runner.py 文件源码

def eval(data_iter, model, args):
    model.eval()
    corrects, avg_loss = 0, 0
    for batch in data_iter:
        feature, target = batch.text, batch.labels
        target.data.sub_(1)  # batch first, index align

        x = feature.data.numpy()
        x = x.T
        feature = autograd.Variable(torch.from_numpy(x))

        if args.cuda:
            feature, target = feature.cuda(), target.cuda()

        logit = model(feature)
        loss = F.cross_entropy(logit, target, size_average=False)

        avg_loss += loss.data[0]
        corrects += (torch.max(logit, 1)
                     [1].view(target.size()).data == target.data).sum()

    size = len(data_iter.dataset)
    avg_loss = loss.data[0] / size
    accuracy = 100.0 * corrects / size
    model.train()
    print('\nEvaluation - loss: {:.6f}  acc: {:.4f}%({}/{}) \n'.format(avg_loss,
                                                                       accuracy,
                                                                       corrects,
                                                                        size))