def test(args):
device = torch.device(('cuda:%d' %
args.gpu) if torch.cuda.is_available() else 'cpu')
BACKBONE = Backbone([args.input_size, args.input_size], args.num_layers,
args.mode)
BACKBONE.load_state_dict(torch.load(args.ckpt_path))
BACKBONE.to(device)
BACKBONE.eval()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
print('Start test at', datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
# accuracy
with open(args.pair_file, 'r') as f:
pairs = f.readlines()
sims = []
labels = []
for pair_id, pair in tqdm.tqdm(enumerate(pairs)):
# print('processing %d/%d...' % (pair_id, len(pairs)), end='\r')
splits = pair.split()
feat1 = get_feature(os.path.join(args.data_root, splits[0]), transform,
BACKBONE, device)
feat2 = get_feature(os.path.join(args.data_root, splits[1]), transform,
BACKBONE, device)
label = int(splits[2])
sim = np.dot(feat1,
feat2) / (np.linalg.norm(feat1) * np.linalg.norm(feat2))
sims.append(sim)
labels.append(label)
acc, th = cal_accuracy(np.array(sims), np.array(labels))
print('acc=%f with threshold=%f' % (acc, th))
print('Finish test at', datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
def train(args):
DEVICE = torch.device(("cuda:%d"%args.gpu[0]) if torch.cuda.is_available() else "cpu")
writer = SummaryWriter(args.log_root)
train_transform = transforms.Compose([transforms.Resize([int(128*args.input_size/112), int(128*args.input_size/112)]),
transforms.RandomCrop([args.input_size, args.input_size]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[args.rgb_mean,args.rgb_mean,args.rgb_mean], std=[args.rgb_std,args.rgb_std,args.rgb_std])
])
train_dataset = datasets.ImageFolder(args.data_root, train_transform)
weights = make_weights_for_balanced_classes(train_dataset.imgs, len(train_dataset.classes))
weights = torch.DoubleTensor(weights)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights))
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, num_workers=8, shuffle=True, drop_last=True)
NUM_CLASS = len(train_loader.dataset.classes)
BACKBONE = Backbone([args.input_size, args.input_size], args.num_layers, args.mode)
HEAD = ArcFace(args.emb_dims, NUM_CLASS, device_id=args.gpu)
LOSS = FocalLoss()
backbone_paras_only_bn, backbone_paras_wo_bn = separate_irse_bn_paras(BACKBONE)
_, head_paras_wo_bn = separate_irse_bn_paras(HEAD)
optimizer = optim.SGD([{'params': backbone_paras_wo_bn+head_paras_wo_bn, 'weight_decay': args.weight_decay},
{'params': backbone_paras_only_bn}], lr=args.lr, momentum=args.momentum)
# optimizer = optim.AdamW([{'params': backbone_paras_wo_bn+head_paras_wo_bn, 'weight_decay': args.weight_decay},
# {'params': backbone_paras_only_bn}], lr=args.lr, momentum=args.momentum)
if args.load_ckpt:
BACKBONE.load_state_dict(torch.load(os.path.join(args.load_ckpt, 'backbone_epoch{}.pth'.format(args.load_epoch))))
HEAD.load_state_dict(torch.load(os.path.join(args.load_ckpt, 'head_epoch{}.pth'.format(args.load_epoch))))
print('Checkpoint loaded')
start_epoch = args.load_epoch if args.load_ckpt else 0
BACKBONE = nn.DataParallel(BACKBONE, device_ids=args.gpu)
BACKBONE = BACKBONE.to(DEVICE)
dispaly_frequency = len(train_loader) // 100
NUM_EPOCH_WARM_UP = args.num_epoch // 25
NUM_BATCH_WARM_UP = len(train_loader) * NUM_EPOCH_WARM_UP
batch = 0
print('Start training at %s!' % datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
for epoch in range(start_epoch, args.num_epoch):
if epoch==args.stages[0] or epoch==args.stages[1] or epoch==args.stages[2]:
for params in optimizer.param_groups:
params['lr'] /= 10.
