def get_model(cfg):
cfg.merge_from_file('../configs/second/car.yaml')
anchors = AnchorGenerator(cfg).anchors
preprocessor = Preprocessor(cfg)
model = Second(cfg).cuda().eval()
ckpt = torch.load('../pvrcnn/ckpts/epoch_12.pth')['state_dict']
model.load_state_dict(ckpt, strict=True)
return model, preprocessor, anchors
def __init__(self,):
self.cfg = cfg
self.cfg.merge_from_file('../configs/second/car.yaml')
self.preprocessor = Preprocessor(cfg)
self.anchors = AnchorGenerator(cfg).anchors.cuda()
self.net = PV_RCNN(cfg).cuda().eval()
# self.net = Second(cfg).cuda().eval()
ckpt = torch.load('./ckpts/epoch_49.pth')
self.net.load_state_dict(ckpt['state_dict'])
pass
def __init__(self, cfg, split):
super(KittiDataset, self).__init__()
self.split = split
self.rootdir = cfg.DATA.ROOTDIR
self.load_annotations(cfg)
if split == 'train':
anchors = AnchorGenerator(cfg).anchors
self.target_assigner = ProposalTargetAssigner(cfg, anchors)
self.augmentation = ChainedAugmentation(cfg)
self.cfg = cfg
def __init__(self, cfg):
super(KittiDatasetTrain, self).__init__(cfg, split='train')
anchors = AnchorGenerator(cfg).anchors
DatabaseBuilder(cfg, self.annotations)
self.target_assigner = ProposalTargetAssigner(cfg, anchors)
self.augmentation = ChainedAugmentation(cfg)