/
score.py
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score.py
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import copy
import os
import torch
import matplotlib.pyplot as plt
import time
import numpy as np
import torch.optim as optim
import torch.nn.functional as F
def data_load(train_path, enroll_path, test_path, trails_path):
train = np.load(train_path)
enroll = np.load(enroll_path)
test = np.load(test_path)
tr_vec = train['vector']
en_vec = enroll['vector']
en_label = enroll['utt']
te_vec = test['vector']
te_label = test['utt']
en_vec = en_vec - tr_vec.mean(axis=0)
te_vec = te_vec - tr_vec.mean(axis=0)
trails = load_trails(trails_path)
return en_vec, en_label, te_vec, te_label, trails
def compute_eer(target_scores, nontarget_scores):
if isinstance(target_scores , list) is False:
target_scores = list(target_scores)
if isinstance(nontarget_scores , list) is False:
nontarget_scores = list(nontarget_scores)
target_scores = sorted(target_scores)
nontarget_scores = sorted(nontarget_scores)
target_size = len(target_scores);
nontarget_size = len(nontarget_scores)
for i in range(target_size-1):
target_position = i
nontarget_n = nontarget_size * float(target_position) / target_size
nontarget_position = int(nontarget_size - 1 - nontarget_n)
if nontarget_position < 0:
nontarget_position = 0
if nontarget_scores[nontarget_position] < target_scores[target_position]:
break
th = target_scores[target_position];
eer = target_position * 1.0 / target_size;
return eer, th
def compute_idr(trails_path, scores):
trails = load_trails(trails_path)
p=0
n=0
for lb in set(trails[1]):
sco_i = scores[trails[1]==lb]
s_index = np.argmax(sco_i)
tar = trails[2][trails[1]==lb]
if tar[s_index]=='target':
p+=1
else:
n+=1
idr = p/(n+p)
return idr
def load_trails(trails_path):
file = open(trails_path,'r')
trails=[]
enroll=[]
test =[]
tar =[]
for line in file:
line = line.strip('\n').split(' ')
enroll.append(line[0])
test.append(line[1])
tar.append(line[2])
trails.append(enroll)
trails.append(test)
trails.append(tar)
trails = np.array(trails)
return trails
def supervise_mean_var(data, label):
assert(data.shape[0] == label.shape[0]), 'data and label must have the same length'
label_class = np.array(list(set(copy.deepcopy(label))))
mean_list = []
label_list = []
for lb in label_class:
data_j = data[label==lb]
mean_j = data_j.mean(0)
mean_list.append(mean_j)
label_list.append(lb)
mean_class = np.array(mean_list)
mlabel = np.array(label_list)
return mean_class, mlabel
def Cosine_score(test_vec, te_label, enroll_vec, enroll_label, trails):
mean_class, mlabel = supervise_mean_var(enroll_vec, enroll_label)
Cosine_tensor_list=[]
tar=[]
for i in range(trails[0].shape[0]):
e = mean_class[mlabel==trails[0][i]][0]
t = test_vec[te_label==trails[1][i]][0]
Cosine_similarity = np.dot(e, t) / (np.linalg.norm(e) * np.linalg.norm(t))
Cosine_tensor_list.append(Cosine_similarity)
tar.append(trails[2][i])
Cosine_scores = np.array(Cosine_tensor_list)
return Cosine_scores, np.array(tar)
def cosine_scoring_by_trails(train_path,enroll_path,test_path,trails_path):
en_vec, en_label, te_vec, te_label, trails = data_load(train_path, enroll_path, test_path, trails_path)
Cosine_scores, tar = Cosine_score(te_vec, te_label, en_vec, en_label, trails)
eer, th = compute_eer(Cosine_scores[tar=='target'], Cosine_scores[tar=='nontarget'])
return eer
'''
if __name__ == "__main__":
train_path = './data/xvector/vox_4k.npz'
enroll_path = './data/xvector/Sitw/enroll.npz'
test_path = './data/xvector/Sitw/test.npz'
trails_path = './data/xvector/Sitw/core-core.lst'
eer = cosine_scoring_by_trails(train_path,enroll_path,test_path,trails_path)
print('eer={:.2f}'.format(eer*100))
'''