assert False, 'Do not support dataset : {}'.format(args.dataset)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
print("=> creating model '{}'".format(args.arch))
if args.dataset=='imagenet':
if args.arch=='resnet34':
net=resnet_imagenet.resnet34(args=args)
elif args.arch=='resnet18':
net=resnet_imagenet.resnet18(args=args)
else:
if args.arch=='dyresnet20':
net=resnet_cifar.resnet20(num_classes=num_classes,args=args)
elif args.arch=='dyresnet32':
net=resnet_cifar.resnet32(num_classes=num_classes,args=args)
elif args.arch=='dyresnet56':
net=resnet_cifar.resnet56(num_classes=num_classes,args=args)
if args.dataset=='imagenet':
print(
' **Train** Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Error@1 {error1:.3f}'
.format(top1=top1, top5=top5, error1=100 - top1.avg), log)
return top1.avg, losses.avg
print("=> creating model '{}'".format(args.arch))
if args.dataset == 'imagenet':
if args.arch == 'resnet101':
net = resnet_imagenet.resnet101()
elif args.arch == 'resnet50':
net = resnet_imagenet.resnet50()
elif args.arch == 'resnet34':
net = resnet_imagenet.resnet34()
elif args.arch == 'resnet18':
net = resnet_imagenet.resnet18()
else:
if args.arch == 'resnet110':
net = models.resnet110(num_classes=10)
elif args.arch == 'resnet56':
net = models.resnet56(num_classes=10)
elif args.arch == 'resnet32':
net = models.resnet32(num_classes=10)
elif args.arch == 'resnet20':
net = models.resnet20(num_classes=10)
if args.dataset == 'imagenet':
if args.arch == 'resnet101':
def load_model(opt):
if opt.from_modelzoo:
if opt.pretrained:
print("=> using pre-trained model '{}'".format(opt.arch))
model = models.__dict__[opt.model_def](pretrained=True)
else:
print("=> creating model '{}'".format(opt.arch))
model = models.__dict__[opt.model_def]()
return model
else:
if opt.pretrained_file != '':
model = torch.load(opt.pretrained_filedir)
else:
if opt.model_def == 'alexnet':
model = alexnet.Net()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'mobilenet':
model = mobilenet.Net(nClasses=opt.nclasses,
width_mult=opt.widthmult,
gtp=opt.grouptype,
gsz=opt.sp,
expsz=opt.exp)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'alexnetexpander':
model = alexnetexpander.Net()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'vgg16cifar':
model = vggcifar.vgg16()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'vgg16cifar_bn':
model = vggcifar.vgg16_bn()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'vgg16cifarexpander':
model = vggcifarexpander.vgg16()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'vgg16cifar_bnexpander':
model = vggcifarexpander.vgg16_bn()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenet_cifar':
model = densenet_cifar.DenseNet3(opt.layers,
opt.nclasses,
opt.growth,
reduction=opt.reduce,
bottleneck=opt.bottleneck,
dropRate=opt.droprate)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenetgrouped_cifar':
model = densenetgrouped_cifar.DenseNet3(
opt.layers,
opt.nclasses,
opt.growth,
reduction=opt.reduce,
bottleneck=opt.bottleneck,
dropRate=opt.droprate)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenetexpander_cifar':
model = densenetexpander_cifar.DenseNet3(
opt.layers,
opt.nclasses,
opt.growth,
reduction=opt.reduce,
bottleneck=opt.bottleneck,
dropRate=opt.droprate,
expandSize=opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenet121':
model = densenet.densenet121()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenet169':
model = densenet.densenet169()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenet161':
model = densenet.densenet161()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenet201':
model = densenet.densenet201()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenetexpander121':
model = densenetexpander.densenet121(expandSize=opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenetexpander169':
model = densenetexpander.densenet169(expandSize=opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenetexpander161':
model = densenetexpander.densenet161(expandSize=opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'densenetexpander201':
model = densenetexpander.densenet201(expandSize=opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnet34':
model = resnet.resnet34()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnet50':
model = resnet.resnet50()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnet101':
model = resnet.resnet101()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnet152':
model = resnet.resnet152()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnetexpander34':
model = resnetexpander.resnet34(opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnetexpander50':
model = resnetexpander.resnet50(opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnetexpander101':
model = resnetexpander.resnet101(opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnetexpander152':
model = resnetexpander.resnet152(opt.expandSize)
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnet18':
model = resnet.resnet18()
if opt.cuda:
model = model.cuda()
elif opt.model_def == 'resnet50':
model = resnet.resnet50()
if opt.cuda:
model = model.cuda()
return model