model_type = args.model_type
batch_size = args.bs
iters = args.iters
eps = args.eps
n = args.n
norm = args.norm
custom_best = args.custom_best
fake_relu = (model_arch != 'vgg19')
analysis = args.analysis
delta_analysis = args.delta_analysis
analysis_start = args.analysis_start
random_restarts = args.random_restarts
# Load model
if args.dataset == 'cifar10':
constants = utils.CIFAR10()
elif args.dataset == 'imagenet':
constants = utils.ImageNet1000()
else:
print("Invalid Dataset Specified")
ds = constants.get_dataset()
# Load model
model = constants.get_model(model_type, model_arch)
# Get stats for neuron activations
senses = constants.get_deltas(model_type, model_arch)
(mean, std) = constants.get_stats(model_type, model_arch)
_, test_loader = ds.make_loaders(batch_size=batch_size,
workers=8,
only_val=True,
delta_values = logits[actual_label] - logits
delta_values /= weights - weights[actual_label]
delta_values[actual_label] = np.inf
return delta_values
if __name__ == "__main__":
import sys
model_arch = sys.argv[1]
model_type = sys.argv[2]
dataset = sys.argv[3]
filename = sys.argv[4]
if dataset == 'cifar10':
dx = utils.CIFAR10()
batch_size = 1024
elif dataset == 'imagenet':
batch_size = 256
dx = utils.ImageNet1000()
else:
raise ValueError("Dataset not supported")
ds = dx.get_dataset()
model = dx.get_model(model_type, model_arch)
_, test_loader = ds.make_loaders(batch_size=batch_size,
workers=8,
only_val=True,
fixed_test_order=True)