def init_weight_rcnn(self, cfg, arg_params, aux_params):
    arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
    arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
    arg_params['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
    arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_bias'])
    arg_params['cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_weight'])
    arg_params['cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_bias'])
    arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
    arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])
def init_weight_rcnn(self, cfg, arg_params, aux_params):
    arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
    arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
    arg_params['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
    arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_bias'])
    arg_params['cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_weight'])
    arg_params['cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_bias'])
    arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
    arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])