def init_weight_fpn(self, cfg, arg_params, aux_params):
    arg_params['fpn_p6_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p6_weight'])
    arg_params['fpn_p6_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p6_bias'])
    arg_params['fpn_p5_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_weight'])
    arg_params['fpn_p5_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_bias'])
    arg_params['fpn_p4_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_weight'])
    arg_params['fpn_p4_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_bias'])
    arg_params['fpn_p3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_weight'])
    arg_params['fpn_p3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_bias'])
    arg_params['fpn_p2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_weight'])
    arg_params['fpn_p2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_bias'])
    arg_params['fpn_p5_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_1x1_weight'])
    arg_params['fpn_p5_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_1x1_bias'])
    arg_params['fpn_p4_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_1x1_weight'])
    arg_params['fpn_p4_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_1x1_bias'])
    arg_params['fpn_p3_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_1x1_weight'])
    arg_params['fpn_p3_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_1x1_bias'])
    arg_params['fpn_p2_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_1x1_weight'])
    arg_params['fpn_p2_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_1x1_bias'])
def init_weight_fpn(self, cfg, arg_params, aux_params):
    arg_params['fpn_p6_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p6_weight'])
    arg_params['fpn_p6_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p6_bias'])
    arg_params['fpn_p5_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_weight'])
    arg_params['fpn_p5_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_bias'])
    arg_params['fpn_p4_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_weight'])
    arg_params['fpn_p4_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_bias'])
    arg_params['fpn_p3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_weight'])
    arg_params['fpn_p3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_bias'])
    arg_params['fpn_p2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_weight'])
    arg_params['fpn_p2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_bias'])
    arg_params['fpn_p5_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p5_1x1_weight'])
    arg_params['fpn_p5_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p5_1x1_bias'])
    arg_params['fpn_p4_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p4_1x1_weight'])
    arg_params['fpn_p4_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p4_1x1_bias'])
    arg_params['fpn_p3_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p3_1x1_weight'])
    arg_params['fpn_p3_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p3_1x1_bias'])
    arg_params['fpn_p2_1x1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fpn_p2_1x1_weight'])
    arg_params['fpn_p2_1x1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fpn_p2_1x1_bias'])