function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def _get_sz_max(self):
got = self.cli('show virtual-service global')
limits = got['TABLE_resource_limits']['ROW_resource_limits']
for resource in limits:
name = resource['media_name']
max_val = int(resource['quota'])
if 'CPU' in name:
self.sz_m... | Apstra/aeon-venos | [
2,
4,
2,
1,
1463407794
] |
def error(errtype, msg, code=42):
sys.stderr.write("{t.red}[ERROR] {t.yellow}{er}: {msg}"
"{t.normal}\n".format(er=errtype, msg=msg, t=log.term))
sys.exit(code) | yac/rdoupdate | [
1,
3,
1,
2,
1380286143
] |
def _get_update_files(args):
if args.files and args.git:
error("invalid invocation", "-g and -f are exclusive.", 19)
if args.files:
files = args.files
else:
if not args.git:
args.git = '.'
f = actions.get_last_commit_update(args.git)
files = [os.path.join(... | yac/rdoupdate | [
1,
3,
1,
2,
1380286143
] |
def _parse_build_filter(fargs):
bf = []
if not fargs:
return bf
for f in fargs:
try:
attr, rex = f.split(':', 1)
except Exception as ex:
raise exception.InvalidFilter(what=f)
bf.append((attr, rex))
return bf | yac/rdoupdate | [
1,
3,
1,
2,
1380286143
] |
def do_move(args):
actions.move_files(args.files, args.dir) | yac/rdoupdate | [
1,
3,
1,
2,
1380286143
] |
def run(*cargs):
parser = get_parser()
args = parser.parse_args(cargs)
action = args.action
return action(args) | yac/rdoupdate | [
1,
3,
1,
2,
1380286143
] |
def do_GET(self):
b_obj = self.barix
NOT_CONNECTED = "<b>NOT CONNECTED</b>"
left_level_avg = 0
right_level_avg = 0
level_avg_window_minutes = 0
if level_history:
N = len(level_history)
left_level_avg = sum(L for L, _ in level_history) / N
... | chirpradio/chirpradio-machine | [
12,
8,
12,
2,
1315249848
] |
def log_message(self, format, *args):
pass # Throw away log messages for now. | chirpradio/chirpradio-machine | [
12,
8,
12,
2,
1315249848
] |
def poll_barix(b_obj, log_fh):
try:
if not b_obj.ping():
return
level_history.append(
(float(b_obj.left_level), float(b_obj.right_level)))
if len(level_history) > LEVEL_HISTORY_MAX_SIZE:
level_history.pop(0)
if log_fh:
now = int(b_obj... | chirpradio/chirpradio-machine | [
12,
8,
12,
2,
1315249848
] |
def __init__(self, categories):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys -
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name e.g., 'cat', 'dog'.
"""
self._categorie... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
groundtruth_dict: A dictionary of groundtruth numpy arrays required
for evaluations.
... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def add_single_detected_image_info(self, image_id, detections_dict):
"""Adds detections for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary of detection numpy arrays required
for evaluation.
"""
... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def evaluate(self):
"""Evaluates detections and returns a dictionary of metrics."""
pass | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
pass | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self,
categories,
matching_iou_threshold=0.5,
evaluate_corlocs=False,
metric_prefix=None,
use_weighted_mean_ap=False,
evaluate_masks=False,
group_of_weight=0.0):
"""Constructor.
