def __init__(self, model_path):
# self.net_name = net_name
self.model_path = model_path
self.num_classes = 81
self.cuda = torch.cuda.is_available()
self.label_map_list = list(coco_label_map.values())
self._init_model()
config_f = 'configs/m2det512_vgg.py'
checkpoint_path = 'weights/m2det512_vgg_coco_epoch_14.pth'
# config_f = 'configs/m2det512_vgg_helmet.py'
# checkpoint_path = 'weights/m2det512_vgg_helmet_epoch_199.pth'
config_f = 'configs/m2det512_vgg_helmet.py'
# checkpoint_path = 'weights/m2det512_vgg_helmet_epoch_550.pth'
# config_f = 'configs/m2det320_vgg_helmet.py'
# checkpoint_path = 'weights/m2det320_vgg_helmet_epoch_246.pth'
# checkpoint_path = 'weights/M2Det_COCO_size320_netresnet101_epoch0.pth'
# config_f = 'configs/m2det320_resnet101.py'
classes = list(coco_label_map.values())
# classes = HELMET_CLASSES
def demo(v_f):
cfg = Config.fromfile(config_f)
anchor_config = anchors(cfg)
priorbox = PriorBox(anchor_config)
net = build_net('test',
size=cfg.model.input_size,
config=cfg.model.m2det_config)
init_net(net, cfg, checkpoint_path)
net.eval().to(device)
with torch.no_grad():
priors = priorbox.forward().to(device)
_preprocess = BaseTransform(