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train.py
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train.py
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import argparse
import os
import sys
import time
import torch
import torch.nn.functional as F
import torchvision
import models
import utils
import tabulate
import torchvision.models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
model_names = sorted(name for name in models.__dict__ if name.islower() and not name.startswith("__") and callable(models.__dict__[name]))
parser = argparse.ArgumentParser(description='SGD/SWA training')
parser.add_argument('--dir', type=str, default=None, required=True, help='training directory (default: None)')
parser.add_argument('--dataset', type=str, default='CIFAR10', help='dataset name (default: CIFAR10)')
parser.add_argument('--data_path', type=str, default=None, required=True, metavar='PATH',
help='path to datasets location (default: None)')
parser.add_argument('--batch_size', type=int, default=128, metavar='N', help='input batch size (default: 128)')
parser.add_argument('--num_workers', type=int, default=4, metavar='N', help='number of workers (default: 4)')
parser.add_argument('--model', type=str, default=None, required=True, metavar='MODEL',
help='model name (default: None)')
parser.add_argument('--resume', type=str, default=None, metavar='CKPT',
help='checkpoint to resume training from (default: None)')
parser.add_argument('--epochs', type=int, default=200, metavar='N', help='number of epochs to train (default: 200)')
parser.add_argument('--save_freq', type=int, default=25, metavar='N', help='save frequency (default: 25)')
parser.add_argument('--eval_freq', type=int, default=5, metavar='N', help='evaluation frequency (default: 5)')
parser.add_argument('--lr_init', type=float, default=0.1, metavar='LR', help='initial learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)')
parser.add_argument('--wd', type=float, default=1e-4, help='weight decay (default: 1e-4)')
parser.add_argument('--swa', action='store_true', help='swa usage flag (default: off)')
parser.add_argument('--swa_start', type=float, default=161, metavar='N', help='SWA start epoch number (default: 161)')
parser.add_argument('--swa_lr', type=float, default=0.05, metavar='LR', help='SWA LR (default: 0.05)')
parser.add_argument('--swa_c_epochs', type=int, default=1, metavar='N',
help='SWA model collection frequency/cycle length in epochs (default: 1)')
parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=model_names,
help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)')
parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')
args = parser.parse_args()
print('Preparing directory %s' % args.dir)
os.makedirs(args.dir, exist_ok=True)
with open(os.path.join(args.dir, 'command.sh'), 'w') as f:
f.write(' '.join(sys.argv))
f.write('\n')
torch.backends.cudnn.benchmark = True
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
print('Using model %s' % args.model)
#model_cfg = getattr(models, args.model)
model_cfg = getattr(torchvision.models, args.model)
print('Loading dataset %s from %s' % (args.dataset, args.data_path))
ds = getattr(torchvision.datasets, args.dataset)
path = os.path.join(args.data_path, args.dataset.lower())
#removing these to port ImageNet
#train_set = ds(path, train=True, download=True, transform=model_cfg.transform_train)
#test_set = ds(path, train=False, download=True, transform=model_cfg.transform_test)
traindir = os.path.join(args.data_path, 'train')
valdir = os.path.join(args.data_path, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(traindir, transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
val_dataset = datasets.ImageFolder(valdir, transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(),
normalize]))
#incase
#if args.distributed:
# train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
#else:
# train_sampler = None
loaders = {
'train': torch.utils.data.DataLoader(
train_dataset, #train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True
),
'test': torch.utils.data.DataLoader(
val_dataset, #test_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True
