adv_x = fast_gradient_method(）调用到gpu进行计算

是的，可以通过在函数调用时传递参数来将计算迁移到GPU上进行加速。例如，可以使用PyTorch的CUDA模块来实现此操作，通过将输入数据和模型参数传递给cuda()函数，并在后续的计算中使用.cuda()来指定GPU计算。其示例如下：

import torch
from torch.autograd import Variable
import numpy as np
from cleverhans_attack import fast_gradient_method

# Load data and model
X = np.load('data.npy')
y = np.load('labels.npy')
model = torch.load('model.pt')

# Convert data to PyTorch variables and move to GPU
X_var = Variable(torch.from_numpy(X).float())
y_var = Variable(torch.from_numpy(y).long())
X_var = X_var.cuda()
y_var = y_var.cuda()

# Move model to GPU
model.cuda()

# Call fast gradient method with GPU acceleration
epsilon = 0.1
adv_x = fast_gradient_method(model, X_var, y_var, epsilon, cuda=True)

# Move adversarial examples back to CPU
adv_x = adv_x.cpu().numpy()