def get_rpn_subnet(self, data, num_anchors, suffix):
    rpn_conv = mx.sym.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=512, name=('rpn_conv_' + suffix), weight=self.shared_param_dict['rpn_conv_weight'], bias=self.shared_param_dict['rpn_conv_bias'])
    rpn_relu = mx.sym.Activation(data=rpn_conv, act_type='relu', name=('rpn_relu_' + suffix))
    rpn_cls_score = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(2 * num_anchors), name=('rpn_cls_score_' + suffix), weight=self.shared_param_dict['rpn_cls_score_weight'], bias=self.shared_param_dict['rpn_cls_score_bias'])
    rpn_bbox_pred = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(4 * num_anchors), name=('rpn_bbox_pred_' + suffix), weight=self.shared_param_dict['rpn_bbox_pred_weight'], bias=self.shared_param_dict['rpn_bbox_pred_bias'])
    rpn_cls_score_t1 = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, (- 1), 0), name=('rpn_cls_score_t1_' + suffix))
    rpn_cls_score_t2 = mx.sym.Reshape(data=rpn_cls_score_t1, shape=(0, 2, (- 1)), name=('rpn_cls_score_t2_' + suffix))
    rpn_cls_prob = mx.sym.SoftmaxActivation(data=rpn_cls_score_t1, mode='channel', name=('rpn_cls_prob_' + suffix))
    rpn_cls_prob_t = mx.sym.Reshape(data=rpn_cls_prob, shape=(0, (2 * num_anchors), (- 1), 0), name=('rpn_cls_prob_t_' + suffix))
    rpn_bbox_pred_t = mx.sym.Reshape(data=rpn_bbox_pred, shape=(0, 0, (- 1)), name=('rpn_bbox_pred_t_' + suffix))
    return (rpn_cls_score_t2, rpn_cls_prob_t, rpn_bbox_pred_t, rpn_bbox_pred)
def get_rpn_subnet(self, data, num_anchors, suffix):
    rpn_conv = mx.sym.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=512, name=('rpn_conv_' + suffix), weight=self.shared_param_dict['rpn_conv_weight'], bias=self.shared_param_dict['rpn_conv_bias'])
    rpn_relu = mx.sym.Activation(data=rpn_conv, act_type='relu', name=('rpn_relu_' + suffix))
    rpn_cls_score = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(2 * num_anchors), name=('rpn_cls_score_' + suffix), weight=self.shared_param_dict['rpn_cls_score_weight'], bias=self.shared_param_dict['rpn_cls_score_bias'])
    rpn_bbox_pred = mx.sym.Convolution(data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=(4 * num_anchors), name=('rpn_bbox_pred_' + suffix), weight=self.shared_param_dict['rpn_bbox_pred_weight'], bias=self.shared_param_dict['rpn_bbox_pred_bias'])
    rpn_cls_score_t1 = mx.sym.Reshape(data=rpn_cls_score, shape=(0, 2, (- 1), 0), name=('rpn_cls_score_t1_' + suffix))
    rpn_cls_score_t2 = mx.sym.Reshape(data=rpn_cls_score_t1, shape=(0, 2, (- 1)), name=('rpn_cls_score_t2_' + suffix))
    rpn_cls_prob = mx.sym.SoftmaxActivation(data=rpn_cls_score_t1, mode='channel', name=('rpn_cls_prob_' + suffix))
    rpn_cls_prob_t = mx.sym.Reshape(data=rpn_cls_prob, shape=(0, (2 * num_anchors), (- 1), 0), name=('rpn_cls_prob_t_' + suffix))
    rpn_bbox_pred_t = mx.sym.Reshape(data=rpn_bbox_pred, shape=(0, 0, (- 1)), name=('rpn_bbox_pred_t_' + suffix))
    return (rpn_cls_score_t2, rpn_cls_prob_t, rpn_bbox_pred_t, rpn_bbox_pred)