def __init__(self, conf, inference=False):
pprint.pprint(conf)
if conf.use_mobilfacenet:
self.model = MobileFaceNet(conf.embedding_size).to(conf.device)
print('MobileFaceNet model generated')
else:
self.model = Backbone(conf.net_depth, conf.drop_ratio,
conf.net_mode).to(conf.device)
print('{}_{} model generated'.format(conf.net_mode,
conf.net_depth))
if not inference:
self.milestones = conf.milestones
self.loader, self.class_num = get_train_loader(conf)
self.writer = SummaryWriter(conf.log_path)
self.step = 0
self.head = Arcface(embedding_size=conf.embedding_size,
classnum=self.class_num).to(conf.device)
print('two model heads generated')
paras_only_bn, paras_wo_bn = separate_bn_paras(self.model)
if conf.use_mobilfacenet:
self.optimizer = optim.SGD(
[{
'params': paras_wo_bn[:-1],
'weight_decay': 4e-5
}, {
'params': [paras_wo_bn[-1]] + [self.head.kernel],
'weight_decay': 4e-4
}, {
'params': paras_only_bn
}],
lr=conf.lr,
momentum=conf.momentum)
else:
self.optimizer = optim.SGD(
[{
'params': paras_wo_bn + [self.head.kernel],
'weight_decay': 5e-4
}, {
'params': paras_only_bn
}],
lr=conf.lr,
momentum=conf.momentum)
print(self.optimizer)
# self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, patience=40, verbose=True)
print('optimizers generated')
self.board_loss_every = len(self.loader) // 5 # originally, 100
self.evaluate_every = len(self.loader) // 5 # originally, 10
self.save_every = len(self.loader) // 2 # originally, 5
# self.agedb_30, self.cfp_fp, self.lfw, self.agedb_30_issame, self.cfp_fp_issame, self.lfw_issame = get_val_data(self.loader.dataset.root.parent)
self.val_112, self.val_112_issame = get_val_pair(
self.loader.dataset.root.parent, 'val_112')
else:
self.threshold = conf.threshold
def verify(learner):
vgg2_fp, vgg2_fp_issame = get_val_pair(conf.emore_folder, 'vgg2_fp')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(
conf, vgg2_fp, vgg2_fp_issame, nrof_folds=10, tta=True)
print('vgg2_fp - accuray:{}, threshold:{}'.format(accuracy,
best_threshold))
trans.ToPILImage()(roc_curve_tensor)
agedb_30, agedb_30_issame = get_val_pair(conf.emore_folder, 'agedb_30')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(
conf, agedb_30, agedb_30_issame, nrof_folds=10, tta=True)
print('agedb_30 - accuray:{}, threshold:{}'.format(accuracy,
best_threshold))
trans.ToPILImage()(roc_curve_tensor)
calfw, calfw_issame = get_val_pair(conf.emore_folder, 'calfw')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(
conf, calfw, calfw_issame, nrof_folds=10, tta=True)
print('calfw - accuray:{}, threshold:{}'.format(accuracy, best_threshold))
trans.ToPILImage()(roc_curve_tensor)
cfp_ff, cfp_ff_issame = get_val_pair(conf.emore_folder, 'cfp_ff')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(
conf, cfp_ff, cfp_ff_issame, nrof_folds=10, tta=True)
print('cfp_ff - accuray:{}, threshold:{}'.format(accuracy, best_threshold))
trans.ToPILImage()(roc_curve_tensor)
cfp_fp, cfp_fp_issame = get_val_pair(conf.emore_folder, 'cfp_fp')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(
conf, cfp_fp, cfp_fp_issame, nrof_folds=10, tta=True)
print('cfp_fp - accuray:{}, threshold:{}'.format(accuracy, best_threshold))
trans.ToPILImage()(roc_curve_tensor)
cplfw, cplfw_issame = get_val_pair(conf.emore_folder, 'cplfw')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(
conf, cplfw, cplfw_issame, nrof_folds=10, tta=True)
print('cplfw - accuray:{}, threshold:{}'.format(accuracy, best_threshold))
trans.ToPILImage()(roc_curve_tensor)
lfw, lfw_issame = get_val_pair(conf.emore_folder, 'lfw')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(