Args:
cat... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def add_single_detected_image_info(self, image_id, detections_dict):
"""Adds detections for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing -
standard_fields.DetectionResultFields.detection_... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
self._evaluation = ObjectDetectionEvaluation(
num_groundtruth_classes=self._num_classes,
matching_iou_threshold=self._matching_iou_threshold,
use_weighted_mean_ap=self._use_weighted_mean_ap,
label_id_offse... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self, categories, matching_iou_threshold=0.5):
super(PascalDetectionEvaluator, self).__init__(
categories,
matching_iou_threshold=matching_iou_threshold,
evaluate_corlocs=False,
metric_prefix='PascalBoxes',
use_weighted_mean_ap=False) | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self, categories, matching_iou_threshold=0.5):
super(WeightedPascalDetectionEvaluator, self).__init__(
categories,
matching_iou_threshold=matching_iou_threshold,
evaluate_corlocs=False,
metric_prefix='WeightedPascalBoxes',
use_weighted_mean_ap=True) | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self, categories, matching_iou_threshold=0.5):
super(PascalInstanceSegmentationEvaluator, self).__init__(
categories,
matching_iou_threshold=matching_iou_threshold,
evaluate_corlocs=False,
metric_prefix='PascalMasks',
use_weighted_mean_ap=False,
evaluate_... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self, categories, matching_iou_threshold=0.5):
super(WeightedPascalInstanceSegmentationEvaluator, self).__init__(
categories,
matching_iou_threshold=matching_iou_threshold,
evaluate_corlocs=False,
metric_prefix='WeightedPascalMasks',
use_weighted_mean_ap=True,
... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self,
categories,
matching_iou_threshold=0.5,
evaluate_corlocs=False,
metric_prefix='OpenImagesV2',
group_of_weight=0.0):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys -
... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self,
categories,
matching_iou_threshold=0.5,
evaluate_corlocs=False,
group_of_weight=1.0):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys -
'id': (required) an integer id uniquely id... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def add_single_detected_image_info(self, image_id, detections_dict):
"""Adds detections for a single image to be used for evaluation.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing -
standard_fields.DetectionResultFields.detection_... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def __init__(self,
num_groundtruth_classes,
matching_iou_threshold=0.5,
nms_iou_threshold=1.0,
nms_max_output_boxes=10000,
use_weighted_mean_ap=False,
label_id_offset=0,
group_of_weight=0.0,
per_image... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def clear_detections(self):
self._initialize_detections() | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def add_single_detected_image_info(self, image_key, detected_boxes,
detected_scores, detected_class_labels,
detected_masks=None):
"""Adds detections for a single image to be used for evaluation.
Args:
image_key: A unique string/int... | cshallue/models | [
6,
3,
6,
1,
1473384593
] |
def testNotImplemented(self):
for name in jnp.linalg._NOT_IMPLEMENTED:
func = getattr(jnp.linalg, name)
with self.assertRaises(NotImplementedError):
func() | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testCholesky(self, shape, dtype):
rng = jtu.rand_default(self.rng())
def args_maker():
factor_shape = shape[:-1] + (2 * shape[-1],)
a = rng(factor_shape, dtype)
return [np.matmul(a, jnp.conj(T(a)))]
self._CheckAgainstNumpy(np.linalg.cholesky, jnp.linalg.cholesky, args_maker,
... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testDet(self, n, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng((n, n), dtype)]
self._CheckAgainstNumpy(np.linalg.det, jnp.linalg.det, args_maker, tol=1e-3)
self._CompileAndCheck(jnp.linalg.det, args_maker,
rtol={np.float64: 1e-13, np.complex128: 1e-1... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testDetGrad(self, shape, dtype):
rng = jtu.rand_default(self.rng())
a = rng(shape, dtype)
jtu.check_grads(jnp.linalg.det, (a,), 2, atol=1e-1, rtol=1e-1)
# make sure there are no NaNs when a matrix is zero
if len(shape) == 2:
pass
jtu.check_grads(
jnp.linalg.det, (jnp.zeros_li... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testDetGradOfSingularMatrixCorank1(self):
# Rank 2 matrix with nonzero gradient
a = jnp.array([[ 50, -30, 45],
[-30, 90, -81],
[ 45, -81, 81]], dtype=jnp.float32)
jtu.check_grads(jnp.linalg.det, (a,), 1, atol=1e-1, rtol=1e-1) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testTensorsolve(self, m, nq, dtype):
rng = jtu.rand_default(self.rng())
# According to numpy docs the shapes are as follows:
# Coefficient tensor (a), of shape b.shape + Q.
# And prod(Q) == prod(b.shape)
# Therefore, n = prod(q)
n, q = nq
b_shape = (n, m)
# To accomplish prod(Q) == ... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSlogdet(self, shape, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
self._CheckAgainstNumpy(np.linalg.slogdet, jnp.linalg.slogdet, args_maker,
tol=1e-3)
self._CompileAndCheck(jnp.linalg.slogdet, args_maker) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSlogdetGrad(self, shape, dtype):
rng = jtu.rand_default(self.rng())
a = rng(shape, dtype)
jtu.check_grads(jnp.linalg.slogdet, (a,), 2, atol=1e-1, rtol=2e-1) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEig(self, shape, dtype, compute_left_eigenvectors,
compute_right_eigenvectors):
rng = jtu.rand_default(self.rng())
n = shape[-1]
args_maker = lambda: [rng(shape, dtype)]
