valloader = DataLoader(dataset=valset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
# model
model = BaseModel(model_name=args.model_name,
num_classes=args.num_classes,
pretrained=args.pretrained)
if args.resume:
state = torch.load(args.resume)
print('Resume from:{}'.format(args.resume))
model.load_state_dict(state['net'], strict=False)
best_acc = state['acc']
start_epoch = state['epoch'] + 1
if 'ols' in args.loss:
criterion['ols'].matrix = state['ols'].cuda()
# sync_bn
if args.sync_bn and multi_gpus:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
print('Using SyncBatchNorm')
if multi_gpus:
device = torch.device("cuda", args.local_rank)
model = model.to(device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank])