BACKBONE.train()
HEAD.train()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
for inputs, labels in train_loader:
if (epoch+1 <= NUM_EPOCH_WARM_UP) and (batch+1 <= NUM_BATCH_WARM_UP):
for params in optimizer.param_groups:
params['lr'] = (batch+1) * args.lr / NUM_BATCH_WARM_UP
inputs = inputs.to(DEVICE)
labels = labels.to(DEVICE).long()
features = BACKBONE(inputs)
outputs = HEAD(features, labels)
loss = LOSS(outputs, labels)
prec1, prec5 = accuracy(outputs.data, labels, topk=(1,5))
losses.update(loss.data.item(), inputs.size(0))
top1.update(prec1.data.item(), inputs.size(0))
top5.update(prec5.data.item(), inputs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch += 1
if batch % dispaly_frequency == 0:
print('%s Epoch %d/%d Batch %d/%d: train loss %f, train prec@1 %f, train prec@5 %f' % (datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
epoch, args.num_epoch, batch, len(train_loader)*args.num_epoch, losses.avg, top1.avg, top5.avg))
writer.add_scalar('Train_Loss', losses.avg, epoch+1)
writer.add_scalar('Train_Top1_Accuracy', top1.avg, epoch+1)
writer.add_scalar('Train_Top5_Accuracy', top5.avg, epoch+1)
torch.save(BACKBONE.module.state_dict(), os.path.join(args.ckpt_root, 'backbone_epoch%d.pth'%(epoch+1)))
torch.save(HEAD.state_dict(), os.path.join(args.ckpt_root, 'head_epoch%d.pth'%(epoch+1)))
class face_learner(object):
def __init__(self, conf, inference=False):
print(conf)
# self.loader, self.class_num = construct_msr_dataset(conf)
self.loader, self.class_num = get_train_loader(conf)
self.model = Backbone(conf.net_depth, conf.drop_ratio, conf.net_mode)
print('{}_{} model generated'.format(conf.net_mode, conf.net_depth))
if not inference:
self.milestones = conf.milestones
self.writer = SummaryWriter(conf.log_path)
self.step = 0
self.head = QAMFace(embedding_size=conf.embedding_size,
classnum=self.class_num).to(conf.device)
self.focalLoss = FocalLoss()
print('two model heads generated')
paras_only_bn, paras_wo_bn = separate_bn_paras(self.model)
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) // 1000
self.evaluate_every = len(self.loader) // 10
self.save_every = len(self.loader) // 2
else:
self.threshold = conf.threshold
# 多GPU训练
self.model = torch.nn.DataParallel(self.model)
self.model.to(conf.device)
self.head = torch.nn.DataParallel(self.head)
self.head = self.head.to(conf.device)
def save_state(self,
conf,
accuracy,
to_save_folder=False,
extra=None,
model_only=False):
if to_save_folder:
save_path = conf.save_path
else:
save_path = conf.model_path
torch.save(
self.model.state_dict(),
save_path / ('model_{}_accuracy:{}_step:{}_{}.pth'.format(
get_time(), accuracy, self.step, extra)))
if not model_only:
torch.save(
self.head.state_dict(),
save_path / ('head_{}_accuracy:{}_step:{}_{}.pth'.format(
get_time(), accuracy, self.step, extra)))
torch.save(
self.optimizer.state_dict(),
save_path / ('optimizer_{}_accuracy:{}_step:{}_{}.pth'.format(
get_time(), accuracy, self.step, extra)))
def load_state(self,
conf,
fixed_str,
from_save_folder=False,
model_only=False):
print('resume model from ' + fixed_str)
if from_save_folder:
save_path = conf.save_path
else:
save_path = conf.model_path
self.model.load_state_dict(
torch.load(save_path / 'model_{}'.format(fixed_str)))
if not model_only:
self.head.load_state_dict(
torch.load(save_path / 'head_{}'.format(fixed_str)))
self.optimizer.load_state_dict(
torch.load(save_path / 'optimizer_{}'.format(fixed_str)))
def board_val(self,
db_name,
accuracy,
best_threshold=0,
roc_curve_tensor=0):
self.writer.add_scalar('{}_accuracy'.format(db_name), accuracy,
self.step)
def train(self, conf, epochs):
self.model.train()
running_loss = 0.
for e in range(epochs):
print('epoch {} started'.format(e))
# manually decay lr
if e in self.milestones:
self.schedule_lr()
for imgs, labels in tqdm(iter(self.loader)):
imgs = (imgs[:, (2, 1, 0)].to(conf.device) * 255) # RGB
labels = labels.to(conf.device)
self.optimizer.zero_grad()
embeddings = self.model(imgs)
thetas = self.head(embeddings, labels)
loss = self.focalLoss(thetas, labels)
loss.backward()
running_loss += loss.item() / conf.batch_size
self.optimizer.step()
if self.step % self.board_loss_every == 0 and self.step != 0:
loss_board = running_loss / self.board_loss_every
self.writer.add_scalar('train_loss', loss_board, self.step)
running_loss = 0.
