic
new_gdf = None
for ind, cat_group in enumerate(columns):
if isinstance(cat_group, tuple):
cat_group = list(cat_group)
elif isinstance(cat_group, str):
cat_group = [cat_group]
if new_gdf is None:
new_gdf = self._op_group_logic(cat_group, gdf, y_mean,
fit_folds, ind)
else:
_df = self._op_group_logic(cat_group, gdf, y_mean, fit_folds,
ind)
new_gdf = cudf.concat([new_gdf, _df], axis=1)
# Drop temporary columns
gdf.drop(columns=[tmp, "__fold__"]
if fit_folds and self.drop_folds else [tmp],
inplace=True)
if fit_folds and not self.drop_folds:
new_gdf[self.fold_name] = gdf[self.fold_name]
return new_gdf
def apply_boxcar_drift(data, metadata):
""" Apply boxcar filter to compensate for doppler smearing
An optimal boxcar is applied per row of drift rate. This retrieves
a sensitivity increase of sqrt(boxcar_size) for a smeared signal.
(Stil down a sqrt(boxcar_size) compared to no smearing case).
Args:
data (np or cp array):
metadata (dict): Dictionary of metadata values
Returns:
data, metadata (array and dict): Data array with filter applied.
"""
logger.debug(
f"apply_boxcar_drift: Applying moving average based on drift rate.")
metadata = deepcopy(metadata)
# Compute drift rates from metadata
dr0, ddr = metadata['drift_rate_start'].value, metadata[
'drift_rate_step'].value
df = metadata['frequency_step'].to('Hz').value
dt = metadata['integration_time'].to('s').value
drates = dr0 + ddr * cp.arange(data.shape[0])
# Compute smearing (array of n_channels smeared for given driftrate)
smearing_nchan = cp.abs(dt * drates / df).astype('int32')
smearing_nchan_max = cp.asnumpy(cp.max(smearing_nchan))
# Apply boxcar filter to compensate for smearing
for boxcar_size in range(2, smearing_nchan_max + 1):
idxs = cp.where(smearing_nchan == boxcar_size)
# 1. uniform_filter1d computes mean. We want sum, so *= boxcar_size
# 2. we want noise to stay the same, so divide by sqrt(boxcar_size)
# combined 1 and 2 give aa sqrt(2) factor
data[idxs] = uniform_filter1d(data[idxs], size=boxcar_size,
axis=2) * np.sqrt(boxcar_size)
return data, metadata
def _major_slice(self, idx, copy=False):
"""Index along the major axis where idx is a slice object.
"""
if idx == slice(None):
return self.copy() if copy else self
M, N = self._swap(*self.shape)
start, stop, step = idx.indices(M)
M = len(range(start, stop, step))
new_shape = self._swap(M, N)
if M == 0:
return self.__class__(new_shape)
row_nnz = cupy.diff(self.indptr)
idx_dtype = self.indices.dtype
res_indptr = cupy.zeros(M + 1, dtype=idx_dtype)
cupy.cumsum(row_nnz[idx], out=res_indptr[1:])
if step == 1:
idx_start = self.indptr[start]
idx_stop = self.indptr[stop]
res_indices = cupy.array(self.indices[idx_start:idx_stop],
copy=copy)
res_data = cupy.array(self.data[idx_start:idx_stop], copy=copy)
else:
rows = cupy.arange(start,
start + (res_indptr.size - 1) * step,
step,
dtype=res_indptr.dtype)
res_indices, res_data = _index._csr_row_index(
rows, self.indptr, self.indices, self.data, res_indptr)
return self.__class__((res_data, res_indices, res_indptr),
shape=new_shape,
copy=False)
def top_k(array, k, axis=0, biggest=True):
""" Return the topK index along the specified dimension,
The returned indices are such that their array values are sorted
-Input:
array: 1d or 2d array
k: the top `k` (k>0, integer)
axis: futile if array is 1d, otherwise sorting along the specified axis
default to 0
biggest: whether the top-k biggest or smallest, default to True
-Output:
inds: indices
vals: array values at the indices
