def init_weight(self, cfg, arg_params, aux_params):
    (arg_params2, aux_params2) = ({}, {})
    for name in self.shared_param_list:
        if ('offset' in name):
            arg_params2[(name + '_weight')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_weight')])
        else:
            arg_params2[(name + '_weight')] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict[(name + '_weight')])
        arg_params2[(name + '_bias')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_bias')])
    self.init_deformable_convnet(cfg, arg_params2, aux_params2)
    self.init_weight_rcnn(cfg, arg_params2, aux_params2)
    self.init_weight_fpn(cfg, arg_params2, aux_params2)
    for k in arg_params2:
        if ((k not in arg_params) or (arg_params[k].shape != arg_params2[k].shape)):
            arg_params[k] = arg_params2[k]
    for k in aux_params2:
        if (k not in aux_params):
            aux_params[k] = aux_params2[k]
def init_weight(self, cfg, arg_params, aux_params):
    (arg_params2, aux_params2) = ({}, {})
    for name in self.shared_param_list:
        if ('offset' in name):
            arg_params2[(name + '_weight')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_weight')])
        else:
            arg_params2[(name + '_weight')] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict[(name + '_weight')])
        arg_params2[(name + '_bias')] = mx.nd.zeros(shape=self.arg_shape_dict[(name + '_bias')])
    self.init_deformable_convnet(cfg, arg_params2, aux_params2)
    self.init_weight_rcnn(cfg, arg_params2, aux_params2)
    self.init_weight_fpn(cfg, arg_params2, aux_params2)
    for k in arg_params2:
        if ((k not in arg_params) or (arg_params[k].shape != arg_params2[k].shape)):
            arg_params[k] = arg_params2[k]
    for k in aux_params2:
        if (k not in aux_params):
            aux_params[k] = aux_params2[k]