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import os
import numpy as np
import torch
import shutil
import torchvision.transforms as transforms
from torch.autograd import Variable
import sklearn
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
import pdb
class AvgrageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.sum = 0
self.cnt = 0
def update(self, val, n=1):
self.sum += val * n
self.cnt += n
self.avg = self.sum / self.cnt
def accuracy(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def get_threshold(score_file):
with open(score_file, 'r') as file:
lines = file.readlines()
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
angle = float(tokens[0])
# pdb.set_trace()
type = int(tokens[1])
data.append({'map_score': angle, 'label': type})
if type == 1:
num_real += 1
else:
num_fake += 1
min_error = count # account ACER (or ACC)
min_threshold = 0.0
min_ACC = 0.0
min_ACER = 0.0
min_APCER = 0.0
min_BPCER = 0.0
for d in data:
threshold = d['map_score']
type1 = len([s for s in data if s['map_score'] <= threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > threshold and s['label'] == 0])
ACC = 1 - (type1 + type2) / count
APCER = type2 / num_fake
BPCER = type1 / num_real
ACER = (APCER + BPCER) / 2.0
if ACER < min_error:
min_error = ACER
min_threshold = threshold
min_ACC = ACC
min_ACER = ACER
min_APCER = APCER
min_BPCER = min_BPCER
# print(min_error, min_threshold)
return min_threshold, min_ACC, min_APCER, min_BPCER, min_ACER
def test_threshold_based(threshold, score_file):
with open(score_file, 'r') as file:
lines = file.readlines()
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
angle = float(tokens[0])
type = int(tokens[1])
data.append({'map_score': angle, 'label': type})
if type == 1:
num_real += 1
else:
num_fake += 1
type1 = len([s for s in data if s['map_score'] <= threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > threshold and s['label'] == 0])
ACC = 1 - (type1 + type2) / count
APCER = type2 / num_fake
BPCER = type1 / num_real
ACER = (APCER + BPCER) / 2.0
return ACC, APCER, BPCER, ACER
def get_err_threhold(fpr, tpr, threshold):
RightIndex = (tpr + (1 - fpr) - 1)
right_index = np.argmax(RightIndex)
best_th = threshold[right_index]
err = fpr[right_index]
differ_tpr_fpr_1 = tpr + fpr - 1.0
right_index = np.argmin(np.abs(differ_tpr_fpr_1))
best_th = threshold[right_index]
err = fpr[right_index]
# print(err, best_th)
return err, best_th
# def performances(dev_scores, dev_labels, test_scores, test_labels):
def performances(map_score_val_filename, map_score_test_filename):
# val
with open(map_score_val_filename, 'r') as file:
lines = file.readlines()
val_scores = []
val_labels = []
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
score = float(tokens[0])
label = float(tokens[1]) # label = int(tokens[1])
val_scores.append(score)
val_labels.append(label)
data.append({'map_score': score, 'label': label})
if label == 1:
num_real += 1
else:
num_fake += 1
fpr, tpr, threshold = roc_curve(val_labels, val_scores, pos_label=1)
val_err, val_threshold = get_err_threhold(fpr, tpr, threshold)
type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
val_ACC = 1 - (type1 + type2) / count
val_APCER = type2 / num_fake
val_BPCER = type1 / num_real
val_ACER = (val_APCER + val_BPCER) / 2.0
# test
with open(map_score_test_filename, 'r') as file2:
lines = file2.readlines()
test_scores = []
test_labels = []
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
score = float(tokens[0])
label = float(tokens[1]) # label = int(tokens[1])
test_scores.append(score)
test_labels.append(label)
data.append({'map_score': score, 'label': label})
if label == 1:
num_real += 1
else:
num_fake += 1
# test based on val_threshold
type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
print([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
print([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
test_ACC = 1 - (type1 + type2) / count
test_APCER = type2 / num_fake
test_BPCER = type1 / num_real
test_ACER = (test_APCER + test_BPCER) / 2.0
# test based on test_threshold
fpr_test, tpr_test, threshold_test = roc_curve(test_labels, test_scores, pos_label=1)
err_test, best_test_threshold = get_err_threhold(fpr_test, tpr_test, threshold_test)
type1 = len([s for s in data if s['map_score'] <= best_test_threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > best_test_threshold and s['label'] == 0])
test_threshold_ACC = 1 - (type1 + type2) / count
test_threshold_APCER = type2 / num_fake
test_threshold_BPCER = type1 / num_real
test_threshold_ACER = (test_threshold_APCER + test_threshold_BPCER) / 2.0
return val_threshold, best_test_threshold, val_ACC, val_ACER, test_ACC, test_APCER, test_BPCER, test_ACER, test_threshold_ACER
def performances_SiW_EER(map_score_val_filename):
# val
with open(map_score_val_filename, 'r') as file:
lines = file.readlines()
val_scores = []
val_labels = []
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