if self.step % self.evaluate_every == 0 and self.step != 0:
self.model.eval()
for bmk in [
'agedb_30', 'lfw', 'calfw', 'cfp_ff', 'cfp_fp',
'cplfw', 'vgg2_fp'
]:
acc = eval_emore_bmk(conf, self.model, bmk)
self.board_val(bmk, acc)
self.model.train()
if self.step % self.save_every == 0 and self.step != 0:
self.save_state(conf, acc)
self.step += 1
self.save_state(conf, acc, to_save_folder=True, extra='final')
def myValidation(self, conf):
self.model.eval()
for bmk in [
'agedb_30', 'lfw', 'calfw', 'cfp_ff', 'cfp_fp', 'cplfw',
'vgg2_fp'
]:
eval_emore_bmk(conf, self.model, bmk)
def schedule_lr(self):
for params in self.optimizer.param_groups:
params['lr'] /= 10
print(self.optimizer)
class face_learner(object):
def __init__(self, conf, inference=False):
print(conf)
self.lr=conf.lr
if conf.use_mobilfacenet:
self.model = MobileFaceNet(conf.embedding_size).to(conf.device)
print('MobileFaceNet model generated')
else:
############################### ir_se50 ########################################
if conf.struct =='ir_se_50':
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))
############################### resnet101 ######################################
if conf.struct =='ir_se_101':
self.model = resnet101().to(conf.device)
print('resnet101 model generated')
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
############################### ir_se50 ########################################
if conf.struct =='ir_se_50':
self.head = Arcface(embedding_size=conf.embedding_size, classnum=self.class_num).to(conf.device)
self.head_race = Arcface(embedding_size=conf.embedding_size, classnum=4).to(conf.device)
############################### resnet101 ######################################
if conf.struct =='ir_se_101':
self.head = ArcMarginModel(embedding_size=conf.embedding_size,classnum=self.class_num).to(conf.device)
self.head_race = ArcMarginModel(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] + [self.head_race.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] + [self.head_race.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')
print('len of loader:',len(self.loader))
self.board_loss_every = len(self.loader)//min(len(self.loader),100)
self.evaluate_every = len(self.loader)//1
self.save_every = len(self.loader)//1
self.agedb_30, self.cfp_fp, self.lfw, self.agedb_30_issame, self.cfp_fp_issame, self.lfw_issame = get_val_data(conf.val_folder)
else:
#self.threshold = conf.threshold
pass
def save_state(self, conf, accuracy, to_save_folder=False, extra=None, model_only=False):
if to_save_folder:
save_path = conf.save_path
else:
save_path = conf.model_path
torch.save(
self.model.state_dict(), save_path /
('model_{}_accuracy:{}_step:{}_{}.pth'.format(get_time(), accuracy, self.step, extra)))
if not model_only:
torch.save(
self.head.state_dict(), save_path /
('head_{}_accuracy:{}_step:{}_{}.pth'.format(get_time(), accuracy, self.step, extra)))
torch.save(
self.head_race.state_dict(), save_path /
('head__race{}_accuracy:{}_step:{}_{}.pth'.format(get_time(), accuracy, self.step, extra)))
torch.save(
self.optimizer.state_dict(), save_path /
('optimizer_{}_accuracy:{}_step:{}_{}.pth'.format(get_time(), accuracy, self.step, extra)))
def load_state(self, model, head=None,head_race=None,optimizer=None):
self.model.load_state_dict(torch.load(model),strict=False)
if head is not None:
self.head.load_state_dict(torch.load(head))
if head_race is not None:
self.head_race.load_state_dict(torch.load(head_race))
if optimizer is not None:
self.optimizer.load_state_dict(torch.load(optimizer))
def board_val(self, db_name, accuracy, best_threshold, roc_curve_tensor,tpr_val):
self.writer.add_scalar('{}_accuracy'.format(db_name), accuracy, self.step)
self.writer.add_scalar('{}_best_threshold'.format(db_name), best_threshold, self.step)
self.writer.add_image('{}_roc_curve'.format(db_name), roc_curve_tensor, self.step)
self.writer.add_scalar('{}[email protected]'.format(db_name), tpr_val, self.step)
# self.writer.add_scalar('{}_val:true accept ratio'.format(db_name), val, self.step)
# self.writer.add_scalar('{}_val_std'.format(db_name), val_std, self.step)
# self.writer.add_scalar('{}_far:False Acceptance Ratio'.format(db_name), far, self.step)
def evaluate(self, conf, carray, issame, nrof_folds = 5, tta = False):
self.model.eval()
idx = 0
embeddings = np.zeros([len(carray), conf.embedding_size])
with torch.no_grad():
while idx + conf.batch_size <= len(carray):
batch = torch.tensor(carray[idx:idx + conf.batch_size])
if tta:
fliped = hflip_batch(batch)
emb_batch = self.model(batch.to(conf.device)) + self.model(fliped.to(conf.device))
embeddings[idx:idx + conf.batch_size] = l2_norm(emb_batch)
else:
embeddings[idx:idx + conf.batch_size] = self.model(batch.to(conf.device)).cpu()
idx += conf.batch_size
if idx < len(carray):
batch = torch.tensor(carray[idx:])
if tta:
fliped = hflip_batch(batch)
emb_batch = self.model(batch.to(conf.device)) + self.model(fliped.to(conf.device))
embeddings[idx:] = l2_norm(emb_batch)
else:
embeddings[idx:] = self.model(batch.to(conf.device)).cpu()
tpr, fpr, accuracy, best_thresholds = evaluate(embeddings, issame, nrof_folds)
try:
tpr_val = tpr[np.less(fpr,0.0012)&np.greater(fpr,0.0008)][0]
except:
tpr_val = 0
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,tpr_val
def find_lr(self,
conf,
init_value=1e-8,
final_value=10.,
beta=0.98,
bloding_scale=3.,
num=None):
if not num:
num = len(self.loader)
mult = (final_value / init_value)**(1 / num)
lr = init_value
for params in self.optimizer.param_groups:
params['lr'] = lr
self.model.train()
avg_loss = 0.
