无法使用经过训练的 NSGA-Net PyTorch 模型生成对抗样本

Question

I have used NSGA-Net neural architecture search to generate and train several architectures. I am trying to generate PGD adversarial examples using my trained PyTorch models. I tried using both Adversarial Robustness Toolbox 1.3 (ART) and torchattacks 2.4 but I get the same error.

These few lines of code describe the main functionality of my code and what I am trying to achieve here:

# net is my trained NSGA-Net PyTorch model

# Defining PGA attack

pgd_attack = PGD(net, eps=4 / 255, alpha=2 / 255, steps=3)

# Creating adversarial examples using validation data and the defined PGD attack

for images, labels in valid_data:
    images = pgd_attack(images, labels).cuda()
    outputs = net(images)

So here is what the error generally looks like:

Traceback (most recent call last):
  File "torch-attacks.py", line 296, in <module>
    main()
  File "torch-attacks.py", line 254, in main
    images = pgd_attack(images, labels).cuda()
  File "\Anaconda3\envs\GPU\lib\site-packages\torchattacks\attack.py", line 114, in __call__
    images = self.forward(*input, **kwargs)
  File "\Anaconda3\envs\GPU\lib\site-packages\torchattacks\attacks\pgd.py", line 57, in forward
    outputs = self.model(adv_images)
  File "\envs\GPU\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "\codes\NSGA\nsga-net\models\macro_models.py", line 79, in forward
    x = self.gap(self.model(x))
  File "\Anaconda3\envs\GPU\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "\Anaconda3\envs\GPU\lib\site-packages\torch\nn\modules\container.py", line 100, in forward
    input = module(input)
  File "\Anaconda3\envs\GPU\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "\codes\NSGA\nsga-net\models\macro_decoder.py", line 978, in forward
    x = self.first_conv(x)
  File "\Anaconda3\envs\GPU\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "\Anaconda3\envs\GPU\lib\site-packages\torch\nn\modules\conv.py", line 345, in forward
    return self.conv2d_forward(input, self.weight)
  File "\Anaconda3\envs\GPU\lib\site-packages\torch\nn\modules\conv.py", line 342, in conv2d_forward
    self.padding, self.dilation, self.groups)
RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #2 'weight' in call to _thnn_conv2d_forward

I have used the same the code with a simple PyTorch model and it worked but I am using NSGA-Net so I haven't designed the model myself. I also tried using .float() on both the model and inputs and still got the same error.

Keep in mind that I only have access to the following files:

• torch-attacks.py
• macro_models.py
• macro_decoder.py

Answer 1

You should convert images to the desired type ( images.float() in your case). Labels must not be converted to any floating type. They are allowed to be either int or long tensors.