def LoadShuffleDefenseAndCIFAR10(vis=False):
modelPlusList = []
#Basic variable and data setup
device = torch.device("cuda")
numClasses = 10
imgSize = 224
batchSize = 8
#Load the CIFAR-10 data
valLoader = DMP.GetCIFAR10Validation(imgSize, batchSize)
#Load ViT-L-16
config = CONFIGS["ViT-L_16"]
model = VisionTransformer(config,
imgSize,
zero_head=True,
num_classes=numClasses,
vis=vis)
dir = "Models/ViT-L_16,cifar10,run0_15K_checkpoint.bin"
dict = torch.load(dir)
model.load_state_dict(dict)
model.eval()
#Wrap the model in the ModelPlus class
modelPlusV = ModelPlus("ViT-L_16",
model,
device,
imgSizeH=imgSize,
imgSizeW=imgSize,
batchSize=batchSize)
modelPlusList.append(modelPlusV)
#Load the BiT-M-R101x3
dirB = "Models/BiT-M-R101x3-Run0.tar"
modelB = BigTransferModels.KNOWN_MODELS["BiT-M-R101x3"](
head_size=numClasses, zero_head=False)
#Get the checkpoint
checkpoint = torch.load(dirB, map_location="cpu")
#Remove module so that it will load properly
new_state_dict = OrderedDict()
for k, v in checkpoint["model"].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
#Load the dictionary
modelB.load_state_dict(new_state_dict)
modelB.eval()
#Wrap the model in the ModelPlus class
#Here we hard code the Big Transfer Model Plus class input size to 160x128 (what it was trained on)
modelBig101Plus = ModelPlus("BiT-M-R101x3",
modelB,
device,
imgSizeH=160,
imgSizeW=128,
batchSize=batchSize)
modelPlusList.append(modelBig101Plus)
#Now time to build the defense
defense = ShuffleDefense.ShuffleDefense(modelPlusList, numClasses)
return valLoader, defense
def LoadViTLAndCIFAR10():
class Arguments():
def __init__(self):
self.cuda = "True"
self.cnn = "E:\Projects\CPSC-597\AdversarialDetection\State\cifar10cnn.model"
self.recon = "E:\Projects\CPSC-597\AdversarialDetection\State\cifar10recon.model"
self.detect = "E:\Projects\CPSC-597\AdversarialDetection\State\cifar10detect.model"
args = Arguments()
metaCNN = MetaCNN(3, args)
# metaCNN = ResNet18()
# stateDict = torch.load( "E:\Projects\CPSC-597\AdversarialDetection\State\cifar10resnet.model" )
# metaCNN.load_state_dict( stateDict['model'] )
#Basic variable and data setup
device = torch.device("cuda")
numClasses = 10
imgSize = 32
batchSize = 128
#Load the CIFAR-10 data
valLoader = DMP.GetCIFAR10Validation(imgSize, batchSize)
#Load ViT-L-16
config = CONFIGS["ViT-L_16"]
#model = VisionTransformer(config, imgSize, zero_head=True, num_classes=numClasses)
#dir = "Models/ViT-L_16,cifar10,run0_15K_checkpoint.bin"
#dict = torch.load(dir)
#model.load_state_dict(dict)
#model.eval()
model = metaCNN
#Wrap the model in the ModelPlus class
modelPlus = ModelPlus("ViT-L_16",
model,
device,
imgSizeH=imgSize,
imgSizeW=imgSize,
batchSize=batchSize)
return valLoader, modelPlus