def test(net: Network, test_loader: torch.utils.data.DataLoader):
net.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for x, y in test_loader:
x = x.to(net.device)
y = y.to(net.device)
pred = net(x.view(-1, 28 * 28))
test_loss += F.nll_loss(pred, y, size_average=False).item()
correct += torch.eq(pred.max(1).indices, y).sum().item()
test_loss /= len(test_loader.dataset)
print(
f"\nTest set, Average loss: {test_loss}, Accuracy: {float(correct) / float(len(test_loader.dataset))}\n"
)
device = "cuda" if torch.cuda.is_available() else "cpu" # Configure device
print('GPU USED?',torch.cuda.is_available())
################# MODELS#####################
models =[]
optimizers =[]
criterions =[]
j = 0
for i in range(len_class):
for event in events:
model = Network(output_dim=numb_class[i])
model = model.to(device)
model.eval()
models.append(model)
weights_list[j] = weights_list[j].to(device)
criterions.append(nn.CrossEntropyLoss(weight=weights_list[j]))
optimizers.append(optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY))
del model
j +=1
#### MODE-SPECIFIC CLASSIFIER SCHEME
# 1 : LW, 2:RA, 3:RD, 4:SA, 5:SD, 6:Stand
# MODEL1: LW(1)-> LW(1), RA(2), RD(3), SA(4), SD(5), Stand(6)
# MODEL2: RA(2)-> LW(1), RA(2)
# MODEL3: RD(3)-> LW(1), RD(3)
# MODEL4: SA(4)-> LW(1), SA(4)
# MODEL5: SD(5)-> LW(1), SD(5)
tmp_dict["id"].append(batch_id)
tmp_dict["epsilon"].append(epsilon)
tmp_dict["y"].append(y.item())
tmp_dict["y_"].append(y_)
return pandas.DataFrame.from_dict(tmp_dict), pertubed_images, pertubation
if __name__ == "__main__":
config = Configuration()
net = Network()
net.load_model()
net.eval()
test_loader = get_test_loader(batch_size=1, shuffle=False)
result = []
for batch_id, (x, y) in enumerate(test_loader):
df, _ = run_attack(net, x, y, config.epsilons, batch_id)
result.append(df)
if batch_id % 100 == 0:
print(f"Step {batch_id}/{len(test_loader.dataset)}")
result_df = pandas.concat(result) # type: pandas.DataFrame
result_df.reset_index(inplace=True, drop=True)
result_path = Path("data/")