conf, lfw, lfw_issame, nrof_folds=10, tta=True)
print('lfw - accuray:{}, threshold:{}'.format(accuracy, best_threshold))
trans.ToPILImage()(roc_curve_tensor)
from config import get_config
import argparse
from Learner import face_learner
from data.data_pipe import get_val_pair
from torchvision import transforms as trans
import time
import pdb
conf = get_config(training=False)
learner = face_learner(conf, inference=True)
learner.load_state(conf, 'ir_se50.pth', model_only=True, from_save_folder=True)
before_time = time.time()
lfw, lfw_issame = get_val_pair(conf.emore_folder, 'lfw')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(conf,
lfw,
lfw_issame,
nrof_folds=10,
tta=True)
print('lfw - accuracy:{}, threshold:{}'.format(accuracy, best_threshold))
# trans.ToPILImage()(roc_curve_tensor)
after_time = time.time()
print(f'time_consumed: {after_time - before_time}')
vgg2_fp, vgg2_fp_issame = get_val_pair(conf.emore_folder, 'vgg2_fp')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(conf,
vgg2_fp,
vgg2_fp_issame,
nrof_folds=10,
tta=True)
from config import get_config
from Learner import face_learner
from data.data_pipe import get_val_pair
from torchvision import transforms as trans
conf = get_config(training=False)
learner = face_learner(conf, inference=True)
learner.load_state(conf, 'ir_se50.pth', model_only=True)
# LFW evaluation
lfw, lfw_issame = get_val_pair(conf.emore_folder, 'lfw')
accuracy, best_threshold, roc_curve_tensor = learner.evaluate(conf,
lfw,
lfw_issame,
nrof_folds=10,
tta=False)
print('lfw - accuray:{}, threshold:{}'.format(accuracy, best_threshold))
trans.ToPILImage()(roc_curve_tensor)
def evaluate_ori(model, path, name, nrof_folds=10, tta=True):
from utils import ccrop_batch, hflip_batch
from models.model import l2_norm
from verifacation import evaluate
idx = 0
from data.data_pipe import get_val_pair
carray, issame = get_val_pair(path, name)
carray = carray[:, ::-1, :, :] # BGR 2 RGB!
if use_mxnet:
carray *= 0.5
carray += 0.5
carray *= 255.
embeddings = np.zeros([len(carray), 512])
if not use_mxnet:
with torch.no_grad():
while idx + bs <= len(carray):
batch = torch.tensor(carray[idx:idx + bs])
if tta:
# batch = ccrop_batch(batch)
fliped = hflip_batch(batch)
emb_batch = model(batch.cuda()) + model(fliped.cuda())
emb_batch = emb_batch.cpu()
embeddings[idx:idx + bs] = l2_norm(emb_batch)
else:
embeddings[idx:idx + bs] = model(batch.cuda()).cpu()
idx += bs
if idx < len(carray):
batch = torch.tensor(carray[idx:])
if tta:
# batch = ccrop_batch(batch)
fliped = hflip_batch(batch)
emb_batch = model(batch.cuda()) + model(fliped.cuda())
emb_batch = emb_batch.cpu()
embeddings[idx:] = l2_norm(emb_batch)
else:
embeddings[idx:] = model(batch.cuda()).cpu()
else:
from sklearn.preprocessing import normalize
while idx + bs <= len(carray):
batch = torch.tensor(carray[idx:idx + bs])
if tta:
fliped = hflip_batch(batch)
emb_batch = model(batch) + model(fliped)
embeddings[idx:idx + bs] = normalize(emb_batch)
else:
embeddings[idx:idx + bs] = model(batch)
idx += bs
if idx < len(carray):
batch = torch.tensor(carray[idx:])
if tta:
fliped = hflip_batch(batch)
emb_batch = model(batch) + model(fliped)
embeddings[idx:] = normalize(emb_batch)
else:
embeddings[idx:] = model(batch)
tpr, fpr, accuracy, best_thresholds = evaluate(embeddings, issame,
nrof_folds)
roc_curve_tensor = None
# buf = gen_plot(fpr, tpr)
# roc_curve = Image.open(buf)
# roc_curve_tensor = trans.ToTensor()(roc_curve)
return accuracy.mean(), best_thresholds.mean(), roc_curve_tensor