code stringlengths 3 6.57k |
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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) |
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