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main.py
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main.py
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import argparse
import time
import math
from os import path, makedirs
import torch
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.backends import cudnn
from torchvision import datasets
from torchvision import transforms
from simsiam.loader import TwoCropsTransform
from simsiam.model_factory import SimSiam
from simsiam.criterion import SimSiamLoss
from simsiam.validation import KNNValidation
parser = argparse.ArgumentParser('arguments for training')
parser.add_argument('--data_root', type=str, help='path to dataset directory')
parser.add_argument('--exp_dir', type=str, help='path to experiment directory')
parser.add_argument('--trial', type=str, default='1', help='trial id')
parser.add_argument('--img_dim', default=32, type=int)
parser.add_argument('--arch', default='resnet18', help='model name is used for training')
parser.add_argument('--feat_dim', default=2048, type=int, help='feature dimension')
parser.add_argument('--num_proj_layers', type=int, default=2, help='number of projection layer')
parser.add_argument('--batch_size', type=int, default=512, help='batch_size')
parser.add_argument('--num_workers', type=int, default=8, help='num of workers to use')
parser.add_argument('--epochs', type=int, default=800, help='number of training epochs')
parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
parser.add_argument('--loss_version', default='simplified', type=str,
choices=['simplified', 'original'],
help='do the same thing but simplified version is much faster. ()')
parser.add_argument('--print_freq', default=10, type=int, help='print frequency')
parser.add_argument('--eval_freq', default=5, type=int, help='evaluate model frequency')
parser.add_argument('--save_freq', default=50, type=int, help='save model frequency')
parser.add_argument('--resume', default=None, type=str, help='path to latest checkpoint')
parser.add_argument('--learning_rate', type=float, default=0.05, help='learning rate')
parser.add_argument('--weight_decay', type=float, default=5e-4, help='weight decay')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
args = parser.parse_args()
def main():
if not path.exists(args.exp_dir):
makedirs(args.exp_dir)
trial_dir = path.join(args.exp_dir, args.trial)
logger = SummaryWriter(trial_dir)
print(vars(args))
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(args.img_dim, scale=(0.2, 1.)),
transforms.RandomHorizontalFlip(),
transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) # not strengthened
], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])
train_set = datasets.CIFAR10(root=args.data_root,
train=True,
download=True,
transform=TwoCropsTransform(train_transforms))
train_loader = DataLoader(dataset=train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
model = SimSiam(args)
optimizer = optim.SGD(model.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
criterion = SimSiamLoss(args.loss_version)
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
criterion = criterion.cuda(args.gpu)
cudnn.benchmark = True
start_epoch = 1
if args.resume is not None:
if path.isfile(args.resume):
start_epoch, model, optimizer = load_checkpoint(model, optimizer, args.resume)
print("Loaded checkpoint '{}' (epoch {})"
.format(args.resume, start_epoch))
else:
print("No checkpoint found at '{}'".format(args.resume))
# routine
best_acc = 0.0
validation = KNNValidation(args, model.encoder)
for epoch in range(start_epoch, args.epochs+1):
adjust_learning_rate(optimizer, epoch, args)
print("Training...")
# train for one epoch
train_loss = train(train_loader, model, criterion, optimizer, epoch, args)
logger.add_scalar('Loss/train', train_loss, epoch)
if epoch % args.eval_freq == 0:
print("Validating...")
val_top1_acc = validation.eval()
print('Top1: {}'.format(val_top1_acc))
# save the best model
if val_top1_acc > best_acc:
best_acc = val_top1_acc
save_checkpoint(epoch, model, optimizer, best_acc,
path.join(trial_dir, '{}_best.pth'.format(args.trial)),
'Saving the best model!')
logger.add_scalar('Acc/val_top1', val_top1_acc, epoch)
# save the model
if epoch % args.save_freq == 0:
save_checkpoint(epoch, model, optimizer, val_top1_acc,
path.join(trial_dir, 'ckpt_epoch_{}_{}.pth'.format(epoch, args.trial)),
'Saving...')
print('Best accuracy:', best_acc)
# save model
save_checkpoint(epoch, model, optimizer, val_top1_acc,
path.join(trial_dir, '{}_last.pth'.format(args.trial)),
'Saving the model at the last epoch.')
def train(train_loader, model, criterion, optimizer, epoch, args):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
progress = ProgressMeter(
len(train_loader),
[batch_time, losses],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for i, (images, _) in enumerate(train_loader):
if args.gpu is not None:
images[0] = images[0].cuda(args.gpu, non_blocking=True)
images[1] = images[1].cuda(args.gpu, non_blocking=True)
# compute output
outs = model(im_aug1=images[0], im_aug2=images[1])
loss = criterion(outs['z1'], outs['z2'], outs['p1'], outs['p2'])
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
losses.update(loss.item(), images[0].size(0))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
return losses.avg
def adjust_learning_rate(optimizer, epoch, args):
"""Decay the learning rate based on schedule"""
lr = args.learning_rate
# cosine lr schedule
lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print('\t'.join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
def save_checkpoint(epoch, model, optimizer, acc, filename, msg):
state = {
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'top1_acc': acc
}
torch.save(state, filename)
print(msg)
def load_checkpoint(model, optimizer, filename):
checkpoint = torch.load(filename, map_location='cuda:0')
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
return start_epoch, model, optimizer
if __name__ == '__main__':
main()