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("]")