"""
assert array.ndim == 1 or array.ndim == 2
assert axis == 0 or axis == 1
if biggest:
array = -array
if array.ndim == 1:
inds = xp.argpartition(array, k)[:k]
vals = array[inds]
sort_inds = xp.argsort(vals)
inds = inds[sort_inds]
vals = vals[sort_inds]
elif axis == 0:
inds = xp.argpartition(array, k, axis=0)[:k, :]
vals = array[inds, xp.arange(array.shape[1])[None, :]]
sort_inds = xp.argsort(vals, axis=0)
inds = inds[sort_inds, xp.arange(array.shape[1])[None, :]]
vals = vals[sort_inds, xp.arange(array.shape[1])[None, :]]
else:
inds = xp.argpartition(array, k, axis=1)[:, :k]
vals = array[xp.arange(array.shape[0])[:, None], inds]
sort_inds = xp.argsort(vals, axis=1)
inds = inds[xp.arange(array.shape[0])[:, None], sort_inds]
vals = vals[xp.arange(array.shape[0])[:, None], sort_inds]
if biggest:
vals = -vals
return inds, vals
def get_session_id_from_session_boundry(session_change_df, last_session_len):
"""
This function returns session starts given a session change df
"""
import cudf
## we dont really need the `session_id` to start from 0
## the total number of sessions per partition should be fairly limited
## and we really should not hit 2,147,483,647 sessions per partition
## Can switch to vec_arange code to match spark 1-1
## see previously commited code
## https://github.com/rapidsai/tpcx-bb/blob/8394f2b8d62540b4077c606c8b687dee96b4f5d3/tpcx-bb1.3.1/tools/sessionization.py
user_session_ids = cp.arange(len(session_change_df), dtype=np.int32)
### up shift the session length df
session_len = session_change_df["t_index"].diff().reset_index(drop=True)
session_len = session_len.shift(-1)
session_len.iloc[-1] = last_session_len
session_id_final_series = (
cudf.Series(user_session_ids).repeat(session_len).reset_index(
drop=True))
return session_id_final_series
def _encode(name, path, gdf, cat_cache, na_sentinel=-1, freq_threshold=0):
value = None
if path:
if cat_cache is not None:
cat_cache = cat_cache.get(name, "disk")
cache = _get_cache()
if cache:
value = cache.get_categories(name, path, cache=cat_cache)
else:
value = cudf.io.read_parquet(path, index=False, columns=[name])
value.index.name = "labels"
value.reset_index(drop=False, inplace=True)
vals = gdf[name].copy(deep=False)
if value is None:
value = cudf.DataFrame({name: [None]})
value[name] = value[name].astype(vals.dtype)
value.index.name = "labels"
value.reset_index(drop=False, inplace=True)
if freq_threshold > 0:
codes = cudf.DataFrame({
name: vals.copy(),
"order": cp.arange(len(vals))
})
codes = codes.merge(value, on=name,
how="left").sort_values("order")["labels"]
codes.fillna(na_sentinel, inplace=True)
return codes.values
else:
# Use `searchsorted` if we are using a "full" encoding
labels = value[name].searchsorted(vals,
side="left",
na_position="first")
labels[labels >= len(value[name])] = na_sentinel
return labels
def create_tokenized_df(str_series, delimiter=" ", ngram_range=(1, 1)):
"""
creates a tokenized df from a string column
where each row is like [token,doc_id],
token = 'string' and doc_id = index from which the word came
Also returns a empty doc_id series
"""
import cudf
token_count_sr = str_series.str.token_count(delimiter=delimiter)
doc_id_ar = cp.arange(start=0, stop=len(str_series), dtype=np.int32)
doc_id_sr = cudf.Series(doc_id_ar)
tokenized_df_ls = []
for n in range(ngram_range[0], ngram_range[1] + 1):
ngram_ser = get_ngram(str_series, n, doc_id_sr, token_count_sr,
delimiter)
tokenized_df_ls.append(ngram_ser)