# Norm, adjusted for dimension and type.
def norm(x):
norm = np.linalg.norm(x, axis=(-2, -1))
r... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEigvalsGrad(self, shape, dtype):
# This test sometimes fails for large matrices. I (@j-towns) suspect, but
# haven't checked, that might be because of perturbations causing the
# ordering of eigenvalues to change, which will trip up check_grads. So we
# just test on small-ish matrices.
rng =... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEigvals(self, shape, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
a, = args_maker()
w1, _ = jnp.linalg.eig(a)
w2 = jnp.linalg.eigvals(a)
self.assertAllClose(w1, w2, rtol={np.complex128: 1e-14}) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEigvalsInf(self):
# https://github.com/google/jax/issues/2661
x = jnp.array([[jnp.inf]])
self.assertTrue(jnp.all(jnp.isnan(jnp.linalg.eigvals(x)))) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEigBatching(self, shape, dtype):
rng = jtu.rand_default(self.rng())
shape = (10,) + shape
args = rng(shape, dtype)
ws, vs = vmap(jnp.linalg.eig)(args)
self.assertTrue(np.all(np.linalg.norm(
np.matmul(args, vs) - ws[..., None, :] * vs) < 1e-3)) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEigh(self, n, dtype, lower):
rng = jtu.rand_default(self.rng())
tol = 1e-3
args_maker = lambda: [rng((n, n), dtype)]
uplo = "L" if lower else "U"
a, = args_maker()
a = (a + np.conj(a.T)) / 2
w, v = jnp.linalg.eigh(np.tril(a) if lower else np.triu(a),
UPLO... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEigvalsh(self, shape, dtype):
rng = jtu.rand_default(self.rng())
n = shape[-1]
def args_maker():
a = rng((n, n), dtype)
a = (a + np.conj(a.T)) / 2
return [a]
self._CheckAgainstNumpy(np.linalg.eigvalsh, jnp.linalg.eigvalsh, args_maker,
tol=1e-3) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEighGrad(self, shape, dtype, lower):
rng = jtu.rand_default(self.rng())
self.skipTest("Test fails with numeric errors.")
uplo = "L" if lower else "U"
a = rng(shape, dtype)
a = (a + np.conj(T(a))) / 2
ones = np.ones((a.shape[-1], a.shape[-1]), dtype=dtype)
a *= np.tril(ones) if lower ... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEighGradVectorComplex(self, shape, dtype, lower, eps):
rng = jtu.rand_default(self.rng())
# Special case to test for complex eigenvector grad correctness.
# Exact eigenvector coordinate gradients are hard to test numerically for complex
# eigensystem solvers given the extra degrees of per-eigenv... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testEighBatching(self, shape, dtype):
rng = jtu.rand_default(self.rng())
shape = (10,) + shape
args = rng(shape, dtype)
args = (args + np.conj(T(args))) / 2
ws, vs = vmap(jsp.linalg.eigh)(args)
self.assertTrue(np.all(np.linalg.norm(
np.matmul(args, vs) - ws[..., None, :] * vs) < 1e-3... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLuPivotsToPermutation(self, shape, dtype):
pivots_size = shape[-1]
permutation_size = 2 * pivots_size
pivots = jnp.arange(permutation_size - 1, pivots_size - 1, -1, dtype=dtype)
pivots = jnp.broadcast_to(pivots, shape)
actual = lax.linalg.lu_pivots_to_permutation(pivots, permutation_size)
... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLuPivotsToPermutationBatching(self, shape, dtype):
shape = (10,) + shape
pivots_size = shape[-1]
permutation_size = 2 * pivots_size
pivots = jnp.arange(permutation_size - 1, pivots_size - 1, -1, dtype=dtype)
pivots = jnp.broadcast_to(pivots, shape)
batched_fn = vmap(
lambda x: l... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testNorm(self, shape, dtype, ord, axis, keepdims):
rng = jtu.rand_default(self.rng())
if (ord in ('nuc', 2, -2) and (
jtu.device_under_test() != "cpu" or
(isinstance(axis, tuple) and len(axis) == 2))):
raise unittest.SkipTest("No adequate SVD implementation available")
