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main_ce.py
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main_ce.py
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from __future__ import print_function
import os
import sys
import argparse
import time
import math
import tensorboard_logger as tb_logger
import torch
import torch.backends.cudnn as cudnn
from torchvision import transforms, datasets
from util import AverageMeter
from util import adjust_learning_rate, warmup_learning_rate, accuracy
from util import set_optimizer, save_model
from networks.resnet_big import SupCEResNet
import torchutils
try:
import apex
from apex import amp, optimizers
except ImportError:
pass
def parse_option():
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--print_freq', type=int, default=200,
help='print frequency')
parser.add_argument('--save_freq', type=int, default=50,
help='save frequency')
parser.add_argument('--batch_size', type=int, default=64,
help='batch_size')
parser.add_argument('--num_workers', type=int, default=16,
help='num of workers to use')
parser.add_argument('--epochs', type=int, default=500,
help='number of training epochs')
# optimization
parser.add_argument('--learning_rate', type=float, default=0.2,
help='learning rate')
parser.add_argument('--lr_decay_epochs', type=str, default='350,400,450',
help='where to decay lr, can be a list')
parser.add_argument('--lr_decay_rate', type=float, default=0.1,
help='decay rate for learning rate')
parser.add_argument('--weight_decay', type=float, default=1e-4,
help='weight decay')
parser.add_argument('--momentum', type=float, default=0.9,
help='momentum')
# model dataset
parser.add_argument('--model', type=str, default='resnet18')
parser.add_argument('--dataset', type=str, default='cifar10',
choices=['cifar10', 'cifar100'], help='dataset')
# other setting
parser.add_argument('--cosine', action='store_true',
help='using cosine annealing')
parser.add_argument('--syncBN', action='store_true',
help='using synchronized batch normalization')
parser.add_argument('--warm', action='store_true',
help='warm-up for large batch training')
parser.add_argument('--trial', type=str, default='0',
help='id for recording multiple runs')
opt = parser.parse_args()
# set the path according to the environment
opt.data_folder = './datasets/'
opt.model_path = './save/SupCon/{}_models'.format(opt.dataset)
opt.tb_path = './save/SupCon/{}_tensorboard'.format(opt.dataset)
iterations = opt.lr_decay_epochs.split(',')
opt.lr_decay_epochs = list([])
for it in iterations:
opt.lr_decay_epochs.append(int(it))
opt.model_name = 'SupCE_{}_{}_lr_{}_decay_{}_bsz_{}_trial_{}'.\
format(opt.dataset, opt.model, opt.learning_rate, opt.weight_decay,
opt.batch_size, opt.trial)
if opt.cosine:
opt.model_name = '{}_cosine'.format(opt.model_name)
# warm-up for large-batch training,
if opt.batch_size > 256:
opt.warm = True
if opt.warm:
opt.model_name = '{}_warm'.format(opt.model_name)
opt.warmup_from = 0.01
opt.warm_epochs = 10
if opt.cosine:
eta_min = opt.learning_rate * (opt.lr_decay_rate ** 3)
opt.warmup_to = eta_min + (opt.learning_rate - eta_min) * (
1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) / 2
else:
opt.warmup_to = opt.learning_rate
opt.tb_folder = os.path.join(opt.tb_path, opt.model_name)
if not os.path.isdir(opt.tb_folder):
os.makedirs(opt.tb_folder)
opt.save_folder = os.path.join(opt.model_path, opt.model_name)
if not os.path.isdir(opt.save_folder):
os.makedirs(opt.save_folder)
if opt.dataset == 'cifar10':
opt.n_cls = 10
elif opt.dataset == 'cifar100':
opt.n_cls = 100
else:
raise ValueError('dataset not supported: {}'.format(opt.dataset))
return opt
def set_loader(opt):
# construct data loader
if opt.dataset == 'cifar10':
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
elif opt.dataset == 'cifar100':
mean = (0.5071, 0.4867, 0.4408)
std = (0.2675, 0.2565, 0.2761)
else:
raise ValueError('dataset not supported: {}'.format(opt.dataset))
normalize = transforms.Normalize(mean=mean, std=std)
train_transform = transforms.Compose([
transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
val_transform = transforms.Compose([
transforms.ToTensor(),
normalize,
])
if opt.dataset == 'cifar10':
train_dataset = datasets.CIFAR10(root=opt.data_folder,
