def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
else:
model = pblm.cifar_model().cuda()
return model
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
elif m == 'resnet':
model = pblm.cifar_model_resnet(N=args.resnet_N, factor=args.resnet_factor).cuda()
else:
model = pblm.cifar_model().cuda()
return model
def select_model(m):
if m == 'small':
model = pblm.cifar_model().cuda()
elif m == 'large':
model = pblm.cifar_model_large().cuda()
# elif m == 'resNet':
# model = pblm.cifar_model_resnet().cuda()
else:
raise ValueError('model argument not recognized for imagenet')
return model
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().to(device)
elif m == 'resnet':
model = pblm.cifar_model_resnet(N=args.resnet_N,
factor=args.resnet_factor).to(device)
else:
model = pblm.cifar_model().to(device)
summary(model, (3, 32, 32))
return model
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
elif m == 'resnet':
model = pblm.cifar_model_resnet(N=args.resnet_N,
factor=args.resnet_factor).cuda()
elif m == 'm1':
print('using a reduced sized network')
model = pblm.cifar_model_m1().cuda()
elif m == 'm2':
print('using a slightly reduced sized network')
model = pblm.cifar_model_m2().cuda()
else:
model = pblm.cifar_model().cuda()
return model