args_maker ... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSVD(self, b, m, n, dtype, full_matrices, compute_uv, hermitian):
if (jnp.issubdtype(dtype, np.complexfloating) and
jtu.device_under_test() == "tpu"):
raise unittest.SkipTest("No complex SVD implementation")
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(b + (m, n), dtype)... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testQr(self, shape, dtype, full_matrices):
rng = jtu.rand_default(self.rng())
m, n = shape[-2:]
if full_matrices:
mode, k = "complete", m
else:
mode, k = "reduced", min(m, n)
a = rng(shape, dtype)
lq, lr = jnp.linalg.qr(a, mode=mode)
# np.linalg.qr doesn't support batch di... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testQrBatching(self, shape, dtype):
rng = jtu.rand_default(self.rng())
args = rng(shape, jnp.float32)
qs, rs = vmap(jsp.linalg.qr)(args)
self.assertTrue(np.all(np.linalg.norm(args - np.matmul(qs, rs)) < 1e-3)) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testCond(self, shape, pnorm, dtype):
if (jnp.issubdtype(dtype, np.complexfloating) and
jtu.device_under_test() == "tpu"):
raise unittest.SkipTest("No complex SVD implementation")
def gen_mat():
# arr_gen = jtu.rand_some_nan(self.rng())
arr_gen = jtu.rand_default(self.rng())
... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testTensorinv(self, shape, dtype):
rng = jtu.rand_default(self.rng())
def tensor_maker():
invertible = False
while not invertible:
a = rng(shape, dtype)
try:
np.linalg.inv(a)
invertible = True
except np.linalg.LinAlgError:
pass
return ... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSolve(self, lhs_shape, rhs_shape, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
self._CheckAgainstNumpy(np.linalg.solve, jnp.linalg.solve, args_maker,
tol=1e-3)
self._CompileAndCheck(jnp.linalg.solve, a... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testInv(self, shape, dtype):
rng = jtu.rand_default(self.rng())
if jtu.device_under_test() == "gpu" and shape == (200, 200):
raise unittest.SkipTest("Test is flaky on GPU")
def args_maker():
invertible = False
while not invertible:
a = rng(shape, dtype)
try:
... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testPinv(self, shape, dtype):
if (jnp.issubdtype(dtype, np.complexfloating) and
jtu.device_under_test() == "tpu"):
raise unittest.SkipTest("No complex SVD implementation")
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
self._CheckAgainstNumpy(np.linalg.pin... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testPinvGradIssue2792(self):
def f(p):
a = jnp.array([[0., 0.],[-p, 1.]], jnp.float32) * 1 / (1 + p**2)
return jnp.linalg.pinv(a)
j = jax.jacobian(f)(jnp.float32(2.))
self.assertAllClose(jnp.array([[0., -1.], [ 0., 0.]], jnp.float32), j)
expected = jnp.array([[[[-1., 0.], [ 0., 0.]], [[... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testMatrixPower(self, shape, dtype, n):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
tol = 1e-1 if jtu.device_under_test() == "tpu" else 1e-3
self._CheckAgainstNumpy(partial(np.linalg.matrix_power, n=n),
partial(jnp.linalg.matrix_power, n=n)... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testMatrixRank(self, shape, dtype):
if (jnp.issubdtype(dtype, np.complexfloating) and
jtu.device_under_test() == "tpu"):
raise unittest.SkipTest("No complex SVD implementation")
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
a, = args_maker()
self._Chec... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testMultiDot(self, shapes, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [[rng(shape, dtype) for shape in shapes]]
np_fun = np.linalg.multi_dot
jnp_fun = partial(jnp.linalg.multi_dot, precision=lax.Precision.HIGHEST)
tol = {np.float32: 1e-4, np.float64: 1e-10,
np.co... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLstsq(self, lhs_shape, rhs_shape, dtype, rcond):
rng = jtu.rand_default(self.rng())
np_fun = partial(np.linalg.lstsq, rcond=rcond)
