I’m trying to incorporate this tutorial: https://adversarial-ml-tutorial.org/adversarial_examples/
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import os
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = models.googlenet(num_classes=5)
num_ftrs = model.aux1.fc2.in_features
model.aux1.fc2 = nn.Linear(num_ftrs, 5)
num_ftrs = model.aux2.fc2.in_features
model.aux2.fc2 = nn.Linear(num_ftrs, 5)
# Handle the primary net
#num_ftrs = model_ft.fc.in_features
#model_ft.fc = nn.Linear(num_ftrs,num_classes)
model.load_state_dict(torch.load("standard_googlenet.pth"))
#phase_transforms = transforms.Compose(transform)
vtype_train = datasets.ImageFolder(os.path.join("data/train"), transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]))
vtype_test = datasets.ImageFolder(os.path.join("data/val"), transforms.Compose([transforms.Resize(224), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]))
train_loader = DataLoader(vtype_train, batch_size = 96, shuffle=True)
test_loader = DataLoader(vtype_test, batch_size = 16, shuffle=False)
def fgsm(model, X, y, epsilon):
""" Construct FGSM adversarial examples on the examples X"""
delta = torch.zeros_like(X, requires_grad=True)
loss = nn.CrossEntropyLoss()(model(X + delta), y)
loss.backward()
return epsilon * delta.grad.detach().sign()
for X,y in test_loader:
X,y = X.to(device), y.to(device)
break
def plot_images(X,y,yp,M,N):
f,ax = plt.subplots(M,N, sharex=True, sharey=True, figsize=(N,M*1.3))
for i in range(M):
for j in range(N):
ax[i][j].imshow(1-X[i*N+j][0].cpu().numpy())
title = ax[i][j].set_title("Pred: {}".format(yp[i*N+j].max(dim=0)[1]))
plt.setp(title, color=('g' if yp[i*N+j].max(dim=0)[1] == y[i*N+j] else 'r'))
ax[i][j].set_axis_off()
plt.tight_layout()
yp = model(X)
#yp = model_dnn_2(X)
plot_images(X, y, yp, 3, 6)
def epoch_adversarial(model, loader, attack, *args):
total_loss, total_err = 0.,0.
for X,y in loader:
X,y = X.to(device), y.to(device)
delta = attack(model, X, y, *args)
yp = model(X+delta)
loss = nn.CrossEntropyLoss()(yp,y)
total_err += (yp.max(dim=1)[1] != y).sum().item()
total_loss += loss.item() * X.shape[0]
return total_err / len(loader.dataset), total_loss / len(loader.dataset)
#print(" GoogLeNet CNN:", epoch_adversarial(model, test_loader, fgsm, 0)[0])
Got this error, while I’m trying to plot:
Traceback (most recent call last):
File "pgd.py", line 55, in <module>
plot_images(X, y, yp, 3, 6)
File "pgd.py", line 48, in plot_images
plt.setp(title, color=('g' if yp[i*N+j].max(dim=0)[1] == y[i*N+j] else 'r'))
RuntimeError: bool value of Tensor with more than one value is ambiguous
And when I comment the plot proceed to epoch_adversarial, got one more error:
Traceback (most recent call last):
File "pgd.py", line 67, in <module>
print(" GoogLeNet CNN:", epoch_adversarial(model, test_loader, fgsm, 0)[0])
File "pgd.py", line 60, in epoch_adversarial
delta = attack(model, X, y, *args)
File "pgd.py", line 35, in fgsm
loss = nn.CrossEntropyLoss()(model(X + delta), y)
File "/home/yolo/Documents/raja/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 489, in __call__
result = self.forward(*input, **kwargs)
File "/home/yolo/Documents/raja/anaconda3/lib/python3.7/site-packages/torch/nn/modules/loss.py", line 904, in forward
ignore_index=self.ignore_index, reduction=self.reduction)
File "/home/yolo/Documents/raja/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py", line 1970, in cross_entropy
return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
File "/home/yolo/Documents/raja/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py", line 1295, in log_softmax
ret = input.log_softmax(dim)
AttributeError: 'GoogLeNetOuputs' object has no attribute 'log_softmax'
corresponding GoogLeNet: https://github.com/pytorch/vision/blob/master/torchvision/models/googlenet.py
any thoughts to solve?