tokenized_df = cudf.concat(tokenized_df_ls)
tokenized_df = tokenized_df.reset_index(drop=True)
empty_doc_ids = doc_id_sr[doc_id_sr[token_count_sr == 0]]
return tokenized_df, empty_doc_ids
def forward_all_cells(self):
""" move all agents in map one time step forward """
agents_durations = self.durations[
cp.arange(0, self.durations.shape[0]),
self.current_state_ids].flatten()
print(
f'DEBUG: agents_durations.shape: {agents_durations.shape}, self.durations.shape: {self.durations.shape}, self.current_state_ids.shape: {self.current_state_ids.shape}'
)
to_transit = (self.current_state_durations == agents_durations)
self.current_state_durations += 1
to_transit = self.agent_ids[to_transit]
self.transit_states(to_transit)
# Contamination at home by end of the period
self.contaminate(self.agent_ids, self.home_cell_ids)
# Update r and associated variables
r = self.n_infected_period / self.n_diseased_period if self.n_diseased_period > 0 else 0
r = cp.array([r])
if self.verbose > 1:
print(f'period {self.current_period}: r={r}')
self.r_factors = append(self.r_factors, r)
self.n_diseased_period = self.get_n_diseased()
self.n_infected_period = 0
#Move one period forward
self.current_period += 1
def get_predicted_traces(matrix_U, matrix_W, sorting_result, time_limits):
W = cp.asarray(matrix_W, dtype=np.float32)
U = cp.asarray(matrix_U, dtype=np.float32)
buffer = W.shape[0]
predicted_traces = cp.zeros(
(U.shape[0], 4 * buffer + (time_limits[1] - time_limits[0])),
dtype=np.int16)
sorting_result = cp.asarray(sorting_result)
all_spike_times = sorting_result[:, 0]
included_spike_pos = cp.asarray(
(time_limits[0] - buffer // 2 < all_spike_times)
& (all_spike_times < time_limits[1] + buffer // 2)).nonzero()[0]
spike_times = all_spike_times[included_spike_pos].astype(np.int32)
spike_templates = sorting_result[included_spike_pos, 1].astype(np.int32)
spike_amplitudes = sorting_result[included_spike_pos, 2]
for s, spike in enumerate(spike_times):
amplitude = spike_amplitudes[s]
U_i = U[:, spike_templates[s], :]
W_i = W[:, spike_templates[s], :]
addendum = cp.ascontiguousarray(cp.matmul(U_i, W_i.T) * amplitude,
dtype=np.int16)
pred_pos = cp.arange(
buffer) + spike - time_limits[0] + buffer + buffer // 2
predicted_traces[:, pred_pos] += addendum
output = predicted_traces[:, buffer * 2:-buffer * 2]
return cp.asnumpy(output).T
def get_session_id(df):
"""
This function creates a session id column for each click
The session id grows in incremeant for each user's susbequent session
Session boundry is defined by the time_out
"""
df["user_change_flag"] = df["wcs_user_sk"].diff(periods=1) != 0
df["session_change_flag"] = df["review_flag"] | df["user_change_flag"]
df = df.reset_index(drop=True)
df["t_index"] = cp.arange(start=0, stop=len(df), dtype=np.int32)
session_change_df = df[df["session_change_flag"]].reset_index(drop=True)
try:
last_session_len = len(df) - session_change_df["t_index"].iloc[-1]
except (AssertionError, IndexError) as e: # IndexError in numba >= 0.48
last_session_len = 0
session_ids = get_session_id_from_session_boundary(session_change_df,
last_session_len)
assert len(session_ids) == len(df)
return session_ids
def create_test_dataset():
cp = np # cpu mode only here
s1 = np.load(test_path.joinpath('my_conv2_input.npy'))
s0 = np.copy(s1)
tmax = np.ceil(4 * sig)
dt = cp.arange(-tmax, tmax + 1)
gauss = cp.exp(-dt**2 / (2 * sig**2))
gauss = (gauss / cp.sum(gauss)).astype(np.float32)
cNorm = lfilter_cpu(gauss, 1., np.r_[np.ones(s1.shape[0]),
