def run(args): dataset = dataloader.VOC12ImageDataset(args.train_list, voc12_root=args.voc12_root, img_normal=None, to_torch=False) dataset = torchutils.split_dataset(dataset, args.num_workers) print('[ ', end='') multiprocessing.spawn(_work, nprocs=args.num_workers, args=(dataset, args), join=True) print(']')
def run(args): model = getattr(importlib.import_module(args.cam_network), 'CAM')() model.load_state_dict(torch.load(args.cam_weights_name + '.pth'), strict=True) model.eval() n_gpus = torch.cuda.device_count() dataset = dataloader.VOC12ClassificationDatasetMSF(args.train_list, voc12_root=args.voc12_root, scales=args.cam_scales) dataset = torchutils.split_dataset(dataset, n_gpus) print('[ ', end='') multiprocessing.spawn(_work, nprocs=n_gpus, args=(model, dataset, args), join=True) print(']') torch.cuda.empty_cache()
def run(args): model = getattr(importlib.import_module(args.irn_network), 'EdgeDisplacement')() model.load_state_dict(torch.load(args.irn_weights_name), strict=False) model.eval() n_gpus = torch.cuda.device_count() dataset = dataloader.VOC12ClassificationDatasetMSF( args.infer_list, voc12_root=args.voc12_root, scales=(1.0, )) dataset = torchutils.split_dataset(dataset, n_gpus) print("[ ", end='') multiprocessing.spawn(_work, nprocs=n_gpus, args=(model, dataset, args), join=True) print("]")