)
}
print(len(loaders['train'].dataset))
print(len(loaders['test'].dataset))
#num_classes = max(train_dataset.train_labels) + 1
#workaround
num_classes = 1000
#print('Preparing model')
#model = model_cfg.base(*model_cfg.args, num_classes=num_classes, **model_cfg.kwargs)
#model.cuda()
#for resnet18
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = torchvision.models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = torchvision.models.__dict__[args.arch]()
model = torch.nn.DataParallel(model).cuda()
if args.swa:
print('SWA training')
#swa_model = model_cfg.base(*model_cfg.args, num_classes=num_classes, **model_cfg.kwargs)
swa_model = torchvision.models.__dict__[args.arch]()
swa_model.cuda()
swa_n = 0
else:
print('SGD training')
def schedule(epoch):
t = (epoch) / (args.swa_start if args.swa else args.epochs)
lr_ratio = args.swa_lr / args.lr_init if args.swa else 0.01
if t <= 0.5:
factor = 1.0
elif t <= 0.9:
factor = 1.0 - (1.0 - lr_ratio) * (t - 0.5) / 0.4
else:
factor = lr_ratio
return args.lr_init * factor
#lr_1 = 0.00000000000001
#lr_2 = 0.00000000000001
#if epoch % 2 == 0:
# return lr_2
#else:
# return lr_1
criterion = F.cross_entropy
optimizer = torch.optim.SGD(
model.parameters(),
lr=args.lr_init,
momentum=args.momentum,
weight_decay=args.wd
)
#test_res = utils.eval(loaders['test'], model, criterion)
#print("accuracy")
#print(test_res['accuracy'])
#print("loss")
#print(test_res['loss'])
#exit()
start_epoch = 0
if args.resume is not None:
print('Resume training from %s' % args.resume)
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
if args.swa:
swa_state_dict = checkpoint['swa_state_dict']
if swa_state_dict is not None:
swa_model.load_state_dict(swa_state_dict)
swa_n_ckpt = checkpoint['swa_n']
if swa_n_ckpt is not None:
swa_n = swa_n_ckpt
columns = ['ep', 'lr', 'tr_loss', 'tr_acc', 'te_loss', 'te_acc', 'time']
if args.swa:
columns = columns[:-1] + ['swa_te_loss', 'swa_te_acc'] + columns[-1:]
swa_res = {'loss': None, 'accuracy': None}
utils.save_checkpoint(
args.dir,
start_epoch,
state_dict=model.state_dict(),
swa_state_dict=swa_model.state_dict() if args.swa else None,
swa_n=swa_n if args.swa else None,
optimizer=optimizer.state_dict()
)
for epoch in range(start_epoch, args.epochs):
time_ep = time.time()
lr = schedule(epoch)
utils.adjust_learning_rate(optimizer, lr)
test_res = utils.eval(loaders['test'], model, criterion)
print("accuracy")
print(test_res['accuracy'])
print("loss")
print(test_res['loss'])
train_res = utils.train_epoch(loaders['train'], model, criterion, optimizer)
if epoch == 0 or epoch % args.eval_freq == args.eval_freq - 1 or epoch == args.epochs - 1:
test_res = utils.eval(loaders['test'], model, criterion)
else:
test_res = {'loss': None, 'accuracy': None}
if args.swa and (epoch + 1) >= args.swa_start and (epoch + 1 - args.swa_start) % args.swa_c_epochs == 0:
utils.moving_average(swa_model, model, 1.0 / (swa_n + 1))
swa_n += 1
if epoch == 0 or epoch % args.eval_freq == args.eval_freq - 1 or epoch == args.epochs - 1:
utils.bn_update(loaders['train'], swa_model)
swa_res = utils.eval(loaders['test'], swa_model, criterion)
else:
swa_res = {'loss': None, 'accuracy': None}
if (epoch + 1) % args.save_freq == 0:
utils.save_checkpoint(
args.dir,
epoch + 1,
state_dict=model.state_dict(),
swa_state_dict=swa_model.state_dict() if args.swa else None,
swa_n=swa_n if args.swa else None,
optimizer=optimizer.state_dict()
)
time_ep = time.time() - time_ep
values = [epoch + 1, lr, train_res['loss'], train_res['accuracy'], test_res['loss'], test_res['accuracy'], time_ep]
if args.swa:
values = values[:-1] + [swa_res['loss'], swa_res['accuracy']] + values[-1:]
table = tabulate.tabulate([values], columns, tablefmt='simple', floatfmt='8.4f')
if epoch % 40 == 0:
table = table.split('\n')
table = '\n'.join([table[1]] + table)
else:
table = table.split('\n')[2]
print(table)
if args.epochs % args.save_freq != 0:
utils.save_checkpoint(
args.dir,
args.epochs,
state_dict=model.state_dict(),
swa_state_dict=swa_model.state_dict() if args.swa else None,
swa_n=swa_n if args.swa else None,
optimizer=optimizer.state_dict()
)