Is this method of calculating the top-5 accuracy in pytorch correct? - computer-vision

Question

I am trying to validate the findings of a paper by testing it on the same model architecture as well as the same dataset reported by the paper. I have been using the imagenet script provided in the official pytorch repository's examples section to do the same.
class AverageMeter(object):
"""Computes and stores the average and current value
Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
"""
def init(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the precision#k for the specified values of k"""
maxk = max(topk)
batchsize = target.size(0)
, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0)
res.append(correctk.mul(100.0 / batch_size))
return res
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
# measure data loading time
print(f"Processing {batch_idx+1}/{len(test_loader)}")
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
# compute output
outputs = model(inputs)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
print(prec1,prec5)
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
print(top1)
print(top5)
However the top 5 error which I am getting by using this script is not matching with the one in the paper. Can anyone tell me what is wrong in this particular snippet?