from networks.HOI import HOI
net = HOI(model_name=args.model)
with_pose = False
# if args.model.__contains__('pose'):
# with_pose = True
coco = False
zero_shot_type = get_zero_shot_type(args.model)
large_neg_for_ho = False
if args.model.endswith('_aug5_new') or args.model.endswith('_aug6_new'):
large_neg_for_ho = True
image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp = obtain_data(
Pos_augment=args.Pos_augment,
Neg_select=args.Neg_select,
augment_type=augment_type,
with_pose=with_pose,
zero_shot_type=zero_shot_type,
)
print('coco', coco)
net.set_ph(image, image_id, num_pos, Human_augmented, Object_augmented,
action_HO, sp)
from models.train_Solver_HICO import train_net
train_net(net,
Trainval_GT,
Trainval_N,
output_dir,
tb_dir,
if args.model.__contains__('res101'):
os.environ['DATASET'] = 'HICO_res101'
from networks.HOI import DisentanglingNet
net = DisentanglingNet(model_name=args.model)
else:
from networks.HOI import DisentanglingNet
net = DisentanglingNet(model_name=args.model)
os.environ['FEATS'] = 'TRUE'
if args.type == 'train':
large_neg_for_ho = False
if args.model.endswith('_aug5_new') or args.model.endswith(
'_aug6_new'):
large_neg_for_ho = True
image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp = obtain_data(
Pos_augment=0, Neg_select=0, augment_type=-1, pattern_type=False)
net.set_ph(image, image_id, num_pos, Human_augmented, Object_augmented,
action_HO, sp)
else:
large_neg_for_ho = False
image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp = obtain_test_data(
Pos_augment=0,
Neg_select=0,
augment_type=-1,
large_neg_for_ho=large_neg_for_ho)
net.set_ph(image, image_id, num_pos, Human_augmented, Object_augmented,
action_HO, sp)
net.init_verbs_objs_cls()
net.create_architecture(False)
augment_type = get_augment_type(args.model)
start_epoch = 0
net = ResNet101(model_name=args.model)
pattern_type = 0
neg_type_ratio = 0
zero_shot_type = get_zero_shot_type(args.model)
human_adj = None
obj_adj = None
image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, obj_mask = obtain_data(
Pos_augment=args.Pos_augment,
Neg_select=args.Neg_select,
augment_type=augment_type,
pattern_type=pattern_type,
zero_shot_type=zero_shot_type,
epoch=start_epoch,
neg_type=neg_type_ratio)
net.set_ph(image, image_id, num_pos, Human_augmented, Object_augmented,
action_HO, sp)
if args.model.__contains__('gan'):
from models.train_Solver_HICO_FCL import train_net
else:
from models.train_Solver_HICO import train_net
train_net(net,
Trainval_GT,
Trainval_N,
output_dir,