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train_warmup.py
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from argparse import ArgumentParser
import os
import json
from datetime import datetime
from tqdm import tqdm
import torch
import torch.backends.cudnn as cudnn
from torch.optim import SGD
from torch.utils.tensorboard import SummaryWriter
from utilities import adv_utils as adv_utils
from kd_lib import utilities as utils
from utilities import dist_utils
from utilities.utils import ModelSaver, check_final
from data.dataset import DATASETS
from data.transforms import build_transforms
from utilities.train import Cure
from utilities.args import get_args
from utilities.results import Results
from utilities.logger import Logger
from copy import deepcopy
# ======================================================================================
# Training Function
# ======================================================================================
def solver(args):
log_dir = os.path.join(args.experiment_name, 'logs')
if args.reinit:
args.model_dir = args.reinit_path
save_model_dir = os.path.join(args.experiment_name, 'checkpoints') if args.adv_mode =='normal' else \
os.path.join(args.experiment_name, 'checkpoints_adv')
os.makedirs(log_dir, exist_ok=True)
os.makedirs(save_model_dir, exist_ok=True)
logger = Logger(os.path.join(log_dir, 'log.txt'), title=args.experiment_name)
logger.set_names(['Nat train loss', 'Nat train acc', 'Nat test acc',
'Adv train acc', 'Adv test acc',
'Nat test acc ema', 'Adv test acc ema',
])
log_path = os.path.join(log_dir, datetime.now().strftime('%Y%m%d_%H%M'))
writer = SummaryWriter(log_path)
use_cuda = args.cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print('device: %s' % device)
if use_cuda:
torch.cuda.set_device(0)
cudnn.benchmark = True
if args.dataset =='imagenet' or args.dataset =='imagenet200':
ds_class = DATASETS[args.dataset](args.data_path, cache_dataset=True)
else:
ds_class = DATASETS[args.dataset](args.data_path)
#load transforms
transform_train, transform_test = build_transforms(args, ds_class)
# load dataset
trainset = ds_class.get_dataset('train', transform_train, transform_test)
testset = ds_class.get_dataset('test', transform_train, transform_test)
#data
train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
shuffle=True, num_workers=args.num_workers)
test_loader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size,
shuffle=False, num_workers=args.num_workers)
# Load model Init
args.num_classes = ds_class.NUM_CLASSES
method = Cure(args, device)
saver = ModelSaver(save_model_dir)
start_epoch = 0
optimizer = SGD(method.model.parameters(), lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
#optimizer = SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
scheduler = None
if args.scheduler == 'multistep':
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.epoch_step, gamma=0.1)
elif args.scheduler == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
# if os.listdir(save_model_dir) and not args.reinit:
# method.model, optimizer, start_epoch = saver.load_checkpoint(method.model, optimizer, save_model_dir)
print('*' * 60 + '\nTraining Mode: %s\n' % args.adv_mode + '*' * 60)
for epoch in tqdm(range(1, args.epochs + 1), desc='training epochs'):
saver.save_models(method.model, optimizer, epoch, 0.0)
if epoch <= start_epoch:
continue
if epoch >= args.epochs_warmup:
args.reinit = True
args.adv_mode = "cure_dual"
args.train_mode = 'cure'
args.reinit_mode = 'rgp'
# method.model_ema = deepcopy(method.model).to(device)
for ema_param, param in zip(method.model_ema.parameters(), method.model.parameters()):
ema_param.data = param.data
method.args = args
# adjust learning rate for SGD
if scheduler:
scheduler.step()
else:
utils.adjust_learning_rate(epoch, args.epoch_step, args.lr_decay_ratio, optimizer)
method.train(train_loader, optimizer, epoch, writer)
# evaluation on natural examples ##
if args.train_eval_freq == 0 or epoch % args.train_eval_freq == 0:
