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mb.conv_transpose(**conv_kargs)
mb.conv(**conv_kargs)
block.remove_ops([conv_op, scale_op])
register_pass(namespace="common")
fuse_conv_scale(AbstractGraphPass)
number (scalar)
is (B, Cout, H, W)
shape (Cout, 1, 1)
and (1, Cout, 1, 1)
conv(%1)
mul(%2, constant)
conv(%1)
__init__(self)
set()
set_ops_to_skip(self, prog)
_fuse_conv_scale_block(self, block)
_match_pattern(op)
len(child_ops)
list(child_ops)
list(block.operations)
self._fuse_conv_scale_block(b)
len(op.blocks)
_match_pattern(op)
_try_to_transform(op, scale_op, block)
apply(self, prog)
self.set_ops_to_skip(prog)
prog.functions.values()
self._fuse_conv_scale_block(f)
provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
TestV1CephFSVolumeSource(unittest.TestCase)
setUp(self)
tearDown(self)
testV1CephFSVolumeSource(self)
kubernetes.client.models.v1_ceph_fs_volume_source.V1CephFSVolumeSource()
unittest.main()
warnings.simplefilter("ignore", category=FutureWarning)
os.path.basename(__file__)
print(FNAME)
len(GRAPH_TYPES)
np.random.seed(23409857)
stashfig(name, **kws)
savefig(name, foldername=FNAME, fmt=DEFAULT_FMT, dpi=DEFUALT_DPI, **kws)
stashskel(name, ids, colors, palette=None, **kws)
ase(adj, n_components)
pass_to_ranks(adj)
AdjacencySpectralEmbed(n_components=n_components)
ase.fit_transform(adj)
np.concatenate(latent, axis=-1)
to_laplace(graph, form="DAD", regularizer=None)
optional (default=None)
D (n_vertices, n_vertices)
TypeError("Unsuported Laplacian normalization")
np.sum(A, axis=0)
np.sum(A, axis=1)
isinstance(regularizer, (int, float)
format(type(regularizer)
ValueError("Regularizer must be greater than or equal to 0")
np.mean(out_degree)
np.errstate(divide="ignore")
np.sqrt(in_degree)
np.sqrt(out_degree)
np.isinf(in_root)
np.isinf(out_root)
np.diag(in_root)
np.diag(out_root)
np.diag(in_degree)
symmetrize(L, method="avg")
lse(adj, n_components, regularizer=None)
pass_to_ranks(adj)
to_laplace(adj, form="R-DAD")
AdjacencySpectralEmbed(n_components=n_components)
ase.fit_transform(lap)
np.concatenate(latent, axis=-1)
omni(adjs, n_components)
pass_to_ranks(a)
OmnibusEmbed(n_components=n_components // len(adjs)
omni.fit_transform(adjs)
np.concatenate(latent, axis=-1)
np.concatenate(latent, axis=-1)
ase_concatenate(adjs, n_components)
pass_to_ranks(a)
AdjacencySpectralEmbed(n_components=n_components // len(adjs)
ase.fit_transform(a)
np.concatenate(latent, axis=-1)
graph_latents.append(latent)
np.concatenate(graph_latents, axis=-1)
sub_ari(known_inds, true_labels, pred_labels)
adjusted_rand_score(true_known_labels, pred_known_labels)
plt.style.use("seaborn-white")
sns.set_palette("deep")
sns.set_context("talk", font_scale=1)
np.where(side_labels == "R")
np.ix_(right_inds, right_inds)
adj.sum(axis=0)
adj.sum(axis=1)
np.argsort(degrees)
np.ix_(sort_inds, sort_inds)
get_lcc(adj, return_inds=True)
np.count_nonzero(adj, axis=0)
np.count_nonzero(adj, axis=1)