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train.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Author : AI Partner
# @Email : [email protected]
import utils.option
import os
args = utils.option.get_args_parser()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
import json
import datetime
import resource
import dataloader
import torch.backends.cudnn
import torch.utils.tensorboard
import model.resnet_cifar
import model.resnet
import model.deit
import model.convnext
import test
import utils.utils
import utils.lr_scheduler
import utils.ema
def get_stats():
stats = {'train_ce_loss': utils.utils.AverageMeter(),
'train_acc': utils.utils.AverageMeter(),
'test_acc': utils.utils.AverageMeter(),
'test_acc_ema': utils.utils.AverageMeter(),
'test_aurc': utils.utils.AverageMeter(),
'test_aurc_ema': utils.utils.AverageMeter(),
'lr' : utils.utils.AverageMeter()}
return stats
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (2048, rlimit[1]))
torch.backends.cudnn.benchmark = True
## tensorboard and logger
writer = torch.utils.tensorboard.SummaryWriter(args.save_dir)
logger = utils.utils.get_logger(args.save_dir)
logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
logger.info('Log saved in {}'.format(args.save_dir))
## define dataloader
if args.dataset == 'CIFAR10':
train_loader = dataloader.Trainloader_cifar10(args.batch_size, args.train_dir, args.train_size)
test_loader = dataloader.Testloader_cifar10(args.batch_size, args.test_dir, args.test_size)
elif args.dataset == 'CIFAR100':
train_loader = dataloader.Trainloader_cifar100(args.batch_size, args.train_dir, args.train_size)
test_loader = dataloader.Testloader_cifar100(args.batch_size, args.test_dir, args.test_size)
elif args.dataset in ['CUB', 'CARS']:
train_loader = dataloader.Trainloader_ImageFolder(args.batch_size, args.train_dir, args.train_size)
test_loader = dataloader.Testloader_ImageFolder(args.batch_size, args.test_dir, args.test_size)
iter_per_epoch = len(train_loader)
## define model
if args.model == 'resnet18_cifar' :
net = model.resnet_cifar.ResNet18(args.nb_cls)
elif args.model == 'resnet18' :
net = model.resnet.ResNet18(args.nb_cls)
if args.pretrained_net is not None :
net = utils.utils.load_pretrained_net(net, args.pretrained_net, logger)
elif args.model == 'resnet50' :
net = model.resnet.ResNet50(args.nb_cls)
if args.pretrained_net is not None :
net = utils.utils.load_pretrained_net(net, args.pretrained_net, logger)
elif args.model == 'deit_base_patch16_384' :
net = model.deit.deit_base_patch16_384(args.nb_cls)
if args.pretrained_net is not None :
net = utils.utils.load_pretrained_net(net, args.pretrained_net, logger)
elif args.model == 'convnext_base' :
net = model.convnext.convnext_base(args.nb_cls)
if args.pretrained_net is not None :
net = utils.utils.load_pretrained_net(net, args.pretrained_net, logger)
net.cuda()
net_ema = utils.ema.ModelEMA(net)
## define optimizer
optimizer = torch.optim.SGD(net.parameters(), lr=args.min_lr, momentum=0.9, weight_decay=args.weight_decay)
## define Warmup cos lr scheduler
lr_scheduler = utils.lr_scheduler.Warmup_cos_lr(args.max_lr, args.min_lr, iter_per_epoch, args.nb_epoch, args.warmup_epoch)
## define criterion
CE_Loss = torch.nn.CrossEntropyLoss()
## define stats
stats = get_stats()
best_acc, best_acc_ema, best_aurc, best_aurc_ema, iter_counter = 0, 0, 1000, 1000, 0
## Sacler: Gradient scaling helps prevent gradients with small magnitudes from flushing to zero (“underflowing”) when training with mixed precision.
scaler = torch.cuda.amp.GradScaler()
# start Train
for epoch in range(args.nb_epoch):
net.train()
for i, batch in enumerate(train_loader) :
lr = lr_scheduler.update_lr(iter_counter)
stats['lr'] = lr
for param_group in optimizer.param_groups:
param_group["lr"] = lr
image, target = batch
image, target = image.cuda(), target.cuda()
optimizer.zero_grad()
with torch.autocast(device_type="cuda"):
logits = net(image)
loss = CE_Loss(logits, target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
net_ema.update(net)
prec, correct = utils.utils.accuracy(logits, target)
stats['train_acc'].update(prec.item(), image.size(0))
stats['train_ce_loss'].update(loss.item(), image.size(0))
if i % 100 == 99 :
logger.info(f"{datetime.datetime.now()} \t LR {stats['lr']:.5f} \t Epoch {epoch} \t Batch {i} \t Train Acc. {stats['train_acc'].avg:.2%} \t Train Loss {stats['train_ce_loss'].avg:.2f}")
iter_counter += 1
net.eval()
stats['test_acc'], stats['test_aurc'], _, _ = test.test(test_loader, net)
stats['test_acc_ema'], stats['test_aurc_ema'], _, _ = test.test(test_loader, net_ema.ema)
msg = f"TEST: {datetime.datetime.now()} \t \
Epoch {epoch} \t \
Train Acc. {stats['train_acc'].avg:.2%} \t \
Test Acc. {stats['test_acc'].avg:.2%} (Prev. Best {best_acc:.2%}) \t \
Test AURC {stats['test_aurc']:.2f} (Prev. Best {best_aurc:.2f}) \t \n \
EMA Test Acc. {stats['test_acc_ema'].avg:.2%} (EMA Prev. Best {best_acc_ema:.2%}) \t \
EMA Test AURC {stats['test_aurc_ema']:.2f} (EMA Prev. Best {best_aurc_ema:.2f}) \t \
Train Loss {stats['train_ce_loss'].avg:.2f}"
logger.info(msg)
for metric in stats:
writer.add_scalar(metric, stats[metric].avg if metric not in ['lr', 'test_aurc', 'test_aurc_ema'] else stats[metric], epoch)
if stats['test_acc'].avg > best_acc :
logger.info(f"Accuracy improved from {best_acc:.2%} to {stats['test_acc'].avg:.2%}!!!")
best_acc = stats['test_acc'].avg
torch.save(net.state_dict(), os.path.join(args.save_dir, 'best_acc_net.pth'))
if stats['test_acc_ema'].avg > best_acc_ema :
logger.info(f"EMA Accuracy improved from {best_acc_ema:.2%} to {stats['test_acc_ema'].avg:.2%}!!!")
best_acc_ema = stats['test_acc_ema'].avg
torch.save(net_ema.ema.state_dict(), os.path.join(args.save_dir, 'best_acc_net_ema.pth'))
if stats['test_aurc'] < best_aurc :
logger.info(f"AURC improved from {best_aurc:.2f} to {stats['test_aurc']:.2f}!!!")
best_aurc = stats['test_aurc']
torch.save(net.state_dict(), os.path.join(args.save_dir, 'best_aurc_net.pth'))
if stats['test_aurc_ema'] < best_aurc_ema :
logger.info(f"EMA AURC improved from {best_aurc_ema:.2f} to {stats['test_aurc_ema']:.2f}!!!")
best_aurc_ema = stats['test_aurc_ema']
torch.save(net_ema.ema.state_dict(), os.path.join(args.save_dir, 'best_aurc_net_ema.pth'))
## re-initialize stats
stats = get_stats()