print("================================================================")
train_loss, train_accuracy, correct = utils.eval(method.model, device, train_loader)
print("Training: Average loss: {:.4f}, Accuracy: {}/{} ({}%)".format(
train_loss, correct, len(train_loader.dataset), train_accuracy * 100))
print("================================================================")
writer.add_scalar("Train/train_loss", train_loss, epoch)
writer.add_scalar("Train/train_accuracy", train_accuracy, epoch)
adv_train_accuracy = 0
if args.adv_mode != 'normal':
_, adv_train_accuracy = adv_utils.eval_adv_robustness(method.model, train_loader, device)
if args.test_eval_freq == 0 or epoch % args.test_eval_freq == 0:
print("================================================================")
nat_test_loss, nat_test_accuracy, correct = utils.eval(method.model, device, test_loader)
print("Test: Average loss: {:.4f}, Accuracy: {}/{} ({}%)".format(
nat_test_loss, correct, len(test_loader.dataset), nat_test_accuracy * 100))
print("================================================================")
writer.add_scalar("Test/test_loss", nat_test_loss, epoch)
writer.add_scalar("Test/test_accuracy", nat_test_accuracy, epoch)
print("================================================================")
nat_test_accuracy2 = adv_test_accuracy = 0
if args.adv_mode != 'normal':
nat_test_accuracy2, adv_test_accuracy = adv_utils.eval_adv_robustness(method.model, test_loader, device)
writer.add_scalar("Test/nat_test_accuracy2", nat_test_accuracy2, epoch)
writer.add_scalar("Test/adv_test_accuracy", adv_test_accuracy, epoch)
nat_test_accuracy_ema = adv_test_accuracy_ema = 0
if 'ema' in args.train_mode:
nat_test_accuracy_ema, adv_test_accuracy_ema = adv_utils.eval_adv_robustness(method.model_ema,
test_loader, device)
if epoch != args.epochs:
saver.save_models(method.model, optimizer, epoch, nat_test_accuracy)
logger.append([train_loss, train_accuracy, nat_test_accuracy, adv_train_accuracy, adv_test_accuracy, nat_test_accuracy_ema, adv_test_accuracy_ema])
# get final test accuracy
test_accuracy_ema = 0
test_loss, test_accuracy, correct = utils.eval(method.model, device, test_loader)
if 'ema' in args.train_mode:
test_loss_ema, test_accuracy_ema, correct_ema = utils.eval(method.model_ema, device, test_loader)
adv_test_accuracy = adv_test_accuracy_ema = 0
if args.adv_mode != 'normal':
nat_test_accuracy2, adv_test_accuracy = adv_utils.eval_adv_robustness(method.model, test_loader, device)
if 'ema' in args.train_mode:
nat_test_accuracy_ema, adv_test_accuracy_ema = adv_utils.eval_adv_robustness(method.model_ema, test_loader, device)
writer.close()
res = Results(args, test_loss, test_accuracy, test_accuracy_ema, adv_test_accuracy, adv_test_accuracy_ema)
# save model
torch.save(method.model, os.path.join(save_model_dir, 'final_model.pth'))
if 'ema' in args.train_mode:
torch.save(method.model_ema, os.path.join(save_model_dir, 'final_model_ema.pth'))
return res #, saver.best, saver.best_epoch
def main(args):
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
#base_name = "%s_%s_%s_mode_%s_%sepochs" % (args.exp_identifier, args.model_architecture, args.mode, args.dataset, args.epochs)
base_name = "%s" % (args.exp_identifier)
base_dir = os.path.join(args.output_dir, base_name)
os.makedirs(base_dir, exist_ok=True)
# save training arguments
args_path = os.path.join(base_dir, 'args.txt')
z = vars(args).copy()
with open(args_path, 'w') as f:
f.write('arguments: ' + json.dumps(z) + '\n')
utils.set_torch_seeds(args.seeds[0])
args.experiment_name = os.path.join(args.output_dir, base_name, base_name + '_seed' + str(args.seeds[0]))
if check_final(args):
exit()
res = solver(args)
res.save_results_normal(os.path.join(args.output_dir, base_name, 'results.csv'))
if __name__ == '__main__':
parser = ArgumentParser(description='mammoth', allow_abbrev=False)
get_args(parser)
args = parser.parse_args()
dist_utils.init_distributed_mode(args)
main(args)