def main():
best_acc = 0
opt = parse_option()
# build data loader
train_loader, val_loader = set_loader(opt)
# build model and criterion
model, classifier, criterion = set_model(opt)
# build optimizer
# optimizer = set_optimizer(opt, [classifier])
optimizer = set_optimizer(opt, [classifier, model])
# training routine
for epoch in range(1, opt.epochs + 1):
adjust_learning_rate(opt, optimizer, epoch)
# train for one epoch
time1 = time.time()
loss, acc = train(train_loader, model, classifier, criterion,
optimizer, epoch, opt)
time2 = time.time()
print('Train epoch {}, total time {:.2f}, accuracy:{:.2f}'.format(
epoch, time2 - time1, acc))
# eval for one epoch
loss, val_acc = validate(val_loader, model, classifier, criterion, opt)
if val_acc > best_acc:
best_acc = val_acc
print('best accuracy: {:.2f}'.format(best_acc))
def main():
best_acc = 0
best_acc5 = 0
opt = parse_option()
# build data loader
train_loader, val_loader = set_loader(opt)
# build model and criterion
model, classifier, criterion = set_model(opt)
# build optimizer
optimizer = set_optimizer(opt, classifier)
logger = tb_logger.Logger(logdir=opt.tb_folder, flush_secs=2)
# training routine
for epoch in range(1, opt.epochs + 1):
adjust_learning_rate(opt, optimizer, epoch)
# train for one epoch
time1 = time.time()
loss, acc, acc5 = train(train_loader, model, classifier, criterion,
optimizer, epoch, opt)
time2 = time.time()
logging.info(
'Train epoch {}, total time {:.2f}, accuracy:{:.2f}'.format(
epoch, time2 - time1, acc))
logger.log_value('classifier/train_loss', loss, epoch)
logger.log_value('classifier/train_acc1', acc, epoch)
logger.log_value('classifier/train_acc5', acc5, epoch)
# eval for one epoch
loss, val_acc, val_acc5 = validate(val_loader, model, classifier,
criterion, opt)
logger.log_value('classifier/val_loss', loss, epoch)
logger.log_value('classifier/val_acc1', val_acc, epoch)
logger.log_value('classifier/val_acc5', val_acc5, epoch)
if val_acc > best_acc:
best_acc = val_acc
best_acc5 = val_acc5
logging.info('best accuracy: {:.2f}, accuracy5: {:.2f}'.format(
best_acc, best_acc5))
def main():
best_acc = 0
best_classifier = None
opt = parse_option()
# build data loader
train_loader, val_loader = set_loader(opt)
# build model and criterion
model, classifier, criterion = set_model(opt)
best_classifier = classifier
# build optimizer
optimizer = set_optimizer(opt, classifier)
if opt.eval:
loss, val_acc = validate(val_loader, model, classifier, criterion, opt)
else:
# training routine
for epoch in range(1, opt.epochs + 1):
adjust_learning_rate(opt, optimizer, epoch)
# train for one epoch
time1 = time.time()
loss, acc = train(train_loader, model, classifier, criterion,
optimizer, epoch, opt)
time2 = time.time()
print('Train epoch {}, total time {:.2f}, accuracy:{:.2f}'.format(
epoch, time2 - time1, acc))
# eval for one epoch
loss, val_acc = validate(val_loader, model, classifier, criterion,
opt)
if val_acc > best_acc:
best_acc = val_acc
best_classifier = classifier
print('best accuracy: {:.2f}'.format(best_acc))
for epsilon in opt.epsilons:
loss, acc, adv_acc = adveval(val_loader, model, best_classifier,
criterion, opt, epsilon)
print('adv accuracy at epsilon {:.2f}: {:.2f}'.format(
epsilon, adv_acc))