np.zeros(int(tmax))])
cNorm = cNorm[int(tmax):]
s1 = lfilter_cpu(gauss,
1,
np.r_[s1, np.zeros((int(tmax), s1.shape[1]))],
axis=0)
s1 = s1[int(tmax):] / cNorm[:, np.newaxis]
# import matplotlib.pyplot as plt
# plt.plot(s0)
# plt.plot(s1)
# np.save(test_path.joinpath('my_conv2_input.npy'), s0)
np.save(test_path.joinpath('my_conv2_output.npy'), s1)
def test_texture_input(self):
width, height, depth = self.dimensions
dim = 3 if depth != 0 else 2 if height != 0 else 1
texobj = self._prep_texture()
ker = getattr(self, f'_prep_kernel{dim}D')()
# prepare input
args = [None, texobj]
size = width
if height > 0:
size *= height
args.append(width)
if depth > 0:
size *= depth
args.append(height)
in_arr = cupy.arange(size, dtype=cupy.float32)
in_arr = in_arr.reshape(self.shape)
args[0] = in_arr
# compute and validate output
out_arr = ker(*args)
expected = in_arr + self.data
testing.assert_allclose(out_arr, expected)
def create_texture_image(textures, texture_size_out=16):
num_faces, texture_size_in = textures.shape[:2]
tile_width = int((num_faces - 1.) ** 0.5) + 1
tile_height = int((num_faces - 1.) / tile_width) + 1
image = np.zeros((tile_height * texture_size_out, tile_width * texture_size_out, 3), 'float32')
vertices = np.zeros((num_faces, 3, 2), 'float32') # [:, :, XY]
face_nums = np.arange(num_faces)
column = face_nums % tile_width
row = face_nums / tile_width
vertices[:, 0, 0] = column * texture_size_out
vertices[:, 0, 1] = row * texture_size_out
vertices[:, 1, 0] = column * texture_size_out
vertices[:, 1, 1] = (row + 1) * texture_size_out - 1
vertices[:, 2, 0] = (column + 1) * texture_size_out - 1
vertices[:, 2, 1] = (row + 1) * texture_size_out - 1
image = chainer.cuda.to_gpu(image)
vertices = chainer.cuda.to_gpu(vertices)
textures = chainer.cuda.to_gpu(textures)
loop = cp.arange(image.size / 3).astype('int32')
chainer.cuda.elementwise(
'int32 j, raw float32 image, raw float32 vertices_all, raw float32 textures',
'',
string.Template('''
const int x = i % (${tile_width} * ${texture_size_out});
const int y = i / (${tile_width} * ${texture_size_out});
const int row = x / ${texture_size_out};
const int column = y / ${texture_size_out};
const int fn = row + column * ${tile_width};
const int tsi = ${texture_size_in};
const float* texture = &textures[fn * tsi * tsi * tsi * 3];
const float* vertices = &vertices_all[fn * 3 * 2];
const float* p0 = &vertices[2 * 0];
const float* p1 = &vertices[2 * 1];
const float* p2 = &vertices[2 * 2];
/* */
// if ((y % ${texture_size_out}) < (x % ${texture_size_out})) continue;
/* compute face_inv */
float face_inv[9] = {
p1[1] - p2[1], p2[0] - p1[0], p1[0] * p2[1] - p2[0] * p1[1],
p2[1] - p0[1], p0[0] - p2[0], p2[0] * p0[1] - p0[0] * p2[1],
p0[1] - p1[1], p1[0] - p0[0], p0[0] * p1[1] - p1[0] * p0[1]};
float face_inv_denominator = (
p2[0] * (p0[1] - p1[1]) +
p0[0] * (p1[1] - p2[1]) +
p1[0] * (p2[1] - p0[1]));
for (int k = 0; k < 9; k++) face_inv[k] /= face_inv_denominator;
/* compute w = face_inv * p */
float weight[3];
float weight_sum = 0;
for (int k = 0; k < 3; k++) {
weight[k] = face_inv[3 * k + 0] * x + face_inv[3 * k + 1] * y + face_inv[3 * k + 2];
weight_sum += weight[k];
}
for (int k = 0; k < 3; k++) weight[k] /= (weight_sum + ${eps});
/* get texture index (float) */
float texture_index_float[3];
for (int k = 0; k < 3; k++) {
float tif = weight[k] * (tsi - 1);
tif = max(tif, 0.);
tif = min(tif, tsi - 1 - ${eps});
texture_index_float[k] = tif;
}
/* blend */
float new_pixel[3] = {0, 0, 0};
