def test_force_cuda(self):
mnist_cnn.to("cuda")
results = maxloss(
mnist_cnn,
nn.CrossEntropyLoss(),
batch_X,
batch_y_cuda,
device="cuda",
)
import seaborn as sns
import torch
from torch import nn
from clustre.attacking import fgsm_perturbs, maxloss_perturbs, pgd_perturbs
from clustre.helpers.datasets import mnist_trainloader
from clustre.models import mnist_cnn, mnist_resnet18
from clustre.models.state_dicts import mnist_cnn_state, mnist_resnet18_state
sns.set()
# %%
mnist_cnn.load_state_dict(mnist_cnn_state)
mnist_resnet18.load_state_dict(mnist_resnet18_state)
mnist_cnn.to("cuda")
mnist_resnet18.to("cuda")
# %%
criterion = nn.CrossEntropyLoss()
# %%
cnn_fgsm = []
for X, y in iter(mnist_trainloader):
X = X.to("cuda")
y = y.to("cuda")
p = fgsm_perturbs(mnist_cnn, criterion, X, y)
cnn_fgsm.append(p)
# %%
cnn_pgd = []
def test_cuda(self):
mnist_cnn.to("cuda")
results = maxloss(mnist_cnn, nn.CrossEntropyLoss(), batch_X_cuda,
batch_y_cuda)
self.assertTrue(results.is_cuda)
def test_maxloss_range(self):
mnist_cnn.to("cuda")
results = maxloss(mnist_cnn, nn.CrossEntropyLoss(), batch_X_cuda,
batch_y_cuda)
self.assertTrue(((results >= -1) & (results <= 1)).all())
def test_maxloss_shape(self):
mnist_cnn.to("cuda")
results = maxloss(mnist_cnn, nn.CrossEntropyLoss(), batch_X_cuda,
batch_y_cuda)
self.assertTupleEqual(results.shape, batch_X.shape)
def test_force_cpu(self):
mnist_cnn.to("cuda")
results = pgd(
mnist_cnn, nn.CrossEntropyLoss(), batch_X, batch_y_cuda, device="cpu"
)