best_loss = 0.
batch_num = 0
losses = []
log_lrs = []
for i, (imgs, labels) in tqdm(enumerate(self.loader), total=num):
imgs = imgs.to(conf.device)
labels = labels.to(conf.device)
batch_num += 1
self.optimizer.zero_grad()
embeddings = self.model(imgs)
thetas = self.head(embeddings, labels)
loss = conf.ce_loss(thetas, labels)
#Compute the smoothed loss
avg_loss = beta * avg_loss + (1 - beta) * loss.item()
self.writer.add_scalar('avg_loss', avg_loss, batch_num)
smoothed_loss = avg_loss / (1 - beta**batch_num)
self.writer.add_scalar('smoothed_loss', smoothed_loss,batch_num)
#Stop if the loss is exploding
if batch_num > 1 and smoothed_loss > bloding_scale * best_loss:
print('exited with best_loss at {}'.format(best_loss))
plt.plot(log_lrs[10:-5], losses[10:-5])
return log_lrs, losses
#Record the best loss
if smoothed_loss < best_loss or batch_num == 1:
best_loss = smoothed_loss
#Store the values
losses.append(smoothed_loss)
log_lrs.append(math.log10(lr))
self.writer.add_scalar('log_lr', math.log10(lr), batch_num)
#Do the SGD step
#Update the lr for the next step
loss.backward()
self.optimizer.step()
lr *= mult
for params in self.optimizer.param_groups:
params['lr'] = lr
if batch_num > num:
plt.plot(log_lrs[10:-5], losses[10:-5])
return log_lrs, losses
def train(self, conf, epochs):
self.model = self.model.to(conf.device)
self.head = self.head.to(conf.device)
self.head_race = self.head_race.to(conf.device)
self.model.train()
self.head.train()
self.head_race.train()
running_loss = 0.
for e in range(epochs):
print('epoch {} started'.format(e))
if e == 8:#5 #train hear_race
#self.init_lr()
conf.loss0 = False
conf.loss1 = True
conf.loss2 = True
conf.model = False
conf.head = False
conf.head_race = True
print(conf)
if e == 16:#10:
#self.init_lr()
self.schedule_lr()
conf.loss0 = True
conf.loss1 = True
conf.loss2 = True
conf.model = True
conf.head = True
conf.head_race = True
print(conf)
if e == 28:#22
self.schedule_lr()
if e == 32:
self.schedule_lr()
if e == 35:
self.schedule_lr()
requires_grad(self.head,conf.head)
requires_grad(self.head_race,conf.head_race)
requires_grad(self.model,conf.model)
for imgs, labels in tqdm(iter(self.loader)):
imgs = imgs.to(conf.device)
labels = labels.to(conf.device)
labels_race = torch.zeros_like(labels)
race0_index = labels.lt(sum(conf.race_num[:1]))
race1_index = labels.lt(sum(conf.race_num[:2])) & labels.ge(sum(conf.race_num[:1]))
race2_index = labels.lt(sum(conf.race_num[:3])) & labels.ge(sum(conf.race_num[:2]))
race3_index = labels.ge(sum(conf.race_num[:3]))
labels_race[race0_index]=0
labels_race[race1_index] = 1
labels_race[race2_index] = 2
labels_race[race3_index] = 3
self.optimizer.zero_grad()
embeddings = self.model(imgs)
thetas ,w = self.head(embeddings, labels)
thetas_race ,w_race = self.head_race(embeddings, labels_race)
loss = 0
loss0 = conf.ce_loss(thetas, labels)
loss1 = conf.ce_loss(thetas_race, labels_race)
loss2 = torch.mm(w_race.t(),w).to(conf.device)
target = torch.zeros_like(loss2).to(conf.device)
target[0][:sum(conf.race_num[:1])] = 1
target[1][sum(conf.race_num[:1]):sum(conf.race_num[:2])] = 1
target[2][sum(conf.race_num[:2]):sum(conf.race_num[:3])] = 1
target[3][sum(conf.race_num[:3]):] = 1
weight = torch.zeros_like(loss2).to(conf.device)
for i in range(4):
weight[i,:] = sum(conf.race_num)/conf.race_num[i]
#loss2 = torch.nn.functional.mse_loss(loss2 , target)
loss2 = F.binary_cross_entropy(torch.sigmoid(loss2),target,weight)
if conf.loss0 ==True:
loss += 2*loss0
if conf.loss1 ==True:
loss += loss1
if conf.loss2 ==True:
loss += loss2
#loss = loss0 + loss1 + loss2
loss.backward()
running_loss += loss.item()
self.optimizer.step()
if self.step % self.board_loss_every == 0 and self.step != 0:
loss_board = running_loss / self.board_loss_every
self.writer.add_scalar('train_loss', loss_board, self.step)
running_loss = 0.
