parser.add_argument('--model',
default="MobileNetV2_skip",
help='MobileNetV2_skip')
args = parser.parse_args()
from models.ilsvrc import mobilenetv2_skip, mobilenetv2
#from models.cifar100 import mobilenetv2_skip
dic_model = {'MobileNetV2_skip': mobilenetv2_skip.MobileNetV2_skip}
if args.model not in dic_model:
print("The model is currently not supported")
sys.exit()
trainloader = utils.get_traindata('ILSVRC2012',
args.dataset_path,
batch_size=args.batch_size,
download=True,
num_workers=16)
testloader = utils.get_testdata('ILSVRC2012',
args.dataset_path,
batch_size=args.batch_size,
num_workers=16)
#args.visible_device sets which cuda devices to be used"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.visible_device
device = 'cuda'
# parallelize
class MyDataParallel(nn.DataParallel):
help='A path to dataset directory')
parser.add_argument('--model',
default="MobileNetV2_skip",
help='MobileNetV2_skip')
args = parser.parse_args()
from models.cifar100 import mobilenetv2_skip
#from models.cifar100 import mobilenetv2_skip
dic_model = {'MobileNetV2_skip': mobilenetv2_skip.MobileNetV2_skip}
if args.model not in dic_model:
print("The model is currently not supported")
sys.exit()
trainloader = utils.get_traindata('CIFAR100',
args.dataset_path,
batch_size=args.batch_size,
download=True)
testloader = utils.get_testdata('CIFAR100',
args.dataset_path,
batch_size=args.batch_size)
#args.visible_device sets which cuda devices to be used"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.visible_device
device = 'cuda'
net = dic_model[args.model](num_classes=100)
net = net.to(device)
#CrossEntropyLoss for accuracy loss criterion
criterion = nn.CrossEntropyLoss()