jnp_fun = partial(jnp.linalg.lstsq, rcond=rcond)
jnp_fun_numpy_resid = partial(jnp.linalg.lstsq, rcond=rcond, numpy_resid=True)
tol = {np.float32: 1e-5, np.float... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testIssue669(self):
def test(x):
val, vec = jnp.linalg.eigh(x)
return jnp.real(jnp.sum(val))
grad_test_jc = jit(grad(jit(test)))
xc = np.eye(3, dtype=np.complex64)
self.assertAllClose(xc, grad_test_jc(xc)) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testIssue1151(self):
rng = self.rng()
A = jnp.array(rng.randn(100, 3, 3), dtype=jnp.float32)
b = jnp.array(rng.randn(100, 3), dtype=jnp.float32)
x = jnp.linalg.solve(A, b)
self.assertAllClose(vmap(jnp.dot)(A, x), b, atol=2e-3, rtol=1e-2)
_ = jax.jacobian(jnp.linalg.solve, argnums=0)(A, b)
... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testIssue1383(self):
seed = jax.random.PRNGKey(0)
tmp = jax.random.uniform(seed, (2,2))
a = jnp.dot(tmp, tmp.T)
def f(inp):
val, vec = jnp.linalg.eigh(inp)
return jnp.dot(jnp.dot(vec, inp), vec.T)
grad_func = jax.jacfwd(f)
hess_func = jax.jacfwd(grad_func)
cube_func = jax.j... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testBlockDiag(self, args):
args_maker = lambda: args
self._CheckAgainstNumpy(osp.linalg.block_diag, jsp.linalg.block_diag,
args_maker)
self._CompileAndCheck(jsp.linalg.block_diag, args_maker) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLu(self, shape, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
x, = args_maker()
p, l, u = jsp.linalg.lu(x)
self.assertAllClose(x, np.matmul(p, np.matmul(l, u)),
rtol={np.float32: 1e-3, np.float64: 1e-12,
... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLuGrad(self, shape, dtype):
rng = jtu.rand_default(self.rng())
a = rng(shape, dtype)
lu = vmap(jsp.linalg.lu) if len(shape) > 2 else jsp.linalg.lu
jtu.check_grads(lu, (a,), 2, atol=5e-2, rtol=3e-1) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLuBatching(self, shape, dtype):
rng = jtu.rand_default(self.rng())
args = [rng(shape, jnp.float32) for _ in range(10)]
expected = list(osp.linalg.lu(x) for x in args)
ps = np.stack([out[0] for out in expected])
ls = np.stack([out[1] for out in expected])
us = np.stack([out[2] for out in ... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLuCPUBackendOnGPU(self):
# tests running `lu` on cpu when a gpu is present.
jit(jsp.linalg.lu, backend="cpu")(np.ones((2, 2))) # does not crash | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLuFactor(self, n, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng((n, n), dtype)]
x, = args_maker()
lu, piv = jsp.linalg.lu_factor(x)
l = np.tril(lu, -1) + np.eye(n, dtype=dtype)
u = np.triu(lu)
for i in range(n):
x[[i, piv[i]],] = x[[piv[i], i],]
self... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testLuSolve(self, lhs_shape, rhs_shape, dtype, trans):
rng = jtu.rand_default(self.rng())
osp_fun = lambda lu, piv, rhs: osp.linalg.lu_solve((lu, piv), rhs, trans=trans)
jsp_fun = lambda lu, piv, rhs: jsp.linalg.lu_solve((lu, piv), rhs, trans=trans)
def args_maker():
a = rng(lhs_shape, dtype)... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSolve(self, lhs_shape, rhs_shape, dtype, sym_pos, lower):
rng = jtu.rand_default(self.rng())
osp_fun = lambda lhs, rhs: osp.linalg.solve(lhs, rhs, sym_pos=sym_pos, lower=lower)
jsp_fun = lambda lhs, rhs: jsp.linalg.solve(lhs, rhs, sym_pos=sym_pos, lower=lower)
def args_maker():
a = rng(lh... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSolveTriangular(self, lower, transpose_a, unit_diagonal, lhs_shape,
rhs_shape, dtype):
rng = jtu.rand_default(self.rng())
k = rng(lhs_shape, dtype)
l = np.linalg.cholesky(np.matmul(k, T(k))
+ lhs_shape[-1] * np.eye(lhs_shape[-1]))
l = l.astyp... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testTriangularSolveGrad(
self, lower, transpose_a, conjugate_a, unit_diagonal, left_side, a_shape,
b_shape, dtype):
rng = jtu.rand_default(self.rng())