if self.step % self.evaluate_every == 0 and self.step != 0:
accuracy=None
accuracy, best_threshold, roc_curve_tensor ,tpr_val= self.evaluate(conf, self.agedb_30, self.agedb_30_issame)
self.board_val('agedb_30', accuracy, best_threshold, roc_curve_tensor,tpr_val)
accuracy, best_threshold, roc_curve_tensor,tpr_val = self.evaluate(conf, self.lfw, self.lfw_issame)
self.board_val('lfw', accuracy, best_threshold, roc_curve_tensor,tpr_val)
accuracy, best_threshold, roc_curve_tensor,tpr_val = self.evaluate(conf, self.cfp_fp, self.cfp_fp_issame)
self.board_val('cfp_fp', accuracy, best_threshold, roc_curve_tensor,tpr_val)
self.model.train()
if self.step % self.save_every == 0 and self.step != 0:
self.save_state(conf, accuracy)
self.step += 1
self.save_state(conf, accuracy, to_save_folder=True, extra='final')
def schedule_lr(self):
for params in self.optimizer.param_groups:
params['lr'] /= 10
print(self.optimizer)
def init_lr(self):
for params in self.optimizer.param_groups:
params['lr'] = self.lr
print(self.optimizer)
def schedule_lr_add(self):
for params in self.optimizer.param_groups:
params['lr'] *= 10
print(self.optimizer)
def infer(self, conf, faces, target_embs, tta=False):
'''
faces : list of PIL Image
target_embs : [n, 512] computed embeddings of faces in facebank
names : recorded names of faces in facebank
tta : test time augmentation (hfilp, that's all)
'''
embs = []
for img in faces:
if tta:
mirror = trans.functional.hflip(img)
emb = self.model(conf.test_transform(img).to(conf.device).unsqueeze(0))
emb_mirror = self.model(conf.test_transform(mirror).to(conf.device).unsqueeze(0))
embs.append(l2_norm(emb + emb_mirror))
else:
embs.append(self.model(conf.test_transform(img).to(conf.device).unsqueeze(0)))
source_embs = torch.cat(embs)
diff = source_embs.unsqueeze(-1) - target_embs.transpose(1,0).unsqueeze(0)
dist = torch.sum(torch.pow(diff, 2), dim=1)
minimum, min_idx = torch.min(dist, dim=1)
min_idx[minimum > self.threshold] = -1 # if no match, set idx to -1
return min_idx, minimum
class face_learner(object):
def __init__(self, conf, inference=False):
print(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) // 100
self.evaluate_every = len(self.loader) // 10
self.save_every = len(self.loader) // 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)
else:
self.threshold = conf.threshold
def save_state(self,
conf,
accuracy,
to_save_folder=False,
extra=None,
model_only=False):
if to_save_folder:
save_path = conf.save_path
else:
save_path = conf.model_path
torch.save(
self.model.state_dict(),
save_path / ('model_{}_accuracy:{}_step:{}_{}.pth'.format(
get_time(), accuracy, self.step, extra)))
if not model_only:
torch.save(
self.head.state_dict(),
save_path / ('head_{}_accuracy:{}_step:{}_{}.pth'.format(
get_time(), accuracy, self.step, extra)))
torch.save(
self.optimizer.state_dict(),
save_path / ('optimizer_{}_accuracy:{}_step:{}_{}.pth'.format(
get_time(), accuracy, self.step, extra)))
def load_state(self,
conf,
fixed_str,
from_save_folder=False,
model_only=False):
if from_save_folder:
save_path = conf.save_path
else:
save_path = conf.model_path
#self.model.load_state_dict(torch.load(save_path/'model_{}'.format(fixed_str)))
state_dict = torch.load(save_path / 'model_{}'.format(fixed_str))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:]
new_state_dict[name] = v
self.model.load_state_dict(new_state_dict)
#
if not model_only:
self.head.load_state_dict(
torch.load(save_path / 'head_{}'.format(fixed_str)))
