File size: 1,698 Bytes
8ffcfd0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | import mat73
import numpy as np
import scipy.io as sio
import torch
import random
from torch.utils.data import Dataset, DataLoader
from utils import *
def get_pairs(E_X, E_Y, neg_prop, train_label):
view0, view1, labels, real_labels, class_labels0, class_labels1 = [], [], [], [], [], []
# construct pos. pairs
for i in range(len(E_X)):
view0.append(E_X[i])
view1.append(E_Y[i])
labels.append(1)
real_labels.append(1)
class_labels0.append(train_label[i])
class_labels1.append(train_label[i])
# construct neg. pairs by taking each sample in view0 as an anchor and randomly sample neg_prop samples from view1,
# which may lead to the so called noisy labels, namely, some of the constructed neg. pairs may in the same category.
for j in range(len(E_X)):
neg_idx = random.sample(range(len(E_Y)), neg_prop)
for k in range(neg_prop):
view0.append(E_X[j])
view1.append(E_Y[neg_idx[k]])
labels.append(0)
class_labels0.append(train_label[j])
class_labels1.append(train_label[neg_idx[k]])
if train_label[j] != train_label[neg_idx[k]]:
real_labels.append(0)
else:
real_labels.append(1)
labels = np.array(labels, dtype=np.int64)
real_labels = np.array(real_labels, dtype=np.int64)
class_labels0, class_labels1 = np.array(class_labels0, dtype=np.int64), np.array(class_labels1, dtype=np.int64)
view0, view1 = np.array(view0, dtype=np.float32), np.array(view1, dtype=np.float32)
return view0, view1, labels, real_labels, class_labels0, class_labels1
|