def test(nnName, dataName, CUDA_DEVICE, epsilon, temperature):
net1 = torch.load("../models/{}.pth".format(nnName))
optimizer1 = optim.SGD(net1.parameters(), lr = 0, momentum = 0)
net1.cuda(CUDA_DEVICE)
if dataName != "Uniform" and dataName != "Gaussian":
testsetout = torchvision.datasets.ImageFolder("../data/{}".format(dataName), transform=transform)
testloaderOut = torch.utils.data.DataLoader(testsetout, batch_size=1,
shuffle=False, num_workers=2)
if nnName == "densenet10" or nnName == "wideresnet10":
testset = torchvision.datasets.CIFAR10(root='../data', train=False, download=True, transform=transform)
testloaderIn = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False, num_workers=2)
if nnName == "densenet100" or nnName == "wideresnet100":
testset = torchvision.datasets.CIFAR100(root='../data', train=False, download=True, transform=transform)
testloaderIn = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False, num_workers=2)
if dataName == "Gaussian":
d.testGaussian(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderIn, nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
elif dataName == "Uniform":
d.testUni(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderIn, nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
else:
d.testData(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderOut, nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
def test(nnName, dataName, CUDA_DEVICE, epsilon, temperature):
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
net1 = vgg1.VGG('VGG16').to(device)
# Test set: Average loss: 0.0015, Accuracy: 9337 / 10000(93 %)
if use_cuda:
net1 = torch.nn.DataParallel(net1)
cudnn.benchmark = True
checkpoint = torch.load("../../model/{}.pth".format(nnName))
net1.load_state_dict(checkpoint['net'])
net1.eval()
if dataName != "Uniform" and dataName != "Gaussian":
testsetout = torchvision.datasets.ImageFolder(
"../../data/{}".format(dataName), transform=transform)
testloaderOut = torch.utils.data.DataLoader(testsetout,
batch_size=1,
shuffle=False,
num_workers=2)
# if nnName == "densenet10" or nnName == "wideresnet10":
testset = torchvision.datasets.CIFAR10(root='../../data',
train=False,
download=True,
transform=transform)
testloaderIn = torch.utils.data.DataLoader(testset,
batch_size=1,
shuffle=False,
num_workers=2)
# if nnName == "densenet100" or nnName == "wideresnet100":
# testset = torchvision.datasets.CIFAR100(root='../../data', train=False, download=True, transform=transform)
# testloaderIn = torch.utils.data.DataLoader(testset, batch_size=1,
# shuffle=False, num_workers=2)
if dataName == "Gaussian":
d.testGaussian(net1, criterion, CUDA_DEVICE, testloaderIn,
testloaderIn, nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
elif dataName == "Uniform":
d.testUni(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderIn,
nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
else:
d.testData(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderOut,
nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
def test(nnName, dataName, CUDA_DEVICE, epsilon, temperature):
net1 = torch.load("../models/{}.pth".format(nnName))
optimizer1 = optim.SGD(net1.parameters(), lr = 0, momentum = 0)
net1.cuda(CUDA_DEVICE)
if dataName != "Uniform" and dataName != "Gaussian":
if dataName == "SVHN":
testsetout = svhn.SVHN("../data/SVHN", split='test', transform=transform, download=True)
elif dataName in ["HFlip","VFlip"]:
testsetout = torchvision.datasets.CIFAR10('../data', train=False, download=True,
transform=Flip[dataName])
elif dataName == "CelebA":
testsetout = torchvision.datasets.ImageFolder(
"../data/{}".format(dataName),
transform=transforms.Compose([transforms.CenterCrop(178), Resize(32), transform]))
else:
testsetout = torchvision.datasets.ImageFolder("../data/{}".format(dataName),
transform=transform)
testloaderOut = torch.utils.data.DataLoader(testsetout, batch_size=1,
shuffle=False, num_workers=2)
if nnName == "densenet10" or nnName == "wideresnet10":
testset = torchvision.datasets.CIFAR10(root='../data', train=False, download=True, transform=transform)
testloaderIn = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False, num_workers=2)
if nnName == "densenet100" or nnName == "wideresnet100":
testset = torchvision.datasets.CIFAR100(root='../data', train=False, download=True, transform=transform)
testloaderIn = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False, num_workers=2)
if dataName == "Gaussian":
d.testGaussian(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderIn, nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
elif dataName == "Uniform":
d.testUni(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderIn, nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)
else:
d.testData(net1, criterion, CUDA_DEVICE, testloaderIn, testloaderOut, nnName, dataName, epsilon, temperature)
m.metric(nnName, dataName)