def eval(self, examples, labels):
assert list(self.results.keys()) == ['top1']
ground_avg = self.results['top1']
ground_output = self.classifier_net(self.normalizer(Variable(examples)))
minibatch = float(examples.shape[0])
ground_accuracy_int = utils.accuracy_int(ground_output,
Variable(labels), topk=1)
ground_avg.update(ground_accuracy_int / minibatch,
n=int(minibatch))
def eval_attack_only(self, adversarials, labels, topk=1):
""" Outputs the accuracy of the adv_inputs only
ARGS:
adv_inputs: Variable NxCxHxW - examples after we did adversarial
perturbation
labels: Variable (longtensor N) - correct labels of classification
output
topk: int - criterion for 'correct' classification
RETURNS:
(int) number of correctly classified examples
"""
normed_advs = self.normalizer.forward(adversarials)
adv_output = self.classifier_net.forward(normed_advs)
return utils.accuracy_int(adv_output, labels, topk=topk)
def eval(self, examples, labels):
assert list(self.results.keys()) == ['top1', 'avg_loss_value']
ground_output = self.classifier_net(self.normalizer(
Variable(examples)))
minibatch = float(examples.shape[0])
# Compute accuracy
ground_avg = self.results['top1']
minibatch_accuracy_int = utils.accuracy_int(ground_output,
Variable(labels),
topk=1)
ground_avg.update(minibatch_accuracy_int / minibatch, n=int(minibatch))
# Compute loss
ground_avg_loss = self.results['avg_loss_value']
minibatch_loss = float(self.loss_fxn(ground_output, labels))
ground_avg_loss.update(minibatch_loss, n=(int(minibatch)))
