Created by: liu-plus-wei

paddlepaddle 1.5 版本，运行结果fetch 出来为nan

复现步骤: git clone https://github.com/advboxes/AdvBox.git

wget http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar tar -xvf ResNet50_pretrained.tar

运行： ` from future import absolute_import from future import division from future import print_function

import os import cv2 import time import sys import math import numpy as np import argparse import functools

import paddle import paddle.fluid as fluid

from sdk.resnet import ResNet50 from sdk.attack_pp import *

class_dim=1000 image_shape=[3,224,224] model_name="resnet50" use_gpu=False pretrained_model="ResNet50_pretrained" TOPK=3

input_layer = fluid.layers.data(name='image', shape=image_shape, dtype='float32')

input_layer.stop_gradient=False

model = ResNet50() out = model.net(input=input_layer, class_dim=class_dim) out = fluid.layers.softmax(out)

eval_program = fluid.default_main_program().clone(for_test=True)

fetch_list = [out.name]

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

fluid.io.load_persistables(exe, pretrained_model)

img_path = "mug227.png" img=process_img(img_path)

init_prog(fluid.default_main_program())

adv=FGSM(o=img,input_layer=input_layer,output_layer=out,step_size=0,isTarget=False,target_label=0) `

FGSM函数代码：https://github.com/advboxes/AdvBox/blob/58731c082b70af329fc671821355fec7b2bb1915/sdk/attack_pp.py#L80

我有把op输出打印出来看过，是conv2d_grad op 输出结果出现了inf：

op desc: ops { inputs { parameter: "Bias" } inputs { parameter: "Filter" arguments: "res5c_branch2c_weights" } inputs { parameter: "Input" arguments: "bn5c_branch2b.output.1.tmp_3" } inputs { parameter: "Output@GRAD" arguments: "res5c_branch2c.conv2d.output.1.tmp_0@GRAD" } outputs { parameter: "Bias@GRAD" } outputs { parameter: "Filter@GRAD" arguments: "res5c_branch2c_weights@GRAD" } outputs { parameter: "Input@GRAD" arguments: "bn5c_branch2b.output.1.tmp_3@GRAD" } type: "conv2d_grad"

运行结果： I0704 11:24:07.686339 18621 operator.cc:66] [input:res5c_branch2c_weights] data: 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I0704 11:24:07.762079 18621 operator.cc:66] [input:res5c_branch2c.conv2d.output.1.tmp_0@GRAD] data: 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I0704 11:24:08.112128 18621 operator.cc:235] CPUPlace Op(conv2d_grad), inputs:{Bias[], Filter[res5c_branch2c_weights:float2048, 512, 1, 1], Input[bn5c_branch2b.output.1.tmp_3:float1, 512, 7, 7], Output@GRAD[res5c_branch2c.conv2d.output.1.tmp_0@GRAD:float1, 2048, 7, 7]}, outputs:{Bias@GRAD[], Filter@GRAD[res5c_branch2c_weights@GRAD:float2048, 512, 1, 1], Input@GRAD[bn5c_branch2b.output.1.tmp_3@GRAD:float1, 512, 7, 7]}.