for (int pn = 0; pn < 8; pn++) {
float w = 1; // weight
int texture_index_int[3]; // index in source (int)
for (int k = 0; k < 3; k++) {
if ((pn >> k) % 2 == 0) {
w *= 1 - (texture_index_float[k] - (int)texture_index_float[k]);
texture_index_int[k] = (int)texture_index_float[k];
} else {
w *= texture_index_float[k] - (int)texture_index_float[k];
texture_index_int[k] = (int)texture_index_float[k] + 1;
}
}
int isc = texture_index_int[0] * tsi * tsi + texture_index_int[1] * tsi + texture_index_int[2];
for (int k = 0; k < 3; k++) new_pixel[k] += w * texture[isc * 3 + k];
}
for (int k = 0; k < 3; k++) image[i * 3 + k] = new_pixel[k];
''').substitute(
num_faces=num_faces,
texture_size_in=texture_size_in,
texture_size_out=texture_size_out,
tile_width=tile_width,
eps=1e-5,
),
'function',
)(loop, image, vertices, textures)
chainer.cuda.elementwise(
'int32 j, raw float32 image, raw float32 vertices_all, raw float32 textures',
'',
string.Template('''
const int x = i % (${tile_width} * ${texture_size_out});
const int y = i / (${tile_width} * ${texture_size_out});
const int row = x / ${texture_size_out};
const int column = y / ${texture_size_out};
const int fn = row + column * ${tile_width};
const int tsi = ${texture_size_in};
const float* texture = &textures[fn * tsi * tsi * tsi * 3];
const float* vertices = &vertices_all[fn * 3 * 2];
const float* p0 = &vertices[2 * 0];
const float* p1 = &vertices[2 * 1];
const float* p2 = &vertices[2 * 2];
/* */
if ((y % ${texture_size_out} + 1) == (x % ${texture_size_out})) {
for (int k = 0; k < 3; k++) image[i * 3 + k] = image[
(y * ${tile_width} * ${texture_size_out} + (x - 1)) * 3 + k];
}
''').substitute(
num_faces=num_faces,
texture_size_in=texture_size_in,
texture_size_out=texture_size_out,
tile_width=tile_width,
eps=1e-5,
),
'function',
)(loop, image, vertices, textures)
vertices[:, :, 0] /= (image.shape[1] - 1)
vertices[:, :, 1] /= (image.shape[0] - 1)
image = image[::-1, ::1]
image = image.get()
vertices = vertices.get()
return image, vertices
# For example, for the P4 (rotation-translation) conv, the input image is a function on Z2,
# which we may think of as a function on P4 that is right-invariant to rotation.
# A right-rotation-invariant P4 function has the same value at (r, u, v) as it has at (r', u, v).
# Naturally, we don't store this invariant P4 function, but we store an array with a length-1 axis for the rotation
# coordinate.
# This is consistent with the numpy convention that lenght-1 axes get broadcast automatically.
# So for Z2 filters, we get the following shapes:
# Filter shape: (output_channels, input_channels, 1, nu, nv)
# Index shape (one per coordinate t, u, v): (output_transforms, 1, nu, nv)
# Result shape: (output_channels, output_transforms, input_channels, 1, nu, nv)
import cupy
from cupy.core.core import compile_with_cache
x = cupy.arange(2, dtype='f') # WORKAROUND - currently, cupy compile_with_cache fails if no cupy code is executed first
# This computes input[..., T, U, V].swapaxes(1, 2)
_index_group_func_str = \
"""
extern "C" __global__ void indexing_kernel(
CArray<{0}, 5> input,
CArray<int, 4> T,
CArray<int, 4> U,
CArray<int, 4> V,
CArray<{0}, 6> output)
{{
CUPY_FOR(i, output.size()) {{
const int* oshape = output.shape();
const int* ostrides = output.strides();
def test_size(self):
x = cupy.arange(3).astype('i')
y = cupy.ElementwiseKernel(
'raw int32 x', 'int32 y', 'y = x.size()', 'test_carray_size',
)(x, size=1)
self.assertEqual(int(y[0]), 3)