score = float(tokens[0])
label = int(tokens[1])
val_scores.append(score)
val_labels.append(label)
data.append({'map_score': score, 'label': label})
if label == 1:
num_real += 1
else:
num_fake += 1
fpr, tpr, threshold = roc_curve(val_labels, val_scores, pos_label=1)
val_err, val_threshold = get_err_threhold(fpr, tpr, threshold)
type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
val_ACC = 1 - (type1 + type2) / count
val_APCER = type2 / num_fake
val_BPCER = type1 / num_real
val_ACER = (val_APCER + val_BPCER) / 2.0
return val_threshold, val_ACC, val_APCER, val_BPCER, val_ACER
def performances_SiWM_EER(map_score_val_filename):
# val
with open(map_score_val_filename, 'r') as file:
lines = file.readlines()
val_scores = []
val_labels = []
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
score = float(tokens[0])
label = int(tokens[1])
val_scores.append(score)
val_labels.append(label)
data.append({'map_score': score, 'label': label})
if label == 1:
num_real += 1
else:
num_fake += 1
fpr, tpr, threshold = roc_curve(val_labels, val_scores, pos_label=1)
val_err, val_threshold = get_err_threhold(fpr, tpr, threshold)
type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
val_ACC = 1 - (type1 + type2) / count
val_APCER = type2 / num_fake
val_BPCER = type1 / num_real
val_ACER = (val_APCER + val_BPCER) / 2.0
return val_threshold, val_err, val_ACC, val_APCER, val_BPCER, val_ACER
def get_err_threhold_CASIA_Replay(fpr, tpr, threshold):
RightIndex = (tpr + (1 - fpr) - 1)
right_index = np.argmax(RightIndex)
best_th = threshold[right_index]
err = fpr[right_index]
differ_tpr_fpr_1 = tpr + fpr - 1.0
right_index = np.argmin(np.abs(differ_tpr_fpr_1))
best_th = threshold[right_index]
err = fpr[right_index]
# print(err, best_th)
return err, best_th, right_index
def performances_CASIA_Replay(map_score_val_filename):
# val
with open(map_score_val_filename, 'r') as file:
lines = file.readlines()
val_scores = []
val_labels = []
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
score = float(tokens[0])
label = float(tokens[1]) # int(tokens[1])
val_scores.append(score)
val_labels.append(label)
data.append({'map_score': score, 'label': label})
if label == 1:
num_real += 1
else:
num_fake += 1
fpr, tpr, threshold = roc_curve(val_labels, val_scores, pos_label=1)
val_err, val_threshold, right_index = get_err_threhold_CASIA_Replay(fpr, tpr, threshold)
type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
print([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
print([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
val_ACC = 1 - (type1 + type2) / count
FRR = 1 - tpr # FRR = 1 - TPR
HTER = (fpr + FRR) / 2.0 # error recognition rate & reject recognition rate
return val_ACC, fpr[right_index], FRR[right_index], HTER[right_index], val_threshold
def performances_ZeroShot(map_score_val_filename):
# val
with open(map_score_val_filename, 'r') as file:
lines = file.readlines()
val_scores = []
val_labels = []
data = []
count = 0.0
num_real = 0.0
num_fake = 0.0
for line in lines:
count += 1
tokens = line.split()
score = float(tokens[0])
label = int(tokens[1])
val_scores.append(score)
val_labels.append(label)
data.append({'map_score': score, 'label': label})
if label == 1:
num_real += 1
else:
num_fake += 1
fpr, tpr, threshold = roc_curve(val_labels, val_scores, pos_label=1)
auc_val = metrics.auc(fpr, tpr)
val_err, val_threshold, right_index = get_err_threhold_CASIA_Replay(fpr, tpr, threshold)
type1 = len([s for s in data if s['map_score'] <= val_threshold and s['label'] == 1])
type2 = len([s for s in data if s['map_score'] > val_threshold and s['label'] == 0])
val_ACC = 1 - (type1 + type2) / count
FRR = 1 - tpr # FRR = 1 - TPR
HTER = (fpr + FRR) / 2.0 # error recognition rate & reject recognition rate
return val_ACC, auc_val, HTER[right_index]
def count_parameters_in_MB(model):
return np.sum(np.prod(v.size()) for name, v in model.named_parameters() if "auxiliary" not in name) / 1e6
def save_checkpoint(state, is_best, save):
filename = os.path.join(save, 'checkpoint.pth.tar')
torch.save(state, filename)
if is_best:
best_filename = os.path.join(save, 'model_best.pth.tar')
shutil.copyfile(filename, best_filename)
def save(model, model_path):
torch.save(model.state_dict(), model_path)
def load(model, model_path):
model.load_state_dict(torch.load(model_path))
def drop_path(x, drop_prob):
if drop_prob > 0.:
keep_prob = 1. - drop_prob
mask = Variable(torch.cuda.FloatTensor(x.size(0), 1, 1, 1, 1).bernoulli_(keep_prob))
x.div_(keep_prob)
x.mul_(mask)
return x
def create_exp_dir(path, scripts_to_save=None):
if not os.path.exists(path):
os.mkdir(path)
print('Experiment dir : {}'.format(path))
if scripts_to_save is not None:
os.mkdir(os.path.join(path, 'scripts'))
for script in scripts_to_save:
dst_file = os.path.join(path, 'scripts', os.path.basename(script))
shutil.copyfile(script, dst_file)
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