trainer.py 文件源码

def test(self, dataset):
        self.model.eval()
        self.embedding_model.eval()
        loss = 0
        predictions = torch.zeros(len(dataset))
        predictions = predictions
        indices = torch.range(1,dataset.num_classes)
        for idx in tqdm(xrange(len(dataset)),desc='Testing epoch  '+str(self.epoch)+''):
            tree, sent, label = dataset[idx]
            input = Var(sent, volatile=True)
            target = Var(map_label_to_target_sentiment(label,dataset.num_classes, fine_grain=self.args.fine_grain), volatile=True)
            if self.args.cuda:
                input = input.cuda()
                target = target.cuda()
            emb = F.torch.unsqueeze(self.embedding_model(input),1)
            output, _ = self.model(tree, emb) # size(1,5)
            err = self.criterion(output, target)
            loss += err.data[0]
            output[:,1] = -9999 # no need middle (neutral) value
            val, pred = torch.max(output, 1)
            predictions[idx] = pred.data.cpu()[0][0]
            # predictions[idx] = torch.dot(indices,torch.exp(output.data.cpu()))
        return loss/len(dataset), predictions