self.optimizer.load_state_dict(
torch.load(save_path / 'optimizer_{}'.format(fixed_str)))
def board_val(self, db_name, accuracy, best_threshold, roc_curve_tensor):
self.writer.add_scalar('{}_accuracy'.format(db_name), accuracy,
self.step)
self.writer.add_scalar('{}_best_threshold'.format(db_name),
best_threshold, self.step)
self.writer.add_image('{}_roc_curve'.format(db_name), roc_curve_tensor,
self.step)
# self.writer.add_scalar('{}_val:true accept ratio'.format(db_name), val, self.step)
# self.writer.add_scalar('{}_val_std'.format(db_name), val_std, self.step)
# self.writer.add_scalar('{}_far:False Acceptance Ratio'.format(db_name), far, self.step)
def evaluate(self, conf, carray, issame, nrof_folds=5, tta=False):
self.model.eval()
idx = 0
embeddings = np.zeros([len(carray), conf.embedding_size])
with torch.no_grad():
while idx + conf.batch_size <= len(carray):
batch = torch.tensor(carray[idx:idx + conf.batch_size])
if tta:
fliped = hflip_batch(batch)
emb_batch = self.model(batch.to(conf.device)) + self.model(
fliped.to(conf.device))
embeddings[idx:idx + conf.batch_size] = l2_norm(emb_batch)
else:
embeddings[idx:idx + conf.batch_size] = self.model(
batch.to(conf.device)).cpu()
idx += conf.batch_size
if idx < len(carray):
batch = torch.tensor(carray[idx:])
if tta:
fliped = hflip_batch(batch)
emb_batch = self.model(batch.to(conf.device)) + self.model(
fliped.to(conf.device))
embeddings[idx:] = l2_norm(emb_batch)
else:
embeddings[idx:] = self.model(batch.to(conf.device)).cpu()
tpr, fpr, accuracy, best_thresholds = evaluate(embeddings, issame,
nrof_folds)
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
def find_lr(self,
conf,
init_value=1e-8,
final_value=10.,
beta=0.98,
bloding_scale=3.,
num=None):
if not num:
num = len(self.loader)
mult = (final_value / init_value)**(1 / num)
lr = init_value
for params in self.optimizer.param_groups:
params['lr'] = lr
self.model.train()
avg_loss = 0.
best_loss = 0.
batch_num = 0
losses = []
log_lrs = []
for i, (imgs, labels) in tqdm(enumerate(self.loader), total=num):
imgs = imgs.to(conf.device)
labels = labels.to(conf.device)
batch_num += 1
self.optimizer.zero_grad()
embeddings = self.model(imgs)
thetas = self.head(embeddings, labels)
loss = conf.ce_loss(thetas, labels)
#Compute the smoothed loss
avg_loss = beta * avg_loss + (1 - beta) * loss.item()
self.writer.add_scalar('avg_loss', avg_loss, batch_num)
smoothed_loss = avg_loss / (1 - beta**batch_num)
self.writer.add_scalar('smoothed_loss', smoothed_loss, batch_num)
#Stop if the loss is exploding
if batch_num > 1 and smoothed_loss > bloding_scale * best_loss:
print('exited with best_loss at {}'.format(best_loss))
plt.plot(log_lrs[10:-5], losses[10:-5])
return log_lrs, losses
#Record the best loss
if smoothed_loss < best_loss or batch_num == 1:
best_loss = smoothed_loss
#Store the values
losses.append(smoothed_loss)
log_lrs.append(math.log10(lr))
self.writer.add_scalar('log_lr', math.log10(lr), batch_num)
#Do the SGD step
#Update the lr for the next step
loss.backward()
self.optimizer.step()
lr *= mult
for params in self.optimizer.param_groups:
params['lr'] = lr
if batch_num > num:
plt.plot(log_lrs[10:-5], losses[10:-5])
return log_lrs, losses
def train(self, conf, epochs):
self.model.train()
# Using GPU
conf.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model = nn.DataParallel(self.model, device_ids=[0, 1, 2, 3])
self.model.to(conf.device)
#
running_loss = 0.
