trainer.py 文件源码

def test(self, dataset):
        self.model.eval()
        loss = 0
        predictions = torch.zeros(len(dataset))
        indices = torch.range(1,dataset.num_classes)
        for idx in tqdm(xrange(len(dataset)),desc='Testing epoch  '+str(self.epoch)+''):
            ltree,lsent,rtree,rsent,label = dataset[idx]
            linput, rinput = Var(lsent, volatile=True), Var(rsent, volatile=True)
            target = Var(map_label_to_target(label,dataset.num_classes), volatile=True)
            if self.args.cuda:
                linput, rinput = linput.cuda(), rinput.cuda()
                target = target.cuda()
            output = self.model(ltree,linput,rtree,rinput)
            err = self.criterion(output, target)
            loss += err.data[0]
            predictions[idx] = torch.dot(indices,torch.exp(output.data.cpu()))
        return loss/len(dataset), predictions