def __init__(self, cfg):
TwoStage.__init__(self, cfg)
# specify the training losses you want to print out.
# The program will call base_model.get_current_losses
# if cfg.display:
# self.visual_names.append('vis_inp')
# specify the models you want to save to the disk.
# The program will call base_model.save_networks and base_model.load_networks
self.model_names = ['detector']
# detector
common_kwargs = self.get_common_args(cfg)
self.detector = BSL(
num_classes=2,
# re-ID
num_train_pids=cfg.num_train_ids,
cls_type=cfg.cls_type,
in_level=cfg.in_level,
**common_kwargs)
# shift network to device
self.net_to_device()
if not cfg.is_test: self.init_optim(cfg)
def modify_commandline_options(parser, is_test):
# common arguments
parser = TwoStage.modify_commandline_options(parser, is_test)
# specific arguments
parser.add_argument('--alpha', type=float, default=0.01, help='warm-up lower lr factor')
parser.add_argument('--cat_c4', action='store_true', help="concat cov4 and cov5 features as re-ID feature")
parser.add_argument('--cls_type', type=str, default='oim', help='type of classifier')
parser.add_argument('--reid_lr', type=float, default=3.5e-4, help='lr of re-ID parameters')
parser.add_argument('--det_thr', type=float, default=0.01)
parser.add_argument('--uni_optim', action='store_true', help="Differs optimizers for detection and re-ID.")
parser.add_argument('--use_mask', action='store_true', help="enable segmentation head")
cfg = parser.parse_args()
parser.set_defaults(num_train_ids=5532 if cfg.benchmark == "ssm" else 483)
# initialize experiment name
model_name = cfg.backbone
model_name += 'cat' if cfg.cat_c4 else ""
model_name += "fs" if cfg.use_mask else "" # foreground segmentation
model_name += cfg.cls_type
augment = "b{}".format(cfg.batch_size)
augment += "uo" if cfg.uni_optim else "do"
augment += "wu" if cfg.warmup_epochs > 0 else ""
augment += "cg" if cfg.clip_grad else ""
augment += "ba" if cfg.bg_aug else ""
if not is_test:
parser.set_defaults(expr_name="{}*{}*{}".format(cfg.benchmark, model_name, augment))
return parser