# Test lax.linalg.triangular_solve instead of scipy.linalg.solve_triangular
# because it exposes more options.
A = jnp.tril(rng(a_shape, ... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testTriangularSolveBatching(self, left_side, a_shape, b_shape, bdims):
rng = jtu.rand_default(self.rng())
A = jnp.tril(rng(a_shape, np.float32)
+ 5 * np.eye(a_shape[-1], dtype=np.float32))
B = rng(b_shape, np.float32)
solve = partial(lax.linalg.triangular_solve, lower=True, transpose... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testExpm(self, n, dtype):
rng = jtu.rand_small(self.rng())
args_maker = lambda: [rng((n, n), dtype)]
osp_fun = lambda a: osp.linalg.expm(a)
jsp_fun = lambda a: jsp.linalg.expm(a)
self._CheckAgainstNumpy(osp_fun, jsp_fun, args_maker)
self._CompileAndCheck(jsp_fun, args_maker)
args_maker... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testIssue2131(self, n, dtype):
args_maker_zeros = lambda: [np.zeros((n, n), dtype)]
osp_fun = lambda a: osp.linalg.expm(a)
jsp_fun = lambda a: jsp.linalg.expm(a)
self._CheckAgainstNumpy(osp_fun, jsp_fun, args_maker_zeros)
self._CompileAndCheck(jsp_fun, args_maker_zeros) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testChoSolve(self, lhs_shape, rhs_shape, dtype, lower):
rng = jtu.rand_default(self.rng())
def args_maker():
b = rng(rhs_shape, dtype)
if lower:
L = np.tril(rng(lhs_shape, dtype))
return [(L, lower), b]
else:
U = np.triu(rng(lhs_shape, dtype))
return [(U, lo... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testExpmFrechet(self, n, dtype):
rng = jtu.rand_small(self.rng())
if dtype == np.float64 or dtype == np.complex128:
target_norms = [1.0e-2, 2.0e-1, 9.0e-01, 2.0, 3.0]
# TODO(zhangqiaorjc): Reduce tol to default 1e-15.
tol = {
np.dtype(np.float64): 1e-14,
np.dtype(np.complex... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testExpmGrad(self, n, dtype):
rng = jtu.rand_small(self.rng())
a = rng((n, n), dtype)
if dtype == np.float64 or dtype == np.complex128:
target_norms = [1.0e-2, 2.0e-1, 9.0e-01, 2.0, 3.0]
elif dtype == np.float32 or dtype == np.complex64:
target_norms = [4.0e-1, 1.0, 3.0]
else:
... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSchur(self, shape, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
self._CheckAgainstNumpy(osp.linalg.schur, jsp.linalg.schur, args_maker)
self._CompileAndCheck(jsp.linalg.schur, args_maker) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSqrtmPSDMatrix(self, shape, dtype):
# Checks against scipy.linalg.sqrtm when the principal square root
# is guaranteed to be unique (i.e no negative real eigenvalue)
rng = jtu.rand_default(self.rng())
arg = rng(shape, dtype)
mat = arg @ arg.T
args_maker = lambda : [mat]
if dtype == n... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSqrtmGenMatrix(self, shape, dtype):
rng = jtu.rand_default(self.rng())
arg = rng(shape, dtype)
if dtype == np.float32 or dtype == np.complex64:
tol = 1e-3
else:
tol = 1e-8
R = jsp.linalg.sqrtm(arg)
self.assertAllClose(R @ R, arg, atol=tol, check_dtypes=False) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSqrtmEdgeCase(self, diag, expected, dtype):
"""
Tests the zero numerator condition
"""
mat = jnp.diag(jnp.array(diag)).astype(dtype)
expected = jnp.diag(jnp.array(expected))
root = jsp.linalg.sqrtm(mat)
self.assertAllClose(root, expected, check_dtypes=False) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def run_test(self, alpha, beta):
n = alpha.shape[-1]