for e in range(epochs):
print('epoch {} started'.format(e))
if e == self.milestones[0]:
self.schedule_lr()
if e == self.milestones[1]:
self.schedule_lr()
if e == self.milestones[2]:
self.schedule_lr()
for imgs, labels in tqdm(iter(self.loader)):
imgs = imgs.to(conf.device)
labels = labels.to(conf.device)
self.optimizer.zero_grad()
embeddings = self.model(imgs)
thetas = self.head(embeddings, labels)
loss = conf.ce_loss(thetas, labels)
loss.backward()
running_loss += loss.item()
self.optimizer.step()
if self.step % self.board_loss_every == 0 and self.step != 0:
loss_board = running_loss / self.board_loss_every
self.writer.add_scalar('train_loss', loss_board, self.step)
running_loss = 0.
if self.step % self.evaluate_every == 0 and self.step != 0:
accuracy, best_threshold, roc_curve_tensor = self.evaluate(
conf, self.agedb_30, self.agedb_30_issame)
self.board_val('agedb_30', accuracy, best_threshold,
roc_curve_tensor)
accuracy, best_threshold, roc_curve_tensor = self.evaluate(
conf, self.lfw, self.lfw_issame)
self.board_val('lfw', accuracy, best_threshold,
roc_curve_tensor)
accuracy, best_threshold, roc_curve_tensor = self.evaluate(
conf, self.cfp_fp, self.cfp_fp_issame)
self.board_val('cfp_fp', accuracy, best_threshold,
roc_curve_tensor)
self.model.train()
if self.step % self.save_every == 0 and self.step != 0:
self.save_state(conf, accuracy)
self.step += 1
self.save_state(conf, accuracy, to_save_folder=True, extra='final')
def schedule_lr(self):
for params in self.optimizer.param_groups:
params['lr'] /= 10
print(self.optimizer)
def infer(self, conf, faces, target_embs, tta=False):
'''
faces : list of PIL Image
target_embs : [n, 512] computed embeddings of faces in facebank
names : recorded names of faces in facebank
tta : test time augmentation (hfilp, that's all)
'''
embs = []
for img in faces:
if tta:
mirror = trans.functional.hflip(img)
emb = self.model(
conf.test_transform(img).to(conf.device).unsqueeze(0))
emb_mirror = self.model(
conf.test_transform(mirror).to(conf.device).unsqueeze(0))
embs.append(l2_norm(emb + emb_mirror))
else:
embs.append(
self.model(
conf.test_transform(img).to(conf.device).unsqueeze(0)))
source_embs = torch.cat(embs)
diff = source_embs.unsqueeze(-1) - target_embs.transpose(
1, 0).unsqueeze(0)
dist = torch.sum(torch.pow(diff, 2), dim=1)
minimum, min_idx = torch.min(dist, dim=1)
min_idx[minimum > self.threshold] = -1 # if no match, set idx to -1
return min_idx, minimum
log_period = config.log_interval
checkpoint_period = config.save_model_interval
eval_period = config.test_interval
device = "cuda"
epochs = 80
logger = logging.getLogger("reid_baseline.train")
logger.info('start training')
if device:
if torch.cuda.device_count() > 1:
print('Using {} GPUs for training'.format(torch.cuda.device_count()))
model = nn.DataParallel(model)
model.to(device)
loss_meter = AverageMeter()
acc_meter = AverageMeter()
evaluator = R1_mAP_eval(num_query, max_rank=50, feat_norm='yes')
model.base._freeze_stages()
logger.info('Freezing the stages number:{}'.format(-1))
# train
for epoch in range(1, epochs + 1):
start_time = time.time()
loss_meter.reset()
acc_meter.reset()
evaluator.reset()
scheduler.step()
model.train()
def train(args):
# gpu init
multi_gpu = False
if len(args.gpus.split(',')) > 1:
multi_gpu = True
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
D = MultiscaleDiscriminator(
input_nc=3,
ndf=64,
n_layers=3,
use_sigmoid=False,
norm_layer=torch.nn.InstanceNorm2d) # pix2pix use MSEloss
G = AAD_Gen()
F = Backbone(50, drop_ratio=0.6, mode='ir_se')
F.load_state_dict(torch.load(args.arc_model_path))
E = Att_Encoder()
optimizer_D = torch.optim.Adam(D.parameters(),
lr=0.0004,
betas=(0.0, 0.999))
optimizer_GE = torch.optim.Adam([{
'params': G.parameters()
}, {
'params': E.parameters()
}],
lr=0.0004,
betas=(0.0, 0.999))
if multi_gpu:
D = DataParallel(D).to(device)
G = DataParallel(G).to(device)
F = DataParallel(F).to(device)
E = DataParallel(E).to(device)
else:
D = D.to(device)
G = G.to(device)
F = F.to(device)
