-
Notifications
You must be signed in to change notification settings - Fork 0
/
eval_adv.py
executable file
·164 lines (137 loc) · 6.15 KB
/
eval_adv.py
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
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
import os
import argparse
import torch
import torch.nn.functional as F
from tqdm import tqdm
import data_loader.data_loaders as module_data
import model.loss as module_loss
import model.metric as module_metric
import model as module_arch
from metrics.evaluate_tDCF_asvspoof19_func import evaluate_tdcf_eer
from parse_config import ConfigParser
from pathlib import Path
from collections import defaultdict
from functools import reduce
import numpy as np
torch.manual_seed(1234) #cpu
torch.cuda.manual_seed(1234) #gpu
np.random.seed(1234) #numpy
# random.seed(1234) #random and transforms
torch.backends.cudnn.benchmark = True
# LCNN-big EER operating point. Dev set: -0.561486, Eval set: 0.694063
EER_POINT = 0.694063
def main(config, resume, adv_data_dir, protocol_file, asv_score_file):
logger = config.get_logger('evaluation')
# setup data_loader instances
# data_loader = getattr(module_data, config['dev_data_loader']['type'])(
# config['dev_data_loader']['args']['scp_file'],
# config['dev_data_loader']['args']['data_dir'],
# batch_size=32,
# shuffle=False,
# validation_split=0.0,
# num_workers=2
# )
data_loader = getattr(module_data, config['dev_data_loader']['type'])(
scp_file=None,
data_dir=adv_data_dir,
batch_size=32,
shuffle=False,
validation_split=0.0,
num_workers=1,
eval=True,
read_protocol=True,
protocol_file=protocol_file
)
# build model architecture
model = config.initialize('arch', module_arch)
# logger.info(model)
# get function handles of loss and metrics
# loss_fn = getattr(module_loss, config['loss'])
# loss_fn = config.initialize('loss', module_loss)
metric_fns = [getattr(module_metric, met) for met in config['metrics']]
logger.info('Loading checkpoint: {} ...'.format(resume))
checkpoint = torch.load(resume)
state_dict = checkpoint['state_dict']
if config['n_gpu'] > 1:
model = torch.nn.DataParallel(model)
model.load_state_dict(state_dict)
# prepare model for testing
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model.eval()
# utt2scores = defaultdict(list)
utt2scores = defaultdict()
# total_loss = 0.0
total_metrics = torch.zeros(len(metric_fns))
suffix = '_'.join(os.path.basename(adv_data_dir).split('_')[-2:])
attack_type = 'wb' if 'lcnn' in adv_data_dir else 'bb'
# attack_type = 'bb' if 'lcnn' in adv_data_dir else 'wb'
ff = open(os.path.join(os.path.dirname(resume), f'adv_pred_wb-{suffix}_WhatINeed.txt'), 'w')
num_spoofing = 0
num_false_positive = 0
with torch.no_grad():
for i, (utt_list, data, target) in enumerate(tqdm(data_loader)):
data, target = data.to(device), target.to(device)
output = model(data, eval=True)
# loss = loss_fn(output, target)
batch_size = data.shape[0]
# total_loss += loss.item() * batch_size
for i, metric in enumerate(metric_fns):
total_metrics[i] += metric(output, target) * batch_size
preds = output.max(1, keepdim=True)[1]
score = output[:, 1] # use the bonafide class for scoring
# score = F.softmax(output, dim=1)[:, 1]
# ======= #
# loglikeli = F.log_softmax(output, dim=1)
# score = loglikeli[:, 1] - loglikeli[:, 0]
# ======= #
for index, utt_id in enumerate(utt_list):
cur_score = score[index].item()
utt2scores[utt_id] = cur_score
pred_label = 'bonafide' if cur_score >= EER_POINT else 'spoofing'
target_label = {0: 'spoofing', 1: 'bonafide'}[target[index].item()]
if target_label == 'spoofing':
num_spoofing += 1
if pred_label == 'bonafide':
num_false_positive += 1
ff.write(utt_id+" "+target_label+" "+pred_label+"\n")
ff.close()
FPR = float(num_false_positive) / num_spoofing
n_samples = len(data_loader.sampler)
# log = {'loss': total_loss / n_samples}
log = { }
log.update({
met.__name__: total_metrics[i].item() / n_samples for i, met in enumerate(metric_fns)
})
logger.info(log)
# compute t-DCF and eer
with open(protocol_file, 'r') as f:
protocol_file_lines = [line.strip().split(' ') for line in f]
sys_id = os.path.basename(protocol_file).split('_')[-1].split('.')[0]
cm_score_file = Path(resume).parent / f'cm_score_eval_adv_whatINeed.txt'
with open(cm_score_file, 'w') as f:
for line in protocol_file_lines:
utt_id = line[1]
label = line[-1]
sco = utt2scores[utt_id]
f.write(utt_id+" "+"-"+" "+label+" "+str(sco)+"\n")
# score_list = utt2scores[utt_id]
# avg_score = reduce(lambda x, y: x + y, score_list) / len(score_list)
# f.write(utt_id+" "+"-"+" "+label+" "+str(avg_score)+"\n")
tdcf, eer = evaluate_tdcf_eer(cm_score_file, asv_score_file, print_cost=False)
logger.info({"min-tDCF": tdcf, "EER": eer, "FPR": FPR})
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ASVSpoof2019 project')
parser.add_argument('-r', '--resume', default=None, type=str,
help='path to latest checkpoint (default: None)')
parser.add_argument('--adv_data', default=None, type=str,
help='path to adversarial examples.')
parser.add_argument('-f', '--protocol_file', default=None, type=str,
help='Protocol file: e.g., data/ASVspoof2019.PA.cm.dev.trl.txt')
parser.add_argument('-a', '--asv_score_file', default=None, type=str,
help='Protocol file: e.g., data/ASVspoof2019_PA_dev_asv_scores_v1.txt')
parser.add_argument('-d', '--device', default=None, type=str,
help='indices of GPUs to enable (default: all)')
args = parser.parse_args()
config = ConfigParser(args)
main(config, args.resume, args.adv_data, args.protocol_file, args.asv_score_file)