# scipy.linalg.eigh_tridiagonal doesn't support complex inputs, so for
# this we call the slower numpy.linalg.eigh.
if np.issubdtype(alpha.dtype, np.complexfloating):
tridiagonal = np.diag(alpha) + np.diag(beta, 1) + np.diag(
np.conj(b... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testToeplitz(self, n, dtype):
for a, b in [[2, -1], [1, 0], [0, 1], [-1e10, 1e10], [-1e-10, 1e-10]]:
alpha = a * np.ones([n], dtype=dtype)
beta = b * np.ones([n - 1], dtype=dtype)
self.run_test(alpha, beta) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testRandomUniform(self, n, dtype):
alpha = jtu.rand_uniform(self.rng())((n,), dtype)
beta = jtu.rand_uniform(self.rng())((n - 1,), dtype)
self.run_test(alpha, beta) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSelect(self, dtype):
n = 5
alpha = jtu.rand_uniform(self.rng())((n,), dtype)
beta = jtu.rand_uniform(self.rng())((n - 1,), dtype)
eigvals_all = jax.scipy.linalg.eigh_tridiagonal(alpha, beta, select="a",
eigvals_only=True)
eps = np.finfo(alp... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def test_tridiagonal_solve(self, dtype):
dl = np.array([0.0, 2.0, 3.0], dtype=dtype)
d = np.ones(3, dtype=dtype)
du = np.array([1.0, 2.0, 0.0], dtype=dtype)
m = 3
B = np.ones([m, 1], dtype=dtype)
X = lax.linalg.tridiagonal_solve(dl, d, du, B)
A = np.eye(3, dtype=dtype)
A[[1, 2], [0, 1]] ... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSchur(self, shape, dtype):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
self._CheckAgainstNumpy(osp.linalg.schur, lax.linalg.schur, args_maker)
self._CompileAndCheck(lax.linalg.schur, args_maker) | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def testSchurBatching(self, shape, dtype):
rng = jtu.rand_default(self.rng())
batch_size = 10
shape = (batch_size, ) + shape
args = rng(shape, dtype)
reconstruct = vmap(lambda S, T: S @ T @ jnp.conj(S.T))
Ts, Ss = vmap(lax.linalg.schur)(args)
self.assertAllClose(reconstruct(Ss... | google/jax | [
22193,
2080,
22193,
1296,
1540502702
] |
def initialize_options(self):
self.test = False | puentesarrin/asyncflux | [
26,
1,
26,
1,
1400633988
] |
def run(self):
if self.test:
path = "docs/_build/doctest"
mode = "doctest"
else:
path = "docs/_build/%s" % __version__
mode = "html"
try:
os.makedirs(path)
except:
pass
status = subprocess.cal... | puentesarrin/asyncflux | [
26,
1,
26,
1,
1400633988
] |
def setUpTestData(cls):
cls.user = User.objects.create_user(username='api_login', email='api_login@email.com', password='123456')
cls.the_user = TheUser.objects.get(id_user=cls.user) | OlegKlimenko/Plamber | [
9,
1,
9,
28,
1487368387
] |
def test_user_login_missing_params(self):
response = self.client.post(reverse('user_login_api'), {'app_key': self.api_key, 'username': 'username'}) | OlegKlimenko/Plamber | [
9,
1,
9,
28,
1487368387
] |
def test_user_login_too_long_username(self):
response = self.client.post(reverse('user_login_api'), {'app_key': self.api_key,
'username': 'a' * 40,
'password': 'somepassword'}) | OlegKlimenko/Plamber | [
9,
1,
9,
28,
1487368387
] |
def test_user_login_too_short_username(self):
response = self.client.post(reverse('user_login_api'), {'app_key': self.api_key,
'username': 'a',
'password': 'somepassword'}) | OlegKlimenko/Plamber | [
9,
1,
9,
28,
1487368387
] |
def test_user_login_username_regex_not_valid(self):
username_patterns = [
'ab#$@cdev', '#$@username', 'username%#&#&', 'db24!!!db34', '#$@234234', '#123dkf%'
] | OlegKlimenko/Plamber | [
9,
1,
9,
28,
1487368387
] |
def test_user_login_email_regex_not_valid(self):
email_patterns = [
'no_extension@ddd', '@first.missing', 'after_at_miss@', '$%#@474**.om', 'em#$@ail@m.com', '#em@ail@m.com'
] | OlegKlimenko/Plamber | [
9,
1,
9,
28,
1487368387
] |
def test_user_login_too_long_password(self):
response = self.client.post(reverse('user_login_api'), {'app_key': self.api_key,
'username': 'test_username',
'password': 'p' * 17}) | OlegKlimenko/Plamber | [
9,
1,
9,
28,
1487368387
] |
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