E = E.to(device)
if args.resume:
if os.path.isfile(args.resume_model_path):
print("Loading checkpoint from {}".format(args.resume_model_path))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint["epoch"]
D.load_state_dict(checkpoint["state_dict_D"])
G.load_state_dict(checkpoint["state_dict_G"])
E.load_state_dict(checkpoint["state_dict_E"])
# optimizer_G.load_state_dict(checkpoint['optimizer_G'])
optimizer_D.load_state_dict(checkpoint['optimizer_D'])
optimizer_GE.load_state_dict(checkpoint['optimizer_GE'])
else:
print('Cannot found checkpoint {}'.format(args.resume_model_path))
else:
args.start_epoch = 1
def print_with_time(string):
print(time.strftime("%Y-%m-%d %H:%M:%S ", time.localtime()) + string)
def weights_init(m):
classname = m.__class__.__name__
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight.data, 0.0, 0.02)
if classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
def set_requires_grad(nets, requires_grad=False):
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
def trans_batch(batch):
t = trans.Compose(
[trans.ToPILImage(),
trans.Resize((112, 112)),
trans.ToTensor()])
bs = batch.shape[0]
res = torch.ones(bs, 3, 112, 112).type_as(batch)
for i in range(bs):
res[i] = t(batch[i].cpu())
return res
set_requires_grad(F, requires_grad=False)
data_transform = trans.Compose([
trans.ToTensor(),
trans.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
#dataset = ImageFolder(args.data_path, transform=data_transform)
dataset = FaceEmbed(args.data_path)
data_loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True)
D.apply(weights_init)
G.apply(weights_init)
E.apply(weights_init)
for epoch in range(args.start_epoch, args.total_epoch + 1):
D.train()
G.train()
F.eval(
) # Only extract features! # input dim=3,256,256 out dim=256 !
E.train()
for batch_idx, data in enumerate(data_loader):
time_curr = time.time()
iteration = (epoch - 1) * len(data_loader) + batch_idx
try:
source, target, label = data
source = source.to(device)
target = target.to(device)
label = torch.LongTensor(label).to(device)
#Zid =F(trans_batch(source)) # bs, 512
Zid = F(
downsample(source[:, :, 50:-10, 30:-30], size=(112, 112)))
Zatt = E(target) # list:8 each：bs,,,
Yst0 = G(Zid, Zatt) # bs,3,256,256
# train discriminators
pred_gen = D(Yst0.detach())
#pred_gen = list(map(lambda x: x[0].detach(), pred_gen))
pred_real = D(target)
optimizer_D.zero_grad()
loss_real, loss_fake = loss_hinge_dis()(pred_gen, pred_real)
L_dis = loss_real + loss_fake
# if batch_idx%3==0:
L_dis.backward()
optimizer_D.step()
# train generators
pred_gen = D(Yst0)
L_gen = loss_hinge_gen()(pred_gen)
#L_id = IdLoss()(F(trans_batch(Yst0)), Zid)
L_id = IdLoss()(F(
downsample(Yst0[:, :, 50:-10, 30:-30], size=(112, 112))),
Zid)
#Zatt = list(map(lambda x: x.detach(), Zatt))
L_att = AttrLoss()(E(Yst0), Zatt)
L_Rec = RecLoss()(Yst0, target, label)
Loss = (L_gen + 10 * L_att + 5 * L_id + 10 * L_Rec).to(device)
optimizer_GE.zero_grad()
Loss.backward()
optimizer_GE.step()
except Exception as e:
print(e)
continue
if batch_idx % args.log_interval == 0 or batch_idx == 20:
time_used = time.time() - time_curr
print_with_time(
'Train Epoch: {} [{}/{} ({:.0f}%)], L_dis:{:.4f}, loss_real:{:.4f}, loss_fake:{:.4f}, Loss:{:.4f}, L_gen:{:.4f}, L_id:{:.4f}, L_att:{:.4f}, L_Rec:{:.4f}'
.format(
epoch, batch_idx * len(data), len(data_loader.dataset),
100. * batch_idx *
len(data) / len(data_loader.dataset), L_dis.item(),
loss_real.item(), loss_fake.item(), Loss.item(),
L_gen.item(), 5 * L_id.item(), 10 * L_att.item(),
10 * L_Rec))
time_curr = time.time()
if epoch % args.save_interval == 0: #or batch_idx*len(data) % 350004==0:
state = {
"epoch": epoch,
"state_dict_D": D.state_dict(),
"state_dict_G": G.state_dict(),
"state_dict_E": E.state_dict(),
"optimizer_D": optimizer_D.state_dict(),
"optimizer_GE": optimizer_GE.state_dict(),
# "optimizer_E": optimizer_E.state_dict(),
}
filename = "../model/train1_{:03d}_{:03d}.pth.tar".format(
epoch, batch_idx * len(data))
torch.save(state, filename)