def run(args):
assert args.voc12_root is not None
assert args.train_list is not None
assert args.cam_weights_name is not None
assert args.cam_network is not None
assert args.cam_out_dir is not None
assert args.cam_network_module is not None
num_classes = 0
if args.category_name == 'make_id':
num_classes = 75
if args.category_name == 'model_id':
num_classes = 431
if args.category_name == 'year':
num_classes = 16
model = getattr(importlib.import_module(args.cam_network_module),
args.cam_network + 'CAM')(num_classes=num_classes)
if use_gpu:
model.load_state_dict(torch.load(args.cam_weights_name + '.pth'),
strict=True)
else:
model.load_state_dict(torch.load(args.cam_weights_name + '.pth',
map_location=torch.device('cpu')),
strict=True)
model.eval()
dataset = dataloader.VOC12ClassificationDatasetMSF(
args.train_list,
voc12_root=args.voc12_root,
scales=args.cam_scales,
category_name=args.category_name)
print('[ ', end='')
if use_gpu:
n_gpus = torch.cuda.device_count()
dataset = torchutils.split_dataset(dataset, n_gpus)
multiprocessing.spawn(_work_gpu,
nprocs=n_gpus,
args=(model, dataset, args),
join=True)
else:
dataset = torchutils.split_dataset(dataset, args.num_workers)
multiprocessing.spawn(_work_cpu,
nprocs=args.num_workers,
args=(model, dataset, args),
join=True)
print(']')
torch.cuda.empty_cache()
def run(args):
assert args.voc12_root is not None
assert args.class_label_dict_path is not None
assert args.infer_list is not None
assert args.sem_seg_out_dir is not None
assert args.irn_weights_name is not None
assert args.cam_out_dir is not None
assert args.irn_network is not None
assert args.irn_network_module is not None
model = getattr(importlib.import_module(args.irn_network_module),
args.irn_network + 'EdgeDisplacement')()
model.load_state_dict(torch.load(args.irn_weights_name), strict=False)
model.eval()
dataset = dataloader.VOC12ClassificationDatasetMSF(
args.infer_list,
voc12_root=args.voc12_root,
scales=(1.0,),
class_label_dict_path=args.class_label_dict_path)
print("[", end='')
if use_gpu:
n_gpus = torch.cuda.device_count()
dataset = torchutils.split_dataset(dataset, n_gpus)
multiprocessing.spawn(_work_gpu,
nprocs=n_gpus,
args=(model, dataset, args),
join=True)
else:
dataset = torchutils.split_dataset(dataset, args.num_workers)
multiprocessing.spawn(_work_cpu,
nprocs=args.num_workers,
args=(model, dataset, args),
join=True)
print("]")
torch.cuda.empty_cache()
def run(args):
assert args.voc12_root is not None
assert args.train_list is not None
assert args.ir_label_out_dir is not None
assert args.cam_out_dir is not None
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(']')