INNER CODE UNIT · Python

correct

adobe/antialiased-cnns · main.py:706

        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

def agreement(output0, output1):
    pred0 = output0.argmax(dim=1, keepdim=False)
    pred1 = output1.argmax(dim=1, keepdim=False)
    agree = pred0.eq(pred1)
    agree = 100.*torch.mean(agree.type(torch.FloatTensor).to(output0.device))
    return agree


if __name__ == '__main__':
    main()

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