transform=train_transform,
download=True)
val_dataset = datasets.CIFAR10(root=opt.data_folder,
train=False,
transform=val_transform)
elif opt.dataset == 'cifar100':
train_dataset = datasets.CIFAR100(root=opt.data_folder,
transform=train_transform,
download=True)
val_dataset = datasets.CIFAR100(root=opt.data_folder,
train=False,
transform=val_transform)
else:
raise ValueError(opt.dataset)
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=opt.batch_size, shuffle=(train_sampler is None),
num_workers=opt.num_workers, pin_memory=True, sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=256, shuffle=False,
num_workers=8, pin_memory=True)
return train_loader, val_loader
def set_model(opt):
model = SupCEResNet(name=opt.model, num_classes=opt.n_cls)
criterion = torch.nn.CrossEntropyLoss()
# enable synchronized Batch Normalization
if opt.syncBN:
model = apex.parallel.convert_syncbn_model(model)
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model = model.cuda()
criterion = criterion.cuda()
cudnn.benchmark = True
return model, criterion
def train(train_loader, model, criterion, optimizer, epoch, opt):
"""one epoch training"""
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
end = time.time()
for idx, (images, labels) in enumerate(train_loader):
data_time.update(time.time() - end)
images = images.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
bsz = labels.shape[0]
# warm-up learning rate
warmup_learning_rate(opt, epoch, idx, len(train_loader), optimizer)
# compute loss
output = model(images)
loss = criterion(output, labels)
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(output, labels, topk=(1, 5))
top1.update(acc1[0], bsz)
# SGD
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# print info
if (idx + 1) % opt.print_freq == 0:
print('Train: [{0}][{1}/{2}]\t'
'BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'DT {data_time.val:.3f} ({data_time.avg:.3f})\t'
'loss {loss.val:.3f} ({loss.avg:.3f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch, idx + 1, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1))
sys.stdout.flush()
return losses.avg, top1.avg
def validate(val_loader, model, criterion, opt):
"""validation"""
model.eval()
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
with torch.no_grad():
end = time.time()
for idx, (images, labels) in enumerate(val_loader):
images = images.float().cuda()
labels = labels.cuda()
bsz = labels.shape[0]
# forward
output = model(images)
loss = criterion(output, labels)
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(output, labels, topk=(1, 5))
top1.update(acc1[0], bsz)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if idx % opt.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
idx, len(val_loader), batch_time=batch_time,
loss=losses, top1=top1))
print(' * Acc@1 {top1.avg:.3f}'.format(top1=top1))
return losses.avg, top1.avg
def main():
torchutils.onceInit(kCUDA=True, cudadevice='cuda:0')
best_acc = 0
opt = parse_option()
# build data loader
train_loader, val_loader = set_loader(opt)
# build model and criterion
model, criterion = set_model(opt)
# build optimizer
optimizer = set_optimizer(opt, model)
# tensorboard
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, train_acc = train(train_loader, model, criterion, optimizer, epoch, opt)
time2 = time.time()
print('epoch {}, total time {:.2f}'.format(epoch, time2 - time1))
# tensorboard logger
logger.log_value('train_loss', loss, epoch)
logger.log_value('train_acc', train_acc, epoch)
logger.log_value('learning_rate', optimizer.param_groups[0]['lr'], epoch)
# evaluation
loss, val_acc = validate(val_loader, model, criterion, opt)
logger.log_value('val_loss', loss, epoch)
logger.log_value('val_acc', val_acc, epoch)
if val_acc > best_acc:
best_acc = val_acc
if epoch % opt.save_freq == 0:
save_file = os.path.join(
opt.save_folder, 'ckpt_epoch_{epoch}.pth'.format(epoch=epoch))
save_model(model, optimizer, opt, epoch, save_file)
# save the last model
save_file = os.path.join(
opt.save_folder, 'last.pth')
save_model(model, optimizer, opt, opt.epochs, save_file)
print('best accuracy: {:.2f}'.format(best_acc))
if __name__ == '__main__':
os.environ['CUDA_VISIBLE_DEVICES'] = '0' #exclude 1 which is a Quadra
main()