如何同时读取两个模型权重做加权？

版本、环境信息：
1）PaddlePaddle 1.5.1
2）GPU V100
3）AI Studio线上环境
4）python 3.7
5）显存16G
复现信息：
代码baseline(https://aistudio.baidu.com/aistudio/datasetdetail/15365)
待读取两个模型(https://aistudio.baidu.com/aistudio/datasetdetail/15366)
修改如下：
attack_example/attack_code/attack_fsgm.py

#coding=utf-8

from future import absolute_import
from future import division
from future import print_function

import argparse
import functools
import numpy as np
import paddle.fluid as fluid

#加载自定义文件
import models
from attack.attack_pp import FGSM, PGD
from utils import init_prog, save_adv_image, process_img, tensor2img, calc_mse, add_arguments, print_arguments

#######parse parameters
parser = argparse.ArgumentParser(description=doc)
add_arg = functools.partial(add_arguments, argparser=parser)

add_arg('class_dim', int, 120, "Class number.")
add_arg('shape', str, "3,224,224", "output image shape")
add_arg('input', str, "./input_image/", "Input directory with images")
add_arg('output', str, "./output_image/", "Output directory with images")

args = parser.parse_args()
print_arguments(args)

######Init args
image_shape = [int(m) for m in args.shape.split(",")]
class_dim=args.class_dim
input_dir = args.input
output_dir = args.output
model_name1="ResNeXt50_32x4d"
model_name2='MobileNetV2_x2_0'
pretrained_model1="./models_parameters/ResNeXt50_32x4d"
pretrained_model2="./models_parameters/MobileNetV2"
val_list = 'val_list.txt'
use_gpu=True

######Attack graph
adv_program=fluid.Program()
#完成初始化
with fluid.program_guard(adv_program):
input_layer = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
#设置为可以计算梯度
input_layer.stop_gradient=False
# model definition
model1 = models.__dict__[model_name1]()
model2 = models.__dict__[model_name2]()
out_logits1 = model1.net(input=input_layer, class_dim=class_dim)
out_logits2 = model2.net(input=input_layer, class_dim=class_dim)
out_logits = 0.5*(out_logits1+out_logits2)
out = fluid.layers.softmax(out_logits)

place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

#记载模型参数
print("Load 1 Start!")
# fluid.io.load_persistables(exe, pretrained_model1)
fluid.io.load_params(exe, pretrained_model1, main_program=fluid.default_main_program())
# fluid.io.load_persistables(exe, pretrained_model2)
fluid.io.load_params(exe, pretrained_model2, main_program=fluid.default_main_program())
print("Load 2 Finish!")
#设置adv_program的BN层状态
init_prog(adv_program)

#创建测试用评估模式
eval_program = adv_program.clone(for_test=True)

#定义梯度
with fluid.program_guard(adv_program):
label = fluid.layers.data(name="label", shape=[1] ,dtype='int64')
loss = fluid.layers.cross_entropy(input=out, label=label)
gradients = fluid.backward.gradients(targets=loss, inputs=[input_layer])[0]

######Inference
def inference(img):
fetch_list = [out.name]
result = exe.run(eval_program,
                 fetch_list=fetch_list,
                 feed={ 'image':img })
result = result[0][0]
pred_label = np.argmax(result)
pred_score = result[pred_label].copy()
return pred_label, pred_score
######FGSM attack
#untarget attack
def attack_nontarget_by_FGSM(img, src_label):
pred_label = src_label
step = 8.0/256.0
eps = 32.0/256.0
while pred_label == src_label:
    #生成对抗样本
    adv=FGSM(adv_program=adv_program,eval_program=eval_program,gradients=gradients,o=img,
             input_layer=input_layer,output_layer=out,step_size=step,epsilon=eps,
             isTarget=False,target_label=0,use_gpu=use_gpu)

    pred_label, pred_score = inference(adv)
    step *= 2
    if step > eps:
        break

print("Test-score: {0}, class {1}".format(pred_score, pred_label))

adv_img=tensor2img(adv)
return adv_img
######PGD attack
#untarget attack
def attack_nontarget_by_PGD(img, src_label):
pred_label = src_label
# Step = 2.0/256效果没有8/256好
step = 8.0/256.0
eps = 32.0/256.0
while pred_label == src_label:
    #生成对抗样本
    adv=PGD(adv_program=adv_program,eval_program=eval_program,gradients=gradients,o=img,
             input_layer=input_layer,output_layer=out,step_size=step,epsilon=eps,
             isTarget=False,target_label=0,use_gpu=use_gpu)

    pred_label, pred_score = inference(adv)
    step *= 2
    if step > eps:
        break

print("Test-score: {0}, class {1}".format(pred_score, pred_label))

adv_img=tensor2img(adv)
return adv_img
####### Main #######
def get_original_file(filepath):
with open(filepath, 'r') as cfile:
full_lines = [line.strip() for line in cfile]
cfile.close()
original_files = []
for line in full_lines:
label, file_name = line.split()
original_files.append([file_name, int(label)])
return original_files

def gen_adv():
mse = 0
original_files = get_original_file(input_dir + val_list)
for filename, label in original_files:
    img_path = input_dir + filename
    print("Image: {0} ".format(img_path))
    img=process_img(img_path)
    adv_img = attack_nontarget_by_FGSM(img, label)
    # adv_img = attack_nontarget_by_PGD(img, label)
    image_name, image_ext = filename.split('.')
    ##Save adversarial image(.png)
    save_adv_image(adv_img, output_dir+image_name+'.png')

    org_img = tensor2img(img)
    score = calc_mse(org_img, adv_img)
    mse += score
print("ADV {} files, AVG MSE: {} ".format(len(original_files), mse/len(original_files)))
def main():
gen_adv()

if name == 'main':
main()

报错信息：

问题描述：
Target : 目标读取入两个模型的权重并加权