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Zero
Running
on
Zero
File size: 3,635 Bytes
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import os
import os.path as osp
import sys
import numpy as np
import scipy.linalg
from tqdm import tqdm
ROOT_DIR = osp.join(osp.abspath(osp.dirname(__file__)), '../')
if ROOT_DIR not in sys.path:
sys.path.append(ROOT_DIR)
from utils_fmaps.misc import KNNSearch
try:
import pynndescent
index = pynndescent.NNDescent(np.random.random((100, 3)), n_jobs=2)
del index
ANN = True
except ImportError:
ANN = False
# https://github.com/RobinMagnet/pyFM
def FM_to_p2p(FM, eigvects1, eigvects2, use_ANN=False):
if use_ANN and not ANN:
raise ValueError('Please install pydescent to achieve Approximate Nearest Neighbor')
k2, k1 = FM.shape
assert k1 <= eigvects1.shape[1], \
f'At least {k1} should be provided, here only {eigvects1.shape[1]} are given'
assert k2 <= eigvects2.shape[1], \
f'At least {k2} should be provided, here only {eigvects2.shape[1]} are given'
if use_ANN:
index = pynndescent.NNDescent(eigvects1[:, :k1] @ FM.T, n_jobs=8)
matches, _ = index.query(eigvects2[:, :k2], k=1)
matches = matches.flatten()
else:
tree = KNNSearch(eigvects1[:, :k1] @ FM.T)
matches = tree.query(eigvects2[:, :k2], k=1).flatten()
return matches
def p2p_to_FM(p2p, eigvects1, eigvects2, A2=None):
if A2 is not None:
if A2.shape[0] != eigvects2.shape[0]:
raise ValueError("Can't compute pseudo inverse with subsampled eigenvectors")
if len(A2.shape) == 1:
return eigvects2.T @ (A2[:, None] * eigvects1[p2p, :])
return eigvects2.T @ A2 @ eigvects1[p2p, :]
return scipy.linalg.lstsq(eigvects2, eigvects1[p2p, :])[0]
def zoomout_iteration(eigvects1, eigvects2, FM, step=1, A2=None, use_ANN=False):
k2, k1 = FM.shape
try:
step1, step2 = step
except TypeError:
step1 = step
step2 = step
new_k1, new_k2 = k1 + step1, k2 + step2
p2p = FM_to_p2p(FM, eigvects1, eigvects2, use_ANN=use_ANN)
FM_zo = p2p_to_FM(p2p, eigvects1[:, :new_k1], eigvects2[:, :new_k2], A2=A2)
return FM_zo
def zoomout_refine(eigvects1,
eigvects2,
FM,
nit=10,
step=1,
A2=None,
subsample=None,
use_ANN=False,
return_p2p=False,
verbose=False):
k2_0, k1_0 = FM.shape
try:
step1, step2 = step
except TypeError:
step1 = step
step2 = step
assert k1_0 + nit*step1 <= eigvects1.shape[1], \
f"Not enough eigenvectors on source : \
{k1_0 + nit*step1} are needed when {eigvects1.shape[1]} are provided"
assert k2_0 + nit*step2 <= eigvects2.shape[1], \
f"Not enough eigenvectors on target : \
{k2_0 + nit*step2} are needed when {eigvects2.shape[1]} are provided"
use_subsample = False
if subsample is not None:
use_subsample = True
sub1, sub2 = subsample
FM_zo = FM.copy()
ANN_adventage = False
iterable = range(nit) if not verbose else tqdm(range(nit))
for it in iterable:
ANN_adventage = use_ANN and (FM_zo.shape[0] > 90) and (FM_zo.shape[1] > 90)
if use_subsample:
FM_zo = zoomout_iteration(eigvects1[sub1], eigvects2[sub2], FM_zo, A2=None, step=step, use_ANN=ANN_adventage)
else:
FM_zo = zoomout_iteration(eigvects1, eigvects2, FM_zo, A2=A2, step=step, use_ANN=ANN_adventage)
if return_p2p:
p2p_zo = FM_to_p2p(FM_zo, eigvects1, eigvects2, use_ANN=False)
return FM_zo, p2p_zo
return FM_zo
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